title: A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification

abstract: $k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.


title: 1 Applications of Advanced Regression Analysis for Trading and Investment ∗

abstract: This chapter examines and analyses the use of regression models in trading and investment with an application to foreign exchange (FX) forecasting and trading models. It is not intended as a general survey of all potential applications of regression methods to the field of quantitative trading and investment, as this would be well beyond the scope of a single chapter. For instance, time-varying parameter models are not covered here as they are the focus of another chapter in this book and Neural Network Regression (NNR) models are also covered in yet another chapter. In this chapter, NNR models are benchmarked against some other traditional regressionbased and alternative forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool. In addition to evaluating the various models using traditional forecasting accuracy measures, such as root-mean-squared errors, they are also assessed using financial criteria, such as risk-adjusted measures of return. Having constructed a synthetic EUR/USD series for the period up to 4 January 1999, the models were developed using the same in-sample data, leaving the remainder for out-ofsample forecasting, October 1994 to May 2000, and May 2000 to July 2001, respectively. The out-of-sample period results were tested in terms of forecasting accuracy, and in terms of trading performance via a simulated trading strategy. Transaction costs are also taken into account. It is concluded that regression models, and in particular NNR models do have the ability to forecast EUR/USD returns for the period investigated, and add value as a forecasting and quantitative trading tool.


title: Disambiguating authors in academic publications using random forests

abstract: Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and last names (complete name change examples are not considered here). In such a case, the user would like the digital library to differentiate among these authors. Name disambiguation can help in many cases; one being a user in a search of all articles written by a particular author. Disambiguation also enables better bibliometric analysis by allowing a more accurate counting and grouping of publications and citations. In this paper, we describe an algorithm for pair-wise disambiguation of author names based on a machine learning classification algorithm, random forests. We define a set of similarity profile features to assist in author disambiguation. Our experiments on the Medline database show that the random forest model outperforms other previously proposed techniques such as those using support-vector machines (SVM). In addition, we demonstrate that the variable importance produced by the random forest model can be used in feature selection with little degradation in the disambiguation accuracy. In particular, the inverse document frequency of author last name and the middle name's similarity alone achieves an accuracy of almost 90%.


A rational account of classical logic argumentation for real-world agents


title: Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video

abstract: We present a computer vision tool that analyses video from a CCTV system installed on fishing trawlers to monitor discarded fish catch. The system aims to support expert observers who review the footage and verify numbers, species and sizes of discarded fish. The operational environment presents a significant challenge for these tasks. Fish are processed below deck under fluorescent lights, they are randomly oriented and there are multiple occlusions. The scene is unstructured and complicated by the presence of fishermen processing the catch. We describe an approach to segmenting the scene and counting fish that exploits the $N^4$-Fields algorithm. We performed extensive tests of the algorithm on a data set comprising 443 frames from 6 belts. Results indicate the relative count error (for individual fish) ranges from 2\% to 16\%. We believe this is the first system that is able to handle footage from operational trawlers.


title: Harvesting Wireless Power: Survey of Energy-Harvester Conversion Efficiency in Far-Field, Wireless Power Transfer Systems

abstract: The idea of wireless power transfer (WPT) has been around since the inception of electricity. In the late 19th century, Nikola Tesla described the freedom to transfer energy between two points without the need for a physical connection to a power source as an ?all-surpassing importance to man? [1]. A truly wireless device, capable of being remotely powered, not only allows the obvious freedom of movement but also enables devices to be more compact by removing the necessity of a large battery. Applications could leverage this reduction in size and weight to increase the feasibility of concepts such as paper-thin, flexible displays [2], contact-lens-based augmented reality [3], and smart dust [4], among traditional point-to-point power transfer applications. While several methods of wireless power have been introduced since Tesla?s work, including near-field magnetic resonance and inductive coupling, laser-based optical power transmission, and far-field RF/microwave energy transmission, only RF/microwave and laser-based systems are truly long-range methods. While optical power transmission certainly has merit, its mechanisms are outside of the scope of this article and will not be discussed.


title: SAMM: A Spontaneous Micro-Facial Movement Dataset

abstract: Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.


title: PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction

abstract: In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.


title: Source Code Analysis: A Road Map

abstract: The automated and semi-automated analysis of source code has remained a topic of intense research for more than thirty years. During this period, algorithms and techniques for source-code analysis have changed, sometimes dramatically. The abilities of the tools that implement them have also expanded to meet new and diverse challenges. This paper surveys current work on source-code analysis. It also provides a road map for future work over the next five-year period and speculates on the development of source-code analysis applications, techniques, and challenges over the next 10, 20, and 50 years.


Running experiments on amazon mechanical turk


title: Design of a Smart Unmanned Ground Vehicle for Hazardous Environments

abstract: A smart Unmanned Ground Vehicle (UGV) is designed and developed for some application specific missions to operate predominantly in hazardous environments. In our work, we have developed a small and lightweight vehicle to operate in general cross-country terrains in or without daylight. The UGV can send visual feedbacks to the operator at a remote location. Onboard infrared sensors can detect the obstacles around the UGV and sends signals to the operator.


title: A Survey on Intrusion Detection System for Advanced Metering Infrastructure

abstract: The security issues of smart grid have drawn more and more attention in the recent years. As a foundational part of smart grid, the cyber and physical security of advanced metering infrastructure (AMI) is of critical importance. Various securing solutions for AMI have been proposed, most of which based on cryptographic technology. However, as learned from experiences in IT environment, intrusion detection system (IDS) can dynamically monitoring the status of network-based systems and provide supplementary defensive capability besides the conventional static solutions, such as encryption, authentication, firewall, etc. In this paper, we analyze the potential threats in AMI and investigate the current academic approaches on intrusion detection systems and techniques for AMI. Then we give some recommendations in designing and deploying a practical AMI-IDS, and propose a comprehensive distributed IDS architecture suitable for AMI. In addition, the existing research problems and challenges are concluded to promote the future progress in this field.


title: A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time Domain Tuning Based on Integral Performance Indices

abstract: A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input-output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI^@lD^@m controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.


title: McPAD: A Multiple Classifier System for Accurate Payload-based Anomaly Detection

abstract: Anomaly-based network intrusion detection systems (IDS) are valuable tools for the defense-in-depth of computer networks. Unsupervised or unlabeled learning approaches for network anomaly detection have been recently proposed. Such anomaly-based network IDS are able to detect (unknown) zero-day attacks, although much care has to be dedicated to controlling the amount of false positives generated by the detection system. As a matter of fact, it is has been shown that the false positive rate is the true limiting factor for the performance of IDS, and that in order to substantially increase the Bayesian detection rate, P(Intrusion|Alarm), the IDS must have a very low false positive rate (e.g., as low as 10^-^5 or even lower). In this paper we present McPAD (multiple classifier payload-based anomaly detector), a new accurate payload-based anomaly detection system that consists of an ensemble of one-class classifiers. We show that our anomaly detector is very accurate in detecting network attacks that bear some form of shell-code in the malicious payload. This holds true even in the case of polymorphic attacks and for very low false positive rates. Furthermore, we experiment with advanced polymorphic blending attacks and we show that in some cases even in the presence of such sophisticated attacks and for a low false positive rate our IDS still has a relatively high detection rate.


title: Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

abstract: Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.


title: Autonomous Landing of a Multirotor Micro Air Vehicle on a High Velocity Ground Vehicle

abstract: While autonomous multirotor micro aerial vehicles (MAVs) are uniquely well suited for certain types of missions benefiting from stationary flight capabilities, their more widespread usage still faces many hurdles, due in particular to their limited range and the difficulty of fully automating their deployment and retrieval. In this paper we address these issues by solving the problem of the automated landing of a quadcopter on a ground vehicle moving at relatively high speed. We present our system architecture, including the structure of our Kalman filter for the estimation of the relative position and velocity between the quadcopter and the landing pad, as well as our controller design for the full rendezvous and landing maneuvers. The system is experimentally validated by successfully landing in multiple trials a commercial quadcopter on the roof of a car moving at speeds of up to 50 km/h.


title: An agent-oriented, trust-aware approach to improve the QoS in dynamic grid federations

abstract: In this paper, a distributed approach aimed at improving the quality of service in dynamic grid federations is presented. Virtual organizations VO are grouped into large-scale federations in which the original goals and scheduling mechanisms are left unchanged, while grid nodes can be quickly instructed to join or leave any VO at any time. Moreover, an agent-oriented framework is designed to observe and characterize past behaviors of nodes in terms of resource sharing and consumption, as well as to determine the trust relationships occurring between each pair of nodes. By combining trust and historical behaviors into a unified convenience measure, software agents are able to evaluate the i advantages of node's membership with VOs and ii whether a specific set of nodes is able to meet the actual requirements, in terms of resource sharing and consumption of a specific VO. The convenience measure has been exploited to design a fully decentralized, greedy procedure, aimed at controlling the grid formation process. Extensive simulations have shown that the coordinated and decentralized process of grid formation provides a powerful means to improve the overall quality of service of the grid federation. Copyright © 2015 John Wiley & Sons, Ltd.


title: Speeding up distributed machine learning using codes

abstract: Distributed machine learning algorithms that are widely run on modern large-scale computing platforms face several types of randomness, uncertainty and system “noise.” These include stragglers1, system failures, maintenance outages, and communication bottlenecks. In this work, we view distributed machine learning algorithms through a coding-theoretic lens, and show how codes can equip them with robustness against this system noise. Motivated by their importance and universality, we focus on two of the most basic building blocks of distributed learning algorithms: data shuffling and matrix multiplication. In data shuffling, we use codes to reduce communication bottlenecks: when a constant fraction of the data can be cached at each worker node, and n is the number of workers, coded shuffling reduces the communication cost by up to a factor Θ(n) over uncoded shuffling. For matrix multiplication, we use codes to alleviate the effects of stragglers, also known as the straggler problem. We show that if the number of workers is n, and the runtime of each subtask has an exponential tail, the optimal coded matrix multiplication is Θ(log n) times faster than the uncoded matrix multiplication or the optimal task replication scheme.


title: Progress towards the development of the SmartHand transradial prosthesis

abstract: This paper presents progress towards the development of an anthropomorphic 16 degree of freedom, 4 degree of mobility prosthetic hand for use by transradial amputees, named SmartHand. The hand is provided with 40 proprioceptive and exteroceptive sensors in the fingers and with a customized control architecture embedded in the palm. The compact size and weight will allow its use to a wide range of amputees in the near future. This paper shows the design and development of the mechanical structure, control architecture, communication protocol developed to deal with the hand, and briefly presents the sensor set embedded in the fingers. Joint sensors plots while a finger was closing at full speed demonstrate hand functionalities in terms of sensory, control and actuation systems. Finally a non-invasive user-prosthesis interface that could be directly connected to the SmartHand for shared control experiments is briefly presented.


title: User-side Wi-Fi Evil Twin Attack detection using SSL/TCP protocols

abstract: Evil Twin Attack (ETA) refers to a rogue Wi-Fi Access Point (AP) that appears to be a legitimate one but actually has been set up to eavesdrop on wireless communications [1]. Most of existing detection techniques assume that the attacker will use the same legitimate wireless network gateway to pass through victim's wireless data. These detection methods will fail if the attacker uses a different gateway, such as using his own broadband cellular connection through his own smartphone. In this paper, we present a new client-side detection method to detect such an ETA that uses a different gateway from the legitimate one. It relies on SSL/TCP connection to an arbitrary remote web server to avoid attacker's misleading message, and trying to detect the changing of gateway's public IP address by switching from one AP to another in the middle of the SSL/TCP connection. The detection method is on the client side which makes it more convenient for users to deploy and ensure their security.


title: A reputation management model using ID based cryptosystem in overlay networks

abstract: Dealing with malicious nodes is an important issue for many types of overlay networks. In overlay networks, malicious nodes can drop, delay or in other ways subvert user traffic, modify packets and break protocols required for the successful operation of the system. We propose a reputation management model supported by an ID based cryptosystem to reveal a malicious node with the aid of altruistic legitimate nodes who wish to identify a malicious node. We propose a deterrent structure using simple mathematical methods to calculate reputation of nodes. Evaluations show that the cost of malicious behaviours for nodes can be extremely high.


Disambiguation of entities in MEDLINE abstracts by combining MeSH terms with knowledge


title: Applications of Holography in Fluid Mechanics and Particle Dynamics

abstract: The quantification of three-dimensional (3D) flow structures and particle dynamics is crucial for unveiling complex interactions in turbulent flows. This review summarizes recent advances in volumetric particle detection and 3D flow velocimetry involving holography. We introduce the fundamental principle of holography and discuss the debilitating depth-of-focus problem, along with methods that have been implemented to circumvent it. The focus of this review is on recent advances in the development of in-line digital holography in general, and digital holographic microscopy in particular. A mathematical background for the numerical reconstruction of digital holograms is followed by a summary of recently introduced 3D particle tracking and velocity measurement techniques. The review concludes with sample applications, including 3D velocity measurements that fully resolve the flow in the inner part of a turbulent boundary layer, the diffusion of oil droplets in high–Reynolds number turbulence, and predator-p...


title: Co-RPL: RPL routing for mobile low power wireless sensor networks using Corona mechanism

abstract: Mobility support for wireless sensor networks has always been a challenging research topic. This paper addresses the issue of mobility support in the Routing Protocol for Low power and lossy networks (RPL), the recently adopted IETF routing protocol standard for low power wireless sensor networks. RPL was originally designed for static networks, with no support for mobility. In this work, we address this gap and propose Co-RPL as an extension to RPL based on the Corona mechanism to support mobility. To demonstrate the effectiveness of Co-RPL, we conducted an extensive simulation study using the Contiki/Cooja simulator and compared the performance against standard RPL. We study the impact of node speed, packet transmission rate and number of Directed Acyclic Graphs (DAG) roots on network performance. The simulation results show that Co-RPL decreases packet loss ratio by 45%, average energy consumption by 50% and end-to-end delay by 2.5 seconds, in comparison with the standard RPL.


title: EVILCOHORT: detecting communities of malicious accounts on online services

abstract: Cybercriminals misuse accounts on online services (e.g., webmails and online social networks) to perform malicious activity, such as spreading malicious content or stealing sensitive information. In this paper, we show that accounts that are accessed by botnets are a popular choice by cybercriminals. Since botnets are composed of a finite number of infected computers, we observe that cybercriminals tend to have their bots connect to multiple online accounts to perform malicious activity. ::: ::: We present EVILCOHORT, a system that detects online accounts that are accessed by a common set of infected machines. EVILCOHORT only needs the mapping between an online account and an IP address to operate, and can therefore detect malicious accounts on any online service (webmail services, online social networks, storage services) regardless of the type of malicious activity that these accounts perform. Unlike previous work, our system can identify malicious accounts that are controlled by botnets but do not post any malicious content (e.g., spam) on the service. We evaluated EVILCOHORT on multiple online services of different types (a webmail service and four online social networks), and show that it accurately identifies malicious accounts.


title: Reinforcement Learning Algorithms for MDPs

abstract: Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. In this article we focus on a few selected algorithms of reinforcement learning which build on the powerful theory of dynamic programming. ::: ::: ::: Keywords: ::: ::: reinforcement learning; ::: Markov Decision Processes; ::: temporal difference learning; ::: stochastic approximation; ::: function approximation; ::: least-squares methods; ::: Q-learning; ::: actor-critic methods; ::: policy gradient; ::: natural gradient


title: Design of ESD Protection Device for $K\!/\!Ka$ -Band Applications in Nanoscale CMOS Process

abstract: An inductor-assisted silicon-controlled rectifier (LASCR) device is proposed to protect the gigahertz ICs in nanoscale CMOS technologies from electrostatic discharge (ESD) damages. The LASCR with the assistance of inductor can provide the bidirectional ESD current paths and fine-tune the high-frequency performances. Each LASCR test device has been implemented in a compact size of $\sim 100\times 100~\mu \text{m}^{2}$ . The LASCR test devices have been successfully verified in a silicon chip to achieve 4–7.5 kV human-body-model ESD robustness with 1–3-dB loss in $K/Ka$ -band (18–40 GHz). With the better performances, the proposed ESD protection device is very suitable for $K/Ka$ -band applications.


title: Gadge me if you can: secure and efficient ad-hoc instruction-level randomization for x86 and ARM

abstract: Code reuse attacks such as return-oriented programming are one of the most powerful threats to contemporary software. ASLR was introduced to impede these attacks by dispersing shared libraries and the executable in memory. However, in practice its entropy is rather low and, more importantly, the leakage of a single address reveals the position of a whole library in memory. The recent mitigation literature followed the route of randomization, applied it at different stages such as source code or the executable binary. However, the code segments still stay in one block. In contrast to previous work, our randomization solution, called Xifer, (1) disperses all code (executable and libraries) across the whole address space, (2) re-randomizes the address space for each run, (3) is compatible to code signing, and (4) does neither require offline static analysis nor source-code. Our prototype implementation supports the Linux ELF file format and covers both mainstream processor architectures x86 and ARM. Our evaluation demonstrates that Xifer performs efficiently at load- and during run-time (1.2% overhead).


title: Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction

abstract: Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.


title: Fog Computing in IoT Aided Smart Grid Transition- Requirements, Prospects, Status Quos and Challenges

abstract: Due to unfolded developments in both the IT sectors viz. Intelligent Transportation and Information Technology contemporary Smart Grid (SG) systems are leveraged with smart devices and entities. Such infrastructures when bestowed with the Internet of Things (IoT) and sensor network make a universe of objects active and online. The traditional cloud deployment succumbs to meet the analytics and computational exigencies decentralized, dynamic cum resource-time critical SG ecosystems. This paper synoptically inspects to what extent the cloud computing utilities can satisfy the mission-critical requirements of SG ecosystems and which subdomains and services call for fog based computing archetypes. The objective of this work is to comprehend the applicability of fog computing algorithms to interplay with the core centered cloud computing support, thus enabling to come up with a new breed of real-time and latency free SG services. The work also highlights the opportunities brought by fog based SG deployments. Correspondingly, we also highlight the challenges and research thrusts elucidated towards the viability of fog computing for successful SG Transition.


title: The Design Space of Wireless Sensor Networks ∗

abstract: In the recent past, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics. As a consequence, it is becoming increasingly difficult to discuss typical requirements regarding hardware issues and software support. This is particularly problematic in a multidisciplinary research area such as wireless sensor networks, where close collaboration between users, application domain experts, hardware designers, and software developers is needed to implement efficient systems. In this article we discuss the consequences of this fact with regard to the design space of wireless sensor networks by considering its various dimensions. We justify our view by demonstrating that specific existing applications occupy different points in the design space.


title: Effects of cross-education on the muscle after a period of unilateral limb immobilization using a shoulder sling and swathe

abstract: The purpose of this study was to apply cross-education during 4 wk of unilateral limb immobilization using a shoulder sling and swathe to investigate the effects on muscle strength, muscle size, and muscle activation. Twenty-five right-handed participants were assigned to one of three groups as follows: the Immob + Train group wore a sling and swathe and strength trained (n = 8), the Immob group wore a sling and swathe and did not strength train (n = 8), and the Control group received no treatment (n = 9). Immobilization was applied to the nondominant (left) arm. Strength training consisted of maximal isometric elbow flexion and extension of the dominant (right) arm 3 days/wk. Torque (dynamometer), muscle thickness (ultrasound), maximal voluntary activation (interpolated twitch), and electromyography (EMG) were measured. The change in right biceps and triceps brachii muscle thickness [7.0 ± 1.9 and 7.1 ± 2.2% (SE), respectively] was greater for Immob + Train than Immob (0.4 ± 1.2 and -1.9 ± 1.7%) and Control (0.8 ± 0.5 and 0.0 ± 1.1%, P < 0.05). Left biceps and triceps brachii muscle thickness for Immob + Train (2.2 ± 0.7 and 3.4 ± 2.1%, respectively) was significantly different from Immob (-2.8 ± 1.1 and -5.2 ± 2.7%, respectively, P < 0.05). Right elbow flexion strength for Immob + Train (18.9 ± 5.5%) was significantly different from Immob (-1.6 ± 4.0%, P < 0.05). Right and left elbow extension strength for Immob + Train (68.1 ± 25.9 and 32.2 ± 9.0%, respectively) was significantly different from the respective limb of Immob (1.3 ± 7.7 and -6.1 ± 7.8%) and Control (4.7 ± 4.7 and -0.2 ± 4.5%, P < 0.05). Immobilization in a sling and swathe decreased strength and muscle size but had no effect on maximal voluntary activation or EMG. The cross-education effect on the immobilized limb was greater after elbow extension training. This study suggests that strength training the nonimmobilized limb benefits the immobilized limb for muscle size and strength.


title: Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system

abstract: Heart rate variability (HRV) parameters can be used as specific indicator of autonomic nervous system (ANS) behavior. ANS, with its main two branches, sympathetic and parasympathetic, may be considered as a coordinated neuronal network which controls heart rate continually. Many parameters define heart rate variability in different domains such as time, frequency or nonlinear. An excessively high computational complexity can occur when developing models for medical applications when the best set of inputs to use is not known. To build a model that can predict a specific process output, it is desirable to select a subset of variables that are truly relevant or the most influential to this output. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study an architecture for modeling complex systems in function approximation and regression was used, based on using adaptive neuro-fuzzy inference system (ANFIS). Variable searching using the ANFIS network was performed to determine how the ANS branches affect the most relevant HRV parameters. The method utilized may work as a basis for examination of ANS influence on HRV activity.


title: Transforming Industrial Business: The Impact of Digital Transformation on Automotive Organizations

abstract: The increasing diffusion of digital technologies throughout industries and aspects of life is transforming businesses even in areas that always have been dependent on physical materiality. In this paper, by conducting an exploratory Delphi study in collaboration with 19 industry experts, we aim to shed light on the specific managerial challenges associated with the impact of digital transformation in the industrial-age, physical product–manufacturing automotive industry. Our results point to three key themes: (1) the radicalization of IT-enabled business transformation going beyond intraorganizational challenges, (2) the transformation of industrial business through digital innovation, and (3) the emergence of physical–digital paradoxes. With our study, we extend the knowledge in IT-enabled business transformation literature by relating it to the emergent topic of digital innovation. We find organizational ambidexterity to be of specific importance when dealing with emergent challenges that arise from the combination of the physical and digital worlds.


title: A universal stability criterion of the foot contact of legged robots - adios ZMP

abstract: This paper proposes a universal stability criterion of the foot contact of legged robots. The proposed method checks if the sum of the gravity and the inertia wrench applied to the COG of the robot, which is proposed to be the stability criterion, is inside the polyhedral convex cone of the contact wrench between the feet of a robot and its environment. The criterion can be used to determine the strong stability of the foot contact when a robot walks on an arbitrary terrain and/or when the hands of the robot are in contact with it under the sufficient friction assumption. The determination is equivalent to check if the ZMP is inside the support polygon of the feet when the robot walks on a horizontal plane with sufficient friction. The criterion can also be used to determine if the foot contact is sufficiently weakly stable when the friction follows a physical law. Therefore, the proposed criterion can be used to judge what the ZMP can, and it can be used in more universal cases


title: Distinguishing War-Related PTSD Resulting From Perpetration- and Betrayal-Based Morally Injurious Events

abstract: OBJECTIVE ::: We investigated whether potentially morally injurious events (PMIEs) during a combat deployment may lead to PTSD through distinct pathways from danger-based events. We also examined the prevalence of perpetration-based PMIEs, during which service members behaved in ways that violated their own moral values, and betrayal-based PMIEs, during which personal moral expectations were violated by trusted others. ::: ::: ::: METHOD ::: Using a sample of 867 active duty Marines from a single infantry battalion that engaged in heavy ground combat while deployed to Afghanistan, a structural equation model was built to examine the relationships between perpetration- and betrayal-based PMIEs, combat experiences, and peritraumatic dissociation reported at 1 month postdeployment, and guilt/shame, anger, and PTSD symptoms reported at 8 months postdeployment. ::: ::: ::: RESULTS ::: The relationship between betrayal-based PMIEs and PTSD was mediated by anger (β = .14). There was marginal evidence of mediation of the relationship between perpetration-based PMIEs and PTSD by shame and guilt (β = .09), and of the relationship between danger-based combat events and PTSD by peritraumatic dissociation (β = .08). No significant direct relationships were found between any of these 3 types of events and subsequent PTSD. Perceived perpetration and betrayal accounted for PTSD symptoms above and beyond combat exposure. Over a third of the sample reported experiencing perpetration- or betrayal-based PMIEs. ::: ::: ::: CONCLUSIONS ::: The associations of perpetration and betrayal with PTSD, controlling for danger-based combat events, highlight the limitations of conceptualizations and treatments of PTSD based on fear or helplessness as sole etiologic factors. (PsycINFO Database Record


title: Improved student psychology prediction & recommendation strategy using 2 state data analysis

abstract: Psychological prediction systems are used by psychologist to assist condition of individual. Various theories are used to map psychological conditions to recommendations such as “Maslow Theory”. The automated prediction and recommendation systems use knowledge mining and analytical algorithms to generate accurate predictions. The aim of proposed model is to implement Maslow theory using improved knowledge mining and analytical algorithms to predict accurately the psychological status of individual and suggest relevant heeling therapy for them. This paper presents the simulation results of implementation of the prediction and recommendation system using association rule mining and statistical method like Pearson's co-relation.


title: Semantic data models

abstract: Semantic data models have emerged from a requirement for more expressive conceptual data models. Current generation data models lack direct support for relationships, data abstraction, inheritance, constraints, unstructured objects, and the dynamic properties of an application. Although the need for data models with richer semantics is widely recognized, no single approach has won general acceptance. This paper describes the generic properties of semantic data models and presents a representative selection of models that have been proposed since the mid-1970s. In addition to explaining the features of the individual models, guidelines are offered for the comparison of models. The paper concludes with a discussion of future directions in the area of conceptual data modeling.


title: Affordance-based agent model for road traffic simulation

abstract: Existing traffic simulations often consider normative driver behavior. Drivers do not always use physically delineated lanes: sometimes drivers use the entire road surface. Thus, current traffic simulations do not reproduce all observed urban and suburban traffic phenomena. To improve the validity of urban and suburban traffic simulations, we propose to consider driving context and driver behavior in terms of occupied space. We endow driver agents with an ego-centered representation of the environment based on the concept of affordances and virtual lanes. Affordances thus identify the possible space occupation actions afforded by the environment and by other agents. The proposed model was implemented using our ArchiSim tool. We show that this model is more efficient and realistic than existing models. The experiments also reproduce real traffic situations and compare simulated data to real data.


title: On the Role of Emotion in Embodied Cognitive Architectures: From Organisms to Robots

abstract: The computational modeling of emotion has been an area of growing interest in cognitive robotics research in recent years, but also a source of contention regarding how to conceive of emotion and how to model it. In this paper, emotion is characterized as (a) closely connected to embodied cognition, (b) grounded in homeostatic bodily regulation, and (c) a powerful organizational principle—affective modulation of behavioral and cognitive mechanisms—that is ‘useful’ in both biological brains and robotic cognitive architectures. We elaborate how emotion theories and models centered on core neurological structures in the mammalian brain, and inspired by embodied, dynamical, and enactive approaches in cognitive science, may impact on computational and robotic modeling. In light of the theoretical discussion, work in progress on the development of an embodied cognitive-affective architecture for robots is presented, incorporating aspects of the theories discussed.


title: Extracting Opinions, Opinion Holders, And Topics Expressed In Online News Media Text

abstract: This paper presents a method for identifying an opinion with its holder and topic, given a sentence from online news media texts. We introduce an approach of exploiting the semantic structure of a sentence, anchored to an opinion bearing verb or adjective. This method uses semantic role labeling as an intermediate step to label an opinion holder and topic using data from FrameNet. We decompose our task into three phases: identifying an opinion-bearing word, labeling semantic roles related to the word in the sentence, and then finding the holder and the topic of the opinion word among the labeled semantic roles. For a broader coverage, we also employ a clustering technique to predict the most probable frame for a word which is not defined in FrameNet. Our experimental results show that our system performs significantly better than the baseline.


title: Performance of an open-source heart sound segmentation algorithm on eight independent databases

abstract: Objective: Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. Approach: Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. Main results: The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The score was shown to increases with an increases in the tolerance window size, as expected. Significance: The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for evaluators who need to test their algorithms with realistic data and share reproducible results.


Multi-task learning for text-dependent speaker verification.


title: Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis

abstract: We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.


title: Detecting multiple changes from multi-temporal images by using stacked denosing autoencoder based change vector analysis

abstract: In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the existing noise, which especially influences the detection of subtle changes. Therefore, the stacked denosing autoencoder (SDAE) which serves as a fine tool for feature extraction is employed to generate a multi-dimensional feature representations. In this way, the C2VA framework can be applied to the inner robust features so that a satisfactory performance can be guaranteed. Experimental results from two datasets show its high accuracy and moderate time complexity, which demonstrates the effectiveness of the proposed SDAE-C2VA approach.


title: Design of Yagi-Uda Antenna using Microstrip Circuit

abstract: This paper gives the design of Yagi-Uda antenna using microstrip circuit. Microstip circuits are used to implement Yagi-Uda antenna so as to reduce the size but here we have shown only two optimization technique. This has been done by varying the length, width and spacing between reflector, driven element and directors. Simulations are conducted to show how return loss and other parameters vary by varying the above mentioned Yagi-Uda parameters. This antenna is operating very near to resonant frequency fr = 2.4 GHz with the specification relative permittivity er= 3.2, height of substrate h=1.6mm, characteristic impedance Z0 = 50 ohm and thickness of strip conductor t=35um. In this paper, two most approximate results after all these perturbations are analyzed and simulations showing return loss and other parameters like directivity, gain and power radiated are discussed. The simulation process has been done using Advanced Design System (ADS) tool. General Terms Advanced Design System (ADS), Microstrip circuit, YagiUda Antenna


title: Discovering Malicious Domains through Passive DNS Data Graph Analysis

abstract: Malicious domains are key components to a variety of cyber attacks. Several recent techniques are proposed to identify malicious domains through analysis of DNS data. The general approach is to build classifiers based on DNS-related local domain features. One potential problem is that many local features, e.g., domain name patterns and temporal patterns, tend to be not robust. Attackers could easily alter these features to evade detection without affecting much their attack capabilities. In this paper, we take a complementary approach. Instead of focusing on local features, we propose to discover and analyze global associations among domains. The key challenges are (1) to build meaningful associations among domains; and (2) to use these associations to reason about the potential maliciousness of domains. For the first challenge, we take advantage of the modus operandi of attackers. To avoid detection, malicious domains exhibit dynamic behavior by, for example, frequently changing the malicious domain-IP resolutions and creating new domains. This makes it very likely for attackers to reuse resources. It is indeed commonly observed that over a period of time multiple malicious domains are hosted on the same IPs and multiple IPs host the same malicious domains, which creates intrinsic association among them. For the second challenge, we develop a graph-based inference technique over associated domains. Our approach is based on the intuition that a domain having strong associations with known malicious domains is likely to be malicious. Carefully established associations enable the discovery of a large set of new malicious domains using a very small set of previously known malicious ones. Our experiments over a public passive DNS database show that the proposed technique can achieve high true positive rates (over 95%) while maintaining low false positive rates (less than 0.5%). Further, even with a small set of known malicious domains (a couple of hundreds), our technique can discover a large set of potential malicious domains (in the scale of up to tens of thousands).


title: StreamingCube: A Unified Framework for Stream Processing and OLAP Analysis

abstract: In most streaming applications, the data streams need to be analyzed continuously to make instant decisions exploiting latest information. Often data streams are multidimensional and are at the low-level of abstraction, whereas analysts are interested in multi-level interactive analysis of data streams across several dimensions. On-line analytical processing (OLAP) is a proven technique for such analysis of static data and has also been studied by some researchers for data streams. Traditionally this is achieved by coupling a stream processing engine with an OLAP engine. We believe that coupling multiple systems is not an efficient solutions as it results in lower performance (due to the transfer of data between multiple systems), resource wastage (due to replication of data for each coupled system) and increased complexity and maintenance cost. To this end, we present StreamingCube, a unified framework for data stream processing and its interactive OLAP analysis. The proposed framework possesses all the essential operators to process data streams and introduces a new operator, cubify, to maintain OLAP lattice nodes (materialized views) incrementally. The novelty of the introduced cubify operator lies in the incremental maintenance of the materialized views. To demonstrate StreamingCube, a web-based GUI has been developed which enables users to register continuous queries (CQs). Once a CQ has been registered, users can perform different OLAP operations through the GUI for the interactive analysis. The results of the OLAP queries/operations are displayed in the form of tables and graphs.


title: Semi-Automatic Image Labelling Using Depth Information

abstract: Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a need for RGB-depth adapted tools. We present a new interactive labeling tool that partially automates image labeling, with two major contributions. First, the method extends the concept of image segmentation from RGB to RGB-depth using Fuzzy C-Means clustering, connected component labeling and superpixels, and generates bounding pixels to extract the desired objects. Second, it minimizes the interaction time needed for object extraction by doing an efficient segmentation in RGB-depth space. Very few clicks are needed for the entire procedure compared to existing, tools. When the desired object is the closest object to the camera, which is often the case in robotics applications, no clicks at all are required to accurately extract the object.


title: Poor adherence to guidelines for preventing central line-associated bloodstream infections (CLABSI): results of a worldwide survey

abstract: BACKGROUND ::: Central line-associated bloodstream infections (CLABSI) are a cause of increased morbidity and mortality, and are largely preventable. We documented attitudes and practices in intensive care units (ICUs) in 2015 in order to assess compliance with CLABSI prevention guidelines. ::: ::: ::: METHODS ::: Between June and October 2015, an online questionnaire was made available to medical doctors and nurses working in ICUs worldwide. We investigated practices related to central line (CL) insertion, maintenance and measurement of CLABSI-related data following the SHEA guidelines as a standard. We computed weighted estimates for high, middle and low-income countries using country population as a weight. Only countries providing at least 10 complete responses were included in these estimates. ::: ::: ::: RESULTS ::: Ninety five countries provided 3407 individual responses; no low income, 14 middle income (MIC) and 27 high income (HIC) countries provided 10 or more responses. Of the total respondents, 80% (MIC, SE = 1.5) and 81% (HIC, SE = 1.0) reported availability of written clinical guidelines for CLABSI prevention in their ICU; 23% (MIC,SE = 1.7) and 62% (HIC,SE = 1.4) reported compliance to the following (combined) recommendations for CL insertion: hand hygiene, full barrier precaution, chlorhexidine >0.5%, no topic or systemic antimicrobial prophylaxis; 60% (MIC,SE = 2.0) and 73% (HIC,SE = 1.2) reported daily assessment for the need of a central line. Most considered CLABSI measurement key to quality improvement, however few were able to report their CLABSI rate. Heterogeneity between countries was high and country specific results are made available. ::: ::: ::: CONCLUSIONS ::: This study has identified areas for improvement in CLABSI prevention practices linked to CL insertion and maintenance. Priorities for intervention differ between countries.


title: Metric learning for weather image classification

abstract: Image classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.


title: A Sound Idea: Phonetic Effects of Brand Names on Consumer Judgments

abstract: In this article we examine a phenomenon known as sound symbolism, where the sound of a word conveys meanings. Specifically, brand names are composed of individual sounds called phonemes and we investigate how this phonetic structure of brand names affects a consumer's evaluation of products and their underlying attributes. We demonstrate that consumers use information they gather from phonemes in brand names to infer product attributes and to evaluate brands. We also demonstrate that the manner in which phonetic effects of brand names manifest is automatic in as much as it is uncontrollable, outside awareness and effortless.


title: Physiological Measures for Human Performance Analysis in Human-Robot Teamwork: Case of Tele-Exploration

abstract: Continuous monitoring of mental workload and situation awareness in operational environments is useful for understanding and prediction of human performance. Such information can be used to develop real-time adaptive systems to enhance human performance. In this paper, we investigate the use of workload- and attention-related physiological measures to predict operator performance and situation awareness in the context of tele-exploration with a small team of robots. A user study is conducted based on a simulated scenario involving visual scanning and manual control tasks with varying levels of task-load. Brain activity and eye movements of the participants are monitored across the experimental tasks using electroencephalogram and eye tracker sensors. The performances of the subjects are evaluated in terms of target detection and situation awareness (primary metrics) as well as reaction time and false detection (secondary metrics). Moreover, individual differences in two specific visual skills, visual search (VS) and multi-object tracking (MOT) are considered as between-subject factors in the experimental design. The main effects of task type and individual differences reveal that VS and MOT skill have significant effects on target detection and situation awareness, respectively. The correlations of physiological measures with the task performance and situation awareness are analyzed. The results suggest that brain-based features (mental workload and distraction) which represent the covert aspect of attention are better suited to predict the secondary performance metrics. On the other hand, glance-based features which represent the overt aspect of attention are shown to be the best predictors of the primary performance metrics.


title: Application Integration on the User Interface Level: an Ontology-Based Approach

abstract: Integration of software applications can be achieved on different levels: the data level, the business logic level, and the user interface level. Integration on the user interface level means assembling the user interfaces of existing applications in a framework allowing seamless, unified interaction with those applications. While integration on the user interface level is desirable both from a software engineering as well as from a usability point of view, most current approaches require detailed knowledge of the integrated applications and make the implementation of a common interaction that involves different applications a difficult issue. In this paper, we propose a framework using ontologies for application integration on the user interface level by encapsulating the applications in plugins. Our approach is to use different ontologies for characterizing applications and the interactions possible with them, and for semantically annotating information objects exchanged between applications. Thus, the domain-independent and the domain-specific parts are untangled, which makes the framework applicable to different domains. An instance-based reasoner is used to process the ontologies and to compute the possible interactions, thus enabling integration at run-time. In an example from the domain of emergency management, we show how our approach helps implementing cross-application interactions more easily, thus significantly lowering the barriers for interoperability.


title: Modelling And Solving English Peg Solitaire

abstract: Peg Solitaire is a well known puzzle, which can prove difficult despite its simple rules. Pegs are arranged on a board such that at least one 'hole' remains. By making draughts/checkers-like moves, pegs are gradually removed until no further moves are possible or some goal configuration is achieved. This paper considers the English variant, consisting of a board in a cross shape with 33 holes. Modelling Peg Solitaire via constraint or integer programming techniques presents a considerable challenge and is examined in detail. The merits of the resulting models are discussed and they are compared empirically. The sequential nature of the puzzle naturally conforms to a planning problem, hence we also present an experimental comparison with several leading AI planning systems. Other variants of the puzzle, such as 'Fool's Solitaire' and 'Long-hop' Solitaire are also considered.


title: The principles and practice of time series forecasting and business modelling using neural nets

abstract: This paper is intended as a ‘hands-on’ practical discussion of how and why neural networks are used in forecasting and business modelling. The need for forecasting is briefly examined. The theory of the multilayer perceptron neural network is then covered both qualitatively and in mathematical detail, including the methods of back-propagation of error and independent validation. The advantages of the neural net approach to forecasting, namely nonlinear modelling capability, plausible interpolations and extrapolations, robustness to noise, ill-conditioning and insufficient data, and ease of use, are discussed. Finally, some working notes are offered for the practical implementation of neural nets in forecasting, and four real-life examples are given from the pursuits of econometrics, sales forecasting, market modelling, and risk evaluation.


title: Performance Analysis of a Multi-tenant In-Memory Data Grid

abstract: Distributed key-value stores have become indispensable for large scale low latency applications. Many cloud services have deployed in-memory data grids for their enterprise infrastructures and support multi-tenancy services. But it is still difficult to provide consistent performance to all tenants for fluctuating workloads that need to scale out. Many popular key-value stores suffer from performance problems at scale and different tenant requirements. To this front, we present our study with Hazelcast, a popular open source data grid, and provide insights to contention and performance bottlenecks. Through experimental analysis, this paper uncovers scenarios of performance degradation followed by optimized performance via end-point multiplexing. Our study suggests that processing increasing number of client requests spawning fewer number of threads help improve performance.


title: Movement and respiration detection using statistical properties of the FMCW radar signal

abstract: This paper presents a 24 GHz FMCW radar system for detection of movement and respiration using change in the statistical properties of the received radar signal, both amplitude and phase. We present the hardware and software segments of the radar system as well as algorithms with measurement results for two distinct use-cases: 1. FMCW radar as a respiration monitor and 2. a dual-use of the same radar system for smart lighting and intrusion detection. By using change in statistical properties of the signal for detection, several system parameters can be relaxed, including, for example, pulse repetition rate, power consumption, computational load, processor speed, and memory space. We will also demonstrate, that the capability to switch between received signal strength and phase difference enables dual-use cases with one requiring extreme sensitivity to movement and the other robustness against small sources of interference.


title: Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

abstract: The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.


title: See Through Walls with COTS RFID System!

abstract: Through-wall tracking has gained a lot of attentions in civilian applications recently. Many applications would benefit from such device-free tracking, e.g. elderly people surveillance, intruder detection, gaming, etc. In this work, we present a system, named Tadar, for tracking moving objects without instrumenting them us- ing COTS RFID readers and tags. It works even through walls and behind closed doors. It aims to enable a see-through-wall technology that is low-cost, compact, and accessible to civilian purpose. In traditional RFID systems, tags modulate their IDs on the backscatter signals, which is vulnerable to the interferences from the ambient reflections. Unlike past work, which considers such vulnerability as detrimental, our design exploits it to detect surrounding objects even through walls. Specifically, we attach a group of RFID tags on the outer wall and logically convert them into an antenna array, receiving the signals reflected off moving objects. This paper introduces two main innovations. First, it shows how to eliminate the flash (e.g. the stronger reflections off walls) and extract the reflections from the backscatter signals. Second, it shows how to track the moving object based on HMM (Hidden Markov Model) and its reflections. To the best of our knowledge, we are the first to implement a through-wall tracking using the COTS RFID systems. Empirical measurements with a prototype show that Tadar can detect objects behind 5" hollow wall and 8" concrete wall, and achieve median tracking errors of 7.8cm and 20cm in the X and Y dimensions.


title: Android permissions: a perspective combining risks and benefits

abstract: The phenomenal growth of the Android platform in the past few years has made it a lucrative target of malicious application (app) developers. There are numerous instances of malware apps that send premium rate SMS messages, track users' private data, or apps that, even if not characterized as malware, conduct questionable actions affecting the user's privacy or costing them money. In this paper, we investigate the feasibility of using both the permissions an app requests, the category of the app, and what permissions are requested by other apps in the same category to better inform users whether the risks of installing an app is commensurate with its expected benefit. Existing approaches consider only the risks of the permissions requested by an app and ignore both the benefits and what permissions are requested by other apps, thus having a limited effect. We propose several risk signals that and evaluate them using two datasets, one consists of 158,062 Android apps from the Android Market, and another consists of 121 malicious apps. We demonstrate the effectiveness of our proposal through extensive data analysis.


title: Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer

abstract: Anton 2 is a second-generation special-purpose supercomputer for molecular dynamics simulations that achieves significant gains in performance, programmability, and capacity compared to its predecessor, Anton 1. The architecture of Anton 2 is tailored for fine-grained event-driven operation, which improves performance by increasing the overlap of computation with communication, and also allows a wider range of algorithms to run efficiently, enabling many new software-based optimizations. A 512-node Anton 2 machine, currently in operation, is up to ten times faster than Anton 1 with the same number of nodes, greatly expanding the reach of all-atom bio molecular simulations. Anton 2 is the first platform to achieve simulation rates of multiple microseconds of physical time per day for systems with millions of atoms. Demonstrating strong scaling, the machine simulates a standard 23,558-atom benchmark system at a rate of 85 µs/day -- 180 times faster than any commodity hardware platform or general-purpose supercomputer.


title: Make it home: automatic optimization of furniture arrangement

abstract: We present a system that automatically synthesizes indoor scenes realistically populated by a variety of furniture objects. Given examples of sensibly furnished indoor scenes, our system extracts, in advance, hierarchical and spatial relationships for various furniture objects, encoding them into priors associated with ergonomic factors, such as visibility and accessibility, which are assembled into a cost function whose optimization yields realistic furniture arrangements. To deal with the prohibitively large search space, the cost function is optimized by simulated annealing using a Metropolis-Hastings state search step. We demonstrate that our system can synthesize multiple realistic furniture arrangements and, through a perceptual study, investigate whether there is a significant difference in the perceived functionality of the automatically synthesized results relative to furniture arrangements produced by human designers.


A Bilingual Attention Network for Code-switched Emotion Prediction


title: SDN & NFV in 5G: Advancements and challenges

abstract: The next generation of mobile networks has risen the expectations and has induced a whole new approach in the networking future. This study summarizes solutions, that meet all the existing requirements integrating Software Defined Network (SDN) and Network Function Virtualization (NFV) in mobile networking. The main contribution of this research activity is that it is a tool for literature reference for scientists and engineers, because it presents and compares the most fundamental existing solutions and the future directions. In this paper, the main characteristics of SDN and NFV are summarized as fundamental alternatives contributing to succeed the goals set by 5G. The main demands and challenges faced by these solutions and the most beneficial applications of the combination of SDN with NFV are described. Finally, the main conclusions of this research and suggestions for possible future activity in the field are described.


title: Speaking To Read: The Effects of Speech Recognition Technology on the Reading and Spelling Performance of Children with Learning Disabilities

abstract: In recent literature on persons with learning disabilities (LD), speech recognition has been discussed primarily as an assistive technology to help compensate for writing difficulties. However, prior research by the authors has suggested that in addition to helping persons to compensate for poor writing skills, speech recognition also may enhance reading and spelling; that is, what was designed as assistive technology appears to serve remedial functions as well. The present study was conducted to determine whether elementary and secondary students with LD who used the technology to write self-selected compositions and class assignments would demonstrate improvements in reading and spelling. Thirty-nine students with LD (ages 9 to 18) participated. Nineteen participants used speech recognition 50 minutes a week for sixteen weeks, and twenty students in a control group received general computer instruction. Results indicated that the speech recognition group showed significantly more improvement than the control group in word recognition (p<.0001), spelling (p<.002) and reading comprehension (p<.01). Pre- and posttests on five reading-related cognitive processing measures (phonological, orthographic, semantic processing, metacognitive reading strategies, and working memory) indicated that for the experimental group, only phonological processing improved significantly over the treatment period when compared to controls (p<.04). Further ANCOVA suggested that growth in phonological processing was associated with significant differences among conditions for all three academic measures: word recognition, spelling, and reading comprehension.


title: Analysis of fetal blood using capillary electrophoresis system: a simple method for prenatal diagnosis of severe thalassemia diseases

abstract: INTRODUCTION: Prenatal diagnosis of severe alpha- and beta-thalasssemia diseases is usually performed by DNA analysis. OBJECTIVE: To establish a simple method, we have evaluated the reliability of prenatal diagnosis by fetal blood analysis using automated capillary electrophoresis system. METHODS: Forty-seven fetal blood specimens collected by cordocentesis at 18-28 wk of gestation were analyzed by the capillary electrophoresis system (Sebia). Fetal DNA was analyzed for respective thalassemia alleles by PCR. RESULTS: Among 47 fetuses, 20 were at risk for the Hb Bart's hydrops fetalis. DNA analysis identified four cases of homozygous alpha degrees -thalassemia (SEA type). Hb analysis by the capillary electrophoresis demonstrated a major peak of Hb Bart's (78.4-81.3%), Hb H (0.8-1.4%) and minor peaks of presumably embryonic Hbs. No Hb F and Hb A was observed. The level of Hb Bart's was found to be 3.4-5.8% in unaffected heterozygote whereas normal fetus had no Hb Bart's. Among the remaining 27 fetuses at risk for Hb E-beta-thalassemia, DNA analysis identified 12 affected fetuses. Hb analysis showed Hb F (94.9-98.9%) and Hb E (1.1-1.8%) without Hb A in all cases. The levels of Hb A were found to be (4.3-7.2%), (1.0-5.5%) and (2.1-3.9%) in normal, heterozygous Hb E and heterozygous beta-thalassemia fetuses, respectively. Affected and unaffected fetuses could be easily distinguished. CONCLUSION: Capillary electrophoresis system is a simple and automated procedure for accurate prenatal diagnosis of severe thalassemia diseases which could readily be performed in routine setting.


title: Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks

abstract: We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning. Our DAN approach builds upon generative adversarial networks (GANs) and conditional GANs but includes the key differentiator of using two discriminators instead of a generator and a discriminator. DAN can be seen as a framework to learn loss functions for predictors that also implements semi-supervised learning in a straightforward manner. We propose instantiations of DAN for two different prediction tasks: classification and ranking. Our experimental results on three datasets of different tasks demonstrate that DAN is a promising framework for both semi-supervised learning and learning loss functions for predictors. For all tasks, the semi-supervised capability of DAN can significantly boost the predictor performance for small labeled sets with minor architecture changes across tasks. Moreover, the loss functions automatically learned by DANs are very competitive and usually outperform the standard pairwise and negative log-likelihood loss functions for both semi-supervised and supervised learning.


title: Analysis on botnet detection techniques

abstract: Botnet detection plays an important role in network security. Botnet are collection of compromised computers called the bot. For detecting the presence of bots in a network, there are many detection techniques available. Network based detection method is the one of the efficient method in detecting bots. Paper reviews four different botnet detection techniques and a comparison of all these techniques are done.


title: An Automatic Text Summarization Approach using Content-Based and Graph-Based Characteristics

abstract: The continuing growth of World Wide Web and on-line text collections makes a large volume of information available to users. Automatic text summarization allows users to quickly understand documents. In this paper, we propose an automated technique for single document summarization which combines content-based and graph-based approaches and introduce the Hopfield Network algorithm as a technique for ranking text segments. A series of experiments are performed using the DUC collection and a Thai-document collection. The results show the superiority of the proposed technique over reference systems, in addition the Hopfield Network algorithm on undirected graph is shown to be the best text segment ranking algorithm in the study.


title: A 28GHz CMOS direct conversion transceiver with packaged antenna arrays for 5G cellular system

abstract: This paper describes a 28GHz CMOS direct conversion transceiver with packaged 2×4 patch antenna arrays for 5G communication. Test results show good RF performances of Rx NF 6.7dB, Maximum Tx EIRP 31.5dBm (1PA P out_sat =10.5dBm), LO integrated phase noise −37.8dBc (0.67°), Rx/Tx EVM around 2.2% (−33.1dB) at mid RF power, and well-fitted beam control capability.


Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning


title: Exploiting geographical influence for collaborative point-of-interest recommendation

abstract: In this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is to explore user preference, social influence and geographical influence for POI recommendations. In addition to deriving user preference based on user-based collaborative filtering and exploring social influence from friends, we put a special emphasis on geographical influence due to the spatial clustering phenomenon exhibited in user check-in activities of LBSNs. We argue that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution. Accordingly, we develop a collaborative recommendation algorithm based on geographical influence based on naive Bayesian. Furthermore, we propose a unified POI recommendation framework, which fuses user preference to a POI with social influence and geographical influence. Finally, we conduct a comprehensive performance evaluation over two large-scale datasets collected from Foursquare and Whrrl. Experimental results with these real datasets show that the unified collaborative recommendation approach significantly outperforms a wide spectrum of alternative recommendation approaches.


title: Taxonomy of Digital Forensics: Investigation Tools and Challenges

abstract: In today's world of computers, any kind of information can be made available within few clicks for different endeavors. The information may be tampered by changing the statistical properties and can be further used for criminal activities. These days, Cyber crimes are happening at a very large scale, and possess big threats to the security of an individual, firm, industry and even to developed countries. To combat such crimes, law enforcement agencies and investment institutions are incorporating supportive examination policies, procedures and protocols to address the complete investigation process. The paper entails a detailed review of several cyber crimes followed by various digital forensics processes involved in the cyber crime investigation. Further various digital forensics tools with detail explanation are discussed with their advantages, disadvantages, challenges, and drawbacks. A comparison among all the selected tools is also presented. Finally the paper recommends the need of training programs for the first res ponder and judgement of signature based image authentication.


title: A Markov Model for Headway/Spacing Distribution of Road Traffic

abstract: In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.


title: Explanations for the Decline in Child Sexual Abuse Cases

abstract: This Bulletin explores the strengths and weaknesses of six possible explanations for the decline by using data from a number of different sources: aggregate data from the National Child Abuse and Neglect Data System (NCANDS); detailed child protective service data from Illinois, Minnesota, Oregon, and Pennsylvania; and self-report data from the National Crime Victimization Survey (NCVS) and from schoolchildren in Minnesota. It provides substantially more evidence about the decline than was available in a previous Bulletin on the same topic, The Decline in Child Sexual Abuse Cases (Jones and Finkelhor, 2001).


title: Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

abstract: Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two tasks are associated by cross&compress units, which automatically share latent features and learn high-order interactions between items in recommender systems and entities in the knowledge graph. We prove that cross&compress units have sufficient capability of polynomial approximation, and show that MKR is a generalized framework over several representative methods of recommender systems and multi-task learning. Through extensive experiments on real-world datasets, we demonstrate that MKR achieves substantial gains in movie, book, music, and news recommendation, over state-of-the-art baselines. MKR is also shown to be able to maintain a decent performance even if user-item interactions are sparse.


title: Optimization and Economic Analysis of an Hybrid Fuel Cell, PhotoVoltaic and Battery Electric Power Generation System

abstract: This paper presents a technical and economical study of a hybrid-fuel cell, photovoltaic, and battery-electric generation system. The system is installed on five different remote sites. These sites are family houses located in different regions in France beyond the reach of electrical power lines. The optimum system configuration is not often easily found and a lot of calculations and/or simulations are required in order to decide which combination gives the best technical and financial benefit. The aim of this study is to design each component of the remote generation system in order to get the best technical and financial combination. In this paper, a method is suggested to find the optimum combination for a hybrid power system dedicated to off-grid electricity users. Indeed, this project permits to save up to 25% of the initial cost.


title: Optimized exosome isolation protocol for cell culture supernatant and human plasma

abstract: Extracellular vesicles represent a rich source of novel biomarkers in the diagnosis and prognosis of disease. However, there is currently limited information elucidating the most efficient methods for obtaining high yields of pure exosomes, a subset of extracellular vesicles, from cell culture supernatant and complex biological fluids such as plasma. To this end, we comprehensively characterize a variety of exosome isolation protocols for their efficiency, yield and purity of isolated exosomes. Repeated ultracentrifugation steps can reduce the quality of exosome preparations leading to lower exosome yield. We show that concentration of cell culture conditioned media using ultrafiltration devices results in increased vesicle isolation when compared to traditional ultracentrifugation protocols. However, our data on using conditioned media isolated from the Non-Small-Cell Lung Cancer (NSCLC) SK-MES-1 cell line demonstrates that the choice of concentrating device can greatly impact the yield of isolated exosomes. We find that centrifuge-based concentrating methods are more appropriate than pressure-driven concentrating devices and allow the rapid isolation of exosomes from both NSCLC cell culture conditioned media and complex biological fluids. In fact to date, no protocol detailing exosome isolation utilizing current commercial methods from both cells and patient samples has been described. Utilizing tunable resistive pulse sensing and protein analysis, we provide a comparative analysis of 4 exosome isolation techniques, indicating their efficacy and preparation purity. Our results demonstrate that current precipitation protocols for the isolation of exosomes from cell culture conditioned media and plasma provide the least pure preparations of exosomes, whereas size exclusion isolation is comparable to density gradient purification of exosomes. We have identified current shortcomings in common extracellular vesicle isolation methods and provide a potential standardized method that is effective, reproducible and can be utilized for various starting materials. We believe this method will have extensive application in the growing field of extracellular vesicle research.


title: Modular proximal optimization for multidimensional total-variation regularization

abstract: We study TV regularization, a widely used technique for eliciting structured sparsity. In particular, we propose efficient algorithms for computing prox-operators for lp-norm TV. The most important among these is l1-norm TV, for whose prox-operator we present a new geometric analysis which unveils a hitherto unknown connection to taut-string methods. This connection turns out to be remarkably useful as it shows how our geometry guided implementation results in efficient weighted and unweighted 1D-TV solvers, surpassing state-of-the-art methods. Our 1D-TV solvers provide the backbone for building more complex (two or higher-dimensional) TV solvers within a modular proximal optimization approach. We review the literature for an array of methods exploiting this strategy, and illustrate the benefits of our modular design through extensive suite of experiments on (i) image denoising, (ii) image deconvolution, (iii) four variants of fused-lasso, and (iv) video denoising. To underscore our claims and permit easy reproducibility, we provide all the reviewed and our new TV solvers in an easy to use multi-threaded C++, Matlab and Python library.


title: Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms

abstract: Extracting good representations from images is essential for many computer vision tasks. In this paper, we propose hierarchical matching pursuit (HMP), which builds a feature hierarchy layer-by-layer using an efficient matching pursuit encoder. It includes three modules: batch (tree) orthogonal matching pursuit, spatial pyramid max pooling, and contrast normalization. We investigate the architecture of HMP, and show that all three components are critical for good performance. To speed up the orthogonal matching pursuit, we propose a batch tree orthogonal matching pursuit that is particularly suitable to encode a large number of observations that share the same large dictionary. HMP is scalable and can efficiently handle full-size images. In addition, HMP enables linear support vector machines (SVM) to match the performance of nonlinear SVM while being scalable to large datasets. We compare HMP with many state-of-the-art algorithms including convolutional deep belief networks, SIFT based single layer sparse coding, and kernel based feature learning. HMP consistently yields superior accuracy on three types of image classification problems: object recognition (Caltech-101), scene recognition (MIT-Scene), and static event recognition (UIUC-Sports).


title: Explicit Reflection in Prolog-Tutor

abstract: This paper describes a reflection-based approach for open learner modeling (OLM). Tutoring dialogues are used by learners to explicitly reveal their own knowledge state to themselves. Dewey's theory of reflective thinking is used to create tutorial strategies which govern these dialogues. Drake's specification of critical thinking, associated to a defined set of skills, is used to define tutoring tactics implementing these strategies. The main contribution of this approach to OLM is that it provides a set of principled and reusable tutorial strategies and tactics to promote reflection, as they are based on domain independent theories. Furthermore, an evaluation of such a principled approach to OLM is straightforward in certain cases, as it refers to theories which already provide evaluation criteria. The approach is integrated in Prolog-Tutor, an existing intelligent tutoring system for Logic Programming. This paper presents a qualitative study of the resulting system, based on think-aloud protocols. A result analysis reveals that explicitly fostering reflection supports reflection based OLM and provides landmarks to explain its manifestations. However, the results also suggest that this openness may be less helpful when used by learners who have already honed a high level of proficiency in logic programming.


title: What do Cochrane systematic reviews say about interventions for vitamin D supplementation?

abstract: CONTEXT AND OBJECTIVE ::: Despite the high prevalence of vitamin D supplementation, its use remains controversial. The objective of this review was to identify and summarize the evidence from Cochrane systematic reviews regarding vitamin D supplementation for preventing ortreating any clinical condition. ::: ::: ::: DESIGN AND SETTING ::: Review of systematic reviews, conducted in the Discipline of Evidence-Based Medicine, Escola Paulista de Medicina, Universidade Federal de São Paulo. ::: ::: ::: METHODS ::: A search was conducted to identify all Cochrane systematic reviews that fulfilled the inclusion criteria. Titles and abstracts were screened by two authors. ::: ::: ::: RESULTS ::: We included 27 Cochrane systematic reviews: 10 assessing use of vitamin D for prevention and 17 for treatment. The reviews found moderate to high quality of evidence regarding the benefit of vitamin D for pregnant women (prevention of adverse events: preterm birth risk [rate ratio, RR 0.36; 95% confidence interval, CI 0.14 to 0.93] and low birthweight risk [RR 0.40; 95% CI 0.24 to 0.67]) and for asthma patients (reduction of severe exacerbations [RR 0.63; 95% CI 0.45 to 0.88]). No benefit was found regarding vitamin D supplementation alone (without calcium) for preventing hip or any new fracture. For all other outcomes assessed under various conditions, the current quality of evidence is low or unknown, and therefore insufficient for any recommendation. ::: ::: ::: CONCLUSION ::: Based on moderate to high quality of evidence, the Cochrane systematic reviews included here showed that there were some benefits from vitamin D supplementation for pregnant women and asthma patients and no benefits for preventing fractures.


title: Quattroped: A Leg--Wheel Transformable Robot

abstract: This paper reports on the design, integration, and performance evaluation of a novel four-leg/four-wheel transformable mobile robot, Quattroped. In contrast to most hybrid platforms that have separate mechanisms and actuators for wheels and legs, this robot is implemented with a unique transformation mechanism that directly switches the morphology of the driving mechanism between the wheels (i.e., a full circle) and 2 degrees of freedom leg (i.e., combining two half circles as a leg), so that the same system of actuation power can be efficiently utilized in both wheeled and legged modes. The design process, mechatronics, software infrastructure, behavioral development, and leg-wheel dynamic characteristics are described. The performance of the robot is evaluated in various scenarios, including driving and turning in wheeled mode, driving, step and bar crossing, irregular terrain passing, and stair climbing in legged mode. Taking advantage of the leg-wheel combination on a single platform, the comparison of the wheeled and legged locomotion is also discussed.


title: Modeling Temporal Structure with LSTM for Online Action Detection

abstract: Online action detection is a challenging problem: a system needs to decide what action is happening at the current frame, based on previous frames only. Fortunately in real-life, human actions are not independent from one another: there are strong (long-term) dependencies between them. An online action detection method should be able to capture these dependencies, to enable a more accurate early detection. At first sight, an LSTM seems very suitable for this problem. It is able to model both short-term and long-term patterns. It takes its input one frame at the time, updates its internal state and gives as output the current class probabilities. In practice, however, the detection results obtained with LSTMs are still quite low. In this work, we start from the hypothesis that it may be too difficult for an LSTM to learn both the interpretation of the input and the temporal patterns at the same time. We propose a two-stream feedback network, where one stream processes the input and the other models the temporal relations. We show improved detection accuracy on an artificial toy dataset and on the Breakfast Dataset [21] and the TVSeries Dataset [7], reallife datasets with inherent temporal dependencies between the actions.