title: Active Learning to Rank using Pairwise Supervision.

abstract: This paper investigates learning a ranking function using pairwise constraints in the context of human-machine interaction. As the performance of a learnt ranking model is predominantly determined by the quality and quantity of training data, in this work we explore an active learning to rank approach. Furthermore, since humans may not be able to confidently provide an order for a pair of similar instances we explore two types of pairwise supervision: (i) a set of “strongly” ordered pairs which contains confidently ranked instances, and (ii) a set of “weakly” ordered pairs which consists of similar or closely ranked instances. Our active knowledge injection is performed by querying domain experts on pairwise orderings, where informative pairs are located by considering both local and global uncertainties. Under this active scheme, querying of pairs which are uninformative or outliers instances would not occur. We evaluate the proposed approach on three real world datasets and compare with representative methods. The promising experimental results demonstrate the superior performance of our approach, and validate the effectiveness of actively using pairwise orderings to improve ranking performance.


title: Analyzing an Agile Solution For Intelligent Distribution Grid Development: A Smart Grid Architecture Method

abstract: The design of future electricity markets requires Distribution System Operators (DSOs) to emerge as an active player by implementing advanced technical solutions which improve their market responsiveness, process adaptability and meet their business needs. This paper presents a methodology that helps DSOs carry out such a complex task. The proposed methodology enhances the Smart Grid Architecture Model (SGAM) framework in the context of transactive energy and multi-agent system (MAS). The proposed method is referred to as agile methodology because it made the SGAM architectures Adaptive, Goal-oriented, Intelligent, Linked and Expandable (AGILE). It is implemented using an object-oriented application that facilitates the analysis of agility in the network management.


title: A sustainable identity: the creativity of an everyday designer

abstract: In this paper we explore sustainability in interaction design by reframing concepts of user identity and use in a domestic setting. Building on our own work on everyday design and Blevis's Sustainable Interaction Design principles, we present examples from an ethnographic study of families in their homes which illustrate design-in-use: the creative and sustainable ways people appropriate and adapt designed artifacts. We claim that adopting a conception of the user as a creative everyday designer generates a new set of design principles that promote sustainable interaction design.


title: Higher-order Lexical Semantic Models for Non-factoid Answer Reranking

abstract: Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models acquire term alignment probabilities from semi-structured data such as question-answer pairs; neural network language models learn term embeddings from unstructured text. All this knowledge is then used to estimate the semantic similarity between question and answer candidates. We introduce a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations. Using a corpus of 10,000 questions from Yahoo! Answers, we experimentally demonstrate that higher-order methods are broadly applicable to alignment and language models, across both word and syntactic representations. We show that an important criterion for success is controlling for the semantic drift that accumulates during graph traversal. All in all, the proposed higher-order approach improves five out of the six lexical semantic models investigated, with relative gains of up to +13\% over their first-order variants.


title: Verified iptables firewall analysis

abstract: We present a fully verified firewall ruleset analysis framework. Ultimately, it computes minimal service matrices, i.e. graphs which partition the complete IPv4 address space and visualize the allowed accesses between partitions for a fixed service. Internally, we are working with a simplified firewall model and a core contribution is the translation of complex real-world iptables firewall rules into this model. The presented algorithms and translation are formally proven correct with the Isabelle theorem prover. A real-world evaluation demonstrates the applicability of our tool. Both the iptables-save datasets and the Isabelle formalization are publicly available.


title: Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks

abstract: Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount of knowledge encoded in imaging data and corresponding reports generated during clinical routine. Weakly supervised learning approaches can link volume-level labels to image content but suffer from the typical label distributions in medical imaging data where only a small part consists of clinically relevant abnormal structures. In this paper we propose to use a semantic representation of clinical reports as a learning target that is predicted from imaging data by a convolutional neural network. We demonstrate how we can learn accurate voxel-level classifiers based on weak volume-level semantic descriptions on a set of 157 optical coherence tomography (OCT) volumes. We specifically show how semantic information increases classification accuracy for intraretinal cystoid fluid (IRC), subretinal fluid (SRF) and normal retinal tissue, and how the learning algorithm links semantic concepts to image content and geometry.


title: A 32.4 ppm/°C 3.2-1.6V self-chopped relaxation oscillator with adaptive supply generation

abstract: A self-chopped relaxation oscillator with adaptive supply generation provides the stable output clock against variations in temperature and supply voltages. The frequency drift is less than ±0.1% for the supply voltage changing from 1.6 to 3.2 V and ±0.1% for a temperature range from −20 to 100°C, which is reduced by 83% with the self-chopped technique. This relaxation oscillator is implemented in a 60-nm CMOS technology with its active area equals to 0.048 mm2. It consumes 2.8 uA from a 1.6-V supply.


Using Wikipedia Edits in Low Resource Grammatical Error Correction


title: On industrial learning and training for the factories of the future: a conceptual, cognitive and technology framework

abstract: The manufacturing education addresses significant challenges in view of paving the way for the human capital of the Factories of the Future. This paper introduces a specification framework for the delivery of industrial learning and training, addressing the needs for the "knowledge" workers of the factories of the future. A review of the relevant background, including the activities associated with the industrial learning and its basic methods as well as some emerging paradigms, is first provided. A Teaching Factory based paradigm is then suggested as the conceptual foundation of this framework. A cognitive framework, addressing the major building blocks of the industrial learning process, namely the attitude the knowledge the skills and the competences is being defined. An appropriate technology framework, dealing with the needs of engineers and blue-collar workers, for practising the manufacturing knowledge content within digital environments, is suggested at this point. Finally, the approach of the future implementation of the specific framework is being discussed.


title: Segmenting Email Message Text into Zones

abstract: In the early days of email, widely-used conventions for indicating quoted reply content and email signatures made it easy to segment email messages into their functional parts. Today, the explosion of different email formats and styles, coupled with the ad hoc ways in which people vary the structure and layout of their messages, means that simple techniques for identifying quoted replies that used to yield 95% accuracy now find less than 10% of such content. In this paper, we describe Zebra, an SVM-based system for segmenting the body text of email messages into nine zone types based on graphic, orthographic and lexical cues. Zebra performs this task with an accuracy of 87.01%; when the number of zones is abstracted to two or three zone classes, this increases to 93.60% and 91.53% respectively.


title: Spectrally efficient telemedicine and in-hospital patient data transfer

abstract: We propose an overlay cognitive radio scheme for telemedicine and in-hospital patient data transfer. We consider the telemedicine transmitter receiver pair to be the primary user or spectrum license holder. The in-hospital patient monitor is the secondary user in this overlay cognitive radio model. Using the fact that the data transmitted to the telemedicine terminal and the in-hospital patient monitor both originate from the coordinator of a medical body area network, the side information at the transmitter can be used to mitigate the effects of interference while the two medical data transmission schemes coexist in the same frequency band. This method can be used to enhance the bit error rate and spectral efficiency in transferring the information generated by wireless body area networks to remote destinations. The method finds applications in E-health medical systems.


title: A dual-band rectifier for RF energy harvesting systems

abstract: The environment of the city is surrounded by a lot of radio frequency (RF) energy that is broadcasted by diverse wireless systems. In order to enhance the value of the energy more than the channels to communicate, the ambient RF energy harvesting system was designed to harvest and recycle the energy for many applications such as battery chargers, sensor devices and portable devices. The main element of the ambient RF energy harvesting system is a rectenna that is the combination of an antenna and a rectifying circuit. Even though the ambient RF energy is widely broadcasted with many systems, the energy is extremely low. Therefore, the high performance of the antenna and the rectifying circuit has to be designed for supporting the small incident power; also the number of frequency channels of the rectenna can enhance the performance and support different harvesting locations. This paper proposes a dual-band rectifier for RF energy harvesting systems that was designed to operate at 2.1 GHz and 2.45 GHz. The first channel can provide the maximum efficiency of 24% with 1.9 V of the output voltage at 10 dBm of input power. On the other hand, a maximum efficiency of 18% and 1.7 V of the output voltage can be achieved by the second channel at 10 dBm of input power.


title: A Survey of Outlier Detection Methods in Network Anomaly Identification

abstract: The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the discovery of outliers leads to more interesting and useful results than the discovery of inliers. Detection of outliers can lead to identification of system faults so that administrators can take preventive measures before they escalate. It is possible that anomaly detection may enable detection of new attacks. Outlier detection is an important anomaly detection approach. In this paper, we present a comprehensive survey of well-known distance-based, density-based and other techniques for outlier detection and compare them. We provide definitions of outliers and discuss their detection based on supervised and unsupervised learning in the context of network anomaly detection.


title: Multifidus muscle recovery is not automatic after resolution of acute, first-episode low back pain.

abstract: Study Design. A clinical study was conducted on 39 patients with acute, first-episode, unilateral low back pain and unilateral, segmental inhibition of the multifidus muscle. Patients were allocated randomly to a control or treatment group. Objectives. To document the natural course of lumbar multifidus recovery and to evaluate the effectiveness of specific, localized, exercise therapy on muscle recovery. Summary of Background Data. Acute low back pain usually resolves spontaneously, but the recurrence rate is high. Inhibition of multifidus occurs with acute, first-episode, low back pain, and pathologic changes in this muscle have been linked with poor outcome and recurrence of symptoms. Methods. Patients in group 1 received medical treatment only. Patients in group 2 received medical treatment and specific, localized, exercise therapy. Outcome measures for both groups included 4 weekly assessments of pain, disability, range of motion, and size of the multifidus cross-sectional area. Independent examiners were blinded to group allocation. Patients were reassessed at a 10-week follow-up examination. Results. Multifidus muscle recovery was not spontaneous on remission of painful symptoms in patients in group 1. Muscle recovery was more rapid and more complete in patients in group 2 who received exercise therapy (P = 0.0001). Other outcome measurements were similar for the two groups at the 4-week examination. Although they resumed normal levels of activity, patients in group 1 still had decreased multifidus muscle size at the 10-week follow-up examination. Conclusions. Multifidus muscle recovery is not spontaneous on remission of painful symptoms. Lack of localized, muscle support may be one reason for the high recurrence rate of low back pain following the initial episode.


title: Energy-efficient deployment of Intelligent Mobile sensor networks

abstract: Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.


DappGuard : Active Monitoring and Defense for Solidity Smart Contracts


title: "OMG! How did it know that?": Reactions to Highly-Personalized Ads

abstract: In this paper, we explore the question "would people be willing to share their personal data in exchange for highly-personalized online ads?" through a Wizard-of-Oz deception study. Our volunteers were exposed via a web browser to three different highly- personalized ads, designed by people who knew them well. They were made believe that the ads had been generated automatically by an Artificial Intelligence engine on the basis of their browsing & location history and/or personal traits. The participants' reactions were surprisingly favorable: in more than 50% of the cases, the ads triggered spontaneous positive emotional reactions; almost 90% of participants would share at least two of the three data sources with advertisers; and about 50% would share all data sources. Our results provide evidence that highly-personalized ads may offset the concerns that people have about sharing their personal data. Thus further efforts in building increasingly personalized online ads would represent a worthwhile endeavour.


title: Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

abstract: So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.


title: A Helmet-Mounted Pedestrian Dead Reckoning System

abstract: Since most persons in emergency service wear some sort of headgear, we investigate how basic navigation sensors could be incorporated into a safety helmet and thereby be made truly wearable. It is well known that the key to solving the generic ubiquitous localization problem is the combination of different positioning techniques and sensor modalities. In this paper, a combined approach of pedestrian dead reckoning (PDR) and GPS positioning is followed. An novel combination of neural-network step length predictions and helmet-mounted sensors is presented. The experimental system shows low accumulated error over an extended walk and indoor/outdoor navigation is demonstrated.


title: A Lyapunov approach based higher order sliding mode controller for grid connected shunt active compensators with a LCL filter

abstract: A Lyapunov approach based homogeneous higher order SM-Controller (HOSMC) is considered here, for grid connected three phases three wires shunt active filter (SAF) via LCL filter. This nonlinear controller is an alternative solution to overcome phase shift caused by a linear controller and a chattering effect caused by a 1st order sliding mode controller that is supposed to be of relative degree 1. The last fact requires a special sliding variable design that yields asymptotic convergence of the tracking error to zero versus a finite time convergence provided by HOSMC. On the other hand, it is well known that VSI based SAFs compensate for the main types of current perturbations in the electrical power systems, while generate some undesired components caused by switching frequency. In order to avoid the spreading of these components to the grid side, a VSI connected to the grid via a LCL output filter is largely proposed, but for renewable energy systems, where the component to be injected into the grid is only the fundamental. Unfortunately, when the currents to be generated by sAf include both fundamental and harmonics, and for a LCL output filter associated to linear controllers, a phase shift appears, between the identified harmonics current and the injected current, and impacts negatively, harmonic current filtration of the SAF. In this article, a PWM-HOSMC is proposed to operate in a fixed frequency without chattering, preventing a variable switching frequency effects. In addition, this article proposes a solution for the non-robustness drawback, from disturbances point of view, of the used control method. The rapidity, tracking and effectiveness of this proposed controller, within the SAF, are validated by simulations carried out by Matlab, Simulink, Simscape-SimPower System code.


title: Fast object recognition and 6D pose estimation using viewpoint oriented color-shape histogram

abstract: In this paper, we present an object recognition and pose estimation framework consisting of a novel global object descriptor, so called Viewpoint oriented Color-Shape Histogram (VCSH), which combines object's color and shape information. During the phase of object modeling and feature extraction, the whole object's color point cloud model is built by registration from multi-view color point clouds. VCSH is trained using partial-view object color point clouds generated from different synthetic viewpoints. During the recognition phase, the object is identified and the closest viewpoint is extracted using the built feature database and object's features from real scene. The estimated closest viewpoint provides a good initialization for object pose estimation optimization using the iterative closest point strategy. Finally, objects in real scene are recognized and their accurate poses are retrieved. A set of experiments is realized where our proposed approach is proven to outperform other existing methods by guaranteeing highly accurate object recognition, fast and accurate pose estimation as well as exhibiting the capability of dealing with environmental illumination changes.


title: Locally Assembled Binary (LAB) feature with feature-centric cascade for fast and accurate face detection

abstract: In this paper, we describe a novel type of feature for fast and accurate face detection. The feature is called Locally Assembled Binary (LAB) Haar feature. LAB feature is basically inspired by the success of Haar feature and Local Binary Pattern (LBP) for face detection, but it is far beyond a simple combination. In our method, Haar features are modified to keep only the ordinal relationship (named by binary Haar feature) rather than the difference between the accumulated intensities. Several neighboring binary Haar features are then assembled to capture their co-occurrence with similar idea to LBP. We show that the feature is more efficient than Haar feature and LBP both in discriminating power and computational cost. Furthermore, a novel efficient detection method called feature-centric cascade is proposed to build an efficient detector, which is developed from the feature-centric method. Experimental results on the CMU+MIT frontal face test set and CMU profile test set show that the proposed method can achieve very good results and amazing detection speed.


title: Task selection in spatial crowdsourcing from worker’s perspective

abstract: With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we propose different approximation algorithms. Finally, to strike a compromise between efficiency and accuracy, we present a progressive algorithms. We conducted a thorough experimental evaluation with both real-world and synthetic data on desktop and mobile platforms to compare the performance and accuracy of our proposed approaches.


title: Personality and transformational and transactional leadership: a meta-analysis

abstract: This study was a meta-analysis of the relationship between personality and ratings of transformational and transactional leadership behaviors. Using the 5-factor model of personality as an organizing framework, the authors accumulated 384 correlations from 26 independent studies. Personality traits were related to 3 dimensions of transformational leadership—idealized influence–inspirational motivation (charisma), intellectual stimulation, and individualized consideration—and 3 dimensions of transactional leadership— contingent reward, management by exception–active, and passive leadership. Extraversion was the strongest and most consistent correlate of transformational leadership. Although results provided some support for the dispositional basis of transformational leadership— especially with respect to the charisma dimension— generally, weak associations suggested the importance of future research to focus on both narrower personality traits and nondispositional determinants of transformational and transactional leadership. A recent PsycINFO search revealed that 1,738 of the 15,000 articles (12%) published since 1990 on the topic of leadership included the keywords personality and leadership. Clearly, scholars have a strong and continuing interest in the dispositional bases of leadership behavior. Indeed, a meta-analysis by Lord, DeVader, and Alliger (1986) reported some associations between personality traits and perceptions of leadership. A more recent meta-analysis (Judge, Bono, Ilies, & Gerhardt, 2002) further advanced this literature, providing evidence that some traits were consistently associated with leadership emergence and effectiveness. Although these meta-analyses made an important contribution to our knowledge of the link between personality and leadership, they do not address the relationship between personality and transformational, transactional, and charismatic leadership. Given the volume of recent research attention focused on these types of leadership, it is important to understand the dispositional bases of transformational and transactional leadership. The purpose of this article was to extend what is known about the association between personality and leadership by focusing directly on the relationship between personality and the eight dimensions of transformational and transactional leadership. These leadership dimensions have been found to be valid predictors of follower job performance and satisfaction (see Fuller, Patterson, Hester, & Stringer, 1996; Lowe, Kroeck, & Sivasubramaniam, 1996). Thus, understanding the personality traits associated with transformational and charismatic leadership has important implications for the selection, training, and development of such leaders. For example, if charisma is linked to stable traits of the individual, organizations may wish to select leaders with these traits. Barling, Weber, and Kelloway (1996) demonstrated that some transformational leadership behaviors can be trained. Thus, an understanding of the role of personality can aid in determining which individuals might gain the most from such training and how training approaches might differ on the basis of trainee personality (i.e., aptitude by treatment interaction).


title: A context-aware Multimodal Biometric Authentication for cloud-empowered systems

abstract: In the context of emerging technologies, Cloud Computing (CC) was introduced as a new paradigm to host and deliver Information Technology Services. In such an environment, privacy and security issues are critical areas that still require to be deeply explored. Yet, highly secured systems are generally met by computationally expensive systems. Such a system may also degrade the user experience and its willingness to adopt it. The aims of this paper are threefold: First, to integrate a Class-Association Rules (CARs) into the process of Multi-factor Authentication Based on Multimodal Biometrics (MFA-MB) for CC; Second, defines a new metric to measure the User Experience and; Third, exhibits an algorithm to authenticate cloud SaaS/PaaS Users with an enhanced MFA-MB scheme. Since, CARs are used to predict the most expected Multimodal Biometric Authentication (MBA) in the basis of Users' authentication habits, mined from their historical authentication data sets, which guarantees continuously improving their Experience. The integration of CARs in the CC authentication process allows also to identify the actual context (Time, Place, Device, etc.) which impacts the choice of biometrics used in MBA according to one User's situation. Therefore, this will help to increase the authentication security level using MBA at a decreased time and improved user experience. Integration of CARs is illustrated by a realistic case of Bimodal Biometric Authentication.


title: Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

abstract: Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multi-view RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at this http URL


title: Overcurrent protection design, failure mode and effect analysis of an electric vehicle inverter

abstract: In the light of rapid developments in electric vehicles, it has raised a concern on the functional safety of inverters. Overcurrent is a common fault in the inverter, which could not only damage the inverter but also be hazardous to drivers and passengers due to unintended motive power loss on the road. Hence, an overcurrent protection design in an inverter is highly needed. In this study, the failure mode and effect analysis on the stated problem is systematically analyzed based on the functional safety standard, ISO26262. A two-level overcurrent protection mechanism with modulation limiter and current limiter strategy is proposed and implemented on a 50kW inverter system. Experimental results show the three-phase current amplitude is regulated within the designated current limit effectively during fault injection, which verifies the validity of the proposed strategies for the electric vehicle application.


title: Influence of Colour and Feature Geometry on Multi-modal 3D Point Clouds Data Registration

abstract: With the current transition of various digital contents from 2D to 3D, the problem of 3D data matching and registration is increasingly important. Registration of multi-modal 3D data acquired from different sensors remains a challenging problem due to the difference in types and characteristics of the data. In this paper, we evaluate the registration performance of 3D feature descriptors with different domains on datasets from various environments and modalities. Datasets are acquired in indoor and outdoor environments with 2D and 3D sensing devices including LIDAR, spherical imaging, digital camera and RGBD camera. FPFH, PFH and SHOT feature descriptors are applied to the 3D point clouds generated from the multi-modal datasets. Local neighbouring point distribution, key points distribution, colour information and their combinations are used for feature description. Finally we analyse their influences on the multi-modal 3D point clouds data registration.


title: Sex Differences in Attraction to Familiar and Unfamiliar Opposite-Sex Faces: Men Prefer Novelty and Women Prefer Familiarity

abstract: Familiarity is attractive in many types of stimuli and exposure generally increases feelings of liking. However, men desire a greater number of sexual partners than women, suggesting a preference for novelty. We examined sex differences in preferences for familiarity. In Study 1 (N = 83 women, 63 men), we exposed individuals to faces twice and found that faces were judged as more attractive on the second rating, reflecting attraction to familiar faces, with the exception that men’s ratings of female faces decreased on the second rating, demonstrating attraction to novelty. In Studies 2 (N = 42 women, 28 men) and 3 (N = 51 women, 25 men), exposure particularly decreased men’s ratings of women’s attractiveness for short-term relationships and their sexiness. In Study 4 (N = 64 women, 50 men), women’s attraction to faces was positively related to self-rated similarity to their current partner’s face, while the effect was significantly weaker for men. Potentially, men’s attraction to novelty may reflect an adaptation promoting the acquisition of a high number of sexual partners.


title: Integrating end-to-end learned steering into probabilistic autonomous driving

abstract: We propose an integrated approach of combining end-to-end learned trajectory proposals with a probabilistic sampling based planning algorithm for autonomous driving. A convolutional neural network is trained based on monocular image data to predict prospective steering angles. By using a local history of image data, we achieve an implicit spatial representation of parked cars or other obstacles commonly found in urban and residential areas. Through this local history, calculated using the vehicle's velocity data, the trajectory proposals are not only capable of lane following, but also comfortably circumnavigate obstacles. Training data is collected by recording video data and the vehicles CAN bus during human driving, thus imitating human behavior. The integration of end-to-end learning into a modularized architecture allows for additional safety constraints and complementary sensor information to be combined with intuitive steering. Our first results take a promising step towards general architectures for autonomous vehicles that combine deep learning with factorized probabilistic modeling.


title: Non-Orthogonal Access with Random Beamforming and Intra-Beam SIC for Cellular MIMO Downlink

abstract: We investigate non-orthogonal access with a successive interference canceller (SIC) in the cellular multiple-input multiple-output (MIMO) downlink for systems beyond LTE-Advanced. Taking into account the overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal multiuser multiplexing and the applicability of the SIC receiver in the MIMO downlink, we propose intra-beam superposition coding of a multiuser signal at the transmitter and the spatial filtering of inter-beam interference followed by the intra-beam SIC at the user terminal receiver. The intra-beam SIC cancels out the inter-user interference within a beam. Furthermore, the transmitter beamforming (precoding) matrix is controlled based on open loop-type random beamforming, which is very efficient in terms of the amount of feedback information from the user terminal. Simulation results show that the proposed non-orthogonal access scheme with random beamforming and the intra-beam SIC simultaneously achieves better sum and cell-edge user throughput compared to orthogonal access, which is assumed in LTE-Advanced.


title: "You Might Also Like:" Privacy Risks of Collaborative Filtering

abstract: Many commercial websites use recommender systems to help customers locate products and content. Modern recommenders are based on collaborative filtering: they use patterns learned from users' behavior to make recommendations, usually in the form of related-items lists. The scale and complexity of these systems, along with the fact that their outputs reveal only relationships between items (as opposed to information about users), may suggest that they pose no meaningful privacy risk. In this paper, we develop algorithms which take a moderate amount of auxiliary information about a customer and infer this customer's transactions from temporal changes in the public outputs of a recommender system. Our inference attacks are passive and can be carried out by any Internet user. We evaluate their feasibility using public data from popular websites Hunch, Last. fm, Library Thing, and Amazon.


title: Graphene 3-port circulator based on edge-guide mode propagation

abstract: In this work we propose and numerically analyze a new type of electromagnetic circulator operating in THz region. The circulator is based on graphene and dielectric substrates. The structure resembles a known three-port microstrip circulator based in edge guided mode. The circulation behaviour is provided by application of a DC magnetic field normal to the graphene plane. In the frequency band (2 ÷ 7) THz the isolation loss of our ultra wideband circulator is −(15 ÷ 50) dB, the insertion loss is −(2 ÷ 5) dB and return loss is better than −10 dB. The chemical potential of the graphene is 0.1 eV and DC magnetic field is 2 T.


title: Is There WiFi Yet?: How Aggressive Probe Requests Deteriorate Energy and Throughput

abstract: WiFi offloading has emerged as a key component of cellular operator strategy to meet the rich data needs of modern mobile devices. Hence, mobile devices tend to aggressively seek out WiFi in order to provide improved user Quality of Experience (QoE) and cellular capacity relief. For home and work environments, aggressive WiFi scans can significantly improve the speed at which mobile nodes join the WiFi network. Unfortunately, the same aggressive behavior that excels in the home environment incurs considerable side effects in crowded wireless environments. In this paper, we analyze empirical data collected from large (stadium) and medium (classroom) venues, and show through controlled experiments (laboratory) how aggressive WiFi scans can have significant implications for energy and throughput for mobile nodes. We close with several thoughts on the disjoint incentives for properly balancing WiFi discovery speed and crowded network interactions.


title: Smart Grid Technologies: Communication Technologies and Standards

abstract: For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.


title: Weldability of High Strength Aluminum Alloys by Friction Stir Welding

abstract: The effects of welding conditions, tool rotation speed, Rt and plate traveling speed, V on weld joints formation and mechanical properties have been investigated on Friction Stir Welding joints of high strength aluminum alloys of 2024-T6 and 7075-T6 in comparison with 5083-O of 4 mm thick plates. Rt/V, Welding parameter, which is closely related to welding heat input per unit length of the welded joint affected joint qualities. Peak temperature of FSW joints near stir zone during welding increased with increasing Rt/V, but lower than solidus temperature for each alloy. As weld defects, lack of bonding and inner defect occurred at low Rt/V for each alloy, and excess high Rt/V caused surface tearing as defect both for 2024-T6 and 7075-T6. As to a range of optimum welding condition, 5083-O was wider than those of 2024-T6 and 7075-T6, and optimum Rt/V range common to three alloys was 3.3 to 5.0. In case of any aluminum alloy, tensile strength of the welded joints increased with increasing Rt/V, and weld defects caused low tensile strength. The maximum tensile strengths for 2024-T6, 7075-T6 and 5083-O were 373, 444 and 296 MPa in as-welded condition, which were almost 76, 83 and 100% to base metal strength. Post-weld artificial aging increased the tensile strength of 7075-T6 joint to 512 MPa, 90% to base metal strength, but in 2024-T6 decreased it slightly.


title: FRESH—FRI-Based Single-Image Super-Resolution Algorithm

abstract: In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes use of the theory of sampling signals with finite rate of innovation (FRI) and combines it with traditional linear reconstruction methods. We combine the two reconstructions by leveraging from the multi-resolution analysis in wavelet theory and show how an FRI reconstruction and a linear reconstruction can be fused using filter banks. We then apply this method along vertical, horizontal, and diagonal directions in an image to obtain a single-image super-resolution algorithm. We also propose a further improvement of the method based on learning from the errors of our super-resolution result at lower resolution levels. Simulation results show that our method outperforms state-of-the-art algorithms under different blurring kernels.


title: Intercity Route Choice Stated-Preference Model to Investigate Driver Response to Road Pricing

abstract: The introduction of innovative transportation policies in general, and road pricing in particular, has become a key priority in both developed and developing countries due to increasing demand for private vehicle usage. The success of a new pricing policy, however, largely depends on public acceptance, and therefore investigating driver responses to pricing has to be treated with utmost priority. This study investigates driver response to the proposed toll motorway project connecting the cities of Corinth and Patras in Greece. Analysis of the intercity route choice behavior of road users and driver sensitivity to road pricing are the main objectives in this study. This toll motorway project has yet to be implemented and therefore, the actual travel data or revealed preference (RP) data do not exist at this stage. Hence, this study uses stated preference (SP) data in the analysis. A binary choice model is used in this study with the choice options, a dual carriageway with full grade separation (proposed toll motorway) and existing alternative routes (existing single carriageway toll motorway and scenic coastal road). The socio-economic and journey related characteristics of the current road users along with their stated preferences (SP) on the proposed toll motorway are explicitly incorporated in the model estimations. Attributes of travel time, travel cost and road type are included in the model to elicit route choice behaviors of intercity travelers. The Greek Government realizes that road pricing is a useful policy in order to finance the road infrastructure and to provide enhanced mobility. Therefore road pricing is considered in this study as a policy option to promote commercial practices thereby enhancing the liberalized markets.


title: Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling

abstract: In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks’ execution latency and the MD’s energy consumption by jointly optimizing the task allocation decision and the MD’s central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks’ execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD’s flexible CPU range can have an impact on the task allocation.


title: Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci

abstract: We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.


title: Which Factors Form Older Adults' Acceptance of Mobile Information and Communication Technologies?

abstract: Technology acceptance has become a key concept for the successful rollout of technical devices. Though the concept is intensively studied for nearly 20 years now, still, many open questions remain. This especially applies to technology acceptance of older users, which are known to be very sensitive to suboptimal interfaces and show considerable reservations towards the usage of new technology. This study investigates long- und short-term effects on technology acceptance for a personal digital assistant (PDA) in older users. We examined the influence of users' personal factors (computer expertise, technical self-confidence) on acceptance (long-term effects). To assess short-term effects on acceptance, PDA acceptance was measured, after participants were given a PDA tutor training and interacted with a simulated PDA. According to the findings, individual factors largely determine people's acceptance showing that acceptance is mainly influenced by the individuals' learning history with technology. Though, also the tutorial training significantly affected acceptance outcomes, especially in the older group.


title: Incremental Instant Radiosity for Real-Time Indirect Illumination

abstract: We present a method for rendering single-bounce indirect illumination in real time on currently available graphics hardware. The method is based on the instant radiosity algorithm, where virtual point lights (VPLs) are generated by casting rays from the primary light source. Hardware shadow maps are then employed for determining the indirect illumination from the VPLs. Our main contribution is an algorithm for reusing the VPLs and incrementally maintaining their good distribution. As a result, only a few shadow maps need to be rendered per frame as long as the motion of the primary light source is reasonably smooth. This yields real-time frame rates even when hundreds of VPLs are used.


title: Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates

abstract: The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scanned healthy human volunteers while they performed a probabilistic instrumental learning task that varied in both the physical effort and the monetary outcome associated with choice options. Behavioral data showed that the same computational rule, using prediction errors to update expectations, could account for both reward maximization and effort minimization. However, these learning-related variables were encoded in partially dissociable brain areas. In line with previous findings, the ventromedial prefrontal cortex was found to positively represent expected and actual rewards, regardless of effort. A separate network, encompassing the anterior insula, the dorsal anterior cingulate, and the posterior parietal cortex, correlated positively with expected and actual efforts. These findings suggest that the same computational rule is applied by distinct brain systems, depending on the choice dimension—cost or benefit—that has to be learned.


title: Feasibility of using tissue microarray cores of paraffin-embedded breast cancer tissue for measurement of gene expression: a proof-of-concept study.

abstract: AIMS ::: To determine whether 0.6 mm cores of formalin-fixed paraffin-embedded (FFPE) tissue, as commonly used to construct immunohistochemical tissue microarrays, may be a valid alternative to tissue sections as source material for quantitative real-time PCR-based transcriptional profiling of breast cancer. ::: ::: ::: METHODS ::: Four matched 0.6 mm cores of invasive breast tumour and two 10 microm whole sections were taken from eight FFPE blocks. RNA was extracted and reverse transcribed, and TaqMan assays were performed on the 21 genes of the Oncotype DX Breast Cancer assay. Expression of the 16 recurrence-related genes was normalised to the set of five reference genes, and the recurrence score (RS) was calculated. ::: ::: ::: RESULTS ::: RNA yield was lower from 0.6 mm cores than from 10 microm whole sections, but was still more than sufficient to perform the assay. RS and single gene data from cores were highly comparable with those from whole sections (RS p=0.005). Greater variability was seen between cores than between sections. ::: ::: ::: CONCLUSIONS ::: FFPE sections are preferable to 0.6 mm cores for RNA profiling in order to maximise RNA yield and to allow for standard histopathological assessment. However, 0.6 mm cores are sufficient and would be appropriate to use for large cohort studies.


title: Analyzing Movie Scripts as Unstructured Text

abstract: This paper describes our initial attempt to analyze movie scripts for understanding patterns and narrative flow that can be present in storytelling. We collected movie scripts, processed the data using simple natural language processing and machine learning techniques. The result suggests that analyzing big movie script may reveal patterns related to narrative structure.


title: A Digital Input Controller for Audio Class-D Amplifiers with 100W 0.004% THD+N and 113dB DR

abstract: A digital input controller for audio class-D amplifiers is presented. The controller utilizes specially configured integrated DAC and power stage feedback loop to suppress distortion components coming from power-stage switching with digital input capability. The class-D amplifier system with the controller and an existing power stage achieves 113dB DR, 0.0018% THD+N with 10W output power, and 0.004% THD+N with 100W output power into 4Omega load


title: Leaky CPW-based slot antenna arrays for millimeter-wave applications

abstract: A uniplanar leaky-wave antenna (LWA) suitable for operation at millimeter-wave frequencies is introduced. Both unidirectional and bidirectional versions of the antenna are presented. The proposed structure consists of a coplanar waveguide fed linear array of closely spaced capacitive transverse slots. This configuration results in a fast-wave structure in which the n=0 spatial harmonic radiates in the forward direction. Since the distance, d, between adjacent elements of the array is small d/spl Lt//spl lambda//sub o/, the slot array essentially becomes a uniform LWA. A comprehensive transmission line model is developed based upon the theory of truncated periodic transmission lines to explain the operation of the antenna and provide a tool for its design. Measured and simulated radiation patterns, directivity, gain, and an associated loss budget are presented for a 32-element antenna operating at 30 GHz. The uniplanar nature of the structure makes the antenna appropriate for integration of shunt variable capacitors such as diode or micro-electromechanical system varactors for fixed frequency beam steering at low-bias voltages.


title: The Study of the Impact of Perceived Quality and Value of Social Enterprises on Customer Satisfaction and Re-Purchase Intention

abstract: Previous studies on social enterprises reported that unlike private enterprise consumers, social enterprise consumers appreciate the social value of the enterprise products and that social value affects customer satisfaction and repurchase intention. However, previous literature also pointed out that focusing only on social value as the factor affecting purchase behavior does not reflect the change in the situation of social enterprises. We expect that not only social but also a variety of other value consumers perceive from the products of social enterprises influence consumer satisfaction and repurchase intention. The purpose of this study is as follows. First, we intend to find the customer value for the products and services of social enterprises. Second, we intend to examine whether the positive relationships between quality and value of products/services reported in numerous previous studies applies to social enterprises. Third, we would like to find out whether satisfaction from social enterprise products and services affect the actual repurchase intention. Finally, in order to find dynamic interaction among the variables, this study models the key flow of the factors influencing the social enterprise consumers’ repurchase intention: perceived quality  perceived value  customer satisfaction  repurchase intention. The results show that there are positive relationships between the consumer perception of quality and that of functional, emotional and social value. We also find positive relationships between the perception of functional, emotional and social value and customer satisfaction. Our findings show that the consumers of social enterprises perceive social value, along with the functional and emotional value, through the quality of products and services. The perceived value has positive effects on customer satisfaction and repurchase intention in the future. This study shows that the positive relationships between quality and value and customer satisfaction and repurchase intention found in numerous previous studies also exist in the context of social enterprises. In the last section, we discuss the practical implications for social enterprises based on the findings of our study and present the directions for future studies.


Editorial: The challenge of dengue vaccine development and introduction


title: A new test set compression scheme for circular scan

abstract: A new test data compression scheme for circular scan is proposed in this paper. For circular scan, the response of the previous test vector is used as the next test vector’s template, and only the conflicting bits between the previous response and the next vector are required to be updated. To reduce the test data volume and test application time, the problem addressed here is minimizing the number of conflicting bits by optimally reordering test vectors. Each vector represents a city, and the number of conflicting bits between two test vectors is regarded as the distance between them. Thus, the problem corresponds to the travelling salesman problem (TSP), which is NP-complete. The genetic algorithm is used to solve this problem. The experimental results show that the proposed scheme could reduce the test data volume efficiently without any additional hardware cost.


title: Wag the dog - The nature and foundations of preschool educators' positive ICT pedagogical beliefs

abstract: It has become evident that the vast majority of early childhood educators feel positive about using ICT with children. However, as they are found to have had no or only a little training or practical experience of pedagogical use of ICT, it is important to explore where the positive attitude comes from, and what pedagogical benefits early childhood educators believe ICT use brings about. This study focuses on the relationships of educators' general and ICT-related pedagogical beliefs, and the foundations of educators' positive ICT pedagogical beliefs.In their general pedagogical beliefs, the educators emphasized the learning of socio-emotional skills, and reported that they carry out practices where children have an active role in their learning. In their ICT pedagogical beliefs, the educators emphasized the learning of academic skills, and the methods they described were usually individual exercises which were either carried out using ICT or by following educators' ICT-supported instructions. Positive ICT pedagogical beliefs were, to a large extent, based on perceptions of what different devices and software promise in terms of children's learning. Outside influences, i.e., the model observed from primary school, also played a significant role in the construction of the educators' ICT pedagogical beliefs. Explores preschool educators' general and ICT-related pedagogical beliefs.Preschool educators' general and ICT-related pedagogical beliefs are incoherent.Preschool educators have uncritical beliefs about the benefits of educational technology.Primary education's ICT culture affects preschool educators' positive ICT pedagogical beliefs.


title: Modeling of the Cell-Electrode Interface Noise for Microelectrode Arrays

abstract: Microelectrodes are widely used in the physiological recording of cell field potentials. As microelectrode signals are generally in the μV range, characteristics of the cell-electrode interface are important to the recording accuracy. Although the impedance of the microelectrode-solution interface has been well studied and modeled in the past, no effective model has been experimentally verified to estimate the noise of the cell-electrode interface. Also in existing interface models, spectral information is largely disregarded. In this work, we developed a model for estimating the noise of the cell-electrode interface from interface impedances. This model improves over existing noise models by including the cell membrane capacitor and frequency dependent impedances. With low-noise experiment setups, this model is verified by microelectrode array (MEA) experiments with mouse muscle myoblast cells. Experiments show that the noise estimated from this model has <;10% error, which is much less than estimations from existing models. With this model, noise of the cell-electrode interface can be estimated by simply measuring interface impedances. This model also provides insights for micro- electrode design to achieve good recording signal-to-noise ratio.


title: Classifying Unsolicited Bulk Email (UBE) using Python Machine Learning Techniques

abstract: Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of spam emails during the past few years. As spammers always try to find a way to evade existing filters, new filters need to be developed to catch spam. Generally, the main tool for email filtering is based on text classification. A classifier then is a system that classifies incoming messages as spam or legitimate (ham) using classification methods. The most important methods of classification utilize machine learning techniques. There are a plethora of options when it comes to deciding how to add a machine learning component to a python email classification. This article describes an approach for spam filtering using Python where the interesting spam or ham words (spam-ham lexicon) are filtered first from the training dataset and then this lexicon is used to generate the training and testing tables that are used by variety of data mining algorithms. Our experimentation using one dataset reveals the affectivity of the Naive Bayes and the SVM classifiers for spam filtering.


A Visual Transactive Memory System Approach Towards Project Information Management


title: Nonlinear system identification by SOM with GP based local modeling

abstract: Genetic programming (GP) is a useful tool of nonlinear model building, however it tends to give much complicated model structure. Local modeling describes one nonlinear model as a set of sweet models, this approach is one of the practical ways to handle nonlinear systems. In this paper, a novel nonlinear system identification technique is proposed by combining GP and local modeling. In this approach, an identification procedure is divided into three steps, which are Self-organizing map (SOM) based clustering of the regression vectors consisting of observed input and output signals, local system identification by using GP, and model fusion of local models by fuzzy inference to provide the global model. The applicability of the proposed method is shown by the results of some numerical experiments.


title: Towards location-based social networking services

abstract: Social networking applications have become very important web services that provide Internet-based platforms for their users to interact with their friends. With the advances in the location-aware hardware and software technologies, location-based social networking applications have been proposed to provide services for their users, taking into account both the spatial and social aspects. Unfortunately, none of existing location-based social networking applications is a holistic system nor equips database management systems to support scalable location-based social networking services. In this paper, we present GeoSocialDB; a holistic system providing three location-based social networking services, namely, location-based news feed, location-based news ranking, and location-based recommendation. In GeoSocialDB, we aim to implement these services as query operators inside a database engine to optimize the query processing performance. Within the GeoSocialDB framework, we discuss research challenges and directions towards the realization of scalable and practical query processing for location-based social networking services. In general, we discuss the challenges in designing location- and/or rank-aware query operators, materializing query answers, supporting continuous query processing, and providing privacy-aware query processing for our three location-based social networking services.


Contextual prediction models for speech recognition


title: Density-Based Data Pruning Method for Deep Reinforcement Learning

abstract: We present a density-based Data Pruning method for Deep Reinforcement Learning (DRL) to improve learning stability and long-term memory in rare situations. The method controls density distribution in the experience pool by discarding high correlation data and preserving rare and unique data. We apply our method to Deep Q-networks (DQN) and Deep Deterministic Policy Gradients (DDPG) for testing in discrete and continuous action space, respectively. We evaluate our method on path following tasks in a simulated physical environment. Compared to other conventional methods such as First-In-First-Out (FIFO), our method provides a significant improvement in performance and learning stability, the average cumulative reward is increased by up to 21% and the standard deviation of the cumulative reward over multiple trials is reduced by 80%. In addition, long-term memory improvement is shown as the agent can remember and perform a behavior corresponding to a past rare event.


title: Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates

abstract: Smartphones are becoming a daily necessity for most undergraduates in Mainland China. Because the present scenario of problematic smartphone use (PSU) is largely unexplored, in the current study we aimed to estimate the prevalence of PSU and to screen suitable predictors for PSU among Chinese undergraduates in the framework of the stress-coping theory. A sample of 1062 undergraduate smartphone users was recruited by means of the stratified cluster random sampling strategy between April and May 2015. The Problematic Cellular Phone Use Questionnaire was used to identify PSU. We evaluated five candidate risk factors for PSU by using logistic regression analysis while controlling for demographic characteristics and specific features of smartphone use. The prevalence of PSU among Chinese undergraduates was estimated to be 21.3%. The risk factors for PSU were majoring in the humanities, high monthly income from the family (≥1500 RMB), serious emotional symptoms, high perceived stress, and perfectionism-related factors (high doubts about actions, high parental expectations). PSU among undergraduates appears to be ubiquitous and thus constitutes a public health issue in Mainland China. Although further longitudinal studies are required to test whether PSU is a transient phenomenon or a chronic and progressive condition, our study successfully identified socio-demographic and psychological risk factors for PSU. These results, obtained from a random and thus representative sample of undergraduates, opens up new avenues in terms of prevention and regulation policies.


Approximation Algorithms for Curvature-Constrained Shortest Paths


Strategic management and Michael Porter: a postmodern reading


title: Comparison of state of the art multilevel inverters

abstract: In this paper, a comparison between existing state of the art multilevel inverter topologies is performed. The topologies examined are the neutral point clamp multilevel inverter (NPCMLI) or diode-clamped multilevel inverter (DCMLI), the flying capacitor multilevel inverter (FCMLI) and the cascaded cell multilevel inverter (CCMLI). The comparison of these inverters is based on the criteria of output voltage quality (Peak value of the fundamental and dominant harmonic components and THD), power circuitry complexity, and implementation cost. The comparison results are based on theoretical results verified by detailed simulation results.


title: Cognition and resting-state functional connectivity in schizophrenia

abstract: Abstract Individuals with schizophrenia consistently display deficits in a multitude of cognitive domains, but the neurobiological source of these cognitive impairments remains unclear. By analyzing the functional connectivity of resting-state functional magnetic resonance imaging (rs-fcMRI) data in clinical populations like schizophrenia, research groups have begun elucidating abnormalities in the intrinsic communication between specific brain regions, and assessing relationships between these abnormalities and cognitive performance in schizophrenia. Here we review studies that have reported analysis of these brain–behavior relationships. Through this systematic review we found that patients with schizophrenia display abnormalities within and between regions comprising (1) the cortico-cerebellar-striatal-thalamic loop and (2) task-positive and task-negative cortical networks. Importantly, we did not observe unique relationships between specific functional connectivity abnormalities and distinct cognitive domains, suggesting that the observed functional systems may underlie mechanisms that are shared across cognitive abilities, the disturbance of which could contribute to the “generalized” cognitive deficit found in schizophrenia. We also note several areas of methodological change that we believe will strengthen this literature.


title: Review: Intelligent tourism recommender systems: A survey

abstract: Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years.


title: Autistic traits affect interpersonal motor coordination by modulating strategic use of role-based behavior

abstract: Background ::: Despite the fact that deficits in social communication and interaction are at the core of Autism Spectrum Conditions (ASC), no study has yet tested individuals on a continuum from neurotypical development to autism in an on-line, cooperative, joint action task. In our study, we aimed to assess whether the degree of autistic traits affects participants' ability to modulate their motor behavior while interacting in a Joint Grasping task and according to their given role.


High-Speed Edge Trimming of CFRP and Online Monitoring of Performance of Router Tools Using Acoustic Emission


title: Leveraging knowledge across media for spammer detection in microblogging

abstract: While microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for spammers to overwhelm other users with unwanted content. Currently, spammer detection in microblogging focuses on using social networking information, but little on content analysis due to the distinct nature of microblogging messages. First, label information is hard to obtain. Second, the texts in microblogging are short and noisy. As we know, spammer detection has been extensively studied for years in various media, e.g., emails, SMS and the web. Motivated by abundant resources available in the other media, we investigate whether we can take advantage of the existing resources for spammer detection in microblogging. While people accept that texts in microblogging are different from those in other media, there is no quantitative analysis to show how different they are. In this paper, we first perform a comprehensive linguistic study to compare spam across different media. Inspired by the findings, we present an optimization formulation that enables the design of spammer detection in microblogging using knowledge from external media. We conduct experiments on real-world Twitter datasets to verify (1) whether email, SMS and web spam resources help and (2) how different media help for spammer detection in microblogging.


title: Wireless Sensor Network Virtualization: Early Architecture and Research Perspectives

abstract: WSNs have become pervasive and are used in many applications and services. Usually, deployments of WSNs are task-oriented and domain-specific, thereby precluding reuse when other applications and services are contemplated. This inevitably leads to the proliferation of redundant WSN deployments. Virtualization is a technology that can aid in tackling this issue, as it enables the sharing of resources/infrastructure by multiple independent entities. In this article we critically review the state of the art and propose a novel architecture for WSN virtualization. The proposed architecture has four layers (physical layer, virtual sensor layer, virtual sensor access layer, and overlay layer) and relies on a constrained application protocol. We illustrate its potential by using it in a scenario where a single WSN is shared by multiple applications, one of which is a fire monitoring application. We present the proof-of-concept prototype we have built along with the performance measurements, and discuss future research directions.


title: Hash Kernels for Structured Data

abstract: We propose hashing to facilitate efficient kernels. This generalizes previous work using sampling and we show a principled way to compute the kernel matrix for data streams and sparse feature spaces. Moreover, we give deviation bounds from the exact kernel matrix. This has applications to estimation on strings and graphs.


title: NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction

abstract: This paper describes our participation in the SemEval-2016 task 5, Aspect Based Sentiment Analysis (ABSA). We participated in two slots in the sentence level ABSA (Subtask 1) namely: aspect category extraction (Slot 1) and sentiment polarity extraction (Slot 3) in English Restaurants and Laptops reviews. For Slot 1, we applied different models for each domain. In the restaurants domain, we used an ensemble classifier for each aspect which is a combination of a Convolutional Neural Network (CNN) classifier initialized with pretrained word vectors, and a Support Vector Machine (SVM) classifier based on the bag of words model. For the Laptops domain, we used only one CNN classifier that predicts the aspects based on a probability threshold. For Slot 3, we incorporated domain and aspect knowledge in one ensemble CNN classifier initialized with fine-tuned word vectors and used it in both domains. In the Restaurants domain, our system achieved the 2 nd and the 3 rd places in Slot 1 and Slot 3 respectively. However, we ranked the 8 th in Slot 1 and the 5 th in Slot 3 in the Laptops domain. Our extended experiments show our system could have ranked 2 nd in the Laptops domain in Slot 1 and Slot 3, had we followed the same approach we followed in the Restaurants domain in slot 1 and trained each domain separately in Slot 3.


title: Resource Provisioning for IoT Services in the Fog

abstract: The advent of the Internet of Things (IoT) leadsto the pervasion of business and private spaces with ubiquitous, networked computing devices. These devices do not simply actas sensors, but feature computational, storage, and networkingresources. These resources are close to the edge of the network, and it is a promising approach to exploit them in order to executeIoT services. This concept is known as fog computing.Despite existing theoretical foundations, the adoption of fogcomputing is still at its very beginning. Especially, there is alack of approaches for the leasing and releasing of resources. Toresolve this shortcoming, we present a conceptual framework forfog resource provisioning. We formalize an optimization problemwhich is able to take into account existing resources in fog/IoTlandscapes. The goal of this optimization problem is to providedelay-sensitive utilization of available fog-based computationalresources. We evaluate the resource provisioning model to showthe benefits of our contributions. Our results show a decrease indelays of up to 39% compared to a baseline approach, yieldingshorter round-trip times and makespans.


Process Modeling


title: On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks

abstract: Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representation layers in a symbiotic relation between synthesis and analysis sparse priors. We explore different iterative methods to solve this new problem in practice, and we propose a new Multi-Layer Iterative Soft Thresholding Algorithm (ML-ISTA), as well as a fast version (ML-FISTA). We show that these nested first order algorithms converge, in the sense that the function value of near-fixed points can get arbitrarily close to the solution of the original problem. We further show how these algorithms effectively implement particular recurrent convolutional neural networks (CNNs) that generalize feed-forward ones without introducing any parameters. We present and analyze different architectures resulting unfolding the iterations of the proposed pursuit algorithms, including a new Learned ML-ISTA, providing a principled way to construct deep recurrent CNNs. Unlike other similar constructions, these architectures unfold a global pursuit holistically for the entire network. We demonstrate the emerging constructions in a supervised learning setting, consistently improving the performance of classical CNNs while maintaining the number of parameters constant.


title: DroidEcho: an in-depth dissection of malicious behaviors in Android applications

abstract: A precise representation for attacks can benefit the detection of malware in both accuracy and efficiency. However, it is still far from expectation to describe attacks precisely on the Android platform. In addition, new features on Android, such as communication mechanisms, introduce new challenges and difficulties for attack detection. In this paper, we propose abstract attack models to precisely capture the semantics of various Android attacks, which include the corresponding targets, involved behaviors as well as their execution dependency. Meanwhile, we construct a novel graph-based model called the inter-component communication graph (ICCG) to describe the internal control flows and inter-component communications of applications. The models take into account more communication channel with a maximized preservation of their program logics. With the guidance of the attack models, we propose a static searching approach to detect attacks hidden in ICCG. To reduce false positive rate, we introduce an additional dynamic confirmation step to check whether the detected attacks are false alarms. Experiments show that DroidEcho can detect attacks in both benchmark and real-world applications effectively and efficiently with a precision of 89.5%.


title: Joint Optimization of Resource Provisioning in Cloud Computing

abstract: Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.


title: Electric Drive System of Dual-Winding Fault-Tolerant Permanent-Magnet Motor for Aerospace Applications

abstract: In this paper, a new electric drive system based on a six-phase ten-pole dual-winding fault-tolerant permanent-magnet (DFPM) motor for aerospace applications is proposed and investigated. The proposed DFPM motor consists of optimal surface-mounted permanent-magnet (PM) rotor and 12-slot stator with two sets of independent three-phase concentrated armature windings on alternate teeth, which incorporates the merits of high power density and high efficiency of the PM motor and high fault tolerance of the dual-winding motor. To achieve the fault-tolerant performance of the electric drive system, the redundant fault-tolerant control strategy based on the failure diagnosis and redundancy communication is proposed and applied to the electric drive system. Then, the operational performance of the drive system under open-circuit and short-circuit fault has been verified by simulation and experiments. The research results show that the open-circuit and short-circuit fault can be detected immediately and the output performance of the DFPM motor under fault conditions is almost the same as the normal condition, which verified the proposed electric drive system, DFPM motor, and fault-tolerant control strategy.


title: EEG and Autonomic Responses During Performance of Matching and Non-Matching to Sample Working Memory Tasks with Emotional Content

abstract: Working memory (WM) is a neural system responsible for the temporary storage of information and its utilization in problem solving. The central executive is theorized as the controller of storage functions that support WM. Neurophysiological data suggest that EEG theta and alpha oscillations in frontal and midline regions are involved in neural communication between the central executive and storage functions during WM performance. Emotion is known to modulate several memory systems, including WM, through central and peripheral pathways. However, the physiological correlations (electroencephalographic – EEG; autonomic nervous activity) of the effect of emotion over WM are not well described. In this study we aimed to identify physiological responses related to emotional WM performance. EEG (21 channels), heart rate (HR) and galvanic skin response (GSR) recordings were obtained from 54 volunteers while performing delayed matching and non-matching to sample tasks (DMTS/DNMTS). Emotional and neutral pictures from the International Affective Picture System and geometric figures were used as stimuli. As expected, WM performance was accompanied by presence of theta (frontal and midline electrodes) and Alpha power (parietal electrodes). Beta and gamma oscillations were concentrated in frontopolar and left temporal regions. DNMTS task was accompanied by increases in Beta power, HR and GSR compared to DMTS task. Correlation analysis showed a positive tendency for gamma in Fp2 site, ratio of LF/HF (HR low and high frequency) and skin conductance in both tasks. The HR results indicate an inverse reaction related to parasympathetic and sympathetic nervous system during the performance of the tasks. Taken together, our results contribute to elucidate the complex interactions between central and autonomic nervous systems in the modulation of emotional WM tasks.


title: Ranking with ordered weighted pairwise classification

abstract: In ranking with the pairwise classification approach, the loss associated to a predicted ranked list is the mean of the pairwise classification losses. This loss is inadequate for tasks like information retrieval where we prefer ranked lists with high precision on the top of the list. We propose to optimize a larger class of loss functions for ranking, based on an ordered weighted average (OWA) (Yager, 1988) of the classification losses. Convex OWA aggregation operators range from the max to the mean depending on their weights, and can be used to focus on the top ranked elements as they give more weight to the largest losses. When aggregating hinge losses, the optimization problem is similar to the SVM for interdependent output spaces. Moreover, we show that OWA aggregates of margin-based classification losses have good generalization properties. Experiments on the Letor 3.0 benchmark dataset for information retrieval validate our approach.


title: An IQ-modulator based heterodyne 77-GHz FMCW colocated MIMO radar system

abstract: In this work the realization of a 77-GHz frequency-division multiple-access-based frequency-modulated continuous-wave multiple-input multiple-output radar with four transceiver channels in conjunction with a non-uniform linear antenna array is presented. The radar system, consisting of an RF-frontend utilizes transceiver chips with integrated inphase/quadrature-modulators to generate the frequency shifted transmit signals and a field programmable gate array-based baseband board, is used for test measurements to verify the system performance and to demonstrate the beamforming capability as well as the accuracy of the digital-beamforming method.


title: Hotbeds of crime and the search for spatial accuracy

abstract: One of the most important aspects of spatial crime analysis is the identification of hotspots: areas of the highest crime concentration. This paper advances a methodology for hotspot detection based on a global moving window approach combined with the use of local statistics to define the hotspot limit. This technique generates hotspots that both follow the urban morphology of the crime distribution and ensures their spatial segregation. The hypothesis that police officers can construct an accurate perception of crime distribution from exposure to daily policing practices is used to demonstrate an application in the use of hotspot analysis. Significant regions generated from recorded crime data are compared with perceived local hotspots catalogued from surveys with police officers. Results from this study show two discrete types of hotspot, here termed hotpoints and hotbeds. The morphology of these crime hotpoints and hotbeds is discussed and possible causes documented.


title: 3.6 A 45Gb/s PAM-4 transmitter delivering 1.3Vppd output swing with 1V supply in 28nm CMOS FDSOI

abstract: The development of next-generation electrical link technology to support 400Gb/s standards is underway [1–5]. Physical constraints paired to the small area available to dissipate heat, impose limits to the maximum number of serial interfaces and therefore their minimum speed. As such, aggregation of currently available 25Gb/s systems is not an option, and the migration path requires serial interfaces to operate at increased rates. According to CEI-56G and IEEE P802.3bs emerging standards, PAM-4 signaling paired to forward error correction (FEC) schemes is enabling several interconnect applications and low-loss profiles [1]. Since the amplitude of each eye is reduced by a factor of 3, while noise power is only halved, a high transmitter (TX) output amplitude is key to preserve high SNR. However, compared to NRZ, the design of a PAM-4 TX is challenged by tight linearity constraints, required to minimize the amplitude distortion among the 4 levels [1]. In principle, current-mode (CM) drivers can deliver a differential peak-to-peak swing up to 4/3(VDD-VOV), but they struggle to generate high-swing PAM-4 levels with the required linearity. This is confirmed by recently published CM PAM-4 drivers, showing limited output swings even with VDD raised to 1.5V [2–4]. Source-series terminated (SST) drivers naturally feature better linearity and represent a valid alternative, but the maximum differential peak-to-peak swing is bounded to VDD only. In [5], a dual-mode SST driver supporting NRZ/PAM-4 was presented, but without FFE for PAM-4 mode. In this paper, we present a PAM-4 transmitter leveraging a hybrid combination of SST and CM driver. The CM part enhances the output swing by 30% beyond the theoretical limit of a conventional SST implementation, while being calibrated to maintain the desired linearity level. A 5b 4-tap FIR filter, where equalization tuning can be controlled independently from output matching, is also embedded. The transmitter, implemented in 28nm CMOS FDSOI, incorporates a half-rate serializer, duty-cycle correction (DCC), »2kV HBM ESD diodes, and delivers a full swing of 1.3Vppd at 45Gb/s while drawing 120mA from a 1V supply. The power efficiency is ∼2 times better than those compared in this paper.


title: Bounds for the chromatic number of graphs with partial information

abstract: We obtain bounds for the chromatic and clique numbers of a simple graph, first in terms of the number of vertices and edges it contains, and then in terms of its vertex degrees. In every case, we examine the tightness of the bounds we obtain. We also briefly discuss lower bounds for the independence number of a graph in terms of the same parameters.


title: On the existence of compactly supported reconstruction functions in a sampling problem

abstract: Assume that samples of a filtered version of a function in a shift-invariant space are avalaible. This work deals with the existence of a sampling formula involving these samples and having reconstruction functions with compact support. Thus, low computational complexity is involved and truncation errors are avoided. This is done in the light of the generalized sampling theory by using the oversampling technique: more samples than strictly necessary are used. For a suitable choice of the sampling period, a necessary and sufficient condition is given in terms of the Kronecker canonical form of a matrix pencil. Comparing with other characterizations in the mathematical literature, the given here has an important advantage: it can be reliable computed by using the GUPTRI form of the matrix pencil. Finally, a practical method for computing the compactly supported reconstruction functions is given for the important case where the oversampling rate is minimum.


title: Safe robot navigation using an omnidirectional camera

abstract: The paper presents a safe robot navigation system based on omnidirectional vision. The 360 degree camera images are analyzed for obstacle detection and avoidance and of course for navigating safely in the given indoor environment. This module can process images in real time and extracts the direction and distance information of the obstacles from the camera system mounted on the robot. This two data is the output of the module. Because of the distortions of the omnidirectional vision, it is necessary to calibrate the camera and not only for that but also to get the right direction and distances information. Several image processing methods and technics were used which are investigated in the rest of this paper.


title: Ladies First: Analyzing Gender Roles and Behaviors in Pinterest

abstract: Online social networks (OSNs) have become popular platforms for people to connect and interact with each other. Among those networks, Pinterest has recently become noteworthy for its growth and promotion of visual over textual content. The purpose of this study is to analyze this image-based network in a gender-sensitive fashion, in order to un- derstand (i) user motivation and usage pattern in the network, (ii) how communications and social interactions happen and (iii) how users describe themselves to others. This work is based on more than 220 million items generated by 683,273 users. We were able to find significant differences w.r.t. all mentioned aspects. We observed that, although the network does not encourage direct social communication, females make more use of lightweight interactions than males. Moreover, females invest more effort in reciprocating social links, are more active and generalist in content generation, and describe themselves using words of affection and positive emotions. Males, on the other hand, are more likely to be specialists and tend to describe themselves in an assertive way. We also observed that each gender has different interests in the network, females tend to make more use of the network's commercial capabilities, while males are more prone to the role of curators of items that reflect their personal taste. It is important to understand gender differences in online social networks, so one can design services and applications that leverage human social interactions and provide more targeted and relevant user experiences.


title: A Novel Video-Based Smoke Detection Method Using Image Separation

abstract: In the state-of-the-art video-based smoke detection methods, the representation of smoke mainly depends on the visual information in the current image frame. In the case of light smoke, the original background can be still seen and may deteriorate the characterization of smoke. The core idea of this paper is to demonstrate the superiority of using smoke component for smoke detection. In order to obtain smoke component, a blended image model is constructed, which basically is a linear combination of background and smoke components. Smoke opacity which represents a weighting of the smoke component is also defined. Based on this model, an optimization problem is posed. An algorithm is devised to solve for smoke opacity and smoke component, given an input image and the background. The resulting smoke opacity and smoke component are then used to perform the smoke detection task. The experimental results on both synthesized and real image data verify the effectiveness of the proposed method.


title: Demystifying Deep Learning in Networking

abstract: We are witnessing a surge of efforts in networking community to develop deep neural networks (DNNs) based approaches to networking problems. Most results so far have been remarkably promising, which is arguably surprising given how intensively these problems have been studied before. Despite these promises, there has not been much systematic work to understand the inner workings of these DNNs trained in networking settings, their generalizability in different workloads, and their potential synergy with domain-specific knowledge. The problem of model opacity would eventually impede the adoption of DNN-based solutions in practice. This position paper marks the first attempt to shed light on the interpretability of DNNs used in networking problems. Inspired by recent research in ML towards interpretable ML models, we call upon this community to similarly develop techniques and leverage domain-specific insights to demystify the DNNs trained in networking settings, and ultimately unleash the potential of DNNs in an explainable and reliable way.


title: Turn Waste into Wealth: On Simultaneous Clustering and Cleaning over Dirty Data

abstract: Dirty data commonly exist. Simply discarding a large number of inaccurate points (as noises) could greatly affect clustering results. We argue that dirty data can be repaired and utilized as strong supports in clustering. To this end, we study a novel problem of clustering and repairing over dirty data at the same time. Referring to the minimum change principle in data repairing, the objective is to find a minimum modification of inaccurate points such that the large amount of dirty data can enhance the clustering. We show that the problem can be formulated as an integer linear programming (ILP) problem. Efficient approximation is then devised by a linear programming (LP) relaxation. In particular, we illustrate that an optimal solution of the LP problem can be directly obtained without calling a solver. A quadratic time approximation algorithm is developed based on the aforesaid LP solution. We further advance the algorithm to linear time cost, where a trade-off between effectiveness and efficiency is enabled. Empirical results demonstrate that both the clustering and cleaning accuracies can be improved by our approach of repairing and utilizing the dirty data in clustering.


title: POM/JLEC: An Automatic Program Equivalence Checker for Java Enterprise Systems

abstract: This paper presents a symbolic-execution-based approach and its implementation by POM/JLEC for checking the logical equivalence between two programs in the system replacement context. The primary contributions lie in the development of POM/JLEC, a fully automatic equivalence checker for Java enterprise systems. POM/JLEC consists of three main components: Domain Specific Pre-Processor for extracting the target code from the original system and adjusting it to a suitable scope for verification, Symbolic Execution for generating symbolic summaries, and solver-based EQuality comparison for comparing the symbolic summaries together and returning counter examples in the case of non-equivalence. We have evaluated POM/JLEC with a large-scale benchmark created from the function layer code of an industrial enterprise system. The evaluation result with 54% test cases passed shows the feasibility for deploying its mature version into software development industry.


title: Single-Image Noise Level Estimation for Blind Denoising

abstract: Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.