title: Adversarial Defense based on Structure-to-Signal Autoencoders

abstract: Adversarial attack methods have demonstrated the fragility of deep neural networks. Their imperceptible perturbations are frequently able fool classifiers into potentially dangerous misclassifications. We propose a novel way to interpret adversarial perturbations in terms of the effective input signal that classifiers actually use. Based on this, we apply specially trained autoencoders, referred to as S2SNets, as defense mechanism. They follow a two-stage training scheme: first unsupervised, followed by a fine-tuning of the decoder, using gradients from an existing classifier. S2SNets induce a shift in the distribution of gradients propagated through them, stripping them from class-dependent signal. We analyze their robustness against several white-box and gray-box scenarios on the large ImageNet dataset. Our approach reaches comparable resilience in white-box attack scenarios as other state-of-the-art defenses in gray-box scenarios. We further analyze the relationships of AlexNet, VGG 16, ResNet 50 and Inception v3 in adversarial space, and found that VGG 16 is the easiest to fool, while perturbations from ResNet 50 are the most transferable.


title: Hybrid excited claw pole rotor for high power density automotive alternators

abstract: This paper presents and investigates an improved 3D-excited hybrid claw pole alternator. The new alternator design is achieved by adding two axial PM rotor components on the lateral sides of the main claw-pole rotor. These additional rotor components are located under the endturn regions of the stator winding. Therefore, the complete machine space including the end-winding region can be utilized to boost the rotor flux, thus to achieve higher alternator power density and efficiency. A validation of the new claw-pole alternator concept has been performed with 3D finite elements. Several simulations are made in order to estimate the boosting effect at different load conditions and with different magnet materials for the lateral rotors. The new rotor with ferrite magnets boost the rotor flux for 2 times, while the NdFeB rotor for 2.8 times. Moreover, the analyses also prove the self-weakening effect of the proposed hybrid rotor. Hence the stator iron losses due to magnets at no load are minimal. Finally, the results of an mechanical comparison indicate that the new construction improves also the structural rigidity of the CPA rotor.


title: The Bender Gestalt test in childhood emotional disorder

abstract: Reflexions sur l'introduction du test Bender-Gestalt dans le cadre hospitalier et sur son utilisation dans le depistage des troubles emotionnels de l'enfant


title: Automatic Construction of an Emoji Sentiment Lexicon

abstract: Emojis have been frequently used to express users' sentiments, emotions, and feelings in text-based communication. To facilitate sentiment analysis of users' posts, an emoji sentiment lexicon with positive, neutral, and negative scores has been recently constructed using manually labeled tweets. However, the number of emojis listed in the lexicon is smaller than that of currently existing emojis, and expanding the lexicon manually requires time and effort to reconstruct the labeled dataset. This paper presents a simple and efficient method for automatically constructing an emoji sentiment lexicon with arbitrary sentiment categories. The proposed method extracts sentiment words from WordNet-Affect and calculates the cooccurrence frequency between the sentiment words and each emoji. Based on the ratio of the number of occurrences of each emoji among the sentiment categories, each emoji is assigned a multidimensional vector whose elements indicate the strength of the corresponding sentiment. In experiments conducted on a collection of tweets, we show a high correlation between the conventional lexicon and our lexicon for three sentiment categories. We also show the results for a new lexicon constructed with additional sentiment categories.


title: Towards Open-World Person Re-Identification by One-Shot Group-Based Verification

abstract: Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods.


title: The Eigenoption-Critic Framework

abstract: Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration. Despite its initial promising results, a couple of issues in current algorithms limit its application, namely: (1) EO methods require two separate steps (eigenoption discovery and reward maximization) to learn a control policy, which can incur a significant amount of storage and computation; (2) EOs are only defined for problems with discrete state-spaces and; (3) it is not easy to take the environment's reward function into consideration when discovering EOs. To addresses these issues, we introduce an algorithm termed eigenoption-critic (EOC) based on the Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement learning (RL) algorithm that allows learning the intra-option policies simultaneously with the policy over options. We also propose a generalization of EOC to problems with continuous state-spaces through the Nystr\"om approximation. EOC can also be seen as extending OC to nonstationary settings, where the discovered options are not tailored for a single task.


title: A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

abstract: Wind energy has seen great development during the past decade. However, wind turbine availability and reliability, especially for offshore sites, still need to be improved, which strongly affect the cost of wind energy. Wind turbine operational cost is closely depending on component failure and repair rate, while fault detection and isolation will be very helpful to improve the availability and reliability factors. In this paper, an efficient machine learning method, random forests (RFs) in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework. In the proposed design, RF is used to rank the features by importance, which are either direct sensor signals or constructed variables from prior knowledge. Then, based on the top-ranking features, XGBoost trains the ensemble classifier for each specific fault. In order to verify the effectiveness of the proposed approach, numerical simulations using the state-of-the-art wind turbine simulator FAST are conducted for three different types of wind turbines in both the below and above rated conditions. It is shown that the proposed approach is robust to various wind turbine models including offshore ones in different working conditions. Besides, the proposed ensemble classifier is able to protect against overfitting, and it achieves better wind turbine fault detection results than the support vector machine method when dealing with multidimensional data.


Designed Ankyrin Repeat Proteins ( DARPins ) : Binding Proteins for Research , Diagnostics , and Therapy


title: Does state boredom cause failures of attention? Examining the relations between trait boredom, state boredom, and sustained attention

abstract: Boredom is an important personal and social problem, but the phenomena itself remains poorly understood. Recent work has shown that boredom is highly related to attention, and that this relationship may be instrumental in revealing boredom’s causes and consequences. In this paper, experimental findings on trait boredom, state boredom, and sustained attention performance are presented. We demonstrate that trait boredom uniquely predicts sustained attention performance, over and above depression and self-report attention problems. We also present exploratory findings consistent with the claim that attention failures may cause boredom and that sustained attention tasks may themselves be boring. Discussion of each of these findings, and potential ramifications for cognitive research as a whole, is included.


title: Secure Signature-Based Authenticated Key Establishment Scheme for Future IoT Applications

abstract: Internet of Things (IoT) is a network of all devices that can be accessed through the Internet. These devices can be remotely accessed and controlled using existing network infrastructure, thus allowing a direct integration of computing systems with the physical world. This also reduces human involvement along with improving accuracy and efficiency, resulting in economic benefit. The devices in IoT facilitate the day-to-day life of people. However, the IoT has an enormous threat to security and privacy due to its heterogeneous and dynamic nature. Authentication is one of the most challenging security requirements in the IoT environment, where a user (external party) can directly access information from the devices, provided the mutual authentication between user and devices happens. In this paper, we present a new signature-based authenticated key establishment scheme for the IoT environment. The proposed scheme is tested for security with the help of the widely used Burrows-Abadi–Needham logic, informal security analysis, and also the formal security verification using the broadly accepted automated validation of Internet security protocols and applications tool. The proposed scheme is also implemented using the widely accepted NS2 simulator, and the simulation results demonstrate the practicability of the scheme. Finally, the proposed scheme provides more functionality features, and its computational and communication costs are also comparable with other existing approaches.


title: Hardware Trojans – Prevention , Detection , Countermeasures ( A Literature Review )

abstract: Abstract : A Hardware Trojan is a malicious, undesired, intentional modification of an electronic circuit or design, resulting in the incorrect behaviour of an electronic device when in operation - a back-door that can be inserted into hardware. A Hardware Trojan may be able to defeat any and all security mechanisms (software or hardware-based) and subvert or augment the normal operation of an infected device. This may result in modifications to the functionality or specification of the hardware, the leaking of sensitive information, or a Denial of Service (DoS) attack. Understanding Hardware Trojans is vital when developing next generation defensive mechanisms for the development and deployment of electronics in the presence of the Hardware Trojan threat. Research over the past five years has primarily focussed on detecting the presence of Hardware Trojans in infected devices. This report reviews the state-of-the-art in Hardware Trojans, from the threats they pose through to modern prevention, detection and countermeasure techniques.


Strategic human resource management : The evolution of the fi eld


title: An eye-tracking study of website complexity from cognitive load perspective

abstract: Abstract Online shopping is becoming one of the most popular applications on the Internet. Websites are the important interfaces in HCI (Human–Computer Interaction). Website design significantly affects online shopping behavior. This research used eye-tracker to track the eye-movement process for 42 college students when they were surfing websites with different levels of complexity and completing simple and complex tasks respectively. The study examines how website complexity and task complexity jointly affect users' visual attention and behavior due to different cognitive loads. The study fills a research gap by examining this phenomenon from the cognitive load perspective and taking the moderate effect of task complexity into consideration. The results show that task complexity can moderate the effect of website complexity on users' visual attention and behavior. Specifically, when users conducted a simple task, fixation count and task completion time were at the highest level on the website with high complexity, while fixation duration was not significantly different on the websites with different complexity. However, when users conducted a complex task on a website with medium complexity, task completion time, fixation count, and fixation duration were all at their highest level. The load theory of attention was used to provide the explanation for the results. The findings provide guidelines for website managers and designers to maximize users' visual attention.


title: Climbing ring robot for inspection of offshore wind turbines

abstract: A rapid expansion of wind turbine farms for sustainable electric power production is planned in Europe by 2020. At least in the UK, these will largely be located offshore to meet growing concerns about the visual intrusiveness and noise generation producedby onshore based farms. The necessary structural integrity inspection of offshore wind turbine blades poses tremendous problems of access, danger to human operatives and costs in the event of blades having to be taken out of service and transported on shore forschedules inspections. For these reasons robotic in-situ blade inspection of offshore wind turbines has been proposed and micro/nano focus computed axial X ray tomography (MNCAT) has been identified as the optimal if not the only solution for identification of safety critical defects in the thickest blade sections. The weight of such an inspection system is very high, typically 200kg and typical cross sectional scanner dimensions of 1 m × 2 m to encircle as blade, clearly involve very high destabilizing moments to be countered by the deployment robot. The solution is a climbing ring robot completely encircling a turbine tower, typically 3 meter in diameter, to provide the necessary adhesion forces and anti-destabilizing force moments. Because of the size and thus development costs of such a huge robot the optimal design path is to prototype a small scale model. First results on such a model are described and from its performance the load carrying capabilities of a full scale version can be computed and the scale model can then berefined by 'reverse engineering' to guarantee that a full scale construction is able tomeet requirements. The key design innovation is that the adhesive forces between the robot and climbing surface a provided entirely by mechanical means rather than by usingthe usual methods of vacuum suction or magnetic force, making the system much cheaper andeasier to manipulate. Furthermore the design is entirely modular. Copyright © 2008 by World Scientific Publishing Co. Pte. Ltd.


title: Experience-based navigation for long-term localisation

abstract: This paper is about long-term navigation in environments whose appearance changes over time, suddenly or gradually. We describe, implement and validate an approach which allows us to incrementally learn a model whose complexity varies naturally in accordance with variation of scene appearance. It allows us to leverage the state of the art in pose estimation to build over many runs, a world model of sufficient richness to allow simple localisation despite a large variation in conditions. As our robot repeatedly traverses its workspace, it accumulates distinct visual experiences that in concert, implicitly represent the scene variation: each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running an independent vision-based pose estimation system. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical time-varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor throughout, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a 3-month period at different times of day, in different weather and lighting conditions. We present extensive results analysing different aspects of the system and approach, in total processing over 136,000 frames captured from 37 km of driving.


title: A System for Interactive Visual Analysis of Large Graphs Using Motifs in Graph Editing and Aggregation

abstract: Network analysis is an important task in a wide variety of application domains including analysis of social, financial, or transportation networks, to name a few. The appropriate visualization of graphs may reveal useful insight into relationships between network entities and subnetworks. However, often further algorithmic analysis of network structures is needed. In this paper, we propose a system for effective visual analysis of graphs which supports multiple analytic tasks. Our system enhances any graph layout algorithm by an analysis stage which detects predefined or arbitrarily specified subgraph structures (motifs). These motifs in turn are used to filter or aggregate the given network, which is particularly useful for search and analysis of interesting structures in large graphs. Our approach is fully interactive and can be iteratively refined, supporting analysis of graph structures at multiple levels of abstraction. Furthermore, our system supports the analysis of data- or user-driven graph dynamics by showing the implications of graph changes on the identified subgraph structures. The interactive facilities may be flexibly combined for gaining deep insight into the network structures for a wide range of analysis tasks. While we focus on directed, weighted graphs, the proposed tools can be easily extended to undirected and unweighted graphs. The usefulness of our approach is demonstrated by application on a phone call data set [18].


title: Transversus abdominis muscle release: a novel approach to posterior component separation during complex abdominal wall reconstruction.

abstract: BACKGROUND ::: Several modifications of the classic retromuscular Stoppa technique to facilitate dissection beyond the lateral border of the rectus sheath recently were reported. We describe a novel technique of transversus abdominis muscle release (TAR) for posterior component separation during major abdominal wall reconstructions. ::: ::: ::: METHODS ::: Retrospective review of consecutive patients undergoing TAR. Briefly, the retromuscular space is developed laterally to the edge of the rectus sheath. The posterior rectus sheath is incised 0.5-1 cm underlying medial to the linea semilunaris to expose the medial edge of the transversus abdominis muscle. The muscle then is divided, allowing entrance to the space anterior to the transversalis fascia. The posterior rectus fascia then is advanced medially. The mesh is placed as a sublay and the linea alba is restored ventral to the mesh. ::: ::: ::: RESULTS ::: Between December 2006 and December 2009, we have used this technique successfully in 42 patients with massive ventral defects. Thirty-two (76.2%) patients had recurrent hernias. The average mesh size used was 1,201 ± 820 cm(2) (range, 600-2,700). Ten (23.8%) patients developed various wound complications requiring reoperation/debridement in 3 patients. At a median follow-up period of 26.1 months, there have been 2 (4.7%) recurrences. ::: ::: ::: CONCLUSIONS ::: Our novel technique for posterior component separation was associated with a low perioperative morbidity and a low recurrence rate. Overall, transversus abdominis muscle release may be an important addition to the armamentarium of surgeons undertaking major abdominal wall reconstructions.


title: Inkjet-printed antennas, sensors and circuits on paper substrate

abstract: Inkjet-printing is a very promising technology for the development of microwave circuits and components. Inkjet- printing technology of conductive silver nanoparticles on an organic flexible paper substrate is introduced in this study. The paper substrate is characterised using the T-resonator method. A variety of microwave passive and active devices, as well as complete circuits inkjet-printed on paper substrates are introduced. This work includes inkjet-printed artificial magnetic conductor structures, a substrate integrated waveguide, solar-powered beacon oscillator for wireless power transfer and localisation, energy harvesting circuits and nanocarbon-based gas-sensing materials such as carbon nanotubes and graphene. This study presents an overview of recent advances of inkjet-printed electronics on paper substrate.


title: Sophia-3: A Semiadaptive Cable-Driven Rehabilitation Device With a Tilting Working Plane

abstract: Sophia-3 (string-operated planar haptic interface for arm rehabilitation) is a planar cable-driven device with a tilting working plane. It represents the first application of the adaptive cable-driven design paradigm recently introduced by the authors, featuring a moving pulley-block that allows the robot to achieve excellent force capabilities, despite the low number of cables. This study presents the design, kinematics, and control of the device and results of experimental validation on healthy subjects.


title: MILS virtualization for Integrated Modular Avionics

abstract: Integrated modular avionics (IMA) is driving computer systems to manage and protect increasingly high value and complex information and applications across the aircraft. Virtualization is a promising emerging technology in avionics systems as a mechanism for consolidating disparate operating environments and functions onto a single computer. However, from a security perspective, commercial virtualization solutions add to the already fertile attack surface of general-purpose operating systems. This paper will provide an overview of embedded hypervisor technology and propose a MILS- and IMA-compliant virtualization architecture that assures the safety and security of critical applications while incorporating highly functional general purpose virtualized environments. A practical avionics application of this architecture will be presented.


title: Cycling to School: Increasing Secondary School Enrollment for Girls in India

abstract: We study the impact of an innovative program in the Indian state of Bihar that aimed to reduce the gender gap in secondary school enrollment by providing girls who continued to secondary school with a bicycle that would improve access to school. Using data from a large representative household survey, we employ a triple difference approach (using boys and the neighboring state of Jharkhand as comparison groups) and find that being in a cohort that was exposed to the Cycle program increased girls' age-appropriate enrollment in secondary school by 30% and also reduced the gender gap in age-appropriate secondary school enrollment by 40%. Parametric and non-parametric decompositions of the triple-difference estimate as a function of distance to the nearest secondary school show that the increases in enrollment mostly took place in villages where the nearest secondary school was further away, suggesting that the mechanism for program impact was the reduction in the time and safety cost of school attendance made possible by the bicycle. We find that the Cycle program was much more cost effective at increasing girls' enrolment than comparable conditional cash transfer programs in South Asia, suggesting that the coordinated provision of bicycles to girls may have generated externalities beyond the cash value of the program, including improved safety from girls cycling to school in groups, and changes in patriarchal social norms that proscribed female mobility outside the village, which inhibited female secondary school participation.


The Russian Orthodox Church 1927–1945: Repression and revival


title: A novel variable stiffness mechanism capable of an infinite stiffness range and unlimited decoupled output motion

abstract: In this paper, a novel variable stiffness mechanism is presented, which is capable of achieving an output stiffness with infinite range and an unlimited output motion, i.e., the mechanism output is completely decoupled from the rotor motion, in the zero stiffness configuration. The mechanism makes use of leaf springs, which are engaged at different positions by means of two movable supports, to realize the variable output stiffness. The Euler–Bernoulli leaf spring model is derived and validated through experimental data. By shaping the leaf springs, it is shown that the stiffness characteristic of the mechanism can be changed to fulfill different application requirements. Alternative designs can achieve the same behavior with only one leaf spring and one movable support pin.


title: MODIFIED WAVENUMBER DOMAIN ALGORITHM FOR THREE-DIMENSIONAL MILLIMETER-WAVE IMAGING

abstract: Millimeter-wave (MMW) imaging techniques have been used for the detection of concealed weapons and contraband carried on personnel at airports and other secure locations. The combination of frequency-modulated continuous-wave (FMCW) technology and MMW imaging techniques should lead to compact, light-weight, and low-cost systems which are especially suitable for security and detection application. However, the long signal duration time leads to the failure of the conventional stop-and-go approximation of the pulsed system. Therefore, the motion within the signal duration time needs to be taken into account. Analytical three- dimensional (3-D) backscattered signal model, without using the stop-and-go approximation, is developed in this paper. Then, a wavenumber domain algorithm, with motion compensation, is presented. In addition, conventional wavenumber domain methods use Stolt interpolation to obtain uniform wavenumber samples and compute the fast Fourier transform (FFT). This paper uses the 3- D nonuniform fast Fourier transform (NUFFT) instead of the Stolt interpolation and FFT. The NUFFT-based method is much faster than the Stolt interpolation-based method. Finally, point target simulations are performed to verify the algorithm.


title: Food classification from images using convolutional neural networks

abstract: The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional neural networks have the capability of estimating the score function directly from image pixels. A 2D convolution layer has been utilised which creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. There are multiple such layers, and the outputs are concatenated at parts to form the final tensor of outputs. We also use the Max-Pooling function for the data, and the features extracted from this function are used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 dataset is recognised using the proposed implementation.


title: Alveolar Osteitis and Osteomyelitis of the Jaws

abstract: Postoperative bone healing after oral surgical procedures occurs uneventfully in most cases because of exceptional vascularity of head and neck structures when compared with other anatomic sites. However, in certain patients, the normal process of osseous healing can be delayed and, in some cases, often because of multiple coexisting factors, the sites can become infected, with extension of the infection into medullary bone. This process is termed osteomyelitis. The exact definition of osteomyelitis is inflammation of the osseous medulla. The term osteitis reflects a more superficial inflammation of the cortex of the bone. Most often, infections of the medulla also involve the cortex by the pathways of haversian systems and often affect the overlying periosteum. Hence the term osteomyelitis is more commonly used to describe alveolar and basal bone infections. The infectious process in the marrow space of bones has beenwell documented in early man. The oldest known case of mandibular osteomyelitis dates back to the Pleistocene epoch about 1.6million years ago and fossil findings in the jaw of a 12-year-old Homo erectus skeleton found in Kenya. Since the discovery of bacteria and the advancement in antimicrobial therapy, there has been a significant decrease in the incidence with improved outcomes in the care of these infectious conditions. Over the years, multiple classification schemes have been proposed,


title: Automated Essay Scoring Using Incremental Latent Semantic Analysis

abstract: Writing has been increasingly regarded by the testers of language tests as an important indicator to assess the language skill of testees. As such tests become more and more popular and the number of testees becomes larger, it is a huge task to score so many essays by raters. So far, many methods have been used to solve this problem and the traditional method is Latent Semantic Analysis (LSA). In this paper, we introduce a new incremental method of LSA to score essays effectively when the dataset is massive. By comparison of the traditional method and our new incremental method, concerning the running time and memory usage, experimental results make it obvious that the incremental method has a huge advantage over the traditional method. Furthermore, we use real corpora of test essays submitted to the MHK test (Chinese Proficiency Test for Minorities), to demonstrate that the incremental method is not only efficient but also effective in performing LSA. The experimental results also show that when using incremental LSA, the scoring accuracy can reach 88.8%.


title: Detecting Malicious Websites by Learning IP Address Features

abstract: Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing various blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information regarding new malicious websites. To tackle this problem, we propose a new method for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URL and DNS. While the strings that form URLs or domain names are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-bytes strings. We develop a lightweight and scalable detection scheme based on the machine learning technique. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. We validate the effectiveness of our approach by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our method can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.


title: SenSec: Mobile security through passive sensing

abstract: We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices. SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device. SenSec calculates the sureness that the mobile device is being used by its owner. Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security. In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75% accuracy in identifying the users and 71.3% accuracy in detecting the non-owners with only 13.1% false alarms.


title: Digital beamforming in a large conformal phased array antenna for satellite TT&C — Demonstration

abstract: This paper presents the design, development, theoretical and measured results of Digital Beam Forming (DBF) hardware for use with a large, multi-beam conformal phased array antennas such as the Geodesic Dome Phased Array Antenna (GDPAA) shown in Figure 1[1]. The GDPAA system with DBF on receive can provide multiple simultaneous links for satellite tracking, telemetry, and command (TT&C) functions including adaptive pattern control for anti-jamming or interference suppression, and high resolution direction finding.


title: Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system

abstract: Abstract As electric vehicles (EVs) have gained an increasing market penetration rate, the traffic on urban roads will tend to be a mix of traditional gasoline vehicles (GVs) and EVs. These two types of vehicles have different energy consumption characteristics, especially the high energy efficiency and energy recuperation system of EVs. When GVs and EVs form a platoon that is recognized as an energy-friendly traffic pattern, it is critical to holistically consider the energy consumption characteristics of all vehicles to maximize the energy efficiency benefit of platooning. To tackle this issue, this paper develops an optimal control model as a foundation to provide eco-driving suggestions to the mixed-traffic platoon. The proposed model leverages the promising connected vehicle technology assuming that the speed advisory system can obtain the information on the characteristics of all platoon vehicles. To enhance the model applicability, the study proposes two eco-driving advisory strategies based on the developed optimal control model. One strategy provides the lead vehicle an acceleration profile, while the other provides a set of targeted cruising speeds. The acceleration-based eco-driving advisory strategy is suitable for platoons with an automated leader, and the speed-based advisory strategy is more friendly for platoons with a human-operated leader. Results of numerical experiments demonstrate the significance when the eco-driving advisory system holistically considers energy consumption characteristics of platoon vehicles.


title: A System-on-Chip Solution for Point-of-Care Ultrasound Imaging Systems: Architecture and ASIC Implementation

abstract: In this paper, we present a novel system-on-chip (SOC) solution for a portable ultrasound imaging system (PUS) for point-of-care applications. The PUS-SOC includes all of the signal processing modules (i.e., the transmit and dynamic receive beamformer modules, mid- and back-end processors, and color Doppler processors) as well as an efficient architecture for hardware-based imaging methods (e.g., dynamic delay calculation, multi-beamforming, and coded excitation and compression). The PUS-SOC was fabricated using a UMC 130-nm NAND process and has 16.8 GFLOPS of computing power with a total equivalent gate count of 12.1 million, which is comparable to a Pentium-4 CPU. The size and power consumption of the PUS-SOC are $27\times 27~{\rm mm}^{2}$ and 1.2 W, respectively. Based on the PUS-SOC, a prototype hand-held US imaging system was implemented. Phantom experiments demonstrated that the PUS-SOC can provide appropriate image quality for point-of-care applications with a compact PDA size ( $200\times 120\times 45~{\rm mm}^{3}$ ) and 3 hours of battery life.


title: On the Structure of Resultative Participles in English

abstract: The article examines the structure of resultative participles in English: participles that denote a state resulting from a prior event, such as The cake is flattened or The metal is hammered.The analysis identifies distinct stative participles that derive from the different heights at which aspectual morphemes attach in a verbalizing structure.The Aspect head involved in resultative participles is shown to attach to a vP that is also found in (a) the formation of deadjectival verbs and (b) verb phrases with resultative secondary predicates, like John hammered the metal flat. These distinct constructions are shown to have a shared structural subcomponent.The analysis proposed here is compared with Lexicalist approaches employing the verbal versus adjectival passive distinction.It is shown that a uniformly syntactic analysis of the participles is superior to the Lexicalist alternative.


title: Salt-and-pepper noise detection and reduction using fuzzy switching median filter

abstract: This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.


title: Latent Dictionary Learning for Sparse Representation Based Classification

abstract: Dictionary learning (DL) for sparse coding has shown promising results in classification tasks, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open question. The existing dictionary learning approaches simply fix a dictionary atom to be either class-specific or shared by all classes beforehand, ignoring that the relationship needs to be updated during DL. To address this issue, in this paper we propose a novel latent dictionary learning (LDL) method to learn a discriminative dictionary and build its relationship to class labels adaptively. Each dictionary atom is jointly learned with a latent vector, which associates this atom to the representation of different classes. More specifically, we introduce a latent representation model, in which discrimination of the learned dictionary is exploited via minimizing the within-class scatter of coding coefficients and the latent-value weighted dictionary coherence. The optimal solution is efficiently obtained by the proposed solving algorithm. Correspondingly, a latent sparse representation based classifier is also presented. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse representation and dictionary learning approaches for action, gender and face recognition.


title: Geodesic image and video editing

abstract: This article presents a new, unified technique to perform general edge-sensitive editing operations on n-dimensional images and videos efficiently. The first contribution of the article is the introduction of a Generalized Geodesic Distance Transform (GGDT), based on soft masks. This provides a unified framework to address several edge-aware editing operations. Diverse tasks such as denoising and nonphotorealistic rendering are all dealt with fundamentally the same, fast algorithm. Second, a new Geodesic Symmetric Filter (GSF) is presented which imposes contrast-sensitive spatial smoothness into segmentation and segmentation-based editing tasks (cutout, object highlighting, colorization, panorama stitching). The effect of the filter is controlled by two intuitive, geometric parameters. In contrast to existing techniques, the GSF filter is applied to real-valued pixel likelihoods (soft masks), thanks to GGDTs and it can be used for both interactive and automatic editing. Complex object topologies are dealt with effortlessly. Finally, the parallelism of GGDTs enables us to exploit modern multicore CPU architectures as well as powerful new GPUs, thus providing great flexibility of implementation and deployment. Our technique operates on both images and videos, and generalizes naturally to n-dimensional data. The proposed algorithm is validated via quantitative and qualitative comparisons with existing, state-of-the-art approaches. Numerous results on a variety of image and video editing tasks further demonstrate the effectiveness of our method.


title: Nuclear DNA phylogeny of the squirrels (Mammalia : Rodentia) and the evolution of arboreality from c-myc and RAG1

abstract: Abstract Although the family Sciuridae is large and well known, phylogenetic analyses are scarce. We report on a comprehensive molecular phylogeny for the family. Two nuclear genes (c-myc and RAG1) comprising approximately 4500 bp of data (most in exons) are applied for the first time to rodent phylogenetics. Parsimony, likelihood, and Bayesian analyses of the separate gene regions and combined data reveal five major lineages and refute the conventional elevation of the flying squirrels (Pteromyinae) to subfamily status. Instead, flying squirrels are derived from one of the tree squirrel lineages. C-myc indels corroborate the sequence-based topologies. The common ancestor of extant squirrels appears to have been arboreal, confirming the fossil evidence. The results also reveal an unexpected clade of mostly terrestrial squirrels with African and Holarctic centers of diversity. We present a revised classification of squirrels. Our results demonstrate the phylogenetic utility of relatively slowly evolving nuclear exonic data even for relatively recent clades.


title: Wireless ethernet for IoT: A case study

abstract: Wireless ethernet is one of the most important wireless technology that enhances our personal environment, either work or private, by means of networking or a variety of personal and wearable devices within the space and with the outside world. It refers to a link layer protocol in the TCP/IP (Transmission Control Protocol/Internet Protocol) stack, describing how networked devices can format data for transmission to other network devices on the same network segment, and how to put that data out on the network connection. This paper represents an overall view of the wireless Ethernet features, advantages, applications and integration of mobile communication. It initially assumed a shared medium: multiple devices on each segment of the network, connected daisy chain at first but later in star topology via Ethernethubs(which replicated all traffic received on any port to every other port).


A Substitution Cipher for Musical Cryptography


title: Hybrid recommendation model based on incremental collaborative filtering and content-based algorithms

abstract: In the past decade, online news consumption has been steadily growing. New articles are published every few minutes, and user preferences are also constantly changing. Traditional recommender systems update model at regular intervals, which cannot adjust recommendation list dynamically according to the changes of user preferences. In this paper, we propose a hybrid recommendation model which contains two key components: incremental update item-based collaborative filtering (CF) and latent semantic analysis based relative term frequency algorithms. The hybrid recommendation model adjusts recommendation list dynamically by updating similarity table of items incrementally in incremental update item-based CF module, moreover, combining collaborative filtering and content-based algorithm ensures the relevance of recommendation articles. Results show that our proposed hybrid recommendation model outperforms traditional recommender approaches.


title: Split hand/foot malformation syndrome (SHFM): rare congenital orthopaedic disorder

abstract: A 17-year-old man, the third child of physically normal parents, was diagnosed with a case of cleft hand (lobster hand) in our outpatient department during a routine visit. He was born with deformed hands and feet in the form of syndactyly of thumb with the index finger in the upper limbs. He had a median cleft of the hands (figures 1–⇓3). There was no facial dysmorphism in the patient. He had a history of congenital heart disease (ventricular septal defect with aortic regurgitation) and seronegative hypothyroididsm. The two siblings of the index case were physically normal. Our patient most probably had autosomal mode of inheritance. The genetic analysis of the patient showed mutation in TP63 gene ( R280C mutation) causing split hand/foot malformation syndrome (SHFM) type 4. ::: ::: ::: ::: Figure 1 ::: Image showing both …


title: ZooplanktoNet: Deep convolutional network for zooplankton classification

abstract: Zooplankton are quite significant to the ocean ecosystem for stabilizing balance of the ecosystem and keeping the earth running normally. Considering the significance of zooplantkon, research about zooplankton has caught more and more attentions. And zooplankton recognition has shown great potential for science studies and mearsuring applications. However, manual recognition on zooplankton is labour-intensive and time-consuming, and requires professional knowledge and experiences, which can not scale to large-scale studies. Deep learning approach has achieved remarkable performance in a number of object recognition benchmarks, often achieveing the current best performance on detection or classification tasks and the method demonstrates very promising and plausible results in many applications. In this paper, we explore a deep learning architecture: ZooplanktoNet to classify zoolankton automatically and effectively. The deep network is characterized by capturing more general and representative features than previous predefined feature extraction algorithms in challenging classification. Also, we incorporate some data augmentation to aim at reducing the overfitting for lacking of zooplankton images. And we decide the zooplankton class according to the highest score in the final predictions of ZooplanktoNet. Experimental results demonstrate that ZooplanktoNet can solve the problem effectively with accuracy of 93.7% in zooplankton classification.


title: Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

abstract: We describe a method for recovering the underlying parametrization of scattered data (m(i)) lying on a manifold M embedded in high-dimensional Euclidean space. The method, Hessian-based locally linear embedding, derives from a conceptual framework of local isometry in which the manifold M, viewed as a Riemannian submanifold of the ambient Euclidean Space R(n), is locally isometric to an open, connected subset Θ of Euclidean space R(d). Because Θ does not have to be convex, this framework is able to handle a significantly wider class of situations than the original ISOMAP algorithm. The theoretical framework revolves around a quadratic form H(f) = ∫(M)||H(f)(m)||²(F)dm defined on functions f: M--> R. Here Hf denotes the Hessian of f, and H(f) averages the Frobenius norm of the Hessian over M. To define the Hessian, we use orthogonal coordinates on the tangent planes of M. The key observation is that, if M truly is locally isometric to an open, connected subset of R(d), then H(f) has a (d + 1)-dimensional null space consisting of the constant functions and a d-dimensional space of functions spanned by the original isometric coordinates. Hence, the isometric coordinates can be recovered up to a linear isometry. Our method may be viewed as a modification of locally linear embedding and our theoretical framework as a modification of the Laplacian eigenmaps framework, where we substitute a quadratic form based on the Hessian in place of one based on the Laplacian.


title: CHOP: Adaptive filter-based DRAM caching for CMP server platforms

abstract: As manycore architectures enable a large number of cores on the die, a key challenge that emerges is the availability of memory bandwidth with conventional DRAM solutions. To address this challenge, integration of large DRAM caches that provide as much as 5× higher bandwidth and as low as 1/3rd of the latency (as compared to conventional DRAM) is very promising. However, organizing and implementing a large DRAM cache is challenging because of two primary tradeoffs: (a) DRAM caches at cache line granularity require too large an on-chip tag area that makes it undesirable and (b) DRAM caches with larger page granularity require too much bandwidth because the miss rate does not reduce enough to overcome the bandwidth increase. In this paper, we propose CHOP (Caching HOt Pages) in DRAM caches to address these challenges. We study several filter-based DRAM caching techniques: (a) a filter cache (CHOP-FC) that profiles pages and determines the hot subset of pages to allocate into the DRAM cache, (b) a memory-based filter cache (CHOP-MFC) that spills and fills filter state to improve the accuracy and reduce the size of the filter cache and (c) an adaptive DRAM caching technique (CHOP-AFC) to determine when the filter cache should be enabled and disabled for DRAM caching. We conduct detailed simulations with server workloads to show that our filter-based DRAM caching techniques achieve the following: (a) on average over 30% performance improvement over previous solutions, (b) several magnitudes lower area overhead in tag space required for cache-line based DRAM caches, (c) significantly lower memory bandwidth consumption as compared to page-granular DRAM caches.


title: Cigarette Advertising and Consumption in the United States: 1961–1990

abstract: This article examines the relationship of aggregate advertising expenditure on cigarette consumption in the United States from 1961 to 1990. The study uses quarterly data in a stepwise regression analysis to determine the relationship of advertising to consumption. The findings and conclusions provide implications for public policy issues related to tobacco advertising.


title: The attention of a society towards corporate brand name and its determinants within the information-rich economy

abstract: Corporate reputation can be a valuable corporate asset but a complicated concept to measure. Similarly, it is difficult to identify and quantify the determinants of corporate reputation. Considering the information rich economy and cognitive limits of economic agents (limited attention), ability to identify the effective channels of corporate communication can be a significant advantage for a corporation. This paper contributes with the methodology that enables to evaluate the attention of a society towards corporations and its determinants. The paper proposes the Googleâ€™s search volume for the specific corporation as a proxy for the attention of a society towards that company. To identify determinants of attention, the paper employs qualitative approaches Corporate Reputation QuotientTM and RepTrakÂ®, that defines the dimensions of corporate reputation. Further, the paper employs Bayesian model averaging (BMA) to handle the uncertainty in a choice of particular determinants. Set of variables identified by BMA is estimated in a linear dynamic panel environment. Delivered results enable to evaluate the current channels of corporate communication with customers and its costs.


title: A Latent Dirichlet Allocation Method for Selectional Preferences

abstract: The computation of selectional preferences, the admissible argument values for a relation, is a well-known NLP task with broad applicability. We present LDA-SP, which utilizes LinkLDA (Erosheva et al., 2004) to model selectional preferences. By simultaneously inferring latent topics and topic distributions over relations, LDA-SP combines the benefits of previous approaches: like traditional class-based approaches, it produces human-interpretable classes describing each relation's preferences, but it is competitive with non-class-based methods in predictive power. ::: ::: We compare LDA-SP to several state-of-the-art methods achieving an 85% increase in recall at 0.9 precision over mutual information (Erk, 2007). We also evaluate LDA-SP's effectiveness at filtering improper applications of inference rules, where we show substantial improvement over Pantel et al.'s system (Pantel et al., 2007).


title: A review of image warping methods

abstract: Image warping is a transformation which maps all positions in one image plane to positions in a second plane. It arises in many image analysis problems, whether in order to remove optical distortions introduced by a camera or a particular viewing perspective, to register an image with a map or template, or to align two or more images. The choice of warp is a compromise between a smooth distortion and one which achieves a good match. Smoothness can be ensured by assuming a parametric form for the warp or by constraining it using differential equations. Matching can be specified by points to be brought into alignment, by local measures of correlation between images, or by the coincidence of edges. Parametric and non-parametric approaches to warping, and matching criteria, are reviewed.


title: Performance Measurements of Virtual Reality Systems: Quantifying the Timing and Positioning Accuracy

abstract: We propose the very first non-intrusive measurement methodology for quantifying the performance of commodity Virtual Reality (VR) systems. Our methodology considers the VR system under test as a black-box and works with any VR applications. Multiple performance metrics on timing and positioning accuracy are considered, and detailed testbed setup and measurement steps are presented. We also apply our methodology to several VR systems in the market, and carefully analyze the experiment results. We make several observations: (i) 3D scene complexity affects the timing accuracy the most, (ii) most VR systems implement the dead reckoning algorithm, which incurs a non-trivial correction latency after incorrect predictions, and (iii) there exists an inherent trade-off between two positioning accuracy metrics: precision and sensitivity.


title: An Efficient Algorithm for Fractal Analysis of Textures

abstract: In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The decomposition of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which we also propose in this work. We evaluated SFTA for the tasks of content-based image retrieval (CBIR) and image classification, comparing its performance to that of other widely employed feature extraction methods such as Haralick and Gabor filter banks. SFTA achieved higher precision and accuracy for CBIR and image classification. Additionally, SFTA was at least 3.7 times faster than Gabor and 1.6 times faster than Haralick with respect to feature extraction time.


title: Mostly-Optimistic Concurrency Control for Highly Contended Dynamic Workloads on a Thousand Cores

abstract: Future servers will be equipped with thousands of CPU cores and deep memory hierarchies. Traditional concurrency control (CC) schemes---both optimistic and pessimistic---slow down orders of magnitude in such environments for highly contended workloads. Optimistic CC (OCC) scales the best for workloads with few conflicts, but suffers from clobbered reads for high conflict workloads. Although pessimistic locking can protect reads, it floods cache-coherence backbones in deep memory hierarchies and can also cause numerous deadlock aborts. ::: ::: This paper proposes a new CC scheme, mostly-optimistic concurrency control (MOCC), to address these problems. MOCC achieves orders of magnitude higher performance for dynamic workloads on modern servers. The key objective of MOCC is to avoid clobbered reads for high conflict workloads, without any centralized mechanisms or heavyweight interthread communication. To satisfy such needs, we devise a native, cancellable reader-writer spinlock and a serializable protocol that can acquire, release and re-acquire locks in any order without expensive interthread communication. For low conflict workloads, MOCC maintains OCC's high performance without taking read locks. ::: ::: Our experiments with high conflict YCSB workloads on a 288-core server reveal that MOCC performs 8× and 23× faster than OCC and pessimistic locking, respectively. It achieves 17 million TPS for TPC-C and more than 110 million TPS for YCSB without conflicts, 170× faster than pessimistic methods.


Chatbots Meet eHealth: Automatizing Healthcare


title: Learning live autonomous navigation: A model car with hardware arduino neurons

abstract: Previously we developed an implementation for an easily expandable hardware Artificial Neural Network (ANN) capable of learning using inexpensive, off-the-shelf Arduino Pro Mini microprocessors. This ANN system is unique, general use, unspecialized, and inexpensive. The implementation involves a one neuron per microchip representation, a ratio which allows for computational parallelism and ANN architecture flexibility inherent to biological neural networks. Learning happens completely on hardware via backpropagation without the need for communication with a computer. Tests showed successful, dynamic learning of the logical operations OR, AND, XOR, and XNOR. In this paper, we demonstrate the usage and strength of this implementation by applying the same framework to learn live obstacle avoidance and autonomous navigation for a 1∶24 scale model car equipped with ultrasonic distance sensors. This test of the application involved a user who supervised the learning and a method to easily transition between testing and training the ANN on the car via Bluetooth. Results show that the hardware ANN consistently learns to navigate the car through an obstacle course from entrance to exit and vice versa with no collisions.


TruSpy: Cache Side-Channel Information Leakage from the Secure World on ARM Devices


title: Generating Coherent Event Schemas at Scale

abstract: Chambers and Jurafsky (2009) demonstrated that event schemas can be automatically induced from text corpora. However, our analysis of their schemas identifies several weaknesses, e.g., some schemas lack a common topic and distinct roles are incorrectly mixed into a single actor. It is due in part to their pair-wise representation that treats subjectverb independently from verb-object. This often leads to subject-verb-object triples that are not meaningful in the real-world. We present a novel approach to inducing open-domain event schemas that overcomes these limitations. Our approach uses cooccurrence statistics of semantically typed relational triples, which we call Rel-grams (relational n-grams). In a human evaluation, our schemas outperform Chambers’s schemas by wide margins on several evaluation criteria. Both Rel-grams and event schemas are freely available to the research community.


title: Query intent prediction and recommendation

abstract: In this tutorial we first characterize user queries before focusing in two important problems related to search engines: query intention prediction and query recommendation


title: Empathizing and Systemizing in Adults with and without Autism Spectrum Conditions: Cross-Cultural Stability

abstract: This study tests the empathizing-systemizing (E-S) theory of sex differences and the extreme male brain (EMB) theory of autism. Three groups of participants took part: n = 48 people with autism spectrum, n = 137 general population controls, and n = 1,250 university student controls. Each participant completed the Empathy Quotient (EQ) and the Systemizing Quotient (SQ). Results: The autism spectrum condition (ASC) group scored significantly lower than controls on the EQ, and significantly higher on the SQ. Among both control groups, females scored significantly higher than males on the EQ, whilst males scored significantly higher than females on the SQ. The distribution of ‘brain types’, based on the difference between EQ and SQ scores, showed distinct profiles for people with ASC, control males and control females.


title: Deconvolving Images with Unknown Boundaries Using the Alternating Direction Method of Multipliers

abstract: The alternating direction method of multipliers (ADMM) has recently sparked interest as a flexible and efficient optimization tool for inverse problems, namely, image deconvolution and reconstruction under non-smooth convex regularization. ADMM achieves state-of-the-art speed by adopting a divide and conquer strategy, wherein a hard problem is split into simpler, efficiently solvable sub-problems (e.g., using fast Fourier or wavelet transforms, or simple proximity operators). In deconvolution, one of these sub-problems involves a matrix inversion (i.e., solving a linear system), which can be done efficiently (in the discrete Fourier domain) if the observation operator is circulant, i.e., under periodic boundary conditions. This paper extends ADMM-based image deconvolution to the more realistic scenario of unknown boundary, where the observation operator is modeled as the composition of a convolution (with arbitrary boundary conditions) with a spatial mask that keeps only pixels that do not depend on the unknown boundary. The proposed approach also handles, at no extra cost, problems that combine the recovery of missing pixels (i.e., inpainting) with deconvolution. We show that the resulting algorithms inherit the convergence guarantees of ADMM and illustrate its performance on non-periodic deblurring (with and without inpainting of interior pixels) under total-variation and frame-based regularization.


title: Real-time Accurate Object Detection using Multiple Resolutions

abstract: We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.


title: A Neural Collaborative Filtering Model with Interaction-based Neighborhood

abstract: Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, they tend to be ineffective to capture localized information. Localized information, such as neighborhood, is important to recommender systems in complementing the user-item interaction data. Based on this consideration, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF). To the best of our knowledge, it is the first time that the neighborhood information is integrated into the neural collaborative filtering methods. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model for the implicit recommendation task.


title: An embedded artificial skin for humanoid robots

abstract: A novel artificial skin for covering the whole body of a humanoid robot is presented. It provides pressure measurements and shape information about the contact surfaces between the robot and the environment. The system is based on a mesh of sensors interconnected in order to form a networked structure. Each sensor has 12 capacitive taxels, has a triangular shape and is supported by a flexible substrate in order to conform to smooth curved surfaces. Three communications ports placed along the sides of each sensor sides allow communications with adjacent sensors. The tactile measurements are sent to embed microcontroller boards using serial bus communication links. The system can adaptively reduce its spatial resolution, improving the response time. This feature is very useful for detecting the first contact very rapidly, at a lower spatial resolution, and then increase the spatial resolution in the region of contact for accurate reconstruction of the contact pressure distribution.


title: The ecosystem of SpatialHadoop

abstract: There is a recent outbreak in the amounts of spatial data generated by different sources, e.g., smart phones, space telescopes, and medical devices, which urged researchers to exploit the existing distributed systems to process such amounts of spatial data. However, as these systems are not designed for spatial data, they cannot fully utilize its spatial properties to achieve high performance. In this paper, we describe SpatialHadoop, a full-fledged MapReduce framework which extends Hadoop to support spatial data efficiently. SpatialHadoop consists of four main layers, namely, language, indexing, query processing, and visualization. The language layer provides a high level language with standard spatial data types and operations to make the system accessible to non-technical users. The indexing layer supports standard spatial indexes, such as grid, R-tree and R+-tree, inside Hadoop file system in order to speed up spatial operations. The query processing layer encapsulates the spatial operations supported by SpatialHadoop such as range query, k nearest neighbor, spatial join and computational geometry operations. Finally, the visualization layer allows users to produce images that describe very large datasets to make it easier to explore and understand big spatial data. SpatialHadoop is already used as a main component in several real systems such as MNTG, TAREEG, TAGHREED, and SHAHED.


title: A novel objective function for improved phoneme recognition using time delay neural networks

abstract: Single-speaker and multispeaker recognition results are presented for the voice-stop consonants /b,d,g/ using time-delay neural networks (TDNNs) with a number of enhancements, including a new objective function for training these networks. The new objective function, called the classification figure of merit (CFM), differs markedly from the traditional mean-squared-error (MSE) objective function and the related cross entropy (CE) objective function. Where the MSE and CE objective functions seek to minimize the difference between each output node and its ideal activation, the CFM function seeks to maximize the difference between the output activation of the node representing incorrect classifications. A simple arbitration mechanism is used with all three objective functions to achieve a median 30% reduction in the number of misclassifications when compared to TDNNs trained with the traditional MSE back-propagation objective function alone. >


title: Deep Word Embeddings for Visual Speech Recognition

abstract: In this paper we present a deep learning architecture for extracting word embeddings for visual speech recognition. The embeddings summarize the information of the mouth region that is relevant to the problem of word recognition, while suppressing other types of variability such as speaker, pose and illumination. The system is comprised of a spatiotemporal convolutional layer, a Residual Network and bidirectional LSTMs and is trained on the Lipreading in-the-wild database. We first show that the proposed architecture goes beyond state-of-the-art on closed-set word identification, by attaining 11.92% error rate on a vocabulary of 500 words. We then examine the capacity of the embeddings in modelling words unseen during training. We deploy Probabilistic Linear Discriminant Analysis (PLDA) to model the embeddings and perform low-shot learning experiments on words unseen during training. The experiments demonstrate that word-level visual speech recognition is feasible even in cases where the target words are not included in the training set.


title: Critical Success Factors for a Customer Relationship Management Strategy

abstract: Most organizations have perceived the customer relationship management (CRM) concept as a technological solution for problems in individual areas, accompanied by a great deal of uncoordinated initiatives. Nevertheless, CRM must be conceived as a strategy, due to its human, technological, and processes implications, at the time an organization decides to implement it. On this basis, the main goal stated in this research is to propose, justify, and validate a model based on critical success factors (CSFs) that will constitute a guide for companies in the implementation and diagnosis of a CRM strategy. The model is conformed by a set of 13 CSFs with their 55 corresponding metrics, which will serve as a guide for organizations wishing to apply this type of strategy. These factors cover the three key aspects of every CRM strategy (human factor, processes, and technology); giving a global focus and propitiating success in the implementation of a CRM strategy. These CSFs – and their metrics – were evaluated by a group of internationally experts allowing determining guidelines for a CRM implementation as well as the probable causes of the deWciencies in past projects. DOI: 10.5901/mjss.2013.v4n10p753


title: Packet Switching in Radio Channels: Part I -- Carrier sense multiple-access modes and their throughput-delay characteristics

abstract: Radio communication is considered as a method for providing remote terminal access to computers. Digital byte streams from each terminal are partitioned into packets (blocks) and transmitted in a burst mode over a shared radio channel. When many terminals operate in this fashion, transmissions may conflict with and destroy each other. A means for controlling this is for the terminal to sense the presence of other transmissions; this leads to a new method for multiplexing in a packet radio environment: carrier sense multiple access (CSMA). Two protocols are described for CSMA and their throughput-delay characteristics are given. These results show the large advantage CSMA provides as compared to the random ALOHA access modes.


title: Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions

abstract: Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fueled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public Clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.


title: Robust Graph Regularized Nonnegative Matrix Factorization for Clustering

abstract: Matrix factorization is often used for data representation in many data mining and machine-learning problems. In particular, for a dataset without any negative entries, nonnegative matrix factorization (NMF) is often used to find a low-rank approximation by the product of two nonnegative matrices. With reduced dimensions, these matrices can be effectively used for many applications such as clustering. The existing methods of NMF are often afflicted with their sensitivity to outliers and noise in the data. To mitigate this drawback, in this paper, we consider integrating NMF into a robust principal component model, and design a robust formulation that effectively captures noise and outliers in the approximation while incorporating essential nonlinear structures. A set of comprehensive empirical evaluations in clustering applications demonstrates that the proposed method has strong robustness to gross errors and superior performance to current state-of-the-art methods.


title: Exploiting Topic based Twitter Sentiment for Stock Prediction

abstract: This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a con- tinuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opin- ion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time series to predict the market. Experiments on real-life S&P100 Index show that our approach is effective and performs better than existing state-of-the-art non-topic based methods.


title: The USC CreativeIT database of multimodal dyadic interactions: from speech and full body motion capture to continuous emotional annotations

abstract: Improvised acting is a viable technique to study expressive human communication and to shed light into actors' creativity. The USC CreativeIT database provides a novel, freely-available multimodal resource for the study of theatrical improvisation and rich expressive human behavior (speech and body language) in dyadic interactions. The theoretical design of the database is based on the well-established improvisation technique of Active Analysis in order to provide naturally induced affective and expressive, goal-driven interactions. This database contains dyadic theatrical improvisations performed by 16 actors, providing detailed full body motion capture data and audio data of each participant in an interaction. The carefully engineered data collection, the improvisation design to elicit natural emotions and expressive speech and body language, as well as the well-developed annotation processes provide a gateway to study and model various aspects of theatrical performance, expressive behaviors and human communication and interaction.


title: Nested saturation control of an UAV carrying a suspended load

abstract: This paper addresses the problem of using unmanned aerial vehicles for the transportation of suspended loads. The proposed solution introduces a novel control law capable of steering the aerial robot to a desired reference while simultaneously limiting the sway of the payload. The stability of the equilibrium is proven rigorously through the application of the nested saturation formalism. Numerical simulations demonstrating the effectiveness of the controller are provided.


title: ScalableBody: a telepresence robot that supports face position matching using a vertical actuator

abstract: Seeing one's partner's face during remote conversation is one of the most important factors for effective communication. When using a telepresence robot, matching face positions with one's partner is sometimes difficult, because face position varies in different situations (e.g., standing or sitting). However, existing telepresence robots cannot change their height. Moreover, due to limited camera angle, the conversation partner's face is often partly cut off in the camera view. Therefore, users cannot communicate while seeing each other's faces. To overcome these problems, we designed a telepresence robot called ScalableBody. ScalableBody has a vertical actuator that allows it to change its height and an omnidirectional camera that provides a wide view. The robot facilitates communication for different contexts using vertical actuation to match the conversation partners' face positions. Furthermore, the operator can see a partner's face in any direction through an omnidirectional camera. This approach can also provide users with the experience of being a different height, as if a giant or a child. In this paper, we describe the vertical actuator mechanism and report our user study on the telepresence robot.


title: The Maturing of a Business Intelligence Maturity Model

abstract: The ongoing high relevance of business intelligence (BI) for the management and competitiveness of organizations requires a continuous, transparent, and detailed assessment of existing BI solutions in the enterprise. This paper presents a BI maturity model (called biMM) that has been developed and refined over years. It is used for both, in surveys to determine the overall BI maturity in German speaking countries and for the individual assessment in organizations. A recently conducted survey shows that the current average BI maturity can be assigned to the third stage (out of five stages). Comparing future (planned) activities and current challenges allows the derivation of a BI research agenda. The need for action includes among others emphasizing BI specific organizational structures, such as the establishment of BI competence centers, a stronger focus on profitability, and improved effectiveness of the BI architecture.


title: Topic-bridged PLSA for cross-domain text classification

abstract: In many Web applications, such as blog classification and new-sgroup classification, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data in a related but different domain. Traditional text classification ap-proaches are not able to cope well with learning across different domains. In this paper, we propose a novel cross-domain text classification algorithm which extends the traditional probabilistic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled data, which come from different but related domains, into a unified probabilistic model. We call this new model Topic-bridged PLSA, or TPLSA. By exploiting the common topics between two domains, we transfer knowledge across different domains through a topic-bridge to help the text classification in the target domain. A unique advantage of our method is its ability to maximally mine knowledge that can be transferred between domains, resulting in superior performance when compared to other state-of-the-art text classification approaches. Experimental eval-uation on different kinds of datasets shows that our proposed algorithm can improve the performance of cross-domain text classification significantly.


title: MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability

abstract: The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering approaches in terms of accuracy, this paper investigates the parallelization and scalability of a common and effective fuzzy clustering algorithm named fuzzy c-means (FCM) algorithm. The algorithm is parallelized using the MapReduce paradigm outlining how the Map and Reduce primitives are implemented. A validity analysis is conducted in order to show that the implementation works correctly achieving competitive purity results compared to state-of-the art clustering algorithms. Furthermore, a scalability analysis is conducted to demonstrate the performance of the parallel FCM implementation with increasing number of computing nodes used.


title: A Combination Approach to Web User Profiling

abstract: In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the “semantic”-based user profile from the Web. Previously, Web user profiling was often undertaken by creating a list of keywords for the user, which is (sometimes even highly) insufficient for main applications. This article formalizes the profiling problem as several subtasks: profile extraction, profile integration, and user interest discovery. We propose a combination approach to deal with the profiling tasks. Specifically, we employ a classification model to identify relevant documents for a user from the Web and propose a Tree-Structured Conditional Random Fields (TCRF) to extract the profile information from the identified documents; we propose a unified probabilistic model to deal with the name ambiguity problem (several users with the same name) when integrating the profile information extracted from different sources; finally, we use a probabilistic topic model to model the extracted user profiles, and construct the user interest model. Experimental results on an online system show that the combination approach to different profiling tasks clearly outperforms several baseline methods. The extracted profiles have been applied to expert finding, an important application on the Web. Experiments show that the accuracy of expert finding can be improved (ranging from +6p to +26p in terms of MAP) by taking advantage of the profiles.


title: TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

abstract: Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the planner. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games, with deeper trees often outperforming shallower ones. We also present a qualitative analysis that sheds light on the trees learned by TreeQN.


title: Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition

abstract: In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR). On one hand, SSDCNN can learn the representation of Online Handwritten Chinese Character (OLHCC) by incorporating the natural sequence information of the strokes. On the other hand, SSDCNN can incorporate eight-directional features in a natural way. In order to train SSDCNN, we divide the process of training into two stages: 1) The training data is used to pre-train the whole architecture until the performance tends to converge. 2) Fully-connected neural network which is used to combine the stroke sequence-dependent representation with eight-directional features and softmax layer are further trained. Experiments were conducted on the OLHCCR competition tasks of ICDAR 2013. Results show that, SSDCNN can reduce the recognition error by 50\% (5.13\% vs 2.56\%) compared to the model which only use eight-directional features. The proposed SSDCNN achieves 97.44\% accuracy which reduces the recognition error by about 1.9\% compared with the best submitted system on ICDAR2013 competition. These results indicate that SSDCNN can exploit the stroke sequence information to learn high-quality representation of OLHCC. It also shows that the learnt representation and the classical eight-directional features complement each other within the SSDCNN architecture.


title: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

abstract: The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. ::: ::: These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.


title: Segmentation-based object tracking using image warping and Kalman filtering

abstract: We propose a segmentation-based method of object tracking using image warping and Kalman filtering. The object region is defined to include a group of patches, which are obtained by a watershed algorithm. In a robust M-estimator framework, we estimate dominant motion of the object region. A linear Kalman filter is employed to predict the estimated affine motion parameters based on a second order kinematic model. Image (affine) warping is performed to predict the object region in the next frame. The warping error of each watershed segment (patch) and its rate of overlapping with the predicted region are utilized for classification of watershed segments near the object border. Applications of head and hand tracking using this method demonstrate its performance.


title: Content Security Problems?: Evaluating the Effectiveness of Content Security Policy in the Wild

abstract: Content Security Policy (CSP) is an emerging W3C standard introduced to mitigate the impact of content injection vulnerabilities on websites. We perform a systematic, large-scale analysis of four key aspects that impact on the effectiveness of CSP: browser support, website adoption, correct configuration and constant maintenance. While browser support is largely satisfactory, with the exception of few notable issues, our analysis unveils several shortcomings relative to the other three aspects. CSP appears to have a rather limited deployment as yet and, more crucially, existing policies exhibit a number of weaknesses and misconfiguration errors. Moreover, content security policies are not regularly updated to ban insecure practices and remove unintended security violations. We argue that many of these problems can be fixed by better exploiting the monitoring facilities of CSP, while other issues deserve additional research, being more rooted into the CSP design.


title: Lift-Off: Using Reference Imagery and Freehand Sketching to Create 3D Models in VR

abstract: Three-dimensional modeling has long been regarded as an ideal application for virtual reality (VR), but current VR-based 3D modeling tools suffer from two problems that limit creativity and applicability: (1) the lack of control for freehand modeling, and (2) the difficulty of starting from scratch. To address these challenges, we present Lift-Off, an immersive 3D interface for creating complex models with a controlled, handcrafted style. Artists start outside of VR with 2D sketches, which are then imported and positioned in VR. Then, using a VR interface built on top of image processing algorithms, 2D curves within the sketches are selected interactively and “lifted” into space to create a 3D scaffolding for the model. Finally, artists sweep surfaces along these curves to create 3D models. Evaluations are presented for both long-term users and for novices who each created a 3D sailboat model from the same starting sketch. Qualitative results are positive, with the visual style of the resulting models of animals and other organic subjects as well as architectural models matching what is possible with traditional fine art media. In addition, quantitative data from logging features built into the software are used to characterize typical tool use and suggest areas for further refinement of the interface.


title: Visual Touch in Virtual Environments: An Exploratory Study of Presence, Multimodal Interfaces, and Cross-Modal Sensory Illusions

abstract: How do users generate an illusion of presence in a rich and consistent virtual environment from an impoverished, incomplete, and often inconsistent set of sensory cues? We conducted an experiment to explore how multimodal perceptual cues are integrated into a coherent experience of virtual objects and spaces. Specifically, we explored whether inter-modal integration contributes to generating the illusion of presence in virtual environments. ::: ::: To discover whether intermodal integration might play a role in presence, we looked for evidence of intermodal integration in the form of cross-modal interactions---perceptual illusions in which users use sensory cues in one modality to “fill in” the “missing” components of perceptual experience. One form of cross-modal interaction, a cross-modal transfer, is defined as a form of synesthesia, that is, a perceptual illusion in which stimulation to a sensory modality connected to the interface (such as the visual modality) is accompanied by perceived stimulation to an unconnected sensory modality that receives no apparent stimulation from the virtual environment (such as the haptic modality). Users of our experimental virtual environment who manipulated the visual analog of a physical force, a virtual spring, reported haptic sensations of “physical resistance”, even though the interface included no haptic displays. A path model of the data suggested that this cross-modal illusion was correlated with and dependent upon the sensation of spatial and sensory presence. ::: ::: We conclude that this is evidence that presence may derive from the process of multi-modal integration and, therefore, may be associated with other illusions, such as cross-modal transfers, that result from the process of creating a coherent mental model of the space. Finally, we suggest that this perceptual phenomenon might be used to improve user experiences with multimodal interfaces, specifically by supporting limited sensory displays (such as haptic displays) with appropriate synesthetic stimulation to other sensory modalities (such as visual and auditory analogs of haptic forces).


title: Cap-and-trade versus carbon taxes: which market mechanism gets the most attention?

abstract: In the debate between carbon taxes and cap-and-trade as a policy response to curbing greenhouse gas emissions, which market-based policy tool tends to garner the most (favorable) media attention? Furthermore, what is the public coverage like of topics such as trade, energy prices, and economic growth and green jobs? Understanding the media’s response to market-based policy mechanisms is important for understanding their ultimate acceptance, or rejection, beyond the academic sphere. One interesting result from this research is that cap-and-trade wins hands down over carbon taxes—it received more, and more positive, media attention in the US in the 22-year time period under study.


title: Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

abstract: We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.


A Concrete Security Treatment of Symmetric Encryption


title: Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks

abstract: Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.


title: Accelerated local anomaly detection via resolving attributed networks

abstract: Attributed networks, in which network connectivity and node attributes are available, have been increasingly used to model real-world information systems, such as social media and e-commerce platforms. While outlier detection has been extensively studied to identify anomalies that deviate from certain chosen background, existing algorithms cannot be directly applied on attributed networks due to the heterogeneous types of information and the scale of real-world data. Meanwhile, it has been observed that local anomalies, which may align with global condition, are hard to be detected by existing algorithms with interpretability. Motivated by the observations, in this paper, we propose to study the problem of effective and efficient local anomaly detection in attributed networks. In particular, we design a collective way for modeling heterogeneous network and attribute information, and develop a novel and efficient distributed optimization algorithm to handle large-scale data. In the experiments, we compare the proposed framework with the state-of-the-art methods on both real and synthetic datasets, and demonstrate its effectiveness and efficiency through quantitative evaluation and case studies.


title: The neural basis of speech perception

abstract: Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1995.


title: Evaluation of energy aware routing metrics for RPL

abstract: In the past few years, the Internet of Things is driving the need for extending the Internet to constrained devices, including sensors and actuators. The IPv6 Routing Protocol for Low power and lossy networks (RPL) is appearing as an emerging IETF standard especially tailored for Low Power Area Networks (6LoWPAN). RPL constructs a Directed Acyclic Graph (DAG) according to an objective function that governs the routing according to some metric(s) and constraint(s). In the last decade, several metrics and constraints have been proposed. In this paper, we survey RPL energy-aware routing metrics and we present to the best of our knowledge the first comparative evaluation considering grid and random topologies. Moreover, we consider in this evaluation two models for the exchange of messages: a model with no packet loss and a second one with 40% of packet loss. Our experiments show that multi-criteria metrics outperform other metrics.