title: PID controller tuning using evolutionary algorithms

abstract: This paper presents the implementation of PID controller tuning using two sets of evolutionary techniques which are differential evolution (DE) and genetic algorithm (GA). The optimal PID control parameters are applied for a high order system, system with time delay and non-minimum phase system. The performance of the two techniques is evaluated by setting its objective function with mean square error (MSE) and integral absolute error (IAE). Both techniques will compete to achieve the globally minimum value of its objective functions. Meanwhile, reliability between DE and GA in consistently maintaining minimum MSE is also been studied. This paper also compares the performance of the tuned PID controller using GA and DE methods with Ziegler-Nichols method.


title: Attendance Management System Using a Mobile Device and a Web Application

abstract: There have been many proposals to optimize the students' attendance management in higher educations. However, each method has pros-and-cons, we have not yet found the ultimate solution. In this study, a novel framework for the attendance management is proposed, which consists of a mobile device and a web application. While the lecture goes on, students participating in the lecture can register their attendance into the mobile device with their selfie or their signature. After the lecture finished, the registration data are sent to the database, and they will be referred in the 'eRollSheet.' This paper reports an overview of the system and the results of evaluation after a trial period, which were conducted in the second semester of the fiscal year 2015.


title: A stick-slip omnidirectional powertrain for low-cost swarm robotics: Mechanism, calibration, and control

abstract: We present an omnidirectional powertrain for swarm robotic platforms that relies on low-cost vibration motors.We describe a mechanism and controller to achieve full 3-DoF motion on the plane. The proposed approach does not require the motors to be in phase, and overcomes differences in manufacturing by a hardware-in-the-loop auto-calibration routine based on the Nelder-Mead algorithm, which issues motion commands via infrared and records the resulting trajectories using an off-the-shelf webcam. We show convergence results of the calibration routine and sample trajectories of the swarm robotic platform “Droplet” demonstrating turning and omnidirectional drive.


title: Compressive Sensing Techniques for Next-Generation Wireless Communications

abstract: A range of efficient wireless processes and enabling techniques are put under a magnifier glass in the quest for exploring different manifestations of correlated processes, where sub-Nyquist sampling may be invoked as an explicit benefit of having a sparse transform-domain representation. For example, wide-band next-generation systems require a high Nyquist-sampling rate, but the channel impulse response (CIR) will be very sparse at the high Nyquist frequency, given the low number of reflected propagation paths. This motivates the employment of compressive sensing based processing techniques for frugally exploiting both the limited radio resources and the network infrastructure as efficiently as possible. A diverse range of sophisticated compressed sampling techniques is surveyed and we conclude with a variety of promising research ideas related to large-scale antenna arrays, non-orthogonal multiple access (NOMA), and ultra-dense network (UDN) solutions, just to name a few.


title: A Survey of Preference-Based Reinforcement Learning Methods

abstract: Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert's preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL.


title: Diffusion tensor imaging of the corpus callosum in Autism

abstract: The corpus callosum is the largest commissural white matter pathway that connects the hemispheres of the human brain. In this study, diffusion tensor imaging (DTI) was performed on subject groups with high-functioning autism and controls matched for age, handedness, IQ, and head size. DTI and volumetric measurements of the total corpus callosum and subregions (genu, body and splenium) were made and compared between groups. The results showed that there were significant differences in volume, fractional anisotropy, mean diffusivity, and radial diffusivity between groups. These group differences appeared to be driven by a subgroup of the autism group that had small corpus callosum volumes, high mean diffusivity, low anisotropy, and increased radial diffusivity. This subgroup had significantly lower performance IQ measures than either the other individuals with autism or the control subjects. Measurements of radial diffusivity also appeared to be correlated with processing speed measured during the performance IQ tests. The subgroup of autism subjects with high mean diffusivity and low fractional anisotropy appeared to cluster with the highest radial diffusivities and slowest processing speeds. These results suggest that the microstructure of the corpus callosum is affected in autism, which may be related to nonverbal cognitive performance.


title: A first-order analysis of lighting, shading, and shadows

abstract: The shading in a scene depends on a combination of many factors---how the lighting varies spatially across a surface, how it varies along different directions, the geometric curvature and reflectance properties of objects, and the locations of soft shadows. In this article, we conduct a complete first-order or gradient analysis of lighting, shading, and shadows, showing how each factor separately contributes to scene appearance, and when it is important. Gradients are well-suited to analyzing the intricate combination of appearance effects, since each gradient term corresponds directly to variation in a specific factor. First, we show how the spatial and directional gradients of the light field change as light interacts with curved objects. This extends the recent frequency analysis of Durand et al. [2005] to gradients, and has many advantages for operations, like bump mapping, that are difficult to analyze in the Fourier domain. Second, we consider the individual terms responsible for shading gradients, such as lighting variation, convolution with the surface BRDF, and the object's curvature. This analysis indicates the relative importance of various terms, and shows precisely how they combine in shading. Third, we understand the effects of soft shadows, computing accurate visibility gradients, and generalizing previous work to arbitrary curved occluders. As one practical application, our visibility gradients can be directly used with conventional ray-tracing methods in practical gradient interpolation methods for efficient rendering. Moreover, our theoretical framework can be used to adaptively sample images in high-gradient regions for efficient rendering.


title: Query enrichment for web-query classification

abstract: Web-search queries are typically short and ambiguous. To classify these queries into certain target categories is a difficult but important problem. In this article, we present a new technique called query enrichment, which takes a short query and maps it to intermediate objects. Based on the collected intermediate objects, the query is then mapped to target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to the ACM Knowledge Discovery and Data Mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this article, we show that, despite the difficulty of an abundance of ambiguous queries and lack of training data, our query-enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result for F1 and precision is 42.4p and 44.4p, respectively, which is 9.6p and 24.3p higher than those from the runner-ups, respectively.


title: An 8-b 400-MS/s 2-b-Per-Cycle SAR ADC With Resistive DAC

abstract: An 8-b 400-MS/s 2-b-per-cycle (2 b/C) successive approximation register (SAR) analog-to-digital converter (ADC) is fabricated in 65-nm CMOS. With the implementation of a low-power and small-area resistive DAC and associated highly integrated circuit implementation, the proposed SAR ADC achieves rapid conversion rate, low power, and compact area, leading to SNDR of 44.5 dB and SFDR of 54.0 dB, at 400 MS/s with 1.9-MHz input. The measured FOM is 73 fJ/conversion-step at 400 MS/s from 1.2-V supply and 42 fJ/conversion-step at 250 MS/s from a 1-V supply. The active area with the digital calibration is 0.028 mm2.


title: Data Mining Algorithms to Classify Students

abstract: In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining techniques easier for instructors. We have used real data from seven Moodle courses with Cordoba University students. We have also applied discretization and rebalance preprocessing techniques on the original numerical data in order to verify if better classifier models are obtained. Finally, we claim that a classifier model appropriate for educational use has to be both accurate and comprehensible for instructors in order to be of use for decision making.


title: Cross validation and MLP architecture selection

abstract: The performance of cross validation (CV) based MLP architecture selection is examined using 14 real world problem domains. When testing many different network architectures the results show that CV is only slightly more likely than random to select the optimal network architecture, and that the strategy of using the simplest available network architecture performs better than CV in this case. Experimental evidence suggests several reasons for the poor performance of CV. In addition, three general strategies which lead to significant increase in the performance of CV are proposed. While this paper focuses on using CV to select the optimal MLP architecture, the strategies are also applicable when CV is used to select between several different learning models, whether the models are neural networks, decision trees, or other types of learning algorithms. When using these strategies the average generalization performance of the network architecture which CV selects is significantly better than the performance of several other well known machine learning algorithms on the data sets tested.


title: DataXFormer: An Interactive Data Transformation Tool

abstract: While syntactic transformations require the application of a formula on the input values, such as unit conversion or date format conversions, semantic transformations, such as "zip code to city", require a look-up in some reference data. We recently presented DataXFormer, a system that leverages Web tables, Web forms, and expert sourcing to cover a wide range of transformations. In this demonstration, we present the user-interaction with DataXFormer and show scenarios on how it can be used to transform data and explore the effectiveness and efficiency of several approaches for transformation discovery, leveraging about 112 million tables and online sources.


title: Road detection using support vector machine based on online learning and evaluation

abstract: Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle.


title: Radio Biometrics: Human Recognition Through a Wall

abstract: In this paper, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity Wi-Fi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from Wi-Fi signals using the time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has a significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight radio. We built a prototype of the TR human identification system using standard Wi-Fi chipsets with $3 \times 3$ multi-in multi-out (MIMO) transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair. Thanks to the ubiquitousness of Wi-Fi, the proposed system shows the promise for future low-cost low-complexity reliable human identification applications based on radio biometrics.


title: XRel: a path-based approach to storage and retrieval of XML documents using relational databases

abstract: This article describes XRel, a novel approach for storage and retrieval of XML documents using relational databases. In this approach, an XML document is decomposed into nodes on the basis of its tree structure and stored in relational tables according to the node type, with path information from the root to each node. XRel enables us to store XML documents using a fixed relational schema without any information about DTDs and also to utilize indices such as the B 1 -tree and the R-tree supported by database management systems. Thus, XRel does not need any extension of relational databases for storing XML documents. For processing XML queries, we present an algorithm for translating a core subset of XPath expressions into SQL queries. Finally, we demonstrate the effectiveness of this approach through several experiments using actual XML documents.


title: Defining the pathway to insulin-like growth factor system targeting in cancer ☆

abstract: The insulin-like growth factors (IGEs; IGF-1 and IGF-2) play central roles in cell growth, differentiation, survival, transformation and metastasis. The biologic effects of the IGFs are mediated by the IGF-1 receptor (IGF-1R), a receptor tyrosine kinase with homology to the insulin receptor (IR). Dysregulation of the ICE system is well recognized as a key contributor to the progression of multiple cancers, with IGF-1R activation increasing the tumorigenic potential of breast, prostate, lung, colon and head and neck squamous cell carcinoma (HNSCC). Despite this relationship, targeting the IGF-1R has only recently undergone development as a molecular cancer therapeutic. As it has taken hold, we are witnessing a robust increase and interest in targeting the inhibition of IGF-1R signaling. This is accentuated by the list of over 30 drugs, including monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs) that are under evaluation as single agents or in combination therapies 1]. The ICE-binding proteins (IGFBPs) represent the third component of the ICE system consisting of a class of six soluble secretory proteins. They represent a unique class of naturally occurring ICE-antagonists that bind to and sequester IGF-1 and IGF-2, inhibiting their access to the IGF-1R. Due to their dual targeting of the IGFs without affecting insulin action, the IGFBPs are an untapped ``third'' class of IGF-1R inhibitors. in this commentary, we highlight some of the significant aspects of and prospects for targeting the IGF-1R and describe what the future may hold. (C) 2010 Elsevier Inc. All rights reserved.


title: Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices

abstract: Volumetric methods provide efficient, flexible and simple ways of integrating multiple depth images into a full 3D model. They provide dense and photorealistic 3D reconstructions, and parallelised implementations on GPUs achieve real-time performance on modern graphics hardware. To run such methods on mobile devices, providing users with freedom of movement and instantaneous reconstruction feedback, remains challenging however. In this paper we present a range of modifications to existing volumetric integration methods based on voxel block hashing, considerably improving their performance and making them applicable to tablet computer applications. We present (i) optimisations for the basic data structure, and its allocation and integration; (ii) a highly optimised raycasting pipeline; and (iii) extensions to the camera tracker to incorporate IMU data. In total, our system thus achieves frame rates up 47 Hz on a Nvidia Shield Tablet and 910 Hz on a Nvidia GTX Titan XGPU, or even beyond 1.1 kHz without visualisation.


title: Designed Interdependence, Growing Social Capital and Knowledge Sharing in IS Project Teams

abstract: This study brings in the task interdependence as the antecedent for growing team social capital and increasing knowledge sharing among team members in IS project context in which heterogeneous members need to interact very closely. Data collected from 203 IS projects largely confirm the proposed research model. Between the task interdependence and team performance, the team social capital as well as the knowledge sharing among members fully mediates the relationship. Interestingly, direct path from task interdependence onto performance is found to be insignificant, rebutting the suggestions of the work design theory which says well designed interdependence directly increase performance in heterogeneous teams. Findings of our study signify the criticality of the mediating roles of social capital and knowledge sharing. Additionally, it is also confirmed that the social capital grows with time. Also, longer term projects revealed a bit different mechanism than shorter ones. Implications are discussed with limitations and further studies.


title: Customer Relationship Management: Implementation Process Perspective

abstract: Customer relationship management (CRM) can help organizations manage customer interactions more effectively to maintain competitiveness in the present economy. As more and more organizations realize the significance of becoming customer-centric in today's competitive era, they adopted CRM as a core business strategy and invested heavily. CRM, an integration of information technology and relationship marketing, provides the infrastructure that facilitates long-term relationship building with customers at an enterprise-wide level. Successful CRM implementation is a complex, expensive and rarely technical projects. This paper presents the successful implementation of CRM from process perspective in a trans-national organization with operations in different segments. This study will aid in understanding transition, constraints and the implementation process of CRM in such organizations.


title: Student and staff perceptions of the effectiveness of plagiarism detection software

abstract: The aim of this research was to determine student and staff perceptions of the effectiveness of plagiarism detection software. A mixed methods approach was undertaken, using a research model adapted from the literature. Eight hours of interviews were conducted with six students and six teaching staff from Curtin Business School at Curtin University of Technology, which had trialled the plagiarism detection software, EVE2 . A survey questionnaire was completed by 171 students involved in the trial. The summary indication was that students perceived that plagiarism is an important issue; detection software makes it easier for lecturers; it is fair to use detection software; students support its use; and it will have some effect in preventing plagiarism. However, students' concerns included being caught for unintentional plagiarism, teaching staff placing too much emphasis on detection results above student ability, and the accuracy of the software at detecting plagiarism. Concerns for teaching staff included the time taken for the detection process, limitation of the software to publicly based Internet sources and direct copying, and the extra workload involved with pursuing academic misconduct.


title: Detecting Foggy Images and Estimating the Haze Degree Factor

abstract: Limited visibility in haze weather strongly influences the accuracy and the general functions of almost outdoor video surveillance or driver assistance systems. Actual weather condition is valuable information to invoke corresponding approaches. Based on the atmospheric scattering model analysis and the statistics of various outdoor images, for most foggy images, we find that the lowest and highest value in color channels tends to be the same value of atmospheric light. A function for estimating the haze degree is developed for the automatic detection of the foggy image with different haze degrees. Experimental results show that our haze classification method achieves high performance.


1 CROWDFUNDING : TRANSFORMING CUSTOMERS INTO INVESTORS THROUGH INNOVATIVE


title: On relating some particle profile characteristics to the profile fourier coefficients

abstract: Abstract This paper describes a preliminary investigation of the problems inherent in the development of an interface between a particle shape pre-processor and a binary computer. In the experiment described, the set of data points of the particle profiles are converted into sets of Fourier coefficients, which in turn are converted to features that appear to be capable of human operator assessment. Real particle profiles are analyzed to yield features which are tested for clustering tendencies in the pattern space. Advantages and disadvantages of the method are discussed.


title: An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications

abstract: Optogenetics offers a powerful new approach for controlling neural circuits. It has numerous applications in both basic and clinical science. These applications require stimulating devices with small processors that can perform real-time neural signal processing, deliver high-intensity light with high spatial and temporal resolution, and do not consume a lot of power. In this paper, we demonstrate the implementation of neuronal models in a platform consisting of an embedded system module and a portable digital light processing projector. As a replacement for damaged neural circuitry, the embedded module processes neural signals and then directs the projector to optogenetically activate a downstream neural pathway. We present a design in the context of stimulating circuits in the visual system, but the approach is feasible for a broad range of biomedical applications.


title: Adoption of security as a service

abstract: Security as a Service systems enable new opportunities to compose security infrastructures for information systems. However, to date there are no holistic insights about their adoption and relevant predictors. Based on existing technology acceptance models we developed an extended application-specific research model including formative and reflective measures. The model was estimated applying the Partial Least Squares technique to address the prediction-oriented nature of the study. A subsequent online survey revealed that a large number of industries shows significant and steadily growing interest in Security as a Service. Adoption drivers were investigated systematically.


title: Two types of cholinergic neurons in the rat tegmental pedunculopontine nucleus: electrophysiological and morphological characterization

abstract: Abstract Two types of tegmental pedunculopontine nucleus neurons have been reported previously based on their electrophysiological characteristics: type I neurons were characterized by low-threshold Ca spikes and type II neurons displayed a transient outward current. This report describes the membrane properties, synaptic inputs, morphologies and axonal projections of two subgroups of type II neurons examined in an in vitro slice preparation. Type II neurons were divided into two groups based on their spike durations: short-duration neurons with an action potential duration of 0.7–1.5 ms and long-duration neurons with an action potential duration of 1.6–2.9 ms. Choline acetyltransferase immunohistochemistry combined with biocytin labeling indicated that 56% of short-duration neurons and 61% of long-duration neurons were immunopositive for choline acetyltransferase. Short-duration neurons had a high input resistance and the capacity to discharge with high frequency. By contrast, long-duration neurons had a low input resistance and low firing frequency and upon current injection displayed an accommodation (spike-frequency adaptation) before reaching a steady firing frequency. Microstimulation of the substantia nigra pars compacta evoked antidromic responses in both short-duration neurons ( n =5/14, 36%) and long-duration neurons ( n =20/39, 51%). Stimulations of the subthalamic nucleus and the substantia nigra pars reticulata induced in these neurons excitatory and inhibitory postsynaptic potentials, respectively. Short-duration neurons were dispersed equally throughout the extent of the tegmental pedunculopontine nucleus area, while long-duration neurons were located more in the rostral tegmental pedunculopontine nucleus. Short-duration neurons were small with two to four thin primary dendrites. Long-duration neurons were medium to large with three to six thick primary dendrites. Cell size was positively correlated with spike duration and axonal conduction velocity, but negatively with input resistance and spontaneous firing frequency. Both groups of neurons had ascending (toward thalamus, pretectal areas and tectum) and descending (toward pontomedullary reticular formation) axons in addition to nigropetal axons. Ascending axons were observed in 75% (6/8) of short-duration neurons and in 45% (15/33) of long-duration neurons, while nigropetal axons were observed in 50% (4/8) of short-duration neurons and in 76% (25/33) of long-duration neurons. These results suggest that the tegmental pedunculopontine nucleus cholinergic projection system is composed of heterogeneous populations of neurons in terms of electrophysiological and morphological characteristics as well as their distribution patterns in the nucleus.


title: Learning 3D Object Categories by Looking Around Them

abstract: Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.


title: Multi-output RNN-LSTM for multiple speaker speech synthesis and adaptation

abstract: Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with single speaker model. Moreover, we also tackle the problem of speaker adaptation by adding a new output branch to the model and successfully training it without the need of modifying the base optimized model. This fine tuning method achieves better results than training the new speaker from scratch with its own model.


title: Raindrop Detection and Removal from Long Range Trajectories

abstract: In rainy scenes, visibility can be degraded by raindrops which have adhered to the windscreen or camera lens. In order to resolve this degradation, we propose a method that automatically detects and removes adherent raindrops. The idea is to use long range trajectories to discover the motion and appearance features of raindrops locally along the trajectories. These motion and appearance features are obtained through our analysis of the trajectory behavior when encountering raindrops. These features are then transformed into a labeling problem, which the cost function can be optimized efficiently. Having detected raindrops, the removal is achieved by utilizing patches indicated, enabling the motion consistency to be preserved. Our trajectory based video completion method not only removes the raindrops but also complete the motion field, which benefits motion estimation algorithms to possibly work in rainy scenes. Experimental results on real videos show the effectiveness of the proposed method.


title: Emergent Complexity via Multi-Agent Competition

abstract: Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment. This suggests that a highly capable agent requires a complex environment for training. In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself. We also point out that such environments come with a natural curriculum, because for any skill level, an environment full of agents of this level will have the right level of difficulty. This work introduces several competitive multi-agent environments where agents compete in a 3D world with simulated physics. The trained agents learn a wide variety of complex and interesting skills, even though the environment themselves are relatively simple. The skills include behaviors such as running, blocking, ducking, tackling, fooling opponents, kicking, and defending using both arms and legs. A highlight of the learned behaviors can be found here: https://goo.gl/eR7fbX


title: Counting public transport passenger using WiFi signatures of mobile devices

abstract: Public transport, or mass transit, is believed to bring several benefits, including energy efficiency, air quality and an economy of scale with respect to total public transport costs. An important factor in providing the public with cost-efficient and timely service, is an ability to do resource planning and optimization based on up-to-date data on passenger counts and flows. In this work we give a novel approach to counting the number of passengers on public bus transport based on WiFi signatures from any mobile devices carried onto the transport vehicle. This provides traffic planners with an up-to-date and relatively cost-effective data source.


title: Automated microsoft office macro malware detection using machine learning

abstract: Macro malware in Microsoft (MS) Office files has long persisted as a cybersecurity threat. Though it ebbed after its initial rampages around the turn of the century, it has reemerged as threat. Attackers are taking a persuasive approach and using document engineering, aided by improved data mining methods, to make MS Office file malware appear legitimate. Recent attacks have targeted specific corporations with malicious documents containing unusually relevant information. This development undermines the ability of users to distinguish between malicious and legitimate MS Office files and intensifies the need for automating macro malware detection. This study proposes a method of classifying MS Office files containing macros as malicious or benign using the K-Nearest Neighbors machine learning algorithm, feature selection, and TFIDF where p-code opcode n-grams (translated VBA macro code) compose the file features. This study achieves a 96.3% file classification accuracy on a sample set of 40 malicious and 118 benign MS Office files containing macros, and it demonstrates the effectiveness of this approach as a potential defense against macro malware. Finally, it discusses the challenges automated macro malware detection faces and possible solutions.


title: Where Do Rewards Come From?

abstract: Where Do Rewards Come From? Satinder Singh Richard L. Lewis Andrew G. Barto baveja@umich.edu Computer Science & Engineering University of Michigan, Ann Arbor rickl@umich.edu Department of Psychology University of Michigan, Ann Arbor barto@cs.umass.edu Department of Computer Science University of Massachusetts, Amherst Abstract Reinforcement learning has achieved broad and successful ap- plication in cognitive science in part because of its general for- mulation of the adaptive control problem as the maximization of a scalar reward function. The computational reinforcement learning framework is motivated by correspondences to ani- mal reward processes, but it leaves the source and nature of the rewards unspecified. This paper advances a general computa- tional framework for reward that places it in an evolutionary context, formulating a notion of an optimal reward function given a fitness function and some distribution of environments. Novel results from computational experiments show how tra- ditional notions of extrinsically and intrinsically motivated be- haviors may emerge from such optimal reward functions. In the experiments these rewards are discovered through auto- mated search rather than crafted by hand. The precise form of the optimal reward functions need not bear a direct relationship to the fitness function, but may nonetheless confer significant advantages over rewards based only on fitness. Introduction In the computational reinforcement learning (RL) frame- work (Sutton & Barto, 1998), rewards—more specifically, re- ward functions—determine the problem the learning agent is trying to solve. Properties of the reward function influence how easy or hard the problem is, and how well an agent may do, but RL theory and algorithms are completely insensitive to the source of rewards (except requiring that their magni- tude be bounded). This is a strength of the framework be- cause of the generality it confers, capable of encompassing both homeostatic theories of motivation in which rewards are defined as drive reduction, as has been done in many motiva- tional systems for artificial agents (Savage, 2000), and non- homeostatic theories that can account, for example, for the behavioral effects of electrical brain stimulation and addic- tive drugs. But it is also a weakness because it defers key questions about the nature of reward functions. Motivating the RL framework are the following correspon- dences to animal reward processes. Rewards in an RL system correspond to primary rewards, i.e., rewards that in animals have been hard-wired by the evolutionary process due to their relevance to reproductive success. In RL, they are thought of as the output of a “critic” that evaluates the RL agent’s be- havior. Further, RL systems that form value functions, us- ing, for example, Temporal Difference (TD) algorithms, ef- fectively create conditioned or secondary reward processes whereby predictors of primary rewards act as rewards them- selves. The learned value function provides ongoing evalu- ations that are consistent with the more intermittent evalu- ations of the hard-wired critic. The result is that the local landscape of a value function gives direction to the system’s preferred behavior: decisions are made to cause transitions to External Environment Actions Sensations Internal Environment Environment Critic Critic Rewards Decisions Rewards States States Actions Agent Agent Organism Figure 1: Agent-environment interactions in reinforcement learn- ing; adapted from Barto et al. (2004). See text for discussion. higher-valued states. A close parallel can be drawn between the gradient of a value function and incentive motivation (Mc- Clure, Daw, & Montague, 2003). In the usual view of an RL agent interacting with an ex- ternal environment (left panel of Figure 1), the primary re- ward comes from the external environment, being generated by a “critic” residing there. But as Sutton and Barto (1998) and Barto, Singh, and Chentanez (2004) point out, this is a seriously misleading view of RL if one wishes to relate this framework to animal reward systems. In a less misleading view of this interaction (right panel of Figure 1), the RL agent’s environment is divided into ex- ternal and internal environments. For an animal, the inter- nal environment consists of the systems that are internal to the animal while still being parts of the learning system’s environment. This view makes it clear that reward signals are always generated within the animal, for example, by its dopamine system. Therefore, all rewards are internal, and the internal/external distinction is not a useful one, a point also emphasized by Oudeyer and Kaplan (2007). This is the viewpoint we adopt in this paper. But what of a distinction between intrinsic and extrinsic reward? Psychologists distinguish between extrinsic motiva- tion, which means doing something because of some specific rewarding outcome, and intrinsic motivation, which refers to “doing something because it is inherently interesting or en- joyable” (Ryan & Deci, 2000). According to this view, in- trinsic motivation leads organisms to engage in exploration, play, and other behavior driven by curiosity in the absence of explicit reinforcement or reward. Barto et al. (2004) used the term intrinsic reward to re- fer to rewards that produce analogs of intrinsic motivation in RL agents, and extrinisic reward to refer to rewards that de- fine a specific task as in standard RL applications. We use this terminology here, but the distinction between intrinsic


title: New research perspectives on Ambient Intelligence

abstract: Ten years of AmI research have led to many new insights and understandings about the way highly interactive environments should be designed to meet the requirement of being truly unobtrusive and supportive from an end-user perspective. Probably the most revealing finding is the fact that, in addition to cognitive intelligence and computing, also elements from social intelligence and design play a dominant role in the realization of the vision. In this paper we discuss these novel insights and their resulting impact on the AmI research landscape. We introduce a number of new AmI research perspectives that are related to social intelligence and in addition we argue that new ways of working are required applying the concept of Experience Research resulting in a true user-centered approach to Ambient Intelligence.


title: Psychosocial stress impairs working memory at high loads: An association with cortisol levels and memory retrieval

abstract: Stress and cortisol are known to impair memory retrieval of well-consolidated declarative material. The effects of cortisol on memory retrieval may in particular be due to glucocorticoid (GC) receptors in the hippocampus and prefrontal cortex (PFC). Therefore, effects of stress and cortisol should be observable on both hippocampal-dependent declarative memory retrieval and PFC-dependent working memory (WM). In the present study, it was tested whether psychosocial stress would impair both WM and memory retrieval in 20 young healthy men. In addition, the association between cortisol levels and cognitive performance was assessed. It was found that stress impaired WM at high loads, but not at low loads in a Sternberg paradigm. High cortisol levels at the time of testing were associated with slow WM performance at high loads, and with impaired recall of moderately emotional, but not of highly emotional paragraphs. Furthermore, performance at high WM loads was associated with memory retrieval. These data extend previous results of pharmacological studies in finding WM impairments after acute stress at high workloads and cortisol-related retrieval impairments.


title: Low power CMOS NAND gate using DVS and mutithreshold CMOS technique

abstract: In digital and analog circuits, power consumption plays an important role in CMOS device. Due to scale down technology in VLSI circuits the threshold voltage of transistors reduced but increases in subthreshold leakage current. To reduce the subthreshold leakage current the effective circuit level technique is proposed. In this paper, the MTCMOS technique is proposed which gives high speed and low power dissipation by maintaining the performance of the circuits. The NAND gate is designed using DVS and MTCMOS technique gives least power consumption. All the simulations have been performed on Tanner EDA Tool version 14.1. The proposed technique reduces the power dissipation by 30% to 70%.


title: Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis

abstract: While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to managers’ knowledge and understanding by demonstrating how business analytics should be implemented to improve DME.


title: Position based fluids

abstract: In fluid simulation, enforcing incompressibility is crucial for realism; it is also computationally expensive. Recent work has improved efficiency, but still requires time-steps that are impractical for real-time applications. In this work we present an iterative density solver integrated into the Position Based Dynamics framework (PBD). By formulating and solving a set of positional constraints that enforce constant density, our method allows similar incompressibility and convergence to modern smoothed particle hydro-dynamic (SPH) solvers, but inherits the stability of the geometric, position based dynamics method, allowing large time steps suitable for real-time applications. We incorporate an artificial pressure term that improves particle distribution, creates surface tension, and lowers the neighborhood requirements of traditional SPH. Finally, we address the issue of energy loss by applying vorticity confinement as a velocity post process.


title: Combined Syntactic and Semantic Kernels for Text Classification

abstract: The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset.


title: ICTCP: Incast Congestion Control for TCP in data center networks

abstract: TCP incast congestion happens in high-bandwidth and low-latency networks, when multiple synchronized servers send data to a same receiver in parallel [15]. For many important data center applications such as MapReduce[5] and Search, this many-to-one traffic pattern is common. Hence TCP in-cast congestion may severely degrade their performances, e.g., by increasing response time. In this paper, we study TCP incast in detail by focusing on the relationship among TCP throughput, round trip time (RTT) and receive window. Different from the previous approach to mitigate the impact of incast congestion by a fine grained timeout value, our idea is to design an ICTCP (Incast congestion Control for TCP) scheme at the receiver side. In particular, our method adjusts TCP receive window proactively before packet drops occur. The implementation and experiments in our testbed demonstrate that we achieve almost zero timeout and high goodput for TCP incast.


title: A Survey on Lightweight Entity Authentication with Strong PUFs

abstract: Physically unclonable functions (PUFs) exploit the unavoidable manufacturing variations of an Integrated Circuit (IC). Their input-output behavior serves as a unique IC “fingerprint.” Therefore, they have been envisioned as an IC authentication mechanism, in particular the subclass of so-called strong PUFs. The protocol proposals are typically accompanied with two PUF promises: lightweight and an increased resistance against physical attacks. In this work, we review 19 proposals in chronological order: from the original strong PUF proposal (2001) to the more complicated noise bifurcation and system of PUF proposals (2014). The assessment is aided by a unified notation and a transparent framework of PUF protocol requirements.


title: Building the Internet of Things with bluetooth smart

abstract: The Internet of Things, or the IoT, is an emerging, disruptive technology that enables physical devices to communicate across disparate networks. IP has been the de facto standard for seamless inte ...


title: WiFi localization and navigation for autonomous indoor mobile robots

abstract: Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We present the results of our exhaustive tests of the WiFi localization independently and in conjunction with the navigation of our custom-built mobile robot in extensive long autonomous runs.


title: Who is Who in the Mailing List? Comparing Six Disambiguation Heuristics to Identify Multiple Addresses of a Participant

abstract: Many software projects adopt mailing lists for the communication of developers and users. Researchers have been mining the history of such lists to study communities' behavior, organization, and evolution. A potential threat of this kind of study is that users often use multiple email addresses to interact in a single mailing list. This can affect the results and tools, when, for example, extracting social networks. This issue is particularly relevant for popular and long-term Open Source Software (OSS) projects, which attract participation of thousands of people. Researchers have proposed heuristics to identify multiple email addresses from the same participant, however there are few studies analyzing the effectiveness of these heuristics. In addition, many studies still do not use any heuristics for authors' disambiguation, which can compromise the results. In this paper, we compare six heuristics from the literature using data from 150 mailing lists from Apache Software Foundation projects. We found that the heuristics proposed by Oliva et al. and a Naive heuristic outperformed the others in most cases, when considering the F-measure metric. We also found that the time window and the size of the dataset influence the effectiveness of each heuristic. These results may help researchers and tool developers to choose the most appropriate heuristic to use, besides highlighting the necessity of dealing with identity disambiguation, mainly in open source software communities with a large number of participants.


Sustainable Development and Innovation: Reviewing the Concept and Malaysian Participation


title: Spanner: Google’s Globally Distributed Database

abstract: Spanner is Google's scalable, multi-version, globally-distributed, and synchronously-replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This paper describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free read-only transactions, and atomic schema changes, across all of Spanner.


title: Multi-task Learning of Cascaded CNN for Facial Attribute Classification

abstract: Recently, facial attribute classification (FAC) has attracted significant attention in the computer vision community. Great progress has been made along with the availability of challenging FAC datasets. However, conventional FAC methods usually firstly pre-process the input images (i.e., perform face detection and alignment) and then predict facial attributes. These methods ignore the inherent dependencies among these tasks (i.e., face detection, facial landmark localization and FAC). Moreover, some methods using convolutional neural network are trained based on the fixed loss weights without considering the differences between facial attributes. In order to address the above problems, we propose a novel multi-task learning of cas- caded convolutional neural network method, termed MCFA, for predicting multiple facial attributes simultaneously. Specifically, the proposed method takes advantage of three cascaded sub-networks (i.e., S_Net, M_Net and L_Net corresponding to the neural networks under different scales) to jointly train multiple tasks in a coarse-to-fine manner, which can achieve end-to-end optimization. Furthermore, the proposed method automatically assigns the loss weight to each facial attribute based on a novel dynamic weighting scheme, thus making the proposed method concentrate on predicting the more difficult facial attributes. Experimental results show that the proposed method outperforms several state-of-the-art FAC methods on the challenging CelebA and LFWA datasets.


title: Face Recognition Time Reduction Based on Partitioned Faces without Compromising Accuracy and a Review of state-of-the-art Face Recognition Approaches

abstract: In this paper, the main objective is to make face recognition system faster by reducing recognition time without compromising accuracy for a constrained environment i.e. classroom, and provide a comparative review of state-of-the-art and classical approaches considering multiple faces that are at variable distance from the camera in the same image. This makes it a more challenging problem. Several models have been developed to partition the faces from a test image into three different levels. We have developed model hybridization by applying some classical but faster face recognition models namely Eigenfaces, Fisherfaces, Local Binary Patterns (LBP), and state-of-the-art yet relatively slower Convolutional Neural Network Model (CNN). Our proposed model hybridization technique based on different levels done by face partitioning has achieved approximately 33.43% faster performance than CNN while maintaining accuracy same as of CNN of our own dataset of faces of a classroom of 15 students while a class was going. The faces were of different students in different places, positions, poses and lighting. The objective of our research is not to enumerate and show how large a dataset we can identify by face, rather it is more interesting. We are interested in recognizing multiple faces at different distances from camera (hence, varying size, posture etc.) which calls for a unique approach as opposed to large dataset headshots from different angles of single people. We used different classification models for different levels of distance from the camera to achieve this faster response, making it a novel hybrid model.


title: Recent automatic text summarization techniques: a survey

abstract: As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.


title: Automatic identification of personal insults on social news sites

abstract: As online communities grow and the volume of user-generated content increases, the need for community management also rises. Community management has three main purposes: to create a positive experience for existing participants, to promote appropriate, socionormative behaviors, and to encourage potential participants to make contributions. Research indicates that the quality of content a potential participant sees on a site is highly influential; off-topic, negative comments with malicious intent are a particularly strong boundary to participation or set the tone for encouraging similar contributions. A problem for community managers, therefore, is the detection and elimination of such undesirable content. As a community grows, this undertaking becomes more daunting. Can an automated system aid community managers in this task? In this paper, we address this question through a machine learning approach to automatic detection of inappropriate negative user contributions. Our training corpus is a set of comments from a news commenting site that we tasked Amazon Mechanical Turk workers with labeling. Each comment is labeled for the presence of profanity, insults, and the object of the insults. Support vector machines trained on these data are combined with relevance and valence analysis systems in a multistep approach to the detection of inappropriate negative user contributions. The system shows great potential for semiautomated community management. © 2012 Wiley Periodicals, Inc.


title: Diagnosis and classification of disorders specifically associated with stress: proposals for ICD-11.

abstract: The diagnostic concepts of post-traumatic stress disorder (PTSD) and other disorders specifically associated with stress have been intensively discussed among neuro- and social scientists, clinicians, epidemiologists, public health planners and humanitarian aid workers around the world. PTSD and adjustment disorder are among the most widely used diagnoses in mental health care worldwide. This paper describes proposals that aim to maximize clinical utility for the classification and grouping of disorders specifically associated with stress in the forthcoming 11th revision of the International Classification of Diseases (ICD-11). Proposals include a narrower concept for PTSD that does not allow the diagnosis to be made based entirely on non-specific symptoms; a new complex PTSD category that comprises three clusters of intra- and interpersonal symptoms in addition to core PTSD symptoms; a new diagnosis of prolonged grief disorder, used to describe patients that undergo an intensely painful, disabling, and abnormally persistent response to bereavement; a major revision of "adjustment disorder" involving increased specification of symptoms; and a conceptualization of "acute stress reaction" as a normal phenomenon that still may require clinical intervention. These proposals were developed with specific considerations given to clinical utility and global applicability in both low- and high-income countries.


title: Analytic moment-based Gaussian process filtering

abstract: We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007).


title: Fast Cost-Volume Filtering for Visual Correspondence and Beyond

abstract: Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.


abstract: CRAIG, C. L., A. L. MARSHALL, M. SJOSTROM, A. E. BAUMAN, M. L. BOOTH, B. E. AINSWORTH, M. PRATT, U. EKELUND, A. YNGVE, J. F. SALLIS, and P. OJA. International Physical Activity Questionnaire: 12-Country Reliability and Validity. Med. Sci. Sports Exerc., Vol. 35, No. 8, pp. 1381-1395, 2003. Background: Physical inactivity is a global concern, but diverse physical activity measures in use prevent international comparisons. The International Physical Activity Questionnaire (IPAQ) was developed as an instrument for cross-national monitoring of physical activity and inactivity. Methods: Between 1997 and 1998, an International Consensus Group developed four long and four short forms of the IPAQ instruments (administered by telephone interview or self-administration, with two alternate reference periods, either the "last 7 d" or a "usual week" of recalled physical activity). During 2000, 14 centers from 12 countries collected reliability and/or validity data on at least two of the eight IPAQ instruments. Test-retest repeatability was assessed within the same week. Concurrent (inter-method) validity was assessed at the same administration, and criterion IPAQ validity was assessed against the CSA (now MTI) accelerometer. Spearman's correlation coefficients are reported, based on the total reported physical activity. Results: Overall, the IPAQ questionnaires produced repeatable data (Spearman's clustered around 0.8), with comparable data from short and long forms. Criterion validity had a median of about 0.30, which was comparable to most other self-report validation studies. The "usual week" and "last 7 d" reference periods performed similarly, and the reliability of telephone administration was similar to the self-administered mode. Conclusions: The IPAQ instruments have acceptable measurement properties, at least as good as other established self-reports. Considering the diverse samples in this study, IPAQ has reasonable measurement properties for monitoring population levels of physical activity among 18- to 65-yr-old adults in diverse settings. The short IPAQ form "last 7 d recall" is recommended for national monitoring and the long form for research requiring more detailed assessment. Key Words: MEASUREMENT, SURVEILLANCE, EPIDEMIOLOGY


title: AVA: A large-scale database for aesthetic visual analysis

abstract: With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks.


title: On Managing Very Large Sensor-Network Data Using Bigtable

abstract: Recent advances and innovations in smart sensor technologies, energy storage, data communications, and distributed computing paradigms are enabling technological breakthroughs in very large sensor networks. There is an emerging surge of next-generation sensor-rich computers in consumer mobile devices as well as tailor-made field platforms wirelessly connected to the Internet. Billions of such sensor computers are posing both challenges and opportunities in relation to scalable and reliable management of the peta- and exa-scale time series being generated over time. This paper presents a Cloud-computing approach to this issue based on the two well-known data storage and processing paradigms: Bigtable and MapReduce.


title: Feasibility analysis of big log data real time search based on Hbase and ElasticSearch

abstract: There are a lot of log events generated very day from modern enterprises. Form the pattern of these log data, enterprises can mine business value; it's even better if they can do it in real time. But managing this big log data is a big challenge because traditional technology is not powerful enough to process huge data. Luckily, Hadoop echo system provides a new way to process big data; ElasticSearch, which is based on Lucene, is the modern search engineen for cloud environment. This paper presents a real-time big data search method: First, Flume agent from the end user's machine collect log events, then ElasticSearch according to the search conditions are needed rowkey list; finally Hbase using these rowkey directly from the database to get the data, the paper-based hardware to create a virtual machine environment, the experiment proved, with the search for more log events, the search response time does not increase linearly. This article will explain Hbase based on modern distributed systems and cloud real-time search search engine ElasticSearch feasibility of large data logs.


title: Acceptance-based interventions for the treatment of chronic pain: A systematic review and meta-analysis

abstract: Acceptance-based interventions such as mindfulness-based stress reduction program and acceptance and commitment therapy are alternative therapies for cognitive behavioral therapy for treating chronic pain patients. To assess the effects of acceptance-based interventions on patients with chronic pain, we conducted a systematic review and meta-analysis of controlled and noncontrolled studies reporting effects on mental and physical health of pain patients. All studies were rated for quality. Primary outcome measures were pain intensity and depression. Secondary outcomes were anxiety, physical wellbeing, and quality of life. Twenty-two studies (9 randomized controlled studies, 5 clinical controlled studies [without randomization] and 8 noncontrolled studies) were included, totaling 1235 patients with chronic pain. An effect size on pain of 0.37 was found for the controlled studies. The effect on depression was 0.32. The quality of the studies was not found to moderate the effects of acceptance-based interventions. The results suggest that at present mindfulness-based stress reduction program and acceptance and commitment therapy are not superior to cognitive behavioral therapy but can be good alternatives. More high-quality studies are needed. It is recommended to focus on therapies that integrate mindfulness and behavioral therapy.


title: On the Application of QR Codes for Robust Self-localization of Mobile Robots in Various Application Scenarios

abstract: This paper presents an experimental analysis of the application of QR matrix codes as landmarks for mobile robot navigation. The QR codes not only provide means for positioning in reference to a global coordinate system over a wide range of viewing configurations, but they may also carry useful information that can be easily read by means of a low-cost camera. Moreover, QR codes can be applied on the mobile robots themselves, providing means for co-operation between the robots and the intelligent environment infrastructure (external cameras). Implementation of these tasks requires to determine how big the landmarks should be, how far from the robot they can be located, at which incidence angles, etc. In the paper we study methods to determine the location of the QR code-based landmarks in two practical setups related to mobile robot navigation. We demonstrate experimental results as to the maximum range of detection of the landmarks and the precision of localization available using off-the-shelf cameras.


title: Burstiness of Verbs and Derived Nouns

abstract: The frequencies of words vary with the discourse context, because any given word is more relevant to some topics of discussion than to others. In the statistical natural language processing literature, the term burstiness is used to characterize the tendency of topical words to occur repeatedly in bursts, separated by lulls in which they occur more rarely This article builds on the study of word burstiness by Altmann et al. (PLoS ONE 4:e7678, 2009). The study analyzed the archive of the USENET discussion group talk.origins, developed a novel method for quantifying burstiness, and showed that the burstiness of words is strongly correlated with their semantic type (in the sense of Montague semantics). Using the same dataset, I here explore the burstiness of abstract derived nouns (such as argument) in relation to their verb stems (e.g. argue) and frequency-matched nonderived nouns (such as science). I ask whether the burstiness of the derived form is inherited from the stem along with other stem features, such as the argument structure, or whether it is determined by the deverbal suffix. Overall, derived nouns pattern just like nonderived nouns, indicating that the suffix acts like the morphological head in determining the discourse statistics. This finding is interpreted in the light of Carlson’s theory of dialogue games (Carlson in Dialogue games: An approach to discourse analysis. Synthese language library, vol. 17. Reidel, Dordrecht, 1983).


title: Graphene mechanical pixels for Interferometric Modulator Displays

abstract: Electro-optic modulators based on micro-electromechanical systems have found success as elements for optical projectors, for simplified optical spectrometers, and as reflective-type screens that make use of light interference (Interferometric Modulator Display technology). The latter concept offers an exciting avenue for graphene nanomechanical structures to replace classical micro-electromechanical devices and bring about enhancement in performance, especially switching speed and voltage. In this work we study the optical response of electrically actuated graphene drumheads by means of spectrometric and stroboscopic experiments. The color reproducibility and speed of these membranes in producing the desired electro-optic modulation makes them suitable as pixels for high refresh rate displays. As a proof of concept, we demonstrate a Graphene Interferometric Modulator Display prototype with 5 μm-in-diameter pixels that compose a high resolution image (2500 pixels per inch)—equivalent to a 5″ display of 12K—whose color can be changed at frame rates of at least 400 Hz.The electro-optical response of suspended graphene membranes measured by change in wavelength-dependent reflectance can enable interferometric modulation display (IMOD) technology. Here, the authors report suspended double layer graphene based IMOD drums with 2500 pixels per inch.


title: SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network

abstract: We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet seamlessly integrates a convolutional neural network (CNN) and a recurrent neural network (RNN) to form a novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos. We effectively train the SV-RCNet in an end-to-end manner so that the visual representations and sequential dynamics can be jointly optimized in the learning process. In order to produce more discriminative spatio-temporal features, we exploit a deep residual network (ResNet) and a long short term memory (LSTM) network, to extract visual features and temporal dependencies, respectively, and integrate them into the SV-RCNet. Moreover, based on the phase transition-sensitive predictions from the SV-RCNet, we propose a simple yet effective inference scheme, namely the prior knowledge inference (PKI), by leveraging the natural characteristic of surgical video. Such a strategy further improves the consistency of results and largely boosts the recognition performance. Extensive experiments have been conducted with the MICCAI 2016 Modeling and Monitoring of Computer Assisted Interventions Workflow Challenge dataset and Cholec80 dataset to validate SV-RCNet. Our approach not only achieves superior performance on these two datasets but also outperforms the state-of-the-art methods by a significant margin.


title: A man-packable unmanned surface vehicle for radiation localization and forensics

abstract: This paper describes the first known man-packable unmanned surface vehicle (USV) system designed for radiation localization, either for nuclear treaty verification or sampling after a nuclear incident. Currently, outdoor radiation sampling is done manually or by ground or aerial unmanned systems. An unmanned surface vehicle can operate in shallow nuclear facility cooling ponds and connected streams, rivers, and bays to sample without entering the facility and while maintaining network connectivity to report readings. The Challenge 1 USV is a Lutra 1.1 autonomous airboat modified to carry an UltraRadiacTM. The debris-free area to survey is determined by teleoperation, then the system autonomously generates and executes a novel spiral path and sampling policy. Challenge 1 was tested for proof-of-concept at Texas A&M Engineering Extension Service's Disaster City® with a cesium-137 source placed near one shore of a pond 72 meters from the deployment point. During this test, 775 radiation readings were taken over an area of 57.61 square meters in 18 minutes. These data were used by an inverse distance weighting interpolation algorithm to produce a visual heatmap.


title: Measuring self-disclosure online: Blurring and non-response to sensitive items in web-based surveys

abstract: People are increasingly required to disclose personal information to computer- and Internet-based systems in order to register, identify themselves or simply for the system to work as designed. In the present paper, we outline two different methods to easily measure people's behavioral self-disclosure to web-based forms. The first, the use of an 'I prefer not to say' option to sensitive questions is shown to be responsive to the manipulation of level of privacy concern by increasing the salience of privacy issues, and to experimental manipulations of privacy. The second, blurring or increased ambiguity was used primarily by males in response to an income question in a high privacy condition. Implications for the study of self-disclosure in human-computer interaction and web-based research are discussed.


title: A novel chromosome 19p13.12 deletion in a child with multiple congenital anomalies

abstract: We describe a patient with multiple congenital anomalies including deafness, lacrimal duct stenosis, strabismus, bilateral cervical sinuses, congenital cardiac defects, hypoplasia of the corpus callosum, and hypoplasia of the cerebellar vermis. Mutation analysis of EYA1, SIX1, and SIX5, genes that underlie otofaciocervical and/or branchio-oto-renal syndrome, was negative. Pathologic diagnosis of the excised cervical sinus tracts was revised on re-examination to heterotopic salivary gland tissue. Using high resolution chromosomal microarray analysis, we identified a novel 2.52 Mb deletion at 19p13.12, which was confirmed by fluorescent in situ hybridization and demonstrated to be a de novo mutation by testing of the parents. Overall, deletions of chromosome 19p13 are rare.


title: Outcomes in Hand Rehabilitation Using Occupational Therapy Services

abstract: OBJECTIVE ::: The purpose of this study was to measure functional outcomes after outpatient occupational therapy for clients who had upper-extremity injury and surgery or both. ::: ::: ::: METHODS ::: A sample of 33 clients referred to occupational therapy outpatient intervention was recruited from five clinics in Ohio. The Canadian Occupational Performance Measure (COPM) was used to guide the occupational therapy sessions and to measure outcomes. The Disability of Arm, Shoulder, and Hand (DASH) questionnaire and the Short Form 36 (SF-36) were also administered to the clients pre and post 6 to 8 weeks of hand rehabilitation services. Two to three months after discharge, the clients responded by the telephone to the Community Integration Questionnaire. ::: ::: ::: RESULTS ::: The clients received a mean of 13 hours of outpatient occupational therapy services and received no other rehabilitation service. Functional performance gains following 6 to 8 weeks of services were significant (COPM; t(32) = -11.5, p < .001; t(32) = -11.31, p < .001; DASH; t(32) = 9.22, p < .001; t(32) = -9.02, p < .001). Effect sizes on the COPM and DASH ranged between 1.43 and 2.52. Progress in the clients' goals was moderately correlated to progress in functional measures. ::: ::: ::: CONCLUSION ::: Clients with upper-extremity injury or surgery made strong, positive gains in functional measures following client-centered occupational therapy services. The COPM was the most sensitive to client change, followed by the DASH, and then the SF-36.


title: Micro-bias and macro-performance

abstract: We use agent-based modeling to investigate the effect of conservatism and partisanship on the efficiency with which large populations solve the density classification task – a paradigmatic problem for information aggregation and consensus building. We find that conservative agents enhance the populations’ ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs.


title: Cooperative flood detection using GSMD via SMS

abstract: This paper proposes architecture for an early warning floods system to alert public against flood disasters. An effective early warning system must be developed with linkages between four elements, which are accurate data collection to undertake risk assessments, development of hazard monitoring services, communication on risk related information and existence of community response capabilities. This project focuses on monitoring water level remotely using wireless sensor network. The project also utilizes Global System for Mobile communication (GSM) and short message service (SMS) to relay data from sensors to computers or directly alert the respective victim's through their mobile phone. It is hope that the proposed architecture can be further develop into a functioning system, which would be beneficial to the community and act as a precautionary action to save lives in the case of flood disaster.


title: Mathematics and geometry education with collaborative augmented reality

abstract: Construct3D is a three-dimensional geometric construction tool specifically designed for mathematics and geometry education. It is based on the mobile collaborative augmented reality system "Studierstube." We describe our efforts in developing a system for the improvement of spatial abilities and maximization of transfer of learning. In order to support various teacher-student interaction scenarios we implemented flexible methods for context and user dependent rendering of parts of the construction. Together with hybrid hardware setups they allow the use of Construct3D in today's classrooms and provide a test bed for future evaluations. Means of application and integration in mathematics and geometry education at the high school, as well as the university, level are being discussed. Anecdotal evidence supports our claim that Construct3D is easy to learn, encourages experimentation with geometric constructions, and improves spatial skills.


title: Adaptive dimension reduction using discriminant analysis and K-means clustering

abstract: We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to generate class labels and use LDA to do subspace selection. The clustering process is thus integrated with the subspace selection process and the data are then simultaneously clustered while the feature subspaces are selected. We show the rich structure of the general LDA-Km framework by examining its variants and their relationships to earlier approaches. Relations among PCA, LDA, K-means are clarified. Extensive experimental results on real-world datasets show the effectiveness of our approach.


title: Application of Levant's differentiator for velocity estimation and increased Z-width in haptic interfaces

abstract: In this paper, we present results from implementation of Levant's differentiator for velocity estimation from optical encoder readings. Levant's differentiator is a sliding mode control theory-based real-time differentiation algorithm proposed as a velocity estimator. The application of the technique allows stable implementation of higher stiffness virtual walls as compared to using the common finite difference method (FDM) cascaded with low-pass filters for velocity estimation. A single degree-of-freedom (DOF) linear haptic device is used as a test bed and an automated virtual wall hitting task is implemented to experimentally demonstrate that it is possible to extend the impedance-width (or Z-width) of a haptic interface via Levant's differentiator.


title: On measuring the impact of DDoS botnets

abstract: Miscreants use DDoS botnets to attack a victim via a large number of malware-infected hosts, combining the bandwidth of the individual PCs. Such botnets have thus a high potential to render targeted services unavailable. However, the actual impact of attacks by DDoS botnets has never been evaluated. In this paper, we monitor C&C servers of 14 DirtJumper and Yoddos botnets and record the DDoS targets of these networks. We then aim to evaluate the availability of the DDoS victims, using a variety of measurements such as TCP response times and analyzing the HTTP content. We show that more than 65% of the victims are severely affected by the DDoS attacks, while also a few DDoS attacks likely failed.


abstract: Formalizing the Pragmatics of Metaphor Understanding Justine T. Kao 1 (justinek@stanford.edu) , Leon Bergen 2 (bergen@mit.edu) , Noah D. Goodman 1 (ngoodman@stanford.edu) 1 Department of Psychology, Stanford University. 2 Department of Brain and Cognitive Science, MIT. Abstract theorists argue that this principle explains how listeners in- fer the meaning of a novel metaphor as well as other forms of loose talk where the meaning of an utterance is underspecified (Sperber & Wilson, 1985). When interpreting the metaphor “My lawyer is a shark,” for example, the listener assumes that the speaker aims to communicate features of “a shark” that are relevant to the person under discussion (“my lawyer”) and do not make use of other shark features—vicious but not has fins or swims. While the ubiquity and importance of nonliteral language are clear, people’s ability to use and understand it remains a mys- tery. Metaphor in particular has been studied extensively across many disciplines in cognitive science. One approach fo- cuses on the pragmatic principles that listeners utilize to infer meaning from metaphorical utterances. While this approach has generated a number of insights about how people under- stand metaphor, to our knowledge there is no formal model showing that effects in metaphor understanding can arise from basic principles of communication. Building upon recent ad- vances in formal models of pragmatics, we describe a com- putational model that uses pragmatic reasoning to interpret metaphorical utterances. We conduct behavioral experiments to evaluate the model’s performance and show that our model produces metaphorical interpretations that closely fit behav- ioral data. We discuss implications of the model for metaphor understanding, principles of communication, and formal mod- els of language understanding. While many linguists and psychologists have argued for the benefits of studying metaphor using a pragmatics frame- work, to our knowledge there is no formal model showing that effects in metaphor understanding may arise from basic principles of communication. On the other hand, a recent body of work presents a series of computational models for pragmatic reasoning, where speaker and listener reason about each other to communicate effectively (Frank & Goodman, 2012; J¨ager & Ebert, 2009). By formalizing principles of communication, these models are able to make quantitative predictions about a range of phenomena in language under- standing, such as scalar implicature and the effect of alter- native utterances (Goodman & Stuhlm¨uller, 2013; Bergen, Goodman, & Levy, 2012). However, a limitation of these models is that they are unable to explain the use of utterances which are known to have false literal interpretations. More re- cent work extends these models to consider affective goals in communication that may be optimally satisfied by nonliteral utterances such as hyperbole (under review). In this paper, we will be considering the role of communicative goals in the in- terpretation of metaphorical utterances. In our formalization, a listener assumes that the speaker chooses an utterance to maximize informativeness about a subject along dimensions that are relevant to the conversation and consistent with the speaker’s communicative goal. This makes it possible for a literally false utterance to be optimal as long as it is informa- tive along the target dimension. Our framework closely aligns with the relevance-theoretic view that a listener considers the relevance of a potential meaning to the speaker’s goal in order to infer what the speaker intended to communicate. Keywords: language understanding; metaphor; pragmatics; computational models Introduction From “Juliet is the sun” to “That woman is a bombshell,” non- literal language is, quite literally, everywhere. Metaphor, hy- perbole, and sarcasm are ubiquitous features of human com- munication, often creating poetic or humorous effects that add rich dimensions to language (Glucksberg, 2001; Pilking- ton, 2000; Lakoff & Turner, 2009; Roberts & Kreuz, 1994). Metaphor has inspired a particularly large amount of research in cognitive science, spanning topics such as how metaphors structure and shape our thoughts (Ortony, 1993; Lakoff et al., 1993; Thibodeau & Boroditsky, 2011), whether metaphor processing recruits the same strategies as standard language processing (Giora, 1997; Gibbs, 2002; Glucksberg & Keysar, 1993) and what factors determine people’s interpretation a novel metaphor (Gentner & Wolff, 1997; Blasko & Connine, 1993; Tourangeau & Sternberg, 1981; Kintsch & Bowles, 2002). This overwhelming interest in metaphor research is due to both the ubiquity of metaphor in everyday language and the potential role of metaphor for helping us understand how the mind creates meaning. One approach to studying metaphor focuses on the prag- matic principles that listeners utilize to infer meaning from metaphorical utterances (Tendahl & Gibbs, 2008). Rather than view metaphor as a separate mode of communication that requires specialized language processing strategies, this approach argues that basic principles of communication drive the meaning that a listener infers from a metaphor (Sperber & Wilson, 2008; Wilson & Carston, 2006). Relevance the- ory, in particular, posits that listeners interpret utterances with the assumption that speakers produced them because they are maximally relevant (Wilson & Sperber, 2002). Relevance To reasonably limit the scope of our work, we focus on metaphors of the classic form “X is a Y .” We describe a com- putational model that can interpret such sentences metaphor- ically and conduct behavioral experiments to evaluate the model’s performance. We show that peoples’ interpretations of metaphors are driven by conversational context and that our model captures this effect. Finally, we show that our model predictions correlate significantly with people’s fine- grained interpretations of metaphorical utterances.


title: Micro parts assembly system with micro gripper and RCC unit

abstract: This paper presents a new micro assembly system, which is composed of a micro gripper, a micro remote center compliance (RCC) unit, a voice coil motor-driving mechanism and precision motion stages. The micro gripper is actuated by two shape memory alloy (SMA) wires, and its grip is 1 mm. The micro RCC unit has low translational and rotational stiffness sufficient for micro parts assembly. The voice coil motor-driving mechanism can generate linear motion with an adjustable stiffness, and it can also measure external force in the moving direction. An algorithm for the automatic assembly of micro parts is also proposed, and assembly experiments are performed.


title: Design and Fabrication of Low-Loss Toroidal Air-Core Inductors

abstract: The design and fabrication of high-Q air-core toroidal inductors are described. Designs are optimized for maximum Q for a given size and inductance, independent any particular fabrication process. A fabrication process using a combination of thin-film processes and conventional machining has been used to make prototypes. Measurements performed on the first prototypes confirm the predicted Q of 115 at 50 MHz.


title: Energy efficient IoT-based smart home

abstract: Smart Home technology is the future of residential related technology which is designed to deliver and distribute number of services inside and outside the house via networked devices in which all the different applications & the intelligence behind them are integrated and interconnected. These smart devices have the potential to share information with each other given the permanent availability to access the broadband internet connection. Hence, Smart Home Technology has become part of IoT (Internet of Things). In this work, a home model is analyzed to demonstrate an energy efficient IoT based smart home. Several Multiphysics simulations were carried out focusing on the kitchen of the home model. A motion sensor with a surveillance camera was used as part of the home security system. Coupled with the home light and HVAC control systems, the smart system can remotely control the lighting and heating or cooling when an occupant enters or leaves the kitchen.


title: Securing IOT devices using SDN and edge computing

abstract: In the current technological perspective, we are witnessing a tremendous increase in the connection of heterogeneous devices and controlling them remotely. One of the latest technologies enabling this concept is “IoT” (Internet of Things). According to historical evidence, anything which is connected to a network is vulnerable to hacking and illegal monitoring. Since the devices which are being connected would be directly controlling human life, the security of these devices becomes paramount. Especially when IoT is turning “heterogeneous networks” into “super-heterogeneous networks” of intelligent devices, securing it becomes very complex. There are different methods of providing security to a network available today but none is infallible. SDN being an intelligent networking paradigm which can rapidly and automatically reconfigure network devices, reroute traffic and apply authentication and access rules can open up a way for better security and access control mechanisms. In this paper, an attempt has been made to devise a method of providing security to IoT using SDN (Software Defined Network) & Edge Computing.


title: Immunohistochemical Localization of Glycogen Synthase and GSK3β: Control of Glycogen Content in Retina

abstract: Glycogen has an important role in energy handling in several brain regions. In the brain, glycogen is localized in astrocytes and its role in several normal and pathological processes has been described, whereas in the retina, glycogen metabolism has been scarcely investigated. The enzyme glycogen phosphorylase has been located in retinal Müller cells; however the cellular location of glycogen synthase (GS) and its regulatory partner, glycogen synthase kinase 3β (GSK3β), has not been investigated. Our aim was to localize these enzymes in the rat retina by immunofluorescence techniques. We found both GS and GSK3β in Müller cells in the synaptic layers, and within the inner segments of photoreceptor cells. The presence of these enzymes in Müller cells suggests that glycogen could be regulated within the retina as in other tissues. Indeed, we showed that glycogen content in the whole retina in vitro was increased by high glucose concentrations, glutamate, and insulin. In contrast, retina glycogen levels were not modified by norepinephrine nor by depolarization with high KCl concentrations. Insulin also induced an increase in glycogen content in cultured Müller cells. The effect of insulin in both, whole retina and cultured Müller cells was blocked by inhibitors of phosphatidyl-inositol 3-kinase, strongly suggesting that glycogen content in retina is modulated by the insulin signaling pathway. The expression of GS and GSK3β in the synaptic layers and photoreceptor cells suggests an important role of GSK3β regulating glycogen synthase in neurons, which opens multiple feasible roles of insulin within the retina.


title: Generating and exploring good building layouts

abstract: Good building layouts are required to conform to regulatory guidelines, while meeting certain quality measures. While different methods can sample the space of such good layouts, there exists little support for a user to understand and systematically explore the samples. Starting from a discrete set of good layouts, we analytically characterize the local shape space of good layouts around each initial layout, compactly encode these spaces, and link them to support transitions across the different local spaces. We represent such transitions in the form of a portal graph. The user can then use the portal graph, along with the family of local shape spaces, to globally and locally explore the space of good building layouts. We use our framework on a variety of different test scenarios to showcase an intuitive design, navigation, and exploration interface.


title: Combining control-flow integrity and static analysis for efficient and validated data sandboxing

abstract: In many software attacks, inducing an illegal control-flow transfer in the target system is one common step. Control-Flow Integrity (CFI) protects a software system by enforcing a pre-determined control-flow graph. In addition to providing strong security, CFI enables static analysis on low-level code. This paper evaluates whether CFI-enabled static analysis can help build efficient and validated data sandboxing. Previous systems generally sandbox memory writes for integrity, but avoid protecting confidentiality due to the high overhead of sandboxing memory reads. To reduce overhead, we have implemented a series of optimizations that remove sandboxing instructions if they are proven unnecessary by static analysis. On top of CFI, our system adds only 2.7% runtime overhead on SPECint2000 for sandboxing memory writes and adds modest 19% for sandboxing both reads and writes. We have also built a principled data-sandboxing verifier based on range analysis. The verifier checks the safety of the results of the optimizer, which removes the need to trust the rewriter and optimizer. Our results show that the combination of CFI and static analysis has the potential of bringing down the cost of general inlined reference monitors, while maintaining strong security.


title: Bayesian Multi-view Tensor Factorization

abstract: We introduce a Bayesian extension of the tensor factorization problem to multiple coupled tensors. For a single tensor it reduces to standard PARAFAC-type Bayesian factorization, and for two tensors it is the first Bayesian Tensor Canonical Correlation Analysis method. It can also be seen to solve a tensorial extension of the recent Group Factor Analysis problem. The method decomposes the set of tensors to factors shared by subsets of the tensors, and factors private to individual tensors, and does not assume orthogonality. For a single tensor, the method empirically outperforms existing methods, and we demonstrate its performance on multiple tensor factorization tasks in toxicogenomics and functional neuroimaging.