title: Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms

abstract: Two different formal definitions of gray-scale reconstruction are presented. The use of gray-scale reconstruction in various image processing applications discussed to illustrate the usefulness of this transformation for image filtering and segmentation tasks. The standard parallel and sequential approaches to reconstruction are reviewed. It is shown that their common drawback is their inefficiency on conventional computers. To improve this situation, an algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels is introduced. Its combination with the sequential technique results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm. >


title: Single-Feed Dual-Layer Dual-Band E-Shaped and U-Slot Patch Antenna for Wireless Communication Application

abstract: A new design for single-feed dual-layer dual-band patch antenna with linear polarization is presented in this letter. The dual-band performance is achieved by E-shaped and U-slot patches. The proposed bands of the antenna are WLAN (2.40-2.4835 GHz) and WiMAX (3.40-3.61 GHz) bands. The fundamental modes of the two bands are TM01 mode, and the impedance bandwidths ( ) of 26.9% and 7.1% are achieved at central frequencies of 2.60 and 3.50 GHz. The peak gains of two different bands are 7.1 and 7.4 dBi, and good band isolation is achieved between the two bands. The advantages of the antenna are simple structure, wideband performance at low band, and high gains.


title: BoostCluster: boosting clustering by pairwise constraints

abstract: Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering,termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (K-means, partitional SingleLink, spectral clustering), and its performance is comparable to the state-of-the-art algorithms for data clustering with side information.


title: A New Scalable Parallel Algorithm for Fock Matrix Construction

abstract: Hartree-Fock (HF) or self-consistent field (SCF) calculations are widely used in quantum chemistry, and are the starting point for accurate electronic correlation methods. Existing algorithms and software, however, may fail to scale for large numbers of cores of a distributed machine, particularly in the simulation of moderately-sized molecules. In existing codes, HF calculations are divided into tasks. Fine-grained tasks are better for load balance, but coarse-grained tasks require less communication. In this paper, we present a new parallelization of HF calculations that addresses this trade-off: we use fine grained tasks to balance the computation among large numbers of cores, but we also use a scheme to assign tasks to processes to reduce communication. We specifically focus on the distributed construction of the Fock matrix arising in the HF algorithm, and describe the data access patterns in detail. For our test molecules, our implementation shows better scalability than NWChem for constructing the Fock matrix.


title: Automatically Assessing Review Helpfulness

abstract: User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.


title: Optimal Dynamic Pricing of Perishable Items by a Monopolist Facing Strategic Consumers

abstract: We introduce a dynamic pricing model for a monopolistic company selling a perishable product to a finite population of strategic consumers (customers who are aware that pricing is dynamic and may time their purchases strategically). This problem is modeled as a stochastic dynamic game in which the company’s objective is to maximize total expected revenues, and each customer maximizes the expected present value of utility. We prove the existence of a unique subgame-perfect equilibrium pricing policy, provide equilibrium optimality conditions for both customer and seller, and prove monotonicity results for special cases. We demonstrate through numerical examples that a company that ignores strategic consumer behavior may receive much lower total revenues than one that uses the strategic equilibrium pricing policy. We also show that, when the initial capacity is a decision variable, it can be used together with the appropriate pricing policy to effectively reduce the impact of strategic consumer behavior. The proposed model is computationally tractable for problems of realistic size.


title: Software process improvement through the Lean Measurement (SPI-LEAM) method

abstract: Software process improvement methods help to continuously refine and adjust the software process to improve its performance (e.g., in terms of lead-time, quality of the software product, reduction of change requests, and so forth). Lean software development propagates two important principles that help process improvement, namely identification of waste in the process and considering interactions between the individual parts of the software process from an end-to-end perspective. A large shift of thinking about the own way of working is often required to adopt lean. One of the potential main sources of failure is to try to make a too large shift about the ways of working at once. Therefore, the change to lean has to be done in a continuous and incremental way. In response to this we propose a novel approach to bring together the quality improvement paradigm and lean software development practices, the approach being called Software Process Improvement through the Lean Measurement (SPI-LEAM) Method. The method allows to assess the performance of the development process and take continuous actions to arrive at a more lean software process over time. The method is under implementation in industry and an initial evaluation of the method has been performed.


title: Theory and Applications of Robust Optimization

abstract: In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.


title: Toward Socially Intelligent Service Robots

abstract: In the Intelligent Robotics Laboratory at Vanderbilt University, we seek to develop service robots with a high level of social intelligence and interactivity. In order to achieve this goal, we have identified two main issues for research. The first issue is how to achieve a high level of interaction between the human and the robot. This has lead to the formulation of our philosophy of Human Directed Local Autonomy (HuDL), a guiding principle for research, design, and implementation of service robots. The motivation for integrating humans into a service robot system is to take advantage of human intelligence and skill. Human intelligence can be used to interpret robot sensor data, eliminating computationally expensive and possibly error-prone automated analyses. Human skill is a valuable resource for trajectory and path planning as well as for simplifying the search process. In this article, we present our plans for integrating humans into a service robot system. We present our paradigm for human-robot int...


title: SpotGarbage: smartphone app to detect garbage using deep learning

abstract: Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on their neighborhoods, this paper presents a novel smartphone app, called SpotGarbage, which detects and coarsely segments garbage regions in a user-clicked geo-tagged image. The app utilizes the proposed deep architecture of fully convolutional networks for detecting garbage in images. The model has been trained on a newly introduced Garbage In Images (GINI) dataset, achieving a mean accuracy of 87.69%. The paper also proposes optimizations in the network architecture resulting in a reduction of 87.9% in memory usage and 96.8% in prediction time with no loss in accuracy, facilitating its usage in resource constrained smartphones.


title: Using Temporal Logic and Model Checking in Automated Recognition of Human Activities for Ambient-Assisted Living

abstract: Automated monitoring and the recognition of activities of daily living (ADLs) is a key challenge in ambient-assisted living (AAL) for the assistance of the elderly. Within this context, a formal approach may provide a means to fill the gap between the low-level observations acquired by sensing devices and the high-level concepts that are required for the recognition of human activities. We describe a system named ARA (Automated Recognizer of ADLs) that exploits propositional temporal logic and model checking to support automated real-time recognition of ADLs within a smart environment. The logic is shown to be expressive enough for the specification of realistic patterns of ADLs in terms of basic actions detected by a sensorized environment. The online model checking engine is shown to be capable of processing a stream of detected actions in real time. The effectiveness and viability of the approach are evaluated within the context of a smart kitchen, where different types of ADLs are repeatedly performed.


title: Dissipative fault-tolerant control for T-S fuzzy descriptor systems

abstract: This paper focuses on the robust dissipative fault-tolerant control problem for one kind of Takagi-Sugeno (T-S) fuzzy descriptor system with actuator failures. The solvable conditions of the robust dissipative fault-tolerant controller are given by using of the Lyapunov theory, Lagrange interpolation polynomial theory, etc. These solvable conditions not only make the closed loop system dissipative, but also integral for the actuator failure situation. The dissipative fault-tolerant controller design methods are given by the aid of the linear matrix inequality toolbox, the function of randomly generated matrix, loop statement, and numerical solution, etc. Thus, simulation process is fully intelligent and efficient. At the same time, the design methods are also obtained for the passive and H∞ fault-tolerant controllers. This explains the fact that the dissipative control unifies H∞ control and passive control. Finally, we give example that illustrates our results.


Flash! Mobs in the Age of Mobile Connectivity


title: Brain Networks and Cognitive Architectures

abstract: Most accounts of human cognitive architectures have focused on computational accounts of cognition while making little contact with the study of anatomical structures and physiological processes. A renewed convergence between neurobiology and cognition is well under way. A promising area arises from the overlap between systems/cognitive neuroscience on the one side and the discipline of network science on the other. Neuroscience increasingly adopts network tools and concepts to describe the operation of collections of brain regions. Beyond just providing illustrative metaphors, network science offers a theoretical framework for approaching brain structure and function as a multi-scale system composed of networks of neurons, circuits, nuclei, cortical areas, and systems of areas. This paper views large-scale networks at the level of areas and systems, mostly on the basis of data from human neuroimaging, and how this view of network structure and function has begun to illuminate our understanding of the biological basis of cognitive architectures.


title: Lessons Learnt from Network Modelling of a Low Heat Density District Heating System

abstract: This paper presents the lessons learnt during the development of a library for the modelling of district heating systems (DH systems), especially focusing on the distribution network. The library was built based on elements from the Modelica Standard Library (Modelica Association, 2012) and the NewThermal library (Lopez, del Hoyo, 2014). The modelling strategy chosen is described. Furthermore, the requirements established by the DH networks are set out as well as the models created in response to these demands. Finally, the artificial diffusion phenomenon, present in the simulation of this kind of thermo-fluid systems, is described.


title: A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

abstract: Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.


title: Adversarial Examples for Evaluating Reading Comprehension Systems

abstract: Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of $75\%$ F1 score to $36\%$; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to $7\%$. We hope our insights will motivate the development of new models that understand language more precisely.


title: The Initial Factors Influencing Successful Final Stereoacuity in Refractive Accommodative Esotropia

abstract: Purpose: To evaluate the initial factors influencing successful final stereoacuity in patients with refractive accommodative esotropia. Methods: The charts of 48 refractive accommodative esotropia patients 2 years of age or older andassessed using the Titmus stereoacuity test were retrospectively reviewed. Hyperopic refractive error on post-cycloplegic refraction, age at first glasses, stereoacuity after refractive correction and amblyopia were categorized and evaluated as the factors influencing final stereoacuity. Results: The mean follow-up period was 43.51 ± 30.02 months. The mean hypermetropia at the initial examination was 4.67 ± 2.18 diopters (D), the mean age at first glasses was 48.09 ± 20.22 months and the mean stereoacuity after refractive correction was 1243.75 ± 1378.24 seconds of arc. The rates of successful stereoacuity at the final visit were 83.3% without amblyopia, 75.0% with mild amblyopia and 42.9% with moderate amblyopia which were statistically significant (p = 0.039). Hyperopic refractive error and age at first glasses were categorized as +1.0 to <3.0 D, +3.0 to <5.0 D, ≥+5.0 D and 2 to <4 years, 4 to <6 years and ≥6 years, respectively, and the rates of successful stereoacuity at the final visit were 83.3%, 66.7%, 61.9% (p = 0.362) and 71.4%, 75.0% and 54.5% (p = 0.334), respectively. Conclusions: The amblyopia at the initial examination was significantly associated with the outcome of stereoacuity at the final visit in patients with refractive accommodative esotropia. J Korean Ophthalmol Soc 2014;55(6):877-881


title: Disaster survivability in elastic optical datacenter networks

abstract: Due to the increasing network risk of large-scale disaster, it is important to protect failed requests for network operators. For the disaster survivable problem in an elastic optical datacenter networks with a bandwidth constraint, we formulate an integer linear program (ILP) for shared protection model. The algorithm utilizes the priori method to maximize the network throughput firstly, and then to minimize used spectrum resources. Unicast and anycast methods in shared protection are investigated. Simulation results show that both methods provide the same network throughput, but the anycast method can use much less spectrum resources than the unicast method.


title: Infinite Variational Autoencoder for Semi-Supervised Learning

abstract: This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data. Experiments show the flexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available.


title: An overview of spoken language technology for education

abstract: This paper reviews research in spoken language technology for education and more specifically for language learning. It traces the history of the domain and then groups main issues in the interaction with the student. It addresses the modalities of interaction and their implementation issues and algorithms. Then it discusses one user population - children - and an application for them. Finally it has a discussion of overall systems. It can be used as an introduction to the field and a source of reference materials.


Multi-tool and Multi-axis CNC Accumulation for Fabricating Conformal Features on Curved Surfaces


title: One pot synthesis of two Mn(II) perchlorate complexes with s -triazine NNN -pincer ligand; molecular structure, Hirshfeld analysis and DFT studies

abstract: Abstract Self assembly of Mn(II) perchlorate and bis(pyrazolo)-s-triazine pincer ligand (L) in methanol-water mixture afforded the homoleptic [MnL2](ClO4)2 complex (1) as plate colorless crystals. Following the crystallization process till the near dryness of the solution, we noted few needle like crystals of the heteroleptic [MnL(H2O)3](ClO4)2·H2O complex (2). Their molecular and supramolecular structures were analyzed using single crystal structure combined with Hirshfeld analysis. The packing of complexes 1 and 2 is dominated by weak C H⋯O and strong O H⋯O hydrogen bonds, respectively, as well as anion-π stacking interactions. Using Hirshfeld analysis, the percentages of the O⋯H intermolecular contacts are 32.7% and 36.8% for 1 and 2, respectively. The Mn N distances correlated well with the atoms in molecules (AIM) topological parameters. The amount of electron density transferred from the ligand units to the manganese centre are nearly the same (0.9 e) in both complexes.


title: Reactive power control in microgrid by using Photovoltaic Generators

abstract: The objective of this paper is to analyse the reactive power control of the micro grid system using Photo Voltaic (PV) as a source. This paper focuses on the compensation of reactive power and improving the real power. This system process is implemented in micro grid system. The harmonic distortion levels and reduction methods were increased penetration levels of Photovoltaic Generators (PVGs) in the distribution network. The maximum IHDv and THDv of the system do not exceed the IEEE-519 limit. To achieve the Maximum Power,role="author"Micro grid system with Perturb and Observe algorithm and Maximum Power Point Tracking (MPPT) has been proposed. This Paper proposes the several control algorithms through which the capability of PV generators for active and reactive power (P-Q) in microgrid connected could be harnessed. A closed loop is implemented which improves the voltage. In this closed loop system PID controller is used which maintains the stability of the system. A DC-DC converter is implemented which improves the output voltage. The chosen control parameters in the proposed control methodologies can seamlessly control inverter P-Q in grid connected system. The proposed technique is designed by Matlab/simulink. The simulation of PQ control is analysed. The simulation is carried out by the waveform.


title: Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers

abstract: Document classification tasks were primarily tackled at word level. Recent research that works with character-level inputs shows several benefits over word-level approaches such as natural incorporation of morphemes and better handling of rare words. We propose a neural network architecture that utilizes both convolution and recurrent layers to efficiently encode character inputs. We validate the proposed model on eight large scale document classification tasks and compare with character-level convolution-only models. It achieves comparable performances with much less parameters.


title: A study on the position of CDO for improving competitiveness based big data in cluster computing environment

abstract: So many companies became enthusiastic over accumulating and managing data, decision making through data analysis, and improvement of competitiveness. This definitely requires budget and work force for data management and utilization. However, organizations do not try to raise budget and increase work force recklessly, for they must be within the range of being managed. For this reason, Chief Data Officer (CDO) was introduced. Even though CDO is highly necessary and important in organizations, CDO is not even recognized by many organizations in Korea. Also, it is hard to find any studies or reports about CDO. Also, CDO is a sub-department or sub-organization of CIO so it has a weak position in organizations. For this reason, the purpose of this study is to support to strengthen competiveness of current organization by suggesting a plan for a desirable position and role of CDO in organizations. To achieve the purpose, actual status of CDO organization in Korea is surveyed and analyzed. This study suggests the alternative to enhance the desirable position for CDO based on the implication from analysis.


title: We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones

abstract: Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users’ privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack.


title: Computing Distrust in Social Media

abstract: Abstract : A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.


title: Learning Precise Timing with LSTM Recurrent Networks

abstract: The temporal distance between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While Hidden Markov Models tend to ignore this information, recurrent neural networks (RNNs) can in principle learn to make use of it. We focus on Long Short-Term Memory (LSTM) because it has been shown to outperform other RNNs on tasks involving long time lags. We find that LSTM augmented by "peephole connections" from its internal cells to its multiplicative gates can learn the fine distinction between sequences of spikes spaced either 50 or 49 time steps apart without the help of any short training exemplars. Without external resets or teacher forcing, our LSTM variant also learns to generate stable streams of precisely timed spikes and other highly nonlinear periodic patterns. This makes LSTM a promising approach for tasks that require the accurate measurement or generation of time intervals.


title: Evolution of Open Source Software Projects: A Systematic Literature Review

abstract: Open Source Software (OSS) is continuouslygaining acceptance in commercial organizations. It is inthis regard that those organizations strive for a betterunderstanding of evolutionary aspects of OSS projects.The study of evolutionary patterns of OSS projects andcommunities has received substantial attention from theresearch community over the last decade. These effortshave resulted in an ample set of research results for whichthere is a need for up-to-date comprehensive overviews andliterature surveys. This paper reports on a systematic literature survey aimedat the identiﬁcation and structuring of research on evolutionof OSS projects. In this review we systematically selectedand reviewed 101 articles published in relevant venues. Thestudy outcome provides insight in what constitutes the maincontributions of the ﬁeld, identiﬁes gaps and opportunities,and distills several important future research directions.


title: DEVELOPMENT OF AN AUTOMATED HANDWRITING ANALYSIS SYSTEM

abstract: In the present study a method has been proposed for the behavioral prediction of a person through automated handwriting analysis. The present work identifies the psychological traits in the writing namely size, slant and pressure, baseline, number of breaks, margins, speed of writing and spacing between the words. The handwriting is analyzed through Image Processing in MATLAB. The behavioral pattern of the person is predicted from the above traits of the handwriting. The developed system identifies handwriting closely which may not be possible for a graphologist. It is real time and involves less image preprocessing. The proposed system is calibrated with manual analysis. The results obtained through the system are in good agreement to more than 80 percent of the cases with ideal manual analysis.


title: A novel image encryption algorithm using AES and visual cryptography

abstract: With the current emergence of the Internet, there is a need to securely transfer images between systems. In this context, we propose a secure image encryption algorithm that uses both AES and Visual Cryptographic techniques to protect the image. The image is encrypted using AES and an encoding schema has been proposed to convert the key into shares based on Visual Secret Sharing. The cryptanalysis of the algorithm is then performed and is proved to be secure. The proposed algorithm is then implemented using python and the results are discussed along with the possible future modifications.


title: Happiness Economics, Eudaimonia and Positive Psychology: From Happiness Economics to Flourishing Economics

abstract: A remarkable current development, happiness economics focuses on the relevance of people’s happiness in economic analyses. As this theory has been criticised for relying on an incomplete notion of happiness, this paper intends to support it with richer philosophical and psychological foundations. Specifically, it suggests that happiness economics should be based on Aristotle’s philosophical eudaimonia concept and on a modified version of ‘positive psychology’ that stresses human beings’ relational nature. First, this analysis describes happiness economics and its shortcomings. Next, it introduces Aristotle’s eudaimonia and takes a look at positive psychology with this lens, elaborating on the need to develop a new approach that goes beyond the economics of happiness: the economics of flourishing. Finally, the paper specifies some possible socio-economic objectives of a eudaimonic economics of happiness.


title: IEMOCAP: interactive emotional dyadic motion capture database

abstract: Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately 12 h of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication.


title: Recommending scientific articles using citeulike

abstract: We describe the use of the social reference management website CiteULike for recommending scientific articles to users, based on their reference library. We test three different collaborative filtering algorithms, and find that user-based filtering performs best. A temporal analysis of the data indexed by CiteULike shows that it takes about two years for the cold-start problem to disappear and recommendation performance to improve.


title: MRI of pregnancy-related issues: abnormal placentation.

abstract: OBJECTIVE ::: In this article, we review the clinical significance of abnormal placentation and the role of MRI in diagnosis and management of this potentially morbid condition. We present our clinical perspective on diagnosing this challenging problem with MRI and review the imaging findings that can lead to a correct diagnosis. ::: ::: ::: CONCLUSION ::: As abnormal placentation becomes more prevalent, in large part due to the markedly rising rates of cesarean delivery, there is a need for accurate antenatal diagnosis of this condition to prevent maternal morbidity and mortality. Maternal and fetal outcomes can be optimized through multidisciplinary planning to achieve accurate diagnosis and anticipation of the extent of abnormal placentation in the antenatal period. Imaging findings of abnormal placentation have been described for both ultrasound and MRI, although limitations exist for each technique. Although ultrasound remains the primary screening modality for the detection of abnormal placentation, MRI is a complementary technique that should be considered when ultrasound is inconclusive or incomplete. Familiarity with MRI techniques to assess the placenta, MRI appearance of normal placenta, and imaging findings that suggest abnormal placentation can help radiologists contribute to a successful maternal outcome.


title: QR code recognition based on image processing

abstract: To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions, different acquisition angles to be quickly identified. Based on other recognition algorithm, some improvements are presented in the image tilt correction, image orientation, image normalization and so on to speed up the image processing and to achieve more simply. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy.


title: Pathophysiology of depression and innovative treatments: remodeling glutamatergic synaptic connections

abstract: Despite the complexity and heterogeneity of mood disorders, basic and clinical research studies have begun to elucidate the pathophysiology of depression and to identify rapid, efficacious antidepressant agents. Stress and depression are associated with neuronal atrophy, characterized by loss of synaptic connections in key cortical and limbic brain regions implicated in depression. This is thought to occur in part via decreased expression and function of growth factors, such as brain-derived neurotrophic factor (BDNF), in the prefrontal cortex (PFC) and hippocampus. These structural alterations are difficult to reverse with typical antidepressants. However, recent studies demonstrate that ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist that produces rapid antidepressant actions in treatment-resistant depressed patients, rapidly increases spine synapses in the PFC and reverses the deficits caused by chronic stress. This is thought to occur by disinhibition of glutamate transmission, resulting in a rapid but transient burst of glutamate, followed by an increase in BDNF release and activation of downstream signaling pathways that stimulate synapse formation. Recent work demonstrates that the rapid-acting antidepressant effects of scopolamine, a muscarinic receptor antagonist, are also associated with increased glutamate transmission and synapse formation. These findings have resulted in testing and identification of additional targets and agents that influence glutamate transmission and have rapid antidepressant actions in rodent models and in clinical trials. Together these studies have created tremendous excitement and hope for a new generation of rapid, efficacious antidepressants.


Wireless Infrared Communications


title: High-Q VHF micromechanical contour-mode disk resonators

abstract: A micromechanical, laterally vibrating disk resonator, fabricated via a technology that combines polysilicon surface-micromachining and metal electroplating to attain submicron lateral capacitive gaps, has been demonstrated at frequencies approaching 160 MHz with Q's as high as 9,400-the highest demonstrated to date for an on-chip resonator in this frequency range. This frequency also represents the highest to date for an electrostatically transduced micromechanical resonator and is an important step towards reaching the frequencies required by the RF front-ends in wireless transceivers. The geometric dimensions necessary to reach a given frequency are larger for this contour-mode than for the flexural-modes used by previous resonators. This, coupled with its unprecedented Q value, makes this disk resonator a choice candidate for use in the IF and RF stages of future miniaturized transceivers.


title: Perceiving Affective Content in Ambiguous Visual Stimuli: A Component of Emotional Intelligence

abstract: Emotional intelligence involves the accurate appraisal and expression of emotions in oneself and others and the regulation of emotion in a way that enhances living. One aspect of emotional intelligence is the ability to recognize the consensually agreed upon emotional qualities of objects in the environment. One hundred thirty-nine adults viewed 18 reproductions of faces, color swatches, and abstract designs and rated the emotional content of these visual stimuli. Three scores were extracted, including consensual accuracy, amount, and range of emotion perceived. These scores were compared with other aspects of emotional intelligence such as empathy and related to constructs such as alexithymia and neuroticism. A general ability to perceive consensual emotional content in visual stimuli was found, and it was most strongly associated with the ability to respond empathically to others.


title: A Highly Efficient Runtime and Graph Library for Large Scale Graph Analytics

abstract: Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing systems have emerged targeting various perspectives of a graph application such as in memory and on disk representations, persistent storage, database capability, runtimes and execution models for exploiting parallelism, etc. In this paper we discuss a novel graph processing system called System G Native Store which allows for efficient graph data organization and processing on modern computing architectures. In particular we describe a runtime designed to exploit multiple levels of parallelism and a generic infrastructure that allows users to express graphs with various in memory and persistent storage properties. We experimentally show the efficiency of System G Native Store for processing graph queries on state-of-the-art platforms.


title: Quantum Simulation

abstract: Simulating quantum mechanics is known to be a difficult computational problem, especially when dealing with large systems. However, this difficulty may be overcome by using some controllable quantum system to study another less controllable or accessible quantum system, i.e., quantum simulation. Quantum simulation promises to have applications in the study of many problems in, e.g., condensed-matter physics, high-energy physics, atomic physics, quantum chemistry and cosmology. Quantum simulation could be implemented using quantum computers, but also with simpler, analog devices that would require less control, and therefore, would be easier to construct. A number of quantum systems such as neutral atoms, ions, polar molecules, electrons in semiconductors, superconducting circuits, nuclear spins and photons have been proposed as quantum simulators. This review outlines the main theoretical and experimental aspects of quantum simulation and emphasizes some of the challenges and promises of this fast-growing field.


title: A State-of-the-Art Mention-Pair Model for Coreference Resolution

abstract: Most recent studies on coreference resolution advocate accurate yet relatively complex models, relying on, for example, entitymention or graph-based representations. As it has been convincingly demonstrated at the recent CoNLL 2012 shared task, such algorithms considerably outperform popular basic approaches, in particular mention-pair models. This study advocates a novel approach that keeps the simplicity of a mention-pair framework, while showing state-of-the-art results. Apart from being very efficient and straightforward to implement, our model facilitates experimental work on the pairwise classifier, in particular on feature engineering. The proposed model achieves the performance level of up to 61.82% (MELA F, v4 scorer) on the CoNLL test data, on par with complex state-of-the-art systems.


title: A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization

abstract: Total variation (TV) is a one of the most popular regularizers for stabilizing the solution of ill-posed inverse problems. This paper proposes a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functionals. Unlike traditional methods that require nested iterations for computing the proximal step of TV, our algorithm approximates the latter with several simple proximals that have closed form solutions. We theoretically prove that the proposed parallel proximal method achieves the TV solution with arbitrarily high precision at a global rate of converge that is equivalent to the fast proximal-gradient methods. The results in this paper have the potential to enhance the applicability of TV for solving very large-scale imaging inverse problems.


title: A proposed model and measurement instrument for the formation of IS satisfaction: the case of end-user computing satisfaction

abstract: This paper presents a model that explicitly defines satisfaction and the antecedent factors that help form it. The model distinguishes between the notions of expectations and desires and argues that both have an impact on overall satisfaction in the form of the difference between priors and post hoc usage perceptions coupled with the individual’s evaluation of these discrepancies. These two types of satisfaction, in turn, will have both direct and multiplicative impact on overall satisfaction. Given this understanding, we highlight possible limitations in existing instruments and provide a solution for creating new measures that should overcome these limitations. A complete set of measures is provided in this paper for future empirical testing, which are general enough to allow researchers to create measures for other aspects related to IS satisfaction beyond those targeted in this paper. We employ our model within the context of the five satisfaction areas outlined by Doll and Torzadeh (1988). Building upon their initial set of perceptual measures, we examine whether there are indeed two types of discrepancy effects, whether there are multiplicative effects for each discrepancy, and whether there is a higher order interaction between the two discrepancy components. Partial least squares analyses of data consisting of over 200 instructional staff member at a large university related to their satisfaction with an online grading system are employed and the results presented at the conference.


title: Metalinguistic Information Extraction for Terminology

abstract: This paper describes and evaluates the Metalinguistic Operation Processor (MOP) system for automatic compilation of metalinguistic information from technical and scientific documents. This system is designed to extract non-standard terminological resources that we have called Metalinguistic Information Databases (or MIDs), in order to help update changing glossaries, knowledge bases and ontologies, as well as to reflect the metastable dynamics of special-domain knowledge.


title: High-performance long range obstacle detection using stereo vision

abstract: Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars. We present a novel method for the joint detection and localization of distant obstacles using a stereo vision system on a moving platform. The approach is applicable to both static and moving obstacles and pushes the limits of detection performance as well as localization accuracy.


title: Epithelium-stroma classification in histopathological images via convolutional neural networks and self-taught learning

abstract: Epithelium-stroma classification is always considered as an important preprocessing step for morphological quantitative analysis in image-based histological researches of oncologic diseases. However, large-scale accurate ground-truth labeling is expensive in histopathological image analysis, thus the classification performances will still be limited with the insufficient labeled training samples. Considering that acquisition of public unlabeled histopathological images is much cheaper, an epithelium-stroma classification framework is developed, based on the deep convolutional neural network framework and the strategies of self-taught learning. The method has the ability of taking advantage of large-scale unlabeled public histopathological data as auxiliary data, and then transferring the knowledge to enhance the performances in epithelium-stroma classification with limited labeled training data. The experiments demonstrate that the proposed method outperforms traditional CNNs when the labeled training data size is decreasing dramatically.


Periopathogenic bacteria in dental plaque of Congolese patients with periodontitis: A pilot study


title: SpotOn: a batch computing service for the spot market

abstract: Cloud spot markets enable users to bid for compute resources, such that the cloud platform may revoke them if the market price rises too high. Due to their increased risk, revocable resources in the spot market are often significantly cheaper (by as much as 10×) than the equivalent non-revocable on-demand resources. One way to mitigate spot market risk is to use various fault-tolerance mechanisms, such as checkpointing or replication, to limit the work lost on revocation. However, the additional performance overhead and cost for a particular fault-tolerance mechanism is a complex function of both an application's resource usage and the magnitude and volatility of spot market prices. We present the design of a batch computing service for the spot market, called SpotOn, that automatically selects a spot market and fault-tolerance mechanism to mitigate the impact of spot revocations without requiring application modification. SpotOn's goal is to execute jobs with the performance of on-demand resources, but at a cost near that of the spot market. We implement and evaluate SpotOn in simulation and using a prototype on Amazon's EC2 that packages jobs in Linux Containers. Our simulation results using a job trace from a Google cluster indicate that SpotOn lowers costs by 91.9% compared to using on-demand resources with little impact on performance.


title: Effects of Magnetic Coupling of Nonadjacent Resonators on Wireless Power Domino-Resonator Systems

abstract: In this paper, the effects of the magnetic coupling of nonadjacent resonators on the optimal frequency of wireless power transfer are addressed. A power analysis has been carried out to identify the adjacent and nonadjacent power flow components. It is found that such cross-coupling effects of nonadjacent resonators would cause the maximum efficiency operation to slightly shift away from the resonance frequency of the resonators. Theoretical reasons for such phenomena are provided and experimentally confirmed with practical measurements in a wireless power transfer system comprising several magnetically coupled resonators arranged in a straight domino form.


title: Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data

abstract: Over the past few years, large human populations around the world have been affected by an increase in significant seismic activities. For both conducting basic scientific research and for setting critical government policies, it is crucial to be able to explore and understand seismic and geographical information obtained through all scientific instruments. In this work, we present a visual analytics system that enables explorative visualization of seismic data together with satellite-based observational data, and introduce a suite of visual analytical tools. Seismic and satellite data are integrated temporally and spatially. Users can select temporal ;and spatial ranges to zoom in on specific seismic events, as well as to inspect changes both during and after the events. Tools for designing high dimensional transfer functions have been developed to enable efficient and intuitive comprehension of the multi-modal data. Spread-sheet style comparisons are used for data drill-down as well as presentation. Comparisons between distinct seismic events are also provided for characterizing event-wise differences. Our system has been designed for scalability in terms of data size, complexity (i.e. number of modalities), and varying form factors of display environments.


title: Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of “Far Transfer”

abstract: It has been claimed that working memory training programs produce diverse beneficial effects. This article presents a meta-analysis of working memory training studies (with a pretest-posttest design and a control group) that have examined transfer to other measures (nonverbal ability, verbal ability, word decoding, reading comprehension, or arithmetic; 87 publications with 145 experimental comparisons). Immediately following training there were reliable improvements on measures of intermediate transfer (verbal and visuospatial working memory). For measures of far transfer (nonverbal ability, verbal ability, word decoding, reading comprehension, arithmetic) there was no convincing evidence of any reliable improvements when working memory training was compared with a treated control condition. Furthermore, mediation analyses indicated that across studies, the degree of improvement on working memory measures was not related to the magnitude of far-transfer effects found. Finally, analysis of publication bias...


title: User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

abstract: Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.


title: Keystroke and Eye-Tracking Biometrics for User Identification

abstract: Intelligent Information Technology Research Laboratory,Jodrey School of Computer Science, Acadia University,Wolfville, Nova Scotia, Canada B4P 2R6danny.silver@acadiau.caphone:902-585-1105, fax: 902-585-1067Abstract. Two sources of weak biometric data are investigated for thedevelopment of user identiﬁcation models. A probabilistic neural net-work model is created from keystroke digraph features extracted fromraw typing data. A second model is developed from scan-path featuresextracted from raw eye-tracking data. A third model is created froma combination of features from the two biometric sources. Experimen-tal results show that models based on keystroke biometric data performvery well, whereas the models involving the eye-tracking data are not assuccessful but encourage further study.Keywords: biometric modeling, probabilistic neural networks, eye-tracking, keystrokeanalysis.


title: Plantar Fasciitis: Diagnosis and Therapeutic Considerations

abstract: Plantar fasciitis is the most common cause of inferior heel pain. The pain and discomfort associated with this condition can have a dramatic impact on physical mobility. The etiology of this condition is not clearly understood and is probably multi-factorial in nature. Weight gain, occupation-related activity, anatomical variations, poor biomechanics, overexertion, and inadequate footwear are contributing factors. Although plantar fasciitis is generally regarded as a self-limited condition, it can take months to years to resolve, presenting a challenge for clinicians. Many treatment options are available that demonstrate variable levels of efficacy. Conservative therapies include rest and avoidance of potentially aggravating activities, stretching and strengthening exercises, orthotics, arch supports, and night splinting. Other considerations include use of anti-inflammatory agents, ultrasonic shockwave therapy, and, in the most extreme cases, surgery. This article reviews plantar fasciitis, presents the most effective treatment options currently available, and proposes nutritional considerations that may be beneficial in the management of this condition. (Altern Med Rev 2005;10(2):83-93)


Semantic Annotation of Clinical Text : The CLEF Corpus


title: Binomial Filters

abstract: Binomial filters are simple and efficient structures based on the binomial coefficients for implementing Gaussian filtering. They do not require multipliers and can therefore be implemented efficiently in programmable hardware. There are many possible variations of the basic binomial filter structure, and they provide a wide range of space-time trade-offs; a number of these designs have been captured in a parametrised form and their features are compared. This technique can be used for multi-dimensional filtering, provided that the filter is separable. The numerical performance of binomial filters, and their implementation using field-programmable devices for an image processing application, are also discussed.


title: Invariant visual representation by single neurons in the human brain

abstract: Stars in their eyesIt takes moments for the human brain to recognize a person or an object even if seen under very different conditions. This raises the question: can a single neuron respond selectively to a given face regardless of view, age, pose or context? That question — it has been called the search for the ‘grandmother neuron’ — is difficult to test. But now, in patients with intractable epilepsy who were implanted with depth electrodes for a clinical process, an answer has been obtained. Patients were asked to respond to images on computer screens, and the results showed that neurons are pretty single-minded in what they respond to. For instance, one neuron will respond selectively to different pictures of the actress Jennifer Aniston, one to basketball player Michael Jordan, and another to different views of the Tower of Pisa.AbstractIt takes a fraction of a second to recognize a person or an object even when seen under strikingly different conditions. How such a robust, high-level representation is achieved by neurons in the human brain is still unclear1,2,3,4,5,6. In monkeys, neurons in the upper stages of the ventral visual pathway respond to complex images such as faces and objects and show some degree of invariance to metric properties such as the stimulus size, position and viewing angle2,4,7,8,9,10,11,12. We have previously shown that neurons in the human medial temporal lobe (MTL) fire selectively to images of faces, animals, objects or scenes13,14. Here we report on a remarkable subset of MTL neurons that are selectively activated by strikingly different pictures of given individuals, landmarks or objects and in some cases even by letter strings with their names. These results suggest an invariant, sparse and explicit code, which might be important in the transformation of complex visual percepts into long-term and more abstract memories.


title: Intergroup emotions: Explaining offensive action tendencies in an intergroup context

abstract: Purdue University Three studies tested the idea that when social identity is salient, group-based appraisals elicit specific emotions and action tendencies toward out-groups. Participants' group memberships were made salient and the collective support apparently enjoyed by the in-group was measured or manipulated. The authors then measured anger and fear (Studies 1 and 2) and anger and contempt (Study 3), as well as the desire to move against or away from the out-group. Intergroup anger was distinct from intergroup fear, and the inclination to act against the out-group was distinct from the tendency to move away from it. Participants who perceived the in-group as strong were more likely to experience anger toward the out-group and to desire to take action against it. The effects of perceived in-group strength on offensive action tendencies were mediated by anger. The annals of history and contemporary news sources bear overwhelming witness to the variety of ways in which out-groups are devalued, discriminated against, and sometimes decimated by the members of other groups. One group is shunned and avoided, a second economically exploited, another belittled and scape- goated, and yet another systematically murdered. In contributing a social psychological perspective to the understanding of negative intergroup behavior, social psychologists have typically focused on prejudice---a negative evaluation of a group and its mem- bers-as the cause of discrimination. Despite the insights provided by such an approach (for reviews, see Brewer & Brown, 1998; Dovidio, Brigham, Johnson, & Gaertner, 1996; Fiske, 1998; Macrae, Stangor, & Hewstone, 1996; Mackie & Smith, 1998a), conceptualizing prejudice as a negative evaluation of a group has proved of little help in explaining the wide variety of negative reactions to out-groups (Mackie & Smith, 1998a, 1998b; Schnei- der, 1996; E. R. Smith, 1993). Why does one out-group attract fear or contempt while another becomes the target of anger? If out- groups uniformly attract negative evaluation, what factors explain the impulse, desire, intention, or tendency to move against some groups and away from others? Unfortunately, very little work has been done to differentiate action tendencies elicited in intergroup contexts (Grant & Brown, 1995; Mackie & Smith, 1998a; Messick & Mackie, 1989; Wright, Taylor, & Moghaddam, 1990; important Diane M. Mackie and Thierry Devos, Department of Psychology, Uni- versity of California, Santa Barbara; Eliot R. Smith, Department of Psy- chological Sciences, Purdue University. This research was supported by National Science Foundation Grant SBR 9209995, a Swiss National Science Foundation Fellowship, and National Institute of Mental Health Grants RO1 MH46840 and KO2 MH01178. Thierry Devos is now at the Department of Psychology, Yale University. Correspondence concerning this article should be addressed to Diane M. Mackie, Department of Psychology, University of California, Santa Barbara, California 93106-9660. Electronic mail may be sent to mackie @psych.ucsb.edu. 602 exceptions are discussed later). The research reported here consti- tutes an initial empirical investigation of a novel theoretical ap- proach to answering these questions. Although intergroup theorists have by and large concentrated on negative attitude or evaluation as a precursor to discrimination, emotion theorists have typically differentiated both the diversity of feelings that may be directed toward a particular target and the specificity of behavior that can follow from those feelings. Ap- praisal theories of emotion (Frijda, 1986; Roseman, 1984; Scherer, 1988; C. A. Smith & Ellsworth, 1985), in particular, conceptualize personal emotions as complex reactions to specific situations or events that include quite differentiated cognitions, feelings, and action tendencies. Specific emotions experienced by an individual are triggered by appraisals (cognitions or interpretations) of whether an event appears to favor or harm the individual's goals or desires and whether the individual has the resources to cope or not, for example. Depending on their particular configuration, cogni- tive appraisals trigger specific emotional experiences (Ellsworth & Smith, 1988; Roseman, Spindel, & Jose, 1990; C. A. Smith & Ellsworth, 1985) and these emotional experiences in turn promote certain behaviors (Frijda, Kuipers, & ter Schure, 1989; Roseman, Wiest, & Swartz, 1994). Anger at another individual, for example, is typically conceptualized as resulting from appraisals that the other has harmed the self and that the self is strong. Such anger in turn leads to tendencies to aggress against that other person. Appraisal theory was developed to explain personal emotions experienced by individuals, and this focus was maintained in an important application of it to the intergroup context. Dijker (1987; Dijker, Koomen, van den Heuvel, & Frijda, 1996) asked native Dutch participants their emotional reactions to individual members of naturally occurring out-groups, with the assumption that these reactions depended on the nature of interpersonal interactions that participants had experienced. Respondents reported feeling a range of distinct negative and positive emotions about out-group mem- bers indicating that their personal emotions were indeed influenced by their individual experiences in encounters with other groups.


title: Let's hear it for audio mining

abstract: he Web, databases, and other digitized information storehouses contain a growing volume of audio content. Sources include newscasts, sporting events, telephone conversations, recordings of meetings, Webcasts, documentary archives such as the Visual History Foundation's interviews with Holocaust survivors (http://www.vhf. org), and media files in libraries. Users want to make the most of this material by searching and indexing the digitized audio content. In the past, companies had to create and manually analyze written transcripts of audio content because using computers to recognize, interpret, and analyze digitized speech was difficult. However, the development of faster microprocessors, larger storage capacities, and better speech-recognition algorithms has made audio mining easier. Now, the technology is on the verge of becoming a powerful tool that could help many organizations. For example, companies could use audio mining to analyze customer-service and helpdesk conversations or even voice mail. Law enforcement and intelligence organizations could use the technology to analyze intercepted phone conversations. Public relations firms could use it to analyze news broadcasts to find coverage of clients.


title: Anisotropic plate diffusion filtering for detection of cell membranes in 3D microscopy images

abstract: We propose an anisotropic diffusion method to denoise and aid the reconstruction of planar objects in three-dimensional images. The contribution of this paper is the development of a planarity function characterizing plate-like structures using an image Hessian's eigensystem. We then construct a diffusion tensor for anisotropically smoothing plates and satisfying necessary scale-space properties. Our method finds applications in improving the fidelity of highly noisy cell membrane images from confocal microscopy. In dense cellular regions, cell membranes assume linear shapes (planar) between neighbors. The imaging process makes cell membranes appear as diffuse structures owing to the non-uniform fluorescent marker distribution, point-spread function of the optics, and anisotropic voxel resolution which make automatic cell segmentation difficult. We apply diffusion filtering to identify and enhance membranes. We demonstrate the use of our methods on 3D cell membrane images of a zebrafish embryo acquired using fluorescent microscopy and quantify the improvement in image quality.


title: Challenges of migrating to agile methodologies

abstract: Software development methodologies are constantly evolving due to changing technologies and new demands from users. Today's dynamic business environment has given rise to emergent organizations that continuously adapt their structures, strategies, and policies to suit the new environment [12]. Such organizations need information systems that constantly evolve to meet their changing requirements---but the traditional, plan-driven software development methodologies lack the flexibility to dynamically adjust the development process.


title: Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks

abstract: Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.


title: Caveolae-mediated endocytosis as a novel mechanism of resistance to trastuzumab emtansine (T-DM1)

abstract: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate (ADC) that has demonstrated clinical benefit for patients with HER2+ metastatic breast cancer, however its clinical activity is limited by inherent or acquired drug resistance. The molecular mechanisms that drive clinical resistance to T-DM1, especially in HER2+ tumors, are not well understood. We used HER2+ cell lines to develop models of T-DM1 resistance utilizing a cyclical dosing schema in which cells received T-DM1 in an "on-off" routine until a T-DM1 resistant population was generated. T-DM1 resistant N87 cells (N87-TM) were cross-resistant to a panel of trastuzumab-ADCs (T-ADCs) with non-cleavable-linked auristatins. N87-TM cells do not have a decrease in HER2 protein levels or an increase in drug transporter protein (e.g. MDR1) expression compared to parental N87 cells. Intriguingly, T-ADCs utilizing auristatin payloads attached via an enzymatically cleavable linker overcome T-DM1 resistance in N87-TM cells. Importantly, N87-TM cells implanted into athymic mice formed T-DM1 refractory tumors which remain sensitive to T-ADCs with cleavable-linked auristatin payloads. Comparative proteomic profiling suggested enrichment in proteins that mediate caveolae formation and endocytosis in the N87-TM cells. Indeed, N87-TM cells internalize T-ADCs into intracellular caveolin-1 (CAV1) positive puncta and alter their trafficking to the lysosome compared to N87 cells. T-DM1 colocalization into intracellular CAV1 positive puncta correlated with reduced response to T-DM1 in a panel of HER2+ cell lines. Together, these data suggest caveolae-mediated endocytosis of T-DM1 may serve as a novel predictive biomarker for patient response to T-DM1.


title: Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness

abstract: Object Detection is critical for automatic military operations. However, the performance of current object detection algorithms is deficient in terms of the requirements in military scenarios. This is mainly because the object presence is hard to detect due to the indistinguishable appearance and dramatic changes of object's size which is determined by the distance to the detection sensors. Recent advances in deep learning have achieved promising results in many challenging tasks. The state-of-the-art in object detection is represented by convolutional neural networks (CNNs), such as the fast R-CNN algorithm. These CNN-based methods improve the detection performance significantly on several public generic object detection datasets. However, their performance on detecting small objects or undistinguishable objects in visible spectrum images is still insufficient. In this study, we propose a novel detection algorithm for military objects by fusing multi-channel CNNs. We combine spatial, temporal and thermal information by generating a three-channel image, and they will be fused as CNN feature maps in an unsupervised manner. The backbone of our object detection framework is from the fast R-CNN algorithm, and we utilize cross-domain transfer learning technique to fine-tune the CNN model on generated multi-channel images. In the experiments, we validated the proposed method with the images from SENSIAC (Military Sensing Information Analysis Centre) database and compared it with the state-of-the-art. The experimental results demonstrated the effectiveness of the proposed method on both accuracy and computational efficiency.


title: A High Power Density Micro-Thermoelectric Generator Fabricated by an Integrated Bottom-Up Approach

abstract: In this paper, we report a high power density cross-plane micro-thermoelectric generator (TEG) fabricated by integrating pulsed electroplating with micro-fabrication processes. The TEG consists of a total of 127 pairs of n-type Bi2Te3 and p-type Sb2Te3 thermoelectric pillars embedded in a SU-8 matrix in order to enhance the overall mechanical strength of the device. Both bottom and top electrical connections are formed by electroplating, which is advantageous because of facile and low cost fabrication and low parasitic electrical resistances. The device demonstrates a maximum power of 2990 $\mu \text{W}$ at a temperature difference of 52.5 K, corresponding to a power density as high as 9.2 mW $\cdot $ cm $^{-2}$ . The power density of our device is more than two times the highest value reported for the electroplated micro-TEGs in the literature, which can be attributed to the low internal resistance and high packing density of thermoelectric pillars. [2015-0348]


title: Downgrade Attack on TrustZone

abstract: Security-critical tasks require proper isolation from untrusted software. Chip manufacturers design and include trusted execution environments (TEEs) in their processors to secure these tasks. The integrity and security of the software in the trusted environment depend on the verification process of the system. ::: We find a form of attack that can be performed on the current implementations of the widely deployed ARM TrustZone technology. The attack exploits the fact that the trustlet (TA) or TrustZone OS loading verification procedure may use the same verification key and may lack proper rollback prevention across versions. If an exploit works on an out-of-date version, but the vulnerability is patched on the latest version, an attacker can still use the same exploit to compromise the latest system by downgrading the software to an older and exploitable version. ::: We did experiments on popular devices on the market including those from Google, Samsung and Huawei, and found that all of them have the risk of being attacked. Also, we show a real-world example to exploit Qualcomm's QSEE. ::: In addition, in order to find out which device images share the same verification key, pattern matching schemes for different vendors are analyzed and summarized.


title: Comparison of IC conducted emission measurement methods

abstract: This paper deals with the electromagnetic emissions of integrated circuits. In particular, four measurement techniques to evaluate integrated circuit conducted emissions are described in detail and they are employed for the measurement of the power supply conducted emission delivered by a simple integrated circuit composed of six synchronous switching drivers. Experimental results obtained by employing such measurement methods are presented and the influence of each test setup on the measured quantities is discussed.


title: Multiple Source Domain Adaptation with Adversarial Training of Neural Networks

abstract: We propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances. The new bound has an interesting interpretation and reduces to an existing bound when there is only one source domain. Compared with existing bounds, the new bound does not require expert knowledge about the target distribution, nor the optimal combination rule for multisource domains. Interestingly, our theory also leads to an efficient implementation using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. To this end, we propose two models, both of which we call multisource domain adversarial networks (MDANs): the first model optimizes directly our bound, while the second model is a smoothed approximation of the first one, leading to a more data-efficient and task-adaptive model. The optimization tasks of both models are minimax saddle point problems that can be optimized by adversarial training. To demonstrate the effectiveness of MDANs, we conduct extensive experiments showing superior adaptation performance on three real-world datasets: sentiment analysis, digit classification, and vehicle counting.


title: Pilot Contamination and Precoding in Multi-Cell TDD Systems

abstract: This paper considers a multi-cell multiple antenna system with precoding used at the base stations for downlink transmission. For precoding at the base stations, channel state information (CSI) is essential at the base stations. A popular technique for obtaining this CSI in time division duplex (TDD) systems is uplink training by utilizing the reciprocity of the wireless medium. This paper mathematically characterizes the impact that uplink training has on the performance of such multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multi-cell systems. Furthermore, it develops a new multi-cell MMSE-based precoding method that mitigate this problem. In addition to being a linear precoding method, this precoding method has a simple closed-form expression that results from an intuitive optimization problem formulation. Numerical results show significant performance gains compared to certain popular single-cell precoding methods.


title: TableQA: Question Answering on Tabular Data

abstract: Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools or even without having to fully understand the dataset structure. The goal of our demonstration is to showcase answering natural language questions from tabular data, and to discuss related system configuration and model training aspects. Our prototype is publicly available and open-sourced (see this https URL).


title: A Tutorial on Evolutionary Multiobjective Optimization

abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making.


title: Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models

abstract: Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples. We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and inference. We propose Batch Renormalization, a simple and effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch. Models trained with Batch Renormalization perform substantially better than batchnorm when training with small or non-i.i.d. minibatches. At the same time, Batch Renormalization retains the benefits of batchnorm such as insensitivity to initialization and training efficiency.


title: Copy detection mechanisms for digital documents

abstract: In a digital library system, documents are available in digital form and therefore are more easily copied and their copyrights are more easily violated. This is a very serious problem, as it discourages owners of valuable information from sharing it with authorized users. There are two main philosophies for addressing this problem: prevention and detection. The former actually makes unauthorized use of documents difficult or impossible while the latter makes it easier to discover such activity.In this paper we propose a system for registering documents and then detecting copies, either complete copies or partial copies. We describe algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security). We also describe a working prototype, called COPS, describe implementation issues, and present experimental results that suggest the proper settings for copy detection parameters.


abstract: Degenerative cervical radiculopathy: clinical diagnosis and conservative treatment. A review. To provide a state-of-the-art assessment of diagnosis and non-surgical treatment of degenerative cervical radiculopathy a literature search for studies on epidemiology, diagnosis including electrophysiological examination and imaging studies, and different types of conservative treatment was undertaken. The most common causes of cervical root compression are spondylarthrosis and disc herniation. Diagnosis is made mainly on clinical grounds, although there are no well-defined criteria. Provocative tests like the foraminal compression test are widely used but not properly evaluated. The clinical diagnosis of degenerative cervical radiculopathy can be confirmed by magnetic resonance imaging. The role of electromyography is mainly to rule out other conditions. Cervical radiculopathy is initially treated conservatively, although no treatment modality has been evaluated in a randomized controlled trial. Degenerative cervical radiculopathy: diagnosis and conservative treatment. A review.


title: Popularity-driven content caching

abstract: This paper presents a novel cache replacement method — Popularity-Driven Content Caching (PopCaching). PopCaching learns the popularity of content and uses it to determine which content it should store and which it should evict from the cache. Popularity is learned in an online fashion, requires no training phase and hence, it is more responsive to continuously changing trends of content popularity. We prove that the learning regret of PopCaching (i.e., the gap between the hit rate achieved by PopCaching and that by the optimal caching policy with hindsight) is sublinear in the number of content requests. Therefore, PopCaching converges fast and asymptotically achieves the optimal cache hit rate. We further demonstrate the effectiveness of PopCaching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40% when the cache capacity is limited. In addition, PopCaching has low complexity.


title: Specific Clinical Findings of Secondary Syphilis In The Oral Mucosa: ASeries of Six Case Reports

abstract: Syphilis (SyS) is a systemic infectious disease caused by the anaerobic spirochetes Treponema pallidum. This condition is further divided into primary, secondary latent and tertiary stages. Oral manifestations are most common in secondary stage and frequently are the only clinical sign of the disease. These lesions are characterized by having high rates of infection and peculiar clinical characteristics. Therefore, the purpose of this article is to highlight the common and not-so-common clinical appearances of secondary SyS in adults. Tongue, lips, jugal mucosa and palate lesions were selected to illustrate the similarity of oral SyS with other diseases. The diagnosis was performed by clinical and serological correlation. Furthermore, it was emphasized the importance of the knowledge of different forms of oral SyS lesions by clinicians in order to provide early diagnosis and faster treatment for infected patients.


title: MEMS-based pico projector display

abstract: A pico-projector based on scanned 3-color laser light has been developed. The scanning element is a dual-axis MEMS scanning mirror that produces WVGA display resolution. The laser light sources are red and blue laser diodes and a second harmonic green laser. Use of a MEMS and laser light sources leads to a small volume and a thickness of 7mm, enabling the use of the projector system as a component embedded into portable consumer electronics.


title: Classifier Architectures for Acoustic Scenes and Events: Implications for DNNs, TDNNs, and Perceptual Features from DCASE 2016

abstract: This paper evaluates neural network NN based systems and compares them to Gaussian mixture model GMM and hidden Markov model HMM approaches for acoustic scene classification SC and polyphonic acoustic event detection AED that are applied to data of the “Detection and Classification of Acoustic Scenes and Events 2016” DCASE'16 challenge, task 1 and task 3, respectively. For both tasks, the use of deep neural networks DNNs and features based on an amplitude modulation filterbank and a Gabor filterbank GFB are evaluated and compared to standard approaches. For SC, additionally a time-delay NN approach is proposed that enables analysis of long contextual information similar to recurrent NNs but with training efforts comparable to conventional DNNs. The SC system proposed for task 1 of the DCASE'16 challenge attains a recognition accuracy of 77.5%, which is 5.6% higher compared to the DCASE'16 baseline system. For the AED task, DNNs are adopted in tandem and hybrid approaches, i.e., as part of HMM-based systems. These systems are evaluated for the polyphonic data of task 3 from the DCASE'16 challenge. Several strategies to address the issue of polyphony are considered. It is shown that DNN-based systems perform less accurate than the traditional systems for this task. Best results are achieved using GFB features in combination with a multiclass GMM-HMM back end.


title: Flip Chip Packaging of Digital Silicon Photonics MEMS Switch for Cloud Computing and Data Centre

abstract: We report on the flip chip packaging of Micro-Electro-Mechanical System (MEMS)-based digital silicon photonic switching device and the characterization results of 12 × 12 switching ports. The challenges in packaging N 2 electrical and 2N optical interconnections are addressed with single-layer electrical redistribution lines of 25 μ m line width and space on aluminum nitride interposer and 13° polished 64-channel lidless fiber array (FA) with a pitch of 127 μ m. 50 μ m diameter solder spheres are laser-jetted onto the electrical bond pads surrounded by suspended MEMS actuators on the device before fluxless flip-chip bonding. A lidless FA is finally coupled near-vertically onto the device gratings using a 6-degree-of-freedom (6-DOF) alignment system. Fiber-to-grating coupler loss of 4.25 dB/facet, 10–11 bit error rate (BER) through the longest optical path, and 0.4 μ s switch reconfiguration time have been demonstrated using 10 Gb/s Ethernet data stream.


title: Effects of Platform-Switching on Peri-implant Soft and Hard Tissue Outcomes: A Systematic Review and Meta-analysis.

abstract: PURPOSE ::: This systematic review and meta-analysis was aimed at evaluating the longitudinal effect of platform switching on implant survival rates as well as on soft and hard tissue outcomes. ::: ::: ::: MATERIALS AND METHODS ::: An electronic search of the databases of the National Center for Biotechnology Information, PubMed, Ovid (MEDLINE), EMBASE, Web of Science, and Cochrane Collaboration Library was conducted in February 2015. Studies published in English with at least 10 human participants and a 12-month post-loading follow-up were included. Random effects meta-analyses of selected studies were applied to compare the primary and secondary outcomes of platform-switched (PS) and regular-platform (RP) implants, as well as the experimental designs and clinical outcomes. ::: ::: ::: RESULTS ::: A total of 26 studies involving 1,511 PS implants and 1,123 RP implants were evaluated. Compared to RP implants, PS implants showed a slight increase in vertical marginal bone loss (VMBL) and pocket depth reduction (weighted mean differences were -0.23 mm and -0.20 mm, respectively). The PS implants had a mean VMBL of 0.36 ± 0.15 mm within the first year of service. The meta-regression suggested a trend of decreased bone resorption at sites with thick soft tissues at baseline. ::: ::: ::: CONCLUSION ::: This study suggested that platform switching may have an indirect protective effect on implant hard tissue outcomes.


title: The Direction of Lightweight Ciphers in Mobile Big Data Computing

abstract: Abstract It is too fast. The advances of the computing technology are moving very fast and far from the era of gigantic machine. This advanced technology offers easy, fast and wide range of computing activities particularly users who want to use the Internet, regardless of time and place. In addition, this advanced technology can also connect more communication tool. At the same time, greater storage platform is also available as mobile computing cloud computing architecture adopted to carry out computer activities. However, the larger the network which is connected to a computer, the more susceptible the computer to the outside threats. Indirectly, the communication system and the information stored in the computer are also exposed. Therefore, in this paper, we has discussed on the evolution of the computing which begin with the distributed system until recent computing technology which we called Mobile Big Data Computing. Besides, in this paper, we define the term Mobile Big Data Computing. Our discussion focuses on the information security aspects for the security of storage and transmitted data. Ultimately, this paper discusses the direction of the lightweight cipher design consideration towards Mobile Big Data Computing.


title: Beyond Self-Selection in Video Game Play: An Experimental Examination of the Consequences of Massively Multiplayer Online Role-Playing Game Play

abstract: There is burgeoning interest in the study of video games. Existing work is limited by the use of correlational designs and is thus unable to make causal inferences or remove self-selection biases from observed results. The recent development of online, socially integrated video games (massively multiplayer online role-playing games [MMORPGs]) has created a new experience for gamers. This randomized, longitudinal study examined the effects of being assigned to play different video game types on game usage, health, well-being, sleep, socializing, and academics. One hundred 18- to 20-year-old participants (73% male; 68% Caucasian) were randomly assigned to play arcade, console, solo computer, or MMORPG games for 1 month. The MMORPG group differed significantly from other groups after 1 month, reporting more hours spent playing, worse health, worse sleep quality, and greater interference in “real-life” socializing and academic work. In contrast, this group also reported greater enjoyment in playing, greater i...


title: Algorithm for 3D Point Cloud Denoising

abstract: The raw data of point cloud produced by 3D scanning tools contains additive noise from various sources. This paper proposes a method for 3D unorganized point cloud denoising by making full use of the depth information of unorganized points and space analytic geometry theory, applying over-domain average method for 2D image of image denoising theory to 3D point data. The point cloud noises are filtered by using irregular polyhedron based on the limited local neighborhoods. The experiment shows that the proposed method successfully removes noise from point cloud with the features of the scattered point model reserved. Furthermore, the presented algorithm excels in its simplicity both in implementation and operation.


title: Finding minimal enclosing boxes

abstract: The problem of finding minimal volume boxes circumscribing a given set of three-dimensional points is investigated. It is shown that it is not necessary for a minimum volume box to have any sides flush with a face of the convex hull of the set of points, which makes a naive search problematic. Nevertheless, it is proven that at least two adjacent box sides are flush with edges of the hull, and this characterization enables anO(n3) algorithm to find all minimal boxes for a set ofn points.


title: Evidence Combination From an Evolutionary Game Theory Perspective

abstract: Dempster–Shafer evidence theory is a primary methodology for multisource information fusion because it is good at dealing with uncertain information. This theory provides a Dempster’s rule of combination to synthesize multiple evidences from various information sources. However, in some cases, counter-intuitive results may be obtained based on that combination rule. Numerous new or improved methods have been proposed to suppress these counter-intuitive results based on perspectives, such as minimizing the information loss or deviation. Inspired by evolutionary game theory, this paper considers a biological and evolutionary perspective to study the combination of evidences. An evolutionary combination rule (ECR) is proposed to help find the most biologically supported proposition in a multievidence system. Within the proposed ECR, we develop a Jaccard matrix game to formalize the interaction between propositions in evidences, and utilize the replicator dynamics to mimick the evolution of propositions. Experimental results show that the proposed ECR can effectively suppress the counter-intuitive behaviors appeared in typical paradoxes of evidence theory, compared with many existing methods. Properties of the ECR, such as solution’s stability and convergence, have been mathematically proved as well.


title: iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones

abstract: As computing moves beyond the desktop, human activity becomes an essential component of many applications. Activity classification is an active research area and several research systems have been constructed. Most have focused on fragile custom hardware only available in limited quantities. We instead seek to use commodity hardware to lower the barrier to creating activity-informed mobile applications. We describe iLearn, our system for classifying human activities using the Apple iPhone‟s three-axis accelerometer and the Nike+iPod Sport Kit. Our results suggest activities including running, walking, bicycling, and sitting can be recognized at accuracies of 97% without any training by an end-user.


title: Completions, coordination, and alignment in dialogue

abstract: Collaborative completions are among the strongest evidence that dialogue requires ::: coordination even at the sub-sentential level; the study of sentence completions may thus ::: shed light on a number of central issues both at the `macro’ level of dialogue management ::: and at the `micro’ level of the semantic interpretation of utterances. We propose a treatment ::: of collaborative completions in PTT, a theory of interpretation in dialogue that provides ::: some of the necessary ingredients for a formal account of completions at the ‘micro’ level, ::: such a theory of incremental utterance interpretation and an account of grounding. We ::: argue that an account of semantic interpretation in completions can be provided through ::: relatively straightforward generalizations of existing theories of syntax such as Lexical Tree ::: Adjoining Grammar (LTAG) and of semantics such as (Compositional) DRT and Situation ::: Semantics. At the macro level, we provide an intentional account of completions, as well as a ::: preliminary account within Pickering and Garrod’s alignment theory.


Combining Visual and Spatial Appearance for Loop Closure Detection in SLAM


Dynamic Pricing in a Labor Market : Surge Pricing and the Supply of Uber Driver-Partners


title: Detection of profile injection attacks in social recommender systems using outlier analysis

abstract: As systems based on social networks grow, they get affected by huge number of fake user profiles. Particularly, social recommender systems are vulnerable to profile injection attacks where malicious profiles are injected into the rating system to affect user's opinion. The objective of attackers is to inject a large set of biased profiles that provide favorable or unfavorable recommendations for a product. In this paper, we propose a classification technique for detection of attackers. First, we define the attributes that provide the likelihood of a user having a profile of that of an attacker. Using user-item rating matrix, user-connection matrix, and similarity between users, we find if the ratings are abnormal and if there are random connections in the network. Then, we use fc-means clustering to categorize users into authentic users and attackers. To evaluate our framework, we use Epinions dataset and inject intelligent push and nuke attacks. These attacks make arbitrary connections to existing users and provide biased ratings. To evaluate the performance, we use precision and recall to show that fc-means clustering can identify the attackers with high accuracy and low false positives.