title: The product of the distances of a point inside a regular polytope to its vertices

abstract: We show that the maximum of the product of the distances from a point inside an n-dimensional regular simplex, cross-polytope or cube to the vertices is attained at the midpoint of an edge for small n, but is attained at symmetrically placed pairs on an edge for sufficiently high dimensions. We also examine the problem for regular polygons and general triangles in the plane.


title: OverLay: Practical Mobile Augmented Reality

abstract: The idea of augmented reality - the ability to look at a physical object through a camera and view annotations about the object - is certainly not new. Yet, this apparently feasible vision has not yet materialized into a precise, fast, and comprehensively usable system. This paper asks: What does it take to enable augmented reality (AR) on smartphones today? To build a ready-to-use mobile AR system, we adopt a top-down approach cutting across smartphone sensing, computer vision, cloud offloading, and linear optimization. Our core contribution is in a novel location-free geometric representation of the environment - from smartphone sensors - and using this geometry to prune down the visual search space. Metrics of success include both accuracy and latency of object identification, coupled with the ease of use and scalability in uncontrolled environments. Our converged system, OverLay, is currently deployed in the engineering building and open for use to regular public; ongoing work is focussed on campus-wide deployment to serve as a "historical tour guide" of UIUC. Performance results and user responses thus far have been promising, to say the least.


title: Combining Digital Forensic Practices and Database Analysis as an Anti-Money Laundering Strategy for Financial Institutions

abstract: Digital forensics is the science that identify, preserve, collect, validate, analyse, interpret, and report digital evidence that may be relevant in court to solve criminal investigations. Conversely, money launderingis a form of crime that is compromising the internal policies in financial institutions, which is investigated by analysing large amount of transactional financial data. However, the majority of financial institutions have adopted ineffective detection procedures and extensive reporting tasks to detect money laundering without incorporating digital forensic practices to handle evidence. Thus, in this article, we propose an anti-money laundering model by combining digital forensics practices along with database tools and database analysis methodologies. As consequence, admissible Suspicious Activity Reports (SARs) can be generated, based on evidence obtained from forensically analysing database financial logs in compliance with Know-Your-Customer policies for money laundering detection.


title: The effect of mammogram databases on algorithm performance

abstract: One of the caveats of the process of designing image processing algorithms is that they are often tested on insignificant numbers of images. This paper discusses the influence of mammogram databases on the performance evaluation of algorithms. We hope to demonstrate that the use of extensive collections of mammograms with varied characteristics to assess algorithm performance can lead to more robust algorithms.


title: Gaining customer knowledge in low cost airlines through text mining

abstract: Purpose – The purpose of this paper is to study the consumer opinion towards the low-cost airlines or low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in revealing current customers’ satisfaction level towards low-cost airlines. Design/methodology/approach – About 10,895 tweets (data collected for two and a half months) are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical techniques are used to segment the customers’ opinion. Findings – The results with two different sentiment algorithms show that there is more positive than negative polarity across the different algorithms. Clustering results show that both K-Means and spherical K-Means algorithms delivered similar results and the four main topics that are discussed by the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and d...


title: A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

abstract: Sorting is at the core of many database operations, such as index creation, sort-merge joins, and user-requested output sorting. As GPUs are emerging as a promising platform to accelerate various operations, sorting on GPUs becomes a viable endeavour. Over the past few years, several improvements have been proposed for sorting on GPUs, leading to the first radix sort implementations that achieve a sorting rate of over one billion 32-bit keys per second. Yet, state-of-the-art approaches are heavily memory bandwidth-bound, as they require substantially more memory transfers than their CPU-based counterparts. Our work proposes a novel approach that almost halves the amount of memory transfers and, therefore, considerably lifts the memory bandwidth limitation. Being able to sort two gigabytes of eight-byte records in as little as 50 milliseconds, our approach achieves a 2.32-fold improvement over the state-of-the-art GPU-based radix sort for uniform distributions, sustaining a minimum speed-up of no less than a factor of 1.66 for skewed distributions. To address inputs that either do not reside on the GPU or exceed the available device memory, we build on our efficient GPU sorting approach with a pipelined heterogeneous sorting algorithm that mitigates the overhead associated with PCIe data transfers. Comparing the end-to-end sorting performance to the state-of-the-art CPU-based radix sort running 16 threads, our heterogeneous approach achieves a 2.06-fold and a 1.53-fold improvement for sorting 64 GB key-value pairs with a skewed and a uniform distribution, respectively.


title: Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

abstract: This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data -- as commonly encountered in robotics applications -- and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.


title: A planar VHF reconfigurable slot antenna

abstract: This paper presents a novel electronically tunable slot antenna. The design is based on a resonant slot structure loaded with a series of PIN diode switches. The tuning of the operating frequency is realized by varying the electrical length of the antenna, which is controlled by the DC bias voltages of the switches. The fabricated antenna is capable of resonating at four different frequencies ranging from 550 to 900 MHz. Very good matching is achieved for all resonant frequencies without the need for any special matching network. Additionally, it is demonstrated that the radiation pattern, efficiency and polarization remain essentially unaffected by the frequency tuning.


title: Millimeter-Wave EBG-Based Aperture-Coupled Dielectric Resonator Antenna

abstract: Design, fabrication and testing of millimeter-wave (MMW) dielectric resonator antenna (DRA) surrounded by electromagnetic band-gap (EBG) structure are presented. For this purpose, MMW mushroom-like, circular patch EBG (CP-EBG) cell is designed and fabricated. The propagation characteristics of the proposed CP-EBG structure are measured using the asymmetric microstrip line method. A cylindrical DRA incorporating the developed CP-EBG structure is then designed and its performance is evaluated with and without the CP-EBG around the 60 GHz bandwidth. Measurements show a significant improvement in the antenna radiation characteristics when it is surrounded by CP-EBG structure. A gain increase of up to 3.2 dBi is obtained while preserving the gain flatness over the suggested bandwidth ( ± 0.7 dB). An additional backlobe suppression of up to 6.5 dBi is achieved. Moreover, radiation toward substrate edges is significantly reduced.


title: Electrical Impedance Tomography (EIT) in the frontal plane using ring electrode configuration

abstract: Electrical Impedance Tomography (EIT) basically gives a two dimensional transverse image of the human thorax. Based on the understanding of the effects of objects in the third dimension it has been possible to obtain and interprete images of the lungs ventilation and stomach emptying using a ring electrode configuration in the frontal plane. A reusable spring loaded electrode system mounted on a single frame has been developed which has made it very convenient to obtain images besides reducing the cost of investigations using considerably.


title: Robot Arm Control Exploiting Natural Dynamics

abstract: This thesis presents an approach to robot arm control exploiting natural dynamics. The approach consists of using a compliant arm whose joints are controlled with simple non-linear oscillators. The arm has special actuators which makes it robust to collisions and gives it a smooth compliant motion. The oscillators produce rhythmic commands of the joints of the arm, and feedback of the joint motions is used to modify the oscillator behavior. The oscillators enable the resonant properties of the arm to be exploited to perform a variety of rhythmic and discrete tasks. These tasks include tuning into the resonant frequencies of the arm itself, juggling, turning cranks, playing with a Slinky toy, sawing wood, throwing balls, hammering nails and drumming. ::: For most of these tasks, the controllers at each joint are completely independent, being coupled by mechanical coupling through the physical arm of the robot. The thesis shows that this mechanical coupling allows the oscillators to automatically adjust their commands to be appropriate for the arm dynamics and the task. This coordination is robust to large changes in the oscillator parameters, and large changes in the dynamic properties of the arm. ::: As well as providing a wealth of experimental data to support this approach, the thesis also provides a range of analysis tools, both approximate and exact. These can be used to understand and predict the behavior of current implementations, and design new ones. These analysis techniques improve the value of oscillator solutions. ::: The results in the thesis suggest that the general approach of exploiting natural dynamics is a powerful method for obtaining coordinated dynamic behavior of robot arms. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)


title: Alignment By Agreement

abstract: We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions.


title: Automated pulmonary nodule CT image characterization in lung cancer screening

abstract: Purpose ::: In lung cancer screening, pulmonary nodules are first identified in low-dose chest CT images. Costly follow-up procedures could be avoided if it were possible to establish the malignancy status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the malignancy status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a lung cancer screening context are addressed.


title: Automatic Indexing: An Approach Using an Index Term Corpus and Combining Linguistic and Statistical Methods

abstract: This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with


title: UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

abstract: We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%. To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.


title: Fast shadows and lighting effects using texture mapping

abstract: Generating images of texture mapped geometry requires projecting surfaces onto a two-dimensional screen. If this projection involves perspective, then a division must be performed at each pixel of the projected surface in order to correctly calculate texture map coordinates. We show how a simple extension to perspective-comect texture mapping can be used to create various lighting effects, These in- clude arbitrary projection of two-dimensional images onto geom- etry, realistic spotlights, and generation of shadows using shadow maps( 10). These effects are obtained in real time using hardware that performs correct texture mapping.


title: Retinex by two bilateral filters

abstract: Retinex theory deals with the removal of unfavorable illumination effects from images. This ill-posed inverse problem is typically regularized by forcing spatial smoothness on the recoverable illumination. Recent work in this field suggested exploiting the knowledge that the illumination image bounds the image from above, and the fact that the reflectance is also expected to be smooth. In this paper we show how the above model can be improved to provide a non-iterative retinex algorithm that handles better edges in the illumination, and suppresses noise in dark areas. This algorithm uses two specially tailored bilateral filters – the first evaluates the illumination and the other is used for the computation of the reflectance. This result stands as a theoretic justification and refinement for the recently proposed heuristic use of the bilateral filter for retinex by Durand and Dorsey. In line with their appealing way of speeding up the bilateral filter, we show that similar speedup methods apply to our algorithm.


title: Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints

abstract: We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient des- cent constraint optimisation to provide further control over the genera- tion process. Among other things, this allows for the use of a “template” piece, from which some structural properties can be extracted, and trans- ferred as constraints to the newly generated material. The sampling pro- cess is guided with Simulated Annealing to avoid local optima, and to find solutions that both satisfy the constraints, and are relatively stable with respect to the C-RBM. Results show that with this approach it is possible to control the higher-level self-similarity structure, the meter, and the tonal properties of the resulting musical piece, while preserving its local musical coherence.


title: Voting-based pose estimation for robotic assembly using a 3D sensor

abstract: We propose a voting-based pose estimation algorithm applicable to 3D sensors, which are fast replacing their 2D counterparts in many robotics, computer vision, and gaming applications. It was recently shown that a pair of oriented 3D points, which are points on the object surface with normals, in a voting framework enables fast and robust pose estimation. Although oriented surface points are discriminative for objects with sufficient curvature changes, they are not compact and discriminative enough for many industrial and real-world objects that are mostly planar. As edges play the key role in 2D registration, depth discontinuities are crucial in 3D. In this paper, we investigate and develop a family of pose estimation algorithms that better exploit this boundary information. In addition to oriented surface points, we use two other primitives: boundary points with directions and boundary line segments. Our experiments show that these carefully chosen primitives encode more information compactly and thereby provide higher accuracy for a wide class of industrial parts and enable faster computation. We demonstrate a practical robotic bin-picking system using the proposed algorithm and a 3D sensor.


title: Diagnosing the root-causes of failures from cluster log files

abstract: System event logs are often the primary source of information for diagnosing (and predicting) the causes of failures for cluster systems. Due to interactions among the system hardware and software components, the system event logs for large cluster systems are comprised of streams of interleaved events, and only a small fraction of the events over a small time span are relevant to the diagnosis of a given failure. Furthermore, the process of troubleshooting the causes of failures is largely manual and ad-hoc. In this paper, we present a systematic methodology for reconstructing event order and establishing correlations among events which indicate the root-causes of a given failure from very large syslogs. We developed a diagnostics tool, FDiag, to extract the log entries as structured message templates and uses statistical correlation analysis to establish probable cause and effect relationships for the fault being analyzed. We applied FDiag to analyze failures due to br eakdowns in interactions between the Lustre file system and its clients on the Ranger supercomputer at the Texas Advanced Computing Center (TACC). The results are positive. FDiag is able to identify the dates and the time periods that contain the significant events which eventually led to the occurrence of compute node soft lockups.


title: Using Theatre to Heal Culture Wars in the United States

abstract: I presented this unpublished paper at an international symposium held in Okinawa City, Japan, in July 2007.


Adaptive Observation Strategies with the Local Ensemble Transform Kalman Filter


title: Towards Unified Programmability of Cloud and Carrier Infrastructure

abstract: The rise of cloud services poses considerable challenges on the control of both cloud and carrier network infrastructures. While traditional telecom network services rely on rather static processes (often involving manual steps), the wide adoption of mobile devices including tablets, smartphones and wearables introduce previously unseen dynamics in the creation, scaling and withdrawal of new services. These phenomena require optimal flexibility in the characterization of services, as well as on the control and orchestration of both carrier and cloud infrastructure. This paper proposes a unified programmability framework addressing: the unification of network and cloud resources, the integrated control and management of cloud and network, the description for programming networked/cloud services, and the provisioning processes of these services. In addition proofs-of-concept are provided based on existing open source control software components.


title: An integer projected fixed point method for graph matching and

abstract: Graph matching and MAP inference are essential problems in computer vision and machine learning. We introduce a novel algorithm that can accommodate both problems and solve them efficiently. Recent graph matching algorithms are based on a general quadratic programming formulation, which takes in consideration both unary and second-order terms reflecting the similarities in local appearance as well as in the pairwise geometric relationships between the matched features. This problem is NP-hard, therefore most algorithms find approximate solutions by relaxing the original problem. They find the optimal continuous solution of the modified problem, ignoring during optimization the original discrete constraints. Then the continuous solution is quickly binarized at the end, but very little attention is put into this final discretization step. In this paper we argue that the stage in which a discrete solution is found is crucial for good performance. We propose an efficient algorithm, with climbing and convergence properties, that optimizes in the discrete domain the quadratic score, and it gives excellent results either by itself or by starting from the solution returned by any graph matching algorithm. In practice it outperforms state-or-the art graph matching algorithms and it also significantly improves their performance if used in combination. When applied to MAP inference, the algorithm is a parallel extension of Iterated Conditional Modes (ICM) with climbing and convergence properties that make it a compelling alternative to the sequential ICM. In our experiments on MAP inference our algorithm proved its effectiveness by significantly outperforming [13], ICM and Max-Product Belief Propagation.


title: Learning to Compose Task-Specific Tree Structures

abstract: For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel tree-structured long short-term memory architecture that learns how to compose task-specific tree structures only from plain text data efficiently. Our model uses Straight-Through Gumbel-Softmax estimator to decide the parent node among candidates dynamically and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language inference and sentiment analysis, and show that our model outperforms or is at least comparable to previous models. We also find that our model converges significantly faster than other models.


title: Soft Robotics: Challenges and Perspectives

abstract: There has been an increasing interest in the use of unconventional materials and morphologies in robotic systems because the underlying mechanical properties (such as body shapes, elasticity, viscosity, softness, density and stickiness) are crucial research topics for our in-depth understanding of embodied intelligence. The detailed investigations of physical system-environment interactions are particularly important for systematic development of technologies and theories of emergent adaptive behaviors. Based on the presentations and discussion in the Future Emerging Technology (fet11) conference, this article introduces the recent technological development in the field of soft robotics, and speculates about the implications and challenges in the robotics and embodied intelligence research. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.


title: Exploring Electronic Marketplace Performance: The 3 Pillars

abstract: The literature on electronic marketplaces has a number of limitations; inconsistencies in defining electronic marketplaces and measuring their performance; a limited amount of empirical research focusing on electronic marketplace performance; and the absence of an electronic marketplace performance model. This study examines 8 electronic marketplaces operating in diverse geographical and product markets. The study reveals the factors that affect electronic marketplace performance, and illustrates how these factors impact performance. Using the concept of fit as a theoretical lens, this study illustrates how electronic marketplaces, through their actions, improved their performance. Finally, the presents a model of electronic marketplace performance.


title: Machine learning approach for automated screening of malaria parasite using light microscopic images.

abstract: The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification.


title: CNP: An FPGA-based processor for Convolutional Networks

abstract: Convolutional Networks (ConvNets) are biologicallyinspired hierarchical architectures that can be trained to perform a variety of detection, recognition and segmentation tasks. ConvNets have a feed-forward architecture consisting of multiple linear convolution filters interspersed with pointwise non-linear squashing functions. This paper presents an efficient implementation of ConvNets on a low-end DSPoriented Field Programmable Gate Array (FPGA). The implementation exploits the inherent parallelism of ConvNets and takes full advantage of multiple hardware multiplyaccumulate units on the FPGA. The entire system uses a single FPGA with an external memory module, and no extra parts. A network compiler software was implemented, which takes a description of a trained ConvNet and compiles it into a sequence of instructions for the ConvNet Processor (CNP). A ConvNet face detection system was implemented and tested. Face detection on a 512 × 384 frame takes 100ms (10 frames per second), which corresponds to an average performance of 3.4×109 connections per second for this 340 million connection network. The design can be used for low-power, lightweight embedded vision systems for micro-UAVs and other small robots.


title: Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

abstract: We address the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.


title: Reservoir Computing and Self-Organized Neural Hierarchies

abstract: There is a growing understanding that machine learning architectures have to be much bigger and more complex to approach any intelligent behavior. There is also a growing understanding that purely supervised learning is inadequate to train such systems. A recent paradigm of artificial recurrent neural network (RNN) training under the umbrella-name Reservoir Computing (RC) demonstrated that training big recurrent networks (the reservoirs) differently than supervised readouts from them is often better. It started with Echo State Networks (ESNs) and Liquid State Machines ten years ago where the reservoir was generated randomly and only linear readouts from it were trained. Rather surprisingly, such simply and fast trained ESNs outperformed classical fully-trained RNNs in many tasks. While full supervised training of RNNs is problematic, intuitively there should also be something better than a random network. In recent years RC became a vivid research field extending the initial paradigm from fixed random reservoir and trained output into using different methods for training the reservoir and the readout. In this thesis we overview existing and investigate new alternatives to the classical supervised training of RNNs and their hierarchies. First we present a taxonomy and a systematic overview of the RNN training approaches under the RC umbrella. Second, we propose and investigate the use of two different neural network models for the reservoirs together with several unsupervised adaptation techniques, as well as unsupervisedly layer-wise trained deep hierarchies of such models. We rigorously empirically test the proposed methods on two temporal pattern recognition datasets, comparing it to the classical reservoir computing state of art.


title: Exploring the patterns of social behavior in GitHub

abstract: Social coding paradigm is reshaping the distributed software development with a surprising speed in recent years. Github, a remarkable social coding community, attracts a huge number of developers in a short time. Various kinds of social networks are formed based on social activities among developers. Why this new paradigm can achieve such a great success in attracting external developers, and how they are connected in such a massive community, are interesting questions for revealing power of social coding paradigm. In this paper, we firstly compare the growth curves of project and user in GitHub with three traditional open source software communities to explore differences of their growth modes. We find an explosive growth of the users in GitHub and introduce the Diffusion of Innovation theory to illustrate intrinsic sociological basis of this phenomenon. Secondly, we construct follow-networks according to the follow behaviors among developers in GitHub. Finally, we present four typical social behavior patterns by mining follow-networks containing independence-pattern, group-pattern, star-pattern and hub-pattern. This study can provide several instructions of crowd collaboration to newcomers. According to the typical behavior patterns, the community manager could design corresponding assistive tools for developers.


title: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing

abstract: In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.


title: Exploratory Access to Wikipedia through Faceted Dynamic Taxonomies

abstract: Users currently access Wikipedia through two traditional paradigms, text search and hypertext navigation. We believe that user access can be significantly improved by supporting a systematic conceptual exploration of the knowledge base through dynamic taxonomies with a faceted taxonomy organization. This approach allows the easy manipulation of sets of documents and the systematic and intuitive exploration of complex knowledge bases.


title: Improved Deep Learning Baselines for Ubuntu Corpus Dialogs

abstract: This paper presents results of our experiments for the next utterance ranking on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging predictions of multiple models. The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset. Finally, we discuss our future plans using this corpus.


Spectral Graph-Based Method of Multimodal Word Embedding


title: Lexical Event Ordering with an Edge-Factored Model

abstract: Extensive lexical knowledge is necessary for temporal analysis and planning tasks. We address in this paper a lexical setting that allows for the straightforward incorporation of rich features and structural constraints. We explore a lexical event ordering task, namely determining the likely temporal order of events based solely on the identity of their predicates and arguments. We propose an “edgefactored” model for the task that decomposes over the edges of the event graph. We learn it using the structured perceptron. As lexical tasks require large amounts of text, we do not attempt manual annotation and instead use the textual order of events in a domain where this order is aligned with their temporal order, namely cooking recipes.


title: Device-to-Device Discovery for Proximity-Based Service in LTE-Advanced System

abstract: In this paper, we propose a device-to-device (D2D) discovery scheme as a key enabler for a proximity-based service in the Long-Term Evolution Advanced (LTE-A) system. The proximity-based service includes a variety of services exploiting the location information of user equipment (UE), for example, the mobile social network and the mobile marketing. To realize the proximity-based service in the LTE-A system, it is necessary to design a D2D discovery scheme by which UE can discover another UE in its proximity. We design a D2D discovery scheme based on the random access procedure in the LTE-A system. The proposed random-access-based D2D discovery scheme is advantageous in that 1) the proposed scheme can be readily applied to the current LTE-A system without significant modification; 2) the proposed scheme discovers pairs of UE in a centralized manner, which enables the access or core network to centrally control the formation of D2D communication networks; and 3) the proposed scheme adaptively allocates resource blocks for the D2D discovery to prevent underutilization of radio resources. We analyze the performance of the proposed D2D discovery scheme. A closed-form formula for the performance is derived by means of the stochastic geometry-based approach. We show that the analysis results accurately match the simulation results.


title: Scalable Meta-Learning for Bayesian Optimization

abstract: Bayesian optimization has become a standard technique for hyperparameter optimization, including data-intensive models such as deep neural networks that may take days or weeks to train. We consider the setting where previous optimization runs are available, and we wish to use their results to warm-start a new optimization run. We develop an ensemble model that can incorporate the results of past optimization runs, while avoiding the poor scaling that comes with putting all results into a single Gaussian process model. The ensemble combines models from past runs according to estimates of their generalization performance on the current optimization. Results from a large collection of hyperparameter optimization benchmark problems and from optimization of a production computer vision platform at Facebook show that the ensemble can substantially reduce the time it takes to obtain near-optimal configurations, and is useful for warm-starting expensive searches or running quick re-optimizations.


title: The Good, the Bad, and the Ugly: The Many Faces of Constructivism

abstract: Across the broad fields of educational theory and research, constructivism has become something akin to a secular religion. In her book Evolution as a Religion (1985), Mary Midgley wrote that the theory of evolution "is not just an inert piece of theoretical science. It is, and cannot help being, also a powerful folk-tale about human origins. Any such narrative must have symbolic force" (1985, p. 1). She might well have written the same about constructivism, which is, whatever else it may be, a "powerful folktale" about the origins of human knowledge. As in all living religions, constructivism has many sects-each of which harbors some distrust of its rivals. This descent into


title: State Repression and Political Order

abstract: ▪ Abstract State repression includes harassment, surveillance/spying, bans, arrests, torture, and mass killing by government agents and/or affiliates within their territorial jurisdiction. Over the past 40 years, the systematic study of state repression has grown considerably. The development of this work, however, has been uneven. Though unified in their focus on the problem of order (i.e., trying to ascertain how political authorities wield coercive power amid potential and actual domestic challengers), different scholars tend to emphasize distinct aspects of the topic. Consequently, a great deal of progress has been made in specific areas but others have lagged behind. In this review, I attempt to identify the dominant traditions in the repression literature, the core empirical findings, and some persisting puzzles.


title: Arch Sex Behav DOI 10.1007/s10508-011-9761-1 ORIGINAL PAPER Stability and Change in Sexual Orientation Identity Over a 10-Year Period in Adulthood

abstract: We examined reports of sexual orientation identity stability and change over a 10-year period drawing on data from the National Survey of Midlife Development in the United States (MIDUS I and II) and tested for three patterns: (1) heterosexual stability, (2) female sexual fluidity, and (3) bisexual fluidity. Fifty-four percent of the 2,560 participants were female and the average age was approximately 47 years. At Wave 1, 2,494 (97.42%) reported a heterosexual identity, 32 (1.25%) a homosexual identity, and 34 (1.33%) a bisexual identity and somewhat more than 2% reported a different sexual orientation identity at Wave 2. Although some support for each hypothesis was found, initial sexual orientation identity interacted with gender to predict a more complex pattern. For the sample as a whole, heterosexuality was the most stable identity. For women, bisexuality and homosexuality were equally unstable and significantly less stable than heterosexuality, suggesting that sexual orientation identity fluidity is a pattern that applies more to sexual minority women than heterosexual women. For men, heterosexuality and homosexuality were both relatively stable compared to bisexuality, which stood out as a particularly unstable identity. This pattern of results was consistent with previous findings and helps to address methodological limitations of earlier research by showing the characteristics of a population-based sample of heterosexual, homosexual, and bisexual identified men and women over time.


title: Elongation factor-2 phosphorylation in dendrites and the regulation of dendritic mRNA translation in neurons

abstract: Neuronal activity results in long lasting changes in synaptic structure and function by regulating mRNA translation in dendrites. These activity dependent events yield the synthesis of proteins known to be important for synaptic modifications and diverse forms of synaptic plasticity. Worthy of note, there is accumulating evidence that the eukaryotic Elongation Factor 2 Kinase (eEF2K)/eukaryotic Elongation Factor 2 (eEF2) pathway may be strongly involved in this process. Upon activation, eEF2K phosphorylates and thereby inhibits eEF2, resulting in a dramatic reduction of mRNA translation. eEF2K is activated by elevated levels of calcium and binding of Calmodulin (CaM), hence its alternative name calcium/CaM-dependent protein kinase III (CaMKIII). In dendrites, this process depends on glutamate signaling and N-methyl-D-aspartate receptor (NMDAR) activation. Interestingly, it has been shown that eEF2K can be activated in dendrites by the metabotropic glutamate receptor (mGluR) 1/5 signaling, as well. Therefore, neuronal activity can induce local proteomic changes at the postsynapse by altering eEF2K activity. Well-established targets of eEF2K in dendrites include Brain-derived neurotrophic factor (BDNF), activity-regulated cytoskeletal-associated protein (Arc), the alpha subunit of calcium/CaM-dependent protein kinase II (αCaMKII), and Microtubule-associated protein 1B (MAP1B), all of which have well-known functions in different forms of synaptic plasticity.In this review we will give an overview of the involvement of the eEF2K/eEF2 pathway at dendrites in regulating the translation of dendritic mRNA in the context of altered NMDAR- and neuronal activity, and diverse forms of synaptic plasticity, such as metabotropic glutamate receptor-dependent-long-term depression (mGluR-LTD). For this, we draw on studies carried out both in vitro and in vivo.


title: False data injection attacks against state estimation in wireless sensor networks

abstract: In this paper we study the effect of false data injection attacks on state estimation carried over a sensor network monitoring a discrete-time linear time-invariant Gaussian system. The steady state Kalman filter is used to perform state estimation while a failure detector is employed to detect anomalies in the system. An attacker wishes to compromise the integrity of the state estimator by hijacking a subset of sensors and sending altered readings. In order to inject fake sensor measurements without being detected the attacker will need to carefully design his actions to fool the estimator as abnormal sensor measurements would result in an alarm. It is important for a designer to determine the set of all the estimation biases that an attacker can inject into the system without being detected, providing a quantitative measure of the resilience of the system to such attacks. To this end, we will provide an ellipsoidal algorithm to compute its inner and outer approximations of such set. A numerical example is presented to further illustrate the effect of false data injection attack on state estimation.


Ad-Hoc-Group Signatures from Hijacked Keypairs


title: XSnippet: mining For sample code

abstract: It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.


title: EXPLICATURE AND SEMANTICS

abstract: According to the relevance-theoretic account, identifying what is communicated explicitly by an utterance (its explicature) involves several pragmatic processes: disambiguation, saturation of indexical elements, the recovery of unarticulated constituents and ad hoc concept construction, both of the latter being free from any linguistic indication. This is at odds with a current philosophical position according to which contextual contributions to the proposition expressed are confined to the processes of disambiguation and saturation; this view necessitates positing a number of hidden (unarticulated) indexicals in the logical form of linguistic expressions. The arguments for the two positions are assessed.


title: A Performance Study of General Purpose Applications on Graphics Processors

abstract: Graphics processors (GPUs) provide a vast number of simple, data-parallel, deeply multithreaded cores and high memory bandwidths. GPU architectures are becoming increasingly programmable, offering the potential for dramatic speedups for a variety of general-purpose applications compared to contemporary general-purpose processors (CPUs). This paper uses NVIDIA's C-like CUDA language and an engineering sample of their recently introduced GTX 260 GPU to explore the effectiveness of GPUs for a variety of application types, and describes some specific coding idioms that improve their performance on the GPU. GPU performance is compared to both single-core and multicore CPU performance, with multicore CPU implementations written using OpenMP. The paper also discusses advantages and inefficiencies of the CUDA programming model and some desirable features that might allow for greater ease of use and also more readily support a larger body of applications.


title: Reducing Uncertainty of Low-Sampling-Rate Trajectories

abstract: The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.


title: HLA-G expression defines a novel regulatory T-cell subset present in human peripheral blood and sites of inflammation

abstract: Regulatory T cells can inhibit harmful immunopathologic responses directed against self and foreign antigens and play a major role in controlling autoimmunity. Here we have identified and characterized a subpopulation of CD4 and CD8 T cells in human peripheral blood expressing the immune tolerizing molecule HLA-G. HLA-G–expressing T cells are hypoproliferative, are CD25- and FOXP3-negative, and exhibit potent suppressive properties that are partially mediated by HLA-G. HLA-G–positive (HLA-Gpos) T cells are found at low percentages among CD4 and CD8 single-positive thymocytes, suggesting a thymic origin. The presence of HLA-Gpos T cells at sites of inflammation such as inflamed skeletal muscle in myositis or the cerebrospinal fluid of patients with acute neuroinflammatory disorders suggests an important function in modulating parenchymal inflammatory responses in vivo.


Mechatronics Design of Small Electric Vehicles; Research and Education


title: n-Gram Statistics for Natural Language Understanding and Text Processing

abstract: n-gram (n = 1 to 5) statistics and other properties of the English language were derived for applications in natural language understanding and text processing. They were computed from a well-known corpus composed of 1 million word samples. Similar properties were also derived from the most frequent 1000 words of three other corpuses. The positional distributions of n-grams obtained in the present study are discussed. Statistical studies on word length and trends of n-gram frequencies versus vocabulary are presented. In addition to a survey of n-gram statistics found in the literature, a collection of n-gram statistics obtained by other researchers is reviewed and compared.


title: Multilayer Perceptron Neural Network (MLPs) For Analyzing the Properties of Jordan Oil Shale 1

abstract: In this paper, we introduce the multilayer preceptron neural network and describe how it can be used for function approximation. The back propagation algorithm (including its variants) is the principle procedure for training multilayer perceptrons. Car must be taken when training perceptron network to ensure that they do not over fit the training data and then fail to generalize well in new situations. So the main purpose of this paper lies in studying the effect of changing both the No of hidden layers of MLPs and the No of processing elements that exist in the hidden layers on the analyzed properties of Jordan Oil Shale. After constructing such a MLP and changing the number of hidden layers, we found that with increasing the number of processing elements in the hidden layers. We reach to an optional output results w.r.t the experimental one or the analytical formulated one. This obtained output is completely matched with the main concepts of theoretical visions of ANN.


title: Child Abuse Training and Knowledge: A National Survey of Emergency Medicine, Family Medicine, and Pediatric Residents and Program Directors

abstract: OBJECTIVE ::: The objective of this study was to determine the level of knowledge, comfort, and training related to the medical management of child abuse among pediatrics, emergency medicine, and family medicine residents. ::: ::: ::: METHODS ::: Surveys were administered to program directors and third-year residents at 67 residency programs. The resident survey included a 24-item quiz to assess knowledge regarding the medical management of physical and sexual child abuse. Sites were solicited from members of a network of child abuse physicians practicing at institutions with residency programs. ::: ::: ::: RESULTS ::: Analyzable surveys were received from 53 program directors and 462 residents. Compared with emergency medicine and family medicine programs, pediatric programs were significantly larger and more likely to have a medical provider specializing in child abuse pediatrics, have faculty primarily responsible for child abuse training, use a written curriculum for child abuse training, and offer an elective rotation in child abuse. Exposure to child abuse training and abused patients was highest for pediatric residents and lowest for family medicine residents. Comfort with managing child abuse cases was lowest among family medicine residents. On the knowledge quiz, pediatric residents significantly outperformed emergency medicine and family medicine residents. Residents with high knowledge scores were significantly more likely to come from larger programs and programs that had a center, provider, or interdisciplinary team that specialized in child abuse pediatrics; had a physician on faculty responsible for child abuse training; used a written curriculum for child abuse training; and had a required rotation in child abuse pediatrics. ::: ::: ::: CONCLUSIONS ::: By analyzing the relationship between program characteristics and residents' child abuse knowledge, we found that pediatric programs provide far more training and resources for child abuse education than emergency medicine and family medicine programs. As leaders, pediatricians must establish the importance of this topic in the pediatric education of residents of all specialties.


title: CANDID: preventing sql injection attacks using dynamic candidate evaluations

abstract: SQL injection attacks are one of the topmost threats for applications written for the Web. These attacks are launched through specially crafted user input on web applications that use low level string operations to construct SQL queries. In this work, we exhibit a novel and powerful scheme for automatically transforming web applications to render them safe against all SQL injection attacks. A characteristic diagnostic feature of SQL injection attacks is that they change the intended structure of queries issued. Our technique for detecting SQL injection is to dynamically mine the programmer-intended query structure on any input, and detect attacks by comparing it against the structure of the actual query issued. We propose a simple and novel mechanism, called C ANDID , for mining programmer intended queries by dynamically evaluating runs over benign candidate inputs. This mechanism is theoretically well founded and is based on inferring intended queries by considering the symbolic query computed on a program run. Our approach has been implemented in a tool called C ANDID , that retrofits Web applications written in Java to defend them against SQL injection attacks. We report extensive experimental results that show that our approach performs remarkably well in practice.


title: Applications of machine learning in cryptography: a survey

abstract: Machine learning techniques have had a long list of applications in recent years. However, the use of machine learning in information and network security is not new. Machine learning and cryptography have many things in common. The most apparent is the processing of large amounts of data and large search spaces. In its varying techniques, machine learning has been an interesting field of study with massive potential for application. In the past three decades, machine learning techniques, whether supervised or unsupervised, have been applied in cryptographic algorithms, cryptanalysis, steganography, among other data-security-related applications. This paper presents an updated survey of applications of machine learning techniques in cryptography and cryptanalysis. The paper summarizes the research done in these areas and provides suggestions for future directions in research.


title: Application of Data Mining Classification Algorithms for Breast Cancer Diagnosis

abstract: Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. Data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making. The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor. Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector Machine (SVM), k-nearest neighbors algorithm (Knn), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.


title: Formant-frequency discrimination of synthesized vowels in budgerigars (Melopsittacus undulatus) and humans.

abstract: Vowels are complex sounds with four to five spectral peaks known as formants. The frequencies of the two lowest formants, F1and F2, are sufficient for vowel discrimination. Behavioral studies show that many birds and mammals can discriminate vowels. However, few studies have quantified thresholds for formant-frequency discrimination. The present study examined formant-frequency discrimination in budgerigars (Melopsittacus undulatus) and humans using stimuli with one or two formants and a constant fundamental frequency of 200 Hz. Stimuli had spectral envelopes similar to natural speech and were presented with random level variation. Thresholds were estimated for frequency discrimination of F1, F2, and simultaneous F1 and F2 changes. The same two-down, one-up tracking procedure and single-interval, two-alternative task were used for both species. Formant-frequency discrimination thresholds were as sensitive in budgerigars as in humans and followed the same patterns across all conditions. Thresholds expressed as percent frequency difference were higher for F1 than for F2, and were unchanged between stimuli with one or two formants. Thresholds for simultaneous F1 and F2 changes indicated that discrimination was based on combined information from both formant regions. Results were consistent with previous human studies and show that budgerigars provide an exceptionally sensitive animal model of vowel feature discrimination.


title: Approximate Parameter Synthesis for Probabilistic Time-Bounded Reachability

abstract: This paper proposes a technique to synthesize parametric rate values in continuous-time Markov chains that ensure the validity of bounded reachability properties. Rate expressions over variables indicate the average speed of state changes and are expressed using the polynomials over reals. The key contribution is an algorithm that approximates the set of parameter values for which the stochastic real-time system guarantees the validity of bounded reachability properties. This algorithm is based on discretizing parameter ranges together with a refinement technique. This paper describes the algorithm, analyzes its time complexity, and shows its applicability by deriving parameter constraints for a real-time storage system with probabilistic error checking facilities.


Multi-objective Assignment Problem with Generalized Trapezoidal Fuzzy Numbers


title: Wireless sensor network survey

abstract: A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.


A Gentle Introduction to Model Predictive Control ( MPC ) Formulations based on Discrete Linear State Space Models


title: Your Installed Apps Reveal Your Gender and More!

abstract: In this paper, we highlight a potential privacy threat in the current smartphone platforms, which allows any third party to collect a snapshot of installed applications without the user's consent. This can be exploited by third parties to infer various user attributes similar to what is done through tracking. We show that using only installed apps, user's gender, a demographic attribute that is frequently used in targeted advertising, can be instantly predicted with an accuracy around 70%, by training a classifier using established supervised learning techniques.


title: Total Moving Face Reconstruction

abstract: We present an approach that takes a single video of a person’s face and reconstructs a high detail 3D shape for each video frame. We target videos taken under uncontrolled and uncalibrated imaging conditions, such as youtube videos of celebrities. In the heart of this work is a new dense 3D flow estimation method coupled with shape from shading. Unlike related works we do not assume availability of a blend shape model, nor require the person to participate in a training/capturing process. Instead we leverage the large amounts of photos that are available per individual in personal or internet photo collections. We show results for a variety of video sequences that include various lighting conditions, head poses, and facial expressions.


title: A linear time natural evolution strategy for non-separable functions

abstract: We present a novel Natural Evolution Strategy (NES) variant, the Rank-One NES (R1-NES), which uses a low-rank approximation of the search distribution covariance matrix. The algorithm allows computation of the natural gradient with cost linear in the dimensionality of the parameter space, and excels in solving high-dimensional non-separable problems.


title: Visual vibrometry: Estimating material properties from small motions in video

abstract: The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. The frequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequencies from video of a vibrating object, we can often make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video.


title: Ilyonectria palmarum sp. nov. causing dry basal stem rot of Arecaceae

abstract: During surveys conducted in 2010–2013, a complete breakage or bending of the trunk and a dry basal stem rot were observed on containerised Brahea armata, B. edulis, Howea forsteriana and Trachycarpus princeps plants in different nurseries located in eastern Sicily (southern Italy). A cylindrocarpon-like species was consistently obtained from diseased palm tissues, while known pathogens of these hosts such as Ganoderma, Phytophthora and Thielaviopsis were not found associated with symptomatic tissues or isolated on standard or selective media. A total of 40 cylindrocarpon-like isolates were collected and characterised based on morphology and DNA phylogeny. Multigene analyses based on the β-tubulin, histone H3, translation elongation factor 1-α, and the internal transcribed spacers (ITS1, 5.8S, ITS2) genes facilitated the identification of a new species, described here as Ilyonectria palmarum. The pathogenicity of one representative isolate collected from each palm species was tested on plants cultivated under nursery conditions and in a growth chamber. All isolates were pathogenic to B. armata, B. edulis, H. forsteriana, and T. princeps and symptoms identical to that observed in nurseries were reproduced. Dry basal stem rot and stem bending caused by Ilyonectria palmarum represents a potentially serious problem for nurseries cultivating containerised palms.


title: The eyes have it: a task by data type taxonomy for information visualizations

abstract: A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts).


title: Beyond data warehousing: what's next in business intelligence?

abstract: During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A new approach to BI, called Business Performance Management (BPM), is emerging from this framework: it includes DW but it also requires a reactive component capable of monitoring the time-critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy. The aim of this paper is to encourage the research community to acknowledge the coming of a second era in BI, to propose a general architecture for BPM, and to lay the premises for investigating the most challenging of the related issues.


title: A Comprehensive Analysis of Knowledge Management Cycles

abstract: At present knowledge and its proper management became an essential issue for every organization. In the modern globalized world, organizations cannot survive in a sustainable way without efficient knowledge management. Knowledge management cycle (KMC) is a process of transforming information into knowledge within an organization, which explains how knowledge is captured, processed, and distributed in an organization. For the better performance organizations require a practical and coherent strategy and comprehensive KMC. The aim of this study is to examine the KMCs and how they are playing vital role for the development of organizations.


title: Urban sprawl: metrics, dynamics and modelling using GIS

abstract: Urban sprawl refers to the extent of urbanisation, which is a global phenomenon mainly driven by population growth and large scale migration. In developing countries like India, where the population is over one billion, one-sixth of the world’s population, urban sprawl is taking its toll on the natural resources at an alarming pace. Urban planners require information related to the rate of growth, pattern and extent of sprawl to provide basic amenities such as water, sanitation, electricity, etc. In the absence of such information, most of the sprawl areas lack basic infrastructure facilities. Pattern and extent of sprawl could be modelled with the help of spatial and temporal data. GIS and remote sensing data along with collateral data help in analysing the growth, pattern and extent of sprawl. With the spatial and temporal analyses along with modelling it was possible to identify the pattern of sprawl and subsequently predict the nature of future sprawl. This paper brings out the extent of sprawl taking place over a period of nearly three decades using GIS and Remote Sensing. The study also attempts to describe some of the landscape metrics required for quantifying sprawl. For understanding and modelling this dynamic phenomenon, prominent causative factors are considered.


title: A slacker coherence protocol for pull-based monitoring of on-line data sources

abstract: An increasing number of online applications operate on data from disparate, and often wide-spread, data sources. This paper studies the design of a system for the automated monitoring of on-line data sources. In this system a number of ad-hoc data warehouses, which maintain client-specified views, are interposed between clients and data sources. We present a model of coherence, referred to here as slacker coherence, to address the freshness problem in the context of pull-based protocols. We experimentally examine various techniques for estimating update rates and polling adaptively. We also look at the impact on the coherence model performance of the request scheduling algorithm at the source.


title: An Introduction to Normalization and Calibration Methods in Functional MRI

abstract: In functional magnetic resonance imaging (fMRI), the blood oxygenation level dependent (BOLD) signal is often interpreted as a measure of neural activity. However, because the BOLD signal reflects the complex interplay of neural, vascular, and metabolic processes, such an interpretation is not always valid. There is growing evidence that changes in the baseline neurovascular state can result in significant modulations of the BOLD signal that are independent of changes in neural activity. This paper introduces some of the normalization and calibration methods that have been proposed for making the BOLD signal a more accurate reflection of underlying brain activity for human fMRI studies.


title: Cognitive Radio: From Spectrum Sharing to Adaptive Learning and Reconfiguration

abstract: This paper introduces important cognitive radio developments like spectrum sharing, learning and adaptation algorithms, and the software and hardware architecture to support these functions. A cognitive radio is defined here as a transceiver that is aware of its environment and can combine this awareness with knowledge of its user's priorities, needs, operational procedures, and governing regulatory rules. It adapts to its environment and configures itself in an appropriate fashion. The radio learns through experience and is capable of generating solutions for communications problems unforeseen by its designers. Our spectrum sharing cognitive radio is built upon GNU radio and uses the universal software radio peripheral (USRP) device as our radio front end platform. We use cyclostationary feature analysis to detect low SNR modulated signals because of its ability to distinguish between modulated signals, interference, and noise in low signal to noise ratios. A parallel algorithm running on a cell broadband engine (Cell BE) is used to attack the associated high computational complexity. A new spectrum sensing scheme, incorporating spectrum monitoring, data transmission, and dynamic channel switching, is designed to fully utilize the idle time of the primary user. Our work is based on the concept of a cognitive engine: an intelligent software package that "reads the meters" and "turns the knobs" of any attached software defined radio (SDR) platform. Using an eclectic combination of artificial intelligence techniques including case-based decision theory, multi-objective genetic algorithms, and neural networks, it implements a system of nested cognition loops. Applied to public safety communications, this technology is the basis of a working prototype Public Safety Cognitive Radio that can scan the public safety spectrum (multiple bands and multiple waveforms, all incompatible) and configure itself to interoperate with any public safety waveform that it finds within 0.1 seconds of determining that a signal is present.


title: UNDERMINING CHILDREN'S INTRINSIC INTEREST WITH EXTRINSIC REWARD: A TEST OF THE "OVERJUSTIFICATION" HYPOTHESIS

abstract: A field experiment was conducted with children to test the "overjustification" hypothesis suggested by self-percepti on theory—the proposition that a person's intrinsic interest in an activity may be decreased by inducing him to engage in that activity as an explicit means to some extrinsic goal. Children showing intrinsic interest in a target activity during base-line observations were exposed to one of three conditions: In the expected-award condition, subjects agreed to engage in the target activity in order to obtain an extrinsic reward; in the unexpected-award condition, subjects had no knowledge of the reward until after they had finished with the activity; and in the no-award condition, subjects neither expected nor received the reward. The results supported the prediction that subjects in the expected-award condition would show less subsequent intrinsic interest in the target activity than subjects in either of the other two conditions. The process by which man seeks to understand his environment—to discern the causes of events which surround him and explain the behavior of others toward him—has been of central concern to social psychology for many years (e.g., Brunswik, 1934; Heider, 1958;


title: Single-structure 3-axis lorentz force magnetometer with sub-30 nT/√HZ resolution

abstract: This work demonstrates a 3-axis Lorentz force magnetometer for electronic compass purposes. The magnetometer measures magnetic flux in 3 axes using a single structure. With 1 mW power consumption, the sensor achieves sub-30 nT/√Hz resolution in each of the 3 axes. Compared to the 3-axis Hall sensors currently used in smartphones, the 3-axis magnetometer shown here has the advantages of 10× lower noise floor and the ability to be co-fabricated with MEMS inertial sensors.


title: The social psychology of perception experiments : Hills , backpacks , glucose and the problem of generalizability

abstract: Experiments take place in a physical environment but also a social environment. Generalizability from experimental manipulations to more typical contexts may be limited by violations of ecological validity with respect to either the physical or the social environment. A replication and extension of a recent study (a blood glucose manipulation) was conducted to investigate the effects of experimental demand (a social artifact) on participant behaviors judging the geographical slant of a large-scale outdoor hill. Three different assessments of experimental demand indicate that even when the physical environment is naturalistic, and the goal of the main experimental manipulation was primarily concealed, artificial aspects of the social environment (such as an explicit requirement to wear a heavy backpack while estimating the slant of a hill) may still be primarily responsible for altered judgments of hill orientation.


title: Yoga Asana sessions increase brain GABA levels: a pilot study.

abstract: OBJECTIVES ::: The aim of this study was to compare changes in brain gamma-aminobutyric (GABA) levels associated with an acute yoga session versus a reading session. It was hypothesized that an individual yoga session would be associated with an increase in brain GABA levels. ::: ::: ::: DESIGN ::: This is a parallel-groups design. ::: ::: ::: SETTINGS/LOCATION ::: Screenings, scan acquisitions, and interventions took place at medical school-affiliated centers. ::: ::: ::: SUBJECTS ::: The sample comprised 8 yoga practitioners and 11 comparison subjects. ::: ::: ::: INTERVENTIONS ::: Yoga practitioners completed a 60-minute yoga session and comparison subjects completed a 60-minute reading session. ::: ::: ::: OUTCOME MEASURES ::: GABA-to-creatine ratios were measured in a 2-cm axial slab using magnetic resonance spectroscopic imaging immediately prior to and immediately after interventions. ::: ::: ::: RESULTS ::: There was a 27% increase in GABA levels in the yoga practitioner group after the yoga session (0.20 mmol/kg) but no change in the comparison subject group after the reading session ( -0.001 mmol/kg) (t = -2.99, df = 7.87, p = 0.018). ::: ::: ::: CONCLUSIONS ::: These findings demonstrate that in experienced yoga practitioners, brain GABA levels increase after a session of yoga. This suggests that the practice of yoga should be explored as a treatment for disorders with low GABA levels such as depression and anxiety disorders. Future studies should compare yoga to other forms of exercise to help determine whether yoga or exercise alone can alter GABA levels.


title: Recurrent Neural Word Segmentation with Tag Inference

abstract: In this paper, we present a Long Short-Term Memory (LSTM) based model for the task of Chinese Weibo word segmentation. The model adopts a LSTM layer to capture long-range dependencies in sentence and learn the underlying patterns. In order to infer the optimal tag path, we introduce a transition score matrix for jumping between tags of successive characters. Integrated with some unsupervised features, the performance of the model is further improved. Finally, our model achieves a weighted F1-score of 0.8044 on close track, 0.8298 on the semi-open track.


title: Biases in Self‐Ratings of Second Language Proficiency: The Role of Language Anxiety

abstract: Previous studies have shown a strong link between participants' apprehension about communicating and their perception of communicative competence in both native (L1) and second (L2) languages. This apprehension may intensify when participants communicate in the L2, especially if they believe their level of L2 competence to be very low. This study examines perceived competence in an L2 as a function of actual competence and language anxiety. Thirty-seven young adult Anglophone students, with widely varied competence in French, participated. They completed scales of language anxiety and a modified version of the “can-do” test, which assessed their self-perceptions of competence on 26 French tasks. They then attempted each of those tasks. We found that L2 language anxiety, perceived L2 competence, and actual L2 competence intercorrelated. However, regression analysis with actual proficiency level controlled showed that anxious students tended to underestimate their competence relative to less anxious students, who tended to overestimate their competence. Theoretical and practical implications are discussed.


title: Presenting a Holistic Framework for Scalable, Marker-less Motion Capturing: Skeletal Tracking Performance Analysis, Sensor Fusion Algorithms and Usage in Automotive Industry

abstract: JVRB, 13(2016), no. 3. - Even though there is promising technological progress, input is currently still one of virtual reality's biggest issues. Off-the-shelf depth cameras have the potential to resolve these tracking problems. These sensors have become common in several application areas due to their availability and affordability. However, various applications in industry and research still require large-scale tracking systems e.g. for interaction with virtual environments. As single depth-cameras have limited performance in this context, we propose a novel set of methods for multiple depth-camera registration and heuristic-based sensor fusion using skeletal tracking. An in-depth accuracy analysis of Kinect v2 skeletal tracking is presented in which a robot moves a mannequin for accurate, reproducible motion paths. Based on the results of this evaluation, a distributed and service-oriented marker-less tracking system consisting of multiple Kinect v2 sensors is developed for real-time interaction with virtual environments. Evaluation shows that this approach helps in increasing tracking areas, resolving occlusions and improving human posture analysis. Additionally, an advanced error prediction model is proposed to further improve skeletal tracking results. The overall system is evaluated by using it for realistic ergonomic assessments in automotive production verification workshops. It is shown that performance and applicability of the system is suitable for the use in automotive industry and may replace conventional high-end marker-based systems partially in this domain.


title: A Survey on Optical Character Recognition Techniques

abstract: This paper presents a review on Optical Character Recognition Techniques. Optical Character recognition (OCR) is a technology that allows machines to automatically recognize the characters through an optical mechanism. OCR can be described as Mechanical or electronic conversion of scanned images where images can be handwritten, typewritten or printed text. It converts the images into machine-encoded text that can be used in machine translation, text-to-speech and text mining. Various techniques are available for character recognition in optical character recognition system. This material can be useful for the researchers who wish to work in character recognition area.


title: Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment

abstract: Three-dimension path planning of uninhabited combat aerial vehicle UCAV is a complicated optimal problem, which mainly focused on optimizing the flight route considering the different types of constrains under complex combating environment. A novel predator-prey pigeon-inspired optimization PPPIO is proposed to solve the UCAV three-dimension path planning problem in dynamic environment. Pigeon-inspired optimization PIO is a new bio-inspired optimization algorithm. In this algorithm, map and compass operator model and landmark operator model are used to search the best result of a function. The prey-predator concept is adopted to improve global best properties and enhance the convergence speed. The characteristics of the optimal path are presented in the form of a cost function. The comparative simulation results show that our proposed PPPIO algorithm is more efficient than the basic PIO, particle swarm optimization PSO, and different evolution DE in solving UCAV three-dimensional path planning problems.


title: A finger-vein imaging and liveness detection for identity authentication using 2-axis MEMS scanner

abstract: A novel finger-vein imaging system based on a laser and scanner is proposed to improve identity authentication. Imaging of vein pattern using enhanced-resolution speckle-based blood flow for liveness detection is experimentally demonstrated.


title: Weakly-supervised text-to-speech alignment confidence measure

abstract: This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.


title: Relationships among community interaction characteristics, perceived benefits, community commitment, and oppositional brand loyalty in online brand communities

abstract: Abstract To stand up for the brands they support, members of brand communities develop “oppositional brand loyalty” towards other rival brands. This study identifies how the interaction characteristics of brand community affect the perceived benefits of community members, and whether the perceived benefits cause members to develop community commitment, as well as the relationship between community commitment and oppositional brand loyalty. This study examined members of online automobile communities in Taiwan, and obtained a total of 283 valid samples. The analytical results reveal that interaction characteristics of brand community make members perceive many benefits, with “brand community engagement” being the most noticeable. Furthermore, hedonic, social, and learning benefits are the main factors to form community commitments. When members have community commitments, they will form oppositional brand loyalty to other rival brands. Based on the analytical results, this study provides suggestions to enterprises regarding online brand community operations.


Flexible On-chip Memory Architecture for DCNN Accelerators


title: Silicon MEMS Disk Resonator Gyroscope With an Integrated CMOS Analog Front-End

abstract: We present a 2-mm diameter, 35-μm-thick disk resonator gyro (DRG) fabricated in silicon with integrated 0.35-μm CMOS analog front-end circuits. The device is fabricated in the commercial InvenSense Fabrication MEMSCMOS integrated platform, which incorporates a wafer-level vacuum seal, yielding a quality factor (Q) of 2800 at the DRGs 78-kHz resonant frequency. After performing electrostatic tuning to enable mode-matched operation, this DRG achieves a 55 μV/°/s sensitivity. Resonator vibration in the sense and drive axes is sensed using capacitive transduction, and amplified using a lownoise, on-chip integrated circuit. This allows the DRG to achieve Brownian noise-limited performance. The angle random walk is measured to be 0.008°/s/√(Hz) and the bias instability is 20°/h.


title: The Female Genital Self-Image Scale (FGSIS): Results from a Nationally Representative Probability Sample of Women in the

abstract: Introduction. Over the past two decades, an expanding body of research has examined women's and men's genital self-image. Support for the reliability and validity of the 7-item Female Genital Self-Image Scale (FGSIS) has been found in a convenience sample of women. Aims. The purpose of this study was to assess the reliability and validity of the FGSIS, its model of fit, and its association with women's scores on the Female Sexual Function Index (FSFI) in a nationally representative prob- ability sample of women in the United States ages 18 to 60. A second purpose was to assess the temporal stability of the scale in a subset of this sample. Methods. A nationally representative sample of 3,800 women ages 18 to 60 were invited to participate in a cross-sectional Internet-based survey; 2,056 (54.1%) participated. Main Outcome Measures. Demographic items (e.g., age, race/ethnicity, marital status, sexual orientation, geo- graphic region), having had a gynecological examination in the past year, having performed a genital self-examination in the past month, frequency of masturbation in the past month, vibrator use in the past month, the FGSIS, and the FSFI. Results. An abbreviated 4-item version of the scale, the FGSIS-4, was a better fit to the data than the original 7-item scale. Women's scores on the FGSIS-4 were significantly related to vibrator use, frequency of masturbation, having had a gynecological exam in the past year, having performed genital self examination in the past month, and all FSFI subscales. Conclusion. Most of the participants felt generally positively about their genitals and female genital self-image was significantly related to female sexual function, women's sexual behavior and their sexual and genital healthcare behaviors. In addition, the FGSIS-4 has evidence of reliability, validity, and temporal stability in a nationally representative probability sample of women in the United States. Herbenick D, Schick V, Reece M, Sanders S, Dodge B, and Fortenberry JD. The female genital self-image scale (FGSIS): Results from a nationally representative probability sample of women in the United States. J Sex Med 2011;8:158-166.


Periungual pilonidal sinus


abstract: Recent years have seen a revival of interest in the Alternating Direction Method of Multipliers (ADMM), due to its simplicity, versatility, and scalability. As a first order method for general convex problems, the rate of convergence of ADMM is O(1=k) [4, 25]. Given the scale of modern data mining problems, an algorithm with similar properties as ADMM but faster convergence rate can make a big difference in real world applications. In this paper, we introduce the Accelerated Alternating Direction Method of Multipliers (A2DM2) which solves problems with the same structure as ADMM. When the objective function is strongly convex, we show that A2DM2 has a O(1=k2) convergence rate. Unlike related existing literature on trying to accelerate ADMM, our analysis does not need any additional restricting assumptions. Through experiments, we show that A2DM2 converges faster than ADMM on a variety of problems. Further, we illustrate the versatility of the general A2DM2 on the problem of learning to rank, where it is shown to be competitive with the state-of-the-art specialized algorithms for the problem on both scalability and accuracy.


CHAPTER 4 Bias in Psychological Assessment An Empirical Review and Recommendations


title: Differential Privacy

abstract: In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database. The techniques developed in a sequence of papers [8, 13, 3], culminating in those described in [12], can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy


title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

abstract: We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.


title: Full-body visible human project® female computational phantom and its applications for biomedical electromagnetic modeling

abstract: This study describes the development to date of a computational full-body human phantom based on the VHP female dataset. Its unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D. Applications for low-frequency and radio-frequency electromagnetic modeling are considered.


title: Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning

abstract: To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe for workers in the factory. In this paper, we extend previously submitted work [1] which consist in rapid learning of local high accuracy behaviors. By exploration and regression, linear and quadratic models are learnt for respectively the dynamics and cost function. Iterative Linear Quadratic Gaussian Regulator combined with cost quadratic regression can converge rapidly in the final stages towards high accuracy behavior as the cost function is modelled quite precisely. In this paper, both a different cost function and a second order improvement method are implemented within this framework. We also propose an analysis of the algorithm parameters through simulation for a positioning task. Finally, an experimental validation on a KUKA LBR iiwa robot is carried out. This collaborative robot manipulator can be easily programmed into safety mode, which makes it qualified for the second industry constraint stated above.


title: The evolution of innovation management towards contextual innovation

abstract: Purpose – In recent decades, innovation management has changed. This article provides an overview of the changes that have taken place, focusing on innovation management in large companies, with the aim of explaining that innovation management has evolved toward a contextual approach, which it will explain and illustrate using two cases.Design/methodology/approach – The basic approach in this article is to juxtapose a review of existing literature regarding trends in innovation management and research and development (R&D) management generations, and empirical data about actual approaches to innovation.Findings – The idea that there is a single mainstream innovation approach does not match with the (successful) approaches companies have adopted. What is required is a contextual approach. However, research with regard to such an approach is fragmented. Decisions to adapt the innovation management approach to the newness of an innovation or the type of organization respectively have thus far been investigat...


title: Multiport Bidirectional SRM Drives for Solar-Assisted Hybrid Electric Bus Powertrain With Flexible Driving and Self-Charging Functions

abstract: The hybrid electric bus (HEB) presents an emerging solution to exhaust gas emissions in urban transport. This paper proposes a multiport bidirectional switched reluctance motor (SRM) drive for solar-assisted HEB (SHEB) powertrain, which not only improves the motoring performance, but also achieves flexible charging functions. To extend the driving miles and achieve self-charging ability, photovoltaic (PV) panels are installed on the bus to decrease the reliance on fuelsbatteries and charging stations. A bidirectional front-end circuit with a PV-fed circuit is designed to integrate electrical components into one converter. Six driving and five charging modes are achieved. The dc voltage is boosted by the battery in generator control unit (GCU) driving mode and by the charge capacitor in battery driving mode, where the torque capability is improved. Usually, an extra converter is needed to achieve battery charging. In this paper, the battery can be directly charged by the demagnetization current in GCU or PV driving mode, and can be quickly charged by the PV panels and GCUAC grids at SHEB standstill conditions, by utilizing the traction motor windings and integrated converter circuit, without external charging converters. Experiments on a three-phase 128 SRM confirm the effectiveness of the proposed drive and control scheme.


title: Lonely People Are No Longer Lonely on Social Networking Sites: The Mediating Role of Self-Disclosure and Social Support

abstract: Abstract Most previous studies assert the negative effect of loneliness on social life and an individual's well-being when individuals use the Internet. To expand this previous research tradition, the current study proposes a model to test whether loneliness has a direct or indirect effect on well-being when mediated by self-disclosure and social support. The results show that loneliness has a direct negative impact on well-being but a positive effect on self-disclosure. While self-disclosure positively influences social support, self-disclosure has no impact on well-being, and social support positively influences well-being. The results also show a full mediation effect of social support in the self-disclosure to well-being link. The results imply that even if lonely people's well-being is poor, their well-being can be enhanced through the use of SNSs, including self-presentation and social support from their friends.