title: Automatic camera self-calibration for immersive navigation of free viewpoint sports video

abstract: In recent years, the demand of immersive experience has triggered a great revolution in the applications and formats of multimedia. Particularly, immersive navigation of free viewpoint sports video has become increasingly popular, and people would like to be able to actively select different viewpoints when watching sports videos to enhance the ultra realistic experience. In the practical realization of immersive navigation of free viewpoint video, the camera calibration is of vital importance. Especially, automatic camera calibration is very significant in real-time implementation and the accuracy of camera parameter directly determines the final experience of free viewpoint navigation. In this paper, we propose an automatic camera self-calibration method based on a field model for free viewpoint navigation in sports events. The proposed method is composed of three parts, namely, extraction of field lines in a camera image, calculation of crossing points, determination of the optimal camera parameter. Experimental results show that the camera parameter can be automatically estimated by the proposed method for a fixed camera, dynamic camera and multi-view cameras with high accuracy. Furthermore, immersive free viewpoint navigation in sports events can also be completely realized based on the camera parameter estimated by the proposed method.


title: Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

abstract: Learning from instructions or demonstrations is a fundamental property of our brain necessary to acquire new knowledge and develop novel skills or behavioral patterns. This type of learning is thought to be involved in most of our daily routines. Although the concept of instruction-based learning has been studied for several decades, the exact neural mechanisms implementing this process remain unrevealed. One of the central questions in this regard is, How do neurons learn to reproduce template signals (instructions) encoded in precisely timed sequences of spikes? Here we present a model of supervised learning for biologically plausible neurons that addresses this question. In a set of experiments, we demonstrate that our approach enables us to train spiking neurons to reproduce arbitrary template spike patterns in response to given synaptic stimuli even in the presence of various sources of noise. We show that the learning rule can also be used for decision-making tasks. Neurons can be trained to classify categories of input signals based on only a temporal configuration of spikes. The decision is communicated by emitting precisely timed spike trains associated with given input categories. Trained neurons can perform the classification task correctly even if stimuli and corresponding decision times are temporally separated and the relevant information is consequently highly overlapped by the ongoing neural activity. Finally, we demonstrate that neurons can be trained to reproduce sequences of spikes with a controllable time shift with respect to target templates. A reproduced signal can follow or even precede the targets. This surprising result points out that spiking neurons can potentially be applied to forecast the behavior (firing times) of other reference neurons or networks.


title: SecGraph: a uniform and open-source evaluation system for graph data anonymization and de-anonymization

abstract: In this paper, we analyze and systematize the state-of-the-art graph data privacy and utility techniques. Specifically, we propose and develop SecGraph (available at [1]), a uniform and open-source Secure Graph data sharing/publishing system. In SecGraph, we systematically study, implement, and evaluate 11 graph data anonymization algorithms, 19 data utility metrics, and 15 modern Structure-based De-Anonymization (SDA) attacks. To the best of our knowledge, SecGraph is the first such system that enables data owners to anonymize data by state-of-the-art anonymization techniques, measure the data's utility, and evaluate the data's vulnerability against modern De-Anonymization (DA) attacks. In addition, SecGraph enables researchers to conduct fair analysis and evaluation of existing and newly developed anonymization/DA techniques. Leveraging SecGraph, we conduct extensive experiments to systematically evaluate the existing graph data anonymization and DA techniques. The results demonstrate that (i) most anonymization schemes can partially or conditionally preserve most graph utilities while losing some application utility; (ii) no DA attack is optimum in all scenarios. The DA performance depends on several factors, e.g., similarity between anonymized and auxiliary data, graph density, and DA heuristics; and (iii) all the state-of-the-art anonymization schemes are vulnerable to several or all of the modern SDA attacks. The degree of vulnerability of each anonymization scheme depends on how much and which data utility it preserves.


title: ON THE NON-LINEAR STATISTICS OF RANGE IMAGE PATCHES HENRY ADAMS

abstract: In [A. B. Lee, K. S. Pedersen, and D. Mumford, Int. J. Comput. Vis., 54 (2003), pp. 83-103], the authors study the distributions of $3\times3$ patches from optical images and from range images. In [G. Carlsson, T. Ishkanov, V. de Silva, and A. Zomorodian, Int. J. Comput. Vis., 76 (2008), pp. 1-12], the authors apply computational topological tools to the data set of optical patches studied by Lee, Pedersen, and Mumford and find geometric structures for high density subsets. One high density subset is called the primary circle and essentially consists of patches with a line separating a light and a dark region. In this paper, we apply the techniques of Carlsson et al. to range patches. By enlarging to $5\times5$ and $7\times7$ patches, we find core subsets that have the topology of the primary circle, suggesting a stronger connection between optical patches and range patches than was found by Lee, Pedersen, and Mumford.


title: Detecting Insider Threat from Enterprise Social and Online Activity Data

abstract: Insider threat is a significant security risk for organizations. In this paper, we attempt to discover insider threat by identifying abnormal behavior in enterprise social and online activity data of employees. To this end, we process and extract relevant features that are possibly indicative of insider threat behavior. This includes features extracted from social data including email communication patterns and content, and online activity data such as web browsing patterns, email frequency, and file and machine access patterns. Subsequently, we detect statistically abnormal behavior with respect to these features using state-of-the-art anomaly detection methods, and declare this abnormal behavior as a proxy for insider threat activity. We test our approach on a real world data set with artificially injected insider threat events. We obtain a ROC score of 0.77, which shows that our proposed approach is fairly successful in identifying insider threat events. Finally, we build a visualization dashboard that enables managers and HR personnel to quickly identify employees with high threat risk scores which will enable them to take suitable preventive measures and limit security risk.


title: CORMAN: A Novel Cooperative Opportunistic Routing Scheme in Mobile Ad Hoc Networks

abstract: The link quality variation of wireless channels has been a challenging issue in data communications until recent explicit exploration in utilizing this characteristic. The same broadcast transmission may be perceived significantly differently, and usually independently, by receivers at different geographic locations. Furthermore, even the same stationary receiver may experience drastic link quality fluctuation over time. The combination of link-quality variation with the broadcasting nature of wireless channels has revealed a direction in the research of wireless networking, namely, cooperative communication. Research on cooperative communication started to attract interests in the community at the physical layer but more recently its importance and usability have also been realized at upper layers of the network protocol stack. In this article, we tackle the problem of opportunistic data transfer in mobile ad hoc networks. Our solution is called Cooperative Opportunistic Routing in Mobile Ad hoc Networks (CORMAN). It is a pure network layer scheme that can be built atop off-the-shelf wireless networking equipment. Nodes in the network use a lightweight proactive source routing protocol to determine a list of intermediate nodes that the data packets should follow en route to the destination. Here, when a data packet is broadcast by an upstream node and has happened to be received by a downstream node further along the route, it continues its way from there and thus will arrive at the destination node sooner. This is achieved through cooperative data communication at the link and network layers. This work is a powerful extension to the pioneering work of ExOR. We test CORMAN and compare it to AODV, and observe significant performance improvement in varying mobile settings.


title: Impact of mobile apps to combat obesity in children and adolescents: A systematic literature review

abstract: PURPOSE ::: This review examines the impact of mobile app technology on obesity-related anthropometric, psychosocial, and behavioral outcomes in children and adolescents. ::: ::: ::: CONCLUSIONS ::: Nine research articles retrieved from a systematic review of the literature met criteria. Evidence is limited and mixed, but argues for an impact of mobile app use on motivation and goal-setting behavior, and supports further study of the impact on childhood obesity-related outcomes such as attitudes, perceptions, physical activity, and dietary habits. ::: ::: ::: PRACTICE IMPLICATIONS ::: Nurses can use this evidence to discuss potential benefits of health promotion mobile apps with parents, children, and adolescents to combat childhood obesity.


title: The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios

abstract: The QLBS model is a discrete-time option hedging and pricing model that is based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines the famous Q-Learning method for RL with the Black-Scholes (-Merton) model's idea of reducing the problem of option pricing and hedging to the problem of optimal rebalancing of a dynamic replicating portfolio for the option, which is made of a stock and cash. Here we expand on several NuQLear (Numerical Q-Learning) topics with the QLBS model. First, we investigate the performance of Fitted Q Iteration for a RL (data-driven) solution to the model, and benchmark it versus a DP (model-based) solution, as well as versus the BSM model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for the model, where we only observe prices and actions (re-hedges) taken by a trader, but not rewards. Third, we outline how the QLBS model can be used for pricing portfolios of options, rather than a single option in isolation, thus providing its own, data-driven and model independent solution to the (in)famous volatility smile problem of the Black-Scholes model.


title: Cannabis: Use, harms, disorder, and interventions

abstract: Cannabis is the most common illicit drug of dependence in the Western world. Frequent cannabis use, especially when first used before the age of 16 years, is associated with addiction, poor mental health, impaired memory and learning, reduced educational attainment, in addition to social and legal difficulties. Despite the harms associated with cannabis use, dependent cannabis users are less likely to present for treatment than other illicit drug users and problematic cannabis use is rarely detected by health professionals. It is important that clinicians regularly include screening and assessment of cannabis use and account for cannabis use in case formulations. This paper will outline the prevalence of cannabis use, its associated harms, diagnostic considerations, and a brief review of best-practice treatment.


Walking in VR: Measuring Presence and Simulator Sickness in First-Person Virtual Reality Games


title: Big Data: Deep Learning for financial sentiment analysis

abstract: Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset.


An Algorithm Faster than NegaScout and SSS * in Practice


title: Zero-Suppressed BDDs for Set Manipulation in Combinatorial Problems

abstract: In this paper, we propose Zero-Suppressed BDDs (0-Sup-BDDs), which are BDDs based on a new reduction rule. This data structure brings unique and compact representation of sets which appear in many combinatorial problems. Using 0-Sup-BDDs, we can manipulate such sets more simply and efficiently than using original BDDs. We show the properties of 0-Sup-BDDs, their manipulation algorithms, and good applications for LSI CAD systems.


Continual Learning for Deep Dense Prediction


THE DESIGN THEORY NEXUS 1 2 By :


title: Christia vespertilionis plant extracts as novel antiproliferative agent against human neuroendocrine tumor cells

abstract: Neuroendocrine tumors respond poorly to radiation and conventional chemotherapy, hence surgical removal of the neoplastic tissue is still the most effective way of treatment. In an attempt to find new therapeutic plant extracts of Christia vespertilionis (CV) their antitumor potential in human medullary thyroid carcinoma (MTC) and human small intestinal neuroendocrine tumor (SI-NET) cell lines were tested. Proliferation and viability were analyzed using cell counting and WST-1 assay. Apoptosis was determined by microscopy, luminescence assays for caspases 3/7, and expression studies of apoptosis-related genes. CV extracts showed antiproliferative and proapoptotic effects in all MTC and SI-NET cell lines, whereby high growth inhibition was observed by treatment with the ethylacetate-extracts (CV-45) in tumor cell lines but not in normal human fibroblasts. Furthermore CV-45 treatment resulted in alterations of gene expression of PDCD5, MTDH and TNFRSF10b in MTC as well as in SI-NET cells. The results indicate that Christia vespertilionis could serve as an anticancer therapeutic for treatment of neuroendocrine tumors.


title: Predicting future states in DotA 2 using value-split models of time series attribute data

abstract: In Multiplayer Online Battle Arena (MOBA) games, teams of players compete in combat to complete an objective and defeat the opposing team. To stay alive, players must closely monitor their character's status, especially remaining health. Understanding how health may change in the near future can be vital in determining what tactics a player may use. We analyzed replay logs of the game Defense of the Ancients 2 (DotA 2) to discover methods to predict how players' health evolves over time. For DotA 2, our results suggest that forecasting changes in a player's health can be done by viewing gameplay as two separate processes: normal gameplay flow in which changes in health are smaller and more regular, and less frequent but higher-impact events in which players experience larger changes in their health, such as team battles. We accomplished this by considering health data as two separate, but interleaved, time series in which separate processes govern low magnitude changes in health from high magnitude changes. In this paper, we present a value-split approach to predicting changes in health and describe the results of our approach using autoregressive moving-average models for low magnitude health changes and a combination of statistical models for the larger changes.


title: Differentiable programming for image processing and deep learning in halide

abstract: Gradient-based optimization has enabled dramatic advances in computational imaging through techniques like deep learning and nonlinear optimization. These methods require gradients not just of simple mathematical functions, but of general programs which encode complex transformations of images and graphical data. Unfortunately, practitioners have traditionally been limited to either hand-deriving gradients of complex computations, or composing programs from a limited set of coarse-grained operators in deep learning frameworks. At the same time, writing programs with the level of performance needed for imaging and deep learning is prohibitively difficult for most programmers. ::: We extend the image processing language Halide with general reverse-mode automatic differentiation (AD), and the ability to automatically optimize the implementation of gradient computations. This enables automatic computation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort. A key challenge is to structure the gradient code to retain parallelism. We define a simple algorithm to automatically schedule these pipelines, and show how Halide's existing scheduling primitives can express and extend the key AD optimization of "checkpointing." ::: Using this new tool, we show how to easily define new neural network layers which automatically compile to high-performance GPU implementations, and how to solve nonlinear inverse problems from computational imaging. Finally, we show how differentiable programming enables dramatically improving the quality of even traditional, feed-forward image processing algorithms, blurring the distinction between classical and deep methods.


title: Measuring service quality in E-retailing using SERVQUAL model

abstract: Practitioners and academics are eager on measuring service quality accurately in order to have better understanding of its indispensable antecedent and consequences, and eventually ascertain methods for improving and measuring service quality in search for competitive advantage. The aim of this study is to rank the dimensions of service quality that affect the customerspsila expectation in online purchasing in Iran from the customerspsila perspective. A questionnaire used in this study was published in Cloob.com which is an Iranian virtual society Web site. The measurements used were based on the widely accepted SERVQUAL model which is the most common method for measuring service quality. This study also examined the service quality gap by comparing customerspsila expectations and their actual perceptions. The results of the study indicated that all of the service quality factors are important. Tangibility was rated as the most important dimension followed by assurance, reliability, responsiveness, and empathy.


title: Toward Automated Dynamic Malware Analysis Using CWSandbox

abstract: Malware is notoriously difficult to combat because it appears and spreads so quickly. In this article, we describe the design and implementation of CWSandbox, a malware analysis tool that fulfills our three design criteria of automation, effectiveness, and correctness for the Win32 family of operating systems


Comprehensive promoter analyses using a database of transcription start sites


title: Timed Commitments

abstract: We introduce and construct timed commitment schemes, an extension to the standard notion of commitments in which a potential forced opening phase permits the receiver to recover (with effort) the committed value without the help of the committer. An important application of our timed-commitment scheme is contract signing: two mutually suspicious parties wish to exchange signatures on a contract. We show a two-party protocol that allows them to exchange RSA or Rabin signatures. The protocol is strongly fair: if one party quits the protocol early, then the two parties must invest comparable amounts of time to retrieve the signatures. This statement holds even if one party has many more machines than the other. Other applications, including honesty preserving auctions and collective coin-flipping, are discussed.


title: Understanding the Intrinsic Motivations of User Acceptance of Hedonic Information Systems: Towards a Unified Research Model

abstract: Although user acceptance of entertainment-oriented information systems (IS), which are called Hedonic IS (HIS), has drawn considerable attention in literature, our understanding of user acceptance of HIS is still limited. This article focuses on exploring the intrinsic motivations of HIS acceptance from a unique perspective. It proposes a hybrid HIS acceptance model that considers the unique characteristics of HIS and the multiple conceptual identities of an HIS user. The model integrates intrinsic motivation factors from Hedonic theory, Flow theory, and the PAD (Pleasure, Arousal, and Dominance) emotion model with the Technology Acceptance Model. The proposed hybrid HIS acceptance model has been empirically tested by a quantitative field survey. The results indicate that emotional responses, imaginal responses, and flow experience are three main predictors of HIS acceptance.


title: Identifying HTTPS-Protected Netflix Videos in Real-Time

abstract: After more than a year of research and development, Netflix recently upgraded their infrastructure to provide HTTPS encryption of video streams in order to protect the privacy of their viewers. Despite this upgrade, we demonstrate that it is possible to accurately identify Netflix videos from passive traffic capture in real-time with very limited hardware requirements. Specifically, we developed a system that can report the Netflix video being delivered by a TCP connection using only the information provided by TCP/IP headers. To support our analysis, we created a fingerprint database comprised of 42,027 Netflix videos. Given this collection of fingerprints, we show that our system can differentiate between videos with greater than 99.99% accuracy. Moreover, when tested against 200 random 20-minute video streams, our system identified 99.5% of the videos with the majority of the identifications occurring less than two and a half minutes into the video stream.


title: Bounded-skew clock and Steiner routing

abstract: We study the minimum-cost bounded-skew routing tree problem under the pathlength (linear) and Elmore delay models. This problem captures several engineering tradeoffs in the design of routing topologies with controlled skew. Our bounded-skew routing algorithm, called the BST/DME algorithm, extends the DME algorithm for exact zero-skew trees via the concept of a merging region . For a prescribed topology , BST/DME constructs a bounded-skew tree (BST) in two phases: (i) a bottom-up phase to construct a binary tree of merging regions which represent the loci of possible embedding points of the internal nodes, and (ii) a top-down phase to determine the exact locations of the internal nodes. We present two approaches to construct the merging regions: (i) the Boundary Merging and Embedding (BME) method which utilizes merging points that are restricted to the boundaries of merging regions, and (ii) the Interior Merging and Embedding (IME) algorithm which employs a sampling strategy and a dynamic programming-based selection technique to consider merging points that are interior to, as well as on the boundary of, the merging regions. When the topology is not prescribed, we propose a new Greedy -BST/DME algorithm which combines the merging region computation with topology generation. The Greedy-BST/DME algorithm very closely matches the best known heuristics for the zero-skew case and for the unbounded-skew case (i.e., the Steiner minimal tree problem). Experimental results show that our BST algorithms can produce a set of routing solutions with smooth skew and wire length tradeoffs.


title: Stream-based speaker segmentation using speaker factors and eigenvoices

abstract: This paper presents a stream-based approach for unsupervised multi-speaker conversational speech segmentation. The main idea of this work is to exploit prior knowledge about the speaker space to find a low dimensional vector of speaker factors that summarize the salient speaker characteristics. This new approach produces segmentation error rates that are better than the state of the art ones reported in our previous work on the segmentation task in the NIST 2000 Speaker Recognition Evaluation (SRE). We also show how the performance of a speaker recognition system in the core test of the 2006 NIST SRE is affected, comparing the results obtained using single speaker and automatically segmented test data.


Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection


LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives


Modeling a Configuration Problem with Generic Tasks


title: Predicting Inter-Data-Center Network Traffic Using Elephant Flow and Sublink Information

abstract: With the ever increasing number of large scale Internet applications, inter-data-center (inter-DC) data transfers are becoming more and more common. Traditional inter-DC transfers suffer from both low utilization and congestion, and traffic prediction is an important method to optimize these transfers. Inter-DC traffic is harder to predict than many other types of network traffic because it is dominated by a few large applications. We propose a model that significantly reduces the prediction errors. In our model, we combine wavelet transform with artificial neural network to improve prediction accuracy. Specifically, we explicitly add information of sublink traffic and elephant flows, the least predictable yet dominating traffic in inter-DC network, into our prediction model. To reduce the amount of monitoring overhead for the elephant flow information, we add interpolation to fill in the unknown values in the elephant flows. We demonstrate that we can reduce prediction errors over existing methods by 5% $ \sim 30$ %. Our prediction is in production as part of the traffic scheduling system at Baidu, one of the largest Internet companies in China, helping to reduce the peak network bandwidth.


title: Creating consistent scene graphs using a probabilistic grammar

abstract: Growing numbers of 3D scenes in online repositories provide new opportunities for data-driven scene understanding, editing, and synthesis. Despite the plethora of data now available online, most of it cannot be effectively used for data-driven applications because it lacks consistent segmentations, category labels, and/or functional groupings required for co-analysis. In this paper, we develop algorithms that infer such information via parsing with a probabilistic grammar learned from examples. First, given a collection of scene graphs with consistent hierarchies and labels, we train a probabilistic hierarchical grammar to represent the distributions of shapes, cardinalities, and spatial relationships of semantic objects within the collection. Then, we use the learned grammar to parse new scenes to assign them segmentations, labels, and hierarchies consistent with the collection. During experiments with these algorithms, we find that: they work effectively for scene graphs for indoor scenes commonly found online (bedrooms, classrooms, and libraries); they outperform alternative approaches that consider only shape similarities and/or spatial relationships without hierarchy; they require relatively small sets of training data; they are robust to moderate over-segmentation in the inputs; and, they can robustly transfer labels from one data set to another. As a result, the proposed algorithms can be used to provide consistent hierarchies for large collections of scenes within the same semantic class.


title: Drawing planar graphs using the canonical ordering

abstract: We introduce a new method to optimize the required area, minimum angle, and number of bends of planar graph drawings on a grid. The main tool is a new type of ordering on the vertices and faces of triconnected planar graphs. Using this method linear-time-and-space algorithms can be designed for many graph-drawing problems. Our main results are as follows: ::: ::: ::: Every triconnected planar graphG admits a planar convex grid drawing with straight lines on a (2n−4)×(n−2) grid, wheren is the number of vertices. ::: ::: ::: Every triconnected planar graph with maximum degree 4 admits a planar orthogonal grid drawing on ann×n grid with at most [3n/2]+4 bends, and ifn>6, then every edge has at most two bends. ::: ::: ::: Every planar graph with maximum degree 3 admits a planar orthogonal grid drawing with at most [n/2]+1 bends on an [n/2]×[n/2] grid. ::: ::: ::: Every triconnected planar graphG admits a planar polyline grid drawing on a (2n−6)×(3n−9) grid with minimum angle larger than 2/d radians and at most 5n−15 bends, withd the maximum degree.


title: Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans

abstract: Convolutional neural networks (CNNs) have emerged as the most powerful technique for a range of different tasks in computer vision. Recent work suggested that CNN features are generic and can be used for classification tasks outside the exact domain for which the networks were trained. In this work we use the features from one such network, OverFeat, trained for object detection in natural images, for nodule detection in computed tomography scans. We use 865 scans from the publicly available LIDC data set, read by four thoracic radiologists. Nodule candidates are generated by a state-of-the-art nodule detection system. We extract 2D sagittal, coronal and axial patches for each nodule candidate and extract 4096 features from the penultimate layer of OverFeat and classify these with linear support vector machines. We show for various configurations that the off-the-shelf CNN features perform surprisingly well, but not as good as the dedicated detection system. When both approaches are combined, significantly better results are obtained than either approach alone. We conclude that CNN features have great potential to be used for detection tasks in volumetric medical data.


title: Automatic pothole and speed breaker detection using android system

abstract: Recent studies have shown that road surface monitoring is essential for the municipal corporations as well as for travelers for choosing the best road possible. Such schemes provide the comfort and security to the vehicle travelers. This paper presents a vibration based approach for automatic detection of potholes and speed breakers along with their co-ordinates. In this approach, a database is maintained for each road, which is made available to the public with the help of global database or through a portal. Potholes and speed breakers are detected along with their severity using android's built-in accelerometer. The results of the proposed approach are tested over a 4 km flat road and compared to manual inspection of pothole and speed breakers on the same considered road. The accuracy of the proposed approach came out to be 93.75% for detection of potholes and speed breakers. This approach is cost efficient and very effective for road surface monitoring.


title: Nodular melanoma: five consecutive cases in a general practice with polarized and non-polarized dermatoscopy and dermatopathology

abstract: BACKGROUND ::: The incidence of nodular melanoma (NM) has been consistently described as at least 10-15% of total melanomas for over 15 years despite advances in diagnostic algorithms and medical technology. NMs are strongly correlated with faster rates of growth and poorer prognosis and thus provide clinicians with a challenge for early recognition. ::: ::: ::: OBJECTIVE ::: To evaluate diagnostic clues of consecutive histopathologically proven NMs in one general practice with particular emphasis on dermatoscopic characteristics and compare this to the published literature. ::: ::: ::: METHOD ::: A retrospective observational study was performed of five consecutive histologically proven NM, from a total of 212 consecutive melanomas from a general practice in Brisbane, Queensland, Australia. Dermatoscopic images, both polarized and non-polarized, which appears to be a unique resource, and dermatopathologic slides were available for all lesions. ::: ::: ::: RESULTS ::: All of the NMs in this series were pigmented although one was hypomelanotic. Two of them were symmetrical. The most highly sensitive clues to NM were gray or blue structures and polarizing-specific white lines. ::: ::: ::: LIMITATIONS ::: Due to the small number of NMs in this report no statistical significance can be attributed to the observational findings. ::: ::: ::: CONCLUSION ::: THIS SMALL SERIES SUPPORTS WHAT IS ALREADY KNOWN: that a significant proportion of NMs may be dermatoscopically symmetrical but that known clues to melanoma are frequently present. Nodular lesions, pigmented or non-pigmented, should be excised to exclude NM if there is any clue to malignancy, regardless of symmetry, unless a confident specific benign diagnosis can be made.


title: MOEA/D-GO for Fragmented Antenna Design

abstract: In this paper, a hybrid multiobjective evolutionary algorithm, MOEA/D-GO (Multiobjective Evolutionary Algorithm Based on Decomposition combined with Enhanced Genetic Operators), is proposed for fragment-type antenna design. It combines the ability and e-ciency of MOEA/D to deal with multiobjective optimization problems with the speciflc character of two-dimensional chromosome coding of genetic algorithm. And enhanced genetic operators are also introduced to generate new individuals. Numerical results of a set of six multiobjective 0/1 knapsack problems show that MOEA/D- GO with weighted sum decomposition approach outperforms original MOEA/D and MOEA/D-PR (MOEA/D combined with Path- Relinking operator). Then it's applied to optimize a CPW-fed monopole antenna to achieve band-notch characteristic. Both numerical and test results show that MOEA/D-GO is promising for solving multiobjective optimization problems about fragmented antenna.


title: Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models

abstract: Bluetooth Low Energy (BLE) is a wireless technology for exchanging data, over short distances, designed for the Internet-of-Things era. As widely supported by wearable devices, BLE has the potential to become an alternative for indoor-localization and proximity sensing. The aim of this work was to perform a thorough characterization of the RSSI-distance relationship under controlled conditions using two BLE devices. Four calibration models underwent to a comparative evaluation analysis. The best results were obtained using a polynomial model with a mean distance percentage error equal to 25.7% (0.4 m) in the range 0–3 m. An overall improvement of 14.3% (0.24 m) in the distance estimate compared to the exponential model commonly adopted in the literature was reported.


title: DETECTION OF ROAD CRACKS WITH MULTIPLE IMAGES Sylvie CHAMBON

abstract: Extracting the defects of the road pavement in images is difficult and, most of the time, one image is used alone. The difficulties of this task are: illumination changes, objects on the road, artefacts due to the dynamic acquisition. In this work, we try to solve some of these problems by using acquisitions from different points of view. In consequence, we present a new methodology based on these steps : the detection of defects in each image, the matching of the images and the merging of the different extractions. We show the increase in performances and more particularly how the false detections are reduced.


title: A critical analysis of Customer Relationship Management from strategic perspective

abstract: Customer relationship management (CRM) is the collection of Processes, people and technology that seeks to find costumers of organizations. CRM develops customers' retention and relationship. Progress in Information Technology and organizational changes in customer-centric procedures have positive effect in the development of CRM. Organizations which implement CRM successfully, will gain customer loyalty and profitability. One reason of CRM failure is, viewing CRM only as a technology, but to make it successful organizations need to integrate and make balance between process, people and technology. An attempt is made to gauge the impact of environmental elements Viz., Political, Economical, Social and Technological factors on CRM. SWOT analysis is also done and based on the analysis few suggestive strategies and tips are recommended to make CRM practices successful. Keywords-CRM; Customer centric; Customer retention; SWOT


title: A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures

abstract: Virtual reality (VR) has been proposed as a methodological tool to study the basic science of psychology and other fields. One key advantage of VR is that sharing of virtual content can lead to more robust replication and representative sampling. A database of standardized content will help fulfil this vision. There are two objectives to this study. First, we seek to establish and allow public access to a database of immersive VR video clips that can act as a potential resource for studies on emotion induction using virtual reality. Second, given the large sample size of participants needed to get reliable valence and arousal ratings for our video, we were able to explore the possible links between the head movements of the observer and the emotions he or she feels while viewing immersive VR. To accomplish our goals, we sourced for and tested 73 immersive VR clips which participants rated on valence and arousal dimensions using self-assessment manikins. We also tracked participants’ rotational head movements as they watched the clips, allowing us to correlate head movements and affect. Based on past research, we predicted relationships between the standard deviation of head yaw and valence and arousal ratings. Results showed that the stimuli varied reasonably well along the dimensions of valence and arousal, with a slight underrepresentation of clips that are of negative valence and highly arousing. The standard deviation of yaw positively correlated with valence, while a significant positive relationship was found between head pitch and arousal. The immersive VR clips tested are available online as supplemental material.


title: A 1.2-pJ/bit 16-Gb/s 60-GHz OOK Transmitter in 65-nm CMOS for Wireless Network-On-Chip

abstract: This paper presents a high-efficiency 60-GHz on-off keying (OOK) transmitter (TX) designed for wireless network-on-chip applications. Aiming at an intra-chip commu- nication distance of 20 mm, the TX consists of a drive amplifier (DA), a high-speed OOK modulator, and a transformer-coupled voltage-controlled oscillator. For high efficiency, a common-source topology with a drain-to-gate neutralization technique is chosen for the DA. A detailed mathematical design methodology is de- rived for the neutralization technique. The bulk-driven OOK modulator employs a novel dual feedthrough cancellation tech- nique, resulting in a 30-dB on-off ratio. Fabricated in a 65-nm bulk CMOS process, the TX consumes only 19 mW from a 1-V supply, and occupies an active area of 0.077 mm . A maximum modulation data rate of 16 Gb/s with 0.75-dBm output power is demonstrated through measurements, which translates to a bit-energy efficiency of 1.2 pJ/bit. Index Terms—Bulk driven, CMOS, drive amplifier (DA), low power, millimeter wave, modulator, neutralization, on-off keying (OOK), transmitter (TX), voltage-controlled oscillator (VCO), wireless network-on-chip (WiNoC).


title: A Wearable Virtual Chair with the Passive Stability Assist

abstract: This paper introduces a wearable device which performs function of swinging chair with worn status on the legs. The users with the proposed device can sit in anyplace and experience the stable swing motion. The device is designed to maintain the stability within the stable swing region while moving back and forth by external forces or user intension. The coupled motion between ankle and knee provides the users concave swing motion in chair mode, while the joints passively follows the motion of the legs in normal gait mode. The key feature of this stable motion is a CAM-drive implemented around the ankle frame and connected to the knee joint by wires. With any directional motion of the ankle joint, the knee joint rotate only one direction to lift up the body of the user. So it can move following concave equilibrium line. We verified the payload of the device is more than 70 kg in computer-aided stress simulation as well as in experiments.


title: Machine Learning and Cognitive Technology for Intelligent Wireless Networks.

abstract: The ability to dynamically and efficiently allocate resources to meet the need of growing diversity in services and user behavior marks the future of wireless networks, giving rise to intelligent processing, which aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive technology while modern machine learning techniques project effectiveness in system adaptation. In this paper, we discuss the development of the cognitive technology and machine learning techniques and emphasize their roles in improving both spectrum and energy efficiency of the future wireless networks. We describe in detail the state-of-the-art of cognitive technology, covering spectrum sensing and access approaches that may enhance spectrum utilization and curtail energy consumption. We discuss powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques to the existing and future wireless communication systems, such as heterogeneous networks and device-to-device communications, and identify some research opportunities and challenges in cognitive technology and machine learning as applied to future wireless networks.


title: Reading handwritten digits: a ZIP code recognition system

abstract: A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail. >


title: A Novel Efficient Pairing-Free CP-ABE Based on Elliptic Curve Cryptography for IoT

abstract: Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic technique that integrates data encryption with access control for ensuring data security in IoT systems. However, the efficiency problem of CP-ABE is still a bottleneck limiting its development and application. A widespread consensus is that the computation overhead of bilinear pairing is excessive in the practical application of ABE, especially for the devices or the processors with limited computational resources and power supply. In this paper, we proposed a novel pairing-free data access control scheme based on CP-ABE using elliptic curve cryptography, abbreviated PF-CP-ABE. We replace complicated bilinear pairing with simple scalar multiplication on elliptic curves, thereby reducing the overall computation overhead. And we designed a new way of key distribution that it can directly revoke a user or an attribute without updating other users’ keys during the attribute revocation phase. Besides, our scheme use linear secret sharing scheme access structure to enhance the expressiveness of the access policy. The security and performance analysis show that our scheme significantly improved the overall efficiency as well as ensured the security.


title: AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks

abstract: Software defined Internet of Things (SD-IoT) networks profit from centralized management and interactive resource sharing, which enhances the efficiency and scalability of Internet of Things applications. But with the rapid growth in services and applications, they are vulnerable to possible attacks and face severe security challenges. Intrusion detection has been widely used to ensure network security, but classical detection methods are usually signature-based or explicit-behavior-based and fail to detect unknown attacks intelligently, which makes it hard to satisfy the requirements of SD-IoT networks. In this paper, we propose an artificial intelligence-based two-stage intrusion detection empowered by software defined technology. It flexibly captures network flows with a global view and detects attacks intelligently. We first leverage Bat algorithm with swarm division and binary differential mutation to select typical features. Then, we exploit Random Forest through adaptively altering the weights of samples using the weighted voting mechanism to classify flows. Evaluation results prove that the modified intelligent algorithms select more important features and achieve superior performance in flow classification. It is also verified that our solution shows better accuracy with lower overhead compared with existing solutions.


title: An Empirical Study into Class Testability 1

abstract: In this paper we investigate factors of the testability of object-oriented software systems. The starting point is given by a study of the literature to obtain both an initial model of testability and existing object-oriented metrics related to testability. Subsequently, these metrics are evaluated by means of five case studies of commercial and open source Java systems for which JUnit test cases exist. The goal of this paper is to identify and evaluate a set of metrics that can be used to assess the testability of the classes of a Java system.


title: Dynamic adaptation of MAC layer re-transmissions based on deadline aware client feedback

abstract: We propose an application layer algorithm for dynamic adaptation of number of medium access control (MAC) layer re-transmissions based on a deadline aware client feedback. The proposed approach is mainly designed for audio, video streaming on IEEE 802.11b wireless networks. The proposed approach allows a server side to set its number of MAC layer re-transmissions based on the client application needs. The proposed algorithm can result in a lower number of "effectively'' lost packets. This includes the packets actually lost (never received by the receiver) and the late packets. The proposed approach caters to applications with different latency requirements. Thus the proposed approach can be used for one-way and/or two-way streaming media.


title: From novel to familiar: Tuning the brain for metaphors

abstract: Abstract Metaphors are fundamental to creative thought and expression. Newly coined metaphors regularly infiltrate our collective vocabulary and gradually become familiar, but it is unclear how this shift from novel to conventionalized meaning happens in the brain. We investigated the neural career of metaphors in a functional magnetic resonance imaging study using extensively normed new metaphors and simulated the ordinary, gradual experience of metaphor conventionalization by manipulating participants' exposure to these metaphors. Results showed that the conventionalization of novel metaphors specifically tunes activity within bilateral inferior prefrontal cortex, left posterior middle temporal gyrus, and right postero-lateral occipital cortex. These results support theoretical accounts attributing a role for the right hemisphere in processing novel, low salience figurative meanings, but also show that conventionalization of metaphoric meaning is a bilaterally-mediated process. Metaphor conventionalization entails a decreased neural load within semantic networks rather than a hemispheric or regional shift across brain areas.


title: A study on balancing parallelism, data locality, and recomputation in existing PDE solvers

abstract: Structured-grid PDE solver frameworks parallelize over boxes, which are rectangular domains of cells or faces in a structured grid. In the Chombo framework, the box sizes are typically 163 or 323, but larger box sizes such as 1283 would result in less surface area and therefore less storage, copying, and/or ghost cells communication overhead. Unfortunately, current on node parallelization schemes perform poorly for these larger box sizes. In this paper, we investigate 30 different inter-loop optimization strategies and demonstrate the parallel scaling advantages of some of these variants on NUMA multicore nodes. Shifted, fused, and communication-avoiding variants for 1283 boxes result in close to ideal parallel scaling and come close to matching the performance of 163 boxes on three different multicore systems for a benchmark that is a proxy for program idioms found in Computational Fluid Dynamic (CFD) codes.


title: Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation

abstract: How do humans use target-predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, humans can learn that a certain combination of objects may define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. The ARTSCENE Search model is developed to illustrate the neural mechanisms of such memory-based context learning and guidance and to explain challenging behavioral data on positive-negative, spatial― object, and local-distant cueing effects during visual search, as well as related neuroanatomical, neurophysiological, and neuroimaging data. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined as a scene is scanned with saccadic eye movements. The model simulates the interactive dynamics of object and spatial contextual cueing and attention in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortex (area 46) primes possible target locations in posterior parietal cortex based on goal-modulated percepts of spatial scene gist that are represented in parahippocampal cortex. Model ventral prefrontal cortex (area 47/12) primes possible target identities in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex.


title: Extending finite automata to efficiently match Perl-compatible regular expressions

abstract: Regular expression matching is a crucial task in several networking applications. Current implementations are based on one of two types of finite state machines. Non-deterministic finite automata (NFAs) have minimal storage demand but have high memory bandwidth requirements. Deterministic finite automata (DFAs) exhibit low and deterministic memory bandwidth requirements at the cost of increased memory space. It has already been shown how the presence of wildcards and repetitions of large character classes can render DFAs and NFAs impractical. Additionally, recent security-oriented rule-sets include patterns with advanced features, namely back-references, which add to the expressive power of traditional regular expressions and cannot therefore be supported through classical finite automata. In this work, we propose and evaluate an extended finite automaton designed to address these shortcomings. First, the automaton provides an alternative approach to handle character repetitions that limits memory space and bandwidth requirements. Second, it supports back-references without the need for back-tracking in the input string. In our discussion of this proposal, we address practical implementation issues and evaluate the automaton on real-world rule-sets. To our knowledge, this is the first high-speed automaton that can accommodate all the Perl-compatible regular expressions present in the Snort network intrusion and detection system.


title: Ambidextrous leadership, ambidextrous employee, and the interaction between ambidextrous leadership and employee innovative performance

abstract: Leader opening and closing behaviors are assumed to foster high levels of employee exploration and exploitation behaviors, hence motivating employee innovative performance. Applying the ambidexterity theory of leadership for innovation, results revealed that leader opening and closing behaviors positively predicted employee exploration and exploitation behaviors, respectively, above and beyond the control variables. Moreover, results showed that employee innovative performance was significantly predicted by leader opening behavior, leader closing behavior, and the interaction between leaders’ opening and closing behaviors, above and beyond control variables.


title: The direct exchange mechanism of induced spin polarization of low-dimensional π-conjugated carbon- and h -BN fragments at LSMO(001) MnO-terminated interfaces

abstract: Abstract Induced spin polarization of π-conjugated carbon and h-BN low dimensional fragments at the interfaces formed by deposition of pentacene molecule and narrow zigzag graphene and h-BN nanoribbons on MnO2-terminated LSMO(001) thin film was studied using GGA PBE+U PAW D3-corrected approach. Induced spin polarization of π-conjugated low-dimensional fragments is caused by direct exchange with Mn ions of LSMO(001) MnO-derived surface. Due to direct exchange, the pentacene molecule changes its diamagnetic narrow-band gap semiconducting nature to the ferromagnetic semiconducting state with 0.15 eV energy shift between spin-up and spin-down valence bands and total magnetic moment of 0.11 μB. Direct exchange converts graphene nanoribbon to 100% spin-polarized half-metal with large amplitude of spin-up electronic density at the Fermi level. The direct exchange narrows the h-BN nanoribbon band gap from 4.04 to 1.72 eV in spin-up channel and converts the h-BN ribbon semiconducting diamagnetic nature to a semiconducting magnetic one. The electronic structure calculations demonstrate a possibility to control the spin properties of low-dimensional π-conjugated carbon and h-BN fragments by direct exchange with MnO-derived LSMO(001) surface for spin-related applications.


title: BLE Beacon Based Patient Tracking in Smart Care Facilities

abstract: Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.


title: Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification

abstract: Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.


title: A modification of Shanks' baby-step giant-step algorithm

abstract: I describe a modification to Shanks' baby-step giant-step algorithm for computing the order n of an element g of a group G, assuming n is finite. My method has the advantage of being able to compute n quickly, which Shanks' method fails to do when the order of G is infinite, unknown, or much larger than n. I describe the algorithm in detail. I also present the results of implementations of my algorithm, as well as those of a similar algorithm developed by Buchmann, Jacobson, and Teske, for calculating the order of various ideal classes of imaginary quadratic orders.


title: SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication

abstract: Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal processing and recommender systems. Real-world tensors can be enormous in size and often very sparse. There is a need for efficient, high-performance tools capable of processing the massive sparse tensors of today and the future. This paper introduces SPLATT, a C library with shared-memory parallelism for three-mode tensors. SPLATT contains algorithmic improvements over competing state of the art tools for sparse tensor factorization. SPLATT has a fast, parallel method of multiplying a matricide tensor by a Khatri-Rao product, which is a key kernel in tensor factorization methods. SPLATT uses a novel data structure that exploits the sparsity patterns of tensors. This data structure has a small memory footprint similar to competing methods and allows for the computational improvements featured in our work. We also present a method of finding cache-friendly reordering and utilizing them with a novel form of cache tiling. To our knowledge, this is the first work to investigate reordering and cache tiling in this context. SPLATT averages almost 30x speedup compared to our baseline when using 16 threads and reaches over 80x speedup on NELL-2.


title: My Smart Home is Under Attack

abstract: Home Automation is becoming more and more common in everyday life. In order to permit a remote control of domestic devices a bridge between the Internet network and the Home Network is necessary. This function is commonly operated by the Smart Home gateway which thus results a critical point for the security of the network. Recently several general purpose devices exist that are capable of connecting both to the Home Network and the Internet network, leading to have additional, dynamic and unpredictable, bridges between the two networks. This possibility makes such devices additional attack vectors for the Smart Home. In this work we show the risks resulting from such devices. By using an emulated scenario we also demonstrate the implementation of a real attack exploiting a general purpose device connected to both the Internet and the Home Network. Preliminary experimental results are presented confirming the success of our attack.


title: Automatic construction and ranking of topical keyphrases on collections of short documents

abstract: We introduce a framework for topical keyphrase generation and ranking, based on the output of a topic model run on a collection of short documents. By shifting from the unigramcentric traditional methods of keyphrase extraction and ranking to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. Our method defines a function to rank topical keyphrases so that more highly ranked keyphrases are considered to be more representative phrases for that topic. We study the performance of our framework on multiple real world document collections, and also show that it is more scalable than comparable phrase-generating models.


title: Music and language side by side in the brain: a PET study of the generation of melodies and sentences

abstract: Parallel generational tasks for music and language were compared using positron emission tomography. Amateur musicians vocally improvised melodic or linguistic phrases in response to unfamiliar, auditorily presented melodies or phrases. Core areas for generating melodic phrases appeared to be in left Brodmann area (BA) 45, right BA 44, bilateral temporal planum polare, lateral BA 6, and pre-SMA. Core areas for generating sentences seemed to be in bilateral posterior superior and middle temporal cortex (BA 22, 21), left BA 39, bilateral superior frontal (BA 8, 9), left inferior frontal (BA 44, 45), anterior cingulate, and pre-SMA. Direct comparisons of the two tasks revealed activations in nearly identical functional brain areas, including the primary motor cortex, supplementary motor area, Broca’s area, anterior insula, primary and secondary auditory cortices, temporal pole, basal ganglia, ventral thalamus, and posterior cerebellum. Most of the differences between melodic and sentential generation were seen in lateralization tendencies, with the language task favouring the left hemisphere. However, many of the activations for each modality were bilateral, and so there was significant overlap. While clarification of this overlapping activity awaits higher-resolution measurements and interventional assessments, plausible accounts for it include component sharing, interleaved representations, and adaptive coding. With these and related findings, we outline a comparative model of shared, parallel, and distinctive features of the neural systems supporting music and language. The model assumes that music and language show parallel combinatoric generativity for complex sound structures (phonology) but distinctly different informational content (semantics).


title: A first look at cellular network performance during crowded events

abstract: During crowded events, cellular networks face voice and data traffic volumes that are often orders of magnitude higher than what they face during routine days. Despite the use of portable base stations for temporarily increasing communication capacity and free Wi-Fi access points for offloading Internet traffic from cellular base stations, crowded events still present significant challenges for cellular network operators looking to reduce dropped call events and improve Internet speeds. For effective cellular network design, management, and optimization, it is crucial to understand how cellular network performance degrades during crowded events, what causes this degradation, and how practical mitigation schemes would perform in real-life crowded events. This paper makes a first step towards this end by characterizing the operational performance of a tier-1 cellular network in the United States during two high-profile crowded events in 2012. We illustrate how the changes in population distribution, user behavior, and application workload during crowded events result in significant voice and data performance degradation, including more than two orders of magnitude increase in connection failures. Our findings suggest two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Using trace-driven simulations, we show that more aggressive release of radio resources via 1-2 seconds shorter RRC timeouts as compared to routine days helps to achieve better tradeoff between wasted radio resources, energy consumption, and delay during crowded events; and opportunistic connection sharing can reduce connection failures by 95% when employed by a small number of devices in each cell sector.


Applications of Artificial Neural Networks in Information System of Management Accounting


title: Graph-Based Statistical Language Model for Code

abstract: n-gram statistical language model has been successfully applied to capture programming patterns to support code completion and suggestion. However, the approaches using n-gram face challenges in capturing the patterns at higher levels of abstraction due to the mismatch between the sequence nature in n-grams and the structure nature of syntax and semantics in source code. This paper presents GraLan, a graph-based statistical language model and its application in code suggestion. GraLan can learn from a source code corpus and compute the appearance probabilities of any graphs given the observed (sub)graphs. We use GraLan to develop an API suggestion engine and an AST-based language model, ASTLan. ASTLan supports the suggestion of the next valid syntactic template and the detection of common syntactic templates. Our empirical evaluation on a large corpus of open-source projects has shown that our engine is more accurate in API code suggestion than the state-of-the-art approaches, and in 75% of the cases, it can correctly suggest the API with only five candidates. ASTLan also has high accuracy in suggesting the next syntactic template and is able to detect many useful and common syntactic templates.


title: A Comparative Analysis of Trust-based Service Composition Algorithms in Service-Oriented Ad Hoc Networks

abstract: In this paper we analyze performance characteristics of trust-based service composition and binding algorithms for composing composite services in service-oriented ad hoc networks. We first show that trust is an effective mechanism to cope with malicious nodes acting for their own gain and colluding with each other to increase their chances of being selected for service execution. Then we perform a comparative performance analysis of single-trust-based, multi-trust-based, and context-aware trust-based service composition and binding algorithms for composing composite services in service-oriented ad hoc networks. Using an encounter-based service composition application with multi-objective optimization goals as a running example, we show that context-aware trust-based service composition and binding outperforms single-trust-based and multi-trust-based counterparts for achieving multi-objective optimization and user satisfaction. Physical interpretations of the results are provided.


title: A passive technique for fingerprinting wireless devices with Wired-side Observations

abstract: In this paper, we introduce GTID, a technique that passively fingerprints wireless devices and their types from the wired backbone. GTID exploits the heterogeneity of devices, which is a function of different device hardware compositions and variations in devices' clock skew. We use statistical techniques to create unique, reproducible device and device type signatures that represent time variant behavior in network traffic and use artificial neural networks (ANNs) to classify devices and device types. We demonstrate the efficacy of our technique on both an isolated testbed and a live campus network (during peak hours) using a corpus of 27 devices representing a wide range of device classes. We collected more than 100 GB of traffic captures for ANN training and classification. We assert that for any fingerprinting technique to be practical, it must be able to detect previously unseen devices (i.e., devices for which no stored signature is available) and must be able to withstand various attacks. GTID is the first fingerprinting technique to detect previously unseen devices and to illustrate its resilience under various attacker models. We measure the performance of GTID by considering accuracy, recall, and processing time and illustrate how it can be used to complement existing authentication systems and to detect counterfeit devices.


title: Studying the Impact of Using Multimedia Interactive Programs at Children Ability to Learn Basic Math Skills.

abstract: The continuous inventions and evolutions in all information technology fields open new channels and opportunities to enhance teaching and educational methods. On one side, these may improve the abilities of educators to present information in interactive and media-enhanced formats relative to traditional methods. This may help students or learners through offering them the information in channels and methods that can be easier to understand, deal with and retrieve. On the other hand, offering those alternative methods of teaching can be helpful particularly for children, people with special needs, or students in rural areas where they can have virtual or remote instructors, especially for majors who have shortages. The purpose of this study is to investigate the impact of utilising multimedia technologies on enhancing, or not, the effectiveness of teaching students at early stages in Jordanian primary schools. To achieve this objective, a program has been developed to test the students' ability to understand basic mathematical knowledge and skills. Two groups were selected from a local school based on their own class distribution, where one group was taught the subject in basic math using a program developed for this purpose and the second class was taught the same subject using traditional methods of teaching (i.e. direct student-to-child instruction, board, etc.). Results showed that in such math skills at this age, using programs or multimedia-enhanced methods of teaching can be effective in getting students' attention, especially when cartoon characters are used. Results also showed that there is no significant difference in learning and knowledge skills and information absorption based on gender distribution, as a comparison of the results between little boys and girls showed no significant difference in their learning skills.


title: New image descriptors based on color, texture, shape, and wavelets for object and scene image classification

abstract: Abstract This paper presents new image descriptors based on color, texture, shape, and wavelets for object and scene image classification. First, a new three Dimensional Local Binary Patterns (3D-LBP) descriptor, which produces three new color images, is proposed for encoding both color and texture information of an image. The 3D-LBP images together with the original color image then undergo the Haar wavelet transform with further computation of the Histograms of Oriented Gradients (HOG) for encoding shape and local features. Second, a novel H-descriptor, which integrates the 3D-LBP and the HOG of its wavelet transform, is presented to encode color, texture, shape, as well as local information. Feature extraction for the H-descriptor is implemented by means of Principal Component Analysis (PCA) and Enhanced Fisher Model (EFM) and classification by the nearest neighbor rule for object and scene image classification. And finally, an innovative H-fusion descriptor is proposed by fusing the PCA features of the H-descriptors in seven color spaces in order to further incorporate color information. Experimental results using three datasets, the Caltech 256 object categories dataset, the UIUC Sports Event dataset, and the MIT Scene dataset, show that the proposed new image descriptors achieve better image classification performance than other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), Spatial Envelope, Color SIFT four Concentric Circles (C4CC), Object Bank, the Hierarchical Matching Pursuit, as well as LBP.


title: Types of Information Technology Capabilities and Their Role in Competitive Advantage: An Empirical Study

abstract: During the past two decades, both business managers and academic researchers have shown considerable interest in understanding how information technologies (IT) help to create competitive advantage for a firm. While recently the idea of competitive differentiation through IT has been challenged, this study contrasts the traditional thinking about competitive advantage with the resource-based view. Specifically, it is argued that by demarcating specific types of capabilities, we can contribute to better understanding of the sources of IT-based competitive advantage. Conceptually, we distinguish here between value, competitive, and dynamic capabilities as three distinct types of capabilities. Within each type, we identify specific capabilities, such as quality of the IT infrastructure, IT business experience, relationship infrastructure, and intensity of organizational learning, and present a model that describes relationships between these capabilities and competitive advantage. We then empirically test the model using data collected via a national mail survey from chief IT executives from 202 manufacturing firms. While the quality of the IT infrastructure is hypothesized as a value capability and expectedly did not have any significant effect on competitive advantage, the quality of IT business expertise and the relationship infrastructure (competitive capabilities) did. The results of the study also indicate that the intensity of organizational learning (dynamic capability) was significantly related to all of the capabilities. These results point to the importance of delineating capabilities such as relationship infrastructure that can facilitate differentiation in the marketplace, and dynamic capabilities such as organizational learning as an important antecedent to IT capability building.


title: A Bayesian computer vision system for modeling human interactions

abstract: We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.


title: CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval

abstract: We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more challenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face image datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) descriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.


title: Should CRISPR Scientists Play God

abstract: Will CRISPR usher in a new era of Promethean overreach? CRISPR makes gene editing widely available and cheap. Anti-play-god bioethicists fear that geneticists will play god and precipitate a backlash from nature that could be devastating. In contrast to the anti-play-god bioethicists, this article recommends that laboratory science invoke the Precautionary Principle: pause at the yellow caution light, but then with constant risk-assessment proceed ahead.


title: Contextual Explanation Networks

abstract: We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs combine deep networks with context-specific probabilistic models and construct explanations in the form of locally-correct hypotheses. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs can easily match or outperform the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.


title: Learning to rank with click-through features in a reinforcement learning framework

abstract: Purpose ::: ::: ::: ::: ::: Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function.


title: Multi-Granularity Generator for Temporal Action Proposal

abstract: Temporal action proposal generation is an important task, aiming to localize the video segments containing human actions in an untrimmed video. In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information. First, we propose to use a bilinear matching model to exploit the rich local information within the video sequence. Afterwards, two components, namely segment proposal producer (SPP) and frame actionness producer (FAP), are combined to perform the task of temporal action proposal at two distinct granularities. SPP considers the whole video in the form of feature pyramid and generates segment proposals from one coarse perspective, while FAP carries out a finer actionness evaluation for each video frame. Our proposed MGG can be trained in an end-to-end fashion. Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets. Moreover, we employ existing action classifiers to perform the classification of the proposals generated by MGG, leading to significant improvements compared against the competing methods for the video detection task.


title: Compact UWB Antenna With Multiple Band-Notches for WiMAX and WLAN

abstract: In order to prevent interference problem due to existing nearby communication systems within the UWB operating frequency, a compact triple band-notch UWB antenna is presented in this communication. This antenna, designed for the rejection of interference with Worldwide Interoperability for Microwave Access (WiMAX), lower and upper wireless local area networks (WLANs) covering 3.3-3.6 GHz, 5.15-5.35 GHz and 5.725-5.825 GHz, provides three notched bands by only one structure with simple design. Based on simulation and measurement, it shows that the proposed antenna can guarantee a wide bandwidth from 3.03 to 11.4 GHz with triple unwanted band-notches successfully.


title: Coreset-Based Neural Network Compression

abstract: We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters. We exploit the redundancies extant in the space of CNN weights and neuronal activations (across samples) in order to obtain compression. Our method requires no retraining, is easy to implement, and obtains state-of-the-art compression performance across a wide variety of CNN architectures. Coupled with quantization and Huffman coding, we create networks that provide AlexNet-like accuracy, with a memory footprint that is $832\times$ smaller than the original AlexNet, while also introducing significant reductions in inference time as well. Additionally these compressed networks when fine-tuned, successfully generalize to other domains as well.


title: Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics

abstract: We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer datasets. On these high-dimensional datasets, our proposed method significantly outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.


title: RADOS: a scalable, reliable storage service for petabyte-scale storage clusters

abstract: Brick and object-based storage architectures have emerged as a means of improving the scalability of storage clusters. However, existing systems continue to treat storage nodes as passive devices, despite their ability to exhibit significant intelligence and autonomy. We present the design and implementation of RADOS, a reliable object storage service that can scales to many thousands of devices by leveraging the intelligence present in individual storage nodes. RADOS preserves consistent data access and strong safety semantics while allowing nodes to act semi-autonomously to self-manage replication, failure detection, and failure recovery through the use of a small cluster map. Our implementation offers excellent performance, reliability, and scalability while providing clients with the illusion of a single logical object store.


title: Dual n-back training increases the capacity of the focus of attention

abstract: Working memory (WM) training has been reported to benefit abilities as diverse as fluid intelligence (Jaeggi et al., Proceedings of the National Academy of Sciences of the United States of America, 105:6829–6833, 2008) and reading comprehension (Chein & Morrison, Psychonomic Bulletin & Review, 17:193–199, 2010), but transfer is not always observed (for reviews, see Morrison & Chein, Psychonomics Bulletin & Review, 18:46–60, 2011; Shipstead et al., Psychological Bulletin, 138:628–654, 2012). In contrast, recent WM training studies have consistently reported improvement on the trained tasks. The basis for these training benefits has received little attention, however, and it is not known which WM components and/or processes are being improved. Therefore, the goal of the present study was to investigate five possible mechanisms underlying the effects of adaptive dual n-back training on working memory (i.e., improvements in executive attention, updating, and focus switching, as well as increases in the capacity of the focus of attention and short-term memory). In addition to a no-contact control group, the present study also included an active control group whose members received nonadaptive training on the same task. All three groups showed significant improvements on the n-back task from pretest to posttest, but adaptive training produced larger improvements than did nonadaptive training, which in turn produced larger improvements than simply retesting. Adaptive, but not nonadaptive, training also resulted in improvements on an untrained running span task that measured the capacity of the focus of attention. No other differential improvements were observed, suggesting that increases in the capacity of the focus of attention underlie the benefits of adaptive dual n-back training.


title: Mechanically programmable bend radius for fiber-reinforced soft actuators

abstract: Established design and fabrication guidelines exist for achieving a variety of motions with soft actuators such as bending, contraction, extension, and twisting. These guidelines typically involve multi-step molding of composite materials (elastomers, paper, fiber, etc.) along with specially designed geometry. In this paper we present the design and fabrication of a robust, fiber-reinforced soft bending actuator where its bend radius and bending axis can be mechanically-programed with a flexible, selectively-placed conformal covering that acts to mechanically constrain motion. Several soft actuators were fabricated and their displacement and force capabilities were measured experimentally and compared to demonstrate the utility of this approach. Finally, a prototype two-digit end-effector was designed and programmed with the conformal covering to shape match a rectangular object. We demonstrated improved gripping force compared to a pure bending actuator. We envision this approach enabling rapid customization of soft actuator function for grasping applications where the geometry of the task is known a priori.


title: English Access to Structured Data

abstract: We present work on using a domain model to guide text interpretation, in the context of a project that aims to interpret English questions as a sequence of queries to be answered from structured databases. We adapt a broad-coverage and ambiguity-enabled natural language processing (NLP) system to produce domain-specific logical forms, using knowledge of the domain to zero in on the appropriate interpretation. The vocabulary of the logical forms is drawn from a domain theory that constitutes a higher-level abstraction of the contents of a set of related databases. The meanings of the terms are encoded in an axiomatic domain theory. To retrieve information from the databases, the logical forms must be instantiated by values constructed from fields in the database. The axiomatic domain theory is interpreted by the first-order theorem prover SNARK to identify the groundings, and then retrieve the values through procedural attachments semantically linked to the database. SNARK attempts to prove the logical form as a theorem by reasoning over the theory that is linked to the database and returns the exemplars of the proof(s) back to the user as answers to the query. The focus of this paper is more on the language task, however, we discuss the interaction that must occur between linguistic analysis and reasoning for an end-to-end natural language interface to databases. We illustrate the process using examples drawn from an HIV treatment domain, where the underlying databases are records of temporally bound treatments of individual patients.


title: Inter-Firm Linkages and the Wealth Effects of Financial Distress Along the Supply Chain

abstract: Extant research examines the extent to which bankruptcy has intra-industry valuation consequences. This study broadens the investigation by examining the wealth effects of distress and bankruptcy filing for suppliers and customers of filing firms. On average, important wealth effects occur prior to and at bankruptcy filings and extend beyond industry competitors along the supply chain. Specifically, distress related to bankruptcy filings is associated with negative and significant stock price effects for suppliers. Supplier wealth effects are more negative when intra-industry contagion is more severe. We also investigate the importance of industry structure, specialized product nature, and leverage on supply chain effects.


title: A Prolegomenon on OLTP Database Systems for Non-Volatile Memory

abstract: The design of a database management system’s (DBMS) architecture is predicated on the target storage hierarchy. Traditional diskoriented systems use a two-level hierarchy, with fast volatile memory used for caching, and slower, durable device used for primary storage. As such, these systems use a buffer pool and complex concurrency control schemes to mask disk latencies. Compare this to main memory DBMSs that assume all data can reside in DRAM, and thus do not need these components. But emerging non-volatile memory (NVM) technologies require us to rethink this dichotomy. Such memory devices are slightly slower than DRAM, but all writes are persistent, even after power loss. We explore two possible use cases of NVM for on-line transaction processing (OLTP) DBMSs. The first is where NVM completely replaces DRAM and the other is where NVM and DRAM coexist in the system. For each case, we compare the performance of a disk-oriented DBMS with a memory-oriented DBMS using two OLTP benchmarks. We also evaluate the performance of different recovery algorithms on these NVM devices. Our evaluation shows that in both storage hierarchies, memory-oriented systems are able to outperform their disk-oriented counterparts. However, as skew decreases the performance of the two architectures converge, showing that neither architecture is ideally suited for an NVM-based storage hierarchy.


title: Surveillance and Conservative Management of a Persistent Sciatic Artery Aneurysm

abstract: A persistent sciatic artery aneurysm is a rare congenital vascular anomaly. Surgical intervention is reserved for symptomatic cases. The authors report the case of a 72-year-old woman who presented with thromboembolic occlusion of an aneurysmal persistent sciatic artery. Although initially symptomatic, she was managed expectantly with the aid of serial duplex sonographic imaging. The persistent sciatic artery subsequently thrombosed as a result of hypotension secondary to a myocardial infarction. No surgical intervention was required. The current literature on this condition is reviewed.


title: Identification and characterization of an injury-induced skeletal progenitor

abstract: The postnatal skeleton undergoes growth, remodeling, and repair. We hypothesized that skeletal progenitor cells active during these disparate phases are genetically and phenotypically distinct. We identified a highly potent regenerative cell type that we term the fracture-induced bone, cartilage, stromal progenitor (f-BCSP) in the fracture callus of adult mice. The f-BCSP possesses significantly enhanced skeletogenic potential compared with BCSPs harvested from uninjured bone. It also recapitulates many gene expression patterns involved in perinatal skeletogenesis. Our results indicate that the skeletal progenitor population is functionally stratified, containing distinct subsets responsible for growth, regeneration, and repair. Furthermore, our findings suggest that injury-induced changes to the skeletal stem and progenitor microenvironments could activate these cells and enhance their regenerative potential.


title: Model of photovoltaic cell circuits under partial shading

abstract: The PV-cell and natural energy production systems have been much attracted. Together with global warning and extensive applications of solar power electric generation, defects on PV-cell under the partial shading become interesting technical issue. This paper investigates the equivalent circuit model and its characteristics under the partial shading condition. And we have clarified the mechanism of PV-cell under the partial shading. As the extension of these considerations, we have proposed the new MPPT method (maximal power point tracking) for the sake of drastic energy generation improvement


title: Using gravity to estimate accelerometer orientation

abstract: Several wearable computing or ubiquitous computing research projects have detected and distinguished user motion activities by attaching accelerometers in known positions and orientations on the user’s body. This paper observes that the orientation constraint can probably be relaxed. An estimate of the constant gravity vector can be obtained by averaging accelerometer samples. This gravity vector estimate in turn enables estimation of the vertical component and the magnitude of the horizontal component of the user’s motion, independently of how the three-axis accelerometer system is oriented.