title: Innovate or die: the need for business model transformations : a case study on business model innovations through professional service platforms

abstract: Over the last decade, the concept of business model innovations was increasingly recognised by both scholars and practitioners as an effective measure to enhance corporate future readiness. This thesis aims to enhance existing knowledge in this field by analysing the influence of digital service platforms on business model innovations of small and medium-sized enterprises. A cross-sectional multi-case research design is employed to address the exploratory research question. Using data/interviews on fifteen companies that are all headquartered in Germany, the transformative impact of digital service platforms is measured. ::: The respective findings suggest that process and organisational innovativeness are decisive in business model transformations among small and medium-sized enterprises. The perceived main advantages of digital service platforms for SMEs are positive network-effects, the joint development of products and services, access to external knowledge and resources, lowered transaction costs, and newly available distribution channels. Key success factors of digital service platforms are low entry barriers, open interfaces to external platforms, a large and heterogeneous user group, and the accumulation of data. ::: The results suggest that joining digital service platforms affects the business model transformation of small and medium sized enterprises positively. Furthermore, it is found that these digital solutions also sustain the future competitiveness of professional service providers.


title: Analyzing protocol implementations for interoperability

abstract: We propose PIC, a tool that helps developers search for non-interoperabilities in protocol implementations. We formulate this problem using intersection of the sets of messages that one protocol participant can send but another will reject as non-compliant. PIC leverages symbolic execution to characterize these sets and uses two novel techniques to scale to real-world implementations. First, it uses joint symbolic execution, in which receiver-side program analysis is constrained based on sender-side constraints, dramatically reducing the number of execution paths to consider. Second, it incorporates a search strategy that steers symbolic execution toward likely non-interoperabilities. We show that PIC is able to find multiple previously unknown noninteroperabilities in large and mature implementations of the SIP and SPDY (v2 through v3.1) protocols, some of which have since been fixed by the respective developers.


1 PrivBayes : Private Data Release via Bayesian Networks


Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation


title: Machine learning in automated text categorization

abstract: The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.


title: OpenCL-Based FPGA-Platform for Stencil Computation and Its Optimization Methodology

abstract: Stencil computation is widely used in scientific computations and many accelerators based on multicore CPUs and GPUs have been proposed. Stencil computation has a small operational intensity so that a large external memory bandwidth is usually required for high performance. FPGAs have the potential to solve this problem by utilizing large internal memory efficiently. However, a very large design, testing and debugging time is required to implement an FPGA architecture successfully. To solve this problem, we propose an FPGA-platform using C-like programming language called open computing language (OpenCL). We also propose an optimization methodology to find the optimal architecture for a given application using the proposed FPFA-platform. According to the experimental results, we achieved 119 $\sim$ 237 Gflop/s of processing power and higher processing speed compared to conventional GPU and multicore CPU implementations.


title: Reliability Analysis for Multi-Component Systems Subject to Multiple Dependent Competing Failure Processes

abstract: For complex multi-component systems with each component experiencing multiple failure processes due to simultaneous exposure to degradation and shock loads, we developed a new multi-component system reliability model, and applied two different preventive maintenance policies. This new model extends previous research, and is different from related previous research by considering an assembled system of degrading components with s-dependent failure times resulting from shared shock exposure. Previous research primarily pertained to a single component or simple system, or systems with s-independent failure processes and failure times. In our new system model, the individual failure processes for each component and the component failure processes are all s-dependent. These models can be applied directly, or customized for many complex systems with multiple components that experience s-dependent competing failure processes. In this model, each component can fail due to a soft failure process, or a hard failure process. These two component failure processes are mutually competing and s-dependent. If one component fails relatively frequently, it is likely that the number of shocks is relatively large, and these shocks impact all components potentially causing them to fail more often as well. Therefore, failure processes of all components are also s-dependent. An age replacement policy and an inspection-based maintenance policy are applied for a system with multiple components. The optimal replacement interval or inspection times are determined by minimizing a cost rate function. The model is demonstrated on several examples.


Supervised Word Sense Disambiguation using Python


title: Urban computing with taxicabs

abstract: Urban computing for city planning is one of the most significant applications in Ubiquitous computing. In this paper we detect flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas. The detected results consist of 1) pairs of regions with salient traffic problems and 2) the linking structure as well as correlation among them. These results can evaluate the effectiveness of the carried out planning, such as a newly built road and subway lines in a city, and remind city planners of a problem that has not been recognized when they conceive future plans. We conduct our method using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and evaluate our results with the real urban planning of Beijing.


title: A Novel Localized Approach to Single Image Dehazing Based on Haze-Lines

abstract: Haze is a common atmospheric degradation which reduces the visibility of outdoor images by creating a thin veil over the scene. Removal of haze from a natural scene is a complex process because of the difficulty in getting any prior information other than the hazy image. In natural images, the assumption of global atmospheric light may not be true because of the non uniform nature of haze. Most of the state-of-the-art methods use a patch based algorithm for the estimation of global atmospheric light and transmission map. In this work, we propose a patch based dehazing technique which estimates localized atmospheric light by clustering the minimum channel of the image using Self Organizing Map (SOM). This local atmospheric light is used in haze-line based dehazing method for the estimation of transmission map. It gives a better estimate of haze content compared to the existing methods and results in better dehazing. The pixel based haze-line model has reduced complexity when compared to dark channel prior based methods. This excludes the requirement of any transmission map refinement operations which makes the algorithm simpler and faster.


title: Development and initial investigation of NTU robotic fish with modular flexible fins

abstract: This paper presents an environment-friendly robotic system mimicking undulating fins of fish. To mimic the actual flexible fin of real fish, we model a fin-like mechanism with a series of connecting linkages and attached it to the robotic fish. By virtue of a specially designed strip, each link is able to turn and slide with respect to the adjacent link. The driving linkages are used to form a mechanical fin consisting of several fin segments, which are able to produce undulations, similar to those produced by the actual fins. Owing to the modular design and the flexible structure of the mechanical fin, we were able to construct various biomimetic robots of fish swimming by fin undulations. Two robotic fish have been designed, constructed, and initially tested in NTU: the first is swimming by undulations of a long anal fin, and the second is swimming by undulations of a pair of lateral fins. The modeling technique of the first designed robotic fish in Singapore is presented. Some qualitative observations obtained by experiments with the robotic fish are also shown and discussed.


title: Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation

abstract: In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (Silberman et al., ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the $$gPb-ucm$$gPb-ucm approach of Arbelaez et al. (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (Felzenszwalb et al., TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art.


title: A modified particle swarm optimizer

abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.


removal of a silver cone by using clinical microscope and ultrasound : case report


title: Solving games Dependence of applicable solving procedures

abstract: We introduce an alternative concept to determine the solvability of two-player games with perfect information. This concept - based on games currently solved - claims that the applicable solving procedures have a significant influence on the solvability of games. This contrasts with current views that suggest that solvability is related to state-space and game-tree complexity. Twenty articles on this topic are discussed, including those that describe the currently obtained solutions. Results include a description of the available solving procedures as well as an overview of essential techniques from the past. Besides well-known classic games, the solvability of popular and recent games zertz, dvonn, and yinsh are analyzed. We conclude that our proposed concept could determine the solvability of games more accurately. Based on our concept, we expect that new solving techniques are required to obtain solutions for unsolved games.


title: Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

abstract: This article provides an improved automated skin lesion segmentation method for dermoscopic images. There are several stages for this method. These include the pre-processing steps such as resizing the images and eliminating noise. Hair was removed and reflective light was reduced using morphological operations and a median filter. The single green channel was rescaled into new intensities, as it provided the highest segmentation accuracy. The threshold value was calculated to separate the skin lesion region from healthy skin. Morphological operations were implemented to merge the small lesion areas around the bigger lesion areas with similar features and trace the boundary of the melanoma. The accuracy of the segmentation was evaluated by comparing the automatic boundary and manual boundary. Compared to other studies, our proposed method achieved the highest average accuracy of 97%.


title: Feature-preserving triangular geometry images for level-of-detail representation of static and skinned meshes

abstract: Geometry images resample meshes to represent them as texture for efficient GPU processing by forcing a regular parameterization that often incurs a large amount of distortion. Previous approaches broke the geometry image into multiple rectangular or irregular charts to reduce distortion, but complicated the automatic level of detail one gets from MIP-maps of the geometry image. We introduce triangular-chart geometry images and show this new approach better supports the GPU-side representation and display of skinned dynamic meshes, with support for feature preservation, bounding volumes, and view-dependent level of detail. Triangular charts pack efficiently, simplify the elimination of T-junctions, arise naturally from an edge-collapse simplification base mesh, and layout more flexibly to allow their edges to follow curvilinear mesh features. To support the construction and application of triangular-chart geometry images, this article introduces a new spectral clustering method for feature detection, and new methods for incorporating skinning weights and skinned bounding boxes into the representation. This results in a tenfold improvement in fidelity when compared to quad-chart geometry images.


title: Software and Hardware Implementations of Stereo Matching

abstract: Stereo matching is one of the key technologies in stereo vision system due to its ultra high data bandwidth requirement, heavy memory accessing and algorithm complexity. To speed up stereo matching, various algorithms are implemented by different software and hardware processing methods. This paper presents a survey of stereo matching software and hardware implementation research status based on local and global algorithm analysis. Based on different processing platforms, including CPU, DSP, GPU, FPGA and ASIC, analysis are made on software or hardware realization performance, which is represented by frame rate, efficiency represented by MDES, and processing quality represented by error rate. Among them, GPU, FPGA and ASIC implementations are suitable for real-time embedded stereo matching applications, because they are low power consumption, low cost, and have high performance. Finally, further stereo matching optimization technologies are pointed out, including both algorithm and parallelism optimization for data bandwidth reduction and memory storage strategy.


title: All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis

abstract: Graph datasets with billions of edges, such as social and web graphs, are prevalent, and scalability is critical. All-distances sketches (ADS) [Cohen 1997], are a powerful tool for scalable approximation of statistics. The sketch is a small size sample of the distance relation of a node which emphasizes closer nodes. Sketches for all nodes are computed using a nearly linear computation and estimators are applied to sketches of nodes to estimate their properties. We provide, for the first time, a unified exposition of ADS algorithms and applications. We present the historic inverse probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of statistics. For the important special cases of neighborhood cardinalities (the number of nodes within some query distance) and closeness centralities, HIP estimators have at most half the variance of previous estimators and we show that this is essentially optimal. Moreover, HIP obtains a polynomial improvement for more general statistics and the estimators are simple, flexible, unbiased, and elegant. For approximate distinct counting on data streams, HIP outperforms the original estimators for the HyperLogLog MinHash sketches (Flajolet et al. 2007), obtaining significantly improved estimation quality for this state-of-the-art practical algorithm.


abstract: We present a new model, called Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural Networks (RNNs) and Predictive State Representations (PSRs), and inherit advantages from both types of models. Like many successful RNN architectures, PSRNNs use (potentially deeply composed) bilinear transfer functions to combine information from multiple sources, so that one source can act as a gate for another. These bilinear functions arise naturally from the connection to state updates in Bayes filters like PSRs, in which observations can be viewed as gating belief states. We show that PSRNNs can be learned effectively by combining backpropogation through time (BPTT) with an initialization based on a statistically consistent learning algorithm for PSRs called two-stage regression (2SR). We also show that PSRNNs can be can be factorized using tensor decomposition, reducing model size and suggesting interesting theoretical connections to existing multiplicative architectures such as LSTMs. We applied PSRNNs to 4 datasets, and showed that we outperform several popular alternative approaches to modeling dynamical systems in all cases.


Do Characters Abuse More Than Words?


title: Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning

abstract: In this paper, we focus on tackling the problem of automatic semantic annotation of high resolution (HR) optical satellite images, which aims to assign one or several predefined semantic concepts to an image according to its content. The main challenges arise from the difficulty of characterizing complex and ambiguous contents of the satellite images and the high human labor cost caused by preparing a large amount of training examples with high-quality pixel-level labels in fully supervised annotation methods. To address these challenges, we propose a unified annotation framework by combining discriminative high-level feature learning and weakly supervised feature transferring. Specifically, an efficient stacked discriminative sparse autoencoder (SDSAE) is first proposed to learn high-level features on an auxiliary satellite image data set for the land-use classification task. Inspired by the motivation that the encoder of the prelearned SDSAE can be regarded as a generic high-level feature extractor for HR optical satellite images, we then transfer the learned high-level features to semantic annotation. To compensate the difference between the auxiliary data set and the annotation data set, the transferred high-level features are further fine-tuned in a weakly supervised scheme by using the tile-level annotated training data. Finally, the fine-tuning process is formulated as an ultimate optimization problem, which can be solved efficiently with our proposed alternate iterative optimization method. Comprehensive experiments on a publicly available land-use classification data set and an annotation data set demonstrate the superiority of our SDSAE-based high-level feature learning method and the effectiveness of our weakly supervised semantic annotation framework compared with state-of-the-art fully supervised annotation methods.


title: Copper-graphene SPR-based biosensor for urea detection

abstract: This paper is intended to investigate the copper-graphene surface plasmon resonance (SPR)-based biosensor by considering the high adsorption efficiency of graphene. Copper (Cu) is used as a plasmonic material whereas graphene is used to prevent Cu from oxidation and enhance the reflectance intensity. Numerical investigation is performed using finite-difference-time-domain (FDTD) method by comparing the sensing performance such as reflectance intensity that explains the sensor sensitivity and the full-width-at-half-maximum (FWHM) of the spectrum for detection accuracy. The measurements were observed with various Cu thin film thicknesses ranging from 20nm to 80nm with 785nm operating wavelength. The proposed sensor shows that the 40nm-thick Cu-graphene (1 layer) SPR-based sensor gave better performance with narrower plasmonic spectrum line width (reflectance intensity of 91.2%) and better FWHM of 3.08°. The measured results also indicate that the Cu-graphene SPR-based sensor is suitable for detecting urea with refractive index of 1.49 in dielectric medium.


Adaptive SIFT / SURF Algorithm for Off-line signature Recognition


title: Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities

abstract: Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes. For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.


title: MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception

abstract: We present the MSP-IMPROV corpus, a multimodal emotional database, where the goal is to have control over lexical content and emotion while also promoting naturalness in the recordings. Studies on emotion perception often require stimuli with fixed lexical content, but that convey different emotions. These stimuli can also serve as an instrument to understand how emotion modulates speech at the phoneme level, in a manner that controls for coarticulation. Such audiovisual data are not easily available from natural recordings. A common solution is to record actors reading sentences that portray different emotions, which may not produce natural behaviors. We propose an alternative approach in which we define hypothetical scenarios for each sentence that are carefully designed to elicit a particular emotion. Two actors improvise these emotion-specific situations, leading them to utter contextualized, non-read renditions of sentences that have fixed lexical content and convey different emotions. We describe the context in which this corpus was recorded, the key features of the corpus, the areas in which this corpus can be useful, and the emotional content of the recordings. The paper also provides the performance for speech and facial emotion classifiers. The analysis brings novel classification evaluations where we study the performance in terms of inter-evaluator agreement and naturalness perception, leveraging the large size of the audiovisual database.


title: Wind Power Prediction with Machine Learning Ensembles

abstract: For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. Machine learning methods can be used as purely data-driven, spatio-temporal prediction models that yield better results than traditional physical models based on weather simulations. The objectives of this thesis are the improvement of prediction errors and the reduction of computation times. This thesis proposes a robust and practical prediction framework based on ensembles, which combines the predictions of numerous and preferably diverse models. A comprehensive experimental evaluation shows that the combination of different techniques to an ensemble outperforms state-of-the-art prediction models while requiring shorter computation times. For model selection we employ evolutionary multi-objective optimization algorithms. The methods offer an efficient and comfortable balancing of preferably low prediction errors and moderate computational costs.


title: Fingerprint shell: Secure representation of fingerprint template

abstract: Abstract Fingerprint is a popular biometric modality which is used extensively in several applications for person authentication, providing high uniqueness and acceptable performance. Most fingerprint systems use minutiae-based representations. However, several studies have proven that the original fingerprint impression can be reconstructed from minutia information, which makes the problem of ensuring the security of fingerprint data very critical. In this paper, we present a new approach for fingerprint template protection. Our objective is to build a non-invertible transformation that meets the requirements of revocability, diversity, security and performance. In this context, we exploit the information provided by the extracted minutiae to construct a new representation based on special spiral curves, which can be used for the recognition task instead of the traditional minutiae-based representation. The proposed approach has been evaluated using the original FVC protocol and compared with existing protection approaches which use the same protocol. Our experimental results illustrate the ability of the proposed representation to preserve the performance of protected systems. Moreover, we demonstrate that the security of our approach is sufficiently robust to the zero effort and brute force attacks.


title: Managing Cloud Storage Obliviously

abstract: Consumers want to ensure that their enterprise data is stored securely and obliviously on the cloud, such that the data objects or their access patterns are not revealed to anyone, including the cloud provider, in the public cloud environment. We have created a detailed ontology describing the oblivious cloud storage models and role based access controls that should be in place to manage this risk. We have developed an algorithm to store cloud data using oblivious data structure defined in this paper. We have also implemented the ObliviCloudManager application that allows users to manage their cloud data by validating it before storing it in an oblivious data structure. Our application uses role-based access control model and collection based document management to store and retrieve data efficiently. Cloud consumers can use our system to define policies for storing data obliviously and manage storage on untrusted cloud platforms even if they are unfamiliar with the underlying technology and concepts of oblivious data structures.


Why There Is No Statistical Test For Confounding, Why Many Think There Is, And Why They Are Almost Right


title: Probfuscation: An Obfuscation Approach Using Probabilistic Control Flows

abstract: Sensitive parts of a program, such as proprietary algorithms or licensing information, are often protected with the help of code obfuscation techniques. Many obfuscation schemes transform the control flow of the protected program. Typically, the control flow of obfuscated programs is deterministic, i.e., recorded execution traces do not differ for multiple executions using the same input values. An adversary can take advantage of this behavior and create multiple traces to perform analyses on the target program in order to deobfuscate it. ::: ::: In this paper, we introduce an obfuscation approach which yields probabilistic control flow within a given method. That is, for the same input values, multiple execution traces differ, whilst preserving semantics. This effectively renders analyses relying on multiple traces impractical. We have implemented a prototype and applied it to several different programs. Our experimental results show that our approach can be used to ensure divergent traces for the same input values and that it can significantly improve the resilience against dynamic analysis.


title: Real-time edge-aware image processing with the bilateral grid

abstract: We present a new data structure---the bilateral grid, that enables fast edge-aware image processing. By working in the bilateral grid, algorithms such as bilateral filtering, edge-aware painting, and local histogram equalization become simple manipulations that are both local and independent. We parallelize our algorithms on modern GPUs to achieve real-time frame rates on high-definition video. We demonstrate our method on a variety of applications such as image editing, transfer of photographic look, and contrast enhancement of medical images.


title: Affordance Discovery using Simulated Exploration

abstract: Allowing robots to understand their world in terms of affordances allows for generalization, learning, and complex planning, while also being intuitive for humans to understand. In recent work, affordances are often learned with hand-coded robot actions, which can limit or bias the model. Real-world training has also been used to learn affordances and manipulation models, but is timeconsuming and unsafe for the robot and its environment.


title: Proactive rumor control in online networks

abstract: The spread of rumors through online networks not only threatens the public safety but also results in loss of financial property. To serve as a reliable platform for spreading critical information, many work study the problem of rumor control which aims at limiting the pernicious influence of rumors. These methods, however, only assume that users are passive receivers of rumors even if the users can browse the rumors on their own. To overcome this issue, in this paper we study the rumor spread from a proactive perspective and introduce a novel rumor control problem, called users' B rowsing based rU mor blocK (BUK). Given a rumor set R, BUK can be summarized as targeting k nodes as 'protectors' to save nodes in G from being influenced by R as many as possible. Different with the previous studies, BUK considers that the rumors spread via users' browsing behaviors, and models the propagation based on the random walk model. Theoretical analysis shows that the problem of BUK is submodular, and we propose two greedy algorithms that can approximate BUK within a ratio of (1 - 1/e). However, both of them consume high spaces and thereby cannot be applied to very large networks. Therefore, we further propose a ranking based method RanSel to solve BUK heuristically, which only consumes a linear space to the graph size. The experiments reveal that the effectiveness of our methods outperforms the baseline by 6% to 59.2%, and our methods can achieve such an effective result in reasonable time.


Polygraph in Civil Commitment Programs for Sexually Violent Predators Polygraph in Civil Commitment Programs for Sexually Violent Predators


title: A cloud-based intelligent car parking services for smart cities

abstract: This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities, as an important application deployed on the Internet of Things (IoT) paradigm. The corresponding IoT sub-system includes sensor layer, communication layer, and application layer. A high-level view of the system architecture is outlined. To demonstrate the provision of car parking services with the proposed platform, a cloud-based intelligent car parking system for use within a University campus is described along with details of its design and implementation.


title: Efficient terahertz electro-absorption modulation employing graphene plasmonic structures

abstract: We propose and discuss terahertz (THz) electro-absorption modulators based on graphene plasmonic structures. The active device consists of a self-gated pair of graphene layers, which are patterned to structures supporting THz plasmonic resonances. These structures allow for efficient control of the effective THz optical conductivity, thus absorption, even at frequencies much higher than the Drude roll-off in graphene where most previously proposed graphene-based devices become inefficient. Our analysis shows that reflectance-based device configurations, engineered so that the electric field is enhanced in the active graphene pair, could achieve very high modulation-depth, even ∼100%, over a wide frequency range up to tens of THz.We propose and discuss terahertz (THz) electro-absorption modulators based on graphene plasmonic structures. The active device consists of a self-gated pair of graphene layers, which are patterned to structures supporting THz plasmonic resonances. These structures allow for efficient control of the effective THz optical conductivity, thus absorption, even at frequencies much higher than the Drude roll-off in graphene where most previously proposed graphene-based devices become inefficient. Our analysis shows that reflectance-based device configurations, engineered so that the electric field is enhanced in the active graphene pair, could achieve very high modulation-depth, even ∼100%, over a wide frequency range up to tens of THz.


NLmaps: A Natural Language Interface to Query OpenStreetMap


title: Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

abstract: Two metaheuristic algorithms (WOA and MFO) are used.These algorithms are applied to multilevel thresholding image segmentation.MFO and WOA are better than compared algorithms.MFO is better than WOA for higher number of thresholds. Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsus fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.


Matching web tables to DBpedia - a feature utility study


title: Easy gesture recognition for Kinect

abstract: Abstract Recent progress in entertainment and gaming systems has brought more natural and intuitive human–computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect.


title: Automatically generating tutorials to enable middle school children to learn programming independently

abstract: Enabling middle school children to learn from code shared on the internet may provide computer science learning opportunities to those who would not otherwise have them. We augmented a programming environment designed for middle school children to automatically generate tutorials from code snippets in order to help users learn new programming skills. In our new system, users select code snippets from a program shared on the web and then complete an automatically generated tutorial in order to re-create that snippet within their own program. To evaluate the potential learning gains from our generated tutorials, we conducted a between-subjects study in which we evaluated the performance of children introduced to new programming constructs through automatically generated tutorials. Participants who used the automatically generated tutorials performed 64% better on a near transfer task compared to participants without generated tutorials.


title: Direct Observation of Intermediates in a Thermodynamically Controlled Solid-State Dynamic Covalent Reaction

abstract: We present the first polymorph interconversion study that uses solid-state dynamic covalent chemistry (DCC). This system exhibits unexpected and rich behavior, including the observation that under appropriate conditions the polymorph interconversion of a heterodimer proceeds through reversible covalent chemistry intermediates, and this route is facilitated by one of the two disulfide homodimers involved in the reaction. Furthermore, we demonstrate experimentally that in all cases a dynamic equilibrium is reached, meaning that changing the milling conditions affects the free energy difference between the two polymorphs and thus their relative stability. We suggest that this effect is due to the surface solvation energy combined with the high surface to volume ratio of the nanocrystalline powder.


title: 28-GHz patch antenna arrays with PCB and LTCC substrates

abstract: Ka-band spectrum is relatively abundant and therefore attractive for services of satellite communication, targeting radar, and wireless broadband access technologies. However, Ka-band patch antenna is difficult in realization because the accurate manufacturing is indeed a challenge in obtaining excellent antenna performance at such high frequencies. This study develops three patch antenna arrays for operation at 28 GHz. Two of the three patch arrays with 2×2 and 4×1 patches, respectively, are realized on microwave printed circuit boards (PCBs). The experimental PCB patch arrays have bandwidths up to 5.7% and gains up to 13 dBi. Another 2×2 stacked-patch antenna array is fabricated with low temperature co-fired ceramic (LTCC) technology for further bandwidth enhancement. This LTCC stacked-patch array comprises a novel opposite-side feeding structure to prevent any electrical effect on the parasitic patch. A measured gain of 10.35 dBi and a wide bandwidth of 10.1% (26.75–29.6 GHz) are achieved.


title: Highly resilient peer-to-peer botnets are here: An analysis of Gameover Zeus

abstract: Zeus is a family of credential-stealing trojans which originally appeared in 2007. The first two variants of Zeus are based on centralized command servers. These command servers are now routinely tracked and blocked by the security community. In an apparent effort to withstand these routine countermeasures, the second version of Zeus was forked into a peer-to-peer variant in September 2011. Compared to earlier versions of Zeus, this peer-to-peer variant is fundamentally more difficult to disable. Through a detailed analysis of this new Zeus variant, we demonstrate the high resilience of state of the art peer-to-peer botnets in general, and of peer-to-peer Zeus in particular.


title: Cancer subtype identification using deep learning approach

abstract: In this paper, a framework using deep learning approach is proposed to identify two subtypes of human colorectal carcinoma cancer. The identification process uses information from gene expression and clinical data which is obtained from data integration process. One of deep learning architecture, multimodal Deep Boltzmann Machines (DBM) is used for data integration process. The joint representation gene expression and clinical is later used as Restricted Boltzmann Machines (RBM) input for cancer subtype identification. Kaplan Meier survival analysis is employed to evaluate the identification result. The curves on survival plot obtained from Kaplan Meier analysis are tested using three statistic tests to ensure that there is a significant difference between those curves. According to Log Rank, Generalized Wilcoxon and Tarone-Ware, the two groups of patients with different cancer subtypes identified using the proposed framework are significantly different.


title: Amazon dynamoDB: a seamlessly scalable non-relational database service

abstract: Reliability and scalability of an application is dependent on how its application state is managed. To run applications at massive scale requires one to operate datastores that can scale to operate seamlessly across thousands of servers and can deal with various failure modes such as server failures, datacenter failures and network partitions. The goal of Amazon DynamoDB is to eliminate this complexity and operational overhead for our customers by offering a seamlessly scalable database service. In this talk, I will talk about how developers can build applications on DynamoDB without having to deal with the complexity of operating a large scale database.


title: No A 4 U: The relationship between multitasking and academic performance

abstract: The proliferation and ease of access to information and communication technologies (ICTs) such as Facebook, text messaging, and instant messaging has resulted in ICT users being presented with more real-time streaming data than ever before. Unfortunately, this has also resulted in individuals increasingly engaging in multitasking as an information management strategy. The purpose of this study was to examine how college students multitask with ICTs and to determine the impacts of this multitasking on their college grade point average (GPA). Using web survey data from a large sample of college students at one university (N=1839), we found that students reported spending a large amount of time using ICTs on a daily basis. Students reported frequently searching for content not related to courses, using Facebook, emailing, talking on their cell phones, and texting while doing schoolwork. Hierarchical (blocked) linear regression analyses revealed that using Facebook and texting while doing schoolwork were negatively associated with overall college GPA. Engaging in Facebook use or texting while trying to complete schoolwork may tax students' capacity for cognitive processing and preclude deeper learning. Our research indicates that the type and purpose of ICT use matters in terms of the educational impacts of multitasking.


title: RTL design of reconfigurable multiplier

abstract: Multiplier is a fundamental unit of digital system and signal processing. A Booth multiplier using state machine has been designed and modeled for n bit multiplication. Functional verification of the design by RTL simulation for 8 bit, 16 bit, 32 bit and 128 bit using Model Sim has been shown. Synthesis has been performed for 16 bit, 32 bit and 128 bit using Xilinx. We have achieved 302.5 MHz as maximum operating frequency for 16 bit multiplication. The proposed Booth multiplier is efficient in terms of speed and area.


title: Cyberbullying and the law: A review of psychological and legal challenges

abstract: Cyberbullying, in its different forms, is common among children and adolescents and is facilitated by the increased use of technology. The consequences of cyberbullying could be severe, especially on mental health, potentially leading to suicide in extreme cases. Although parents, schools and online social networking sites are encouraged to provide a safe online environment, little is known about the legal avenues which could be utilised to prevent cyberbullying or act as a deterrent. This article attempts to explore current laws, and the challenges that exist to establishing cyberbullying legislation in the context of the UK. It is arguable that a number of statutes may be of assistance in relation to cyberbullying, namely Education and Inspections Act 2006, Protection from Harassment Act 1997, Communications Act 2003, Malicious Communications Act 1988, Telecommunications Act 1984, Public Order Act 1986, Obscene Publications Act 1959, Computer Misuse Act 1990, Crime and Disorder Act 1998, and Defamation Act 2013. However, given the lack of clear definition of bullying, the applicability of these laws to cyberbullying is open to debate. Establishing new legislation or a modification to existing laws is particularly challenging for a number of reasons, namely: an absence of consistent bullying/cyberbullying definition, a difficulty in determining intention to harm or evidence of this, a lack of surveillance, a lack of general awareness, issues surrounding jurisdiction, the role of technology, and the age of criminal responsibility. These challenges are elaborated and discussed in this article. Cyberbullying is a prevalent phenomenon, however little is known about its legality.On a review of UK legislation, a number of provisions may cover cyberbullying.Issues such as intent, evidence, jurisdiction, age and the role of technology are challenging.To provide clarity and deterrence, existing legislation should be modified.


title: Analysis and design of business-to-consumer online auctions

abstract: Business-to-consumer online auctions form an important element in the portfolio of mercantile processes that facilitate electronic commerce activity. Much of traditional auction theory has focused on analyzing single-item auctions in isolation from the market context in which they take place. We demonstrate the weakness of such approaches in online settings where a majority of auctions are multiunit in nature. Rather than pursuing a classical approach and assuming knowledge of the distribution of consumers' valuations, we emphasize the largely ignored discrete and sequential nature of such auctions. We derive a general expression that characterizes the multiple equilibria that can arise in such auctions and segregate these into desirable and undesirable categories. Our analytical and empirical results, obtained by tracking real-world online auctions, indicate that bid increment is an important factor amongst the control factors that online auctioneers can manipulate and control. We show that consumer bidding strategies in such auctions are not uniform and that the level of bid increment chosen influences them. With a motive of providing concrete strategic directions to online auctioneers, we derive an absolute upper bound for the bid increment. Based on the theoretical upper bound we propose a heuristic decision rule for setting the bid increment. Empirical evidence lends support to the hypothesis that setting a bid increment higher than that suggested by the heuristic decision rule has a negative impact on the auctioneer's revenue.


title: AU Binding Proteins Recruit the Exosome to Degrade ARE-Containing mRNAs

abstract: Inherently unstable mammalian mRNAs contain AU-rich elements (AREs) within their 3' untranslated regions. Although found 15 years ago, the mechanism by which AREs dictate rapid mRNA decay is not clear. In yeast, 3'-to-5' mRNA degradation is mediated by the exosome, a multisubunit particle. We have purified and characterized the human exosome by mass spectrometry and found its composition to be similar to its yeast counterpart. Using a cell-free RNA decay system, we demonstrate that the mammalian exosome is required for rapid degradation of ARE-containing RNAs but not for poly(A) shortening. The mammalian exosome does not recognize ARE-containing RNAs on its own. ARE recognition requires certain ARE binding proteins that can interact with the exosome and recruit it to unstable RNAs, thereby promoting their rapid degradation.


Mobile Manipulating Unmanned Aerial Vehicle ( MM-UAV ) : Towards Aerial Manipulators


title: Vertical Handover Supporting Pervasive Computing in Future Wireless Networks

abstract: A variety of current and future wired and wireless networking technologies can be transformed into a seamless communication environments through application of context-based vertical handovers. Such seamless communication environments are needed for future pervasive/ubiquitous systems. Pervasive systems are context aware and need to adapt to context changes, including network disconnections and changes in network Quality of Service (QoS). Vertical handover is one of many possible adaptation methods. It allows users to roam freely between heterogeneous networks while maintaining the continuity of their applications. This paper proposes a vertical handover mechanism suitable for multimedia applications in pervasive systems. The paper focuses on the handover decision making process which uses context information regarding user devices, user location, network environment and requested QoS.


title: INGESTBASE: A Declarative Data Ingestion System

abstract: Big data applications have fast arriving data that must be quickly ingested. At the same time, they have specific needs to preprocess and transform the data before it could be put to use. The current practice is to do these preparatory transformations once the data is already ingested, however, this is expensive to run and cumbersome to manage. As a result, there is a need to push data preprocessing down to the ingestion itself. In this paper, we present a declarative data ingestion system, called INGESTBASE, to allow application developers to plan and specify their data ingestion logic in a more systematic manner. We introduce the notion of ingestions plans, analogous to query plans, and present a declarative ingestion language to help developers easily build sophisticated ingestion plans. INGESTBASE provides an extensible ingestion optimizer to rewrite and optimize ingestion plans by applying rules such as operator reordering and pipelining. Finally, the INGESTBASE runtime engine runs the optimized ingestion plan in a distributed and fault-tolerant manner. Later, at query processing time, INGESTBASE supports ingestion-aware data access and interfaces with upstream query processors, such as Hadoop MapReduce and Spark, to post- process the ingested data. We demonstrate through a number of experiments that INGESTBASE: (i) is flexible enough to express a variety of ingestion techniques, (ii) incurs a low ingestion overhead, (iii) provides efficient access to the ingested data, and (iv) has much better performance, up to 6 times, than preparing data as an afterthought, via a query processor.


title: Impact of Using an Educational Robot-Based Learning System on Students’ Motivation in Elementary Education

abstract: Educational robotics has been regarded as an effective instructional tool over the past decade. Many studies have tested the role of robots in supporting educational classroom activities. However, reliable empirical evidence confirming the effectiveness of educational robots remains limited. Therefore, this study developed an educational robot-based learning system that provides an attractive teaching application that combines multimedia objects with an educational robot. This study examined the impact of the proposed learning system on student learning performance and motivation. The four-stage experimental procedure involved a pre-test, post-test, learning activities, and questionnaire survey. The participants comprised one homeroom teacher and 52 second-grade students from two classes at an elementary school in Taiwan. All of the students were assigned to use either the proposed learning system or a PowerPoint-based learning system. The pre-test and post-test results show that the proposed learning system improved student performance more than the PowerPoint-based learning system did. A questionnaire based on the Instructional Materials Motivation Survey (IMMS) was employed to measure four motivational factors (attention, relevance, confidence, and satisfaction). The IMMS survey results show that satisfaction and relevance were the highest rated motivational factors among students who used the proposed learning system. The experimental results indicate that the students were motivated to use the educational robot-based learning system. Thus, using educational robot-based learning systems in classrooms demonstrates a significant advantage for students, by improving overall learning interest and motivation.


title: An ensemble classifier with random projection for predicting multi-label protein subcellular localization

abstract: In protein subcellular localization prediction, a predominant scenario is that the number of available features is much larger than the number of data samples. Among the large number of features, many of them may contain redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble multi-label classifier for predicting protein subcellular localization. Specifically, the frequencies of occurrences of gene-ontology terms are used as feature vectors, which are projected onto lower-dimensional spaces by random projection matrices whose elements conform to a distribution with zero mean and unit variance. The transformed low-dimensional vectors are classified by an ensemble of one-vs-rest multi-label support vector machine (SVM) classifiers, each corresponding to one of the RP matrices. The scores obtained from the ensemble are then fused for making the final decision. Experimental results on two recent datasets suggest that the proposed method can reduce the dimensions by six folds and remarkably improve the classification performance.


title: Deduplication on Encrypted Big Data in Cloud

abstract: Cloud computing offers a new way of service provision by re-arranging various resources over the Internet. The most important and popular cloud service is data storage. In order to preserve the privacy of data holders, data are often stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in cloud. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. They cannot flexibly support data access control and revocation. Therefore, few of them can be readily deployed in practice. In this paper, we propose a scheme to deduplicate encrypted data stored in cloud based on ownership challenge and proxy re-encryption. It integrates cloud data deduplication with access control. We evaluate its performance based on extensive analysis and computer simulations. The results show the superior efficiency and effectiveness of the scheme for potential practical deployment, especially for big data deduplication in cloud storage.


title: D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling

abstract: Exponential growth of social media consumption demands effective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To effectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We first mine and parse data, i.e. textual content, visual content, social context and social relation, from heterogeneous social media feeds. Then, we design a two-branch network to map the social contents and users into a same latent space. Particularly, the network is trained by a large margin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint in an end-to- end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. To demonstrate the effectiveness of D-Sempre in user interests-social contents modeling, we construct a Twitter dataset and conduct extensive experiments on it. As a result, D-Sempre effectively integrates the multi-modal data from heterogeneous social media feeds and captures the hidden semantic correlations between users' interests and social contents.


title: Unit testing in practice

abstract: Unit testing is a technique that receives a lot of criticism in terms of the amount of time that it is perceived to take and in how much it costs to perform. However it is also the most effective means to test individual software components for boundary value behavior and ensure that all code has been exercise adequately (e.g. statement, branch or MC/DC coverage). In this paper we examine the available data from three safety related software projects undertaken by Pi Technology that have made use of unit testing. Additionally we discuss the different issues that have been found applying the technique at different phases of the development and using different methods to generate those test. In particular we provide an argument that the perceived costs of unit testing may be exaggerated and that the likely benefits in terms of defect detection are actually quite high in relation to those costs.


title: A French junior sensation seeking scale (J-SSS)

abstract: Abstract We translated the 50-item Spanish Junior Sensation Seeking Scale (J-SSS) into French. The J-SSS has 10 items for each subscale: Thrill and Adventure Seeking (TAS), Experience Seeking (ES), Disinhibition (Dis) and Boredom Susceptibility (BS), as well as a 10-item Lie (L) scale. After administering the J-SSS to two primary and secondary pupil samples from some French-speaking schools in Belgium, the French version of the J-SSS showed acceptable 1 month test-retest and α reliabilities, and acceptable validity.


title: A Spread Spectrum Clock Generator for DisplayPort Main Link

abstract: This brief presents a spread spectrum clock generator (SSCG) with a process variation compensator for DisplayPort main link. The process variation compensator reduces the error of spread ratio and guarantees reliable operation of an SSCG. The test chip has been implemented in 0.18-μm complementary metal-oxide-semiconductor process. The SSCG supports 10-phase 270- and 162-MHz clocks. The phase noise of an output clock at 270 MHz without spread spectrum clocking is -97.7 and -120.4 dBc/Hz at 1- and 10-MHz offset, respectively. The peak reduction is 8.75 dBm, and the spread ratio of 5000 ppm is achieved with a process variation compensator.


title: VANET Applications: Hot Use Cases

abstract: Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving.


title: Informing design for tangible interaction: a case for children with learning difficulties

abstract: The advent of new technologies is expanding the possibilities for a richer, multi-sensory interaction to support children with learning difficulties in schools. However, little research has yet investigated how such innovative interaction can enhance the learning experience of these children. Effectively informing research and design of innovative educational, technological resources for children with learning difficulties requires relevant field study. Findings from a qualitative classroom study indicate the potential of tangible technologies to enhance these children's experiences by providing a variety of modes of representation, and opportunities for collaboration, physical engagement and hands-on exploration. They also provide an effective foundation for investigating how tangible interaction can help to structure exploratory learning: a recommended but problematic approach for these children.


title: On-line Trend Analysis with Topic Models: \#twitter Trends Detection Topic Model Online

abstract: We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built update mechanism based on time slices and implements a dynamic vocabulary. We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.


title: MRPR: A MapReduce solution for prototype reduction in big data classification

abstract: In the era of big data, analyzing and extracting knowledge from large-scale data sets is a very interesting and challenging task. The application of standard data mining tools in such data sets is not straightforward. Hence, a new class of scalable mining method that embraces the huge storage and processing capacity of cloud platforms is required. In this work, we propose a novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification. These methods aim at representing original training data sets as a reduced number of instances. Their main purposes are to speed up the classification process and reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. However, the standard prototype reduction methods cannot cope with very large data sets. To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one. The proposed model enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss. We test the speeding up capabilities of our model with data sets up to 5.7 millions of instances. The results show that this model is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.


Image classification based on fuzzy logic


title: Grounded Recurrent Neural Networks

abstract: In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process"grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important problem in healthcare of understanding what medical concepts are discussed in clinical text. Using a publicly available dataset derived from Intensive Care Units, we learn to label a patient's diagnoses and procedures from their discharge summary. Our evaluation shows a clear advantage to using our proposed architecture over a variety of strong baselines.


title: Classroom-based assistive technology: collective use of interactive visual schedules by students with autism

abstract: vSked is an interactive and collaborative assistive technology for students with autism, combining visual schedules, choice boards, and a token-based reward system into an integrated classroom system. In this paper, we present the results of a study of three deployments of vSked over the course of a year in two autism classrooms. The results of our study demonstrate that vSked can promote student independence, reduce the quantity of educator-initiated prompts, encourage consistency and predictability, reduce the time required to transition from one activity to another. The findings from this study reveal practices surrounding the use of assistive technologies in classrooms and highlight important considerations for both the design and the evaluation of assistive technologies in the future, especially those destined for classroom use.


title: Dense Optical Flow Based Emotion Recognition Classifier

abstract: In recent years, enabling computer systems to recognize facial expressions and infer emotions from them in real time has become very important since such information can be used in emerging applications such as video games, educational software, computer-based tutoring for special need children for better human computer interactions. However, real time emotion recognition using video streams face challenges due to the varying illuminations. In this paper, we present a real time emotion recognition scheme using dense optical flow based approach and SVM classifier. Via extensive analysis using newly collected datasets of 370 videos, we demonstrate that our approach demonstrates high accuracy in recognizing 4 basic emotions: happy, angry, surprise and sad.


title: On the role of semantics in automated requirements tracing

abstract: In this paper, we investigate the potential benefits of utilizing natural language semantics in automated traceability link retrieval. In particular, we evaluate the performance of a wide spectrum of semantically enabled information retrieval methods in capturing and presenting requirements traceability links in software systems. Our objectives are to gain more operational insights into these methods and to provide practical guidelines for the design and development of effective requirements tracing and management tools. To achieve our research objectives, we conduct an experimental analysis using three datasets from various application domains. Results show that considering more semantic relations in traceability link retrieval does not necessarily lead to higher quality results. Instead, a more focused semantic support, that targets specific semantic relations, is expected to have a greater impact on the overall performance of tracing tools. In addition, our analysis shows that explicit semantic methods, that exploit local or domain-specific sources of knowledge, often achieve a more satisfactory performance than latent methods, or methods that derive semantics from external or general-purpose knowledge sources.


title: Linguistic Affordances: Making Sense of Word Senses

abstract: In this position paper we want to focus the attention on the roles of word senses in standard Natural Language Understanding tasks. We first identify the main problems of having such a rigourous and inflexible way of discriminating among different meanings at word-level. In fact, in human cognition, we know the process of language understanding refers to a more shaded procedure. For this reason, we propose the concept of linguistic affordances, i.e., combinations of objects properties that are involved in specific actions and that help the comprehension of the whole scene being described. The idea is that similar verbs involving similar properties of the arguments may refer to comparable mental scenes. This architecture produces a converging framework where meaning becomes a distributed property between actions and objects, without having to differentiate among terms and relative word senses. We hope that this contribution will stimulate the debate about the actual effectiveness of current Word Sense Disambiguation systems towards more cognitive approaches able to go beyond word-level automatic understanding of natural language.


title: Platform Complexity: Lessons from the Music Industry

abstract: Platforms provide anchor-points for coordinating varied activities within an ecosystem. Understanding platform complexity and its effects is therefore a vital concern for academia and industry. The complexity defines the range of possible activities on the platform and the related aspects of control. This paper argues that abstract models of platforms used in current research remove some of the most important features underlying the inherent complexity of digital platforms. This insight is illustrated with a small study of platforms and their evolving complexity in the music industry. The exploration highlights salient phenomena that a more comprehensive theory of digital platforms should encompass. We posit that advancing theoretical perspective that better embrace the complexity of digital platforms is needed to fully capture the strategic and technological implications of emerging digital platforms.


title: SINGA: Putting Deep Learning in the Hands of Multimedia Users

abstract: Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Two key factors behind deep learning's remarkable achievement are the immense computing power and the availability of massive training datasets, which enable us to train large models to capture complex regularities of the data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by non-experts without much effort. The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this paper, we design a distributed deep learning platform called SINGA which has an intuitive programming model and good scalability. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.


title: Large-scale direct SLAM with stereo cameras

abstract: We propose a novel Large-Scale Direct SLAM algorithm for stereo cameras (Stereo LSD-SLAM) that runs in real-time at high frame rate on standard CPUs. In contrast to sparse interest-point based methods, our approach aligns images directly based on the photoconsistency of all high-contrast pixels, including corners, edges and high texture areas. It concurrently estimates the depth at these pixels from two types of stereo cues: Static stereo through the fixed-baseline stereo camera setup as well as temporal multi-view stereo exploiting the camera motion. By incorporating both disparity sources, our algorithm can even estimate depth of pixels that are under-constrained when only using fixed-baseline stereo. Using a fixed baseline, on the other hand, avoids scale-drift that typically occurs in pure monocular SLAM.We furthermore propose a robust approach to enforce illumination invariance, capable of handling aggressive brightness changes between frames - greatly improving the performance in realistic settings. In experiments, we demonstrate state-of-the-art results on stereo SLAM benchmarks such as Kitti or challenging datasets from the EuRoC Challenge 3 for micro aerial vehicles.


title: From Observational Studies to Causal Rule Mining

abstract: Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many data sets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large data sets. The problem is, causal relationships imply associations, but the reverse is not always true. However we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations. In this paper we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large data sets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real world data sets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach in general is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables, a feature that other causal discovery methods do not possess.


English to Punjabi Transliteration using Orthographic and Phonetic Information.


Learning Sampling Functions for Efficient Object Detection


title: Query by committee

abstract: We propose an algorithm called query by commitee , in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement . The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.


OBJECT-ORIENTED CLASSIFICATION OF REMOTE SENSING DATA FOR THE IDENTIFICATION OF TREE SPECIES COMPOSITION


title: Natural human-robot interaction using speech, head pose and gestures

abstract: In this paper we present our ongoing work in building technologies for natural multimodal human-robot interaction. We present our systems for spontaneous speech recognition, multimodal dialogue processing and visual perception of a user, which includes the recognition of pointing gestures as well as the recognition of a person's head orientation. Each of the components is described in the paper and experimental results are presented. In order to demonstrate and measure the usefulness of such technologies for human-robot interaction, all components have been integrated on a mobile robot platform and have been used for real-time human-robot interaction in a kitchen scenario.


title: Understanding Consumer Churning Behaviors in Mobile Telecommunication Service Industry : Cross-national Comparison between Korea and China

abstract: It is crucial for telecom operators to shift from a subsidy-centered to a servicecentered marketing strategy. As the market reaches its saturation point, the effectiveness of customer acquisition through the traditional subsidy decreases and offering differentiated services for existing customer retention is critical. This study aims at finding out factors affecting consumer churning intention based on PPM (Push-Pull-Mooring) theory in order to exactly understand consumer behaviors in the mobile telecommunication service industry (MTSI). This study also analyzes the difference between Korea (saturation market) and China (growth market) in terms of the causality between the intention to switch and the affecting factors. The research findings will encourage telecom operators to establish the effective service strategy that corresponds to their level of market maturity.


title: Investigation of BEOL plasma process induced damage effect on gate oxide

abstract: Plasma process induced damage (PID) to thin gate oxide of different thickness was investigated in this paper with antenna test structures to enhance the effect of plasma charging. Gate leakage under Fowler-Nordheim (F-N) stress, threshold voltage, and time dependent dielectric breakdown (TDDB) was applied for gate oxide degrading measurement. It is found that gate oxide with around 5nm electrical oxide thickness (EOT) is more sensitive to plasma process and can be easily revealed with gate leakage. Gate oxide TDDB test with short loop wafers indicated that contact (CT) etching stop layer and back end of line (BEOL) process, especially high density plasma (HDP) chemical vapor deposition (CVD) of inter-metal-dielectric (IMD) plays the main role in oxide damage. These phenomena provide important approaches to reduce PID effect in integrated circuit manufacturing.


title: Mobile Data Offloading through Opportunistic Communications and Social Participation

abstract: 3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.


title: Enhancing Advanced Encryption Standard S-Box Generation Based on Round Key

abstract: In 1997, the National Institute of Standards and Technology send out a call for candidates to replace the aging and obsolete Data Encryption Standard (DES). NIST then announced the selection of Rijndael as the proposed Advanced Encryption Standard (AES) [1]. Rijndael, submitted by Joan Daemen and Vincent Rijmen is designed for the use with keys of lengths 128, 192, and 256 bits. Although AES uses the same three key size alternatives, it limits the block length to 128 bits [2].


title: Competitive intelligence: construct exploration, validation and equivalence

abstract: Purpose – Little empirical research has been conducted on competitive intelligence (CI). This paper aims to contribute to the quantitative strand of the CI literature by exploring and validating the theoretical constructs of the CI process.Design/methodology/approach – Data from 601 questionnaires filled out by South African and Flemish exporters were subjected to exploratory factor analysis and construct equivalence analysis between the sub‐samples.Findings – The results showed that the CI process consists of three constructs, while the context in which CI takes place consists of four constructs. This agrees to some extent with the literature. When verifying the constructs for both cultures it was found that all but one CI context construct can be viewed as equivalent in both groups. Bias analysis identified one item in the questionnaire that was biased. Via regression analysis it was also indicated that the context in which CI takes place influences the CI process to a large extent. The research identif...


title: Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/ intelligent systems

abstract: We are living in a world which is undergoing profound changes brought about by rapid advances in science and technology. Among such changes, the most visible are those that relate to what is popularly referred to as the information revolution. The artifacts of this revolution are all around us: the e-mail, the world wide web, the cellular phone; the fax; and the desktop computer, among many others. Linked to the information revolution is another revolution — the intelligent systems revolution. The manifestations of this revolution are not as obvious as those of the information revolution because they involve, for the most part, not new products but higher MIQ (Machine IQ) of existing systems, products and devices. Among the familiar examples are smart appliances, smart cameras, smart robots and smart software for browsing, diagnosis, fraud detection and quality control. The information and intelligent systems revolutions are in a symbiotic relationship. Intelligence requires information and vice-versa. The confluence of intelligent systems and information systems leads to intelligent information systems. In this sense, the union of information systems, intelligent systems and intelligent information systems constitutes what might be referred to as information/intelligent systems, or I/IS for short. In my perception, in coming years, the design, construction and utilization of information/intelligent systems will become the primary focus of science and technology, and I/IS systems will become a dominant presence in our daily lives. When we take a closer look at information/intelligent systems what we see is the increasingly important role of soft computing (SC) in their conception, design and utilization. Basically, soft computing is an association of computing methodologies which includes as its principal members fuzzy


title: Contactless Palm Vein Recognition Using a Mutual Foreground-Based Local Binary Pattern

abstract: Local binary pattern (LBP) is popular for the texture representation owing to its discrimination ability and computational efficiency, but when used to describe the sparse texture in palm vein images, the discrimination ability is diluted, leading to lower performance, especially for contactless palm vein matching. In this paper, an improved mutual foreground LBP method is presented for achieving a better matching performance for contactless palm vein recognition. First, the normalized gradient-based maximal principal curvature algorithm and $k$ -means method are utilized for texture extraction, which can effectively suppress noise and improve accuracy and robustness. Then, an LBP matching strategy was adopted for similarity measurements on the basis of extracted palm veins and their neighborhoods, which include the vast majority of useful distinctive information for identification while eliminating interference by excluding the background. To further improve the LBP performance, the matched pixel ratio was adopted to determine the best matching region (BMR). Finally, the matching score obtained in the process of finding the BMR was fused with results of LBP matching at the score level to further improve the identification performance. A series of rigorous contrast experiments using the palm vein data set in the CASIA multispectral palmprint image database were conducted. The obtained low equal error rate (0.267%) and comparisons with the most state-of-the-art approaches demonstrate that our method is feasible and effective for contactless palm vein recognition.


title: SceneNet: An annotated model generator for indoor scene understanding

abstract: We introduce SceneNet, a framework for generating high-quality annotated 3D scenes to aid indoor scene understanding. SceneNet leverages manually-annotated datasets of real world scenes such as NYUv2 to learn statistics about object co-occurrences and their spatial relationships. Using a hierarchical simulated annealing optimisation, these statistics are exploited to generate a potentially unlimited number of new annotated scenes, by sampling objects from various existing databases of 3D objects such as ModelNet, and textures such as OpenSurfaces and ArchiveTextures. Depending on the task, SceneNet can be used directly in the form of annotated 3D models for supervised training and 3D reconstruction benchmarking, or in the form of rendered annotated sequences of RGB-D frames or videos.


title: I Know Nothing about You But Here is What You Might Like

abstract: Recommenders widely use collaborative filtering schemes. These schemes, however, threaten privacy as user profiles are made available to the service provider hosting the recommender and can even be guessed by curious users who analyze the recommendations. Users can encrypt their profiles to hide them from the service provider and add noise to make them difficult to guess. These precautionary measures hamper latency and recommendation quality. In this paper, we present a novel recommender, X-REC, enabling an effective collaborative filtering scheme to ensure the privacy of users against the service provider (system-level privacy) or other users (user-level privacy). X-REC builds on two underlying services: X-HE, an encryption scheme designed for recommenders, and X-NN, a neighborhood selection protocol over encrypted profiles. We leverage uniform sampling to ensure differential privacy against curious users. Our extensive evaluation demonstrates that X-REC provides (1) recommendation quality similar to non-private recommenders, and (2) significant latency improvement over privacy-aware alternatives.


title: High power induction heating

abstract: Induction heating (IH) by using Common Core Transformer (CCT) to increase power of IH system is presented in this research. Its configuration is composed of rectifier circuit, inverter circuit, resonance circuit, and common core transformer. Three-phase diode rectifier circuit is served as input of the system. The inverter circuit has been designed by using parallel full bridge and used IGBT as switch to control the operation of the circuit. LC series resonance circuit is used as output of the inverter. Transformer resonance is designed by using the two primary and single secondary windings with magnetic core together. The magnetic core is made of ferrite material. The experimental results are shown that the output power of IH is increased by parallel inverter with CCT.


title: Hybrid PAPR Reduction Scheme for FBMC/OQAM Systems Based on Multi Data Block PTS and TR Methods

abstract: The filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is being studied by many researchers as a key enabler for the fifth-generation air interface. In this paper, a hybrid peak-to-average power ratio (PAPR) reduction scheme is proposed for FBMC/OQAM signals by utilizing multi data block partial transmit sequence (PTS) and tone reservation (TR). In the hybrid PTS-TR scheme, the data blocks signal is divided into several segments, and the number of data blocks in each segment is determined by the overlapping factor. In each segment, we select the optimal data block to transmit and jointly consider the adjacent overlapped data block to achieve minimum signal power. Then, the peak reduction tones are utilized to cancel the peaks of the segment FBMC/OQAM signals. Simulation results and analysis show that the proposed hybrid PTS-TR scheme could provide better PAPR reduction than conventional PTS and TR schemes in FBMC/OQAM systems. Furthermore, we propose another multi data block hybrid PTS-TR scheme by exploiting the adjacent multi overlapped data blocks, called as the multi hybrid (M-hybrid) scheme. Simulation results show that the M-hybrid scheme can achieve about 0.2-dB PAPR performance better than the hybrid PTS-TR scheme.


title: The ordinal nature of emotions

abstract: Representing computationally everyday emotional states is a challenging task and, arguably, one of the most fundamental for affective computing. Standard practice in emotion annotation is to ask humans to assign an absolute value of intensity to each emotional behavior they observe. Psychological theories and evidence from multiple disciplines including neuroscience, economics and artificial intelligence, however, suggest that the task of assigning reference-based (relative) values to subjective notions is better aligned with the underlying representations than assigning absolute values. Evidence also shows that we use reference points, or else anchors, against which we evaluate values such as the emotional state of a stimulus; suggesting again that ordinal labels are a more suitable way to represent emotions. This paper draws together the theoretical reasons to favor relative over absolute labels for representing and annotating emotion, reviewing the literature across several disciplines. We go on to discuss good and bad practices of treating ordinal and other forms of annotation data, and make the case for preference learning methods as the appropriate approach for treating ordinal labels. We finally discuss the advantages of relative annotation with respect to both reliability and validity through a number of case studies in affective computing, and address common objections to the use of ordinal data. Overall, the thesis that emotions are by nature relative is supported by both theoretical arguments and evidence, and opens new horizons for the way emotions are viewed, represented and analyzed computationally.


title: Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

abstract: We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.


title: Information Clustering Using Manifold-Based Optimization of the Bag-of-Features Representation

abstract: In this paper, a manifold-based dictionary learning method for the bag-of-features (BoF) representation optimized toward information clustering is proposed. First, the spectral representation, which unwraps the manifolds of the data and provides better clustering solutions, is formed. Then, a new dictionary is learned in order to make the histogram space, i.e., the space where the BoF historgrams exist, as similar as possible to the spectral space. The ability of the proposed method to improve the clustering solutions is demonstrated using a wide range of datasets: two image datasets, the 15-scene dataset and the Corel image dataset, one video dataset, the KTH dataset, and one text dataset, the RT-2k dataset. The proposed method improves both the internal and the external clustering criteria for two different clustering algorithms: 1) the -means and 2) the spectral clustering. Also, the optimized histogram space can be used to directly assign a new object to its cluster, instead of using the spectral space (which requires reapplying the spectral clustering algorithm or using incremental spectral clustering techniques). Finally, the learned representation is also evaluated using an information retrieval setup and it is demonstrated that improves the retrieval precision over the baseline BoF representation.


title: Randomized Trial to Evaluate the Efficacy of Cognitive Therapy for Low-Functioning Patients With Schizophrenia

abstract: Context: Low-functioning patients with chronic schizophrenia have high direct treatment costs and indirect costs incurred due to lost employment and productivity and have a low quality of life; antipsychotic medications and psychosocial interventions have shown limited efficacy to promote improved functional outcomes. Objective: To determine the efficacy of an 18-month recovery-oriented cognitive therapy program to improve psychosocial functioning and negative symptoms (avolition-apathy, anhedonia-asociality) in lowfunctioning patients with schizophrenia. Design, Setting, and Participants: A single-center, 18-month, randomized, single-blind, parallel group trial enrolled 60 low-functioning, neurocognitively impaired patients with schizophrenia (mean age, 38.4 years; 33.3% female; 65.0% African American). Interventions: Cognitive therapy plus standard treatment vs standard treatment alone. Main Outcome Measures: The primary outcome measure was the Global Assessment Scale score at 18 months after randomization. The secondary outcomes were scores on the Scale for the Assessment of Negative Symptoms and the Scale for the Assessment of Positive Symptoms at 18 months after randomization. Results: Patients treated with cognitive therapy showed a clinically significant mean improvement in global functioning from baseline to 18 months that was greater than the improvement seen with standard treatment (withingroup Cohen d, 1.36 vs 0.06, respectively; adjusted mean [SE], 58.3 [3.30] vs 47.9 [3.60], respectively; P=.03; between-groupd=0.56). Patients receiving cognitive therapy as compared with those receiving standard treatment also showed a greater mean reduction in avolition-apathy (adjusted mean [SE], 1.66 [0.31] vs 2.81 [0.34], respectively; P=.01; between-group d=�0.66) and positive symptoms (hallucinations, delusions, disorganization) (adjusted mean [SE], 9.4 [3.3] vs 18.2 [3.8], respectively;P=.04; betweengroup d=�0.46) at 18 months. Age was controlled in the analyses, and there were no meaningful group differences in baseline antipsychotic medications (class or dosage) or in medication changes during the course of the trial. Conclusion: Cognitive therapy can be successful in promoting clinically meaningful improvements in functional outcome, motivation, and positive symptoms in lowfunctioning patients with significant cognitive impairment. Trial Registration: clinicaltrials.gov Identifier: NCT00350883