title: An Organization Ontology for Enterprise Modelling

abstract: The paper presents our exploration into an organization ontology for the TOVE enterprise model. Its primary focus has been in linking structure and behavior through the concept of empowerment. Empowerment is the right of an organization agent to perform status changing actions. This linkage is critical to the unification of enterprise models and their executability.


title: Arabic Sign Language Alphabet Recognition Based on HOG-PCA Using Microsoft Kinect in Complex Backgrounds

abstract: A complex background is a well-known problem in any vision-based sign language recognition system. This paper presents Arabic sign language alphabet recognition system in complex backgrounds using Microsoft Kinect. The proposed system passes by three phases. The first phase which is the signer segmentation process, focuses on the isolation of the active signer rapidly from the background. The system assumes that the active signer is the closest person to the Kinect sensor, so that the system isolates this person from any other persons or any skin-like object that may exist in the scene. After that, hand segmentation is achieved using RGB-Ratio color model. Histogram of Oriented Gradients (HOG) is extracted from the image then Principal Component Analysis (PCA) is applied on HOG so that HOG-PCA is used to train a support vector machine (SVM) classifier. The system is able to recognize the 30 Arabic alphabets with an accuracy of 99.2%. The proposed online system has the ability to recognize Arabic alphabet almost correctly in a reasonable response time.


Comparing the Efficacy of Policy-Capturing Weights and Direct Estimates for Predicting Job Choice


title: A Graph Model for E-Commerce Recommender Systems

abstract: Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.


title: Improving Interaction in HMD-Based Vehicle Simulators through Real Time Object Reconstruction

abstract: Bringing real objects into the virtual world has been shown to increase usability and presence in virtual reality applications. This paper presents a system to generate a real time virtual reconstruction of real world user interface elements for use in a head mounted display based driving simulator. Our system uses sensor fusion algorithms to combine data from depth and color cameras to generate an accurate, detailed, and fast rendering of the user's hands while using the simulator. We tested our system and show in our results that the inclusion of the participants real hands, the wheel, and the shifter in the virtual environment increases the immersion, presence, and usability of the simulation. Our system can also be used to bring other real objects into the virtual world, especially when accuracy, detail, and real time updates are desired.


Image Inpainting Using Pre-trained Classi cation-CNN


title: Beware of BGP attacks

abstract: This note attempts to raise awareness within the network research community about the security of the interdomain routing infrastructure. We identify several attack objectives and mechanisms, assuming that one or more BGP routers have been compromised. Then, we review the existing and proposed countermeasures, showing that they are either generally ineffective (route filtering), or probably too heavyweight to deploy (S-BGP). We also review several recent proposals, and conclude by arguing that a significant research effort is urgently needed in the area of routing security.


title: Differential privacy in new settings

abstract: Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without compromising the the privacy of any individual [7]. We describe new work [10, 9] that extends differentially private data analysis beyond the traditional setting of a trusted curator operating, in perfect isolation, on a static dataset. We ask • How can we guarantee differential privacy, even against an adversary that has access to the algorithm's internal state, eg, by subpoena? An algorithm that achives this is said to be pan-private. • How can we guarantee differential privacy when the algorithm must continually produce outputs? We call this differential privacy under continual observation. We also consider these requirements in conjunction.


title: Palladium-Decorated Hydrogen-Gas Sensors Using Periodically Aligned Graphene Nanoribbons

abstract: Polymer residue-free graphene nanoribbons (GNRs) of 200 nm width at 1 μm pitch were periodically generated in an area of 1 cm2 via laser interference lithography using a chromium interlayer prior to photoresist coating. High-quality GNRs were evidenced by atomic force microscopy, micro-Raman spectroscopy, and X-ray photoelectron spectroscopy measurements. Palladium nanoparticles were then deposited on the GNRs as catalysts for sensing hydrogen gases, and the GNR array was utilized as an electrically conductive path with less electrical noise. The palladium-decorated GNR array exhibited a rectangular sensing curve with unprecedented rapid response and recovery properties: 90% response within 60 s at 1000 ppm and 80% recovery within 90 s in nitrogen ambient. In addition, reliable and repeatable sensing behaviors were revealed when the array was exposed to various gas concentrations even at 30 ppm.


title: The Mondrian Process for Machine Learning

abstract: This report is concerned with the Mondrian process and its applications in machine learning. The Mondrian process is a guillotine-partition-valued stochastic process that possesses an elegant self-consistency property. The first part of the report uses simple concepts from applied probability to define the Mondrian process and explore its properties. ::: The Mondrian process has been used as the main building block of a clever online random forest classification algorithm that turns out to be equivalent to its batch counterpart. We outline a slight adaptation of this algorithm to regression, as the remainder of the report uses regression as a case study of how Mondrian processes can be utilized in machine learning. In particular, the Mondrian process will be used to construct a fast approximation to the computationally expensive kernel ridge regression problem with a Laplace kernel. ::: The complexity of random guillotine partitions generated by a Mondrian process and hence the complexity of the resulting regression models is controlled by a lifetime hyperparameter. It turns out that these models can be efficiently trained and evaluated for all lifetimes in a given range at once, without needing to retrain them from scratch for each lifetime value. This leads to an efficient procedure for determining the right model complexity for a dataset at hand. ::: The limitation of having a single lifetime hyperparameter will motivate the final Mondrian grid model, in which each input dimension is endowed with its own lifetime parameter. In this model we preserve the property that its hyperparameters can be tweaked without needing to retrain the modified model from scratch.


title: Defining the Human Deubiquitinating Enzyme Interaction Landscape

abstract: Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel nonreciprocal proteomic data sets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, subcellular localization, and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway.


title: The Romance of Learning from Disagreement. The Effect of Cohesiveness and Disagreement on Knowledge Sharing Behavior and Individual Performance Within Teams

abstract: PurposeThe purpose of this study was to explore the effects of disagreement and cohesiveness on knowledge sharing in teams, and on the performance of individual team members.Design/methodology/approachData were obtained from a survey among 1,354 employees working in 126 teams in 17 organizations.FindingsThe results show that cohesiveness has a positive effect on the exchange of advice between team members and on openness for sharing opinions, whereas disagreement has a negative effect on openness for sharing opinions. Furthermore, the exchange of advice in a team has a positive effect on the performance of individual team members and acts as a mediator between cohesiveness and individual performance.ImplicationsManagers who want to stimulate knowledge sharing processes and performance within work teams may be advised to take measures to prevent disagreement between team members and to enhance team cohesiveness.Originality/valueAlthough some gurus in organizational learning claim that disagreement has a positive effect on group processes such as knowledge sharing and team learning, this study does not support this claim.


SKEE: A lightweight Secure Kernel-level Execution Environment for ARM


title: Dynamic Analysis of a Novel Moving Magnet Linear Actuator

abstract: A novel moving magnet linear actuator is proposed for linear oscillations in the linear resonant compressors for household refrigerators. This paper provides stator and armature design including CAD model and geometric parameters. Furthermore, the working principle of the proposed actuator is explained. The stator assembly is composed of two reversely wound coils, which are electrically excited with single phase ac power and oscillates the radially magnetized armature. With the help of the electromechanical analytical model, the dynamic parameters such as stroke, velocity, and acceleration of the armature are derived. Additionally, the time-dependent current model of the stator winding is proposed. An experimental setup is used to validate these responses at the resonance excitation frequency with the help of sensors. The system kinetics are discussed to estimate the spring, damping, inertial, and magnetic forces. A simulation is executed to estimate the time-domain responses of these dynamic parameters and the effects of excitation frequency are discussed. The force models are experimentally validated at the resonance frequency excitation. In order to evaluate the performance of the proposed actuator, a comparison of the performance parameters, such as efficiency, stroke, current, and mass flow rate is demonstrated with the conventional rotary as well as the linear motors for linear compressor application.


title: Stepwise two-photon excitation studies in terbium-doped fluoride crystals

abstract: Abstract Energy upconversion signals were observed in the wavelength region 360–430 nm when the Tb3+-doped LaF3 and CaF2 crystals were excited with an argon ion laser. Upconversion signal intensity increased 10× when the samples were excited simultaneously with an argon ion laser and a Ti:Sapphire laser.


title: Adapting Auxiliary Losses Using Gradient Similarity

abstract: One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivial to know if an auxiliary task will be helpful for the main task and when it could start hurting. We propose to use the cosine similarity between gradients of tasks as an adaptive weight to detect when an auxiliary loss is helpful to the main loss. We show that our approach is guaranteed to converge to critical points of the main task and demonstrate the practical usefulness of the proposed algorithm in a few domains: multi-task supervised learning on subsets of ImageNet, reinforcement learning on gridworld, and reinforcement learning on Atari games.


title: Moving target detection in foliage using along track monopulse synthetic aperture radar imaging.

abstract: This paper presents a method for detecting moving targets embedded in foliage from the monostatic and bistatic synthetic aperture radar (SAR) data obtained via two airborne radars. The two radars, which are mounted on the same aircraft, have different coordinates in the along track (cross-range) domain. However, unlike the interferometric SAR systems used for topographic mapping, the two radars possess a common range and altitude (i.e., slant range). The resultant monopulse SAR images are used to construct difference and interferometric images for moving target detection. It is shown that the signatures of the stationary targets are weakened in these images. Methods for estimating a moving target's motion parameters are discussed. Results for an ultrawideband UHF SAR system are presented.


title: Modern Control Systems

abstract: From the Publisher: ::: For more than twenty years, Modern Control Systems has set the standard of excellence for undergraduate control systems textbooks. It has remained a bestseller because Richard Dorf and Robert Bishop have been able to take complex control theory and make it exciting and accessible to students. The book presents a control engineering methodology that, while based on mathematical fundamentals, stresses physical system modeling and practical control system designs with realistic system specifications.


title: Software-Defined Network Virtualization Platform for Enterprise Network Resource Management

abstract: Applications of a software-defined network (SDN) have increased. Notably an SDN has the benefits of programmability and automation for network control. It also realizes network virtualization. Based on the OpenFlow/FlowVisor network virtualization layer, this paper proposes a dynamic resource management scheme, called the EnterpriseVisor engine that manages the distribution of network resources among slices. The EnterpriseVisor engine first slices an enterprise’s network into virtual subnets, and then continues to monitor the resource utilization of each subnet. In the engine, dynamic resource scheduling of the subnet for each service uses the linear programming method. The engine then delivers the schedule as the resource allocation policy of the FlowVisor layer for data plane users. To demonstrate the feasibility of the proposed EnterpriseVisor engine, an OpenFlow testbed with four subnets is designed. Compared with the basic OpenFlow and OpenFlow/FlowVisor platforms, the EnterpriseVisor engine has higher network utilization, 25.7% and 13.4% higher, respectively, with only a minor sacrifice to control message latency.


title: An Enhanced Symmetric Key Cryptography Algorithm to Improve Data Security

abstract: This work uses enhanced symmetric key encryption algorithm, in which same structure of encryption and decryption procedure algorithm is used. In conventional encryption methods the key for encryption and decryption is same and remain secret. The algorithm uses key generation method by random number in algorithm for increasing efficiency of algorithm. The algorithm use key size of 512 bits for providing better security and it also provide the concept of internal key generation at receiver end on the basis of 512 bits key which will entered by the sender. This internal key will store in the sender end database and send to the receiver end by other path for preventing brute force attack and other harmful attacks on security. This algorithm is more efficient for large data where existing algorithms provides efficient encryption and decryption only for 2MB data. This work provides better speed in comparison to existing algorithms for large size of files with less overhead.


Combining Content-Based and Collaborative Filters in an Online Newspaper


title: E-government maturity models: Extension of the Layne and Lee model

abstract: The article proposes a reorientation of the e-government maturity models by focusing IT applications to improve the core activities and bring end-users as the key stakeholders for future e-government investments. The proposed Public Sector Process Rebuilding (PPR) maturity model is an extension of the Layne and Lee model.


title: High-Efficiency MOSFET Inverter with H6-Type Configuration for Photovoltaic Nonisolated AC-Module Applications

abstract: A novel, high-efficiency inverter using MOSFETs for all active switches is presented for photovoltaic, nonisolated, ac-module applications. The proposed H6-type configuration features high efficiency over a wide load range, low ground leakage current, no need for split capacitors, and low-output ac-current distortion. The detailed power stage operating principles, pulsewidth modulation scheme, associated multilevel bootstrap power supply, and integrated gate drivers for the proposed inverter are described. Experimental results of a 300 W hardware prototype show that not only are MOSFET body diode reverse-recovery and ground leakage current issues alleviated in the proposed inverter, but also that 98.3% maximum efficiency and 98.1% European Union efficiency of the dc-ac power train and the associated driver circuit are achieved.


title: Achieving 10Gbps Line-rate Key-value Stores with FPGAs.

abstract: Distributed in-memory key-value stores such as memcached have become a critical middleware application within current web infrastructure. However, typical x86based systems yield limited performance scalability and high power consumption as their architecture with its optimization for single thread performance is not wellmatched towards the memory-intensive and parallel nature of this application. In this paper we present the design of a novel memcached architecture implemented on Field Programmable Gate Arrays (FPGAs) which is the first in literature to achieve 10Gbps line rate processing for all packet sizes. By transformation of the functionality into a dataflow architecture, the implementation can not only provide significant speed-up but also operate at a lower power consumption than any x86. More specifically, with our prototype we have measured an increase of up to a factor of 36x in requests per second per Watt that can be serviced in comparison to the best published numbers for regular servers with optimized software. Additionally, we show that through the tight integration of network interface, memory and compute, round trip latency can be reduced down to below 4.5 microseconds.


title: Secure Monitoring of Patients With Wandering Behavior in Hospital Environments

abstract: Today there is considerable interest for making use of the latest technological advancements for several healthcare applications. However, there are several challenges for making use of different technologies for healthcare applications. In particular, there is a need to ensure that the healthcare related services receive priority during events, such as legitimate failures of devices, congestion, and attacks in the networks. In this paper, we discuss some of the requirements for making use of technology for healthcare applications and propose techniques for secure monitoring of patients with wandering behavior in a hospital or elderly care environment. One of the aims of our work is to use technology for secure monitoring of patients with wandering behavior to keep them away from danger, or detect if the behavior of the patient violates the policies of the hospital, or even violates privacy policies of other patients. Our approach makes use of software defined networking (SDN), Wireless LAN (WLAN), and wearable devices for the patients. Our approach incurs low cost since WLAN is widely deployed. However, there are some challenges for making use of WLAN for monitoring dementia patients, since it is primarily used for accessing the Internet and its open nature is vulnerable to different types of security attacks. Hence we make use of SDN to solve some of these challenges and provide priority for the monitoring services. We have developed a security application for an SDN controller that can be used to enforce fine granular policies for communication between the hosts, real time location tracking of the patients, and deal with attacks on the hospital networks. The policy-based security enforcement helps to differentiate healthcare related traffic from other traffic and provide priority to the healthcare traffic. The real time location tracking detects wandering by patients and if necessary can raise alarms to the staff. The attack detection component makes use of attack signatures and behavior-based intrusion detection to deal with attacks on hospital networks. We will also present the prototype implementation of our model using ONOS SDN controller and OpenFlow Access Points.


title: Visual assistance for drivers by mixed reality

abstract: This paper describes extensive research results of visual assistance for drivers based on mixed reality and discusses the technical advantages of the research framework used. These include utilization of roadside cameras, development of windshield display, error-free image processing system, vision enhancement by mixed reality, and evaluation in real traffic environment. The approaches concentrate on providing the best visual clues that can be understood intuitively and instantly for driver's vision while driving. Novel visual assistance methods are proposed based on these research frameworks, research results of which include a virtual mirror and a virtual slope. For the covering abstract see ITRD E140665.


title: In vitro propagation and genetic fidelity analysis of alginate-encapsulated Bacopa monnieri shoot tips using Gracilaria salicornia extracts

abstract: The scope of the present study is to investigate the effect of plant growth regulators and seaweed liquid extracts (SLEs) on in vitro regeneration of the Ayurvedically important memory plus plant Bacopa monnieri using shoot tips. Sodium alginate-encapsulated shoot tips were cultured on Murashige and Skoog (MS) medium containing 6-benzylaminopurine (BAP), kinetin, zeatin, 1-naphthaleneacetic acid (NAA), indole-3-acetic acid (IAA), and indole-3-butyric acid (IBA), individually for shoot initiation. The effect of Gracilaria salicornia and Kappaphycus alvarezii SLEs (20–80 %) for shoot induction and regeneration using shoot tips was also studied. MS medium containing 1.0 mg L−1 BAP and 0.5 mg L−1 NAA showed highest multiple shoot induction frequency 95.8 %, mean number 147.8, and mean length 12.5 cm of shoots where G. salicornia extract at 60 % showed 85.9 % multiple shoot induction with 145.6 shoots and mean length 12.3 cm after 5 weeks of incubation. An effective rooting frequency 84.1 % was observed on half-strength MS medium containing 0.1 mg L−1 IAA and where 25 % of G. salicornia extract showed 82.2 % of rooting after 1 week of incubation. In vitro-rooted plants were acclimatized in soil cups and maintained in greenhouse with natural conditions. In vitro-propagated and mother plants were analyzed using random amplified polymorphic DNA markers reveled that no genetic variation between control plants and in vitro-regenerated plants using SLEs. These results showed that G. salicornia extracts can be used as PGRs for in vitro propagation of B. monnieri and it is a simple, rapid mass propagation method.


title: Learning via human feedback in continuous state and action spaces

abstract: This paper considers the problem of extending Training an Agent Manually via Evaluative Reinforcement (TAMER) in continuous state and action spaces. Investigative research using the TAMER framework enables a non-technical human to train an agent through a natural form of human feedback (negative or positive). The advantages of TAMER have been shown on tasks of training agents by only human feedback or combining human feedback with environment rewards. However, these methods are originally designed for discrete state-action, or continuous state-discrete action problems. This paper proposes an extension of TAMER to allow both continuous states and actions, called ACTAMER. The new framework utilizes any general function approximation of a human trainer's feedback signal. Moreover, a combined capability of ACTAMER and reinforcement learning is also investigated and evaluated. The combination of human feedback and reinforcement learning is studied in both settings: sequential and simultaneous. Our experimental results demonstrate the proposed method successfully allowing a human to train an agent in two continuous state-action domains: Mountain Car and Cart-pole (balancing).


title: The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applications

abstract: In this paper we discuss the development and application of a large formal ontology to the semantic web. The Suggested Upper Merged Ontology (SUMO) (Niles & Pease, 2001) (SUMO, 2002) is a “starter document” in the IEEE Standard Upper Ontology effort. This upper ontology is extremely broad in scope and can serve as a semantic foundation for search, interoperation, and communication on the semantic web.


title: An Adaptive Method of Speckle Reduction and Feature Enhancement for SAR Images Based on Curvelet Transform and Particle Swarm Optimization

abstract: This paper proposes an adaptive method based on the mirror-extended curvelet transform and the improved particle swarm optimization (PSO) algorithm, which reduce speckle noise and enhance edge features and contrast of synthetic aperture radar (SAR) images. First, an improved gain function, which integrates the speckle reduction with the feature enhancement, is introduced to nonlinearly shrink and stretch the curvelet coefficients. Then, a novel objective criterion for the quality of the despeckled and enhanced images is proposed in order to adaptively obtain the optimal parameters in the gain function. Finally, the PSO algorithm is employed as a global search strategy for the best despeckled and enhanced image. In order to increase the convergence speed and avoid the premature convergence, two further improvements for the classic PSO algorithm are presented. That is, a new learning scheme and a mutation operator are introduced. Experimental results demonstrate that the proposed method can efficiently reduce the speckle and enhance the edge features and the contrast of SAR images and outperforms the wavelet- and curvelet-based nonadaptive despeckling and enhancement methods.


title: A Coverage-Based Approach to Recommendation Diversity On Similarity Graph

abstract: We consider the problem of generating diverse, personalized recommendations such that a small set of recommended items covers a broad range of the user's interests. We represent items in a similarity graph, and we formulate the relevance/diversity trade-off as finding a small set of unrated items that best covers a subset of items positively rated by the user. In contrast to previous approaches, our method does not rely on an explicit trade-off between a relevance objective and a diversity objective, as the estimations of relevance and diversity are implicit in the coverage criterion. We show on several benchmark datasets that our approach compares favorably to the state-of-the-art diversification methods according to various relevance and diversity measures.


Impact of the Optimal Tilt Angle on the Solar Photovoltaic Array Size and Cost for a 100 kWh Solar Power System in Imo State


title: Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems

abstract: Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) are two popular approaches to compute matrix factorization. There has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been explored thoroughly. In this paper, we show that coordinate descent based methods have a more efficient update rule compared to ALS, and are faster and have more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updatesrank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, on a synthetic dataset with 2 billion ratings, CCD++ is 4 times faster than both SGD and ALS using a distributed system with 20 machines.


title: Plastic and viscous deformation of redundant structures—prototypes for a general theory☆

abstract: Abstract A systematic method is presented for the analysis of a wide class of rigid, strain-hardening structures, subjected to arbitrarily prescribed independent loads. It is shown how the behavior of such structures may be fully described geometrically by means of a yield polyhedron and associated flow law in a suitably defined space of generalized forces. The structures in question are prototypes for a very general theory of piecewise linear plasticity. In fact the basic postulates of the general theory are deduced, in the case of the prototype structures, from extremely simple assumptions. The relationship between the theory expounded and that of the so-called kinematic hardening theory is discussed. Finally it is shown that there exists a close analogy between the behavior of linearly viscous structures and that of linearly strain-hardening structures, and that nonlinear or piecewise linear viscous structures possess many features in common with analogous plastic structures.


title: Waste Not, Want Not: Why Rarefying Microbiome Data is Inadmissible

abstract: Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.


abstract: Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial approaches to unsupervised domain adaptation reduce the difference between the training and test domain distributions and thus improve generalization performance. However, while generative adversarial networks (GANs) show compelling visualizations, they are not optimal on discriminative tasks and can be limited to smaller shifts. On the other hand, discriminative approaches can handle larger domain shifts, but impose tied weights on the model and do not exploit a GAN-based loss. In this work, we first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and use this generalized view to better relate prior approaches. We then propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard domain adaptation tasks as well as a difficult cross-modality object classification task.


2 + 1 D Emergent Supersymmetry at First-Order Quantum Phase Transition


title: Research on Control Strategy of Two-Wheeled Self-Balancing Robot

abstract: The structure of two-wheeled self-balancing robot is equivalent to the combination of the linear inverted pendulum and wheeled mobile robot. But compared to the linear inverted pendulum, two-wheeled self-balancing robot can move freely and turn flexibly. So the system is more complex and difficult to control. As the two-wheeled self-balancing robot is a high order and multi variable system, PID controller based on output feedback can't have a satisfactory control effect. The LQR control strategy is implemented, and the weight matrix Q and R of the LQR controller are optimized by using multi-population genetic ideas. Taking two wheeled self balancing robot as the test platform, the two control modes of LQR and PID are used to carry out experiments and analysis. In the simulation experiments of balance control, position control and speed control, the LQR controller has a better performance in the system's robustness and fast response.


title: Synonym set extraction from the biomedical literature by lexical pattern discovery

abstract: Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst [1], but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.


title: Methods for exploring and mining tables on Wikipedia

abstract: Knowledge bases extracted automatically from the Web present new opportunities for data mining and exploration. Given a large, heterogeneous set of extracted relations, new tools are needed for searching the knowledge and uncovering relationships of interest. We present WikiTables, a Web application that enables users to interactively explore tabular knowledge extracted from Wikipedia. In experiments, we show that WikiTables substantially outperforms baselines on the novel task of automatically joining together disparate tables to uncover "interesting" relationships between table columns. We find that a "Semantic Relatedness" measure that leverages the Wikipedia link structure accounts for a majority of this improvement. Further, on the task of keyword search for tables, we show that WikiTables performs comparably to Google Fusion Tables despite using an order of magnitude fewer tables. Our work also includes the release of a number of public resources, including over 15 million tuples of extracted tabular data, manually annotated evaluation sets, and public APIs.


title: Design of biomimetic hand prosthesis with tendon-driven five fingers

abstract: This paper proposes a biomimetic hand prosthesis with tendon-driven five fingers. Each finger is composed of a distal-middle phalange, a proximal phalange and a metacarpal bone, which are connected to a link mechanism. The finger flexion is a resultant motion by pulling a wire to serve as a tendon, but the finger extension is performed by an elastic mechanism composed of a restoration spring. The designed hand prosthesis with tendon-driven five fingers has six degrees of freedom. But its total weight is merely 400.72g. The hand performs various hand functions such as the grasping and the hand postures. From experimental results, we show that the proposed hand prosthesis is applicable to amputees as a prosthetic hand.


title: Beyond Holistic Object Recognition: Enriching Image Understanding with Part States

abstract: Important high-level vision tasks require rich semantic descriptions of objects at part level. Based upon previous work on part localization, in this paper, we address the problem of inferring rich semantics imparted by an object part in still images. Specifically, we propose to tokenize the semantic space as a discrete set of part states. Our modeling of part state is spatially localized, therefore, we formulate the part state inference problem as a pixel-wise annotation problem. An iterative part-state inference neural network that is efficient in time and accurate in performance is specifically designed for this task. Extensive experiments demonstrate that the proposed method can effectively predict the semantic states of parts and simultaneously improve part segmentation, thus benefiting a number of visual understanding applications. The other contribution of this paper is our part state dataset which contains rich part-level semantic annotations.


title: Two-Stream Convolutional Networks for Dynamic Saliency Prediction

abstract: In recent years, visual saliency estimation in images has attracted much attention in the computer vision community. However, predicting saliency in videos has received rela- tively little attention. Inspired by the recent success of deep convolutional neural networks based static saliency mod- els, in this work, we study two different two-stream convo- lutional networks for dynamic saliency prediction. To im- prove the generalization capability of our models, we also introduce a novel, empirically grounded data augmenta- tion technique for this task. We test our models on DIEM dataset and report superior results against the existing mod- els. Moreover, we perform transfer learning experiments on SALICON, a recently proposed static saliency dataset, by finetuning our models on the optical flows estimated from static images. Our experiments show that taking motion into account in this way can be helpful for static saliency estimation.


title: BQP and the polynomial hierarchy

abstract: The relationship between BQP and PH has been an open problem since the earliest days of quantum computing. We present evidence that quantum computers can solve problems outside the entire polynomial hierarchy, by relating this question to topics in circuit complexity, pseudorandomness, and Fourier analysis. ::: First, we show that there exists an oracle relation problem (i.e., a problem with many valid outputs) that is solvable in BQP, but not in PH. This also yields a non-oracle relation problem that is solvable in quantum logarithmic time, but not in AC0. ::: Second, we show that an oracle decision problem separating BQP from PH would follow from the Generalized Linial-Nisan Conjecture, which we formulate here and which is likely of independent interest. The original Linial-Nisan Conjecture (about pseudorandomness against constant-depth circuits) was recently proved by Braverman, after being open for twenty years.


title: Accelerometer-based transportation mode detection on smartphones

abstract: We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.


title: Deep Age Estimation: From Classification to Ranking

abstract: Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel convolutional neural network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a set of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. From a theoretical perspective, we obtain an approximation for the final ranking error, show that it is controlled by the maximum error produced among subranking problems, and thus find a new error bound, which provides helpful guidance for the training and analysis of deep rankers. Based on the new error bound, we theoretically give an explicit formula for the learning of ranking-CNN and demonstrate its convergence using the stochastic approximation method. Moreover, we rigorously prove that ranking-CNN, by considering ordinal relation between ages, is more likely to get smaller estimation errors when compared with multiclass classification approaches. Through extensive experiments, we show that ranking-CNN outperforms other state-of-the-art feature extractors and age estimators on benchmark datasets.


title: Match-Ladder: An efficient event matching algorithm in large-scale content-based publish/subscribe system

abstract: To resolve high-performance content-based event matching problem for large-scale publish/subscribe systems, we have focused on how to use some priori knowledge to improve the efficiency. In this paper, by theoretically analyzing the inherent problem of the matching order of predicates, we propose a matching algorithm called Match-Ladder which based on the best matching order. The Match-Ladder can achieve better trade-off between time efficiency and the usage of memory space. It has been verified through both mathematical and simulation-based evaluation.


title: Oral Hairy Leukoplakia Which Occurred as a Presenting Sign of Acute Myeloid Leukemia in a Child

abstract: Oral hairy leukoplakia (OHL) is caused by the reactivation of a previous Epstein-Barr virus (EBV) infection in the epithelium of the tongue. Most lesions are characterized by corrugated whitish patches on the lateral border of the tongue. It is frequently associated with AIDS, but cases in patients with other immunosuppressed states have also been reported. In leukemia patients, OHL is rarely encountered, and appears only after chemotherapy. We report a case of OHL which occurred as a presenting sign of acute myeloid leukemia (AML) in a previously healthy 15-year-old child. A 15-year-old boy presented with a whitish patch on the left lateral border of the tongue. The biopsy specimen revealed papillomatosis, hyperkeratosis, acanthosis and ballooning degeneration in the stratum spinosum. The patient was EBV seropositive, and PCR analysis of EBV DNA in the lesional tissue was positive. After the diagnosis of OHL in dermatologic department, the patient was referred to pediatrics due to the abnormal peripheral blood smear, and was diagnosed with AML.


title: Xampling: Signal Acquisition and Processing in Union of Subspaces

abstract: We introduce Xampling, a unified framework for signal acquisition and processing of signals in a union of subspaces. The main functions of this framework are two: Analog compression that narrows down the input bandwidth prior to sampling with commercial devices followed by a nonlinear algorithm that detects the input subspace prior to conventional signal processing. A representative union model of spectrally sparse signals serves as a test-case to study these Xampling functions. We adopt three metrics for the choice of analog compression: robustness to model mismatch, required hardware accuracy, and software complexities. We conduct a comprehensive comparison between two sub-Nyquist acquisition strategies for spectrally sparse signals, the random demodulator and the modulated wideband converter (MWC), in terms of these metrics and draw operative conclusions regarding the choice of analog compression. We then address lowrate signal processing and develop an algorithm for that purpose that enables convenient signal processing at sub-Nyquist rates from samples obtained by the MWC. We conclude by showing that a variety of other sampling approaches for different union classes fit nicely into our framework.


title: On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy

abstract: In 1977 Tore Dalenius articulated a desideratum for statistical databases: nothing about ::: ::: an individual should be learnable from the database that cannot be learned without access to the ::: ::: database. We give a general impossibility result showing that a natural formalization of Dalenius’ ::: ::: goal cannot be achieved if the database is useful. The key obstacle is the side information that ::: ::: may be available to an adversary. Our results hold under very general conditions regarding the ::: ::: database, the notion of privacy violation, and the notion of utility. ::: ::: Contrary to intuition, a variant of the result threatens the privacy even of someone not in ::: ::: the database. This state of affairs motivated the notion of differential privacy [15, 16], a strong ::: ::: ad omnia privacy which, intuitively, captures the increased risk to one’s privacy incurred by ::: ::: participating in a database.


title: A compact 0.3–10 GHz broadband stacked amplifier in 65nm standard CMOS

abstract: This work presents the design and implementation of a fully integrated broadband medium power stacked amplifier in 65 nm bulk CMOS. The amplifier topology utilizes three NMOS stack and three PMOS stack to increase the output voltage swing along with the output impedance. The load impedance is further optimized with a resistive feedback which not only results in broadband operation but also avoids a lossy broadband output matching network which reduces area significantly. Further, small interstage peaking inductors are employed to peak the parasitics capacitances that limit the broadband operation. The proposed amplifier shows a measured peak saturated output power from 13 dBm to 8.5 dBm and a P1dB of 7 dBm to 4 dBm from 0.3 GHz to 10 GHz. The measured gain is 9 dB with a gain ripple of ±1.5 dB in the entire frequency range, yielding a fractional bandwidth of 188%. The measured load-pull −1 dB, −2 dB output power contours verify the optimum impedance around 50 Ω. The active chip area is only 0.44mm2.


title: Personalized Privacy Protection in Social Networks

abstract: Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user's personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker's background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users' protection requirements. We verify the effectiveness of the framework through extensive experiments.


title: Parameters optimization of deep learning models using Particle swarm optimization

abstract: Deep learning has been successfully applied in several fields such as machine translation, manufacturing, and pattern recognition. However, successful application of deep learning depends upon appropriately setting its parameters to achieve high quality results. The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm. Manual parameter setting and grid search approaches somewhat ease the users tasks in setting these important parameters. Nonetheless, these two techniques can be very time consuming. In this paper, we show that the Particle swarm optimization (PSO) technique holds great potential to optimize parameter settings and thus saves valuable computational resources during the tuning process of deep learning models. Specifically, we use a dataset collected from a Wi-Fi campus network to train deep learning models to predict the number of occupants and their locations. Our preliminary experiments indicate that PSO provides an efficient approach for tuning the optimal number of hidden layers and the number of neurons in each layer of the deep learning algorithm when compared to the grid search method. Our experiments illustrate that the exploration process of the landscape of configurations to find the optimal parameters is decreased by 77%-85%. In fact, the PSO yields even better accuracy results.


title: LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems

abstract: We describe a parallel iterative least squares solver named \texttt{LSRN} that is based on random normal projection. \texttt{LSRN} computes the min-length solution to $\min_{x \in \mathbb{R}^n} \|A x - b\|_2$, where $A \in \mathbb{R}^{m \times n}$ with $m \gg n$ or $m \ll n$, and where $A$ may be rank-deficient. Tikhonov regularization may also be included. Since $A$ is only involved in matrix-matrix and matrix-vector multiplications, it can be a dense or sparse matrix or a linear operator, and \texttt{LSRN} automatically speeds up when $A$ is sparse or a fast linear operator. The preconditioning phase consists of a random normal projection, which is embarrassingly parallel, and a singular value decomposition of size $\lceil \gamma \min(m,n) \rceil \times \min(m,n)$, where $\gamma$ is moderately larger than 1, e.g., $\gamma = 2$. We prove that the preconditioned system is well-conditioned, with a strong concentration result on the extreme singular values, and hence that the number of iterations is fully predictable when we apply LSQR or the Chebyshev semi-iterative method. As we demonstrate, the Chebyshev method is particularly efficient for solving large problems on clusters with high communication cost. Numerical results demonstrate that on a shared-memory machine, \texttt{LSRN} outperforms LAPACK's DGELSD on large dense problems, and MATLAB's backslash (SuiteSparseQR) on sparse problems. Further experiments demonstrate that \texttt{LSRN} scales well on an Amazon Elastic Compute Cloud cluster.


title: Group A Streptococcus, Acute Rheumatic Fever and Rheumatic Heart Disease: Epidemiology and Clinical Considerations

abstract: Early recognition of group A streptococcal pharyngitis and appropriate management with benzathine penicillin using local clinical prediction rules together with validated rapi-strep testing when available should be incorporated in primary health care. A directed approach to the differential diagnosis of acute rheumatic fever now includes the concept of low-risk versus medium-to-high risk populations. Initiation of secondary prophylaxis and the establishment of early medium to long-term care plans is a key aspect of the management of ARF. It is a requirement to identify high-risk individuals with RHD such as those with heart failure, pregnant women, and those with severe disease and multiple valve involvement. As penicillin is the mainstay of primary and secondary prevention, further research into penicillin supply chains, alternate preparations and modes of delivery is required.


title: Cumulative Effect in Information Diffusion: A Comprehensive Empirical Study on Microblogging Network

abstract: Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.


title: Bringing IoT and Cloud Computing towards Pervasive Healthcare

abstract: Pervasive healthcare applications utilizing body sensor networks generate a vast amount of data that need to be managed and stored for processing and future usage. Cloud computing among with the Internet of Things (IoT) concept is a new trend for efficient managing and processing of sensor data online. This paper presents a platform based on Cloud Computing for management of mobile and wearable healthcare sensors, demonstrating this way the IoT paradigm applied on pervasive healthcare.


title: LDA-based document models for ad-hoc retrieval

abstract: Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.


title: An analysis of training and generalization errors in shallow and deep networks

abstract: Abstract This paper is motivated by an open problem around deep networks, namely, the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case of regression problems when each unit evaluates a periodic activation function. We argue that the minimal expected value of the square loss is inappropriate to measure the generalization error in approximation of compositional functions in order to take full advantage of the compositional structure. Instead, we measure the generalization error in the sense of maximum loss, and sometimes, as a pointwise error. We give estimates on exactly how many parameters ensure both zero training error as well as a good generalization error. We prove that a solution of a regularization problem is guaranteed to yield a good training error as well as a good generalization error and estimate how much error to expect at which test data.


title: Education 4.0 for tall thin engineer in a data driven society

abstract: The rise of digital technology creates disruptive innovation, new opportunities in communication, it transforms industrial production and it changes the way we work, learn and live. That leads to the fourth Industrial Revolution, which changes the working world enormously. New skills, future-ready curricula, new learning and training concepts as well as the flexibility in education are necessary. To deliver that, it needs a fourth revolution in the educational system with a strong active presence and most important Artificial Intelligence (AI) driven features for better performance. Education 4.0 needs also a strong partnership between industry and academicals environment in the formation of human resources for Industry 4.0. We present in this paper our answers of education regarding the challenges of fourth revolution in Industry and the seven AI driven didactic methods identified for the University of Future.


title: Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking

abstract: The evaluation of information retrieval (IR) systems over special collections, such as large book repositories, is out of reach of traditional methods that rely upon editorial relevance judgments. Increasingly, the use of crowdsourcing to collect relevance labels has been regarded as a viable alternative that scales with modest costs. However, crowdsourcing suffers from undesirable worker practices and low quality contributions. In this paper we investigate the design and implementation of effective crowdsourcing tasks in the context of book search evaluation. We observe the impact of aspects of the Human Intelligence Task (HIT) design on the quality of relevance labels provided by the crowd. We assess the output in terms of label agreement with a gold standard data set and observe the effect of the crowdsourced relevance judgments on the resulting system rankings. This enables us to observe the effect of crowdsourcing on the entire IR evaluation process. Using the test set and experimental runs from the INEX 2010 Book Track, we find that varying the HIT design, and the pooling and document ordering strategies leads to considerable differences in agreement with the gold set labels. We then observe the impact of the crowdsourced relevance label sets on the relative system rankings using four IR performance metrics. System rankings based on MAP and Bpref remain less affected by different label sets while the Precision@10 and nDCG@10 lead to dramatically different system rankings, especially for labels acquired from HITs with weaker quality controls. Overall, we find that crowdsourcing can be an effective tool for the evaluation of IR systems, provided that care is taken when designing the HITs.


title: Finding temporal structure in music: blues improvisation with LSTM recurrent networks

abstract: We consider the problem of extracting essential ingredients of music signals, such as a well-defined global temporal structure in the form of nested periodicities (or meter). We investigate whether we can construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style. Because recurrent neural networks (RNNs) can, in principle, learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard RNNs often lacks global coherence. The reason for this failure seems to be that RNNs cannot keep track of temporally distant events that indicate global music structure. Long short-term memory (LSTM) has succeeded in similar domains where other RNNs have failed, such as timing and counting and the learning of context sensitive languages. We show that LSTM is also a good mechanism for learning to compose music. We present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and we believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure, it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen.


title: Endocrown with Leucite-Reinforced Ceramic: Case of Restoration of Endodontically Treated Teeth

abstract: A common problem encountered by dentists is the restorative treatment of nonvital teeth. When the pulp chamber presents appropriate conditions for retention, the endocrown is indicated. This monolithic, ceramic adhesive restoration is singularly used yet warrants wider recognition and use. The endocrown allows preservation of the tooth structure and is minimally invasive. Currently, this treatment option, of a core buildup and full coverage restoration, reduces tooth structure excessively. This treatment presents not only functional limitations but also aesthetic concerns. Recently, the VITA-PM9 system, a leucite-reinforced glass ceramic, has been increasingly used in a variety of clinical situations due to its satisfactory physical-mechanical and aesthetic properties. Therefore, the present study describes a case of surgical restoration of a nonvital tooth using the endocrown technique and the VITA-PM9.


title: Integration of Visual and Tactile Signals From the Hand in the Human Brain: An fMRI Study

abstract: In the non-human primate brain, a number of multisensory areas have been described where individual neurons respond to visual, tactile and bimodal visuotactile stimulation of the upper limb. It has been shown that such bimodal neurons can integrate sensory inputs in a linear or nonlinear fashion. In humans, activity in a similar set of brain regions has been associated with visuotactile stimulation of the hand. However, little is known about how these areas integrate visual and tactile information. In this functional magnetic resonance imaging experiment, we employed tactile, visual, and visuotactile stimulation of the right hand in an ecologically valid setup where participants were looking directly at their upper limb. We identified brain regions that were activated by both visual and tactile stimuli as well as areas exhibiting greater activity in the visuotactile condition than in both unisensory ones. The posterior and inferior parietal, dorsal, and ventral premotor cortices, as well as the cerebellum, all showed evidence of multisensory linear (additive) responses. Nonlinear, superadditive responses were observed in the cortex lining the left anterior intraparietal sulcus, the insula, dorsal premotor cortex, and, subcortically, the putamen. These results identify a set of candidate frontal, parietal and subcortical regions that integrate visual and tactile information for the multisensory perception of one's own hand.


title: A survey of consensus problems in multi-agent coordination

abstract: As a distributed solution to multi-agent coordination, consensus or agreement problems have been studied extensively in the literature. This paper provides a survey of consensus problems in multi-agent cooperative control with the goal of promoting research in this area. Theoretical results regarding consensus seeking under both time-invariant and dynamically changing information exchange topologies are summarized. Applications of consensus protocols to multiagent coordination are investigated. Future research directions and open problems are also proposed.


title: The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

abstract: Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.


title: Bioradiolocation-based sleep stage classification

abstract: This paper presents a method for classifying wakefulness, REM, light and deep sleep based on the analysis of respiratory activity and body motions acquired by a bioradar. The method was validated using data of 32 subjects without sleep-disordered breathing, who underwent a polysomnography study in a sleep laboratory. We achieved Cohen's kappa of 0.49 in the wake-REM-light-deep sleep classification, 0.55 for the wake-REM-NREM classification and 0.57 for the sleep/wakefulness determination. The results might be useful for the development of unobtrusive sleep monitoring systems for diagnostics, prevention, and management of sleep disorders.


title: Lifted probabilistic inference with counting formulas

abstract: Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.'s first-order variable elimination (FOVE) has focused on the sharing of potentials across interchangeable random variables. In this paper, we also exploit interchangeability within individual potentials by introducing counting formulas, which indicate how many of the random variables in a set have each possible value. We present a new lifted inference algorithm, C-FOVE, that not only handles counting formulas in its input, but also creates counting formulas for use in intermediate potentials. C-FOVE can be described succinctly in terms of six operators, along with heuristics for when to apply them. Because counting formulas capture dependencies among large numbers of variables compactly, C-FOVE achieves asymptotic speed improvements compared to FOVE.


title: Whistleblowing in Organizations: An Examination of Correlates of Whistleblowing Intentions, Actions, and Retaliation

abstract: Whistleblowing on organizational wrongdoing is becoming increasingly prevalent. What aspects of the person, the context, and the transgression relate to whistleblowing intentions and to actual whistleblowing on corporate wrongdoing? Which aspects relate to retaliation against whistleblowers? Can we draw conclusions about the whistleblowing process by assessing whistleblowing intentions? Meta-analytic examination of 193 correlations obtained from 26 samples (N = 18,781) reveals differences in the correlates of whistleblowing intentions and actions. Stronger relationships were found between personal, contextual, and wrongdoing characteristics and whistleblowing intent than with actual whistleblowing. Retaliation might best be predicted using contextual variables. Implications for research and practice are discussed.


title: Cancer Genome Landscapes

abstract: Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.


title: HANDEXOS: TOWARDS A SUPPORT DEVICE FOR HAND ACTIVITIES AND TELEPRESENCE

abstract: HANDEXOS is an exoskeleton device for supporting the human hand and performing teleoperation activities. It could be used to operate both in remote-manipulation mode and directly in microgravity environments. In manipulation mode, crew or operators within the space ship could tele-control the endeffector of a robot in the space during the execution of extravehicular activities (EVA) by means of an advanced upper limb exoskeleton. The choice of an appropriate man-machine interface (MMI) is important to allow a correct and dexterous grasp of objects of regular and irregular shapes in the space. Many different technologies have been proposed, from conventional joysticks to exoskeletons, but the arising number of more and more dexterous space manipulators such as Robonaut [1] or Eurobot [2] leads researchers to design novel MMIs with the aim to be more capable to exploit all functional advantages offered by new space robots. From this point of view exoskeletons better suite for execution of remote control-task than conventional joysticks, facilitating commanding of three dimensional trajectories and saving time in crew’s operation and training [3]. Moreover, it’s important to point out that in microgravity environments the astronauts spend most time doing motor exercises, so HANDEXOS can be useful in supporting such motor practice, assisting human operators in overcoming physical limitations deriving from the fatigue in performing EVA. It is the goal of this paper to provide a detailed description of HANDEXOS mechanical design and to present the results of the preliminary simulations derived from the implementation of the exoskeleton/human finger dynamic model for different actuation solutions.


title: Forebrain Engraftment by Human Glial Progenitor Cells Enhances Synaptic Plasticity and Learning in Adult Mice

abstract: Human astrocytes are larger and more complex than those of infraprimate mammals, suggesting that their role in neural processing has expanded with evolution. To assess the cell-autonomous and species-selective properties of human glia, we engrafted human glial progenitor cells (GPCs) into neonatal immunodeficient mice. Upon maturation, the recipient brains exhibited large numbers and high proportions of both human glial progenitors and astrocytes. The engrafted human glia were gap-junction-coupled to host astroglia, yet retained the size and pleomorphism of hominid astroglia, and propagated Ca2+ signals 3-fold faster than their hosts. Long-term potentiation (LTP) was sharply enhanced in the human glial chimeric mice, as was their learning, as assessed by Barnes maze navigation, object-location memory, and both contextual and tone fear conditioning. Mice allografted with murine GPCs showed no enhancement of either LTP or learning. These findings indicate that human glia differentially enhance both activity-dependent plasticity and learning in mice.


title: MIMO wireless linear precoding

abstract: This article provides a tutorial of linear precoding for a frequency- flat, single-user MIMO wireless system, examining both theoretical foundations and practical issues. The article first discusses principles for CSIT (channel-side information at the transmitter) acquisition and develops a dynamic CSIT model, which spans perfectly to statistical CSIT, taking into account channel temporal variation. It then presents the capacity benefits of CSIT and information theoretic arguments for exploiting the CSIT by linear precoding. A precoded system structure is then described, involving an encoder and a linear precoder. Criteria for designing the precoder are then discussed, followed by specific designs for different CSIT scenarios.


title: A tested semantics for getters, setters, and eval in JavaScript

abstract: We present S5, a semantics for the strict mode of the ECMAScript 5.1 (JavaScript) programming language. S5 shrinks the large source language into a manageable core through an implemented transformation. The resulting specification has been tested against real-world conformance suites for the language. This paper focuses on two aspects of S5: accessors (getters and setters) and eval. Since these features are complex and subtle in JavaScript, they warrant special study. Variations on both features are found in several other programming languages, so their study is likely to have broad applicability.


title: Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition

abstract: Recognizing image data across different domains has been a challenging task. For biometrics, heterogeneous face recognition (HFR) deals with recognition problems in which training/gallery images are collected in terms of one modality (e.g., photos), while test/probe images are observed in the other (e.g., sketches). In this paper, we present a domain adaptation approach for solving HFR problems. By utilizing external face images (i.e., those collected from the subjects not of interest) from both source and target domains, we propose a novel Domain-independent Component Analysis (DiCA) algorithm for deriving a common subspace for relating and representing cross-domain image data. In order to introduce improved representation ability, we further advance the self-taught learning strategy for learning a domain-independent dictionary in our DiCA subspace, which can be applied to both gallery and probe images of interest to improve representation and recognition. Different from some prior domain-adaptation approaches, we do not require the data correspondences (i.e., data pairs) when collecting external cross-domain image data, nor the label information is needed for learning the common feature space when associating different domains. Thus, our method is practical for real-world cross-domain classification problems. In our experiments, we consider sketch-to-photo and near-infrared (NIR) to visible spectrum (VIS) face recognition problems for evaluating the performance of our proposed approach.


title: Simulation of Partial Discharges in Single and Double Voids Using SIMULINK

abstract: Partial discharges (PD) in the insulation of power equipment have been a focal point in the field of electrical technology. Numerical simulation of PD is an important approach to knowing the PD mechanics. In this paper, the physical model of PD in voids was modified and the simulation circuit of PD in voids was built using Simulink. The simulations of the epoxy resin samples with a single void and double voids were investigated, respectively. The discharge voltage waveforms, discharge current waveforms, and the single discharge waveforms of voids were obtained by simulation. At the same time, we measured the PD pulse waveforms of epoxy resin samples in time-domain by an ultra-wideband PD measuring system. The test samples enclosing artificial voids have the same size as the simulation samples. The single discharge waveforms of a single void and double voids obtained by simulation are very similar to the measured ones in the same condition. Moreover, the different discharge rules between one void and two voids are found by simulation. The conformity of the simulation results and test ones indicates that the simulation model is effective and the simulation results are reliable.


title: Robot action planning by online optimization in human–robot collaborative tasks

abstract: Collaborative robots are widely used in strict and complex hybrid assembly tasks in the context of intelligent manufacturing. However, the task efficiency and task cost evaluation, which are very significant in the robot action planning to ensure and enhance the task quality in the human–robot collaboration process, are rarely studied in previous works. In this paper, we propose a novel and practical approach based on online optimization for the robot to plan its actions in human–robot collaboration to address this challenge. First, we extract the task model by graphical representations and design the collaboration cost functions which incorporate time consumption and human efforts. After that, the robot action planning algorithms are developed to online plan robot actions by optimizing the collaborative assembly cost. In addition, appropriate robot actions can be planned by the proposed algorithms to ensure the accomplishment of assembly process when the human happens to conduct wrong actions during the collaboration. We performed studies through hybrid assembly tasks with different types of human–robot collaboration scenarios to verify the advantages of our robot action planning algorithms. Experimental results suggested that the proposed algorithms can successfully generate the optimal actions for the robot to guarantee the efficiency in human–robot collaborative assembly.


title: Modeling and Simulation of Wind Turbine Driven Permanent Magnet Generator with New MPPT Algorithm

abstract: This paper presents Maximum Power Point Control for variable speed wind turbine driven permanent- magnet generator. The wind turbine generator is operated such that the rotor speed varies according to wind speed to adjust the duty cycle of power converter and maximizes Wind Energy Conversion System (WECS) efficiency. The maximum power point for each speed value is traced using Maximum Power Point Tracking (MPPT) algorithm. The rotating speed of permanent-magnet generator should be adjusted in the real time to capture maximum wind power. The system includes the wind-turbine, permanent-magnet generator (PMG), three-phase rectifier, boost chopper, inverter and load. The control parameter is the duty cycle of the chopper. PMG is made to operate at variable speed to achieve good performance. The entire WECS model consists of wind turbine model, PMG model and power converters model. The MATLAB / SIMULINK are used for simulation and the results are compared with laboratory setup.


title: Live memory migration with matrix bitmap algorithm

abstract: Live migration of OS instance, a powerful instrument to facilitate system maintenance, load balancing, fault tolerance, has become the central issue of virtualization, while the memory Migration has always been the bottleneck. However, exiting algorithms suffer two problems. First, when facing those frequently modified pages, the performance would degrade profoundly. Second, though they do perform more preferable through some certain algorithms, the accessional resources are requisite, which might extraordinary scarce under heavy load conditions. These imperfections will lead to striking performance degradation of virtual machine services. Therefore, based on the “Program Locality Principle”, this paper presents the “matrix bitmap algorithm” that collects the dirty page information for many times before deciding whether to transfer the page or not. It provides a more reasonable approach to obtain the determination. Experiments demonstrate that when under heavy load conditions, if sufficient dirty page information is collected, the decrease in total migration time will achieve 50% without increasing the system burden of the original domain.


Optimized ChIP-seq method facilitates transcription factor profiling in human tumors


title: Examining the Role of Business Intelligence in Non-profit Organizations to Support Strategic Social Goals

abstract: Many non-profit organizations have prioritized the need to be transparent in operating procedures, allocation of funds, and the impact of programs aimed at reducing social issues. In an effort to support this practical business need, we highlight how non-profits utilize information systems (IS) to support their strategic social goals through a case study. We examine business intelligence (BI) practices at a United Way affiliate that created new knowledge on the impact of its programs in the community. The United Way affiliate derived value from the organization's utilization of an integrated data system which provided visual and spatial map analyses and traditional analytical reports. We examine the relationship between BI-facilitated intellectual capital and its resultant impact on the non-profit's social goal. Lastly, we discuss lessons learned and provide recommendations for other non-profit organizations engaging in BI practices.


title: Arabic Language Sentiment Analysis on Health Services

abstract: The social media network phenomenon leads to a massive amount of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.


title: A Digital Signature Based on a Conventional Encryption Function

abstract: A new digital signature based only on a conventional encryption function (such as DES) is described which is as secure as the underlying encryption function -- the security does not depend on the difficulty of factoring and the high computational costs of modular arithmetic are avoided. The signature system can sign an unlimited number of messages, and the signature size increases logarithmically as a function of the number of messages signed. Signature size in a 'typical' system might range from a few hundred bytes to a few kilobytes, and generation of a signature might require a few hundred to a few thousand computations of the underlying conventional encryption function.


title: Light Field Segmentation From Super-pixel Graph Representation

abstract: Efficient and accurate segmentation of light field is an important task in computer vision and graphics. The large volume of input data and the redundancy of light field make it an open challenge. In the paper, we propose a novel graph representation for interactive light field segmentation based on light field super-pixel (LFSP). The LFSP not only maintains light field redundancy, but also greatly reduces the graph size. These advantages make LFSP useful to improve segmentation efficiency. Based on LFSP graph structure, we present an efficient light field segmentation algorithm using graph-cuts. Experimental results on both synthetic and real dataset demonstrate that our method is superior to previous light field segmentation algorithms with respect to accuracy and efficiency.


title: Compound Embedding Features for Semi-supervised Learning

abstract: To solve data sparsity problem, recently there has been a trend in discriminative methods of NLP to use representations of lexical items learned from unlabeled data as features. In this paper, we investigated the usage of word representations learned by neural language models, i.e. word embeddings. The direct usage has disadvantages such as large amount of computation, inadequacy with dealing word ambiguity and rare-words, and the problem of linear non-separability. To overcome these problems, we instead built compound features from continuous word embeddings based on clustering. Experiments showed that the compound features not only improved the performances on several NLP tasks, but also ran faster, suggesting the potential of embeddings.


title: Anthocyanin biosynthetic genes in Brassica rapa

abstract: BackgroundAnthocyanins are a group of flavonoid compounds. As a group of important secondary metabolites, they perform several key biological functions in plants. Anthocyanins also play beneficial health roles as potentially protective factors against cancer and heart disease. To elucidate the anthocyanin biosynthetic pathway in Brassica rapa, we conducted comparative genomic analyses between Arabidopsis thaliana and B. rapa on a genome-wide level.ResultsIn total, we identified 73 genes in B. rapa as orthologs of 41 anthocyanin biosynthetic genes in A. thaliana. In B. rapa, the anthocyanin biosynthetic genes (ABGs) have expanded and most genes exist in more than one copy. The anthocyanin biosynthetic structural genes have expanded through whole genome and tandem duplication in B. rapa. More structural genes located upstream of the anthocyanin biosynthetic pathway have been retained than downstream. More negative regulatory genes are retained in the anthocyanin biosynthesis regulatory system of B. rapa.ConclusionsThese results will promote an understanding of the genetic mechanism of anthocyanin biosynthesis, as well as help the improvement of the nutritional quality of B. rapa through the breeding of high anthocyanin content varieties.


title: Learning to Filter Unsolicited Commercial E-Mail

abstract: We present a thorough investigation on using machine learning to construct effective personalized anti-spam filters. The investigation includes four learning algorithms, Naive Bayes, Flexible Bayes, LogitBoost, and Support Vector Machines, and four datasets, constructed from the mailboxes of different users. We discuss the model and search biases of the learning algorithms, along with worst-case computational complexity figures, and observe how the latter relate to experimental measurements. We study how classification accuracy is affected when using attributes that represent sequences of tokens, as opposed to single tokens, and explore the effect of the size of the attribute and training set, all within a cost-sensitive framework. Furthermore, we describe the architecture of a fully implemented learning-based anti-spam filter, and present an analysis of its behavior in real use over a period of seven months. Information is also provided on other available learning-based anti-spam filters, and alternative filtering approaches.


title: An evaluation of GUI and kinesthetic teaching methods for constrained-keyframe skills

abstract: Keyframe-based Learning from Demonstration has been shown to be an effective method for allowing end-users to teach robots skills. We propose a method for using multiple keyframe demonstrations to learn skills as sequences of positional constraints (c-keyframes) which can be planned between for skill execution. We also introduce an interactive GUI which can be used for displaying the learned c-keyframes to the teacher, for altering aspects of the skill after it has been taught, or for specifying a skill directly without providing kinesthetic demonstrations. We compare 3 methods of teaching c-keyframe skills: kinesthetic teaching, GUI teaching, and kinesthetic teaching followed by GUI editing of the learned skill (K-GUI teaching). Based on user evaluation, the K-GUI method of teaching is found to be the most preferred, and the GUI to be the least preferred. Kinesthetic teaching is also shown to result in more robust constraints than GUI teaching, and several use cases of K-GUI teaching are discussed to show how the GUI can be used to improve the results of kinesthetic teaching.


title: A new topology for dual rotor/stator BLDC motors applied to marine thrusters

abstract: The aim of this study is to present a new topology for dual-rotor contra-rotating brushless direct current (BLDC) motors specially designed for remotely controlled unmanned underwater vehicles, in the form of “BLDC thrusters”. Once presenting the applied typical design by using sizing procedure, the main results obtained are given via simulation principles applying parametric analysis and performance characteristics of a dual-rotor BLDC thruster. Both external and internal rotor radial-field design in the same structure utilize modular concentrated winding, optimized slot-opening and pole/slot ratio, outer and inner diameter for both stator and rotor and stack length parameters for the machine. The output parameters such as flux linkage, armature current, rated torque, rated power, and cogging torque of each motor for different rotor positions are calculated and evaluated. Moreover, the analytical and numerical studies based on finite element methods are performed and compared. In addition, a coupled-circuit analysis is employed to prove the effectiveness of the achieved design to assess the maximum power and torque capability.


title: Possession vs. Direct Play: Evaluating Tactical Behavior in Elite Soccer

abstract: Measuring and evaluating tactical behavior is one of the main goals in performance analysis in professional soccer. A specific tactical approach may present the difference between winning and losing, especially in the highest level of competition. So fare tactical behavior is mostly determined by using one or two variables, like ball possession or number of passes. Within this study, we introduce a new index (Index of Offensive Behavior, IOB) that combines different variables of offensive actions to evaluate tactical behavior. To evaluate the indexes data of 676 official games (German Bundesliga 2009/10, & 2010/11, FIFA World Cup 2010) were analysed using the "MathBall"- notation software (Algorithma Ltd., 2009). IOB was able to reliable distinguish the two common tactical approaches in soccer: possession and direct play. Furthermore, we could show that successful teams prefer possession play and that game control (measured via Index of Game Control, IOC) is the most important variable of success irrespective of the tactical approach.


Building customer-based brand equity : a blueprint for creating strong brands


title: Disciplinary differences in Twitter scholarly communication

abstract: This paper investigates disciplinary differences in how researchers use the microblogging site Twitter. Tweets from selected researchers in ten disciplines (astrophysics, biochemistry, digital humanities, economics, history of science, cheminformatics, cognitive science, drug discovery, social network analysis, and sociology) were collected and analyzed both statistically and qualitatively. The researchers tended to share more links and retweet more than the average Twitter users in earlier research and there were clear disciplinary differences in how they used Twitter. Biochemists retweeted substantially more than researchers in the other disciplines. Researchers in digital humanities and cognitive science used Twitter more for conversations, while researchers in economics shared the most links. Finally, whilst researchers in biochemistry, astrophysics, cheminformatics and digital humanities seemed to use Twitter for scholarly communication, scientific use of Twitter in economics, sociology and history of science appeared to be marginal.


title: Sociable robot systems for real-world problems

abstract: Human-robot interaction research is maturing to the point where we can begin to build systems that interact with people in their daily lives and provide support for particular needs. We propose that sociable robot systems are systems that comprise a sociable robot, other technological devices, methods for interaction, and methods for relationship creation and maintenance. These systems can be designed as solutions to address particular needs such as health care or behavior change goals. We discuss the social support benefits of creating a relationship between a person and a robot and offer ideas for how this might be done. A system that is currently under development in our lab to help obese patients who have recently lost weight maintain their target weight is presented as an example of this kind of sociable robot system.


title: Tree-structured composition in neural networks without tree-structured architectures

abstract: Tree-structured neural networks encode a particular tree geometry for a sentence in the network design. However, these models have at best only slightly outperformed simpler sequence-based models. We hypothesize that neural sequence models like LSTMs are in fact able to discover and implicitly use recursive compositional structure, at least for tasks with clear cues to that structure in the data. We demonstrate this possibility using an artificial data task for which recursive compositional structure is crucial, and find an LSTM-based sequence model can indeed learn to exploit the underlying tree structure. However, its performance consistently lags behind that of tree models, even on large training sets, suggesting that tree-structured models are more effective at exploiting recursive structure.


title: From the internet of things to the web of things : Resource oriented architecture and best practices

abstract: Creating networks of “smart things” found in the physical world (e.g., with RFID, wireless sensor and actuator networks, embedded devices) on a large scale has become the goal of a variety of recent research activities. Rather than exposing real-world data and functionality through vertical system designs, we propose to make them an integral part of the Web. As a result, smart things become easier to build upon. In such an architecture, popular Web technologies (e.g., HTML, JavaScript, Ajax, PHP, Ruby) can be used to build applications involving smart things, and users can leverage well-known Web mechanisms (e.g., browsing, searching, bookmarking, caching, linking) to interact with and share these devices. In this chapter, we describe the Web of Things (WoT) architecture and best practices based on the RESTful principles that have already contributed to the popular success, scalability, and evolvability of the Web. We discuss several prototypes using these principles, which connect environmental sensor nodes, energy monitoring systems, and RFID-tagged objects to the Web. We also show how Web-enabled smart things can be used in lightweight ad-hoc applications, called “physical Mashups”, and discuss some of the remaining challenges towards the global World Wide Web of Things.