title: Pad: an alternative approach to the computer interface

abstract: We believe that navigation in information spaces is best supported by tapping into our natural spatial and geographic ways of thinking. To this end, we are developing a new computer interface model called Pad. The ongoing Pad project uses a spatial metaphor for computer interface design. It provides an intuitive base for the support of such applications as electronic marketplaces, information services, and on-line collaboration. Pad is an infinite two dimensional information plane that is shared among users, much as a network file system is shared. Objects are organized geographically; every object occupies a well defined region on the Pad surface. For navigation, Pad uses “portals” - magnifying glasses that can peer into and roam over different parts of this single infinite shared desktop; links to specific items are established and broken continually as the portal’s view changes. Portals can recursively look onto other portals. This paradigm enables the sort of peripheral activity generally found in real physical working environments. The apparent size of an object to any user determines the amount of detail it presents. Different users can share and view multiple applications while assigning each a desired degree of interaction. Documents can be visually nested and zoomed as they move back and forth between primary and secondary working attention. Things can be peripherally accessible. In this paper we describe the Pad interface. We discuss how to efficiently implement its graphical aspects, and we illustrate some of our initial applications.


title: A miniature surface tension-driven robot mimicking the water-surface locomotion of water strider

abstract: Aiming at mimicking water strider's water-surface locomotion, this study proposes a new miniature surface tension-driven robot. A key feature of this robot is that its actuating legs possess ellipse-like spatial trajectories like water strider by using a cam-link mechanism, and never pierces water surface when rowing. A set of simple models and equations are proposed to analyze the interaction forces between leg and water as well as the critical condition for a leg penetrating a water surface. The final fabricated robot weights about 3.9 g with a load capacity of 5.6 g. By controlling the motions of actuating legs, the robot can freely and stably walk on water with different gaits. The maximum forward and turning speeds of the robot are measured as 16 cm/s and 23 °/s, respectively. Moreover, a similarity analysis with Bond Number and Weber Number reveals that the locomotion of this robot is quite analogous to that of a water strider: surface tension force dominates the lifting force and plays a major role in the propulsion.


title: Two years of short URLs internet measurement: security threats and countermeasures

abstract: URL shortening services have become extremely popular. However, it is still unclear whether they are an effective and reliable tool that can be leveraged to hide malicious URLs, and to what extent these abuses can impact the end users. With these questions in mind, we first analyzed existing countermeasures adopted by popular shortening services. Surprisingly, we found such countermeasures to be ineffective and trivial to bypass. This first measurement motivated us to proceed further with a large-scale collection of the HTTP interactions that originate when web users access live pages that contain short URLs. To this end, we monitored 622 distinct URL shortening services between March 2010 and April 2012, and collected 24,953,881 distinct short URLs. With this large dataset, we studied the abuse of short URLs. Despite short URLs are a significant, new security risk, in accordance with the reports resulting from the observation of the overall phishing and spamming activity, we found that only a relatively small fraction of users ever encountered malicious short URLs. Interestingly, during the second year of measurement, we noticed an increased percentage of short URLs being abused for drive-by download campaigns and a decreased percentage of short URLs being abused for spam campaigns. In addition to these security-related findings, our unique monitoring infrastructure and large dataset allowed us to complement previous research on short URLs and analyze these web services from the user's perspective.


title: Stator current and power factor optimization in an IPMSM for railway traction application

abstract: The objective of this paper is to compare two control strategies aimed at optimizing stator current and power factor in the constant torque operation zone on railway traction applications when interior permanent magnet synchronous machines (IPMSM) are used. Both control strategies are the classical “zero d-current”, which is used in most of industrial applications, and the Most Torque per Amp (MTPA) technique, which optimizes the torque with the lowest current. A simulation study has been carried out with different saliency interior permanent magnet syncronous machines, in order to demonstrate that MTPA control strategy allows stator current and power factor optimization, specially if high saliency IPMSM are used. Finally, experimental results with a full-scale 100kW prototype have been carried out, confirming the study fulfilled in simulation.


title: QUENN: QUantization engine for low-power neural networks

abstract: Deep Learning is moving to edge devices, ushering in a new age of distributed Artificial Intelligence (AI). The high demand of computational resources required by deep neural networks may be alleviated by approximate computing techniques, and most notably reduced-precision arithmetic with coarsely quantized numerical representations. In this context, Bonseyes comes in as an initiative to enable stakeholders to bring AI to low-power and autonomous environments such as: Automotive, Medical Healthcare and Consumer Electronics. To achieve this, we introduce LPDNN, a framework for optimized deployment of Deep Neural Networks on heterogeneous embedded devices. In this work, we detail the quantization engine that is integrated in LPDNN. The engine depends on a fine-grained workflow which enables a Neural Network Design Exploration and a sensitivity analysis of each layer for quantization. We demonstrate the engine with a case study on Alexnet and VGG16 for three different techniques for direct quantization: standard fixed-point, dynamic fixed-point and k-means clustering, and demonstrate the potential of the latter. We argue that using a Gaussian quantizer with k-means clustering can achieve better performance than linear quantizers. Without retraining, we achieve over 55.64\% saving for weights' storage and 69.17\% for run-time memory accesses with less than 1\% drop in top5 accuracy in Imagenet.


title: Simulation of LoRa in NS-3: Improving LoRa Performance with CSMA

abstract: In this paper, we consider two research issues. First, we present a NS-3 module that simulates the behavior of LoRa in an accurate way. To validate the module, we compare its results with measurements on a real-world testbed and with measured values reported by other work. We also show that the module correctly represents the capture effect that lowers the packet drop rate due to collisions. Second, we want to improve the performance of LoRa devices while not impacting energy consumption, the aspect that usually is not taken into account in the literature. We use the simulator to evaluate CSMA, a simple enhancement to LoRaWAN that lowers the collision ratio. The simulation results show that CSMA considerably lowers the collision ratio while only slightly increasing energy consumption. We also observe that CSMA presents lower energy consumption than LoRa for a large number of devices. Another advantage of CSMA consists of increased throughput and larger network capacity because the ETSI restrictions on the duty cycle do not longer apply.


title: Toward Blockchain-Based Accounting and Assurance.

abstract: ABSTRACT Since 2009, blockchain has served as a potentially transformative information technology expected to be as revolutionary as the Internet. Originally developed as a methodology to record cryptocurrency transactions, blockchain's functionality has evolved into a large number of applications, such as banking, financial markets, insurance, voting systems, leasing contracts, and government service. Despite such advancements, the application of blockchain to accounting and assurance remains under-explored. This paper aims to provide an initial discussion on how blockchain could enable a real-time, verifiable, and transparent accounting ecosystem. Additionally, blockchain has the potential to transform current auditing practices, resulting in a more precise and timely automatic assurance system.


title: Magnetic Adder Based on Racetrack Memory

abstract: The miniaturization of integrated circuits based on complementary metal oxide semiconductor (CMOS) technology meets a significant slowdown in this decade due to several technological and scientific difficulties. Spintronic devices such as magnetic tunnel junction (MTJ) nanopillar become one of the most promising candidates for the next generation of memory and logic chips thanks to their non-volatility, infinite endurance, and high density. A magnetic processor based on spintronic devices is then expected to overcome the issue of increasing standby power due to leakage currents and high dynamic power dedicated to data moving. For the purpose of fabricating such a non-volatile magnetic processor, a new design of multi-bit magnetic adder (MA)-the basic element of arithmetic/logic unit for any processor-whose input and output data are stored in perpendicular magnetic anisotropy (PMA) domain wall (DW) racetrack memory (RM)-is presented in this paper. The proposed multi-bit MA circuit promises nearly zero standby power, instant ON/OFF capability, and smaller die area. By using an accurate racetrack memory spice model, we validated this design and simulated its performance such as speed, power and area, etc.


title: Shifts in selective visual attention: towards the underlying neural circuitry

abstract: A number of psychophysical studies concerning the detection, localization and recognition of objects in the visual field have suggested a two-stage theory of human visual perception. The first stage is the “preattentive” mode, in which simple features are processed rapidly and in parallel over the entire visual field. In the second, “attentive” mode, a specialized processing focus, usually called the focus of attention, is directed to particular locations in the visual field. The analysis of complex forms and the recognition of objects are associated with this second stage.1 The computational justification for such a hypothesis comes from the realization that while it is possible to imagine specific algorithms performing tasks such as shape analysis and recognition at specific locations, it is difficult to imagine these algorithms operating in parallel over the whole visual scene, since such an approach will quickly lead to a combinatorial explosion in terms of required computational resources.2 This is essentially the major critique of Minsky and Papert to a universal application of perceptrons in visual perception.3 Taken together, these empirical and theoretical studies suggest that beyond a certain preprocessing stage, the analysis of visual information proceeds in a sequence of operations, each one applied to a selected location (or locations).


title: Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature

abstract: Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.


title: Computational Sprinting: Architecture, Dynamics, and Strategies

abstract: Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips’ thermal limits and the rack’s power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4--6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.


title: HanGuard: SDN-driven protection of smart home WiFi devices from malicious mobile apps

abstract: A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). As verified in our study, those systems tend to rely on the Wi-Fi router to authenticate other devices. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called HanGuard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. HanGuard uses an SDN-like approach to offer fine-grained protection: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. We implemented our design over both Android and iOS (> 95% of mobile OS market share) and a popular router. Our study shows that HanGuard is both efficient and effective in practice.


INTRODUCTION Taking the Mystery out of the Infamous Formula, "SNR = 6.02N + 1.76dB, " and Why You Should Care


title: Android Root and its Providers: A Double-Edged Sword

abstract: Android root is the voluntary and legitimate process of gaining the highest privilege and full control over a user's Android device. To facilitate the popular demand, a unique Android root ecosystem has formed where a variety of root providers begin to offer root as a service. Even though legitimate, many convenient one-click root methods operate by exploiting vulnerabilities in the Android system. If not carefully controlled, such exploits can be abused by malware author to gain unauthorized root privilege. To understand such risks, we undertake a study on a number of popular yet mysterious Android root providers focusing on 1) if their exploits are adequately protected. 2) the relationship between their proprietary exploits and publicly available ones. We find that even though protections are usually employed, the effort is substantially undermined by a few systematic and sometimes obvious weaknesses we discover. From one large provider, we are able to extract more than 160 exploit binaries that are well-engineered and up-to date, corresponding to more than 50 families, exceeding the number of exploits we can find publicly. We are able to identify at least 10 device driver exploits that are never reported in the public. Besides, for a popular kernel vulnerability (futex bug), the provider has engineered 89 variants to cover devices with different Android versions and configurations. Even worse, we find few of the exploit binaries can be detected by mobile antivirus software.


title: Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit

abstract: Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography—distance decay, structured variation in population density, mental maps, and “the Big Sort” (spatial homophily)—manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.


title: Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

abstract: This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks. HighlightsTwo statistical techniques are proposed for outlier detection in point cloud data.The proposed methods can fit robust plane in laser scanning data.The proposed methods produce robust normal and curvature in point cloud processing.They are faster and robust than RANSAC, robust PCA and other existing efficient methods.They have potential for point cloud denoising, segmentation, and reconstruction.


title: Implicit Learning of Common Sense for Reasoning

abstract: We consider the problem of how enormous databases of "common sense" knowledge can be both learned and utilized in reasoning in a computationally efficient manner. We propose that this is possible if the learning only occurs implicitly, i.e., without generating an explicit representation. We show that it is feasible to invoke such implicitly learned knowledge in essentially all natural tractable reasoning problems. This implicit learning also turns out to be provably robust to occasional counterexamples, as appropriate for such common sense knowledge.


title: Automatically categorizing written texts by author gender

abstract: The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80 per cent accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98 per cent accuracy.


title: Models for Intercultural Collaboration and Negotiation

abstract: This bookis the first to bring together research material from different communities, Computer Science and especially Artificial Intelligence, and Social Sciences, e.g. Anthropology, Social Psychology, Political Science that present ideas and viewpoints, methods and models on inter-cultural collaboration and negotiation. With increasing globalization of business and science, cultural differences of the parties are an important factor that affects the process and outcomes of collaborative and self-interested interactions. The social science literature on culture as well as human collaboration and negotiation is vast. Most of this literature is devoted to work within the same culture. Artificial intelligence researchers, on the other hand, have developed computational models of cooperation, conflict resolution and negotiation, but paying almost no attention to identifying and modeling cultural factors. In recent years, we have witnessed a great increase in interest in understanding inter-cultural interactions. This has led to increased interest of social scientists and computational scientists in theoretical and experimental analysis of inter-cultural exchanges, modeling and support. Currently, these communities are largely unconnected. There is a great need to bring them together to share research work and experiences, discuss ideas and forge interdisciplinary collaborative relations. This book will be of interest to researchers from AI/computer science and social/behavioral sciences fields, such as psychology, sociology, communications, organizational science.


title: Abnormal driving detection using real time Global Positioning System data

abstract: Driving speed and vehicle movement are important factors in road safety. Abnormal driver generally drives above speed limit, changes speed suddenly or changes vehicle lateral position incessantly. One approach to tackling this problem is to develop automated instrument for detecting abnormal drivers. Global Position System (GPS) provides special features which can be utilized for this application as it could supplies location, time, and speed simultaneously. In-vehicle transponders send real time GPS data via radio frequency to a Monitoring Station for processing, and from detection algorithm, abnormal situation will be detected automatically. The normal speed parameter and lateral position changing obtained from our previous study can be used as threshold value in detection algorithm. If the driver behavior is evidently different from normal driving, then abnormal driving is recognized. Experiments and simulated data were used to verify the effectiveness of the detection algorithm.


An Approach to Extract Product Features from Chinese Consumer Reviews and Establish Product Feature Structure Tree


title: Evaluation of Age Related Changes in Cervical Spine in Saudi Arabian Adult Population: Using CT Scan Images

abstract: Purpose: Cervical spine is considered to be the most moving part of vertebral column. It is located in a very important part of the body i.e. neck having some very important structures related to it. Any age related changes occurring in such a part of vertebral column may lead to many disorders and disabilities. Complete familiarity of this entire region anatomically as well as radiologically is therefore of utmost importance for the diagnosis and management of such disorders. Aim of this work is to evaluate the frequency of age related degeneration in cervical spine in Saudi adult asymptomatic subjects using CT scan images. Methods: In this study, 105 cases of symptomless adults ranging between 18 - 90 years of age were included. The cases were classified into 3 groups; adult group (18 - 35 years old), middle age group (36 - 55 years old) and old age group (56 - 90 years old). Their CT scans were performed in the department of radiology, King Khalid Hospital, Al Kharj and studied for any age related changes. Results: The first age group category included 35 cases; 20 had normal and 15 degenerative changes. The second category included 35 cases; 3 had normal and 32 degenerative changes. The third category included 35 cases. Single had normal and 34 degenerative changes. Conclusion: Asymptomatic degenerative changes are common in the cervical spine after 30 years of age in Saudi adult asymptomatic subjects.


title: High-rate ferrous iron oxidation by immobilized Acidithiobacillus ferrooxidans with complex of PVA and sodium alginate

abstract: Abstract By four different methods, Acidithiobacillus ferrooxidans cells were immobilized by the complex of PVA and sodium alginate. The beads formed by these different methods were evaluated in terms of relative mechanical strength, biological activity, dilatability, and so on. The results indicate that the technique utilizing the complex of PVA and sodium alginate crosslinked with Ca(NO 3 ) 2 is more appropriate for the immobilization of A. ferrooxidans than any others. So the PVA–calcium nitrate beads were used in batch and continuous culture. A maximum ferrous iron oxidation rate of 4.6 g/l/h was achieved in batch culture. Long-time performance of packed-bed bioreactor was evaluated systematically over 40 days, depending on the conversion ratio of ferrous iron and the residence time. At a residence time of 2.5 h, 96% of the initial ferrous iron was oxidized. This study shows this new immobilization technique will be a feasible and economical method for A. ferrooxidans .


title: Compact Quad-Mode Planar Phased Array With Wideband for 5G Mobile Terminals

abstract: In this paper, a quad-mode endfire planar phased antenna array with wide scan angle and 1.2 mm clearance is proposed for 5G mobile terminals. The proposed antenna can obtain over 8 GHz of impedance bandwidth. In this paper, it is suggested to efficiently combine a multimode array element with different radiation patterns for each mode into a phased antenna array. In the array, similar and wide embedded radiation patterns are obtained for the all four modes. Furthermore, a coaxial to differential stripline transition is designed in this paper. The differential feeding structure is very compact and utilizes only MMPX connector and vias. The total scan pattern and coverage efficiency of the measured and simulated phased array antenna are calculated in the range from 25 to 33 GHz, and good agreement between measured and simulated results is observed. The mean coverage efficiency along the frequency range is very similar, but minor difference in variance of coverage efficiency is observed in the measurements. The coverage efficiency of around 50% for the threshold gain of 5 dBi is achieved in the chosen frequency range.


title: Structured Deep Neural Network Pruning via Matrix Pivoting

abstract: Deep Neural Networks (DNNs) are the key to the state-of-the-art machine vision, sensor fusion and audio/video signal processing. Unfortunately, their computation complexity and tight resource constraints on the Edge make them hard to leverage on mobile, embedded and IoT devices. Due to great diversity of Edge devices, DNN designers have to take into account the hardware platform and application requirements during network training. In this work we introduce pruning via matrix pivoting as a way to improve network pruning by compromising between the design flexibility of architecture-oblivious and performance efficiency of architecture-aware pruning, the two dominant techniques for obtaining resource-efficient DNNs. We also describe local and global network optimization techniques for efficient implementation of the resulting pruned networks. In combination, the proposed pruning and implementation result in close to linear speed up with the reduction of network coefficients during pruning.


title: Automatic myonuclear detection in isolated single muscle fibers using robust ellipse fitting and sparse representation

abstract: Accurate and robust detection of myonuclei in isolated single muscle fibers is required to calculate myonuclear domain size. However, this task is challenging because: 1) shape and size variations of the nuclei, 2) overlapping nuclear clumps, and 3) multiple z-stack images with out-of-focus regions. In this paper, we have proposed a novel automatic detection algorithm to robustly quantify myonuclei in isolated single skeletal muscle fibers. The original z-stack images are first converted into one all-in-focus image using multi-focus image fusion. A sufficient number of ellipse fitting hypotheses are then generated from the myonuclei contour segments using heteroscedastic errors-in-variables (HEIV) regression. A set of representative training samples and a set of discriminative features are selected by a two-stage sparse model. The selected samples with representative features are utilized to train a classifier to select the best candidates. A modified inner geodesic distance based mean-shift clustering algorithm is used to produce the final nuclei detection results. The proposed method was extensively tested using 42 sets of z-stack images containing over 1,500 myonuclei. The method demonstrates excellent results that are better than current state-of-the-art approaches.


title: Segmenting overlapping cervical cell in Pap smear images

abstract: Accurate segmentation of cervical cells in Pap smear images is an important task for automatic identification of pre-cancerous changes in the uterine cervix. One of the major segmentation challenges is the overlapping of cytoplasm, which was less addressed by previous studies. In this paper, we propose a learning-based method to tackle the overlapping issue with robust shape priors by segmenting individual cell in Pap smear images. Specifically, we first define the problem as a discrete labeling task for multiple cells with a suitable cost function. We then use the coarse labeling result to initialize our dynamic multiple-template deformation model for further boundary refinement on each cell. Multiple-scale deep convolutional networks are adopted to learn the diverse cell appearance features. Also, we incorporate high level shape information to guide segmentation where the cells boundary is noisy or lost due to touching and overlapping cells. We evaluate the proposed algorithm on two different datasets, and our comparative experiments demonstrate the promising performance of the proposed method in terms of segmentation accuracy.


title: RGB-D-based Human Motion Recognition with Deep Learning: A Survey

abstract: Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and multi-modal-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research.


title: Automatic restoration of polygon models

abstract: We present a fully automatic technique which converts an inconsistent input mesh into an output mesh that is guaranteed to be a clean and consistent mesh representing the closed manifold surface of a solid object. The algorithm removes all typical mesh artifacts such as degenerate triangles, incompatible face orientation, non-manifold vertices and edges, overlapping and penetrating polygons, internal redundant geometry, as well as gaps and holes up to a user-defined maximum size ρ. Moreover, the output mesh always stays within a prescribed tolerance e to the input mesh. Due to the effective use of a hierarchical octree data structure, the algorithm achieves high voxel resolution (up to 40963 on a 2GB PC) and processing times of just a few minutes for moderately complex objects. We demonstrate our technique on various architectural CAD models to show its robustness and reliability.


title: Full autonomous navigation for an aerial robot using behavior-based control motion and SLAM

abstract: This work presents a complete navigation architecture for an autonomous aerial robot. The proposed scheme consist of: i) a low-level controller for establishing the attitude and position of the vehicle, ii) a Simultaneous Localization and Mapping (SLAM) system, based in bearing (angular) measurements, which gives the robot the ability for navigating in unknown environments, and iii) a high-level motion control system which generates online trajectories. The high-level motion control system (MCS), which represents the main contribution of this work, is inspired by the behavior-based control strategies. The MCS takes as input a very high level mission target (e.g. “explore as much as you can”) and generates online trajectories according to the mission, but at the same time minimizing uncertainty in the estimations in order to maintain the integrity of the robot. The proposed architecture is explained for simplified 3DOF dynamics, but it could be extended in a straightforward manner in order to be applied to full dynamics. Several simulations are included in order to show the performance of the proposed scheme.


title: Global Requirements for DNA Sample Collections: Results of a Survey of 204 Ethics Committees in 40 Countries

abstract: The Industry Pharmacogenomics Working Group has an interest in attaining a better understanding of global requirements for sample collections intended for pharmacogenetics research. To have adequately powered pharmacogenetics studies representative of the clinical trial population, it is important to collect DNA samples from a majority of consenting study participants under many institutional review board/ethics committee (IRB/EC) jurisdictions. A survey was distributed to gather information from local and central IRBs/ECs. The survey included questions related to the approval of pharmacogenetics studies, collection and banking of samples, and return of data to subjects. A total of 204 responses were received from global IRBs/ECs with pharmacogenetic experience. The data show that requirements for approval of pharmacogenetic research differ between IRBs/ECs within and between countries but not between regions of the United States. A better understanding of differing requirements should facilitate global sample collection of DNA for pharmacogenetics research and may provide the basis for harmonized regulations for collection of genetic samples in the future. ::: ::: ::: ::: Clinical Pharmacology & Therapeutics (2011) 89 4, 554–561. doi:10.1038/clpt.2010.319


title: Improving Bag-of-Visual-Words model using visual n-grams for human action classification

abstract: Abstract The Bag-of-Visual-Words model has emerged as an effective approach to represent local video features for human actions classification. However, one of the major challenges in this model is the generation of the visual vocabulary. In the case of human action recognition, losing spatial-temporal relationships is one of the important reasons that provokes the low descriptive power of classic visual words. In this work we propose a three-level approach to construct visual n-grams for human action classification. First, in order to reduce the number of non-descriptive words generated by K-means clustering of the spatio-temporal interest points, we propose to apply a variant of the classical Leader-Follower clustering algorithm to create an optimal vocabulary from a pre-established number of visual words. Second, with the aim of incorporating spatial and temporal constraints to the Bag-of-Visual-Words model, we exploit the spatio-temporal relationships between interest points to build a graph-based representation of the video. Frequent subgraphs are extracted for each action class and a visual vocabulary of n-grams is constructed from the labels (descriptors) of selected subgraphs. Finally, we build a histogram by using the frequency of each n-gram in the graph representing a video of human action. The proposed approach combines the representational power of graphs with the efficiency of the Bag-of-Visual-Words model. Extensive validation on five challenging human actions datasets demonstrates the effectiveness of the proposed model compared to state-of-the-art methods.


Designing a Program Language for Home Automation


title: An efficient and reliable reservation algorithm for mobile transactions

abstract: In a mobile computing environment, a user carrying a portable computer can execute a mobile t11m,,· action by submitting the ope.rations of the transaction to distributed data servers from different locations. M a result of this mobility, the operations of the transaction may be executed at different servers. The distribution oC operations implies that the transmission of messages (such as those involved in a two phase commit protocol) may be required among these data servers in order to coordinate the execution ofthese operations. In this paper, we will address the distribution oC operations that update partitioned data in mobile environments. We show that, for operations pertaining to resource allocation, the message overhead (e.g., for a 2PC protocol) introduced by the distribution of operations is undesirable and unnecessary. We introduce a new algorithm, the RenlnJation Algorithm (RA), that does not necessitate the incurring of message overheads Cor the commitment of mobile transactions. We address two issues related to the RA algorithm: a termination protocol and a protocol for non_partition.commutotive operation". We perform a comparison between the proposed RA algorithm and existing solutions that use a 2PC protocol.


title: On the Euclidean distance of images

abstract: We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantage of this distance measure is that it can be embedded in most image classification techniques such as SVM, LDA, and PCA. The embedding is rather efficient by involving a transformation referred to as Standardizing Transform (ST). We show that ST is a transform domain smoothing. Using the Face Recognition Technology (FERET) database and two state-of-the-art face identification algorithms, we demonstrate a consistent performance improvement of the algorithms embedded with the new metric over their original versions.


title: A benchmark image database of isolated Bangla handwritten compound characters

abstract: In the present work, we present a benchmark image database of isolated handwritten Bangla compound characters, used in the standard Bangla literature. A thorough survey over more than 2 million Bangla words has revealed that there exist around 334 compound characters in Bangla script. Of which, only around 171 character classes form unique pattern shapes, and some of these classes are often written in multiple styles. Altogether, 55,278 isolated character images, belonging to 199 different pattern shapes, are collected using three different data collection modalities. The database is divided into training and test sets in 4:1 ratio for each pattern class, by considering a balanced distribution of shapes from different modalities. A convex hull and quadtree-based feature set has been designed, and the test set recognition performance is reported with the support vector machine classifier. We have achieved a recognition accuracy of 79.35 % on the test database consisting of 171 character classes. The complete compound character image database is freely available as CMATERdb 3.1.3.3 from the website http://code.google.com/p/cmaterdb/ , which may facilitate research on handwritten character recognition, especially related to Bangla form document processing systems.


title: Permaculture for agroecology: design, movement, practice, and worldview. A review

abstract: Agroecology is a promising alternative to industrial agriculture, with the potential to avoid the negative social and ecological consequences of input-intensive production. Transitioning to agroecological production is, however, a complex project that requires diverse contributions from the outside of scientific institutions. Agroecologists therefore collaborate with traditional producers and agroecological movements. Permaculture is one such agroecological movement, with a broad international distribution and a unique approach to system design. Despite a high public profile, permaculture has remained relatively isolated from scientific research. Though the potential contribution of permaculture to agroecological transition is great, it is limited by this isolation from science, as well as from oversimplifying claims, and the lack of a clear definition. Here, we review scientific and popular permaculture literature. A systematic review discusses quantitative bibliometric data, including keyword analysis. A qualitative review identifies and assesses major themes, proposals, and claims. The manuscript follows a stratified definition of permaculture as design system, best practice framework, worldview, and movement. The major points of our analysis are as follows: (1) Principles and topics largely complement and even extend principles and topics found in the agroecological literature. (2) Distinctive approaches to perennial polyculture, water management, and the importance of agroecosystem configuration exceed what is documented in the scientific literature and thus suggest promising avenues of inquiry. (3) Discussions of practice consistently underplay the complexity, challenges, and risks that producers face in developing diversified and integrated production systems. (4) The movement is mobilizing diverse forms of social support for sustainability, in geographically diverse locations. (5) And scholarship in permaculture has always been a diverse marginal sector, but is growing.


title: Judgments of genuine, suppressed, and faked facial expressions of pain

abstract: The process of discriminating among genuine, suppressed, and faked expressions of pain was examined. Untrained judges estimated the severity of pain being experienced when viewing video-taped facial expressions of chronic pain patients undergoing a painful iagnostic testor dissimulating reactions. Verbal feedback as to whether pain was experienced also was provided, so as to be either consistent or inconsistent with the facial expression. Judges were able to distinguish genuine pain faces from baseline expressions but, relative to genuine pain faces, attributed more pain to faked faces and less pain to suppressed ones


title: CONGENITAL DISORDERS OF GLYCOSYLATION

abstract: Congenital disorders of glycosylation (CDG) are a rapidly growing group of genetic diseases that are due to defects in the synthesis of glycans and in the attachment of glycans to other compounds. Most CDG are multisystem diseases that include severe brain involvement. The CDG causing sialic acid deficiency of N-glycans can be diagnosed by isoelectrofocusing of serum sialotransferrins. An efficient treatment, namely oral D-mannose, is available for only one CDG (CDG-Ib). In many patients with CDG, the basic defect is unknown (CDG-x). Glycan structural analysis, yeast genetics, and knockout animal models are essential tools in the elucidation of novel CDG. Eleven primary genetic glycosylation diseases have been discovered and their basic defects identified: six in the N-glycan assembly, three in the N-glycan processing, and two in the O-glycan (glycosaminoglycan) assembly. This review summarizes their clinical, biochemical, and genetic characteristics and speculates on further developments in this field.


End-to-End Differentiable Adversarial Imitation Learning


title: FANET Based Flights Monitoring Simulation System Over Cloud

abstract: As mounting population and need of advance commutes systems, available vehicular ad hoc networks (VANET) systems will not suffice the communication complex issues between vehicles on land, water and air. In order to meet ever growing challenges, VANET cloud computing techniques start contributing with its supple state line less storage, high end virtual techniques and logical schema connectivity to make commute rich, safe and pleasure experience to users. In this paper, we proposed an architectural model for flight monitoring simulation, which optimizes the scheduling of flights in the most prominent manner by centralizing all resources at single cloud which optimizes the time management of effective communication among resources. This model will be an immense resource not only for commercial flights but also for air force navigation systems. VANET existing systems were designed for vehicular networks while our proposed solution flying ad hoc networks (FANET) or Flights Air Networks adapts VANET approach on high speed air traffic, FANET may use electromagnetic waves for data transfer between flights as electromagnetic waves speed is much higher than flights speeds, it may ensure timely communication between flights and other communicating sources.


Research on the Evaluation Model of a Smart Grid Development Level Based on Differentiation of Development Demand


title: Knowledge Based Radar Detection, Tracking and Classification

abstract: Contributors. 1. Introduction (Fulvio Gini and Muralidhar Rangaswamy). 2. Cognitive Radar (Simon Haykin) 3. Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview (Gerard T. Capraro, Alfonso Farina, Hugh D. Griffiths, and Michael C. Wicks). 4. An Overview of Knowledge-Aided Adaptive Radar at DARPA and Beyond (Joseph R. Guerci and Edward J. Baranoski). 5. Space-Time Adaptive Processing for Airborne Radar: A Knowledge-Based Perspective (Michael C. Wicks, Muralidhar Rangaswamy, Raviraj S. Adve, and Todd B. Hale). 6. CFAR Knowledge-Aided Radar Detection and its Demonstration Using Measured Airborne Data (Christopher T. Capraro, Gerard T. Capraro, Antonio De Maio, Alfonso Farina, and Michael C. Wicks). 7. STAP via Knowledge-Aided Covariance Estimation and the FRACA Meta-Algorithms (Shannon D. Blunt, Karl Gerlach, Muralidhar Rangaswamy, and Aaron K. Shackelford). 8. Knowledge-Based Radar Tracking (Alessio Benavoli, Luigi Chisci, Alfonso Farina, Sandro Immediata, and Luca Timmoneri). 9. Knowledge-Based Radar Target Classification (Igal Bilik and Joseph Tabrikian). 10. Multifunction Radar Resource Management (Sergio Luis de Carvalho Miranda, Chris J. Baker, Karl Woodbridge and Hugh D. Griffiths). Index.


title: A Diffusion Framework for Dimensionality Reduction

abstract: Many fields of research deal with high-dimensional data sets. Hyperspectral images in remote sensing and in hyper-spectral microscopy, transactions in banking monitoring systems are just a few examples for this type of sets. Revealing the geometric structure of these data-sets as a preliminary step facilitates their efficient processing. Often, only a small number of parameters govern the structure of the data-set. This number is the true dimension of the data-set and is the motivation to reduce the dimensionality of the set. Dimensionality reduction algorithms try to discover the true dimension of a data set. In this chapter, we describe a natural framework based on diffusion processes for the multi-scale analysis of high-dimensional data-sets (Coifman and Lafon, 2006). This scheme enables us to describe the geometric structures of such sets by utilizing the Newtonian paradigm according to which a global description of a system can be derived by the aggregation of local transitions. Specifically, a Markov process is used to describe a random walk on the data set. The spectral properties of the Markov matrix that is associated with this process are used to embed the data-set in a low-dimensional space. This scheme also facilitates the parametrization of a data-set when the high dimensional data-set is not accessible and only a pair-wise similarity matrix is at hand.


title: Universal Approximation to Nonlinear Operators by Neural Networks with Arbitrary Activation Functions and Its Application to Dynamical Systems

abstract: The purpose of this paper is to investigate neural network capability systematically. The main results are: 1) every Tauber-Wiener function is qualified as an activation function in the hidden layer of a three-layered neural network; 2) for a continuous function in S'(R/sup 1/) to be a Tauber-Wiener function, the necessary and sufficient condition is that it is not a polynomial; 3) the capability of approximating nonlinear functionals defined on some compact set of a Banach space and nonlinear operators has been shown; and 4) the possibility by neural computation to approximate the output as a whole (not at a fixed point) of a dynamical system, thus identifying the system. >


title: Effect of a 10-Week Traditional Dance Program on Static and Dynamic Balance Control in Elderly Adults

abstract: This preliminary study examined the effect of a 10-wk traditional Greek dance program on static and dynamic balance indices in healthy elderly adults. Twenty-six community-dwelling older adults were randomly assigned to either an intervention group who took supervised Greek traditional dance classes for 10 wk (1 hr, 2 sessions/week, n = 14), or a control group (n = 12). Balance was assessed pre- and postintervention by recording the center-of-pressure (COP) variations and trunk kinematics during performance of the Sharpened-Romberg test, 1-leg (OL) stance, and dynamic weight shifting (WS). After practice, the dance group significantly decreased COP displacement and trunk sway in OL stance. A significant increase in the range of trunk rotation was noted during performance of dynamic WS in the sagittal and frontal planes. These findings support the use of traditional dance as an effective means of physical activity for improving static and dynamic balance control in the elderly.


title: A building block approach to sensornet systems

abstract: We present a building block approach to hardware platform design based on a decade of collective experience in this area, arriving at an architecture in which general-purpose modules that require expertise to de sign and incorporate commonly-used functionality are integrated with application-specific carriers that satisfy the unique sensing, power supply, and mechanical constraints of an application. Of course, modules are widespread, but our focus is far less on the performance of any individual module and far more on an overall architecture that supports the prototype, pilot, and production stages of design, and preserves the artifacts and learnings accumulated along the way. We present heuristics for partitioning functionality between modules and carriers, and identify guidelines for their interconnection. Our approach advocates exporting a wide electrical interface, eliminating the system bus, and supporting many physical interconnect options for modules and carriers. We evaluate this approach by constructing a family of general-purpose modules and application-specific carriers that achieve a high degree of reuse despite very different application requirements. We show that this approach shortens platform development time-to-result for novice graduate students, making custom platforms broadly accessible.


title: Learning Algorithms for Keyphrase Extraction

abstract: Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.


Csp-NN: a Convolutional Neural Network Implementation of Common Spatial Patterns.


title: Fully Differential, Regulated Cascode Amplifier

abstract: A fully differential, regulated cascode transimpedance amplifier for applications with high input capacitance is described. It uses a regulated cascode input stage to achieve low input impedance. The input impedance and noise are reduced further by controlling the common-gate stages with the differential input signal


title: Data set for fall events and daily activities from inertial sensors

abstract: Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data sets. This paper presents a description of the data set and the experimental protocols designed by the authors for the simulation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection algorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%.


title: Tabu Search—Part II

abstract: This is the second half of a two part series devoted to the tabu search metastrategy for optimization problems. Part I introduced the fundamental ideas of tabu search as an approach for guiding other heuristics to overcome the limitations of local optimality, both in a deterministic and a probabilistic framework. Part I also reported successful applications from a wide range of settings, in which tabu search frequently made it possible to obtain higher quality solutions than previously obtained with competing strategies, generally with less computational effort. Part II, in this issue, examines refinements and more advanced aspects of tabu search. Following a brief review of notation, Part II introduces new dynamic strategies for managing tabu lists, allowing fuller exploitation of underlying evaluation functions. In turn, the elements of staged search and structured move sets are characterized, which bear on the issue of finiteness. Three ways of applying tabu search to the solution of integer programmin...


title: Vulvovaginitis in childhood.

abstract: Symptoms related to vulvitis and vulvovaginitis are a frequent complaint in the paediatric age. Knowledge of the risk factors and the pathogenetic mechanisms, combined with thorough clinical examination, helps to distinguish between dermatological diseases, non-specific vulvitis and vulvovaginitis proper. On the basis of microbiological data, the most common pathogens prove to be Streptococcus pyogenes, Haemophilus influenzae and Enterobius vermicularis; fungal and viral infections are less frequent. The possibility of isolating opportunistic pathogens should also be considered. In rare situations, the isolation of a micro-organism normally transmitted by sexual contact should prompt a careful evaluation of possible sexual abuse. Current treatments for specific and non-specific forms are outlined, together with pointers for the evaluation of recurrence.


title: Multiple IMU Integration for Vehicular Navigation

abstract: One of the most widespread applications of the Global Positioning System (GPS) is vehicular navigation. Improving the navigation accuracy continues to be a focus of research, commonly answered by the use of additional sensors. A sensor commonly fused with GPS is the inertial measurement unit (IMU). Due to the fact that the requirements of commercial systems are low cost, small size, and power conservative, micro-electro mechanical sensors (MEMS) IMUs are used. They provide navigation capability even in the absence of GPS signals or in the presence of high multipath or jamming. This paper addresses a centralized filter construction whereby navigation solutions from multiple IMUs are fused together to improve accuracy in GPS degraded areas. The proposed filter is a collection of several single IMU block filters. Each block filter is a 21 state IMU filter. Because each block filter estimates position, velocity and attitude, the system can utilize relative updates between the IMUs. These relative updates provide a method of reducing the position drift in the absence of GPS observations. The proposed filter’s performance is analyzed as a function of the number of IMUs used and relative update type, using a data set consisting of GPS outages, urban canyons and residential open sky conditions. While the use of additional IMUs (including a single IMU) provides negligible improvement in open sky conditions (where GPS alone is sufficient), the use of two, three, four and five IMUs provided a horizontal position improvement of 25 %, 29 %, 32 %, and 34 %, respectively, when GPS observations are removed for 30 seconds. Similarly, the velocity RMS improved by 25 %, 31%, 33%, and 34% for two, three, four and five IMUs, respectively. Attitude estimation also improves significantly ranging from 30 % – 76 %. Results also indicate that the use of more IMUs provides the system with better multipath rejection and performance in urban canyons.


title: Semantic Similarity Strategies for Job Title Classification

abstract: Automatic and accurate classification of items enables numerous downstream applications in many domains. These applications can range from faceted browsing of items to product recommendations and big data analytics. In the online recruitment domain, we refer to classifying job ads to pre-defined or custom occupation categories as job title classification. A large-scale job title classification system can power various downstream applications such as semantic search, job recommendations and labor market analytics. In this paper, we discuss experiments conducted to improve our in-house job title classification system. The classification component of the system is composed of a two-stage coarse and fine level classifier cascade that classifies input text such as job title and/or job ads to one of the thousands of job titles in our taxonomy. To improve classification accuracy and effectiveness, we experiment with various semantic representation strategies such as average W2V vectors and document similarity measures such as Word Movers Distance (WMD). Our initial results show an overall improvement in accuracy of Carotene[1].


title: RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation

abstract: Several approaches to web service selection and recommendation via collaborative filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative filtering algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative filtering algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative filtering algorithms.


title: Hybrid Page Segmentation with Efficient Whitespace Rectangles Extraction and Grouping

abstract: Page segmentation is still a challenging problem due to the large variety of document layouts. Methods examining both foreground and background regions are among the most effective to solve this problem. However, their performance is influenced by the implementation of two key steps: the extraction and selection of background regions, and the grouping of background regions into separators. This paper proposes an efficient hybrid method for page segmentation. The method extracts white space rectangles based on connected component analysis, and filters white space rectangles progressively incorporating foreground and background information such that the remaining rectangles are likely to form column separators. Experimental results on the ICDAR2009 page segmentation competition test set demonstrate the effectiveness and superiority of the proposed method.


title: The E2E Dataset: New Challenges For End-to-End Generation

abstract: This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.


title: Digital Crafts: Industrial Technologies for Applied Artists and Designer Makers

abstract: 1. Introduction 2. Craft-Minded 3. Virtual for Tangible 4. Applying 2D Digital Data 5. Applying 3D Digital Data 6. Accessing Digital Technologies Biographies Glossary Resources Further Reading Index


title: Neonatal lung ultrasound exam guidelines

abstract: Point-of-care ultrasound (POC-US) is increasingly used especially in emergency and critical-care medicine. It is focused, quick and does not expose patients to ionizing radiation. It encompasses all organ systems and has well-defined indications. Lung ultrasound (LUS) represents one of the most exciting applications in the field of POC-US. It is particularly important to emphasize the role of LUS in neonatology due to the specific pathology inherent in lung immaturity as well as in the particular sensitivity of neonates to repeated radiation exposure. One of the main barriers to the more extensive use of the ultrasound technology is a lack of efficient and attractive training solutions followed by the structured quality-check assurance. In an effort to help bridge this gap, based on the most current literature, we developed creative and intuitive neonatal LUS algorithms. We hope they can serve as a clinical imaging guidelines and a valuable complement to the history and physical exam.


title: Effect of the Phosphor Permanent Substrate on the Angular CCT for White Thin-Film Flip-Chip Light-Emitting Diodes

abstract: This paper developed a phosphor layer applied for thin-film flip-chip light-emitting diodes (TFFC-LEDs) to produce uniform phosphor-converted TFFC white LEDs (TFFC-WLEDs) by combination of laser liftoff, secondary transferring, and surface roughening process. The spin-coating method was used for phosphor layer fabrication onto a substrate to form the phosphor permanent substrate. The TFFC-LEDs were then bonded onto the permanent substrate. From the results, the blue TFFC GaN-based LED with roughened u-GaN surface on a glass substrate (TFRG-LED) demonstrated a 54.2% (at 350 mA) enhancement in light output power, compared with a blue flip-chip GaN-on-sapphire based LED. As the TFFC GaN/phosphor-/glass-based white LED with roughened u-GaN surface (TFRG-WLED) was operated at a forward-bias current of 350 mA, the enhancement of luminous flux was increased by 75.5%, compared with a TFFC GaN/phosphor template-/glass-based white LED. The angular correlated color temperature (CCT) deviation of a TFRG-WLED can be reduced to 1279 K in the range from −75° to +75° at 5000–6000 K application. The TFRG-WLED was fabricated on the glass substrate with the roughened u-GaN surface and the structure of phosphor layer closed to the u-GaN. These kinds of FRGB-WLED structure contribute to a better light extraction characteristic and a higher CCT stability.


title: Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata

abstract: While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.


title: Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

abstract: Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, profiling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how efficiently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome profiling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations.


title: Uncovering Like-minded Political Communities on Twitter

abstract: Stance detection systems often integrate social clues in their algorithms. While the influence of social groups on stance is known, there is no evaluation of how well state-of-the-art community detection algorithms perform in terms of detecting like-minded communities, i.e. communities that share the same stance on a given subject. We used Twitter's social interactions to compare the results of community detection algorithms on datasets on the Scottish Independence Referendum and US Midterm Elections. Our results show that algorithms relying on information diffusion perform better for this task and confirm previous observations about retweets being better vectors of stance than mentions.


title: Genetic algorithm-neural network : feature extraction for bioinformatics data.

abstract: With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, ::: for both computer and medical scientists, are which genes are significantly involved in discriminating cancer ::: classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the ::: misconception of the objectives of microarray study. Furthermore, the application of various preprocessing ::: techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the ::: integrity of the findings has been compromised by the improper use of techniques and the ill-conceived ::: objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has ::: reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context ::: of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the ::: proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.


title: Motion Adjustment for Extrinsic Calibration of Cameras with Non-overlapping Views

abstract: This paper adresses the issue of calibrating multiple cameras on a mobile platform. Due to decreasing sensor prices and increasing processing performance, the use of multiple cameras in vehicles becomes an attractive possibility for environment perception. To avoid restrictions relating to the camera arrangement, we focus on non-overlapping camera configurations. Hence, we resign the usage of corresponding features between the cameras. The hand-eye calibration technique based on visual odometry is basically able to solve this problem by exploiting the cameras' motions. However, this technique suffers from inaccuracies in motion estimation. Especially the absolute magnitudes of the translational velocities of each camera are essential for a successful calibration. This contribution presents a novel approach to solve the hand eye calibration problem for two cameras on a mobile platform with non-overlapping fields of view. The so-called motion adjustment simultaneously estimates the extrinsic parameters up to scale as well as the reltive motion magnitudes. Results with simulated and real data are presented.


title: Special issue on “The role of networks in entrepreneurial performance: new answers to old questions?”

abstract: The nature and extent of relationships between entrepreneurial networks and entrepreneurial performance are old questions. Scholars have explored the nature of entrepreneurial networks and have focused on their relationship with, and effects on, performance, which is viewed in this special issue in terms of the strategic development of established businesses and new ventures. However, while much is now known about the origins and effects of social networks there continues to a paucity of research on how social networks are used in various organizations, and when one or more networks are drawn upon for what specific purpose. Each article in this special issue addresses one or more of these questions in a range of industries and environments, namely poor village entrepreneurs who have to work in a highly challenging financial and social environment in Bangladesh, “early internationalizing small firms” in South Africa, high technology “early-stage ventures” in Hong Kong, 3-D technology ventures that operate with an “open” business model, and the “multi-rational” nature of networks in family businesses in and beyond the UK. In all, this collection of papers comprises a body of scholarship with fine-grained studies on how and when specific social networks are drawn on in various forms of organization. The subsequent discussion of these issues extends knowledge of the various ways in which entrepreneurs and entrepreneurial businesses advance their interests by leveraging familiar business and social networks.


title: Sketching Uncertainty into Simulations

abstract: In a variety of application areas, the use of simulation steering in decision making is limited at best. Research focusing on this problem suggests that most user interfaces are too complex for the end user. Our goal is to let users create and investigate multiple, alternative scenarios without the need for special simulation expertise. To simplify the specification of parameters, we move from a traditional manipulation of numbers to a sketch-based input approach. Users steer both numeric parameters and parameters with a spatial correspondence by sketching a change onto the rendering. Special visualizations provide immediate visual feedback on how the sketches are transformed into boundary conditions of the simulation models. Since uncertainty with respect to many intertwined parameters plays an important role in planning, we also allow the user to intuitively setup complete value ranges, which are then automatically transformed into ensemble simulations. The interface and the underlying system were developed in collaboration with experts in the field of flood management. The real-world data they have provided has allowed us to construct scenarios used to evaluate the system. These were presented to a variety of flood response personnel, and their feedback is discussed in detail in the paper. The interface was found to be intuitive and relevant, although a certain amount of training might be necessary.


title: Conformation of human fibrinogen in solution from polarized triplet spectroscopy.

abstract: The rotational motions of human fibrinogen in solution at 20 degrees C have been examined, in the 0.2-12-microseconds time range, by measuring the laser-induced dichroism of the triplet state of an erythrosin probe covalently bonded to the protein. The decay of the anisotropy was multiexponential, and up to three correlation times (phi 1 = 380 +/- 50 ns, phi 2 = 1.1 +/- 0.1 microseconds, and phi 3 = 3.3 +/- 0.6 microseconds) were needed to obtain a satisfactory analysis. The experimental data are consistent with the brownian motions of an elongated, rigid particle. If the correlation times are combined with previous data on the intrinsic viscosity of fibrinogen, the rotational and translational diffusive properties of the protein can be reproduced with high accuracy by idealizing it as an elongated ellipsoid of revolution with dimensions (2a x 2b) of (54 +/- 6) x (7.2 +/- 0.5) nm, having rotational diffusion constants of D parallel = (6.2 +/- 0.7) x 10(5) s-1 and D perpendicular = (5 +/- 1) x 10(4) s-1. The possibility of Ca(2+)-dependent changes in the rigidity or conformation of fibrinogen was excluded by examining the submicrosecond time-resolved fluorescence depolarization of 1-methylpyrene conjugates of the protein in the presence of different calcium concentrations. Although there are inherent difficulties to extrapolate the data on isolated fibrinogen molecules to the polymerizing species, this relatively stiff conformation meets the requirements of the classical half-staggered double-stranded model of fibrin polymerization rather better than those of the recently proposed interlocked single-stranded mechanism.


title: The Quest Data Mining System

abstract: The goal of the Quest project at the IBM Almaden Research center is to develop technology to enable a new breed of data-intensive decision-support applications. This paper is a capsule summary of the current functionality and architecture of the Quest data mining System. Our overall approach has been to identify basic data mining operations that cut across applications and develop fast, scalable algorithms for their execution (Agrawal, Imielinski, & Swami 1993a). We wanted our algorithms to:


title: Fault Effects in FlexRay-Based Networks with Hybrid Topology

abstract: This paper investigates fault effects and error propagation in a FlexRay-based network with hybrid topology that includes a bus subnetwork and a star subnetwork. The investigation is based on about 43500 bit-flip fault injection inside different parts of the FlexRay communication controller. To do this, a FlexRay communication controller is modeled by Verilog HDL at the behavioral level. Then, this controller is exploited to setup a FlexRay-based network composed of eight nodes (four nodes in the bus subnetwork and four nodes in the star subnetwork). The faults are injected in a node of the bus subnetwork and a node of the star subnetwork of the hybrid network Then, the faults resulting in the three kinds of errors, namely, content errors, syntax errors and boundary violation errors are characterized. The results of fault injection show that boundary violation errors and content errors are negligibly propagated to the star subnetwork and syntax errors propagation is almost equal in the both bus and star subnetworks. Totally, the percentage of errors propagation in the bus subnetwork is more than the star subnetwork.


Bridging a Conceptual Divide: How Peer Collaboration Facilitates Science Learning.


title: Lowering Data Dimensionality in Big Data for the Benefit of Precision Agriculture

abstract: Abstract Predictive analytics can be used to make smarter decisions in farming by collecting real-time data on weather, soil and air quality, crop maturity and even equipment and labor costs and availability. This is known as precision agriculture. Big data is expected to play an important role in precision agriculture for managing real-time data analysis with massive streaming data. The data analysis efficiency and throughput would be a challenge with the massive increase in size of big data. The unstructured streaming data received from different agricultural sources would contain multiple dimensions and not the entire content is needed for performing analysis. The core data which is small but that alone enough to represent the entire content should be extracted. This paper explains how to systematically reduce the size of big data by applying a tensor based feature reduction model. The data decomposition and core value extraction is done with the help of IHOSVD algorithm. This way it reduces the overall file size by eliminating unwanted data dimensions. The time involved in data analysis and CPU usage will be significantly reduced when dimensionality reduced data is used in place of raw (unprocessed) data.


title: Derivation of high-order compact finite difference schemes for non-uniform grid using polynomial interpolation

abstract: In this paper simple polynomial interpolation is used to derive arbitrarily high-order compact schemes for the first derivative and tridiagonal compact schemes for the second derivative (consisting of three second derivative nodes in the interior and two on the boundary) on non-uniform grids. Boundary and near boundary schemes of the same order as the interior are also developed using polynomial interpolation and for a general compact scheme on a non-uniform grid it is shown that polynomial interpolation is more efficient than the conventional method of undetermined coefficients for finding coefficients of the scheme. The high-order non-uniform schemes along with boundary closure of up to 14th order thus obtained are shown to be stable on a non-uniform grid with appropriate stretching so that more grid points are clustered near the boundary. The stability and resolution properties of the high-order non-uniform grid schemes are studied and the results of three numerical tests on stability and accuracy properties are also presented.


title: Media Multitasking Is Associated with Symptoms of Depression and Social Anxiety

abstract: Abstract We investigated whether multitasking with media was a unique predictor of depression and social anxiety symptoms. Participants (N=318) completed measures of their media use, personality characteristics, depression, and social anxiety. Regression analyses revealed that increased media multitasking was associated with higher depression and social anxiety symptoms, even after controlling for overall media use and the personality traits of neuroticism and extraversion. The unique association between media multitasking and these measures of psychosocial dysfunction suggests that the growing trend of multitasking with media may represent a unique risk factor for mental health problems related to mood and anxiety. Further, the results strongly suggest that future research investigating the impact of media use on mental health needs to consider the role that multitasking with media plays in the relationship.


title: A polyhedron representation for computer vision

abstract: My approach to computer vision is best characterized as inverse computer graphics. In computer graphics, the world is represented in sufficient detail so that the image forming process can be numerically simulated to generate synthetic television images; in the inverse, perceived television pictures (from a real TV camera) are analyzed to compute detailed geometric models.


Key concepts in ELT. Schemas


title: Using CloudSim to learn cloud computing architecture/system concepts in a graduate course

abstract: The recent expansion of the cloud computing paradigm has motivated educators to include cloud-related topics in computer science and computer engineering curricula. While programming and algorithm topics have been covered in different undergraduate and graduate courses, cloud architecture/system topics are still not usually studied in academic contexts. But design, deployment and management of datacenters, virtualization technologies for cloud, cloud management tools and similar issues should be addressed in current computer science and computer engineering programs. This work presents our approach and experiences in designing and implementing a curricular module covering all these topics. In this approach the utilization of a simulation tool, CloudSim, is essential to allow the students a practical approximation to the course contents.


title: Proportional-resonant controllers and filters for grid-connected voltage-source converters

abstract: The recently introduced proportional-resonant (PR) controllers and filters, and their suitability for current/voltage control of grid-connected converters, are described. Using the PR controllers, the converter reference tracking performance can be enhanced and previously known shortcomings associated with conventional PI controllers can be alleviated. These shortcomings include steady-state errors in single-phase systems and the need for synchronous d-q transformation in three-phase systems. Based on similar control theory, PR filters can also be used for generating the harmonic command reference precisely in an active power filter, especially for single-phase systems, where d-q transformation theory is not directly applicable. Another advantage associated with the PR controllers and filters is the possibility of implementing selective harmonic compensation without requiring excessive computational resources. Given these advantages and the belief that PR control will find wide-ranging applications in grid-interfaced converters, PR control theory is revised in detail with a number of practical cases that have been implemented previously, described clearly to give a comprehensive reference on PR control and filtering.


title: Mining Text Data

abstract: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.


title: Common genetic determinants of vitamin D insufficiency: a genome-wide association study

abstract: BACKGROUND ::: Vitamin D is crucial for maintenance of musculoskeletal health, and might also have a role in extraskeletal tissues. Determinants of circulating 25-hydroxyvitamin D concentrations include sun exposure and diet, but high heritability suggests that genetic factors could also play a part. We aimed to identify common genetic variants affecting vitamin D concentrations and risk of insufficiency. ::: ::: ::: METHODS ::: We undertook a genome-wide association study of 25-hydroxyvitamin D concentrations in 33 996 individuals of European descent from 15 cohorts. Five epidemiological cohorts were designated as discovery cohorts (n=16 125), five as in-silico replication cohorts (n=9367), and five as de-novo replication cohorts (n=8504). 25-hydroxyvitamin D concentrations were measured by radioimmunoassay, chemiluminescent assay, ELISA, or mass spectrometry. Vitamin D insufficiency was defined as concentrations lower than 75 nmol/L or 50 nmol/L. We combined results of genome-wide analyses across cohorts using Z-score-weighted meta-analysis. Genotype scores were constructed for confirmed variants. ::: ::: ::: FINDINGS ::: Variants at three loci reached genome-wide significance in discovery cohorts for association with 25-hydroxyvitamin D concentrations, and were confirmed in replication cohorts: 4p12 (overall p=1.9x10(-109) for rs2282679, in GC); 11q12 (p=2.1x10(-27) for rs12785878, near DHCR7); and 11p15 (p=3.3x10(-20) for rs10741657, near CYP2R1). Variants at an additional locus (20q13, CYP24A1) were genome-wide significant in the pooled sample (p=6.0x10(-10) for rs6013897). Participants with a genotype score (combining the three confirmed variants) in the highest quartile were at increased risk of having 25-hydroxyvitamin D concentrations lower than 75 nmol/L (OR 2.47, 95% CI 2.20-2.78, p=2.3x10(-48)) or lower than 50 nmol/L (1.92, 1.70-2.16, p=1.0x10(-26)) compared with those in the lowest quartile. ::: ::: ::: INTERPRETATION ::: Variants near genes involved in cholesterol synthesis, hydroxylation, and vitamin D transport affect vitamin D status. Genetic variation at these loci identifies individuals who have substantially raised risk of vitamin D insufficiency. ::: ::: ::: FUNDING ::: Full funding sources listed at end of paper (see Acknowledgments).


title: A large-scale benchmark dataset for event recognition in surveillance video

abstract: We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.


title: Analysis and design of a series-parallel resonant converter with capacitive output filter

abstract: The objectives are to propose a LCC-type parallel resonant power converter with a capacitive output filter, to present its different operating modes and analyze the converter for these modes, to obtain design curves based on the analysis to present a simple design procedure for the proposed converter, and, finally to provide experimental results. A high-frequency link series-parallel resonant power converter with capacitive output filter is proposed and analyzed using the state-space approach. Analysis shows that the converter enters into three different modes. The normalized load current and other component stresses are plotted against the converter gain. Detailed experimental results are presented to verify the theory. >


title: Single Switched Capacitor Battery Balancing System Enhancements

abstract: Battery management systems (BMS) are a key element in electric vehicle energy storage systems. The BMS performs several functions concerning to the battery system, its key task being balancing the battery cells. Battery cell unbalancing hampers electric vehicles’ performance, with differing individual cell voltages decreasing the battery pack capacity and cell lifetime, leading to the eventual failure of the total battery system. Quite a lot of cell balancing topologies have been proposed, such as shunt resistor, shuttling capacitor, inductor/transformer based and DC energy converters. The shuttling capacitor balancing systems in particular have not been subject to much research efforts however, due to their perceived low balancing speed and high cost. This paper tries to fill this gap by briefly discussing the shuttling capacitor cell balancing topologies, focusing on the single switched capacitor (SSC) cell balancing and proposing a novel procedure to improve the SSC balancing system performance. This leads to a new control strategy for the SSC system that can decrease the balancing system size, cost, balancing time and that can improve the SSC balancing system efficiency.


title: D ej a Vu: A User Study Using Images for Authentication

abstract: Current secure systems suffer because they neglect the importance of human factors in security. We address a fundamental weakness of knowledge-based authentication schemes, which is the human limitation to remember secure passwords. Our approach to improve the security of these systems relies on recognition-based, rather than recall-based authentication. We examine the requirements of a recognition-based authentication system and propose Deja Vu, which authenticates a user through her ability to recognize previously seen images. Deja Vu is more reliable and easier to use than traditional recall-based schemes, which require the user to precisely recall passwords or PINs. Furthermore, it has the advantage that it prevents users from choosing weak passwords and makes it difficult to write down or share passwords with others. ::: ::: We develop a prototype of Deja Vu and conduct a user study that compares it to traditional password and PIN authentication. Our user study shows that 90% of all participants succeeded in the authentication tests using Deja Vu while only about 70% succeeded using passwords and PINS. Our findings indicate that Deja Vu has potential applications, especially where text input is hard (e.g., PDAs or ATMs), or in situations where passwords are infrequently used (e.g., web site passwords).


Coherence in Computer Graphics


title: Auto cropping for digital photographs

abstract: In this paper, we propose an effective approach to the nearly untouched problem, still photograph auto cropping, which is one of the important features to automatically enhance photographs. To obtain an optimal result, we first formulate auto cropping as an optimization problem by defining an energy function, which consists of three sub models: composition sub model, conservative sub model, and penalty sub model. Then, particle swarm optimization (PSO) is employed to obtain the optimal solution by maximizing the objective function. Experimental results and user studies over hundreds of photographs show that the proposed approach is effective and accurate in most cases, and can be used in many practical multimedia applications.


title: Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach

abstract: This article presents a pragmatic approach to Chinese word segmentation. It differs from most previous approaches mainly in three respects. First, while theoretical linguists have defined Chinese words using various linguistic criteria, Chinese words in this study are defined pragmatically as segmentation units whose definition depends on how they are used and processed in realistic computer applications. Second, we propose a pragmatic mathematical framework in which segmenting known words and detecting unknown words of different types (i.e., morphologically derived words, factoids, named entities, and other unlisted words) can be performed simultaneously in a unified way. These tasks are usually conducted separately in other systems. Finally, we do not assume the existence of a universal word segmentation standard that is application-independent. Instead, we argue for the necessity of multiple segmentation standards due to the pragmatic fact that different natural language processing applications might require different granularities of Chinese words.These pragmatic approaches have been implemented in an adaptive Chinese word segmenter, called MSRSeg, which will be described in detail. It consists of two components: (1) a generic segmenter that is based on the framework of linear mixture models and provides a unified approach to the five fundamental features of word-level Chinese language processing: lexicon word processing, morphological analysis, factoid detection, named entity recognition, and new word identification; and (2) a set of output adaptors for adapting the output of (1) to different application-specific standards. Evaluation on five test sets with different standards shows that the adaptive system achieves state-of-the-art performance on all the test sets.