title: Latent Trees for Coreference Resolution

abstract: We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task datasets, which comprise three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58.69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60.15, which corresponds to a 3.5% error reduction, and is the best performing system for each of the three languages.


title: Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips

abstract: Sample preparation in digital microfluidics refers to the generation of droplets with target concentrations for onchip biochemical applications. In recent years, digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation. However, there remain one major problem associated with sample preparation on a conventional DMFB. For conventional DMFBs, only a (1:1) mixing/splitting model can be used, leading to an increase in the number of fluidic operations required for sample preparation. To overcome the drawback, we adopt a next generation DMFB platform, referred to as micro-electrode-dot-array (MEDA), for sample preparation. We propose the first sample preparation method that exploits the MEDA-specific advantages of fine-grained control of droplet sizes and real-time droplet sensing. Experimental demonstration using a fabricated MEDA biochip and simulation results highlight the effectiveness of the proposed sample-preparation method.


DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion.


title: ECMDB: The E. coli Metabolome Database

abstract: The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to 1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. Each metabolite entry in the ECMDB contains an average of 75 separate data fields, including comprehensive compound descriptions, names and synonyms, chemical taxonomy, compound structural and physicochemical data, bacterial growth conditions and substrates, reactions, pathway information, enzyme data, gene/protein sequence data and numerous hyperlinks to images, references and other public databases. The ECMDB also includes an extensive collection of intracellular metabolite concentration data compiled from our own work as well as other published metabolomic studies. This information is further supplemented with thousands of fully assigned reference nuclear magnetic resonance and mass spectrometry spectra obtained from pure E. coli metabolites that we (and others) have collected. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of E. coli’s importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers but also to molecular biologists, systems biologists and individuals in the biotechnology industry.


title: Traffic lights detection and state estimation using Hidden Markov Models

abstract: The detection of a traffic light on the road is important for the safety of persons who occupy a vehicle, in a normal vehicles or an autonomous land vehicle. In normal vehicle, a system that helps a driver to perceive the details of traffic signals, necessary to drive, could be critical in a delicate driving manoeuvre (i.e crossing an intersection of roads). Furthermore, traffic lights detection by an autonomous vehicle is a special case of perception, because it is important for the control that the autonomous vehicle must take. Multiples authors have used image processing as a base for achieving traffic light detection. However, the image processing presents a problem regarding conditions for capturing scenes, and therefore, the traffic light detection is affected. For this reason, this paper proposes a method that links the image processing with an estimation state routine formed by Hidden Markov Models (HMM). This method helps to determine the current state of the traffic light detected, based on the obtained states by image processing, aiming to obtain the best performance in the determination of the traffic light states. With the proposed method in this paper, we obtained 90.55% of accuracy in the detection of the traffic light state, versus a 78.54% obtained using solely image processing. The recognition of traffic lights using image processing still has a large dependence on the capture conditions of each frame from the video camera. In this context, the addition of a pre-processing stage before image processing could contribute to improve this aspect, and could provide a better results in determining the traffic light state.


abstract: Wilman, D., Irianni, R. A. M., Humphreys, M. O. (2004). Stay-green compared with non-stay-green Lolium perenne in field swards with different cutting and nitrogen treatments. Annals of Applied Biology, 144, 95-101 Key words: Stay-green, Lolium perenne, perennial ryegrass, colour, yield, N, leaves


title: Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition

abstract: This paper performs the first investigation into depth for large-scale human action recognition in video where the depth cues are estimated from the videos themselves. We develop a new framework called depth2action and experiment thoroughly into how best to incorporate the depth information. We introduce spatio-temporal depth normalization (STDN) to enforce temporal consistency in our estimated depth sequences. We also propose modified depth motion maps (MDMM) to capture the subtle temporal changes in depth. These two components significantly improve the action recognition performance. We evaluate our depth2action framework on three large-scale action recognition video benchmarks. Our model achieves state-of-the-art performance when combined with appearance and motion information thus demonstrating that depth2action is indeed complementary to existing approaches.


title: BDO-SD: An efficient scheme for big data outsourcing with secure deduplication

abstract: In the big data era, duplicated copies of data may be generated by various smart devices. Although convergent encryption has been extensively adopted for secure deduplication, it is still very challenging to efficiently and reliably manage the convergent keys and support keyword search over encrypted data. This paper addresses this problem by presenting an efficient scheme for big data outsourcing with secure deduplication, named BDO-SD. Specifically, with the convergent encryption technique, data owners can outsource their data to the cloud server with data deduplication; with the idea of public key encryption and keywords search, a data user can query encrypted data while preserving its privacy. Security analysis demonstrates that our proposed BDO-SD scheme can achieve the data confidentiality, query privacy and key security. Simulation results show that compared with other schemes, our scheme can achieve better efficiency than the existing data deduplication schemes.


title: Design and characterization of tapered transition and inductive window filter based on Substrate Integrated Waveguide technology (SIW)

abstract: Microstrip tapered transition and inductive band-pass filter around 5 GHz, using Substrate Integrated Waveguide technology (SIW) are studied in this paper. All the structures are designed with Finite Element Method (FEM) and fabricated on a single substrate of Epoxy FR4 using a standard PCB process. The return loss of the proposed filter is better than 20 dB. The measured results for all the structures investigated show a good agreement with the simulation results.


title: Artificial sweeteners induce glucose intolerance by altering the gut microbiota

abstract: Non-caloric artificial sweeteners (NAS) are among the most widely used food additives worldwide, regularly consumed by lean and obese individuals alike. NAS consumption is considered safe and beneficial owing to their low caloric content, yet supporting scientific data remain sparse and controversial. Here we demonstrate that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota. These NAS-mediated deleterious metabolic effects are abrogated by antibiotic treatment, and are fully transferrable to germ-free mice upon faecal transplantation of microbiota configurations from NAS-consuming mice, or of microbiota anaerobically incubated in the presence of NAS. We identify NAS-altered microbial metabolic pathways that are linked to host susceptibility to metabolic disease, and demonstrate similar NAS-induced dysbiosis and glucose intolerance in healthy human subjects. Collectively, our results link NAS consumption, dysbiosis and metabolic abnormalities, thereby calling for a reassessment of massive NAS usage.


title: Thermal management in high-density power converters

abstract: This paper gives an overview of basic principles of the thermal management in high density power converters. Methods for heat removal on a device and circuit board level are discussed. New packaging technologies for discrete power semiconductors as well as multifunctional board integration techniques are included. A view is given on future 3D integration techniques that take into account the poor thermal conductivity especially of the passive component materials. With these techniques much higher power densities are possible. Some examples of prototype systems are presented, together with the achieved technical data.


title: A Practical Framework for the Development of Augmented Reality Applications by using ArUco Markers

abstract: The Augmented Reality (AR) is an expanding field of the Computer Graphics (CG) that merges items of the real-world environment (e.g., places, objects) with digital information (e.g., multimedia files, virtual objects) to provide users with an enhanced interactive multi-sensorial experience of the real-world that surrounding them. Currently, a wide range of devices is used to vehicular AR systems. Common devices (e.g., cameras equipped on smartphones) enable users to receive multimedia information about target objects (non-immersive AR). Advanced devices (e.g., virtual windscreens) provide users with a set of virtual information about points of interest (POIs) or places (semi-immersive AR). Finally, an ever-increasing number of new devices (e.g., HeadMounted Display, HMD) support users to interact with mixed reality environments (immersive AR). This paper presents a practical framework for the development of non-immersive augmented reality applications through which target objects are enriched with multimedia information. On each target object is applied a different ArUco marker. When a specific application hosted inside a device recognizes, via camera, one of these markers, then the related multimedia information are loaded and added to the target object. The paper also reports a complete case study together with some considerations on the framework and future work.


title: Bridging the Semantic Gap in Virtual Machine Introspection via Online Kernel Data Redirection

abstract: It is generally believed to be a tedious, time-consuming, and error-prone process to develop a virtual machine introspection (VMI) tool because of the semantic gap. Recent advance shows that the semantic-gap can be largely narrowed by reusing the executed code from a trusted OS kernel. However, the limitation for such an approach is that it only reuses the exercised code through a training process, which suffers the code coverage issues. Thus, in this article, we present Vmst, a new technique that can seamlessly bridge the semantic gap and automatically generate the VMI tools. The key idea is that, through system wide instruction monitoring, Vmst automatically identifies the introspection related data from a secure-VM and online redirects these data accesses to the kernel memory of a product-VM, without any training. Vmst offers a number of new features and capabilities. Particularly, it enables an in-VM inspection program (e.g., ps) to automatically become an out-of-VM introspection program. We have tested Vmst with over 25 commonly used utilities on top of a number of different OS kernels including Linux and Microsoft Windows. The experimental results show that our technique is general (largely OS-independent), and it introduces 9.3X overhead for Linux utilities and 19.6X overhead for Windows utilities on average for the introspected program compared to the native in-VM execution without data redirection.


title: Mining Frequent Patterns in Data Streams at Multiple Time Granularities

abstract: Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time. In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. We extended the framework to mine time-sensitive patterns with approximate support guarantee. We incrementally maintain tilted-time windows for each pattern at multiple time granularities. Interesting


title: Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

abstract: With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning's advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.


title: Defect detection in textile fabric images using wavelet transforms and independent component analysis

abstract: In this paper, a new method based on the use of wavelet transformation prior to independent component analysis for solving the problem of defect detection in textile fabric images is presented. Different subbands of the wavelet packet tree scheme of the defect-free subwindows are obtained and independent components of these subbands are calculated as basis vectors. The true feature vectors corresponding to these basis vectors are computed. The test subwindow is labeled as defective, or not according to the Euclidean distance between the true feature vector representing the non-defective regions and the feature vector of the subwindow under test. The advantage of adding wavelet analysis prior to the independent component analysis is presented.


title: Formation of highly uniform and dense diamond microcrystal thin films using a combustion flame surrounded by an inert‐gas flow

abstract: An inert‐gas curtain method, in which a combustion flame of oxygen/acetylene mixture is protected from the air by a surrounding inert‐gas flow, has been developed for the deposition of microcrystalline diamond films. As a result, highly uniform and highly dense microcrystal films of diamonds possessing the quality of natural diamond were successfully prepared. The inert‐gas flow allows the hydrocarbon radicals in the flame to be varied for preferential deposition of diamonds.


title: Does Vertical Bring more Satisfaction?: Predicting Search Satisfaction in a Heterogeneous Environment

abstract: The study of search satisfaction is one of the prime concerns in search performance evaluation research. Most existing works on search satisfaction primarily rely on the hypothesis that all results on search engine result pages (SERPs) are homogeneous. However, a variety of heterogeneous vertical results such as videos, images and instant answers are aggregated into SERPs by search engines to improve the diversity and quality of search results. In this paper, we carry out a lab-based user study with specifically designed SERPs to determine how verticals with different qualities and presentation styles affect search satisfaction. Users' satisfaction feedback and external assessors' satisfaction annotations are both collected to make a comparison regarding the perception of search satisfaction. Mouse click-through / movement data and eye movement information are also collected such that we can investigate the influence of vertical results from the perspectives of both benefit and cost. Finally, a vertical-aware learning-based prediction method is proposed to predict search satisfaction on aggregated SERPs. To the best of our knowledge, this paper is the first to analyze the effect of verticals on search satisfaction. The results show that verticals with different qualities, presentation styles and positions have different effects on search satisfaction, among which Encyclopedia verticals, as well as Download verticals, will bring the largest improvement. Furthermore, our proposed vertical-aware prediction method outperforms state-of-the-art methods that are designed for search satisfaction prediction in homogeneous environment.


title: CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT

abstract: In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices generating and uploading a deluge of data to the cloud. If successfully extracted, the knowledge buried in the data can significantly improve the quality of life and foster economic growth. However, a critical bottleneck for realizing the efficient IoT is the pressure it puts on the existing communication infrastructures, requiring transfer of enormous data volumes. Aiming at addressing this problem, we propose a novel architecture dubbed Condense which integrates the IoT-communication infrastructure into the data analysis. This is achieved via the generic concept of network function computation. Instead of merely transferring data from the IoT sources to the cloud, the communication infrastructure should actively participate in the data analysis by carefully designed en-route processing. We define the Condense architecture, its basic layers, and the interactions among its constituent modules. Furthermore, from the implementation side, we describe how Condense can be integrated into the Third Generation Partnership Project (3GPP) machine type communications (MTCs) architecture, as well as the prospects of making it a practically viable technology in a short time frame, relying on network function virtualization and software-defined networking. Finally, from the theoretical side, we survey the relevant literature on computing atomic functions in both analog and digital domains, as well as on function decomposition over networks, highlighting challenges, insights, and future directions for exploiting these techniques within practical 3GPP MTC architecture.


title: BreakingNews: Article Annotation by Image and Text Processing

abstract: Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.


title: Performance analysis of low-power 1-bit CMOS full adder cells

abstract: A performance analysis of 1-bit full-adder cell is presented. The adder cell is anatomized into smaller modules. The modules are studied and evaluated extensively. Several designs of each of them are developed, prototyped, simulated and analyzed. Twenty different 1-bit full-adder cells are constructed (most of them are novel circuits) by connecting combinations of different designs of these modules. Each of these cells exhibits different power consumption, speed, area, and driving capability figures. Two realistic circuit structures that include adder cells are used for simulation. A library of full-adder cells is developed and presented to the circuit designers to pick the full-adder cell that satisfies their specific applications.


title: Singularity Analysis of Planar Cable-Driven Parallel Robots

abstract: It is well known that parallel robots may have singular configurations that can result in a loss of full control the mechanisms. This paper analyzes two different categories of singularities of planar cable-driven parallel robots with four or more cables. The unidirectional constraint of cables makes the singularity analysis of cable-driven parallel robots different from that of rigid-link parallel robots even if they have similar kinematical architectures. Based on their natures, singularities of cable-driven parallel robots are classified into two categories: the Jacobian singularity and the force-closure singularity. A Jacobian singularity occurs when the Jacobian matrix of a cable-driven parallel robot loses its full rank. Based on rank analysis of Jacobian matrix, a group of Jacobian singularities is reported with mathematical proof. When the Jacobian matrix of a cable-driven parallel robot has a full rank, the cables' inability to generate tension will lead to force-closure singularities, which can always happen to fully-constrained cable-driven parallel robots. An algorithm of identifying force-closure singularities of planar cable-driven parallel robots is proposed. Understanding of the natures of singularities is important for the design and control of cable-driven parallel robots.


title: Estimating Cosmological Parameters from the Dark Matter Distribution

abstract: A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach in estimating the cosmological parameters is to use the large scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in three dimensions. Information about galaxy locations is typically summarized in a "single" function of scale, such as the galaxy correlation function or power-spectrum. We show that it is possible to estimate these cosmological parameters directly from the distribution of matter. This paper presents the application of deep 3D convolutional networks to volumetric representation of dark-matter simulations as well as the results obtained using a recently proposed distribution regression framework, showing that machine learning techniques are comparable to, and can sometimes outperform, maximum-likelihood point estimates using "cosmological models". This opens the way to estimating the parameters of our Universe with higher accuracy.


title: a.SCAtch - A Sketch-Based Retrieval for Architectural Floor Plans

abstract: Architects’ daily routine means working with drawings. They use either a pen or a computer sketching their ideas or drawing to scale. When beginning a new project they often have to search for similar projects in the past. In this paper a sketch-based approach is proposed to query the floor plan repository. The user searches for semantically similar floor plans just by drawing the new plan. An algorithm extracts the semantic structure sketched by the architect on DFKI’s Touch & Write table and compares the structure of the sketch with the ones from the floor plan repository. The a SCatch system enables the user to easily access knowledge from past projects. While in the current prototype only sketches with a predefined structure are recognized, we will extend the system to work with normal floor plans.


title: Analysis of Functional MRI Data Using Mutual Information

abstract: A new information-theoretic approach is presented for analyzing fMRI data to calculate the brain activation map. The method is based on a formulation of the mutual information between two waveforms-the fMRI temporal response of a voxel and the experimental protocol timeline. Scores based on mutual information are generated for all voxels and then used to compute the activation map of an experiment. Mutual information for fMRI analysis is employed because it has been shown to be robust in quantifying the relationship between any two waveforms. More importantly, our technique takes a principled approach toward calculating the brain activation map by making few assumptions about the relationship between the protocol timeline and the temporal response of a voxel. This is important especially in fMRI experiments where little is known about the relationship between these two waveforms. Experiments are presented to demonstrate this approach of computing the brain activation map. Comparisons to other more traditional analysis techniques are made and the results are presented.


title: Experimental Analysis of a Time-Triggered Network Designed for Train Communications

abstract: Time-triggered protocols are recognized to have a high degree of dependability. The purpose of this paper is to analyze by experiments their application to the networking of the devices installed aboard train. The paper starts by reviewing the current standard for the train communications. Then the operating principles of the time-triggered protocols are illustrated, a possible arrangement for a time-triggered network to be applied to a train is presented, and the TTP/C protocol is introduced. Afterwards the technical data of a commercial TTP/C board are illustrated and a prototypal network is set up with these boards to control an emulated train function. At last, experimental results showing the communication characteristics of the prototypal network are reported.


title: Ontological modelling of form and function for architectural design

abstract: Form, function and the relationship between the two serve a crucial role in design. Within architectural design, key aspects of the anticipated function of buildings, or of spatial environments in general, are supposed to be supported by their structural form, i.e., their shape, layout, or connectivity. Whereas the philosophy of form and function is a well-researched topic, the practical relations and dependencies between form and function are only known implicitly by designers and architects. Specifically, the formal modelling of structural forms and resulting artefactual functions within design and design assistance systems remains elusive.In our work, we aim at making these definitions explicit by ontologically modelling respective domain entities, their properties and related constraints. We interpret “structural form” and “artefactual function” by specifying modular ontologies and their interplay for the architectural design domain. A key aspect in our modelling approach is the use of formal conceptual requirements and qualitative spatial calculi as a link between the structural form of a design and the differing functional capabilities that it affords or leads to. We demonstrate how our ontological modelling reflects types of architectural form and function, and how it facilitates the conceptual modelling of requirement constraints in architectural design.


title: On the effectiveness of address-space randomization

abstract: Address-space randomization is a technique used to fortify systems against buffer overflow attacks. The idea is to introduce artificial diversity by randomizing the memory location of certain system components. This mechanism is available for both Linux (via PaX ASLR) and OpenBSD. We study the effectiveness of address-space randomization and find that its utility on 32-bit architectures is limited by the number of bits available for address randomization. In particular, we demonstrate a derandomization attack that will convert any standard buffer-overflow exploit into an exploit that works against systems protected by address-space randomization. The resulting exploit is as effective as the original exploit, although it takes a little longer to compromise a target machine: on average 216 seconds to compromise Apache running on a Linux PaX ASLR system. The attack does not require running code on the stack. We also explore various ways of strengthening address-space randomization and point out weaknesses in each. Surprisingly, increasing the frequency of re-randomizations adds at most 1 bit of security. Furthermore, compile-time randomization appears to be more effective than runtime randomization. We conclude that, on 32-bit architectures, the only benefit of PaX-like address-space randomization is a small slowdown in worm propagation speed. The cost of randomization is extra complexity in system support.


Design and Implementation of a Homomorphic-Encryption Library


title: The Mobile Data Challenge : Big Data for Mobile Computing Research

abstract: This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. First we review the Lausanne Data Collection Campaign (LDCC) an initiative to collect unique, longitudinal smartphone data set for the basis of the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; and discuss some of the key aspects in order to generate privacy-respecting, challenging, and scientifically relevant mobile data resources for wider use of the research community. The concluding remarks will summarize the paper.


title: Randomized Kinodynamic Motion Planning with Moving Obstacles

abstract: This paper presents a novel randomized motion planner for robots that must achieve a specified goal under kinematic and/or dynamic motion constraints while avoiding collision with moving obstacles with known trajectories. The planner encodes the motion constraints on the robot with a control system and samples the robot's state × time space by picking control inputs at random and integrating its equations of motion. The result is a probabilistic roadmap of sampled state ×time points, called milestones, connected by short admissible trajectories. The planner does not precompute the roadmap; instead, for each planning query, it generates a new roadmap to connect an initial and a goal state×time point. The paper presents a detailed analysis of the planner's convergence rate. It shows that, if the state×time space satisfies a geometric property called expansiveness, then a slightly idealized version of our implemented planner is guaranteed to find a trajectory when one exists, with probability quickly converg...


title: On activating human communications with pet-type robot AIBO

abstract: In this paper we describe effects of human interactions with a pet-type robot, especially with AIBO. First, we describe a design concept for AIBO based on how to increase its "lifelike" appearance. By introducing statistical results of marketing, and experiments involving human-robot interactions using AIBO, we show that this pet-type robot activates human emotions effectively. Furthermore, the experiments demonstrate that AIBO helps in human-human communication. We discuss the phenomena of interaction with AIBO, and attempt to explain why this happened.


title: Enhanced Texture-Based Terrain Synthesis on Graphics Hardware

abstract: Curvilinear features extracted from a 2D user-sketched feature map have been used successfully to constraint a patch-based texture synthesis of real landscapes. This map-based user interface does not give fine control over the height profile of the generated terrain. We propose a new texture-based terrain synthesis framework controllable by a terrain sketching interface. We enhance the realism of the generated landscapes by using a novel patch merging method that reduces boundary artefacts caused by overlapping terrain patches. A more constrained synthesis process is used to produce landscapes that better match user requirements. The high computational cost of texture synthesis is reduced with a parallel implementation on graphics hardware. Our GPU-accelerated solution provides a significant speedup depending on the size of the example terrain. We show experimentally that our framework is more successful in generating realistic landscapes than current example-based terrain synthesis methods. We conclude that texture-based terrain synthesis combined with sketching provides an excellent solution to the user control and realism challenges of virtual landscape generation. © 2012 Wiley Periodicals, Inc.


title: Si-Based Technologies for mmWave Automotive Radar

abstract: Continued advancements in Si-based technologies - in particular SiGe BiCMOS technologies - have enabled mmWave integrated circuits designed for 77GHz automotive radar systems to reach production-level maturity. This paper will discuss the technology requirements for mmWave automotive radar products, and also present the evolution of the respective key figures of merit. Si-based CMOS challenges and opportunities will also be discussed.


title: An accurate contactless position sensor with planar resonators

abstract: In this paper, we report the development of a contactless position sensor with thin and planar structures for both sensor and target. The target is designed to be a compact resonator with resonance near the operating frequency, which improves the signal strength and increases the sensing range. The sensor is composed of a source coil and a pair of symmetrically arranged detecting coils. With differential measurement technique, highly accurate edge detection can be realized. Experiment results show that the sensor operates at varying gap size between the target and the sensor, even when the target is at 30 mm away, and the achieved accuracy is within 2% of the size of the sensing coil.


title: Profiling of amyloid beta peptide variants using SELDI Protein Chip arrays.

abstract: The profile of amyloid beta (A beta) peptide variants secreted into the media of human cultured cells that express the amyloid precursor protein was examined by Surface Enhanced Laser Desorption/Ionization (SELDI) ProteinChip technology from Ciphergen Biosystems using biologically active ProteinChip Arrays. An anti-A beta polyclonal antibody (anti-NTA4) was used to capture and purify multiple immunoreactive A beta fragments from a single microliter of media onto the ProteinChip Array. Fragments retained on the surface of the ProteinChip Array were detected directly by mass in the ProteinChip System to provide detailed information on the identity of different A beta variants secreted from the cultured cells. We discuss existing and potential applications of this immunoassay for the detection and relative quantitation of A beta species from both cultured cell systems and clinical samples.


title: Design of Solar Tricycle for Handicapped Person

abstract: Solar plays a vital role in our day to day life. We have developed the solar tricycle especially for handicapped person of ANANDVAN organization started by Mr. Baba Amate in Warora taluka of Maharashtra state (India). The organization is working for the leprosy affected people. In this paper it is discussed that how solar tricycle will help to reduce the effort of handicapped person. All the designs specification considered after analyzing the problems from the handicapped person. Comfort of the person in the tricycle is an important and we have given importance to it. The main content of the tricycle is Solar PV panel, Brushless PMDC motor, Charge controller and battery. This paper will discuss about the main idea of this project and to get a larger picture on what is the problem in the current technologies, what that I want to achieve in this project and the area that will cover on this project. This paper is divided into some categories that are project background to describe the reasons to do this project, problem statement to inform about the problem or weakness of the existing technology, objective to make sure what actually this project must achieve and scope of this project to specify what will be used in this project.


title: Microstructure changes and residual stresses of each phase caused by shot peening in 304 austenitic stainless steel

abstract: The phase transformation and residual stress induced by shot peening in 304 austenitic stainless steel were investigated. The volume fraction of austenite phase and martensite phase and residual stress of each phase were determined quantitatively by X-ray diffraction technique. The results indicate that volume fraction of austenite phase and martensite phase is function of peening intensity and peening time and that volume fraction of martensite phase increases with time of shot peening. The time to shot peening and peening intensity are two main parameters when peening 304 austenitic stainless steel, the saturated time to ensure 100% coverage is not enough to ensure the maximum volume fraction of martensite phase caused by shot peening under the same peening intensity.


title: Classification of Hyperspectral Data Based on Semi-Supervised Tri-Training Learning Framework

abstract: A semi-supervised learning framework based on the tri-training scheme is proposed for the classification of hyper spectral data. The framework involves two stages: multiple classifier learning by the improved tri-training and integrating the outputs of classifiers to the final hypothesis by decision fusion. To settle the ill-posed classification problem, in the stage of classifier learning, the label confidence of each learner is measured by the improved estimation of classifier error, and self-training is introduced to expand the labeled set using unlabeled samples with confident labels assigned by classifiers. Hyper spectral data classification experiments show the effectiveness of the proposed framework.


title: VT-Mininet: Virtual-time-enabled Mininet for Scalable and Accurate Software-Define Network Emulation

abstract: The advancement of software-defined networking (SDN) technology is highly dependent on the successful transformations from in-house research ideas to real-life products. To enable such transformations, a testbed offering scalable and high fidelity networking environment for testing and evaluating new/existing designs is extremely valuable. Mininet, the most popular SDN emulator by far, is designed to achieve both accuracy and scalability by running unmodified code of network applications in lightweight Linux Containers. However, Mininet cannot guarantee performance fidelity under high workloads, in particular when the number of concurrent active events is more than the number of parallel cores. In this project, we develop a lightweight virtual time system in Linux container and integrate the system with Mininet, so that all the containers have their own virtual clocks rather than using the physical system clock which reflects the serialized execution of multiple containers. With the notion of virtual time, all the containers perceive virtual time as if they run independently and concurrently. As a result, interactions between the containers and the physical system are artificially scaled, making a network appear to be ten times faster from the viewpoint of applications within the containers than it actually is. We also design an adaptive virtual time scheduling subsystem in Mininet, which is responsible to balance the experiment speed and fidelity. Experimental results demonstrate that embedding virtual time into Mininet significantly enhances its performance fidelity, and therefore, results in a useful platform for the SDN community to conduct scalable experiments with high fidelity.


title: Enterprise Architecting: Critical Problems

abstract: An enterprise architecture (EA) identifies the main components of the organization, its information systems, the ways in which these components work together in order to achieve defined business objectives, and the way in which the information systems support the business processes of the organization. The components include staff, business processes, technology, information, financial and other resources, etc. Enterprise architecting is the set of processes, tools, and structures necessary to implement an enterprise-wide coherent and consistent IT architecture for supporting the enterprise's business operations. It takes a holistic view of the enterprise's IT resources rather than an application-by-application view. Given the size and still immature nature of many enterprise architecture efforts, a number of critical challenges and problem continue to exist. This paper surveys a number of these challenges and problems in an attempt to provide a platform for a discussion on enterprise architecture problems and possible solutions.


title: An Empirical Investigation of Systematic Reviews in Software Engineering

abstract: BACKGROUND: Systematic Literature Reviews (SLRs) have gained significant popularity among software engineering (SE) researchers since 2004. Several researchers have also been working on improving the scientific and technological support for SLRs in SE. We argue that there is also an essential need for evidence-based body of knowledge about different aspects of the adoption of SLRs in SE. OBJECTIVE: The main objective of this research is to empirically investigate the adoption and use of SLRs in SE research from various perspectives. METHOD: We used multi-method approach as it is based on a combination of complementary research methods which are expected to compensate each others' limitations. RESULTS: A large majority of the participants are convinced of the value of using a rigorous and systematic methodology for literature reviews. However, there are concerns about the required time and resources for SLRs. One of the most important motivators for performing SLRs is new findings and inception of innovative ideas for further research. The reported SLRs are more influential compared to the traditional literature reviews in terms of number of citations. One of the main challenges of conducting SLRs is drawing a balance between rigor and required effort. CONCLUSIONS: SLR has become a popular research methodology for conducting literature review and evidence aggregation in SE. There is an overall positive perception about this methodology. The findings provide interesting insights into different aspects of SLRs. We expect that the findings can provide valuable information to readers on what can be expected from conducting SLRs and the potential impact of such reviews.


title: Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures

abstract: In this paper we present PasS (Privacy as a Service); a set of security protocols for ensuring the privacy and legal compliance of customer data in cloud computing architectures. PasS allows for the secure storage and processing of users’ confidential data by leveraging the tamper-proof capabilities of cryptographic coprocessors. Using tamper-proof facilities provides a secure execution domain in the computing cloud that is physically and logically protected from unauthorized access. PasS central design goal is to maximize users’ control in managing the various aspects related to the privacy of sensitive data. This is achieved by implementing user-configurable software protection and data privacy mechanisms. Moreover, PasS provides a privacy feedback process which informs users of the different privacy operations applied on their data and makes them aware of any potential risks that may jeopardize the confidentiality of their sensitive information. To the best of our knowledge, PasS is the first practical cloud computing privacy solution that utilizes previous research on cryptographic coprocessors to solve the problem of securely processing sensitive data in cloud computing infrastructures.


title: BIOS chronomancy: fixing the core root of trust for measurement

abstract: In this paper we look at the implementation of the Core Root of Trust for Measurement (CRTM) from a Dell Latitude E6400 laptop. We describe how the implementation of the CRTM on this system doesn't meet the requirements set forth by either the Trusted Platform Module(TPM)PC client specification or NIST 800-155 guidance. We show how novel tick malware, a 51 byte patch to the CRTM, can replay a forged measurement to the TPM, falsely indicating that the BIOS is pristine. This attack is broadly applicable, because all CRTMs we have seen to date are rooted in mutable firmware. We also show how flea malware can survive attempts to reflash infected firmware with a clean image. To fix the untrustworthy CRTM we ported an open source "TPM-timing-based attestation" implementation from running in the Windows kernel, to running in an OEM's BIOS and SMRAM. This created a new, stronger CRTM that detects tick, flea, and other malware embedded in the BIOS. We call our system "BIOS Chronomancy", and we show that it works in a real vendor BIOS, with all the associated complexity, rather than in a simplified research environment.


title: A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments

abstract: We describe our work on multirotor UAVs and focus on our method for autonomous landing and position control. The paper describes the design of our landing pad and the vision based detection algorithm that estimates the 3D-position of the UAV relative to the landing pad. A cascaded controller structure stabilizes velocity and position in the absence of GPS signals by using a dedicated optical flow sensor. Practical experiments prove the quality of our approach.


title: A Bayesian Approach for Policy Learning from Trajectory Preference Queries

abstract: We consider the problem of learning control policies via trajectory preference queries to an expert. In particular, the agent presents an expert with short runs of a pair of policies originating from the same state and the expert indicates which trajectory is preferred. The agent's goal is to elicit a latent target policy from the expert with as few queries as possible. To tackle this problem we propose a novel Bayesian model of the querying process and introduce two methods that exploit this model to actively select expert queries. Experimental results on four benchmark problems indicate that our model can effectively learn policies from trajectory preference queries and that active query selection can be substantially more efficient than random selection.


title: Knowledge and Information Technology Management: Human and Social Perspectives

abstract: As the information technology sector continues to grow, an understanding of KM practices will become increasingly essential to organizational success. Because technical communicators are creators and managers of content/knowledge, they will need to understand KM practices to anticipate how their jobs may change to meet KM needs. The collection Knowledge and information technology management: Human and social perspectives provides technical communicators with both an overview of KM practices and insights into the future development of KM as a field. For this reason, the book is a valuable resource that technical communicators should read to better prepare themselves for future developments in the field.


Cybercrime Classification: A Motivational Model


title: MINIATURE ROBOTS FOR SPACE AND MILITARY MISSIONS

abstract: Miniature robots enable low-cost planetary surface exploration missions as well as new military missions in urban terrain where small robots provide critical assistance to human operations. These space and military missions have many similar technological challenges. This article describes the technologies under development, the applications where these technologies are relevant to both space and military missions, and the status of the most recent technology demonstrations in terrestrial scenarios.


title: Two layers optimization model for time-of-use price based on fuel saving and emission reducing

abstract: Implementation of time-of-use (TOU) is one of demand-side management measures, will help load shifting, energy saving, its effect is dependent on science episodes and appropriate TOU. Based on the shared interests of energy conservation and the goal of establishing a TOU optimization model to arrive at the most time into their corresponding TOU pricing.


title: An Auxiliary Self-Oscillating Preheating System for Self-Oscillating Fluorescent Lamp Electronic Ballasts

abstract: The fluorescent lamp lifetime is very dependent of the start-up lamp conditions. The lamp filament current and temperature during warm-up and at steady-state operation are important to extend the life of a hot-cathode fluorescent lamp, and the preheating circuit is responsible for attending to the start-up lamp requirements. The usual solution for the preheating circuit used in self-oscillating electronic ballasts is simple and presents a low cost. However, the performance to extend the lamp lifetime is not the most effective. This paper presents an effective preheating circuit for self-oscillating electronic ballasts as an alternative to the usual solution.


title: Animal-assisted therapy — magic or medicine?

abstract: Abstract A sound theoretical basis supported by scientifically measured physiological parameters is needed to gain medical support for animal-assisted therapy. Six neurochemicals associated with a decrease in blood pressure were measured in humans ( n =18) and dogs ( n =18) before and after positive interaction. Results ( P


title: MOTION CONTROL OF AN OMNIDIRECTIONAL MOBILE ROBOT

abstract: This paper focuses on the motion control problem of an omnidirectional mobile robot. A new control method based on the inverse input-output linearized kinematic model is proposed. As the actuator saturation and actuator dynamics have important impacts on the robot performance, this control law takes into account these two aspects and guarantees the stability of the closed-loop control system. Real-world experiments with an omnidirectional middle-size RoboCup robot verify the performance of this proposed control algorithm.


title: DARN: A Deep Adversarial Residual Network for Intrinsic Image Decomposition

abstract: We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully convolutional neural network that estimates absolute albedo and shading jointly. Our solution relies on a single end-to-end deep sequence of residual blocks and a perceptually-motivated metric formed by two adversarially trained discriminators. As opposed to classical intrinsic image decomposition work, it is fully data-driven, hence does not require any physical priors like shading smoothness or albedo sparsity, nor does it rely on geometric information such as depth. Compared to recent deep learning techniques, we simplify the architecture, making it easier to build and train, and constrain it to generate a valid and reversible decomposition. We rediscuss and augment the set of quantitative metrics so as to account for the more challenging recovery of non scale-invariant quantities. We train and demonstrate our architecture on the publicly available MPI Sintel dataset and its intrinsic image decomposition, show attenuated overfitting issues and discuss generalizability to other data. Results show that our work outperforms the state of the art deep algorithms both on the qualitative and quantitative aspect.


title: Medical Evaluation of Suspected Child Sexual Abuse: 2011 Update

abstract: The medical evaluation of children with suspected sexual abuse includes more than just the physical examination of the child. The importance of taking a detailed medical history from the parents and a history from the child about physical sensations following sexual contact has been emphasized in other articles in the medical literature. The examination is important, however, and medical providers who examine children need to be aware of published research on findings in nonabused children, studies of healing of injuries, and studies documenting the association between sexual contact and the diagnosis of sexually transmissible infections in children. This article reviews the current approach to interpreting findings in children who may have been sexually abused and why additional research is needed.


title: Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts

abstract: Social commerce has taken the e-tailing world by storm. Business-to-consumer sites and, more important, intermediaries that facilitate shopping experience, continue to offer more and more innovative technologies to support social interaction among like-minded community members or friends who share the same shopping interests. Among these technologies, reviews, ratings, and recommendation systems have become some of the most popular social shopping platforms due to their ease of use and simplicity in sharing buying experience and aggregating evaluations. This paper studies the effect of electronic word of mouth (eWOM) communication among a closed community of book readers. We studied the entire market of Amazon Shorts e-books, which are digital microproducts sold at a low and uniform price. With the minimal role of price in the buying decision, social discussion via eWOM becomes a collective signal of reputation, and ultimately a significant demand driver. Our empirical study suggests that eWOM can be used to convey the reputation of the product (e.g., the book), the reputation of the brand (i.e., the author), and the reputation of complementary goods (e.g., books in the same category). Until newer social shopping technologies gain acceptance, eWOM technologies should be considered by both e-tailers and shoppers as the first and perhaps primary source of social buying experience.


title: Task Variant Allocation in Distributed Robotics

abstract: This paper tackles the problem of allocating tasks to a distributed heterogeneous robotic system, where tasks---named *task variants* in the paper---can vary in terms of trade-off between resource requirements and quality of service provided. Three different methods (constraint programming, greedy, and metaheuristic) are proposed to solve such a problem and are evaluated both in simulation and in a real scenario, showing the goodness of the constraint programming method.


title: iCROSS: toward a scalable infrastructure for cross-domain context management

abstract: With the development of pervasive computing techniques, the world will be filled with interconnected context-aware domains (e.g., homes, offices, hospitals, etc.). While the previous studies focused solely on the management of contexts produced in a single domain, in this paper we discuss the challenges to be addressed for cross-domain context management. By analyzing the requirements from several scenarios, we identify two context producer---consumer patterns in multi-domain environments. Furthermore, to deal with the mobile entity problem raised in cross-domain context sharing, a transparent query mechanism that enables applications to obtain context information about mobile entities from remote domains is proposed. Two prototype applications--smart home and community services in a smart campus--have been developed to demonstrate the key features and usefulness of cross-domain context management. Initial experiments have also been conducted to evaluate the performance of our system.


Successfully Managing Impending Skin Necrosis following Hyaluronic Acid Filler Injection, using High-Dose Pulsed Hyaluronidase


title: Leveraging Big Data

abstract: Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies--such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and "type simulations"--can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.


title: HeMPS - a framework for NoC-based MPSoC generation

abstract: Multi-Processor Systems-on-Chip (MPSoCs) are increasingly popular in embedded systems. Due to their complexity and huge design space to explore for such systems, CAD tools and frameworks to customize MPSoCs are mandatory. Some academic and industrial frameworks are available to support bus-based MPSoCs, but few works target NoCs as underlying communication architecture. A framework targeting MPSoC customization must provide abstract models to enable fast design space exploration, flexible application mapping strategies, all coupled to features to evaluate the performance of running applications. This paper proposes a framework to customize NoC-based MPSoCs with support to static and dynamic task mapping and C/SystemC simulation models for processors and memories. A simple, specifically designed microkernel executes in each processor, enabling multitasking at the processor level. Graphical tools enable debug and system verification, individualizing data for each task. Practical results highlight the benefit of using dynamic mapping strategies (total execution time reduction) and abstract models (total simulation time reduction without losing accuracy).


title: Toward An Understanding of the Behavioral Intention to Use Mobile Banking Services

abstract: Although millions of dollars have been spend on building mobile banking systems, report on mobile banking systems has shown that potential users may not use the systems in spite of their availability. There is a need for research to identify the factors that determine users’ acceptance of mobile banking. According to the technology acceptance model (TAM), perceived ease of use and perceived usefulness constructs are believed to be fundamental in determining the acceptance and use of various IT. These beliefs, however, may not fully explain the user’s behavior toward the newly emerging mobile banking. Using the technology acceptance model (TAM) as a theoretical framework, this study introduces “perceived credibility” as a new factor that reflects the user’s security and privacy concern in the acceptance of mobile banking, and examines the effect of computer self-efficacy on intention to use mobile banking. Data collected from 267 users in Taiwan were tested against the extended TAM using the structural equation modeling approach. The results strongly support the extended TAM in predicting users’ intention to adopt mobile banking systems, and demonstrate the significant effect of computer self-efficacy on behavioral intention through perceived ease of use, perceived usefulness, and perceived credibility.


title: Experimental analysis of mechanical vibrations and wind speed for a rail vehicle WSN fed by energy harvesters

abstract: Real-time diagnostic and monitoring on railway vehicles for goods transportation is considered. The results of a preliminary experimental activity aimed to model mechanical vibrations and wind speed for autonomous monitoring and diagnostic wireless sensor network fed by means of energy harvesters are reported and commented.


title: Large-Scale Visual Active Learning with Deep Probabilistic Ensembles

abstract: Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provide an effective solution to this. Despite being theoretically principled, BNNs require approximations to be applied to large-scale problems, where both performance and uncertainty estimation are crucial. In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep BNN. We conduct a series of large-scale visual active learning experiments to evaluate DPEs on classification with the CIFAR-10, CIFAR-100 and ImageNet datasets, and semantic segmentation with the BDD100k dataset. Our models require significantly less training data to achieve competitive performances, and steadily improve upon strong active learning baselines as the annotation budget is increased.


title: Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems

abstract: A strategy for grid power peak shaving and valley filling using vehicle-to-grid systems (V2G) is proposed. The architecture of the V2G systems and the logical relationship between their sub-systems are described. An objective function of V2G peak-shaving control is proposed and the main constraints are formulated. The influences of the number of connected EVs and the average value of the target curve are analyzed. The rms and the standard deviation of the difference between the target and planned curves are proposed as indices for measuring the degree of matching between the two curves. The simulation results demonstrate that peaking shaving using V2G can be effective and controllable, and the proposed control algorithm is feasible.


title: Sequential Deep Learning for Disaster-Related Video Classification

abstract: Videos serve to convey complex semantic information and ease the understanding of new knowledge. However, when mixed semantic meanings from different modalities (i.e., image, video, text) are involved, it is more difficult for a computer model to detect and classify the concepts (such as flood, storm, and animals). This paper presents a multimodal deep learning framework to improve video concept classification by leveraging recent advances in transfer learning and sequential deep learning models. Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) models are then used to obtain the sequential semantics for both audio and textual models. The proposed framework is applied to a disaster-related video dataset that includes not only disaster scenes, but also the activities that took place during the disaster event. The experimental results show the effectiveness of the proposed framework.


title: Tensor Subspace Analysis

abstract: Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. The typical linear sub-space learning algorithms include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). All of these methods consider an n1 x n2 image as a high dimensional vector in ℝn1 x n2, while an image represented in the plane is intrinsically a matrix. In this paper, we propose a new algorithm called Tensor Subspace Analysis (TSA). TSA considers an image as the second order tensor in Rn1 ⊗ Rn2, where Rn1 and Rn2 are two vector spaces. The relationship between the column vectors of the image matrix and that between the row vectors can be naturally characterized by TSA. TSA detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace. We compare our proposed approach with PCA, LDA and LPP methods on two standard databases. Experimental results demonstrate that TSA achieves better recognition rate, while being much more efficient.


title: Computational Creativity Infrastructure for Online Software Composition: A Conceptual Blending Use Case

abstract: Computational creativity (CC) is a multidisciplinary research field, studying how to engineer software that exhibits behavior that would reasonably be deemed creative. This paper shows how composition of software solutions in this field can effectively be supported through a CC infrastructure that supports user-friendly development of CC software components and workflows, their sharing, execution, and reuse. The infrastructure allows CC researchers to build workflows that can be executed online and be easily reused by others through the workflow web address. Moreover, it enables the building of procedures composed of software developed by different researchers from different laboratories, leading to novel ways of software composition for computational purposes that were not expected in advance. This capability is illustrated on a workflow that implements a Concept Generator prototype based on the Conceptual Blending framework. The prototype consists of a composition of modules made available as web services, and is explored and tested through experiments involving blending of texts from different domains, blending of images, and poetry generation.


title: High Speed VLSI Implementation of the Hyperbolic Tangent Sigmoid Function

abstract: The hyperbolic tangent function is commonly used as the activation function in artificial neural networks. In this work two different hardware implementations for the hyperbolic tangent function are proposed. Both methods are based on the approximation of the function rather than calculating it, since it has exponential nature. The first method uses a lookup table to approximate the function, while the second method reduces the size of the table by using range addressable decoding as opposed to the classic decoding scheme. Hardware synthesis results show the proposed methods perform significantly faster, and use less area compared to other similar methods with the same amount of error.


title: Energy efficiency in wireless sensor networks: A top-down survey

abstract: The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energy-constrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energy-saving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture. We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.


title: Precision Performance Surgery for PostgreSQL: LLVM-based Expression Compilation, Just in Time

abstract: We demonstrate how the compilation of SQL expressions into machine code leads to significant query runtime improvements in PostgreSQL 9. Our primary goal is to connect recent research in query code generation with one of the most widely deployed database engines. The approach calls on LLVM to translate arithmetic and filter expressions into native x86 instructions just before SQL query execution begins. We deliberately follow a non-invasive design that does not turn PostgreSQL on its head: interpreted and compiled expression evaluation coexist and both are used to execute the same query. We will bring an enhanced version of PostgreSQL that exhibits notable runtime savings and provides visual insight into exactly where and how execution plans can benefit from SQL expression compilation.


title: Claper: Recommend classical papers to beginners

abstract: Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas.


title: Abstract Using Natural Language Program Analysis to Locate and Understand Action-Oriented Concerns

abstract: Most current software systems contain undocumented high-level ideas implemented across multiple files and modules. When developers perform program maintenance tasks, they often waste time and effort locating and understanding these scattered concerns. We have developed a semi-automated concern location and comprehension tool, Find-Concept, designed to reduce the time developers spend on maintenance tasks and to increase their confidence in the results of these tasks. Find-Concept is effective because it searches a unique natural language-based representation of source code, uses novel techniques to expand initial queries into more effective queries, and displays search results in an easy-to-comprehend format. We describe the Find-Concept tool, the underlying program analysis, and an experimental study comparing Find-Concept's search effectiveness with two state-of-the-art lexical and information retrieval-based search tools. Across nine action-oriented concern location tasks derived from open source bug reports, our Eclipse-based tool produced more effective queries more consistently than either competing search tool with similar user effort.


title: Assessing the Effects of Building Social Intelligence in a Robotic Interface for the Home. Interacting with Computers, Volume 17

abstract: This paper reports an exploration of the concept of social intelligence in the context of designing home dialogue systems for an Ambient Intelligence home. It describes a Wizard of Oz experiment involving a robotic interface capable of simulating several human social behaviours. Our results show that endowing a home dialogue system with some social intelligence will: (a) create a positive bias in the user's perception of technology in the home environment, (b) enhance user acceptance for the home dialogue system, and (c) trigger social behaviours by the user in relation to the home dialogue system.


title: A compact branch-line coupler using folded microstrip lines

abstract: This paper presents a compact 3 dB Quadrature branch-line coupler using folded microstrip line geometry. Commercially available IE3D software has been used to design and simulate the structure. The proposed structure is simple and takes lesser optimization time. It has been shown that the structure provides about 43.63% compactness compared to the conventional branch line coupler. The structure, therefore, can be incorporated in the microwave circuit design where medium level compactness and faster design are preferred.


title: A monocular pose estimation system based on infrared LEDs

abstract: We present an accurate, efficient, and robust pose estimation system based on infrared LEDs. They are mounted on a target object and are observed by a camera that is equipped with an infrared-pass filter. The correspondences between LEDs and image detections are first determined using a combinatorial approach and then tracked using a constant-velocity model. The pose of the target object is estimated with a P3P algorithm and optimized by minimizing the reprojection error. Since the system works in the infrared spectrum, it is robust to cluttered environments and illumination changes. In a variety of experiments, we show that our system outperforms state-of-the-art approaches. Furthermore, we successfully apply our system to stabilize a quadrotor both indoors and outdoors under challenging conditions. We release our implementation as open-source software.


title: Hierarchical interest graph from tweets

abstract: Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt spreading activation theory to assign user interest score to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of users' explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source.


title: Scapular Winging: Evaluation and Treatment

abstract: Scapular winging is a rare, underreported, and debilitating disorder that produces abnormal scapulothoracic kinematics, which can lead to shoulder weakness, decreased range of motion, and substantial pain. Although there are numerous underlying etiologies, injuries to the long thoracic nerve or spinal accessory nerve are the most common, with resultant neuromuscular imbalance in the scapulothoracic stabilizing muscles. Early diagnosis followed by initiation of a treatment algorithm is important for successful outcomes. Most cases resolve with nonsurgical management. However, in patients with persistent symptoms despite nonsurgical management, appropriate dynamic muscle transfers can effectively treat the scapular winging, with good clinical outcomes.


title: Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

abstract: This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions. Deep learning encapsulates a family of machine learning algorithms that over the last decade has transformed many important subfields of artificial intelligence (AI) and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical, and demographic datasets.


The Influences of Personality and Motivation on the Sharing of Misinformation on Social Media


title: Ontop: Answering SPARQL queries over relational databases

abstract: We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases.


title: A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods

abstract: The sigmoid kernel was quite popular for support vector machines due to its origin from neural networks. Although it is known that the kernel matrix may not be positive semi-definite (PSD), other properties are not fully studied. In this paper, we discuss such non-PSD kernels through the viewpoint of separability. Results help to validate the possible use of non-PSD kernels. One example shows that the sigmoid kernel matrix is conditionally positive definite (CPD) in certain parameters and thus are valid kernels there. However, we also explain that the sigmoid kernel is not better than the RBF kernel in general. Experiments are given to illustrate our analysis. Finally, we discuss how to solve the non-convex dual problems by SMO-type decomposition methods. Suitable modifications for any symmetric non-PSD kernel matrices are proposed with convergence proofs.


title: Duality between Time Series and Networks

abstract: Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways.


title: Focus: Querying Large Video Datasets with Low Latency and Low Cost

abstract: Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering"after the fact"queries: identify video frames with objects of certain classes (cars, bags) from many days of recorded video. While advancements in convolutional neural networks (CNNs) have enabled answering such queries with high accuracy, they are too expensive and slow. We build Focus, a system for low-latency and low-cost querying on large video datasets. Focus uses cheap ingestion techniques to index the videos by the objects occurring in them. At ingest-time, it uses compression and video-specific specialization of CNNs. Focus handles the lower accuracy of the cheap CNNs by judiciously leveraging expensive CNNs at query-time. To reduce query time latency, we cluster similar objects and hence avoid redundant processing. Using experiments on video streams from traffic, surveillance and news channels, we see that Focus uses 58X fewer GPU cycles than running expensive ingest processors and is 37X faster than processing all the video at query time.


title: Risk analysis in information systems: a fuzzification of the MAGERIT methodology

abstract: Several methodologies based on ISO/IEC 27000 international standard have been developed to deal with risk analysis in information systems (IS). These methodologies do not, however, consider imprecise valuations, but use precise values on different, usually percentage, scales. ::: ::: We propose an extension of the MAGERIT methodology based on classical fuzzy computational models. A linguistic term scale is used to represent asset values, their dependencies and frequency and asset degradation associated with threats. Computations are based on trapezoidal fuzzy numbers associated with linguistic terms. A similarity function is used to associate a linguistic term on the previously defined scale to the trapezoidal fuzzy numbers resulting from computations. Finally, regarding the selection of preventive safeguards to reduce risks in IS, we propose a dynamic programming-based method that incorporates simulated annealing to tackle optimizations problems with the aim of minimizing costs while keeping the risk at acceptable levels. ::: ::: An example of an administrative unit using in-house and third-party information systems internally and to provide public information services is used to illustrate the methodology.


title: A Supervised KeyPhrase Extraction System

abstract: In this paper, we present a multi-featured supervised automatic keyword extraction system. We extracted salient semantic features which are descriptive of candidate keyphrases, a Random Forest classifier was used for training. The system achieved an accuracy of 58.3 % precision and has shown to outperform two top performing systems when benchmarked on a crowdsourced dataset. Furthermore, our approach achieved a personal best Precision and F-measure score of 32.7 and 25.5 respectively on the Semeval Keyphrase extraction challenge dataset. The paper describes the approaches used as well as the result obtained.


title: Evidence for van der Waals adhesion in gecko setae

abstract: Geckos have evolved one of the most versatile and effective adhesives known. The mechanism of dry adhesion in the millions of setae on the toes of geckos has been the focus of scientific study for over a century. We provide the first direct experimental evidence for dry adhesion of gecko setae by van der Waals forces, and reject the use of mechanisms relying on high surface polarity, including capillary adhesion. The toes of live Tokay geckos were highly hydrophobic, and adhered equally well to strongly hydrophobic and strongly hydrophilic, polarizable surfaces. Adhesion of a single isolated gecko seta was equally effective on the hydrophobic and hydrophilic surfaces of a microelectro-mechanical systems force sensor. A van der Waals mechanism implies that the remarkable adhesive properties of gecko setae are merely a result of the size and shape of the tips, and are not strongly affected by surface chemistry. Theory predicts greater adhesive forces simply from subdividing setae to increase surface density, and suggests a possible design principle underlying the repeated, convergent evolution of dry adhesive microstructures in gecko, anoles, skinks, and insects. Estimates using a standard adhesion model and our measured forces come remarkably close to predicting the tip size of Tokay gecko seta. We verified the dependence on size and not surface type by using physical models of setal tips nanofabricated from two different materials. Both artificial setal tips stuck as predicted and provide a path to manufacturing the first dry, adhesive microstructures.


title: LEXA: Towards Automatic Legal Citation Classification

abstract: In this paper we present our approach towards legal citation classification using incremental knowledge acquisition. This forms a part of our more ambitious goal of automatic legal text summarization. We created a large training and test corpus from court decision reports in Australia. We showed that, within less than a week, it is possible to develop a good quality knowledge base which considerably outperforms a baseline Machine Learning approach. We note that the problem of legal citation classification allows the use of Machine Learning as classified training data is available. For other subproblems of legal text summarization this is unlikely to be the case.


title: Sleep problems and their relation to cognitive factors, anxiety, and depressive symptoms in children and adolescents

abstract: Background: Existing research indicates sleep problems to be prevalent in youth with internalizing disorders. However, childhood sleep problems are common in the general population and few data are available examining unique relationships between sleep, specific types of anxiety and depressive symptoms among nonclinical samples of children and adolescents. Methods: The presence of sleep problems was examined among a community sample of children and adolescents (N=175) in association with anxiety and depressive symptoms, age, and gender. Based on emerging findings from the adult literature we also examined associations between cognitive biases and sleep problems. Results: Overall findings revealed significant associations between sleep problems and both anxiety and depressive symptoms, though results varied by age. Depressive symptoms showed a greater association with sleep problems among adolescents, while anxiety symptoms were generally associated with sleep problems in all youth. Cognitive factors (cognitive errors and control beliefs) linked with anxiety and depression also were associated with sleep problems among adolescents, though these correlations were no longer significant after controlling for internalizing symptoms. Conclusions: Results are discussed in terms of their implications for research and treatment of sleep and internalizing disorders in youth. Depression and Anxiety 26:503–512, 2009. Published 2008 Wiley-Liss, Inc. y


Rethinking the Internet of Things


title: Individual differences in disgust sensitivity: comparisons and evaluations of paper-and-pencil versus behavioural measures. Journal of Research in. Personality 33:330–51

abstract: Abstract Sixty-eight undergraduate students experienced 32 hands-on tasks designed to provide a behavioral validation for the paper-and-pencil Disgust Scale, which the students had completed 2 months before. Tasks assessed participant-determined degree of exposure (looking at, picking up, touching, and in some cases eating) to objects such as a cockroach, cremated ashes, and a freshly killed pig's head and to disgusting video clips (seconds watching). These tasks elicited strong negative affect in a way that was ethical and not very disturbing to participants; they may be useful for future laboratory study of emotion. Participants also experienced nondisgusting control tasks, such as imitating a chicken or holding one's hand in ice-water. Analysis of task intercorrelations indicated four factors: food-related disgust, body-violation-and-death-related disgust, compliance motivation, and embarrassability. Only the two disgust factors correlated significantly with the paper-and-pencil Disgust Scale; a combination of the two correlated .58 with Disgust Scale scores obtained months before the laboratory assessment and correlated .71 with scores obtained immediately after this assessment. Most generally, these results are a reminder that there is no gold standard for personality assessment. As with paper-and-pencil measures, behavioral measures require getting beyond face validity to assess threats to validity from factors such as embarrassment and compliance motivation.


title: A RESEARCH AGENDA ON MANAGERIAL INTENTION TO GREEN IT ADOPTION: FROM NORM ACTIVATION PERSPECTIVE

abstract: Green IT adoption is a plausible attempt for organizations to tackle the current environmental problem, and can also improve the economic performance of an organization. Therefore, organizational adoption of Green IT is beneficial to the society. Investigating how the decision maker in an organization would come to adopt Green IT is necessary to establish strategies in promoting organizational adoption of Green IT adoption because decision makers make decisions, rather than organizations. However, to our knowledge, studies in Green IT literature seldom investigate this area. In Green IT literature, emphasis has been given on the effects of institutional pressures and organizational resources on organizational adoption of Green IT. The current study adapts a Norm Activation Model in organizational context to investigate on how an intention to Green IT adoption is formed. The decision makers’ personal norm on environmental interpretation is argued to be activated when environmental preservation is interpreted as an opportunity, which may lead to intention to Green IT adoption. Perceived competitive advantage of Green IT will affect how environmental preservation is interpreted, as well as affect intention to Green IT adoption. Moreover, discretionary slack will also affect how environmental preservation is interpreted. All of these should be tested empirically to see if the results support our proposed hypotheses. Empirical data for this study will be collected via a survey.


title: Experimental Analysis of a Vibrating Tribocharging Device of Plastic Particles for a Free-Fall Electrostatic Separator

abstract: The selective sorting of mixed granular plastics can be achieved by tribocharging in a vibrated parallelepiped chamber of variable height prior to introducing them in a free-fall electrostatic separator. Although such vibrating tribochargers are currently used in industry, no experimental investigation of these devices has been carried out yet. The objective of this work is to characterize this class of tribochargers by estimating both the electric charge imparted to the granules and the electrostatic separation efficiency. The granular product, which is a mixture of high-density polyethylene (HDPE) and polyvinyl chloride (PVC), is introduced through a funnel into the vibrating chamber, where the particles get triboelectrically charged by undergoing collisions between themselves and with the walls of the device. The obtained results showed that the efficiencies of both charging and separation depend on the geometry and the operating conditions of the tribocharger (height, length, inclination angle, and motor speed). Charge per mass ratios beyond 3 nC/g have been easily attained, and the efficiency of the separation is excellent: more than 90% of the HDPE and PVC in the feed have been recovered at the outlet of the installation.


PLAGIARISM DETECTION ALGORITHM USING NATURAL LANGUAGE PROCESSING BASED ON GRAMMAR ANALYZING


title: Modelling and managing deployment costs of microservice-based cloud applications

abstract: We present an approach to model the deployment costs, including compute and IO costs, of Microservice-based applications deployed to a public cloud. Our model, which we dubbed CostHat, supports both, Microservices deployed on traditional IaaS or PaaS clouds, and services that make use of novel cloud programming paradigms, such as AWS Lambda. CostHat is based on a network model, and allows for what-if and cost sensitivity analysis. Further, we have used this model to implement tooling that warns cloud developers directly in the Integrated Development Environment (IDE) about certain classes of potentially costly code changes. We illustrate our work based on a case study, and evaluate the CostHat model using a standalone Python implementation. We show that, once instantiated, cost calculation in CostHat is computationally inexpensive on standard hardware (below 1 ms even for applications consisting of thousand services and endpoints). This enables its use in real-time for developer tooling which continually re-evaluates the costs of an application in the background, while the developer is working on the code.


title: Thin-Wafer Silicon IGBT With Advanced Laser Annealing and Sintering Process

abstract: A novel insulated gate bipolar transistor (IGBT) featuring thin-wafer processing and a combined dopant activation laser annealing and contact metal laser sintering is presented. The device concept includes a new back-side boron anode (collector) activation process by laser annealing through a titanium layer to enhance the absorption of the deposited energy from the laser beam. This technology enables improved activation control of the anode injection efficiency for thin-wafer-based IGBTs rated normally below 1700 V. The IGBT concept will therefore be provided with a wider range of performance options on the loss technology curve when compared to state-of-the-art devices processed with conventional activation techniques.


title: Genetic affinities among Mongol ethnic groups and their relationship to Turks.

abstract: The central Asian country Mongolia is home to more than 20 tribes and ethnic groups, some of which are related to neighboring Turkic populations. The main Mongolian people, Khalkha, live in central and eastern Mongolia while the Tsaatan minority lives in the north of the country. The Oold minority is from the western Altai mountain region and live in close proximity with Turkic people. We have typed the HLA-A, -B, -Cw, -DRB1 and -DQB1 loci by PCR-SSP in these three Mongolian populations as well as a sample of the German population. To examine their genetic relationships, a sample of the Turkish population already typed at the HLA-A, -B and -DRB1 loci were used. Altogether five populations were analyzed: Khalkha (n = 100), Tsaatan (n = 72), Oold (n = 52), German (n = 260) and (Anatolian) Turkish (n = 498). Nei's unbiased genetic identity (GI) and genetic distance (GD) were estimated from genotypes using PopGene v1.31, and dendrograms were constructed using phylip. The results suggested a close relationship of the Khalkha to the Tsaatan. The Turks and Germans were equally distant to all three Mongolian populations. These results confirmed the lack of strong genetic relationship between the Mongols and the Turks despite the close relationship of their languages (Altaic group) and shared historical neighborhood. This study has provided useful population data for genetic and anthropologic studies bridging eastern and western populations.