title: Online Learning for Matrix Factorization and Sparse Coding

abstract: Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set in order to adapt it to specific data. Variations of this problem include dictionary learning in signal processing, non-negative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems. A proof of convergence is presented, along with experiments with natural images and genomic data demonstrating that it leads to state-of-the-art performance in terms of speed and optimization for both small and large data sets.


title: Exploring IT-enabled innovation: A new paradigm?

abstract: Abstract Innovation is a major strategic issue for many organizations. This paper reports on findings from 10 case studies of successful IT-enabled innovation covering a wide range of organizations and projects. They were carried out as an exploratory study to identify practices that contribute to innovation and specifically consider the implications of a focus on innovation for earlier work on benefits-led approaches to IT. The research identifies a number of practices from benefits-led approaches to IT that contribute to IT-enabled innovation. There is also evidence of organizations developing a new practice of ideation drawing on ‘crowd sourcing’ and exploiting social media technologies. Existing principles and practices for benefits realization can contribute to innovation, but the practices have to be applied in different ways according to the context. The overall conclusion emerging from the detailed analysis is that a focus on IT-enabled business innovation represents a new paradigm in terms of the mindset and approach involved. It amounts to a third IT paradigm, different from previous paradigms of technology implementation and a planned approach to benefits realization. Further work is required to explore this paradigm and how organizations can make the ‘paradigm shift’ required.


What the Neurosciences can Tell Educators about Reading and Arithmetic


title: CDM protection design for CMOS applications using RC-triggered rail clamps

abstract: A new ESD protection design method is introduced that optimizes the CDM circuit response of an RC-triggered rail clamp network for I/O protection. The design is verified by VF-TLP and CDM test data of I/O test rings with two different output driver options.


title: Solid State Transformer (SST) as an energy router: Economic dispatch based energy routing strategy

abstract: The Solid State Transformer (SST) is a revolutionary technology being developed by the authors. It has a tremendous number of features, which include power management, fault management and energy management. Its autonomous and distributed power management capability enables large-scale integration of distributed energy resources (DER) into the power grid. Specifically, it supports AC or DC connected Energy Cells: a combination of DERs, energy storage devices and loads. The SST can achieve real-time power flow regulation via the Energy Cell, therefore forming the foundation of its capability to become a real-time Energy Router. This paper introduces an economic based energy routing strategy that utilizes energy storage to reduce consumption of grid power. Predictive photovoltaic and load forecasting are used to optimize charging and discharging of energy storage. This rule-based algorithm is implemented and demonstrated in a SST enabled 380 V DC Energy Cell.


12 B . 2 OBSERVED CHANGES IN ORGANIZED TROPICAL DEEP CONVECTION AS IDENTIFIED BY CLOUD REGIME ANALYSIS


title: Three metamaterial-based gap waveguides between parallel metal plates for mm/submm waves

abstract: This paper presents the basic ideas of how local waveguides and transmission lines can be designed to propagate along desired paths in the air gap between two metal surfaces. The principle of operation is related to the performance of artificial magnetic conductors, EBG surfaces and soft and hard surfaces. Three different major types of gap waveguides are described: ridge gap waveguides, microstrip gap lines and groove gap waveguides. Different realizations of the cut-off structures suppressing normal parallel plate modes are described, as well as expected applications, and possible problem areas preferably seen as research challenges.


title: Broadband folded printed quadrifilar helical antenna

abstract: The printed quadrifilar helical antenna (PQHA) represents a very attractive candidate for many applications such as mobile satellite communications, broadcasting satellite systems or global positioning systems, due to hemispherical radiation, circular polarization, good axial ratio, and low cost. Nevertheless, the bandwidth of a conventional PQHA could be insufficient for such applications. Therefore, in this paper, a technique for increasing the bandwidth of a PQHA by adding a shorted parasitic helix is presented. The new folded printed quadrifilar helical antenna (FPQHA) is designed to achieve 30% of bandwidth in axial-mode. Moreover, due to the printed arms on one substrate face, the FPQHA manufacturing is very simple. Measured results are presented to validate the concept


title: Similarity search and locality sensitive hashing using ternary content addressable memories

abstract: Similarity search methods are widely used as kernels in various data mining and machine learning applications including those in computational biology, web search/clustering. Nearest neighbor search (NNS) algorithms are often used to retrieve similar entries, given a query. While there exist efficient techniques for exact query lookup using hashing, similarity search using exact nearest neighbors suffers from a "curse of dimensionality", i.e. for high dimensional spaces, best known solutions offer little improvement over brute force search and thus are unsuitable for large scale streaming applications. Fast solutions to the approximate NNS problem include Locality Sensitive Hashing (LSH) based techniques, which need storage polynomial in n with exponent greater than 1, and query time sublinear, but still polynomial in n, where n is the size of the database. In this work we present a new technique of solving the approximate NNS problem in Euclidean space using a Ternary Content Addressable Memory (TCAM), which needs near linear space and has O(1) query time. In fact, this method also works around the best known lower bounds in the cell probe model for the query time using a data structure near linear in the size of the data base. TCAMs are high performance associative memories widely used in networking applications such as address lookups and access control lists. A TCAM can query for a bit vector within a database of ternary vectors, where every bit position represents 0, 1 or *. The * is a wild card representing either a 0 or a 1. We leverage TCAMs to design a variant of LSH, called Ternary Locality Sensitive Hashing (TLSH) wherein we hash database entries represented by vectors in the Euclidean space into {0,1,*}. By using the added functionality of a TLSH scheme with respect to the * character, we solve an instance of the approximate nearest neighbor problem with 1 TCAM access and storage nearly linear in the size of the database. We validate our claims with extensive simulations using both real world (Wikipedia) as well as synthetic (but illustrative) datasets. We observe that using a TCAM of width 288 bits, it is possible to solve the approximate NNS problem on a database of size 1 million points with high accuracy. Finally, we design an experiment with TCAMs within an enterprise ethernet switch (Cisco Catalyst 4500) to validate that TLSH can be used to perform 1.5 million queries per second per 1Gb/s port. We believe that this work can open new avenues in very high speed data mining.


title: Ongoing Evolution of Visual SLAM from Geometry to Deep Learning: Challenges and Opportunities

abstract: Visual simultaneous localization and mapping (SLAM) has been investigated in the robotics community for decades. Significant progress and achievements on visual SLAM have been made, with geometric model-based techniques becoming increasingly mature and accurate. However, they tend to be fragile under challenging environments. Recently, there is a trend to develop data-driven approaches, e.g., deep learning, for visual SLAM problems with more robust performance. This paper aims to witness the ongoing evolution of visual SLAM techniques from geometric model-based to data-driven approaches by providing a comprehensive technical review. Our contribution is not only just a compilation of state-of-the-art end-to-end deep learning SLAM work, but also an insight into the underlying mechanism of deep learning SLAM. For such a purpose, we provide a concise overview of geometric model-based approaches first. Next, we identify visual depth estimation using deep learning is a starting point of the evolution. It is from depth estimation that ego-motion or pose estimation techniques using deep learning flourish rapidly. In addition, we strive to link semantic segmentation using deep learning with emergent semantic SLAM techniques to shed light on simultaneous estimation of ego-motion and high-level understanding. Finally, we visualize some further opportunities in this research direction.


title: A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging

abstract: In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and a protective coating that is too thin. The fingerprint recognition sensor can also be placed under a glass screen to meet the needs of the full screen design of the mobile phone. In this paper, the electric field distribution around the fingerprint is analyzed. The electrostatic imaging sensor design is carried out based on the electrostatic detection principle and MEMS (micro-electro-mechanical system) technology. The MEMS electrostatic imaging array, analog, and digital signal processing circuit structure are designed. Simulation and testing are carried out as well. According to the simulation and prototype test device test results, it is confirmed that our proposed electrostatic imaging-based fingerprint sensing technology can increase fingerprint recognition distance by 46% compared to the existing capacitive fingerprint sensing technology. A distance of more than 439 μm is reached.


title: Not So Naive Bayes: Aggregating One-Dependence Estimators

abstract: Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and Super-Parent TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers. In extensive experiments this technique delivers comparable prediction accuracy to LBR and Super-Parent TAN with substantially improved computational efficiency at test time relative to the former and at training time relative to the latter. The new algorithm is shown to have low variance and is suited to incremental learning.


title: Fingerprint enhancement using STFT analysis

abstract: Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is still an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on short time Fourier transform (STFT) Analysis. STFT is a well-known technique in signal processing to analyze non-stationary signals. Here we extend its application to 2D fingerprint images. The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local ridge frequency. Furthermore we propose a probabilistic approach of robustly estimating these parameters. We experimentally compare the proposed approach to other filtering approaches in literature and show that our technique performs favorably.


title: Accelerating Convergency Genetic Algorithm of Improved Operator

abstract: To overcome low performance and premature convergence of simple Genetic Algorithm(GA),an improved operator genetic algotithm is proposed.It improves the operators such as initialization,selection,crossover and mutation,which make the algorithm more adaptive.The 3 groups of experiments show that improved algorithm has a quick speed convergence and more stable than simple GA,and also can efficiently avoid premature convergence.


title: Organizational culture and knowledge sharing: Critical success factors

abstract: Purpose – This research aims at investigating the role of certain factors in organizational culture in the success of knowledge sharing. Such factors as interpersonal trust, communication between staff, information systems, rewards and organization structure play an important role in defining the relationships between staff and in turn, providing possibilities to break obstacles to knowledge sharing. This research is intended to contribute in helping businesses understand the essential role of organizational culture in nourishing knowledge and spreading it in order to become leaders in utilizing their know-how and enjoying prosperity thereafter. Design/methodology/approach – The conclusions of this study are based on interpreting the results of a survey and a number of interviews with staff from various organizations in Bahrain from the public and private sectors. Findings – The research findings indicate that trust, communication, information systems, rewards and organization structure are positively related to knowledge sharing in organizations. Research limitations/implications – The authors believe that further research is required to address governmental sector institutions, where organizational politics dominate a role in hoarding knowledge, through such methods as case studies and observation. Originality/value – Previous research indicated that the Bahraini society is influenced by traditions of household, tribe, and especially religion of the Arab and Islamic world. These factors define people’s beliefs and behaviours, and thus exercise strong influence in the performance of business organizations. This study is motivated by the desire to explore the role of the national organizational culture on knowledge sharing, which may be different from previous studies conducted abroad.


title: Surviving Failures with Performance-Centric Bandwidth Allocation in Private Datacenters

abstract: In the context of private datacenters that are operated by Web service providers such as Google, multiple applications using data parallel frameworks, such as MapReduce, coexist and share a limited supply of link bandwidth capacities. It has been shown that failures are the norm, rather than the exception, in datacenters, and will negatively affect the performance of data parallel applications as failed tasks need to be relaunched and placed on newly selected servers. In this paper, we argue that even with the presence of failures, link bandwidth should be allocated to competing applications with performance-centric fairness, in that the performance that applications enjoy should be proportional to their weights. We formulate and solve the open challenge of jointly optimizing placement decisions for relaunched tasks and bandwidth allocation, so that the adverse effects of failures on application performance are minimized. With our proposed algorithm implemented in the Mininet emulation testbed, our experiments show the effectiveness of our solutions towards minimizing the negative effects of failures, while still achieving performance-centric fairness.


title: S-CNN: Subcategory-Aware Convolutional Networks for Object Detection

abstract: The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.


title: Fear and decision-making in narcissistic personality disorder—a link between psychoanalysis and neuroscience

abstract: Linking psychoanalytic studies with neuroscience has proven increasingly productive for identifying and understanding personality functioning. This article focuses on pathological narcissism and narcissistic personality disorder (NPD), with the aim of exploring two clinically relevant aspects of narcissistic functioning also recognized in psychoanalysis: fear and decision-making. Evidence from neuroscientific studies of related conditions, such as psychopathy, suggests links between affective and cognitive functioning that can influence the sense of self-agency and narcissistic self-regulation. Attention can play a crucial role in moderating fear and self-regulatory deficits, and the interaction between experience and emotion can be central for decision-making. In this review we will explore fear as a motivating factor in narcissistic personality functioning, and the impact fear may have on decision-making in people with pathological narcissism and NPD. Understanding the processes and neurological underpinnings of fear and decision-making can potentially influence both the diagnosis and treatment of NPD.


title: AUTOMATIC FIRE DETECTION: A SURVEY FROM WIRELESS SENSOR NETWORK PERSPECTIVE

abstract: Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a certain field (e.g., event detection for wireless sensor networks) or the main concern for which techniques have been specifically designed (e.g., fire detection using remote sensing techniques). These different approaches stem from different backgrounds of researchers dealing with fire, such as computer science, geography and earth observation, and fire safety. In this report we survey previous studies from three perspectives: (1) fire detection techniques for residential areas, (2) fire detection techniques for forests, and (3) contributions of sensor networks to early fire detection.


title: Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study

abstract: Background and objective: Combined evaluation of lumbosacral structures (e.g. nerves, bone) on multimodal radiographic images is routinely conducted prior to spinal surgery and interventional procedures. Generally, magnetic resonance imaging is conducted to differentiate nerves, while computed tomography (CT) is used to observe bony structures. The aim of this study is to investigate the feasibility of automatically segmenting lumbosacral structures (e.g. nerves & bone) on non-contrast CT with deep learning. Methods: a total of 50 cases with spinal CT were manually labeled for lumbosacral nerves and bone with Slicer 4.8. The ratio of training: validation: testing is 32:8:10. A 3D-Unet is adopted to build the model SPINECT for automatically segmenting lumbosacral structures. Pixel accuracy, IoU, and Dice score are used to assess the segmentation performance of lumbosacral structures. Results: the testing results reveals successful segmentation of lumbosacral bone and nerve on CT. The average pixel accuracy is 0.940 for bone and 0.918 for nerve. The average IoU is 0.897 for bone and 0.827 for nerve. The dice score is 0.945 for bone and 0.905 for nerve. Conclusions: this pilot study indicated that automatic segmenting lumbosacral structures (nerves and bone) on non-contrast CT is feasible and may have utility for planning and navigating spinal interventions and surgery.


title: Factored Displays: Improving resolution, dynamic range, color reproduction, and light field characteristics with advanced signal processing

abstract: Today, direct-view displays constitute the primary means by which humans visually interact with computers. These displays come in many forms: mobile phones, laptops, workstations, digital projectors, and digital signage; while ubiquitous, our two-dimensional (2-D) displays still pale in comparison to the three-dimensional (3-D) physical world. Gabriel Lippmann, a Nobel Prize-winning physicist and an early photographic innovator, lamented on the similar limitations of photography.


title: PC-based PID speed control in DC motor

abstract: This paper presents a LabVIEW-aided PID designed controller to monitor DC motor speed and uses the software simulation of VisSim to analysis its response. First, design the drive circuit of DC motor and as the feedback signal through the photo sensor and 8051 chip modules to produce rotational speed signal, and show from SEG7 displays. To take out the analog signal through D/A converter at the same time, acquire the signal via the NIDAQ USB-6008 card. By the LabVIEW-aided PID controller, the parameters are adjusted to control the motor speed. The front panel will display the speed of DC motor on the screen. The simulation results are quite match with the theoretical prediction for the behavior of the PID controller. In this proposed paper, it demonstrates the humanized operation interface that not only can replace the traditional instrument, but also facilitate the amateur engineer's operation under the remote control and monitor.


title: Online book recommendation system by using collaborative filtering and association mining

abstract: Recommendation systems is used for the purpose of suggesting items to purchase or to see. They direct users towards those items which can meet their needs through cutting down large database of Information. A various techniques have been introduced for recommending items i.e. content, collaborative and association mining techniques are used. This paper solves the problem of data sparsity problem by combining the collaborative-based filtering and association rule mining to achieve better performance. The results obtained are demonstrated and the proposed recommendation algorithms perform better and solve the challenges such as data sparsity and scalability.


title: Multiview image rectification algorithm for parallel camera arrays

abstract: An algorithm for multiview images captured by a parallel multicamera array is presented. An ergodic method is proposed for tracing the ideal rotation matrix and the preferred locations of the optical centers. Moreover, the camera's intrinsic parameters are optimized in order to obtain the ideal projection matrix from which the image rectifying transform matrix (IRTM) is further derived. Then, the multiview image is rectified based on the IRTM. Experimental results show that this proposed algorithm dramatically improves accuracy. Moreover, the robustness of this algorithm is superior to the robustness of the previous method. © 2014 SPIE and IS&T (DOI: 10.1117/1.JEI.23.3.033001)


title: Churn detection and prediction in automotive supply industry

abstract: Companies have both large certified enterprises and small unauthorized service providers as their competitors in the automotive supply industry. As technology related industries undergo more intensive competition, churn detection and prediction become essential to be precautious about leaving customers. The literature for churn detection offers numerous statistical and intelligent methods. In this study, Artificial Neural Networks and Decision Trees are applied to detect the churn in and analyze the validity of these methods for the automotive supply industry. The problem involves both categorical and continuous numerical decision inputs which cannot simultaneously fed into Decision Trees. In this case, continuous inputs should be divided into binary categorical ones by splitting into various intervals which are called buckets. Particle Swarm Optimization algorithm is implemented for finding optimal buckets for the churn problem data. Results indicate that while both algorithms are promising, the bucket tuning for Decision Trees complicate the churn detection process


title: Virtual Battery: A testing tool for power-aware software

abstract: Virtualization is an inexpensive and convenient method for setting up software test environments. Thus it is being widely used as a test tool for software products requiring high reliability such as mission critical cyber-physical systems. However, existing virtualization platforms do not fully virtualize the battery subsystem. Therefore, it is difficult to test battery-related features of guest systems. In this paper, we propose Virtual Battery, a battery virtualization scheme for type II full virtualization platforms. Virtual Battery takes the form of an ACPI-compatible battery device driver dedicated to each virtual machine, which virtualizes a target system. Through Virtual Battery, developers can easily manipulate the charging and battery status of each virtual machine (VM), regardless of the existence or current status of the host system's battery. In addition, Virtual Battery emulates the behavior of batteries by discharging the virtual batteries according to the resource usages of their VMs. This feature enables VMs to act as battery resource containers. Three case studies demonstrate the effectiveness of the proposed scheme.


title: 2003, "Information systems research and Hofstede's culture's consequences: an uneasy and incomplete partnership

abstract: Information systems (IS) researchers have begun to investigate how national culture, as articulated by Hofstede, affects a wide variety of issues. A citation analysis of IS articles that cite Hofstede's research on national culture suggests that most research is focused on issues related to IS management and to IS, while issues related to IS development and operations and to IS usage remain relatively unexamined. Within the dominant categories, research is concentrated in the IS management and types of information systems subcategories. Furthermore, the dimensions of national culture outlined by Hofstede have not been frequently used to develop and to build theory. Research opportunities and approaches to develop a stronger cumulative tradition and theory for international IS issues are proposed.


title: On-line relevant anomaly detection in the Twitter stream: an efficient bursty keyword detection model

abstract: On-line social networks have become a massive communication and information channel for users world-wide. In particular, the microblogging platform Twitter, is characterized by short-text message exchanges at extremely high rates. In this type of scenario, the detection of emerging topics in text streams becomes an important research area, essential for identifying relevant new conversation topics, such as breaking news and trends. Although emerging topic detection in text is a well established research area, its application to large volumes of streaming text data is quite novel. Making scalability, efficiency and rapidness, the key aspects for any emerging topic detection algorithm in this type of environment. Our research addresses the aforementioned problem by focusing on detecting significant and unusual bursts in keyword arrival rates or bursty keywords. We propose a scalable and fast on-line method that uses normalized individual frequency signals per term and a windowing variation technique. This method reports keyword bursts which can be composed of single or multiple terms, ranked according to their importance. The average complexity of our method is O(n log n), where n is the number of messages in the time window. This complexity allows our approach to be scalable for large streaming datasets. If bursts are only detected and not ranked, the algorithm remains with lineal complexity O(n), making it the fastest in comparison to the current state-of-the-art. We validate our approach by comparing our performance to similar systems using the TREC Tweet 2011 Challenge tweets, obtaining 91% of matches with LDA, an off-line gold standard used in similar evaluations. In addition, we study Twitter messages related to the SuperBowl football events in 2011 and 2013.


RelVis: Benchmarking OpenIE Systems


Empirical findings on bullying in the workplace


title: A Stepwise Approach for the Management of Poisoning with Extracorporeal Treatments

abstract: The use of an extracorporeal treatment (ECTR) in a poisoned patient may be life-saving in a limited number of scenarios. The decision-processes surrounding the use of ECTR in poisoning is complex: most nephrologists are not trained to assess a poisoned patient while clinical toxicologists rarely prescribe ECTRs. Deciding on which ECTR is most appropriate for a poison requires a good understanding of the poison's physicochemical and pharmacokinetic properties. Further, a detailed understanding of the capabilities and limitations of the different ECTRs can be useful to select the most appropriate ECTR for a given clinical situation. This manuscript provides a stepwise approach to assess the usefulness of ECTRs in poisoning.


title: Pose-Invariant 3D Face Alignment

abstract: Face alignment aims to estimate the locations of a set of landmarks for a given image. This problem has received much attention as evidenced by the recent advancement in both the methodology and performance. However, most of the existing works neither explicitly handle face images with arbitrary poses, nor perform large-scale experiments on non-frontal and profile face images. In order to address these limitations, this paper proposes a novel face alignment algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for a face image with an arbitrary pose. By integrating a 3D point distribution model, a cascaded coupled-regressor approach is designed to estimate both the camera projection matrix and the 3D landmarks. Furthermore, the 3D model also allows us to automatically estimate the 2D landmark visibilities via surface normal. We use a substantially larger collection of all-pose face images to evaluate our algorithm and demonstrate superior performances than the state-of-the-art methods.


title: Few-shot Learning by Exploiting Visual Concepts within CNNs

abstract: Convolutional neural networks (CNNs) are one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major obstacles on the road to achieving ideal machine intelligence. One is that CNNs are complex and hard to interpret. Another is that standard CNNs require large amounts of annotated data, which is sometimes hard to obtain, and it is desirable to learn to recognize objects from few examples. In this work, we address these limitations of CNNs by developing novel, flexible, and interpretable models for few-shot learning. Our models are based on the idea of encoding objects in terms of visual concepts (VCs), which are interpretable visual cues represented by the feature vectors within CNNs. We first adapt the learning of VCs to the few-shot setting, and then uncover two key properties of feature encoding using VCs, which we call category sensitivity and spatial pattern. Motivated by these properties, we present two intuitive models for the problem of few-shot learning. Experiments show that our models achieve competitive performances, while being more flexible and interpretable than alternative state-of-the-art few-shot learning methods. We conclude that using VCs helps expose the natural capability of CNNs for few-shot learning.


A Taxonomic Framework for Task Modeling and Knowledge Transfer in Manufacturing Robotics.


title: Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners

abstract: The aim of this study was to investigate whether nocturnal heart rate variability (HRV) can be used to predict changes in endurance performance during 28 weeks of endurance training. The training was divided into 14 weeks of basic training (BTP) and 14 weeks of intensive training periods (ITP). Endurance performance characteristics, nocturnal HRV, and serum hormone concentrations were measured before and after both training periods in 28 recreational endurance runners. During the study peak treadmill running speed (Vpeak ) improved by 7.5 ± 4.5%. No changes were observed in HRV indices after BTP, but after ITP, these indices increased significantly (HFP: 1.9%, P=0.026; TP: 1.7%, P=0.007). Significant correlations were observed between the change of Vpeak and HRV indices (TP: r=0.75, P<0.001; HFP: r=0.71, P<0.001; LFP: r=0.69, P=0.01) at baseline during ITP. In order to lead to significant changes in HRV among recreational endurance runners, it seems that moderate- and high-intensity training are needed. This study showed that recreational endurance runners with a high HRV at baseline improved their endurance running performance after ITP more than runners with low baseline HRV.


title: Deep learning approach for active classification of electrocardiogram signals

abstract: In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods.


title: IoT-Forensics Meets Privacy: Towards Cooperative Digital Investigations

abstract: IoT-Forensics is a novel paradigm for the acquisition of electronic evidence whose operation is conditioned by the peculiarities of the Internet of Things (IoT) context. As a branch of computer forensics, this discipline respects the most basic forensic principles of preservation, traceability, documentation, and authorization. The digital witness approach also promotes such principles in the context of the IoT while allowing personal devices to cooperate in digital investigations by voluntarily providing electronic evidence to the authorities. However, this solution is highly dependent on the willingness of citizens to collaborate and they may be reluctant to do so if the sensitive information within their personal devices is not sufficiently protected when shared with the investigators. In this paper, we provide the digital witness approach with a methodology that enables citizens to share their data with some privacy guarantees. We apply the PRoFIT methodology, originally defined for IoT-Forensics environments, to the digital witness approach in order to unleash its full potential. Finally, we show the feasibility of a PRoFIT-compliant digital witness with two use cases.


title: TRIFECTA: Security, Energy Efficiency, and Communication Capacity Comparison for Wireless IoT Devices

abstract: The widespread proliferation of sensor nodes in the era of Internet of Things (IoT) coupled with increasing sensor fidelity and data acquisition modality is expected to generate 3+ Exabytes of data per day by 2018. Since most of these IoT devices will be wirelessly connected at the last few feet, wireless communication is an integral part of the future IoT scenario. The ever-shrinking size of unit computation (Moore's Law) and continued improvements in efficient communication (Shannon's Law) is expected to harness the true potential of the IoT revolution and produce dramatic societal impact. However, reducing size of IoT nodes and lack of significant improvement in energy-storage density leads to reducing energy-availability. Moreover, smaller size and energy means less resources available for securing IoT nodes, making the energy-sparse low-cost leaf nodes of the network as prime targets for attackers. In this paper, we survey six prominent wireless technologies with respect to the three dimensions - security, energy efficiency, and communication capacity. We point out the state-of-the-art, open issues, and the road ahead for promising research directions.


title: Monitor and understand pilgrims: data collection using smartphones and wearable devices

abstract: Each year, millions of people visit the sacred sites in Makkah and Madinah. Even though the Hajj pilgrimage is one of the biggest annual events in the world, with many of the pilgrims reporting it as a life-changing experience, quite a little is done to objectively monitor the pilgrims and to understand from the crowd and from the individual point of view what makes this event so special. We present a data collection phase of 8 days of pilgrimage in April 2013 with 41 pilgrims carrying Android smartphones and 10 pilgrims wearing two physiological sensors, namely chest belts and wrist-worn devices. We describe the data recording itself, and emphasize the problems raised and the challenges faced during the study. We provide the best practices for performing solid and efficient user studies in such a difficult environment, and give first insights towards measuring important aspects of the Hajj pilgrimage such as recognition of activities and stages, analysis of group behavior, detection of stressful situations and health monitoring of pilgrims in general.


title: Meta-Learning with Temporal Convolutions

abstract: Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. Recent work in meta-learning seeks to overcome this shortcoming by training a meta-learner on a distribution of similar tasks; the goal is for the meta-learner to generalize to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, most recent approaches to meta-learning are extensively hand-designed, either using architectures that are specialized to a particular application, or hard-coding algorithmic components that tell the meta-learner how to solve the task. We propose a class of simple and generic meta-learner architectures, based on temporal convolutions, that is domain- agnostic and has no particular strategy or algorithm encoded into it. We validate our temporal-convolution-based meta-learner (TCML) through experiments pertaining to both supervised and reinforcement learning, and demonstrate that it outperforms state-of-the-art methods that are less general and more complex.


title: Brain Activity-Based Image Classification From Rapid Serial Visual Presentation

abstract: We report the design and performance of a brain-computer interface (BCI) system for real-time single-trial binary classification of viewed images based on participant-specific dynamic brain response signatures in high-density (128-channel) electroencephalographic (EEG) data acquired during a rapid serial visual presentation (RSVP) task. Image clips were selected from a broad area image and presented in rapid succession (12/s) in 4.1-s bursts. Participants indicated by subsequent button press whether or not each burst of images included a target airplane feature. Image clip creation and search path selection were designed to maximize user comfort and maintain user awareness of spatial context. Independent component analysis (ICA) was used to extract a set of independent source time-courses and their minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from 128-channel EEG data recorded during clip burst presentations in a training session. The naive Bayes fusion of two Fisher discriminant classifiers, computed from the 100 most discriminative time and time-frequency features, respectively, was used to estimate the likelihood that each clip contained a target feature. This estimator was applied online in a subsequent test session. Across eight training/test session pairs from seven participants, median area under the receiver operator characteristic curve, by tenfold cross validation, was 0.97 for within-session and 0.87 for between-session estimates, and was nearly as high (0.83) for targets presented in bursts that participants mistakenly reported to include no target features.


title: A Temperature and Process Compensated Ultralow-Voltage Rectifier in Standard Threshold CMOS for Energy-Harvesting Applications

abstract: This brief presents an ultralow-voltage multistage rectifier built with standard threshold CMOS for energy-harvesting applications. A threshold-compensated diode (TCD) is developed to minimize the forward voltage drop while maintaining low reverse leakage flow. In addition, an interstage compensation scheme is proposed that enables efficient power conversion at input amplitudes below the diode threshold. The new rectifier also features an inherent temperature and process compensation mechanism, which is achieved by precisely tracking the diode threshold by an auxiliary dummy. Although the design is optimized for an ac input at 13.56 MHz, the presented enhancement techniques are also applicable for low- or ultrahigh-frequency energy scavengers. The rectifier prototype is fabricated in a 0.35-μm four-metal two-poly standard CMOS process with the worst-case threshold voltage of 600 mV/- 780 mV for nMOS/pMOS, respectively. With a 13.56 MHz input of a 500 mV amplitude, the rectifier is able to deliver more than 35 μW at 2.5 V VDD, and the measured deviation in the output voltage is as low as 180 mV over 100°C for a cascade of ten TCDs.


Category Theory and the Foundations of Classical Space–Time Theories


title: BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

abstract: Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4 V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data).


title: Implementing the real-time publisher/subscriber model on the controller area network (CAN)

abstract: Designing distributed real-time systems as being composed of communicating objects offers many advantages with respect to modularity and extensibility of these systems. However, distributed real-time applications exhibit communication patterns that significantly differ from the traditional object invocation style. The publisher/subscriber model for inter-object communication matches well with these patterns. Any implementation of that model must address the problems of binding subscribers to publishers, of routing and filtering of messages, as well as reliability, efficiency and latency of message delivery. In the context of real-time applications, all these issues must be subject to a rigid inspection with respect to meeting real-time requirements. We argue that for embedded control systems built around smart microcontroller-powered devices these requirements can only be met when exploiting the properties of the underlying network. The CAN-Bus (CAN: Controller Area Network) which is an emerging standard in the field of real-time embedded systems is particularly suited to implement a publisher/subscriber model of communication. We present an implementation of the real-time publisher/subscriber model that exploits the underlying facilities of the CAN-Bus. In particular, we introduce a novel addressing scheme for publisher/subscriber communication that makes efficient use of the CAN-Bus addressing method. We provide a detailed design and implementation details along with some preliminary performance estimations.


title: Noise2Noise: Learning Image Restoration without Clean Data

abstract: We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans -- all corrupted by different processes -- based on noisy data only.


title: Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification

abstract: The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with Log-Euclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the matrix logarithms from the original tangent space to a new tangent space of more discriminability. Under the tangent map framework, the novel metric learning can then be formulated as an optimization problem of seeking a Mahalanobis-like matrix, which can take the advantage of traditional metric learning techniques. Extensive evaluations on several image set classification tasks demonstrate the effectiveness of our proposed metric learning method.


title: Enterprise information systems project implementation: A case study of ERP in Rolls-Royce

abstract: Economic globalisation and internationalisation of operations are essential factors in integration of suppliers, partners and customers within and across national borders, the objective being to achieve integrated supply chains. In this effort, implementation of information technologies and systems such as enterprise resource planning (ERP) facilitate the desired level of integration. There are cases of successful and unsuccessful implementations. The principal reason for failure is often associated with poor management of the implementation process. This paper examines key dimensions of implementation of ERP system within a large manufacturing organisation and identifies core issues to confront in successful implementation of enterprise information system. A brief overview of the application of ERP system is also presented and in particular, ERP software package known as SAP R/3, which was the ERP software package selected by Rolls-Royce plc. The paper takes an in-depth look at the issues behind the process of ERP implementation via a case study methodology. It focuses on business and technical as well as cultural issues at the heart of the Rolls-Royce implementation. The case study also looks at the implementation time scales and assesses the benefits from the project both tangible and intangible.


title: Ontology modeling of Malaysian food composition

abstract: Each new design solution must contain a novelty element. Thus, case adaptation becomes an important subtask in case-based design. This subtask plays the role of modifying the previous solution of the best retrieved case. Generally, adaptation task requires for domain knowledge, and domain- and task-heuristics. This paper demonstrates the process of modeling a domain knowledge using ontology. The domain ontology models the dietitians' knowledge where they use it in designing the dietary menu plan. The method used for ontology modeling is Ontology Development 101 (OD 101). The domain ontology was built from scratch. MyFCO has successfully modeled the domain knowledge of the dietary menu planning. We evaluated the quality of the domain ontology by using it in the design case adaptation for dietary menu planning application. The iterative process emphasized by OD 101 enabled a novice ontology developer to model the ontology in a simpler and more flexible way. This contributes to the success factor of ontology modeling for domain knowledge.


title: An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

abstract: Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.


Uses and Gratification Theory – Why Adolescents Use Facebook?


title: Separate-and-Conquer Rule Learning

abstract: This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.


title: High-Resolution Multispectral Dataset for Semantic Segmentation

abstract: Unmanned aircraft have decreased the cost required to collect remote sensing imagery, which has enabled researchers to collect high-spatial resolution data from multiple sensor modalities more frequently and easily. The increase in data will push the need for semantic segmentation frameworks that are able to classify non-RGB imagery, but this type of algorithmic development requires an increase in publicly available benchmark datasets with class labels. In this paper, we introduce a high-resolution multispectral dataset with image labels. This new benchmark dataset has been pre-split into training/testing folds in order to standardize evaluation and continue to push state-of-the-art classification frameworks for non-RGB imagery.


title: Boosting XML Filtering with a Scalable FPGA-based Architecture

abstract: The growing amount of XML encoded data exchanged over the Internet increases the importance of XML based publish-subscribe (pub-sub) and content based routing systems. The input in such systems typically consists of a stream of XML documents and a set of user subscriptions expressed as XML queries. The pub-sub system then filters the published documents and passes them to the subscribers. Pub-sub systems are characterized by very high input ratios, therefore the processing time is critical. In this paper we propose a"pure hardware"based solution, which utilizes XPath query blocks on FPGA to solve the filtering problem. By utilizing the high throughput that an FPGA provides for parallel processing, our approach achieves drastically better throughput than the existing software or mixed (hardware/software) architectures. The XPath queries (subscriptions) are translated to regular expressions which are then mapped to FPGA devices. By introducing stacks within the FPGA we are able to express and process a wide range of path queries very efficiently, on a scalable environment. Moreover, the fact that the parser and the filter processing are performed on the same FPGA chip, eliminates expensive communication costs (that a multi-core system would need) thus enabling very fast and efficient pipelining. Our experimental evaluation reveals more than one order of magnitude improvement compared to traditional pub/sub systems.


title: Derivation and comparison of SAR and frequency-wavenumber migration within a common inverse scalar wave problem formulation

abstract: Two common Fourier imaging algorithms used in ground penetrating radar (GPR), synthetic aperture radar (SAR), and frequency-wavenumber (F-K) migration, are reviewed and compared from a theoretical perspective. The two algorithms, while arising from seemingly different physical models: a point-scatterer model for SAR and the exploding source model for F-K migration, result in similar imaging equations. Both algorithms are derived from an integral equation formulation of the inverse scalar wave problem, which allows a clear understanding of the approximations being made in each algorithm and allows a direct comparison. This derivation brings out the similarities of the two techniques which are hidden by the traditional formulations based on physical scattering models. The comparison shows that the approximations required to derive each technique from the integral equation formulation of the inverse problem are nearly identical, and hence the two imaging algorithms and physical models are making similar assumptions about the solution to the inverse problem, thus clarifying why the imaging equations are so similar. Sample images of landmine-like targets buried in sand are obtained from experimental GPR data using both algorithms.


title: IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing

abstract: Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.


title: Compact Printed MIMO Antenna for UWB Applications

abstract: A compact multiple-input-multiple-output (MIMO) antenna is presented for ultrawideband (UWB) applications. The antenna consists of two open L-shaped slot (LS) antenna elements and a narrow slot on the ground plane. The antenna elements are placed perpendicularly to each other to obtain high isolation, and the narrow slot is added to reduce the mutual coupling of antenna elements in the low frequency band (3-4.5 GHz). The proposed MIMO antenna has a compact size of 32 ×32 mm ::: 2 ::: , and the antenna prototype is fabricated and measured. The measured results show that the proposed antenna design achieves an impedance bandwidth of larger than 3.1-10.6 GHz, low mutual coupling of less than 15 dB, and a low envelope correlation coefficient of better than 0.02 across the frequency band, which are suitable for portable UWB applications.


title: DyDroid: Measuring Dynamic Code Loading and Its Security Implications in Android Applications

abstract: Android has provided dynamic code loading (DCL) since API level one. DCL allows an app developer to load additional code at runtime. DCL raises numerous challenges with regards to security and accountability analysis of apps. While previous studies have investigated DCL on Android, in this paper we formulate and answer three critical questions that are missing from previous studies: (1) Where does the loaded code come from (remotely fetched or locally packaged), and who is the responsible entity to invoke its functionality? (2) In what ways is DCL utilized to harden mobile apps, specifically, application obfuscation? (3) What are the security risks and implications that can be found from DCL in off-the-shelf apps? We design and implement DyDroid, a system which uses both dynamic and static analysis to analyze dynamically loaded code. Dynamic analysis is used to automatically exercise apps, capture DCL behavior, and intercept the loaded code. Static analysis is used to investigate malicious behavior and privacy leakage in that dynamically loaded code. We have used DyDroid to analyze over 46K apps with little manual intervention, allowing us to conduct a large-scale measurement to investigate five aspects of DCL, such as source identification, malware detection, vulnerability analysis, obfuscation analysis, and privacy tracking analysis. We have several interesting findings. (1) 27 apps are found to violate the content policy of Google Play by executing code downloaded from remote servers. (2) We determine the distribution, pros/cons, and implications of several common obfuscation methods, including DEX encryption/loading. (3) DCL's stealthiness enables it to be a channel to deploy malware, and we find 87 apps loading malicious binaries which are not detected by existing antivirus tools. (4) We found 14 apps that are vulnerable to code injection attacks due to dynamically loading code which is writable by other apps. (5) DCL is mainly used by third-party SDKs, meaning that app developers may not know what sort of sensitive functionality is injected into their apps.


Bend, stretch, and touch: Locating a finger on an actively deformed transparent sensor array


The effect of various polishing systems on surface roughness and phase transformation of monolithic zirconia


title: An Empirical Study on Network Anomaly Detection Using Convolutional Neural Networks

abstract: Deep learning has been widely applied to network anomaly detection to improve performance. In our past research, we empirically evaluated a set of deep learning models, including Fully Connected Network (FCN), Variational Auto Encoder (VAE), and Sequence to Sequence model with Long Short-Term Memory (Seq2Seq-LSTM), for network anomaly detection. Additionally, we evaluate Convolution Neural Networks (CNNs) for network anomaly detection in this paper. We set up three simple CNN models with different internal depths (shallow CNN, moderate CNN, and deep CNN) to see the impact of the depth to the performance. We evaluate the models using three different types of traffic datasets. Our experimental results show that deeper structures do not make any performance improvement. In addition, we observed that the evaluated CNN models occasionally outperform the VAE models, but do not work better than the other deep learning models based on FCN and Seq2Seq-LSTM.


title: Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations

abstract: Spiking Neural Networks (SNNs) play an important role in neuroscience as they help neuroscientists understand how the nervous system works. To model the nervous system, SNNs incorporate the concept of time into neurons and inter-neuron interactions called spikes; a neuron's internal state changes with respect to time and input spikes, and a neuron fires an output spike when its internal state satisfies certain conditions. As the neurons forming the nervous system behave differently, SNN simulation frameworks must be able to simulate the diverse behaviors of the neurons. To support any neuron models, some frameworks rely on general-purpose processors at the cost of inefficiency in simulation speed and energy consumption. The other frameworks employ specialized accelerators to overcome the inefficiency; however, the accelerators support only a limited set of neuron models due to their model-driven designs, making accelerator-based frameworks unable to simulate target SNNs. In this paper, we present Flexon, a flexible digital neuron which exploits the biologically common features shared by diverse neuron models, to enable efficient SNN simulations. To design Flexon, we first collect SNNs from prior work in neuroscience research and analyze the neuron models the SNNs employ. From the analysis, we observe that the neuron models share a set of biologically common features, and that the features can be combined to simulate a significantly larger set of neuron behaviors than the existing model-driven designs. Furthermore, we find that the features share a small set of computational primitives which can be exploited to further reduce the chip area. The resulting digital neurons, Flexon and spatially folded Flexon, are flexible, highly efficient, and can be easily integrated with existing hardware. Our prototyping results using TSMC 45 nm standard cell library show that a 12-neuron Flexon array improves energy efficiency by 6,186x and 422x over CPU and GPU, respectively, in a small footprint of 9.26 mm2. The results also show that a 72-neuron spatially folded Flexon array incurs a smaller footprint of 7.62 mm2 and achieves geomean speedups of 122.45x and 9.83x over CPU and GPU, respectively.


title: A cyber-crime investigation framework

abstract: Epistemic uncertainty is an unavoidable attribute which is present in criminal investigations and could affect negatively the effectiveness of the process. A cyber-crime investigation involves a potentially large number of individuals and groups who need to communicate, share and make decisions across many levels and boundaries. This paper presents an approach adopting elements of the Strategic Systems Thinking Framework (SST) by which conflicting information due to the unavoidable uncertainty can be captured and processed, in support of the investigation process. A formal description of this approach is proposed as a basis for developing a cyber-crime investigation support system.


title: Once daily dolutegravir (S/GSK1349572) in combination therapy in antiretroviral-naive adults with HIV: planned interim 48 week results from SPRING-1, a dose-ranging, randomised, phase 2b trial

abstract: Summary Background Dolutegravir (S/GSK1349572) is a new HIV-1 integrase inhibitor that has antiviral activity with once daily, unboosted dosing. SPRING-1 is an ongoing study designed to select a dose for phase 3 assessment. We present data from preplanned primary and interim analyses. Methods In a phase 2b, multicentre, dose-ranging study, treatment-naive adults were randomly assigned (1:1:1:1) to receive 10 mg, 25 mg, or 50 mg dolutegravir or 600 mg efavirenz. Dose but not drug allocation was masked. Randomisation was by a central integrated voice-response system according to a computer-generated code. Study drugs were given with either tenofovir plus emtricitabine or abacavir plus lamivudine. Our study was done at 34 sites in France, Germany, Italy, Russia, Spain, and the USA beginning on July 9, 2009. Eligible participants were seropositive for HIV-1, aged 18 years or older, and had plasma HIV RNA viral loads of at least 1000 copies per mL and CD4 counts of at least 200 cells per μL. Our primary endpoint was the proportion of participants with viral load of less than 50 copies per mL at week 16 and we present data to week 48. Analyses were done on the basis of allocation group and included all participants who received at least one dose of study drug. This study is registered with ClinicalTrials.gov, number NCT00951015. Findings 205 patients were randomly allocated and received at least one dose of study drug: 53, 51, and 51 to receive 10 mg, 25 mg, and 50 mg dolutegravir, respectively, and 50 to receive efavirenz. Week 16 response rates to viral loads of at most 50 copies per mL were 93% (144 of 155 participants) for all doses of dolutegravir (with little difference between dose groups) and 60% (30 of 50) for efavirenz; week 48 response rates were 87% (139 of 155) for all doses of dolutegravir and 82% (41 of 50) for efavirenz. Response rates between nucleoside reverse transcriptase inhibitor subgroups were similar. We identified three virological failures in the dolutegravir groups and one in the efavirenz group—we did not identify any integrase inhibitor mutations. We did not identify any dose-related clinical or laboratory toxic effects, with more drug-related adverse events of moderate-or-higher intensity in the efavirenz group (20%) than the dolutegravir group (8%). We did not judge that any serious adverse events were related to dolutegravir. Interpretation Dolutegravir was effective when given once daily without a pharmacokinetic booster and was well tolerated at all assessed doses. Our findings support the assessment of once daily 50 mg dolutegravir in phase 3 trials. Funding Shionogi-GlaxoSmithKline Pharmaceuticals, LLC, now Shionogi-ViiV Healthcare, LLC.


title: Corticomotor Excitability Effects of Peripheral Nerve Electrical Stimulation to the Paretic Arm in Stroke

abstract: Objective This study aimed to examine the corticomotor excitability changes after peripheral nerve electrical stimulation (PNS) on the stroke-impaired arm. Design This randomized cross-over study included 32 subjects with chronic stroke. They received a 1-hr PNS or placebo PNS by random order to the ulnar and radial nerves of the paretic arm in separate sessions. The primary outcome was excitability of the corticospinal projections for the contralateral first dorsal interosseous hand muscle in terms of slope of the recruitment curve, peak motor-evoked potential amplitude, and duration of the cortical silent period, measured with transcranial magnetic stimulation on both cerebral hemispheres. Seventeen of the subjects were measured for their paretic hand dexterity (using Purdue Pegboard Test) and pinch strength as secondary outcomes. Results Peripheral nerve electrical stimulation, but not placebo PNS, increased recruitment curve slope and peak motor-evoked potential amplitude in both the lesioned and nonlesioned hemispheres and lengthened the cortical silent period duration in the nonlesioned hemisphere. Peripheral nerve electrical stimulation significantly improved hand dexterity scores compared with placebo PNS. Pinch strength was not changed by the interventions. Conclusions A 1-hr PNS alone to the paretic arm could increase corticomotor excitability in both hemispheres, together with hand dexterity improvement in people presented with mild-to-moderate motor impairment in the paretic upper limb after stroke. To Claim CME Credits Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME Objectives Upon completion of this article, the readers should be able to: (1) Understand the effects of a stroke on the corticomotor excitability of the lesioned and intact hemisphere; (2) Describe the effect of peripheral nerve electrical stimulation on the lesioned and intact hemisphere corticomotor excitability following stroke; and (3) Understand the effect of peripheral nerve electrical stimulation on dexterity of the paretic hand following stroke. Level Advanced Accreditation The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 0.75 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.


title: Cooling system investigation of thermoelectric generator used for marine waste heat recovery

abstract: There has been great interest on exploring the potential of waste heat extraction using thermoelectric generator (TEG) system on marine application, due to great amount of exhaust gas volume, many potential locations with consistent high temperature and availability of infinite cooling sea water. To improve the overall output performance of TEG system in terms of efficiency, proper system level engineering and integration have been considered crucial besides development of thermoelectric materials with larger ZT (figure of merit) value and optimal control strategy of electrical interface system. In this work, preliminary experimental investigation and numerical modelling optimization on the cooling system for a thermoelectric module level device are conducted. The predicted open-circuit voltages by thermoelectric simulation for device with conventional tubed water cooling plate are in good agreement with the measured experimental data for various hot exhaust temperatures. The model is then revised for further simulation to optimize the cooling plate design by examining detailed thermal performance and electrical current-voltage characteristics for the thermoelectric module device with varying electrical load. These investigations demonstrate that a local effective cooling system is important for high efficiency TEG implementation. This modulelevel observation serves to perceive how TEG system integrates as well as indicate potential improvement for TEG power conversion system. With good accuracy, the employed simulation tool is capable of rapidly characterizing TEG system level performance in future work.


title: Survey on Privacy Protection of Android Devices

abstract: Nowadays, the ubiquity of smart phones make them carry large amounts of personal sensitive information, but at the same time, there are also many Apps in Android APP market that target to collect users' sensitive data. So it becomes quite important to prevent users from the threat of privacy leakage. In this paper, we analyze the Android's privacy protection mechanism, and describe various threats to users' different types of privacy data. After that, we enumerate two ways that can leak sensitive information, and discuss the current solutions and techniques from aspects of privacy protection enhancement and privacy leakage detection. We also make a fine-grained classification for these two aspects, and study the difference between solutions in each category. Finally, we summarize the deficiency of existing research of Android privacy protection and propose the future research direction.


title: Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges

abstract: The solid-state lighting is revolutionizing the indoor illumination. Current incandescent and fluorescent lamps are being replaced by the LEDs at a rapid pace. Apart from extremely high energy efficiency, the LEDs have other advantages such as longer lifespan, lower heat generation, and improved color rendering without using harmful chemicals. One additional benefit of LEDs is that they are capable of switching to different light intensity at a very fast rate. This functionality has given rise to a novel communication technology (known as visible light communication—VLC) where LED luminaires can be used for high speed data transfer. This survey provides a technology overview and review of existing literature of visible light communication and sensing. This paper provides a detailed survey of 1) visible light communication system and characteristics of its various components such as transmitter and receiver; 2) physical layer properties of visible light communication channel, modulation methods, and MIMO techniques; 3) medium access techniques; 4) system design and programmable platforms; and 5) visible light sensing and application such as indoor localization, gesture recognition, screen-camera communication, and vehicular networking. We also outline important challenges that need to be addressed in order to design high-speed mobile networks using visible light communication.


title: Khan academy gamifies computer science

abstract: Gamification is the buzzword for adding gaming elements such as points or badges to learning experiences to make them more engaging and to increase motivation. In this paper we explore how Khan Academy has incorporated gaming elements into its CS learning platform. By mapping the literature on motivational processes to popular games we critically analyze how successful Khan Academy is at gamifying their site.


title: An Improvement of a Fuzzy Logic-Controlled Maximum Power Point Tracking Algorithm for Photovoltic Applications

abstract: This paper presents an improved maximum power point tracking (MPPT) algorithm using a fuzzy logic controller (FLC) in order to extract potential maximum power from photovoltaic cells. The objectives of the proposed algorithm are to improve the tracking speed, and to simultaneously solve the inherent drawbacks such as slow tracking in the conventional perturb and observe (P and O) algorithm. The performances of the conventional P and O algorithm and the proposed algorithm are compared by using MATLAB/Simulink in terms of the tracking speed and steady-state oscillations. Additionally, both algorithms were experimentally validated through a digital signal processor (DSP)-based controlled-boost DC-DC converter. The experimental results show that the proposed algorithm performs with a shorter tracking time, smaller output power oscillation, and higher efficiency, compared with the conventional P and O algorithm.


title: The Disciplinary Status of Consumer Behavior: A Sociology of Science Perspective on Key Controversies

abstract: Critics within the consumer behavior field have consistently debated three fundamental issues about the field’s defining properties and goals: (1) whether consumer behavior should be an independent discipline, (2) what is (and is not) consumer behavior, and (3) whether our field should be interdisciplinary. Taking the perspective of the sociology of science leads us to conclude that (1) consumer behavior is not an independent discipline; (2) consumer behavior is distinguished from other fields by its focus on a consumer role, emphasizing the acquisition, consumption, and disposal of marketplace products, services, and experiences; and (3) consumer behavior is not an interdisciplinary field.


First Classified Annotated Bibliography of NLP Tasks in the Burmese Language of Myanmar


title: Two novel NIPBL gene mutations in Chinese patients with Cornelia de Lange syndrome.

abstract: Cornelia de Lange syndrome (CdLS) is a dominantly inherited developmental disorder characterized by distinctive facial features, mental retardation, and upper limb defects, with the involvement of multiple organs and systems. To date, mutations have been identified in five genes responsible for CdLS: NIPBL, SMC1A, SMC3, RAD21, and HDAC8. Here, we present a clinical and molecular characterization of five unrelated Chinese patients whose clinical presentation is consistent with that of CdLS. There were no chromosomal abnormalities in the five children. In three patients, DNA sequencing revealed a previously reported frameshift mutation c.2479delA (p.Arg827GlyfsX20), and two novel mutations including a heterozygous mutation c.6272 G>T (p.Cys2091Phe) and a frameshift mutation c.1672delA (p.Thr558LeufsX7) in NIPBL. For the remaining patients, large deletions and/or duplications within the NIPBL gene were excluded as playing a role in the pathogenesis, by Multiplex Ligation-dependent Probe Amplification (MLPA) analysis. These findings broaden the mutation spectrum of NIPBL and further our understanding of the diverse and variable effects of NIPBL mutations on CdLS.


title: User-Interface Support to Group Interaction

abstract: This paper describes a user-interface system developed to support group interaction for same-time/different-place cooperative applications. The user-interface system is based on a model which defines four types of objects, each one dedicated to address a specific issue of group interaction support: information sharing, interaction control, structuring of group interactions and awareness of user activities in the system. Examples of combination of the above objects are given for the implementation of talk, joint editing and telepointer services.


Wireless Sensor Network Optimization: Multi-Objective Paradigm


title: Multilevel Inverter Topologies With Reduced Device Count: A Review

abstract: Multilevel inverters have created a new wave of interest in industry and research. While the classical topologies have proved to be a viable alternative in a wide range of high-power medium-voltage applications, there has been an active interest in the evolution of newer topologies. Reduction in overall part count as compared to the classical topologies has been an important objective in the recently introduced topologies. In this paper, some of the recently proposed multilevel inverter topologies with reduced power switch count are reviewed and analyzed. The paper will serve as an introduction and an update to these topologies, both in terms of the qualitative and quantitative parameters. Also, it takes into account the challenges which arise when an attempt is made to reduce the device count. Based on a detailed comparison of these topologies as presented in this paper, appropriate multilevel solution can be arrived at for a given application.


title: Regularization paths for generalized linear models via coordinate descent

abstract: We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include l1 (the lasso), l2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.


title: SOR-HDFS: a SEDA-based approach to maximize overlapping in RDMA-enhanced HDFS

abstract: In this paper, we propose SOR-HDFS, a SEDA (Staged Event-Driven Architecture)-based approach to improve the performance of HDFS Write operation. This design not only incorporates RDMA-based communication over InfiniBand but also maximizes overlapping among different stages of data transfer and I/O. Performance evaluations show that, the new design improves the aggregated write throughput of Enhanced DFSIO benchmark in Intel HiBench by up to 64% and reduces the job execution time by 37% compared to IPoIB (IP over InfiniBand). Compared to the previous best RDMA-enhanced design [4], the improvements in throughput and execution time are 30% and 20%, respectively. Our design can also improve the performance of HBase Put operation by up to 53% over IPoIB and 29% compared to the previous best RDMA-enhanced HDFS. To the best of our knowledge, this is the first design of SEDA-based HDFS in the literature.


title: Beekup: A distributed and safe P2P storage framework for IoE applications

abstract: Cloud computing and, in particular, cloud storage, has successfully boosted a variety of applications, in several domains; with proven acceptance and benefits by end users. While peculiar features of the cloud, e.g., redundancy, scalability and accessibility, are still highly desirable, several new scenarios are nowadays emerging, which call for additional features and/or different interpretations of the underlying paradigm. With the recent rising of the Internet of Everything, for example, some of the cloud principles should be partly reconsidered to better address scenarios involving a high number of devices, each carrying limited storage capacity, which can possibly operate in connection “islands” due to erratic or unreliable network connections. In this paper we start addressing these needs by presenting the Beekup system: a P2P storage framework, based on Tahoe-LAFS. Beekup enables cloud-like storage on edge devices supporting a variety of applications: from private secure file backup, to sensor-level distributed storage of raw data. Two main testbeds centered on private cloud storage and distributed sensor-data backup are analyzed in the paper, together with corresponding user applications. Initial outcomes from experiments and encouraging feedback from users involved in system trials, confirm the potential viability of the approach.


title: Analysis of data mining techniques for heart disease prediction

abstract: Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. This paper addresses the issue of prediction of heart disease according to input attributes on the basis of data mining techniques. We have investigated the heart disease prediction using KStar, J48, SMO, Bayes Net and Multilayer Perceptron through Weka software. The performance of these data mining techniques is measured by combining the results of predictive accuracy, ROC curve and AUC value using a standard data set as well as a collected data set. Based on performance factor SMO and Bayes Net techniques show optimum performances than the performances of KStar, Multilayer Perceptron and J48 techniques.


title: Robot reinforcement learning using EEG-based reward signals

abstract: Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, there are situations in which this reward is hard to encode, and so would require a supervised approach of reinforcement learning, where a user directly types the reward on each trial. This paper proposes to use brain activity recorded by an EEG-based BCI system as reward signals. The idea is to obtain the reward from the activity generated while observing the robot solving the task. This process does not require an explicit model of the reward signal. Moreover, it is possible to capture subjective aspects which are specific to each user. To achieve this, we designed a new protocol to use brain activity related to the correct or wrong execution of the task. We showed that it is possible to detect and classify different levels of error in single trials. We also showed that it is possible to apply reinforcement learning algorithms to learn new similar tasks using the rewards obtained from brain activity.


title: Predicting User-Topic Opinions in Twitter with Social and Topical Context

abstract: With popular microblogging services like Twitter, users are able to online share their real-time feelings in a more convenient way. The user generated data in Twitter is thus regarded as a resource providing individuals' spontaneous emotional information, and has attracted much attention of researchers. Prior work has measured the emotional expressions in users' tweets and then performed various analysis and learning. However, how to utilize those learned knowledge from the observed tweets and the context information to predict users' opinions toward specific topics they had not directly given yet, is a novel problem presenting both challenges and opportunities. In this paper, we mainly focus on solving this problem with a Social context and Topical context incorporated Matrix Factorization (ScTcMF) framework. The experimental results on a real-world Twitter data set show that this framework outperforms the state-of-the-art collaborative filtering methods, and demonstrate that both social context and topical context are effective in improving the user-topic opinion prediction performance.


title: Targeting Strategies for the Combination Treatment of Cancer Using Drug Delivery Systems

abstract: Cancer cells have characteristics of acquired and intrinsic resistances to chemotherapy treatment—due to the hostile tumor microenvironment—that create a significant challenge for effective therapeutic regimens. Multidrug resistance, collateral toxicity to normal cells, and detrimental systemic side effects present significant obstacles, necessitating alternative and safer treatment strategies. Traditional administration of chemotherapeutics has demonstrated minimal success due to the non-specificity of action, uptake and rapid clearance by the immune system, and subsequent metabolic alteration and poor tumor penetration. Nanomedicine can provide a more effective approach to targeting cancer by focusing on the vascular, tissue, and cellular characteristics that are unique to solid tumors. Targeted methods of treatment using nanoparticles can decrease the likelihood of resistant clonal populations of cancerous cells. Dual encapsulation of chemotherapeutic drug allows simultaneous targeting of more than one characteristic of the tumor. Several first-generation, non-targeted nanomedicines have received clinical approval starting with Doxil® in 1995. However, more than two decades later, second-generation or targeted nanomedicines have yet to be approved for treatment despite promising results in pre-clinical studies. This review highlights recent studies using targeted nanoparticles for cancer treatment focusing on approaches that target either the tumor vasculature (referred to as ‘vascular targeting’), the tumor microenvironment (‘tissue targeting’) or the individual cancer cells (‘cellular targeting’). Recent studies combining these different targeting methods are also discussed in this review. Finally, this review summarizes some of the reasons for the lack of clinical success in the field of targeted nanomedicines.


title: Memory and reward systems coproduce ‘nostalgic’ experiences in the brain

abstract: People sometimes experience an emotional state known as 'nostalgia', which involves experiencing predominantly positive emotions while remembering autobiographical events. Nostalgia is thought to play an important role in psychological resilience. Previous neuroimaging studies have shown involvement of memory and reward systems in such experiences. However, it remains unclear how these two systems are collaboratively involved with nostalgia experiences. Here, we conducted a functional magnetic resonance imaging study of healthy females to investigate the relationship between memory-reward co-activation and nostalgia, using childhood-related visual stimuli. Moreover, we examined the factors constituting nostalgia and their neural correlates. We confirmed the presence of nostalgia-related activity in both memory and reward systems, including the hippocampus (HPC), substantia nigra/ventral tegmental area (SN/VTA), and ventral striatum (VS). We also found significant HPC-VS co-activation, with its strength correlating with individual 'nostalgia tendencies'. Factor analyses showed that two dimensions underlie nostalgia: emotional and personal significance and chronological remoteness, with the former correlating with caudal SN/VTA and left anterior HPC activity, and the latter correlating with rostral SN/VTA activity. These findings demonstrate the cooperative activity of memory and reward systems, where each system has a specific role in the construction of the factors that underlie the experience of nostalgia.


title: Immediate Effect of Ayres's Sensory Integration-Based Occupational Therapy Intervention on Children With Autism Spectrum Disorders

abstract: OBJECTIVE. This study examined the effects of Ayres�s sensory integration intervention on the behavior and task engagement of young children with autism spectrum disorders (ASD). Clinical observations and caregiver reports of behavior and engagement also were explored to help guide future investigations. ::: ::: METHOD. This single-subject study used an ABAB design to compare the immediate effect of Ayres�s sensory integration and a play scenario on the undesired behavior and task engagement of 4 children with ASD. ::: ::: RESULTS. No clear patterns of change in undesired behavior or task management emerged through objective measurement. Subjective data suggested that each child exhibited positive changes during and after intervention. ::: ::: CONCLUSION. When effects are measured immediately after intervention, short-term Ayres�s sensory integration does not have a substantially different effect than a play scenario on undesired behavior or engagement of young children with ASD. However, subjective data suggest that Ayres�s sensory integration may produce an effect that is evident during treatment sessions and in home environments.


title: A Meta-Analysis of Research on Sensory Integration Treatment

abstract: OBJECTIVE ::: The purpose of this study was to find whether existing studies of treatment using sensory integration approaches support the efficacy of these approaches. ::: ::: ::: METHOD ::: With meta-analysis, the results of sensory integration efficacy research studies published from 1972 to the present were synthesized and analyzed. Sixteen studies were used to compare sensory integration effect with no treatment (SI/NT), and 16 were used to compare sensory integration effect with alternative treatments (SI/ALT). Overall average effect sizes, comparisons of the effect sizes for different dependent variables, and secondary factors associated with effect size variation were examined. ::: ::: ::: RESULTS ::: The weighted average effect size of SI/NT studies was .29. However, there was a significant difference between the average effect sizes of the earlier studies (.60) and the more recent studies (.03). Of the outcome measures, larger effect sizes were found in the psychoeducational category (.39) and motor category (.40). Of SI/ALT studies, the average effect size was .09, not significantly different from zero. ::: ::: ::: CONCLUSION ::: Three central conclusions can be made. First, in the SI/NT comparison, a significant effect was replicated for sensory integration treatment effects in earlier studies, but more recent studies did not show overall positive effects. Second, larger effect sizes were found in psychoeducational and motor categories. Third, sensory integration treatment methods were found to be as effective as various alternative treatment methods.


title: A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation

abstract: BACKGROUND ::: Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). ::: ::: ::: OBJECTIVE ::: To develop an equation to predict GFR from serum creatinine concentration and other factors. ::: ::: ::: DESIGN ::: Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. ::: ::: ::: PATIENTS ::: 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. ::: ::: ::: METHODS ::: The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. ::: ::: ::: RESULTS ::: To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. ::: ::: ::: CONCLUSION ::: The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.


title: SoRec: social recommendation using probabilistic matrix factorization

abstract: Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system confronts. Many existing approaches to recommender systems can neither handle very large datasets nor easily deal with users who have made very few ratings or even none at all. Moreover, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the social interactions or connections among users. In view of the exponential growth of information generated by online social networks, social network analysis is becoming important for many Web applications. Following the intuition that a person's social network will affect personal behaviors on the Web, this paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results shows that our method performs much better than the state-of-the-art approaches, especially in the circumstance that users have made few or no ratings.