title: Investigating Language Universal and Specific Properties in Word Embeddings

abstract: Recently, many NLP tasks have benefited from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent architecture. Here we investigate three representative models on a large set of language samples by mapping dense embedding to sparse linguistic property space. Experiment results reveal the language universal and specific properties encoded in various word representation. Additionally, strong evidence supports the utility of word form, especially for inflectional languages.


title: The construction system of the brain

abstract: The ability to construct a hypothetical situation in one's imagination prior to it actually occurring may afford greater accuracy in predicting its eventual outcome. The recollection of past experiences is also considered to be a reconstructive process with memories recreated from their component parts. Construction, therefore, plays a critical role in allowing us to plan for the future and remember the past. Conceptually, construction can be broken down into a number of constituent processes although little is known about their neural correlates. Moreover, it has been suggested that some of these processes may be shared by a number of other cognitive functions including spatial navigation and imagination. Recently, novel paradigms have been developed that allow for the isolation and characterization of these underlying processes and their associated neuroanatomy. Here, we selectively review this fast-growing literature and consider some implications for remembering the past and predicting the future.


title: Automatic image captioning

abstract: We examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.


title: BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm301 Data and text mining BioText Search Engine: beyond abstract search

abstract: Summary: The BioText Search Engine is a freely available Web-based application that provides biologists with new ways to access the scientific literature. One novel feature is the ability to search and browse article figures and their captions. A grid view juxtaposes many different figures associated with the same keywords, providing new insight into the literature. An abstract/title search and list view shows at a glance many of the figures associated with each article. The interface is carefully designed according to usability principles and techniques. The search engine is a work in progress, and more functionality will be added over time. ::: ::: Availability: http://biosearch.berkeley.edu ::: ::: Contact:divoli@ischool.berkeley.edu and hearst@ischool.berkeley.edu


title: A Comparative Study of Type 1 Singleton Fuzzy Logic Systems in Machining Application

abstract: This paper presents a comparison of two different techniques of Soft Computing (SC), in union with a novel approach of a hybrid model with Central Composite Design (CCD) with capacity of performing a rule reduction, without loss of reliability, and also performs a reduction of the computational time spent in prediction. Results obtained with this method show better performance thought generating a minimum error rate when it is compared with the Adaptive Network Fuzzy Inference Systems (ANFIS), or with the least squares method in case of pure the Fuzzy Singleton system.


title: Advertisement clicking prediction by using multiple criteria mathematical programming

abstract: In online advertisement industry, it is important to predict potentially profitable users who will click target ads (i.e., Behavioral targeting). The task selects the potential users that are likely to click the ads by analyzing user's clicking/web browsing information and displaying the most relevant ads to them. This paper proposes four multiple criteria mathematical programming models for advertisement clicking problems. First two are multi-criteria linear regression (MCLR) and kernel-based multiple criteria regression (KMCR) algorithms for click-through rate (CTR) prediction. The second two are multi-criteria linear programming (MCLP) and kernel-based multiple criteria programming (KMCP) algorithms, which are used to predict ads clicking events, such as identifying clicked ads in a set of ads. Using the experimental datasets from KDD Cup 2012, the paper first conducts a comparison of the proposed MCLR and KMCR with the methods of support vector regression (SVR) and logistic regression (LR), which shows that both MCLR and KMCR are good alternatives. Then the paper further studies the performance between the proposed MCLP and KMCP algorithms with known algorithms, including support vector machines (SVM), LR, radial basis function network (RBFN), k-nearest neighbor algorithm (KNN) and Naive Bayes (NB) in both prediction and selection processes. The studies show that the MCLP and KMCP models have better performance stability and can be used to effectively handle behavioral targeting application for online advertisement problems.


title: Sparse Coding and Dictionary Learning with Linear Dynamical Systems

abstract: Linear Dynamical Systems (LDSs) are the fundamental tools for encoding spatio-temporal data in various disciplines. To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs. Rather than approximate the extended observability with a finite-order matrix, we represent the space of LDSs by an infinite Grassmannian consisting of the orthonormalized extended observability subspaces. Via a homeomorphic mapping, such Grassmannian is embedded into the space of symmetric matrices, where a tractable objective function can be derived for sparse coding. Then, we propose an efficient method to learn the system parameters of the dictionary atoms explicitly, by imposing the symmetric constraint to the transition matrices of the data and dictionary systems. Moreover, we combine the state covariance into the algorithm formulation, thus further promoting the performance of the models with symmetric transition matrices. Comparative experimental evaluations reveal the superior performance of proposed methods on various tasks including video classification and tactile recognition.


title: Automatic patch generation learned from human-written patches

abstract: Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations. To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (Par), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically. We experimentally evaluated Par on 119 real bugs. In addition, a user study involving 89 students and 164 developers confirmed that patches generated by our approach are more acceptable than those generated by GenProg. Par successfully generated patches for 27 out of 119 bugs, while GenProg was successful for only 16 bugs.


title: Power amplifiers and transmitters for RF and microwave

abstract: The generation of RF/microwave power is required not only in wireless communications, but also in applications such as jamming, imaging, RF heating, and miniature dc/dc converters. Each application has its own unique requirements for frequency, bandwidth, load, power, efficiency, linearity, and cost. RF power is generated by a wide variety of techniques, implementations, and active devices. Power amplifiers are incorporated into transmitters in a similarly wide variety of architectures, including linear, Kalm, envelope tracking, outphasing, and Doherty. Linearity can be improved through techniques such as feedback, feedforward, and predistortion.


title: Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition

abstract: Multichannel automatic speech recognition (ASR) systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture, which performs multichannel filtering in the first layer of the network, and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. Next, we show how performance can be improved by factoring the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. Finally, we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel waveform model.


title: Disturbance and mosquito diversity in the lowland tropical rainforest of central Panama

abstract: The Intermediate Disturbance Hypothesis (IDH) is well-known in ecology providing an explanation for the role of disturbance in the coexistence of climax and colonist species. Here, we used the IDH as a framework to describe the role of forest disturbance in shaping the mosquito community structure, and to identify the ecological processes that increase the emergence of vector-borne disease. Mosquitoes were collected in central Panama at immature stages along linear transects in colonising, mixed and climax forest habitats, representing different levels of disturbance. Species were identified taxonomically and classified into functional categories (i.e., colonist, climax, disturbance-generalist, and rare). Using the Huisman-Olff-Fresco multi-model selection approach, IDH testing was done. We did not detect a unimodal relationship between species diversity and forest disturbance expected under the IDH; instead diversity peaked in old-growth forests. Habitat complexity and constraints are two mechanisms proposed to explain this alternative postulate. Moreover, colonist mosquito species were more likely to be involved in or capable of pathogen transmission than climax species. Vector species occurrence decreased notably in undisturbed forest settings. Old-growth forest conservation in tropical rainforests is therefore a highly-recommended solution for preventing new outbreaks of arboviral and parasitic diseases in anthropic environments.


title: A Framework for Process-to-Module Modeling of a-Si/c-Si (HIT) Heterojunction Solar Cells to Investigate the Cell-to-Module Efficiency Gap

abstract: The cell-to-module efficiency gap observed in a-Si/c-Si heterojunction solar cells is a key challenge to the broad adoption of this technology. To systematically address this issue, we describe an end-to-end modeling framework to explore the implications of process and device variations at the module level. First, a process model is developed to connect the a-Si deposition parameters to the material properties. Next, a physics-based device model is presented; the model uses the thermionic emission/diffusion theory to capture the essential features of photocurrent and diode injection current. Using the process and device models, the effects of process conditions on cell performance are explored. Finally, the performance of the module, as a function of device and process parameters, is explored to establish the cell-to-module efficiency gap. The insights developed through this process-to-module modeling framework will help close the cell-to-module efficiency gap of this commercially promising technology.


title: Scalable Attestation Resilient to Physical Attacks for Embedded Devices in Mesh Networks

abstract: Interconnected embedded devices are increasingly used invarious scenarios, including industrial control, building automation, or emergency communication. As these systems commonly process sensitive information or perform safety critical tasks, they become appealing targets for cyber attacks. A promising technique to remotely verify the safe and secure operation of networked embedded devices is remote attestation. However, existing attestation protocols only protect against software attacks or show very limited scalability. In this paper, we present the first scalable attestation protocol for interconnected embedded devices that is resilient to physical attacks. Based on the assumption that physical attacks require an adversary to capture and disable devices for some time, our protocol identifies devices with compromised hardware and software. Compared to existing solutions, our protocol reduces ommunication complexity and runtimes by orders of magnitude, precisely identifies compromised devices, supports highly dynamic and partitioned network topologies, and is robust against failures. We show the security of our protocol and evaluate it in static as well as dynamic network topologies. Our results demonstrate that our protocol is highly efficient in well-connected networks and robust to network disruptions.


title: Compressing and indexing labeled trees, with applications

abstract: Consider an ordered, static tree T where each node has a label from alphabet Σ. Tree T may be of arbitrary degree and shape. Our goal is designing a compressed storage scheme of T that supports basic navigational operations among the immediate neighbors of a node (i.e. parent, ith child, or any child with some label,…) as well as more sophisticated path-based search operations over its labeled structure. We present a novel approach to this problem by designing what we call the XBW-transform of the tree in the spirit of the well-known Burrows-Wheeler transform for strings [1994]. The XBW-transform uses path-sorting to linearize the labeled tree T into two coordinated arrays, one capturing the structure and the other the labels. For the first time, by using the properties of the XBW-transform, our compressed indexes go beyond the information-theoretic lower bound, and support navigational and path-search operations over labeled trees within (near-)optimal time bounds and entropy-bounded space. Our XBW-transform is simple and likely to spur new results in the theory of tree compression and indexing, as well as interesting application contexts. As an example, we use the XBW-transform to design and implement a compressed index for XML documents whose compression ratio is significantly better than the one achievable by state-of-the-art tools, and its query time performance is order of magnitudes faster.


title: J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features

abstract: Methods for Named Entity Recognition and Disambiguation (NERD) perform NER ::: and NED in two separate stages. Therefore, NED may be penalized with ::: respect to precision by NER false positives, and suffers in recall from ::: NER false negatives. Conversely, NED does not fully exploit information ::: computed by NER such as types of mentions. This paper presents J-NERD, a ::: new approach to perform NER and NED jointly, by means of a probabilistic ::: graphical model that captures mention spans, mention types, and the ::: mapping of mentions to entities in a knowledge base. We present ::: experiments with different kinds of texts from the CoNLL’03, ACE’05, and ::: ClueWeb’09-FACC1 corpora. J-NERD consistently outperforms state-of-the-art ::: competitors in end-to-end NERD precision, recall, and F1.


title: The role of gut microbiota in the development of type 1, type 2 diabetes mellitus and obesity

abstract: Diabetes is a group of metabolic disorders characterized by persistent hyperglycemia and has become a major public health concern. Autoimmune type 1 diabetes (T1D) and insulin resistant type 2 diabetes (T2D) are the two main types. A combination of genetic and environmental factors contributes to the development of these diseases. Gut microbiota have emerged recently as an essential player in the development of T1D, T2D and obesity. Altered gut microbiota have been strongly linked to disease in both rodent models and humans. Both classic 16S rRNA sequencing and shot-gun metagenomic pyrosequencing analysis have been successfully applied to explore the gut microbiota composition and functionality. This review focuses on the association between gut microbiota and diabetes and discusses the potential mechanisms by which gut microbiota regulate disease development in T1D, T2D and obesity.


title: 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

abstract: Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2). The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data [13]. Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid. Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing. Our extensive experimental analysis shows that our reconstruction framework (i) outperforms the state-of-the-art methods for single view reconstruction, and (ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).


title: Teaching UAVs to Race Using UE4Sim

abstract: Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in the recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of data for training. In this paper, we develop a photo-realistic simulator that can afford the generation of large amounts of training data (both images rendered from the UAV camera and its controls) to teach a UAV to autonomously race through challenging tracks. We train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing. Training is done through imitation learning enabled by data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots.


title: Automated Human Recognition by Gait using Neural Network

abstract: We describe a new method for recognizing humans by their gait using back-propagation neural network. Here, the gait motion is described as rhythmic and periodic motion, and a 2D stick figure is extracted from gait silhouette by motion information with topological analysis guided by anatomical knowledge. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature extraction based on motion parameters. Then, a back-propagation neural network algorithm is used to recognize humans by their gait patterns. In experiments, higher gait recognition performances have been achieved.


Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm


title: A survey of intrusion detection in Internet of Things

abstract: Internet of Things (IoT) is a new paradigm that integrates the Internet and physical objects belonging to different domains such as home automation, industrial process, human health and environmental monitoring. It deepens the presence of Internet-connected devices in our daily activities, bringing, in addition to many benefits, challenges related to security issues. For more than two decades, Intrusion Detection Systems (IDS) have been an important tool for the protection of networks and information systems. However, applying traditional IDS techniques to IoT is difficult due to its particular characteristics such as constrained-resource devices, specific protocol stacks, and standards. In this paper, we present a survey of IDS research efforts for IoT. Our objective is to identify leading trends, open issues, and future research possibilities. We classified the IDSs proposed in the literature according to the following attributes: detection method, IDS placement strategy, security threat and validation strategy. We also discussed the different possibilities for each attribute, detailing aspects of works that either propose specific IDS schemes for IoT or develop attack detection strategies for IoT threats that might be embedded in IDSs.


title: Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective

abstract: In order to anticipate and prepare effectively for future experiences, the brain must cope with the inherent uncertainty of future events. In this Review, Grupe and Nitschke show how alterations in the processes by which the brain deals with future uncertainty may contribute to the development of pathological anxiety.


title: A 1.2 V 8 Gb 8-Channel 128 GB/s High-Bandwidth Memory (HBM) Stacked DRAM With Effective I/O Test Circuits

abstract: Motivated by a graphics memory system that achieves multiplied bandwidth by the number of memories per system, HBM DRAM adopts a brand new architecture, with many technical changes and challenges. The first main change in the architecture is the stacked memory structure with TSV array, which has independent bandwidth per slice. The second is semi-independent row, column command interface, which enhances effective performance. For supporting high bandwidth, this chip has fine pitch microbump interface. Methods for testing microbump are explained. 8 Gb stacked HBM is fabricated with chip-on-wafer process and tested with high-frequency wafer probing. Using chip-on-wafer test results, 128 GB/s at 1.2 V supply voltage is achieved.


title: A Scalable 6-to-18GHz Concurrent Dual-Band Quad-Beam Phased-Array Receiver in CMOS

abstract: This paper describes the general architecture and the signal-path behavior of a CMOS programmable phased-array receiver element that simultaneously operates at two frequencies between 6 and 18GHz (a tritave) while forming four independently controlled beams.


title: A Parallel Floyd-Warshall algorithm based on TBB

abstract: The paper presents one novel algorithms: Parallel Floyd-Warshall algorithm(PF) based on multi-core computer with Threading Building Blocks (TBB). TBB offers a rich and complete approach to express parallelism in a C++ program. Serial Floyd-Warshall algorithm(SF) and PF are implemented and verified with TBB for the all-pairs shortest path problem. Experiments show that the efficiency of multi-threaded of PF is obviously superior to single-threaded of PF which is enormously better than SF. At this point, PF can be efficiently applied to the systems of large node number and it leads to a significant improvement in running efficiency. Furthermore, the running results of multi-threaded of PF are stable when thread number is relatively large.


title: Compact modified bow-tie slot antenna fed by CPW for ultra-wideband applications

abstract: A coplanar waveguide fed compact modified bow-tie slot antenna is proposed for use in the ultra-wideband systems. The modification is made by attaching a pair of meandered slotlines to the upper ends of the small bow-tie slot. With a proper slotline length, the impedance bandwidth of the proposed antenna can be adjusted to cover the entire ultra-wideband and the antenna size remains compact. Moderate in-band antenna gains and bi-directional broadside radiation patterns are also measured and presented.


title: Enhancing Cross-Device Interaction Scripting with Interactive Illustrations

abstract: Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process.


Buruli Ulcer Disease and Its Association with Land Cover in Southwestern Ghana


title: Emergence of neural encoding of auditory objects while listening to competing speakers

abstract: A visual scene is perceived in terms of visual objects. Similar ideas have been proposed for the analogous case of auditory scene analysis, although their hypothesized neural underpinnings have not yet been established. Here, we address this question by recording from subjects selectively listening to one of two competing speakers, either of different or the same sex, using magnetoencephalography. Individual neural representations are seen for the speech of the two speakers, with each being selectively phase locked to the rhythm of the corresponding speech stream and from which can be exclusively reconstructed the temporal envelope of that speech stream. The neural representation of the attended speech dominates responses (with latency near 100 ms) in posterior auditory cortex. Furthermore, when the intensity of the attended and background speakers is separately varied over an 8-dB range, the neural representation of the attended speech adapts only to the intensity of that speaker but not to the intensity of the background speaker, suggesting an object-level intensity gain control. In summary, these results indicate that concurrent auditory objects, even if spectrotemporally overlapping and not resolvable at the auditory periphery, are neurally encoded individually in auditory cortex and emerge as fundamental representational units for top-down attentional modulation and bottom-up neural adaptation.


title: Mathematical Methods in Linguistics

abstract: Preface. Part A. Set Theory. 1. Basic Concepts of Set Theory. 2. Relations and Functions. 3. Properties of Relations. 4. Infinities. Appendix A1. Part B. Logic and Formal Systems. 5. Basic Concepts of Logic. 6.Statement Logic. 7. Predicate Logic. 8. Formal Systems, Axiomatization, and Model Theory. Appendix B1. Appendix BII. Part C. Algebra. 9. Basic Concepts of Algebra. 10. Operational Structures. 11. Lattices. 12. Boolean and Heyting Algebras. Part D. English as a Formal Language. 13. Basic Concepts of Formal Languages. 14. Generalized Quantifiers. 15. Intensionality. Part E. Languages, Grammars, and Automata. 16. Basic Concepts of Languages, Grammars, and Automata. 17. Finite Automata, Regular Languages and Type 3 Grammars. 18. Pushdown Automata, Context-Free Grammars and Languages. 19. Turing Machines, Recursively Enumberable Languages, and Type 0 Grammars. 20. Linear Bounded Automata, Context-Sensitive Languages and Type 1 Grammars. 21. Languages Between Context-Free and Context-Sensitive. 22. Transformational Grammars. Appendix EI. Appendix EII. Review Problems. Index.


title: An Exploration of Serverless Architectures for Information Retrieval

abstract: Serverless architectures represent a new approach to designing applications in the cloud without having to explicitly provision or manage servers. The developer specifies functions with well-defined entry and exit points, and the cloud provider handles all other aspects of execution. In this paper, we explore a novel application of serverless architectures to information retrieval and describe a search engine built in this manner with Amazon Web Services: postings lists are stored in the DynamoDB NoSQL store and the postings traversal algorithm for query evaluation is implemented in the Lambda service. The result is a search engine that scales elastically with a pay-per-request model, in contrast to a server-based model that requires paying for running instances even if there are no requests. We empirically assess the performance and economics of our serverless architecture. While our implementation is currently too slow for interactive searching, analysis shows that the pay-per-request model is economically compelling, and future infrastructure improvements will increase the attractiveness of serverless designs over time.


title: Smart, Sparse Contours to Represent and Edit Images.

abstract: We study the problem of reconstructing an image from information stored at sparse contour locations. Existing contour-based image reconstruction methods struggle to balance contour sparsity and reconstruction fidelity. Therefore, denser contours are needed to capture subtle texture information even though contours were not meant for textures. We propose a novel image representation where image content is characterized by contours with gradient information via an encoder-decoder network, while image details are modeled by a conditional generative adversarial network. We show that high-quality reconstructions with high fidelity to the source image can be obtained from extremely sparse input, e.g., comprising less than 6% of image pixels. Our model synthesizes texture, details and fine structures in regions where no input information is provided. The semantic knowledge encoded into our model and the sparsity of the input allows using contours as an intuitive interface for semantically-aware image manipulation: local edits in contour domain such as scaling, translation and erasing, translate to long-range and coherent changes in the pixel space. Experiments on a variety of datasets verify the versatility and convenience afforded by our models.


title: Depressive Moods of Users Portrayed in Twitter

abstract: Potential benets of using online social network data for clinical studies on depression are tremendous. In this paper, we present a preliminary result on building a research framework that utilizes real-time moods of users captured in the Twitter social network and explore the use of language in describing depressive moods. First, we analyzed a random sample of tweets posted by the general Twitter population during a two-month period to explore how depression is talked about in Twitter. We found remarkable activities related to depression in Twitter. A large number of tweets contained detailed information about depressed feelings, status, as well as treatment history. Going forward, we conducted a study on 69 participants to determine whether the use of sentiment words of depressed users diered from a typical user. We found that the use of words related to negative emotions and anger signicantly increased among Twitter users with major depressive symptoms compared to those otherwise. However, no dierence was found in the use of words related to positive emotions between the two groups. Our work provides several evidences that online social networks provide meaningful data for capturing depressive moods of users.


title: Network-Induced Constraints in Networked Control Systems—A Survey

abstract: Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.


title: An Active Neck Brace Controlled by a Joystick to Assist Head Motion

abstract: Dropped head syndrome is often seen in patients with neurological disorders, such as amyotrophic lateral sclerosis. Static collars are often prescribed to these patients to support their head. These collars support the head in one position but do not allow motion to the head. In this letter, we present a novel active neck brace to address this issue and provide assistance to the patients to improve their neck range of motion. This brace has three degrees-of-freedom and allows improved range of motion to the head and neck. We have developed a joystick interface for this brace to allow the user to carry out their intended head rotation with assistance of the brace. In order to assess the feasibility of the use of this brace, we present a study that recruited eight healthy subjects. We simultaneously recorded the motion of the head, electromyography of the neck muscles, and force/torque applied through the brace to the head. The results show that the brace provides support to the head and subjects activate their muscles less when assisted by the brace using the joystick.


title: Evidence-based interventions for reading and language difficulties: Creating a virtuous circle

abstract: Background. Children may experience two very different forms of reading problem: decoding difficulties (dyslexia) and reading comprehension difficulties. Decoding difficulties appear to be caused by problems with phonological (speech sound) processing. Reading comprehension difficulties in contrast appear to be caused by problems with ‘higher level’ language difficulties including problems with semantics (including deficient knowledge of word meanings) and grammar (knowledge of morphology and syntax). ::: ::: Aims. We review evidence concerning the nature, causes of, and treatments for children's reading difficulties. We argue that any well-founded educational intervention must be based on a sound theory of the causes of a particular form of learning difficulty, which in turn must be based on an understanding of how a given skill is learned by typically developing children. Such theoretically motivated interventions should in turn be evaluated in randomized controlled trials (RCTs) to establish whether they are effective, and for whom. ::: ::: Results. There is now considerable evidence showing that phonologically based interventions are effective in ameliorating children's word level decoding difficulties, and a smaller evidence base showing that reading and oral language (OL) comprehension difficulties can be ameliorated by suitable interventions to boost vocabulary and broader OL skills. ::: ::: Conclusions. The process of developing theories about the origins of children's educational difficulties and evaluating theoretically motivated treatments in RCTs, produces a ‘virtuous circle’ whereby theory informs practice, and the evaluation of effective interventions in turn feeds back to inform and refine theories about the nature and causes of children's reading and language difficulties.


BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research


title: An adaptive PAM-4 5 Gb/s backplane transceiver in 0.25 /spl mu/m CMOS

abstract: This paper describes a novel backplane transceiver, which uses PAM-4 (pulse amplitude modulated four level) signalling and continuously adaptive transmit based equalization to move 5 Gcb/s (channel bits per second) across typical FR-4 backplanes for total distances of up to 50 inches through two sets of backplane connectors. The paper focuses on the implementation of the equalizer and the adaptation algorithms, and includes measured results. The 17 mm/sup 2/ device is implemented in a 0.25 /spl mu/m CMOS process, operates on 2.5 V and 3.3 V supplies and consumes 1.2 W.


Sherpa: Hyperparameter Optimization for Machine Learning Models


title: A Framework for Self-Tuning Optimization Algorithm

abstract: The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.


title: Opening up the collaborative problem-solving process to solvers

abstract: In software systems, having features of openness means that some of the internal components of the system are made available for examination by users. Researchers have looked at different effects of open systems a great deal in the area of educational technology, but also in areas outside of education. Properly used, openness has the potential to teach users about the system, and about themselves, without drowning the user in a sea of confusing information. ::: This work explores features of openness that I have added to the collaborative problem-solving system known as CoSolve. Collaborative problem-solving software allows several solvers, often remotely and possibly asynchronously, to work together to create a solution to a problem known to all of them. For the purposes of this work, the problem being solved is well defined. In addition to making remote and asynchronous solving more practical, collaborative problem-solving systems can enhance the problem-solving process by providing additional services to solvers, such as record-keeping, suggestion and evaluation tools. ::: The contributions I present here are 1) the addition of features of openness to CoSolve, and an evaluation of those open features, and 2) a collection of tools for analyzing and understanding CoSolve behavior. The first contribution improves CoSolve by making it easier for solvers to collaborate. The second introduces a way to understand the seemingly chaotic and complex behavior of solvers in the CoSolve system.


title: A Field-Based Representation of Surrounding Vehicle Motion from a Monocular Camera

abstract: Sensing and presenting on-road information of moving vehicles is essential for fully and semi-automated driving. It is challenging to track vehicles from affordable on-board cameras in crowded scenes. The mismatch or missing data are unavoidable and it is ineffective to directly present uncertain cues to support the decision-making. In this paper, we propose a physical model based on incompressible fluid dynamics to represent the vehicle’s motion, which provides hints of possible collision as a continuous scalar riskmap. We estimate the position and velocity of other vehicles from a monocular on-board camera located in front of the ego-vehicle. The noisy trajectories are then modeled as the boundary conditions in the simulation of advection and diffusion process. We then interactively display the animating distribution of substances, and show that the continuous scalar riskmap well matches the perception of vehicles even in presence of the tracking failures. We test our method on real-world scenes and discuss about its application for driving assistance and autonomous vehicle in the future.


A Capability Maturity Model for Corporate Performance Management – An Empirical Study in Large Finnish Manufacturing Companies


title: A Generic Scheme for Progressive Point Cloud Coding

abstract: In this paper, we propose a generic point cloud encoder that provides a unified framework for compressing different attributes of point samples corresponding to 3D objects with an arbitrary topology. In the proposed scheme, the coding process is led by an iterative octree cell subdivision of the object space. At each level of subdivision, the positions of point samples are approximated by the geometry centers of all tree-front cells, whereas normals and colors are approximated by their statistical average within each of the tree-front cells. With this framework, we employ attribute-dependent encoding techniques to exploit the different characteristics of various attributes. All of these have led to a significant improvement in the rate-distortion (R-D) performance and a computational advantage over the state of the art. Furthermore, given sufficient levels of octree expansion, normal space partitioning, and resolution of color quantization, the proposed point cloud encoder can be potentially used for lossless coding of 3D point clouds.


title: Factors affecting attitudes and intentions towards knowledge sharing in the Dubai Police Force

abstract: Abstract This study contributes to the limited research base on knowledge sharing in public sector organisations, specifically police forces, and organisations in the Middle East through a case study investigation into the factors that affect knowledge sharing in the Dubai Police Force. A questionnaire-based survey was conducted with staff in key departments in the Dubai Police Force. Informed by the literature and by interviews conducted in a previous phase, the core of the questionnaire was a bank of Likert-style questions covering the dependent variables intention to knowledge share, and attitude towards knowledge sharing, and the independent variables, trust, organisational structure, leadership, reward, time, and information technology. Data was analysed using structured equation modelling, in order to test the measurement model using confirmatory factor analysis, and to test the structural model. The structural model suggests a strong relationship between attitude to knowledge sharing, and intention to share knowledge. Hypotheses regarding the influence of leadership, trust, organisational structure, time, and information technology on attitude to knowledge sharing were upheld. Rewards did not to influence attitude to knowledge sharing. Recommendations are offered for practice and further research.


title: Bohatei: flexible and elastic DDoS defense

abstract: DDoS defense today relies on expensive and proprietary hardware appliances deployed at fixed locations. This introduces key limitations with respect to flexibility (e.g., complex routing to get traffic to these "chokepoints") and elasticity in handling changing attack patterns. We observe an opportunity to address these limitations using new networking paradigms such as software-defined networking (SDN) and network functions virtualization (NFV). Based on this observation, we design and implement Bohatei, a flexible and elastic DDoS defense system. In designing Bohatei, we address key challenges with respect to scalability, responsiveness, and adversary-resilience. We have implemented defenses for several DDoS attacks using Bohatei. Our evaluations show that Bohatei is scalable (handling 500 Gbps attacks), responsive (mitigating attacks within one minute), and resilient to dynamic adversaries.


title: Semi-supervised convolutional neural networks for text categorization via region embedding

abstract: This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks.


title: FM-based indoor localization

abstract: The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this work, we demonstrate through a detailed experimental study in 3 different buildings across the US, that FM radio signal RSSI values can be used to achieve room-level indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5%. More importantly, we experimentally demonstrate that the localization errors of FM andWiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83% (when accounting for wireless signal temporal variations) compared to when WiFi RSSI only is used as a signature.


title: McDRAM: Low Latency and Energy-Efficient Matrix Computations in DRAM

abstract: We propose a novel memory architecture for in-memory computation called McDRAM, where DRAM dies are equipped with a large number of multiply accumulate (MAC) units to perform matrix computation for neural networks. By exploiting high internal memory bandwidth and reducing off-chip memory accesses, McDRAM realizes both low latency and energy efficient computation. In our experiments, we obtained the chip layout based on the state-of-the-art memory, LPDDR4 where McDRAM is equipped with 2048 MACs in a single chip package with a small area overhead (4.7%). Compared with the state-of-the-art accelerator, TPU and the power-efficient GPU, Nvidia P4, McDRAM offers $9.5{\times }$ and $14.4{\times }$ speedup, respectively, in the case that the large-scale MLPs and RNNs adopt the batch size of 1. McDRAM also gives $2.1{\times }$ and $3.7{\times }$ better computational efficiency in TOPS/W than TPU and P4, respectively, for the large batches.


title: Understanding video interactions in youtube

abstract: This paper seeks understanding the user behavior in a social network created essentially by video interactions. We present a characterization of a social network created by the video interactions among users on YouTube, a popular social networking video sharing system. Our results uncover typical user behavioral patterns as well as show evidences of anti-social behavior such as self-promotion and other types of content pollution.


title: Cortical mechanism for the visual guidance of hand grasping movements in the monkey: A reversible inactivation study

abstract: Summary Picking up an object requires two basic motor operations: reaching and grasping. Neurophysiological studies in monkeys have suggested that the visuomotor transformations necessary for these two operations are carried out by separate parietofrontal circuits and that, for grasping, a key role is played by a specific sector of the ventral premotor cortex: area F5. The aim of the present study was to test the validity of this hypothesis by reversibly inactivating area F5 in monkeys trained to grasp objects of different shape, size and orientation. In separate sessions, the hand field of the primary motor cortex (area F1 or area 4) was also reversibly inactivated. The results showed that after inactivation of area F5 buried in the bank of the arcuate sulcus (the F5 sector where visuomotor neurones responding to object presentation are located), the hand shaping preceding grasping was markedly impaired and the hand posture was not appropriate for the object size and shape. The monkeys were eventually able to grasp the objects, but


title: Towards a business analytics capability maturity model

abstract: Business analytics (BA) systems are an important strategic investment for many organisations and can potentially contribute significantly to firm performance. Establishing strong BA capabilities is currently one of the major concerns of chief information officers. This research project aims to develop a BA capability maturity model (BACMM). The BACMM will help organisations to scope and evaluate their BA initiatives. This research-in-progress paper describes the current BACMM, relates it to existing capability maturity models and explains its theoretical base. It also discusses the design science research approach being used to develop the BACMM and provides details of further work within the research project. Finally, the paper concludes with a discussion of how the BACMM might be used in practice.


title: Coh-Metrix: Capturing Linguistic Features of Cohesion

abstract: This study addresses the need in discourse psychology for computational techniques that analyze text on multiple levels of cohesion and text difficulty. Discourse psychologists often investigate phenomena related to discourse processing using lengthy texts containing multiple paragraphs, as opposed to single word and sentence stimuli. Characterizing such texts in terms of cohesion and coherence is challenging. Some computational tools are available, but they are either fragmented over different databases or they assess single, specific features of text. Coh-Metrix is a computational linguistic tool that measures text cohesion and text difficulty on a range of word, sentence, paragraph, and discourse dimensions. This study investigated the validity of Coh-Metrix as a measure of cohesion in text using stimuli from published discourse psychology studies as a benchmark. Results showed that Coh-Metrix indexes of cohesion (individually and combined) significantly distinguished the high- versus low-cohesion vers...


Ensemble modeling of denoising autoencoder for speech spectrum restoration.


title: Interactive Sketch-Based Normal Map Generation with Deep Neural Networks

abstract: High-quality normal maps are important intermediates for representing complex shapes. In this paper, we propose an interactive system for generating normal maps with the help of deep learning techniques. Utilizing the Generative Adversarial Network (GAN) framework, our method produces high quality normal maps with sketch inputs. In addition, we further enhance the interactivity of our system by incorporating user-specified normals at selected points. Our method generates high quality normal maps in real time. Through comprehensive experiments, we show the effectiveness and robustness of our method. A thorough user study indicates the normal maps generated by our method achieve a lower perceptual difference from the ground truth compared to the alternative methods.


title: 70 kHz, 15 kW silicon-carbide MOSFET inverter for industrial induction heating systems

abstract: This paper discusses the applicability of SiC MOSFET to high-frequency induction heating applications. In this paper, a 15-kW, 70-kHz SiC MOSFET inverter prototype is constructed to evaluate the performance of the SiC MOSFETs. Power loss breakdown is also conducted from the experimental results. As a results, the inverter prototype has exhibited a power conversion efficiency as high as 98.5% at the rated power operation, and demonstrated the possibility of applications of SiC MOSFETs to high-frequency induction heating systems.


title: Introducing a ROS based planning and execution framework for human-robot interaction

abstract: Working in human populated environments requires fast and robust action selection and execution especially when deliberately trying to interact with humans. This work presents the combination of a high-level planner (ROSPlan) for action sequencing and automatically generated finite state machines (PNP) for execution. Using this combined system we are able to exploit the speed and robustness of the execution and the flexibility of the sequence generation and combine the positive aspects of both approaches.


title: Crowd analysis using visual and non-visual sensors, a survey

abstract: This paper proposes a critical survey of crowd analysis techniques using visual and non-visual sensors. Automatic crowd understanding has a massive impact on several applications including surveillance and security, situation awareness, crowd management, public space design, intelligent and virtual environments. In case of emergency, it enables practical safety applications by identifying crowd situational context information. This survey identifies different approaches as well as relevant work on crowd analysis by means of visual and non-visual techniques. Multidisciplinary research groups are addressing crowd phenomenon and its dynamics ranging from social, and psychological aspects to computational perspectives. The possibility to use smartphones as sensing devices and fuse this information with video sensors data, allows to better describe crowd dynamics and behaviors. Eventually, challenges and further research opportunities with reference to crowd analysis are exposed.


title: A comparative study of fairness-enhancing interventions in machine learning

abstract: Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions that require investigation for these algorithms to receive broad adoption. We present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures and existing datasets. We find that although different algorithms tend to prefer specific formulations of fairness preservations, many of these measures strongly correlate with one another. In addition, we find that fairness-preserving algorithms tend to be sensitive to fluctuations in dataset composition (simulated in our benchmark by varying training-test splits) and to different forms of preprocessing, indicating that fairness interventions might be more brittle than previously thought.


title: SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

abstract: In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm.


title: Discontinuous PWM for low switching losses in indirect matrix converter drives

abstract: This paper proposes a discontinuous pulse width modulation (DPWM) method for an indirect matrix converter (IMC) driving induction motor (IM) in order to reduce switching losses. The IMC has advantages that long lifetime and reduced volume depending on the lack of capacitors. However, the IMC has many electric switching devices because it is composed of two stages such as a rectifier stage and an inverter stage for AC/AC power conversion. Therefore, the IMC has higher switching losses according to a number of switching devices consequently. The other topologies are researched for reduction in the number of switching devices in the rectifier stage such as sparse matrix converter. On the contrary, in this paper, the DPWM method is used to reduce the switching losses of the inverter stage. The effectiveness of the proposed method for reduce the switching losses of the inverter stage is verified by simulation results.


title: Clustering Validity Based on the Improved Hubert \Gamma Statistic and the Separation of Clusters

abstract: The validity of clustering is one important research field in clustering analysis, and many clustering validity functions have been proposed, especially those based on the geometrical structure of data set, such as Dunn's index and Xie-Beni index. In this way, the compactness and the separation of clusters are usually taken into account. Xie-Beni index decreases with the number of partitions increasing. It is difficult to choose the optimal number of clusters when there are lots of clusters in data. In this paper, a novel clustering validity function is proposed, which is based on the improved Huber \Gamma statistic combined with the separation of clusters. Unlike other clustering validity, the function has the only maximum with the clustering number increasing. The experiments indicate that the function can be used as the optimal index for the choice of the clustering numbers.


Fundamentals for Future Mobile-Health (mHealth): A Systematic Review of Mobile Phone and Web-Based Text Messaging in Mental Health


CoTO: A novel approach for fuzzy aggregation of semantic similarity measures


Arm and hand movement control


title: Achieving both high precision and high recall in near-duplicate detection

abstract: To find near-duplicate documents, fingerprint-based paradigms such as Broder's shingling and Charikar's simhash algorithms have been recognized as effective approaches and are considered the state-of-the-art. Nevertheless, we see two aspects of these approaches which may be improved. First, high score under these algorithms' similarity measurement implies high probability of similarity between documents, which is different from high similarity of the documents. But how similar two documents are is what we really need to know. Second, there has to be a tradeoff between hash-code length and hash-code multiplicity in fingerprint paradigms, which makes it hard to maintain a satisfactory recall level while improving precision. In this paper our contributions are two-folded. First, we propose a framework for implementing the longest common subsequence (LCS) as a similarity measurement in reasonable computing time, which leads to both high precision and recall. Second, we present an algorithm to get a trustable partition from the LCS to reduce the negative impact from templates used in web page design. A comprehensive experiment was conducted to evaluate our method in terms of its effectiveness, efficiency, and quality of result. More specifically, the method has been successfully used to partition a set of 430 million web pages into 68 million subsets of similar pages, which demonstrates its effectiveness. For quality, we compared our method with simhash and a Cosine-based method through a sampling process (Cosine is compared to LCS as an alternative similarity measurement). The result showed that our algorithm reached an overall precision of 0.95 while simhash was 0.71 and Cosine was 0.82. At the same time our method obtains 1.86 times as much recall as simhash and 1.56 times as much recall as Cosine. Comparison experiment was also done for documents in the same web sites. For that, our algorithm, simhash and Cosine find almost the same number of true-positives at a precision of 0.91, 0.50 and 0.63 respectively. In terms of efficiency, our algorithm takes 118 hours to process the whole archive of 430 million topic-type pages on a cluster of six Linux boxes, at the same time the processing time of simhash and Cosine is 94 hours and 68 hours respectively. When considering the need of word segmentation for languages such as Chinese, the processing time of Cosine should be multiplied and in our experiment it is 602 hours.


title: Dominance of green sulfur bacteria in the chemocline of the meromictic Lake Suigetsu, Japan, as revealed by dissimilatory sulfite reductase gene analysis

abstract: This study investigated the spatiotemporal abundance and diversity of the α-subunit of the dissimilatory sulfite reductase gene (dsrA) in the meromictic Lake Suigetsu for assessing the sulfur-oxidizing bacterial community. The density of dsrA in the chemocline reached up to 3.1 × 106 copies ml−1 in summer by means of quantitative real-time PCR and it was generally higher than deeper layers. Most of the dsrA clones sequenced were related to green sulfur bacteria such as Chlorobium phaeovibrioides, C. limicola, and C. luteolum. Below the chemocline of the lake, we also detected other dsrA clones related to the purple sulfur bacterium Halochromatium salexigens and some branching lineages of diverse sequences that were related to chemotrophic sulfur bacterial species such as Magnetospirillum gryphiswaldense, Candidatus Ruthia magnifica, and Candidatus Thiobios zoothamnicoli. The abundance and community compositions of sulfur-oxidizing bacteria changed depending on the water depth and season. This study indicated that the green sulfur bacteria dominated among sulfur-oxidizing bacterial population in the chemocline of Lake Suigetsu and that certain abiotic environmental variables were important factors that determined sulfur bacterial abundance and community structure.


title: Deep Neural Networks for traffic flow prediction

abstract: Traffic flow prediction is an essential function of traffic information systems. Conventional approaches, using artificial neural networks with narrow network architecture and poor training samples for supervised learning, have been only partially successful. In this paper, a deep-learning neural-network based on TensorFlow™ is suggested for the prediction traffic flow conditions, using real-time traffic data. Until now, no research has applied the TensorFlow™ deep learning neural network model to the estimation of traffic conditions. The suggested supervised model is trained by a deep learning algorithm, which uses real traffic data aggregated every five minutes. Results demonstrate that the model's accuracy rate is around 99%.


Intelligent Predictive Decision Support System ( IPDSS ) for Condition-Based Maintenance


title: Towards Transactional Memory Support for GCC

abstract: Transactional memory is a parallel programming model providing many advantages over lock-based concurrency. It is one important attempt to exploit the potential of multicore architectures while preserving software development productivity. This paper describes the design of a transactional memory extension for GCC, and highlights research challenges and perspectives enabled by this design.


title: $V_{T}$ Compensation Circuit for AM OLED Displays Composed of Two TFTs and One Capacitor

abstract: In this paper, we propose the threshold-voltage compensation pixel circuit that is composed of two thin-film transistors (TFTs) and one capacitor (2T1C). It not only compensates the deviation of the threshold voltage of the driver TFT but also actualizes the large aperture ratio for organic light-emitting diode (OLED) devices as well as the traditional 2T1C circuit. We show the result of SPICE simulation for the pixel circuit; it indicates that the circuit can allocate the relatively large aperture ratio for OLED devices


title: Application of Data Mining Techniques in Weather Prediction and Climate Change Studies

abstract: Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2000 and 2009 from the city of Ibadan, Nigeria. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies.


title: Csrs-Siat: A Benchmark Remote Sensing Dataset to Semantic-Enabled and Cross-Scales Scene Recognition

abstract: The deep learning has been widely used in scene recognition of remote sensing images. However, the accuracy of deep learning relays on the size of training dataset to the utmost. The remote sensing images have various spatial scales and semantics, which are not fully considered in the existing datasets. In this paper, a benchmark remote sensing dataset named as Cross-Scale Remote Sensing dataset of Shenzhen Institutes of Advanced Technology (CSRS-SIAT) is proposed, which has about 100 classes according to the land cover and land use field, and due to the cross-scale characteristics of remote sensing images, the experiments using traditional and state-of-the-art deep learning algorithms shows that there still need more efforts to achieve better results.


title: The assessment of subjective well-being (issues raised by the

abstract: This commentary raises conceptual issues related to recent efforts to develop measures of subjective wellbeing (SWB).Specifically, Hills’ and Argyle’s (2002) article on the development of the 29-item Oxford Happiness Questionnaire (OHQ), and its predecessor, the 20-item Oxford Happiness Inventory (Argyle, Martin & Crossland, 1989).Instead of assessing the structure of subjective well-being (SWB), items of the OHQ tap into self-esteem, sense of purpose, social interest and kindness, sense of humor, and aesthetic appreciation.The item content of the OHQ fails to differentiate the assessment of SWB from the predictors, correlates, and consequences of SWB.In contrast to published SWB findings with other measures, data are presented suggesting that the OHQ has artificially inflated correlations with those constructs tapped by the OHQ: self-esteem, sense of purpose, and social interest/extraversion.The operationalization of SWB by the OHQ is not based on relevant definition and theory and appears to invite nonrandom error into the study of SWB.The article concludes with an appeal for the use of more stringent conceptual and analytic approaches. # 2003 Elsevier Ltd.All rights reserved.


title: Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation

abstract: Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and 'appreciates') of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer 'visually consistent' content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves.


title: In vivo absorption and metabolism of leptosperin and methyl syringate, abundantly present in manuka honey.

abstract: SCOPE ::: Manuka honey, which shows strong nonperoxide-dependent antibacterial activity, contains unique components, such as methyl syringate 4-O-β-D-gentiobioside (leptosperin) and its aglycone, methyl syringate (MSYR). To determine the potential for biological activity evoked by the ingestion of leptosperin and MSYR, we investigated the absorption and metabolism of these components in manuka honey. ::: ::: ::: METHODS AND RESULTS ::: The incubation of MSYR with liver microsomes or S9 fractions in vitro resulted in the formation of MSYR-glucuronide (MSYR-GA), MSYR-sulfate (MSYR-S), and syringic acid as metabolites. Then, manuka honey (15 g) was fed to healthy human volunteers. MSYR-GA, MSYR-S, and MSYR were detected in both plasma and urine. Within plasma, their levels were highest within 0.5 h to 1 h post-ingestion, and most metabolites disappeared within 3 h. In conjunction with the disappearances, a significant amount of metabolites along with trace leptosperin was excreted in urine within 4 h. To elucidate the detailed metabolisms of leptosperin and MSYR, each compound was separately administered to mice. In each case, MSYR-GA, MSYR-S, and MSYR were detected in both plasma and urine. ::: ::: ::: CONCLUSION ::: This study shows the major molecular pathway for leptosperin and MSYR metabolism and could facilitate an understanding of biological functions of manuka honey post ingestion.


title: The Relationship Between Gender Social Identity and Support for Feminism

abstract: This study is an application of social identity theory to feminist consciousness and activism. For women, strong gender identifications may enhance support for equality struggles, whereas for men, they may contribute to backlashes against feminism. University students (N = 276), primarily Euroamerican, completed a measure of gender self-esteem (GSE, that part of one's self-concept derived from one's gender), and two measures of feminism. High GSE in women and low GSE in men were related to support for feminism. Consistent with past research, women were more supportive of feminism than men, and in both genders, support for feminist ideas was greater than self-identification as a feminist.


title: A Survey of Dimension Reduction Techniques

abstract: Advances in data collection and storage capabilities during the past decades have led to an information overload in most sciences. Researchers working in domains as diverse as engineering, astronomy, biology, remote sensing, economics, and consumer transactions, face larger and larger observations and simulations on a daily basis. Such datasets, in contrast with smaller, more traditional datasets that have been studied extensively in the past, present new challenges in data analysis. Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data, is the number of variables that are measured on each observation. High-dimensional datasets present many mathematical challenges as well as some opportunities, and are bound to give rise to new theoretical developments. One of the problems with high-dimensional datasets is that, in many cases, not all the measured variables are ''important'' for understanding the underlying phenomena of interest. While certain computationally expensive novel methods can construct predictive models with high accuracy from high-dimensional data, it is still of interest in many applications to reduce the dimension of the original data prior to any modeling ofmore » the data. In this paper, we described several dimension reduction methods.« less


title: A Methodology for Outlier Detection in Audit Logs for Financial Transactions

abstract: Database audit logs contain the information about database operations which are helpful to verify accuracy, lawfulness and to report risks. In financial systems, the audit logs should be monitored on continuous basis in order to detect and take action against any reasonably abnormal behavior. Outlier detection is a very important concept in the data mining which is useful in data analysis. Nowadays, a direct mapping can be found between the data outliers and the real world anomalies, and hence the outlier detection techniques can be applied to detect the abnormal activities in financial transactions. The purpose of the present study is to automate the process of abnormal activity detection and report generation. In the proposed framework, we will detect outliers based on rules and then we will apply Bayesian classification technique for abnormal activity risk classification. We will also generate user transaction profiles by obtaining user behavior according to historic transactions based on categorical or numerical attributes. Later we will monitor new transactions and will compare it against the corresponding user profile, in order to determine if the transaction is unusual. This framework will work on audit logs from a financial system, and thus will be useful for financial system auditors performing data analysis tasks.


title: A theory of biological pattern formation

abstract: One of the elementary processes in morphogenesis is the formation of a spatial pattern of tissue structures, starting from almost homogeneous tissue. It will be shown that relatively simple molecular mechanisms based on auto- and cross catalysis can account for a primary pattern of morphogens to determine pattern formation of the tissue. The theory is based on short range activation, long range inhibition, and a distinction between activator and inhibitor concentrations on one hand, and the densities of their sources on the other. While source density is expected to change slowly, e.g. as an effect of cell differentiation, the concentration of activators and inhibitors can change rapidly to establish the primary pattern; this results from auto- and cross catalytic effects on the sources, spreading by diffusion or other mechanisms, and degradation.


title: Long Short-Term Memory Over Recursive Structures

abstract: The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process. We call the model S-LSTM, which provides a principled way of considering long-distance interaction over hierarchies, e.g., language or image parse structures. We leverage the models for semantic composition to understand the meaning of text, a fundamental problem in natural language understanding, and show that it outperforms a state-of-the-art recursive model by replacing its composition layers with the S-LSTM memory blocks. We also show that utilizing the given structures is helpful in achieving a performance better than that without considering the structures.


title: Haptic Shared Control in Tele-Manipulation: Effects of Inaccuracies in Guidance on Task Execution

abstract: Haptic shared control is a promising approach to improve tele-manipulated task execution, by making safe and effective control actions tangible through guidance forces. In current research, these guidance forces are most often generated based on pre-generated, errorless models of the remote environment. Hence such guidance forces are exempt from the inaccuracies that can be expected in practical implementations. The goal of this research is to quantify the extent to which task execution is degraded by inaccuracies in the model on which haptic guidance forces are based. In a human-in-the-loop experiment, subjects (n = 14) performed a realistic tele-manipulated assembly task in a virtual environment. Operators were provided with various levels of haptic guidance, namely no haptic guidance (conventional tele-manipulation), haptic guidance without inaccuracies, and haptic guidance with translational inaccuracies (one large inaccuracy, in the order of magnitude of the task, and a second smaller inaccuracy). The quality of natural haptic feedback (i.e., haptic transparency) was varied between high and low to identify the operator’s ability to detect and cope with inaccuracies in haptic guidance. The results indicate that haptic guidance is beneficial for task execution when no inaccuracies are present in the guidance. When inaccuracies are present, this may degrade task execution, depending on the magnitude and the direction of the inaccuracy. The effect of inaccuracies on overall task performance is dominated by effects found for the Constrained Translational Movement, due to its potential for jamming. No evidence was found that a higher quality of haptic transparency helps operators to detect and cope with inaccuracies in the haptic guidance.


title: VENUS: A System for Streamlined Graph Computation on a Single PC

abstract: Recent studies show that disk-based graph computation systems on just a single PC can be as highly competitive as cluster-based systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale graphs efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-centric “streamlined” processing—the graph is sequentially loaded and an update function is executed for each vertex in parallel on the fly. VENUS deliberately avoids loading batch edge data by separating read-only structure data from mutable vertex data on disk, and minimizes random IOs by caching vertex data in the main memory whenever possible. The streamlined processing is realized with efficient sequential scan over massive structure data and fast feeding the update function for a large number of vertices. Extensive evaluation on large real-world and synthetic graphs has demonstrated the efficiency of VENUS. For example, to run the PageRank algorithm on a Twitter graph of 42 million vertices and 1.4 billion edges, Spark needs 8.1 minutes with 50 machines and GraphChi spends 13 minutes using high-speed SSD, while VENUS only takes 5 minutes on one machine with an ordinary hard disk.


title: Supply-Doubled Pulse-Shaping High Voltage Pulser for CMUT Arrays

abstract: A supply-doubled pulse-shaping high voltage (HV) pulser is presented for medical ultrasound imaging applications, particularly those that use capacitive micromachined ultrasonic transducers (CMUTs). The pulser employs a bootstrap circuit combined with dynamically biased stacked transistors, which allow HV operation above process limit without lowering device reliability. The new pulser overcomes supply voltage limitation of conventional unipolar pulsers by generating output signals that are almost twice the supply level. It also can generate three-level pulses to further optimize the transmit pressure signals. A proof-of-concept prototype has been implemented in 0.18- ${\mu }\text{m}$ HV CMOS/DMOS technology with 60 V devices. Measurement results show that the HV pulser can safely generate controllable three-level pulses with up to 85 $\text{V}_{{\mathrm{ pp}}}$ from 45 V supply. Acoustic measurements are conducted connecting the pulser to a CMUT with 2 pF capacitance and 8.3 MHz center frequency. The pulse shape has been adjusted for the CMUT under test to generate maximum pressure output and the results are in good agreement with a large signal CMUT model.


title: Building a State-of-the-Art Grammatical Error Correction System

abstract: This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system. We do this by analyzing the Illinois system that placed first among seventeen teams in the recent CoNLL-2013 shared task on grammatical error correction. The system focuses on five different types of errors common among non-native English writers. We describe four design principles that are relevant for correcting all of these errors, analyze the system along these dimensions, and show how each of these dimensions contributes to the performance.


title: Security Risk Management via Dynamic Games with Learning

abstract: This paper presents a game-theoretic and learning approach to security risk management based on a model that captures the diffusion of risk in an organization with multiple technical and business processes. Of particular interest is the way the interdependencies between processes affect the evolution of the organization's risk profile as time progresses, which is first developed as a probabilistic risk framework and then studied within a discrete Markov model. Using zero-sum dynamic Markov games, we analyze the interaction between a malicious adversary whose actions increases the risk level of the organization and a defender agent, e.g. security and risk management division of the organization, which aims to mitigate risks. We derive min-max (saddle point) solutions of this game to obtain the optimal risk management strategies for the organization to achieve a certain level of performance. This methodology also applies to worst-case scenario analysis where the adversary can be interpreted as a nature player in the game. In practice, the parameters of the Markov game may not be known due to the costly nature of collecting and processing information about the adversary as well an organization with many components itself. We apply ideas from Q-learning to analyze the behavior of the agents when little information is known about the environment in which the attacker and defender interact. The framework developed and results obtained are illustrated with a small example scenario and numerical analysis.


title: Exploring solutions put forth to solve computer forensic investigations of large storage media.

abstract: The capacity of digital storage media is growing at a phenomenal rate, leading to an increase in the overall time it takes to process a typical digital forensics investigation. Conventional tools and techniques simply do not cater for the size of potential evidence that investigators have to analyse. With digital evidence being available on an increasing number of digital media types, ranging from portable media players, to Global Positioning System (GPS) devices, to rack-mountable servers, in addition to the fact that there is a rising trend for digitizing information in the business world, the problem is only getting worse. This paper endeavours to initiate an investigation into the current solutions put forth to solve computer forensic investigations of large storage media for the purpose of stimulating ideas and encouraging expansion of current solutions within the research community.


title: Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria

abstract: BackgroundEstuaries are among the most productive habitats on the planet. Bacteria in estuary sediments control the turnover of organic carbon and the cycling of nitrogen and sulfur. These communities are complex and primarily made up of uncultured lineages, thus little is known about how ecological and metabolic processes are partitioned in sediments.ResultsDe novo assembly and binning resulted in the reconstruction of 82 bacterial genomes from different redox regimes of estuary sediments. These genomes belong to 23 bacterial groups, including uncultured candidate phyla (for example, KSB1, TA06, and KD3-62) and three newly described phyla (White Oak River (WOR)-1, WOR-2, and WOR-3). The uncultured phyla are generally most abundant in the sulfate-methane transition (SMTZ) and methane-rich zones, and genomic data predict that they mediate essential biogeochemical processes of the estuarine environment, including organic carbon degradation and fermentation. Among the most abundant organisms in the sulfate-rich layer are novel Gammaproteobacteria that have genes for the oxidation of sulfur and the reduction of nitrate and nitrite. Interestingly, the terminal steps of denitrification (NO3 to N2O and then N2O to N2) are present in distinct bacterial populations.ConclusionsThis dataset extends our knowledge of the metabolic potential of several uncultured phyla. Within the sediments, there is redundancy in the genomic potential in different lineages, often distinct phyla, for essential biogeochemical processes. We were able to chart the flow of carbon and nutrients through the multiple geochemical layers of bacterial processing and reveal potential ecological interactions within the communities.


title: A traffic-aware street lighting scheme for Smart Cities using autonomous networked sensors

abstract: Display Omitted TALiSMaN detects road users and sets streetlight brightness appropriately.A utility model is detailed to quantify the usefulness of street lighting.Street lighting schemes are evaluated with StreetlightSim.TALiSMaN offers comparable usefulness as conventional lighting schemes.TALiSMaN consumes 2-55% of the energy of conventional/state-of-the-art schemes. Street lighting is a ubiquitous utility, but sustaining its operation presents a heavy financial and environmental burden. Many schemes have been proposed which selectively dim lights to improve energy efficiency, but little consideration has been given to the usefulness of the resultant street lighting system. This paper proposes a real-time adaptive lighting scheme, which detects the presence of vehicles and pedestrians and dynamically adjusts their brightness to the optimal level. This improves the energy efficiency of street lighting and its usefulness; a streetlight utility model is presented to evaluate this. The proposed scheme is simulated using an environment modelling a road network, its users, and a networked communication system - and considers a real streetlight topology from a residential area. The proposed scheme achieves similar or improved utility to existing schemes, while consuming as little as 1-2% of the energy required by conventional and state-of-the-art techniques.


title: N‐alkylation of chitosan by β‐halopropionic acids in the presence of various acceptors

abstract: N-carboxyethylation of chitosan by β-halopropionic acids in the presence of various proton and halogen ion acceptors was investigated. It has been observed that carboxyethylation of chitosan in aqueous medium is accompanied by the by-processes of hydrolysis and dehydrohalogenation of the β-halopropionic acids yielding β-hydroxypropionic acid, bis(2-carboxyethyl) ether, and acrylic acid. Degree of carboxyethyl substitution (DS) of chitosan and the relative rates of the by-processes varied significantly depending on the conditions used and nature of the proton or halogen ion acceptor. At carboxyethylation of chitosan with the alkaline β-bromopropionates, the DS increased in the order Cs+ < Rb+ < K+ ∼ Na+ < Li+. For alkaline earth salts BrCH2CH2COOM0.5 (M = Be2+, Mg2+, Ca2+, Sr2+, Ba2+), the highest DS was obtained with strontium and barium salts, which could be subsequently removed from the reaction mixture by precipitation as sulfates. Among the organic bases applied (tetrabutylammonium hydroxide, triethylamine, trimethylamine, pyridine, 4-N,N-dimethylaminopyridine, 2,6-lutidine, and 1,5-diazabicyclo[4.3.0] non-5-ene), the highest DS was obtained using a moderately strong base triethylamine. For the halogen acceptors (Pb2+, Ag+, Tl+), the stoichiometrically highest DS was achieved in a system comprising iodopropionic acid plus Tl+ and a comparable conversion rate was obtained using also a combination of chloropropionic acid and Ag+. A novel alternative preparative approach—gel-state synthesis—was suggested that provides for the highest DS at the optimum reaction conditions. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008


title: Defining Enterprise Architecture: A Systematic Literature Review

abstract: In recent years, there has been an increasing interest among researchers and practitioners concerning Enterprise Architecture (EA). Despite this increase, several studies have reported a lack of common understanding in EA. Some specific expressions like lack of common terminology, lack of shared meaning and fragmented literature have been frequently used to describe this lack. However, very few systematic studies have been conducted to gain a better understanding about the nature and the extend of these definition differences. This study presents a Systematic Literature Review considering journal articles that contain explicit definitions of EA. 145 definitions were identified and analyzed in order to determine their similarities and differences. The analysis was guided by concepts from the field of terminology. The findings of this study show that there are significant divergences of multiple kind between definitions. The next steps for the EA community would be to achieve something comparable to MacGregor's Theory X and Theory Y model or Mintzberg's schools of thought.


Comparative Study of Verse Similarity for Multi-lingual Representations of the Qur ’ an


title: PhishMonger: A free and open source public archive of real-world phishing websites

abstract: The number of active, online phishing websites continues to grow unabated in recent years. This has created an ever-increasing security risk for both individual and enterprise users in terms of identity theft, malware, financial loss, etc. Although resources exist for tracking, cataloguing, and blacklisting these types of sites (e.g., PhishTank.com), the ephemeral nature of phishing websites makes in-depth analysis exceptionally difficult. In order to better understand how these phishing sites exploit user and system weaknesses, we have crafted a platform named PhishMonger for capturing live phishing websites in real-time on an ever-present, rolling basis, which we outline in this paper. Moreover, we present details regarding our growing database of verified phishing websites, which currently encompasses over 88,754 sites, spanning 10,956,415 files and folders, utilizing 108GB of compressed storage. We offer recommendations on how this corpus can be leveraged by the cybersecurity and security informatics research communities to examine several important research problems.


title: One-Shot Face Recognition via Generative Learning

abstract: One-shot face recognition measures the ability to recognize persons with only seeing them once, which is a hallmark of human visual intelligence. It is challenging for existing machine learning approaches to mimic this way, since limited data cannot well represent the data variance. To this end, we propose to build a large-scale face recognizer, which is capable to fight off the data imbalance difficulty. To seek a more effective general classifier, we develop a novel generative model attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes. Specifically, we formulate conditional generative adversarial networks and the general Softmax classifier into a unified framework. Such a two-player minimax optimization can guide the generation of more effective data, which benefit the classifier learning for one-shot classes. The experimental results on a large-scale face benchmark with 21K persons verify the effectiveness of our proposed algorithm in one-shot classification, as our generative model significantly improves the recognition coverage rate from 25:65% to 94:84% at the precision of 99% for the one-shot classes, while still keeps an overall Top-1 accuracy at 99:80% for the normal classes.


Simultaneous estimation of several size distributions of income


title: System G Distributed Graph Database

abstract: Motivated by the need to extract knowledge and value from interconnected data, graph analytics on big data is a very active area of research in both industry and academia. To support graph analytics efficiently a large number of in memory graph libraries, graph processing systems and graph databases have emerged. Projects in each of these categories focus on particular aspects such as static versus dynamic graphs, off line versus on line processing, small versus large graphs, etc. While there has been much advance in graph processing in the past decades, there is still a need for a fast graph processing, using a cluster of machines with distributed storage. In this paper, we discuss a novel distributed graph database called System G designed for efficient graph data storage and processing on modern computing architectures. In particular we describe a single node graph database and a runtime and communication layer that allows us to compose a distributed graph database from multiple single node instances. From various industry requirements, we find that fast insertions and large volume concurrent queries are critical parts of the graph databases and we optimize our database for such features. We experimentally show the efficiency of System G for storing data and processing graph queries on state-of-the-art platforms.


title: Structural description and classification of signature images

abstract: This paper introduces a new method for describing signature images. The description involves features of the signatures and relations among its parts organized in a hierarchical structure. The description is used to classify the signatures and globally analyse their structure.


title: Leveraging RF-channel fluctuation for activity recognition: Active and passive systems, continuous and RSSI-based signal features

abstract: We consider the recognition of activities from passive entities by analysing radio-frequency (RF)-channel fluctuation. In particular, we focus on the recognition of activities by active Software-defined-radio (SDR)-based Device-free Activity Recognition (DFAR) systems and investigate the localisation of activities performed, the generalisation of features for alternative environments and the distinction between walking speeds. Furthermore, we conduct case studies for Received Signal Strength (RSS)-based active and continuous signal-based passive systems to exploit the accuracy decrease in these related cases. All systems are compared to an accelerometer-based recognition system.


title: A delay-tolerant network architecture for challenged internets

abstract: The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.


title: Estimation and removal of clock skew from network delay measurements

abstract: Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze network performance. The clocks on the end-systems used to measure the delays, however, are not always synchronized, and this lack of synchronization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. We introduce a linear programming-based algorithm to estimate the clock skew in network delay measurements and compare it with three other algorithms. We show that our algorithm has a time complexity of O(N), leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew estimate is independent of the magnitude of the skew. We use traces of real Internet delay measurements to assess the algorithm, and compare its performance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimate is better (smaller) than existing algorithms.