title: Cannabinoids provide neuroprotection against 6-hydroxydopamine toxicity in vivo and in vitro: Relevance to Parkinson's disease

abstract: Cannabinoids have been reported to provide neuroprotection in acute and chronic neurodegeneration. In this study, we examined whether they are also effective against the toxicity caused by 6-hydroxydopamine, both in vivo and in vitro, which may be relevant to Parkinson's disease (PD). First, we evaluated whether the administration of cannabinoids in vivo reduces the neurodegeneration produced by a unilateral injection of 6-hydroxydopamine into the medial forebrain bundle. As expected, 2 weeks after the application of this toxin, a significant depletion of dopamine contents and a reduction of tyrosine hydroxylase activity in the lesioned striatum were noted, and were accompanied by a reduction in tyrosine hydroxylase-mRNA levels in the substantia nigra. None of these events occurred in the contralateral structures. Daily administration of delta9-tetrahydrocannabinol (delta9-THC) during these 2 weeks produced a significant waning in the magnitude of these reductions, whereas it failed to affect dopaminergic parameters in the contralateral structures. This effect of delta9-THC appeared to be irreversible since interruption of the daily administration of this cannabinoid after the 2-week period did not lead to the re-initiation of the 6-hydroxydopamine-induced neurodegeneration. In addition, the fact that the same neuroprotective effect was also produced by cannabidiol (CBD), another plant-derived cannabinoid with negligible affinity for cannabinoid CB1 receptors, suggests that the antioxidant properties of both compounds, which are cannabinoid receptor-independent, might be involved in these in vivo effects, although an alternative might be that the neuroprotection exerted by both compounds might be due to their anti-inflammatory potential. As a second objective, we examined whether cannabinoids also provide neuroprotection against the in vitro toxicity of 6-hydroxydopamine. We found that the non-selective cannabinoid agonist HU-210 increased cell survival in cultures of mouse cerebellar granule cells exposed to this toxin. However, this effect was significantly lesser when the cannabinoid was directly added to neuronal cultures than when these cultures were exposed to conditioned medium obtained from mixed glial cell cultures treated with HU-210, suggesting that the cannabinoid exerted its major protective effect by regulating glial influence to neurons. In summary, our results support the view of a potential neuroprotective action of cannabinoids against the in vivo and in vitro toxicity of 6-hydroxydopamine, which might be relevant for PD. Our data indicated that these neuroprotective effects might be due, among others, to the antioxidant properties of certain plant-derived cannabinoids, or exerted through the capability of cannabinoid agonists to modulate glial function, or produced by a combination of both mechanisms.


title: Expression of surfactant proteins in embryonic rat lung.

abstract: Because surfactant proteins A, B, and C (SP-A, SP-B, and SP-C) are putative markers for alveolar epithelial type II cells, we investigated their expression in embryonic rat lung (12 to 15 days, term = 22 days). The expression of the messages for SP-A, SP-B, and SP-C was assessed by the reverse-transcriptase polymerase chain reaction (RT-PCR). Embryonic rat lung at 12 days' gestation lacked detectable mRNAs for all three surfactant proteins. Messages for SP-A, SP-B, and SP-C were, however, present in embryonic rat lung at 13 days' gestation. Expression of SP-A mRNA increased in embryonic rat lung with advancing gestation. Surfactant protein mRNA expression during the embryonic period of lung development was limited to the epithelial cells. The expression of SP-A mRNA, while limited to the lung, did not appear to be a marker for cells destined to become type II cells, since it was detected in the trachea and the presumptive main bronchial ducts of embryonic rat lung at 13 days' gestation, as well as in the ...


Digital processing of synthetic aperture radar data


title: Caliber-Persistent Labial Artery: A Rarely Recognized Cause of a Lower Lip Swelling-Report of 5 Cases and Review of the Literature.

abstract: BACKGROUND ::: Caliber-persistent labial artery (CPLA) is a vascular anomaly of the labial artery that penetrates into the submucosa of the lip without reduction in diameter. It commonly presents as a bluish or normal-colored elevated mass and usually pulsates on manual palpation. It can resemble a mucocele or squamous cell carcinoma if surface ulceration presented. CPLA carries the risk of profuse bleeding if the artery undergoes transection during biopsy. ::: ::: ::: METHODOLOGY ::: Five patients aged between 28 and 88 years presented with discrete lesions of the lower lip. All cases were diagnosed clinically as either mucocele or squamous cell carcinoma and were treated with excisional biopsy. ::: ::: ::: RESULTS ::: The specimens showed a prominent muscular vessel in the stroma that was associated with ulceration in 2 cases. Hemostasis was achieved by either application of surgical diathermy or ligation with deep sutures at the wound area. Persistence of the lesion after excision was seen in only 1 case, another single case the patient complained of persistent paresthesia at the surgical site at 7 months review after biopsy. ::: ::: ::: CONCLUSION ::: CPLA should be considered in the differential diagnosis of any raised soft tissue lesion affecting the lip. Careful inspection with palpation for pulse during clinical examination should permit an accurate clinical diagnosis and may prevent unnecessary surgical treatment or prepare the operator for the possibility of hemorrhage during surgery. Pathologists can be alerted to the possibility of CPLA where an isolated muscular vessel is encountered during lip biopsy.


title: A scale-invariant framework for image classification with deep learning

abstract: In this paper, we propose a scale-invariant framework based on Convolutional Neural Networks (CNNs). The network exhibits robustness to scale and resolution variations in data. Previous efforts in achieving scale invariance were made on either integrating several variant-specific CNNs or data augmentation. However, these methods did not solve the fundamental problem that CNNs develop different feature representations for the variants of the same image. The topology proposed by this paper develops a uniform representation for each of the variants of the same image. The uniformity is acquired by concatenating scale-variant and scale-invariant features to enlarge the feature space so that the case when input images are of diverse variations but from the same class can be distinguished from another case when images are of different classes. Higher-order decision boundaries lead to the success of the framework. Experimental results on a challenging dataset substantiates that our framework performs better than traditional frameworks with the same number of free parameters. Our proposed framework can also achieve a higher training efficiency.


title: PRE–IRRIGATION OF A SEVERELY–SALINE SOIL WITH IN–SITU WATER TO ESTABLISH DRYLAND FORAGES

abstract: Alfalfa serves as one of the most important forage crops in North America. It is also one of the recommended ::: remedial crops for dryland salinity control. But, because of its limited salt tolerance, alfalfa does not establish satisfactorily ::: in severely or moderately saline soils. Pre–irrigations with the in–situ ground water located beneath a severely–saline site ::: were applied to seedbeds located within the same saline site before seeding ‘Beaver’ alfalfa (Medicago sativa L.) and a ::: ‘RS–H’ wheatgrass hybrid (Elymus Hoffmannii K.B. Jensen and K.H. Asay). In a field study conducted in semiarid ::: Saskatchewan, fall irrigations with 4.6 dS/m water from a shallow, on–site well fitted with a solar–powered pump preceded ::: spring seeding. Plant emergence, spacing, height, cover, and forage yield of alfalfa compared by analysis–of–covariance with ::: and without irrigation were significantly improved following pre–irrigation. Mean alfalfa emergence increased from 20% to ::: 79%. The pre–irrigation significantly improved wheatgrass height and forage yield, but only indicated a trend toward ::: improvement in plant emergence, spacing, and cover. The mean plant height in July increased from 90 to 159 mm for the ::: wheatgrass and from 35 to 140 mm for the alfalfa. The quantities of water applied as part of the non–irrigation and ::: pre–irrigation treatments ranged from zero to 2530 mm. Based on linear regression of irrigated amounts, every 119.3 mm of ::: irrigated, in–situ water up to 2530 mm increased alfalfa forge yield by 10 g/m 2.


title: Biology as information

abstract: Biology is being rapidly transformed by the ability to collect comprehensive information about cellular and organismal processes, as well as to systematically manipulate pathways. This keynote talk will survey the biological implications of such research programs as: genome-scale sequence from many related organisms; large-scale human genotyping to study the basis of disease; systematic recognition of signatures of cellular responses; comprehensive analysis of mutations in cancer; and systematic inhibition of genes through RNAi interference. It will address the laboratory and computational issues in fulfilling the opportunities presented by this revolution.


title: A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

abstract: Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.


title: Stepparents and parenting stress: The roles of gender, marital quality, and views about gender roles

abstract: Previous research suggests that stepparenting can be stressful, although the mechanisms that contribute to the experience of parenting stress in stepfamilies are less clear. This study examines gender, marital quality, and views about gendered family roles as correlates of parenting stress among 310 stepmothers, stepfathers, and biological mothers and fathers. Findings suggest that stepparents, and especially stepmothers, experience higher levels of parenting stress than biological parents. Findings also suggest that less traditional views about gendered family roles and higher dyadic adjustment are associated with lower parenting stress for stepparents, particularly in combination. Stepparents reporting both of these protective factors were indistinguishable in terms of parenting stress from biological parents. These findings indicate potential pathways to mitigate the stress associated with stepparenting.


Chemical Gas Sensors for Aeronautic and Space Applications


title: Approximate Distribution Matching for Sequence-to-Sequence Learning

abstract: Sequence-to-Sequence models were introduced to tackle many real-life problems like machine translation, summarization, image captioning, etc. The standard optimization algorithms are mainly based on example-to-example matching like maximum likelihood estimation, which is known to suffer from data sparsity problem. Here we present an alternate view to explain sequence-to-sequence learning as a distribution matching problem, where each source or target example is viewed to represent a local latent distribution in the source or target domain. Then, we interpret sequence-to-sequence learning as learning a transductive model to transform the source local latent distributions to match their corresponding target distributions. In our framework, we approximate both the source and target latent distributions with recurrent neural networks (augmenter). During training, the parallel augmenters learn to better approximate the local latent distributions, while the sequence prediction model learns to minimize the KL-divergence of the transformed source distributions and the approximated target distributions. This algorithm can alleviate the data sparsity issues in sequence learning by locally augmenting more unseen data pairs and increasing the model's robustness. Experiments conducted on machine translation and image captioning consistently demonstrate the superiority of our proposed algorithm over the other competing algorithms.


title: Cross-Sentence N-ary Relation Extraction with Graph LSTMs

abstract: Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.


title: Attitudes and Practices of Euthanasia and Physician-Assisted Suicide in the United States, Canada, and Europe

abstract: IMPORTANCE ::: The increasing legalization of euthanasia and physician-assisted suicide worldwide makes it important to understand related attitudes and practices. ::: ::: ::: OBJECTIVE ::: To review the legal status of euthanasia and physician-assisted suicide and the available data on attitudes and practices. ::: ::: ::: EVIDENCE REVIEW ::: Polling data and published surveys of the public and physicians, official state and country databases, interview studies with physicians, and death certificate studies (the Netherlands and Belgium) were reviewed for the period 1947 to 2016. ::: ::: ::: FINDINGS ::: Currently, euthanasia or physician-assisted suicide can be legally practiced in the Netherlands, Belgium, Luxembourg, Colombia, and Canada (Quebec since 2014, nationally as of June 2016). Physician-assisted suicide, excluding euthanasia, is legal in 5 US states (Oregon, Washington, Montana, Vermont, and California) and Switzerland. Public support for euthanasia and physician-assisted suicide in the United States has plateaued since the 1990s (range, 47%-69%). In Western Europe, an increasing and strong public support for euthanasia and physician-assisted suicide has been reported; in Central and Eastern Europe, support is decreasing. In the United States, less than 20% of physicians report having received requests for euthanasia or physician-assisted suicide, and 5% or less have complied. In Oregon and Washington state, less than 1% of licensed physicians write prescriptions for physician-assisted suicide per year. In the Netherlands and Belgium, about half or more of physicians reported ever having received a request; 60% of Dutch physicians have ever granted such requests. Between 0.3% to 4.6% of all deaths are reported as euthanasia or physician-assisted suicide in jurisdictions where they are legal. The frequency of these deaths increased after legalization. More than 70% of cases involved patients with cancer. Typical patients are older, white, and well-educated. Pain is mostly not reported as the primary motivation. A large portion of patients receiving physician-assisted suicide in Oregon and Washington reported being enrolled in hospice or palliative care, as did patients in Belgium. In no jurisdiction was there evidence that vulnerable patients have been receiving euthanasia or physician-assisted suicide at rates higher than those in the general population. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Euthanasia and physician-assisted suicide are increasingly being legalized, remain relatively rare, and primarily involve patients with cancer. Existing data do not indicate widespread abuse of these practices.


title: Safety assessment of mixed traffic based on accident scenario

abstract: Abstract Nowadays, vehicles are more and more equipped with systems that help or replace drivers to avoid or reduce critical situations. These systems are designed to alert them and to maintain a safety time gap with his leader in car following driving tasks. Because of the high cost of these systems, a diversity of driving modes are encountered in traffic, mixing human and automated driving. Safety analysis in mixed road traffic has not received much attention. This paper deals with driver safety studies using two new suitable descriptors in case of critical situations like accident. Simulation results, using the two evaluation approaches, confirm that the traffic is more secured in the presence of more automated vehicles with quantitative analysis.


title: Automotive V2X on phones: Enabling next-generation mobile ITS apps

abstract: Automotive connectivity standards promise to usher in new apps and services that utilize vehicle-to-vehicle communications, and mobile app trends point to the potential of direct device-to-device connections. The convergence of the two, made possible by new hardware, system software, and programming abstractions, promises to realize next-generation mobile ITS apps. The city-scale impact of such convergence can now be projected using new evaluation infrastructure that captures interactions between humans, vehicles, devices and networks.


title: Four-element dual-band printed slot antenna array for the future 5G mobile communication networks

abstract: In this article, a four-element dual-band printed slot antenna array for the future fifth generation (5G) mobile networks is proposed. The antenna element has a compact with size of 0.8 λ 0 × 0.75 λ 0 at 28 GHz. The simulated results show that the designed antenna has a dual-band function at 28/38 GHz that covers future 5G applications. The designed 1-to-4 modified Wilkinson power divider is used to feed the proposed array. For further enhancement of the designed power divider, an electromagnetic bandgap (EBG) structures are used. The proposed antenna array provides directional patterns, relatively flat gain, and high radiation efficiency through the frequency band excluding the rejected band.


title: Single-Ended and Differential Radial Power Combiners Implemented With a Compact Broadband Probe

abstract: A radial power-combiner design is presented using broadband probes and a radial cavity configuration. The 8:1 combiner (which can also be used as a power divider) is demonstrated experimentally in both single-ended and differential configurations. The broadband probe is composed of cylindrical conductors and dielectric spacers, arranged on a rod for mechanical stability. The proposed radial power combiner provides low loss, broad bandwidth (800-1800 MHz), and high power capability.


title: A survey of intrusion detection techniques for cyber-physical systems

abstract: Pervasive healthcare systems, smart grids, and unmanned aircraft systems are examples of Cyber-Physical Systems (CPSs) that have become highly integrated in the modern world. As this integration deepens, the importance of securing these systems increases. In order to identify gaps and propose research directions in CPS intrusion detection research, we survey the literature of this area. Our approach is to classify modern CPS Intrusion Detection System (IDS) techniques based on two design dimensions: detection technique and audit material. We summarize advantages and drawbacks of each dimension’s options. We also summarize the most and least studied CPS IDS techniques in the literature and provide insight on the effectiveness of IDS techniques as they apply to CPSs. Finally, we identify gaps in CPS IDS research and suggest future research areas.


title: A Trust Model Based Analysis of Social Networks

abstract: In this paper, we analyse the sustainability of social networks using STrust, our social trust model. The novelty of the model is that it introduces the concept of engagement trust and combines it with the popularity trust to derive the social trust of the community as well as of individual members in the community. This enables the recommender system to use these different types of trust to recommend different things to the community, and identify (and recommend) different roles. For example, it recommends mentors using the engagement trust and leaders using the popularity trust. We then show the utility of the model by analysing data from two types of social networks. We also study the sustainability of a community through our social trust model. We observe that a 5% drop in highly trusted members causes more than a 50% drop in social capital that, in turn, raises the question of sustainability of the community. We report our analysis and its results.


title: Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?

abstract: Three important properties of a classification machinery are i) the system preserves the core information of the input data; ii) the training examples convey information about unseen data; and iii) the system is able to treat differently points from different classes. In this paper, we show that these fundamental properties are satisfied by the architecture of deep neural networks. We formally prove that these networks with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data. Similar points at the input of the network are likely to have a similar output. The theoretical analysis of deep networks here presented exploits tools used in the compressed sensing and dictionary learning literature, thereby making a formal connection between these important topics. The derived results allow drawing conclusions on the metric learning properties of the network and their relation to its structure, as well as providing bounds on the required size of the training set such that the training examples would represent faithfully the unseen data. The results are validated with state-of-the-art trained networks.


title: Exploiting Matrix Dependency for Efficient Distributed Matrix Computation

abstract: Distributed matrix computation is a popular approach for many large-scale data analysis and machine learning tasks. However existing distributed matrix computation systems generally incur heavy communication cost during the runtime, which degrades the overall performance. In this paper, we propose a novel matrix computation system, named DMac, which exploits the matrix dependencies in matrix programs for efficient matrix computation in the distributed environment. We decompose each matrix program into a sequence of operations, and reveal the matrix dependencies between operations in the program. We next design a dependency-oriented cost model to select an optimal execution strategy for each operation, and generate a communication efficient execution plan for the matrix computation program. To facilitate the matrix computation in distributed systems, we further divide the execution plan into multiple un-interleaved stages which can run in a distributed cluster with efficient local execution strategy on each worker. The DMac system has been implemented on a popular general-purpose data processing framework, Spark. The experimental results demonstrate that our techniques can significantly improve the performance of a wide range of matrix programs.


title: Real time Detection of Lane Markers in Urban Streets

abstract: We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.


title: Reliable and Efficient Performance Monitoring in Linux

abstract: Processor hardware performance counters have recently improved in quality and features, while performance monitoring support in Linux has been significantly revamped with the development of the perf_events subsystem, which contributed in making performance analysis an increasingly common practice among developers. However, no performance analysis is possible without an efficient monitoring interface and reliable hardware counter data. In this paper, we first address a reliability issue in the Performance Monitoring Unit of recent Intel processors with Hyper-Threading enabled. A published erratum causes cross hyper-thread hardware counter corruption and may produce unreliable results. We propose a cache-coherence style protocol which we implement in the Linux kernel to address the issue by introducing cross hyper-thread dynamic event scheduling. Second, we improve event scheduling efficiency by introducing an algorithm which optimally schedules events onto hardware counters consistently. The proposed optimizations do not require any user level changes. They leverage the internal design of the perf_events subsystem and have broader applicability in processors. The improvements have been contributed to the upstream Linux kernel 4.1.


title: Novel hyaluronic acid dermal filler: dermal gel extra physical properties and clinical outcomes.

abstract: BACKGROUND ::: Dermal gel extra (DGE) is a new, tightly cross-linked hyaluronic acid (HA)-based dermal filler containing lidocaine engineered to resist gel deformation and degradation. ::: ::: ::: OBJECTIVES ::: To develop a firmer gel product (DGE) and compare the efficacy and safety of DGE with nonanimal stabilized HA (NASHA) for correction of nasolabial folds (NLFs). ::: ::: ::: METHODS ::: DGE physical properties were characterized, and 140 subjects with moderate to deep NLFs were treated with DGE and NASHA in a randomized, multicenter, split-face design study. Efficacy, pain, and satisfaction were measured using appropriate standard instruments. Adverse events were monitored throughout the study. ::: ::: ::: RESULTS ::: DGE has a higher modulus and a higher gel:fluid ratio than other HA fillers. Similar optimal correction was observed with DGE and NASHA through 36 weeks (9 months). Study subjects required less volume (p<.001) and fewer touch-ups (p=.005) and reported less injection pain (p<.001) with DGE treatment. Most adverse events were mild to moderate skin reactions. ::: ::: ::: CONCLUSIONS ::: DGE is a firm HA gel that required significantly less volume and fewer touch-ups to provide equivalent efficacy to NASHA for NLF correction; both dermal gels were well tolerated. DGE will provide a comfortable and cost-effective dermal filler option for clinicians and patients.


title: Ethical Considerations in Artificial Intelligence Courses

abstract: The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.


title: Adaptive e-learning web-based English tutor using data mining techniques and Jackson's learning styles

abstract: With the emanation of educational data mining field, it is being increasingly connected to a number of research areas such as adaptive and intelligent web-based tutors, intelligent educational applications and other accommodating online educational data mining systems. The applications of educational data mining takes into account the system academic aspects, the academic background, and the learner's classification. This paper proposes a new adaptive e-learning system. The proposed system integrates a well known intelligent web-based English e-learning tutor with data mining techniques. Also, the data minig techniques are used in order to cluster students' learning styles according to Jackson's learning styles. The ultimate goal of the proposed system is to determine the best teaching pattern for each learner. The proposed system can be made available through the web Everywhere as well as Every Time (EWET). It also offers adaptive facilities such as learning videos, adaptive presentations, and quizzes for the students. Moreover, it helps both teachers and students to follow the best learning process and achieve the highest academic rates. The results show that the highest student's achievement pattern is the pattern (Speaking — Reading — Grammar — Writing) with score of at least 87.4%.


title: Detecting Hepatitis B Surface Antigen Mutants

abstract: Hepatitis B viral mutants can emerge in patients as a result of selection pressure from either immune response or treatment options. Mutations that occur within the immunodominant epitopes of hepatitis B surface antigen (HBsAg) allow mutant virus to propagate in the presence of a neutralizing immune response, while wild-type virus is reduced to undetectable levels. HBsAg mutants present as false-negative results in some immunoassays. An understanding of immunoassay reactivity with HBsAg mutants is key to establishing an appropriate testing algorithm for hepatitis B virus detection programs.


title: Semi-automatic model integration using matching transformations and weaving models

abstract: Model transformations are at the heart of model driven engineering (MDE) and can be used in many different application scenarios. For instance, model transformations are used to integrate very large models. As a consequence, they are becoming more and more complex. However, these transformations are still developed manually. Several code patterns are implemented repetitively, increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper we propose a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Matching transformations are a special kind of transformations that implement heuristics and algorithms to create weaving models. Weaving models are models that capture different kinds of relationships between models. Our solution enables to rapidly implement and to customize these heuristics. We combine different heuristics, and we propose a new metamodel-based heuristic that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations.


title: CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.


Liver cirrhosis mortality, alcohol consumption and tobacco consumption over a 62 year period in a high alcohol consumption country: a trend analysis


The Gift of Generations


title: The role of perceived risk on intention to use online banking in vietnam

abstract: Online banking is a substantial service in the enlargement strategy of the modern bank sector. Nevertheless, this service has not been widely used, because of users are still scared of the risks of online transactions. Thus, the in-depth and distinct works of risks in risk awareness are an essential and meaningful assignment for the banking sector. This study investigates the role of perceived risk on intention to use online banking. The results of SEM (structural equation modelling) evidence risk factors (privacy risk, security risk, social risk, time risk, and financial-performance risk) in perceived risk, which has a negative effect on intention to use online banking in Vietnam. Research results can help to propose solutions for enhancing the safety and mitigating the risks in online banking.


Consumers ’ Perceptions , Attitudes and Purchase Intention towards Private Label Food Products in Malaysia


title: A high-level overview of fundamental limits studies for the DARPA SSPARC program

abstract: This paper presents a high-level overview of the Fundamental Limits studies for the DARPA SSPARC program. It focuses on the key techniques and insights that have resulted from this effort and presents possible future research directions that have been suggested by these studies1.


title: Current status of proteomic standards development

abstract: The generation of proteomic data is becoming ever more high throughput. Both the technologies and experimental designs used to generate and analyze data are becoming increasingly complex. The need for methods by which such data can be accurately described, stored and exchanged between experimenters and data repositories has been recognized. Work by the Proteome Standards Initiative of the Human Proteome Organization has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. The Minimum Information About a Proteomic Experiment data model describes both the scope and purpose of a proteomics experiment and encompasses the development of more specific interchange formats such as the mzData model of mass spectrometry. The eXtensible Mark-up Language-MI data interchange format, which allows exchange of molecular interaction data, has already been published and major databases within this field are supplying data downloads in this format.


title: Towards social user profiling: unified and discriminative influence model for inferring home locations

abstract: Users' locations are important to many applications such as targeted advertisement and news recommendation. In this paper, we focus on the problem of profiling users' home locations in the context of social network (Twitter). The problem is nontrivial, because signals, which may help to identify a user's location, are scarce and noisy. We propose a unified discriminative influence model, named as UDI, to solve the problem. To overcome the challenge of scarce signals, UDI integrates signals observed from both social network (friends) and user-centric data (tweets) in a unified probabilistic framework. To overcome the challenge of noisy signals, UDI captures how likely a user connects to a signal with respect to 1) the distance between the user and the signal, and 2) the influence scope of the signal. Based on the model, we develop local and global location prediction methods. The experiments on a large scale data set show that our methods improve the state-of-the-art methods by 13%, and achieve the best performance.


title: Optimized Stochastic Policies for Task Allocation in Swarms of Robots

abstract: We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.


title: Identification and prioritization of critical issues for the promotion of e-learning in Pakistan

abstract: Identification of state-of-the-art issues for the promotion of e-learning in Pakistan.Analysis of the impact of issues on the promotion of e-learning in HEIs of Pakistan.Classifying the identified issues in to respective dimensions.Development of taxonomy for the identified issues of e-learning.Prioritization of the issues according to their importance for the promotion of e-learning. Integration of information and communication technology in education is emerging as the new paradigm of learning and training. Higher education institutions are struggling to shift to this new paradigm to facilitate more and more learners with the flexibility of any time-anywhere learning. E-learning is not gaining as much popularity in the developing countries as it was expected in the last decade. Little work has been done in this area of research in the developing countries. This study contributes to identify and analyze the impact of critical issues which are creating barriers in the promotion of e-learning in the developing countries like Pakistan. Furthermore, this study contributes in devising taxonomy and proposing new category software for the identified critical issues. A mix mode research model has been applied to collect data from the e-learning experts of different public sector universities of Pakistan to get a deeper understanding of the issues and their impact on the promotion of e-learning in Pakistan. The findings of this study reveal sixteen (16) critical issues which are classified in five (5) dimensions, to be addressed on priority basis to promote e-learning in Pakistan. The identified dimensions and issues have been prioritized according to their importance using the Analytical Hierarchy Process method.


title: Design, Analysis, and Optimization of Ironless Stator Permanent Magnet Machines

abstract: This paper presents a methodology for the design, analysis, and graphical optimization of ironless brushless permanent magnet machines primarily for generator applications. Magnetic flux in this class of electromagnetic machine tends to be 3-D due to the lack of conventional iron structures and the absence of a constrained magnetic flux path. The proposed methodology includes comprehensive geometric, magnetic and electrical dimensioning followed by detailed 3-D finite element (FE) modeling of a base machine for which parameters are determined. These parameters are then graphically optimized within sensible volumetric and electromagnetic constraints to arrive at improved design solutions. This paper considers an ironless machine design to validate the 3-D FE model to optimize power conversion for the case of a low-speed, ironless stator generator. The machine configuration investigated in this paper has concentric arrangement of the rotor and the stator, solenoid-shaped coils, and a simple mechanical design considered for ease of manufacture and maintenance. Using performance and material effectiveness as the overriding optimization criteria, this paper suggests optimal designs configurations featuring two different winding arrangements, i.e., radial and circumferentially mounted. Performance and material effectiveness of the studied ironless stator designs are compared to published ironless machine configurations.


title: Universal Value Function Approximators

abstract: Value functions are a core component of reinforcement learning systems. The main idea is to to construct a single function approximator V (s; θ) that estimates the long-term reward from any state s, using parameters θ. In this paper we introduce universal value function approximators (UVFAs) V (s, g; θ) that generalise not just over states s but also over goals g. We develop an efficient technique for supervised learning of UVFAs, by factoring observed values into separate embedding vectors for state and goal, and then learning a mapping from s and g to these factored embedding vectors. We show how this technique may be incorporated into a reinforcement learning algorithm that updates the UVFA solely from observed rewards. Finally, we demonstrate that a UVFA can successfully generalise to previously unseen goals.


title: Using S-parameter measurements to determine the threshold voltage, gain factor, and mobility degradation factor for microwave bulk-MOSFETs

abstract: Abstract In this paper, we present an extraction and characterization methodology which allows for the determination, from S-parameter measurements, of the threshold voltage, the gain factor, and the mobility degradation factor, neither requiring data regressions involving multiple devices nor DC measurements. This methodology takes into account the substrate effects occurring in MOSFETs built in bulk technology so that physically meaningful parameters can be obtained. Furthermore, an analysis of the substrate impedance is presented, showing that this parasitic component not only degrades the performance of a microwave MOSFET, but may also lead to determining unrealistic values for the model parameters when not considered during a high-frequency characterization process. Measurements were made on transistors of different lengths, the shortest being 80 nm, in the 10 MHz to 40 GHz frequency range.


title: A Demand Response Energy Management Scheme for Industrial Facilities in Smart Grid

abstract: Demand response (DR) smart grid technology provides an opportunity for electricity consumers to actively participate in the management of power systems. Industry is one of the major consumers of electric power. In this study, we propose a DR energy management scheme for industrial facilities based on the state task network (STN) and mixed integer linear programming (MILP). The scheme divides the processing tasks in industrial facilities into nonschedulable tasks (NSTs) and schedulable tasks (STs), and takes advantage of distributed energy resources (DERs) to implement DR. Based on day-ahead hourly electricity prices, the scheme determines the scheduling of STs and DERs in order to shift the demand from peak periods (with high electricity prices) to off-peak periods (with low electricity prices), which not only improves the reliability of the electric power system, but also reduces energy costs for industrial facilities.


title: Accumulators with Applications to Anonymity-Preserving Revocation

abstract: Membership revocation is essential for cryptographic applications, from traditional PKIs to group signatures and anonymous credentials. Of the various solutions for the revocation problem that have been explored, dynamic accumulators are one of the most promising. We propose Braavos, a new, RSA-based, dynamic accumulator. It has optimal communication complexity and, when combined with efficient zero-knowledge proofs, provides an ideal solution for anonymous revocation. For the construction of Braavos we use a modular approach: we show how to build an accumulator with better functionality and security from accumulators with fewer features and weaker security guarantees. We then describe an anonymous revocation component (ARC) that can be instantiated using any dynamic accumulator. ARC can be added to any anonymous system, such as anonymous credentials or group signatures, in order to equip it with a revocation functionality. Finally, we implement ARC with Braavos and plug it into Idemix, the leading implementation of anonymous credentials. This work resolves, for the first time, the problem of practical revocation for anonymous credential systems.


title: Internet Gaming Disorder: An Emergent Health Issue for Men

abstract: Internet gaming is a legitimate leisure activity worldwide; however, there are emerging concerns that vast numbers of gamers are becoming addicted. In 2013, the American Psychiatric Association (APA) classified Internet Gaming Disorder (IGD) as a condition warranting more clinical research ahead of formalizing it as a mental disorder. Proposed as a behavioral addiction, IGD shares many similarities in both physical and psychosocial manifestations with substance use disorder, including cerebral changes on functional magnetic resonance imaging (fMRI). Among the gaming population, compared to females, adolescent and adult males demonstrate far more addictive internet gaming use in terms of screen hours, craving, and negative impacts on health, which have, in isolated incidents, also caused death. The current article draws findings from a scoping review of literature related to IGD as a means to raising awareness about an emergent men’s health issue. Included are three themes: (a) unveiling the nature, impacts and symptoms of IGD; (b) conceptualizing IGD through neuroscience; and (c) treatment approaches to IGD. Afforded by these themes is an overview and synthesis of the existing literature regarding IGD as a means of providing direction for much needed research on gaming addiction and orientating primary care providers (PCPs) to the specificities of IGD in men’s health. The findings are applied to a discussion of the connections between IGD and masculinity and the importance of recognizing how behaviors such as social isolation and game immersion can be maladaptive coping strategies for males.


title: Greek Teachers’ Attitudes toward the Inclusion of Students with Special Educational Needs

abstract: The aim of this study is to examine the teachers’ attitudes toward the inclusion of students with special educational needs, in public schools and how these attitudes are influenced by their self-efficacy perceptions. The sample is comprised of 416 preschool, primary and secondary education teachers. The results show that, in general, teachers develop positive attitude toward the inclusive education. Higher self-efficacy was associated rather with their capacity to come up against negative experiences at school, than with their attitude toward disabled learners in the classroom and their ability to meet successfully the special educational needs students. The results are consistent with similar studies and reveal the need of establishing collaborative support networks in school districts and the development of teacher education programs, in order to achieve the enrichment of their knowledge and skills to address diverse needs appropriately.


Towards Lexical Chains for Knowledge-Graph-based Word Embeddings


title: Feature extraction based on the high-pass filtering of audio signals for Acoustic Event Classification

abstract: HighlightsWe propose a new front-end for Acoustic Event Classification tasks (AEC).The proposed front-end relies on the high pass filtering of acoustic event signals.Results show that it outperforms the baseline system in clean and noisy conditions. In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC). First, we study the spectral characteristics of different acoustic events in comparison with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel-Frequency Cepstral Coefficients (MFCC) and is based on the high pass filtering of the acoustic event signal. The proposed front-end have been tested in clean and noisy conditions and compared to the conventional MFCC in an AEC task. Results support the fact that the high pass filtering of the audio signal is, in general terms, beneficial for the system, showing that the removal of frequencies below 100-275Hz in the feature extraction process in clean conditions and below 400-500Hz in noisy conditions, improves significantly the performance of the system with respect to the baseline.


title: Turing Patterns in Memristive Cellular Nonlinear Networks

abstract: The formation of ordered structures, in particular Turing patterns, in complex spatially extended systems has been observed in many different contexts, spanning from natural sciences (chemistry, physics, and biology) to technology (mechanics and electronics). In this paper, it is shown that the use of memristors in a simple cell of a spatially-extended circuit architecture allows us to design systems able to generate Turing patterns. In addition, the memristor parameters play a key role in the selection of the type and characteristics of the emerging pattern, which is also influenced by the initial conditions. The problem of finding the regions of parameters where Turing patterns may emerge in the proposed cellular architecture is solved in an analytic way, and numerical results are shown to illustrate the system behavior with respect to its parameters.


title: Modeling MOS snapback for circuit-level ESD simulation using BSIM3 and VBIC models

abstract: A novel macro model approach for modeling ESD MOS snapback is introduced. The macro model consists of standard components only. It includes a MOS transistor modeled by BSIM3v3, a bipolar transistor modeled by VBIC, and a resistor for substrate resistance. No external current source, which is essential in most publicly reported macro models, is included since both BSIM3vs and VBIC have formulations built in to model the relevant effects. The simplicity of the presented macro model makes behavior languages, such as Verilog-A, and special ESD equations not necessary in model implementation. This offers advantages of high simulation speed, wider availability, and less convergence issues. Measurement and simulation of the new approach indicates that good silicon correlation can be achieved.


title: Increasing Mechanical Strength of Gelatin Hydrogels by Divalent Metal Ion Removal

abstract: The usage of gelatin hydrogel is limited due to its instability and poor mechanical properties, especially under physiological conditions. Divalent metal ions present in gelatin such as Ca(2+) and Fe(2+) play important roles in the gelatin molecule interactions. The objective of this study was to determine the impact of divalent ion removal on the stability and mechanical properties of gelatin gels with and without chemical crosslinking. The gelatin solution was purified by Chelex resin to replace divalent metal ions with sodium ions. The gel was then chemically crosslinked by 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC). Results showed that the removal of divalent metal ions significantly impacted the formation of the gelatin network. The purified gelatin hydrogels had less interactions between gelatin molecules and form larger-pore network which enabled EDC to penetrate and crosslink the gel more efficiently. The crosslinked purified gels showed small swelling ratio, higher crosslinking density and dramatically increased storage and loss moduli. The removal of divalent ions is a simple yet effective method that can significantly improve the stability and strength of gelatin hydrogels. The in vitro cell culture demonstrated that the purified gelatin maintained its ability to support cell attachment and spreading.


title: A Single-Switch AC/DC Flyback Converter Using a CCM/DCM Quasi-Active Power Factor Correction Front-End

abstract: This paper discusses the major issues that exist in the single-stage ac/dc converters with power factor correction (PFC) and presents a novel converter based on a quasi-active PFC scheme. Two additional windings wound in the transformer of a conventional dc/dc flyback converter are used to drive and achieve continuous current mode operation of an input inductor. In addition, direct energy transfer paths are provided through the additional windings to improve the conversion efficiency and to reduce the dc bus capacitor voltage below 450 V for universal line applications. The proposed converter can be easily designed to comply with IEC 61000-3-2 Class D requirement and to achieve fast output voltage regulation. By properly tuning the converter parameters, a good tradeoff between efficiency, dc bus capacitor voltage stress, and harmonic content can be achieved. Operating principles, analysis, and experimental results of the proposed method are presented.


title: Analyzing emotion words to predict severity of software bugs: a case study of open source projects

abstract: A successful software development project becomes an essential part of a software company's reputation. Thus, lots of project managers focus more on maintenance than on other management processes. Previous works studied how to help the maintenance process by detecting bug duplication and predicting the severity of bugs. This paper continues that kind of special work by analyzing emotion words for bug-severity prediction. In detail, we construct an emotion words-based dictionary for verifying bug reports' textual emotion analyses based on positive and negative terms. Then, we modify a machine learning algorithm, the Naive Bayes multinomial, calling the new algorithm EWD-Multinomial. We compare this EWD-Multinomial study with our baselines, including Naive Bayes multinomial and a Lamkanfi study, for open source projects such as Eclipse, Android, and JBoss. The result shows this study's algorithm outperforms the others.


title: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs

abstract: Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task. This paper tackles this challenging and essentially underdetermined problem by regression on deep convolutional neural network (DCNN) features, combined with a post-processing refining step using conditional random fields (CRF). Our framework works at two levels, super-pixel level and pixel level. First, we design a DCNN model to learn the mapping from multi-scale image patches to depth or surface normal values at the super-pixel level. Second, the estimated super-pixel depth or surface normal is refined to the pixel level by exploiting various potentials on the depth or surface normal map, which includes a data term, a smoothness term among super-pixels and an auto-regression term characterizing the local structure of the estimation map. The inference problem can be efficiently solved because it admits a closed-form solution. Experiments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.


title: Over-expression of miR397 improves plant tolerance to cold stress in Arabidopsis thaliana

abstract: Cold stress is an environmental factor that limits the geographical distribution and growing season of plants. The plant response to cold involves different metabolic pathways and gene regulation. Although the expression of some miRNAs is significantly altered in cold stress, little is known about the regulatory function of miRNAs in plant resistance to cold. In this study, we generated transgenic Arabidopsis plants which harbor an over-expression construct of 35S::miR397a. Analysis by RNA blotting revealed high levels of miR397a transcripts in the transformed plants. Plant cold tolerance assays demonstrated that over-expression of miR397a improved plant tolerance to chilling and freezing stresses. In addition, this study showed that the freezing tolerance of miR397a-ov plants was improved by cold acclimation at 4°C. Northern blot analysis revealed that overexpression of miR397a affected the expression of coldregulated CBF genes and downstream COR genes. These findings provide evidence demonstrating a regulatory role of miR397 in the cold signaling pathway and plant tolerance to chilling and freezing stresses.


title: Evaluating data storage structures of MapReduce

abstract: MapReduce framework and its open-source implementation Hadoop, a scalable and fault-tolerant infrastructure for big data analysis on large clusters, can achieve different performance with different data storage structures. This paper evaluates the performance about three kinds of data storage structures of MapReduce, namely row-store, column-store, and RCFile. The evaluating experiments are designed to test three data storage structures in terms of data loading time, data storage space, and query execution time. The experimental results show that RCFile data storage structure can achieve better performance in most cases.


title: Microstrip bandpass filters for Ultra-Wideband (UWB) wireless communications

abstract: A new technique is developed for designing a composite microstrip bandpass filter (BPF) with a 3 dB fractional bandwidth of more than 100%. The BPF is suitable for ultra-wideband (UWB) wireless communications. The design utilizes embedding individually designed highpass structures and lowpass filters (LPF) into each other, followed by an optimization for tuning in-band performance. The stepped-impedance LPF is employed to attenuate the upper stopband and quarter-wave short-circuited stubs are used to realize the lower stopband. Two such BPFs are fabricated and measured to demonstrate the performance.


title: Back-iron effect of tubular linear motors with dual Halbach permanent magnet arrays

abstract: Ferromagnetic materials affect magnetic field distribution and force output of electromagnetic motors significantly. This paper studies effect of back iron on tubular linear motors with dual Halbach array. The structure and operating principle of linear motors with dual Halbach array are presented. Numerical method in Ansoft environment is employed to compute the magnetic field and force output of linear motors with various types of back iron patterns including designs with internal back-iron, external back-iron, double-sided back-iron, and without back-iron. The study shows that the employment of back irons does help to enhance magnetic field and increase force output of electromagnetic linear motors with dual Halbach arrays.


title: A concise introduction to autonomic computing

abstract: 1. IntroductionThe advances in computing and communication tech-nologies and software have resulted in an explosive growthin computing systems and applications that impact allaspects of our life. However, as the scale and complexity ofthese systems and applications grow, their development,conﬁguration and management challenges are beginning tobreak current paradigms, overwhelm the capabilities ofexisting tools and methodologies, and rapidly render thesystems and applications brittle, unmanageable andinsecure.This has led researchers to consider alternativeapproaches based on strategies used by biological systemsto successfully deal with similar challenges of complexity,dynamism, heterogeneity and uncertainty. Autonomiccomputing is emerging as a signiﬁcant new strategic andholistic approach to the design of complex distributedcomputer systems. It is inspired by the functioning of thehuman nervous system and is aimed at designing andbuilding systems that are self-managing. Morespeciﬁcally, an autonomic system is a self-managing,autonomous and ubiquitous computing environment thatcompletely hides its complexity, thus providing the userwith an interface that exactly meets her/his needs. Thesystem will always decide on its own, using high-levelguidance from humans, what needs to be done to keep itstable. It will constantly check and optimize its status,and automatically adapt itself to changing conditions.Self-management is achieved through key aspects such asself-governing, self-adaptation, self-organization, self-optimization, self-conﬁguration, self-diagnosis of faults,self-protection, self-healing, self-recovery, and autonomy.Achieving these goals come down to bringing pre-emptiveand proactive approaches to all areas of a computingsystem. Meeting these challenges of autonomic computingrequires scientiﬁc and technological advances in a widevariety of ﬁelds, and new architectures that supporteffective integration of the constituent technologies.2. ConceptsBiological systems have inspired systems design in manyways—Artiﬁcial Intelligence, Artiﬁcial Neural Networks,Genetic Algorithms, Genetic Programming, and HolonicSystems to name a few. The most recent is the inspiration tocreate self-managing systems.2.1. Autonomic nervous systemThe human body’s Autonomic Nervous System (ANS) isthe part of the nervous system that controls the vegetativefunctions of the body such as circulation of the blood,intestinal activity and secretion, and the production ofchemical ‘messengers’, i.e. hormones, that circulate in


title: Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

abstract: Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.


title: Know Your Phish: Novel Techniques for Detecting Phishing Sites and Their Targets

abstract: Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they require a large amount of training data and are not adept at detecting phishing attacks against new targets. In this paper, we begin with two core observations: (a) although phishers try to make a phishing webpage look similar to its target, they do not have unlimited freedom in structuring the phishing webpage, and (b) a webpage can be characterized by a small set of key terms, how these key terms are used in different parts of a webpage is different in the case of legitimate and phishing webpages. Based on these observations, we develop a phishing detection system with several notable properties: it requires very little training data, scales well to much larger test data, is language-independent, fast, resilient to adaptive attacks and implemented entirely on client-side. In addition, we developed a target identification component that can identify the target website that a phishing webpage is attempting to mimic. The target detection component is faster than previously reported systems and can help minimize false positives in our phishing detection system.


title: Flattening Gamma: Radiometric Terrain Correction for SAR Imagery

abstract: Enabling intercomparison of synthetic aperture radar (SAR) imagery acquired from different sensors or acquisition modes requires accurate modeling of not only the geometry of each scene, but also of systematic influences on the radiometry of individual scenes. Terrain variations affect not only the position of a given point on the Earth's surface but also the brightness of the radar return as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover induced backscatter differences, and comparison of backscatter from multiple satellites, modes, or tracks loses meaning. The ASAR & PALSAR sensors provide state vectors and timing with higher absolute accuracy than was previously available, allowing them to directly support accurate tie-point-free geolocation and radiometric normalization of their imagery. Given accurate knowledge of the acquisition geometry of a SAR image together with a digital height model (DHM) of the area imaged, radiometric image simulation is applied to estimate the local illuminated area for each point in the image. Ellipsoid-based or sigma naught (σ0) based incident angle approximations that fail to reproduce the effect of topographic variation in their sensor model are contrasted with a new method that integrates terrain variations with the concept of gamma naught (γ0) backscatter, converting directly from beta naught (β0) to a newly introduced terrain-flattened γ0 normalization convention. The interpretability of imagery treated in this manner is improved in comparison to processing based on conventional ellipsoid or local incident angle based σ0 normalization.


Estimation of Synaptic Conductances in Presence of Nonlinear Effects Caused by Subthreshold Ionic Currents


title: Theta activity and meditative states: spectral changes during concentrative meditation

abstract: Brain oscillatory activity is associated with different cognitive processes and plays a critical role in meditation. In this study, we investigated the temporal dynamics of oscillatory changes during Sahaj Samadhi meditation (a concentrative form of meditation that is part of Sudarshan Kriya yoga). EEG was recorded during Sudarshan Kriya yoga meditation for meditators and relaxation for controls. Spectral and coherence analysis was performed for the whole duration as well as specific blocks extracted from the initial, middle, and end portions of Sahaj Samadhi meditation or relaxation. The generation of distinct meditative states of consciousness was marked by distinct changes in spectral powers especially enhanced theta band activity during deep meditation in the frontal areas. Meditators also exhibited increased theta coherence compared to controls. The emergence of the slow frequency waves in the attention-related frontal regions provides strong support to the existing claims of frontal theta in producing meditative states along with trait effects in attentional processing. Interestingly, increased frontal theta activity was accompanied reduced activity (deactivation) in parietal–occipital areas signifying reduction in processing associated with self, space and, time.


title: Automatic Accident Detection and reporting framework for two wheelers

abstract: Technology must work for human race and improve the way help reaches a person in distress in the shortest possible time. In a developing nation like India, with the advancement in the transportation technology and rise in the total number of vehicles, road accidents are increasing at an alarming rate. If an accident occurs, the victim's survival rate increases when you give immediate medical assistance. You can give medical assistance to an accident victim only when you know the exact location of the accident. This paper presents an inexpensive but intelligent framework that can identify and report an accident for two-wheelers. This paper targets two-wheelers because the mortality ratio is highest in two-wheeler accidents in India. This framework includes a microcontroller-based low-cost Accident Detection Unit (ADU) that contains a GPS positioning system and a GSM modem to sense and generate accidental events to a centralized server. The ADU calculates acceleration along with ground clearance of the vehicle to identify the accidental situation. On detecting an accident, ADU sends accident detection parameters, GPS coordinates, and the current time to the Accident Detection Server (ADS). ADS maintain information on the movement of the vehicle according to the historical data, current data, and the rules that you configure in the system. If an accident occurs, ADS notifies the emergency services and the preconfigured mobile numbers for the vehicle that contains this unit.


Linux Based Control Framework for Mecnaum Based Omnidirectional Automated Guided Vehicles


title: Containerization and the PaaS Cloud

abstract: Containerisation is widely discussed as a lightweight virtualisation solution. Apart from exhibiting benefits over traditional virtual machines in the cloud, containers are especially relevant for Platform-as-a-Service (PaaS) clouds to manage and orchestrate applications through containers as an application packaging mechanism. We discuss the requirements that arise from having to facilitate applications through distributed multi-cloud platforms.


title: Query Optimization for Dynamic Graphs

abstract: Given a query graph that represents a pattern of interest, the emerging pattern detection problem can be viewed as a continuous query problem on a dynamic graph. We present an incremental algorithm for continuous query processing on dynamic graphs. The algorithm is based on the concept of query decomposition; we decompose a query graph into smaller subgraphs and assemble the result of sub-queries to find complete matches with the specified query. The novelty of our work lies in using the subgraph distributional statistics collected from the dynamic graph to generate the decomposition. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query decomposition strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over competing approaches.


title: SaaS business model for software enterprise

abstract: Revenue of traditional software enterprises are from software copyright licensing and software upgrades, maintenance and other services fees. As a rapidly growing operation model of software contrast to traditional perpetual-use license, SaaS (Software as a Service) softwares are subscripted by users from vendors. From views of business and technique, software product/service selection, software service classification, service pattern and charges, advantages and disadvantages of SaaS business model were analyzed in the paper. A conclusion is that SaaS business model could not only provide vendors more development opportunities but also contribute to SME's informationization.


DYNAMIC MODELING OF ONE DEGREE OF FREEDOM PNEUMATIC MUSCLE-BASED ACTUATOR FOR INDUSTRIAL APPLICATIONS


title: Optimal Power Flow in Microgrids With Energy Storage

abstract: Energy storage may improve power management in microgrids that include renewable energy sources. The storage devices match energy generation to consumption, facilitating a smooth and robust energy balance within the microgrid. This paper addresses the optimal control of the microgrid's energy storage devices. Stored energy is controlled to balance power generation of renewable sources to optimize overall power consumption at the microgrid point of common coupling. Recent works emphasize constraints imposed by the storage device itself, such as limited capacity and internal losses. However, these works assume flat, highly simplified network models, which overlook the physical connectivity. This work proposes an optimal power flow solution that considers the entire system: the storage device limits, voltages limits, currents limits, and power limits. The power network may be arbitrarily complex, and the proposed solver obtains a globally optimal solution.


title: A control perspective for centralized and distributed convex optimization

abstract: In this paper, we want to study how natural and engineered systems could perform complex optimizations with limited computational and communication capabilities. We adopt a continuous-time dynamical system view rooted in early work on optimization and more recently in network protocol design, and merge it with the dynamic view of distributed averaging systems. We obtain a general approach, based on the control system viewpoint, that allows to analyze and design (distributed) optimization systems converging to the solution of given convex optimization problems. The control system viewpoint provides many insights and new directions of research. We apply the framework to a distributed optimal location problem and demonstrate the natural tracking and adaptation capabilities of the system to changing constraints.


title: Hello, is grandma there? let's read! StoryVisit: family video chat and connected e-books

abstract: StoryVisit allows children and long-distance adults to experience a sense of togetherness by reading children's story books together over a distance. StoryVisit combines video conferencing and connected books: remote grown-up and child readers can see and hear each other, and can also see and control the same e-book. We report on research with 61 families - over 200 users including parents, children and long-distance readers - who used StoryVisit in their homes with a long-distance reader for at least one reading session. In addition, we report qualitative findings regarding nineteen of the families who participated in telephone interviews and four families who were monitored and interviewed by researchers at home. Results show that connected e-book video chat sessions last about five times as long as the typical video chats reported in previous research on families with young children. Moreover, the addition of an animated character increased session lengths by another 50%. StoryVisit usage peaked for families with three year olds, showing that sustained distance interactions with very young children are possible if communication technologies incorporate joint activities that engage children and adults.


title: Causal video object segmentation from persistence of occlusions

abstract: Occlusion relations inform the partition of the image domain into “objects” but are difficult to determine from a single image or short-baseline video. We show how long-term occlusion relations can be robustly inferred from video, and used within a convex optimization framework to segment the image domain into regions. We highlight the challenges in determining these occluder/occluded relations and ensuring regions remain temporally consistent, propose strategies to overcome them, and introduce an efficient numerical scheme to perform the partition directly on the pixel grid, without the need for superpixelization or other preprocessing steps.


title: Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents

abstract: There is a wealth of information about financial systems that is embedded in document collections. In this paper, we focus on a specialized text extraction task for this domain. The objective is to extract mentions of names of financial institutions, or FI names, from financial prospectus documents, and to identify the corresponding real world entities, e.g., by matching against a corpus of such entities. The tasks are Named Entity Recognition (NER) and Entity Resolution (ER); both are well studied in the literature. Our contribution is to develop a rule-based approach that will exploit lists of FI names for both tasks; our solution is labeled Dict-based NER and Rank-based ER. Since the FI names are typically represented by a root, and a suffix that modifies the root, we use these lists of FI names to create specialized root and suffix dictionaries. To evaluate the effectiveness of our specialized solution for extracting FI names, we compare Dict-based NER with a general purpose rule-based NER solution, ORG NER. Our evaluation highlights the benefits and limitations of specialized versus general purpose approaches, and presents additional suggestions for tuning and customization for FI name extraction. To our knowledge, our proposed solutions, Dict-based NER and Rank-based ER, and the root and suffix dictionaries, are the first attempt to exploit specialized knowledge, i.e., lists of FI names, for rule-based NER and ER.


title: Deep Reinforcement Learning for Join Order Enumeration

abstract: Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for"learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency.


Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs


title: Alkamides Activate Jasmonic Acid Biosynthesis and Signaling Pathways and Confer Resistance to Botrytis cinerea in Arabidopsis thaliana

abstract: Alkamides are fatty acid amides of wide distribution in plants, structurally related to N-acyl-L-homoserine lactones (AHLs) from Gram-negative bacteria and to N- acylethanolamines (NAEs) from plants and mammals. Global analysis of gene expression changes in Arabidopsis thaliana in response to N-isobutyl decanamide, the most highly active alkamide identified to date, revealed an overrepresentation of defense-responsive transcriptional networks. In particular, genes encoding enzymes for jasmonic acid (JA) biosynthesis increased their expression, which occurred in parallel with JA, nitric oxide (NO) and H₂O₂ accumulation. The activity of the alkamide to confer resistance against the necrotizing fungus Botrytis cinerea was tested by inoculating Arabidopsis detached leaves with conidiospores and evaluating disease symptoms and fungal proliferation. N-isobutyl decanamide application significantly reduced necrosis caused by the pathogen and inhibited fungal proliferation. Arabidopsis mutants jar1 and coi1 altered in JA signaling and a MAP kinase mutant (mpk6), unlike salicylic acid- (SA) related mutant eds16/sid2-1, were unable to defend from fungal attack even when N-isobutyl decanamide was supplied, indicating that alkamides could modulate some necrotrophic-associated defense responses through JA-dependent and MPK6-regulated signaling pathways. Our results suggest a role of alkamides in plant immunity induction.


Arabic Script Recognition : A Survey


title: Very Early Nimodipine Use in Stroke (VENUS) : A Randomized, Double-Blind, Placebo-Controlled Trial

abstract: BACKGROUND AND PURPOSE ::: The Very Early Nimodipine Use in Stroke (VENUS) trial was designed to test the hypothesis that early treatment with nimodipine has a positive effect on survival and functional outcome after stroke. This was suggested in a previous meta-analysis on the use of nimodipine in stroke. However, in a recent Cochrane review we were unable to reproduce these positive results. This led to the early termination of VENUS after an interim analysis. ::: ::: ::: METHODS ::: In this randomized, double-blind, placebo-controlled trial, treatment was started by general practitioners or neurologists within 6 hours after stroke onset (oral nimodipine 30 mg QID or identical placebo, for 10 days). Main analyses included comparisons of the primary end point (poor outcome, defined as death or dependency after 3 months) and secondary end points (neurological status and blood pressure 24 hours after inclusion, mortality after 10 days, and adverse events) between treatment groups. Subgroup analyses (on final diagnosis and based on the per-protocol data set) were performed. ::: ::: ::: RESULTS ::: At trial termination, after inclusion of 454 patients (225 nimodipine, 229 placebo), no effect of nimodipine was found. After 3 months of follow-up, 32% (n=71) of patients in the nimodipine group had a poor outcome compared with 27% (n=62) in the placebo group (relative risk, 1.2; 95% CI, 0.9 to 1.6). A treatment effect was not found for secondary outcomes and in the subgroup analyses. ::: ::: ::: CONCLUSIONS ::: The results of VENUS do not support the hypothesis of a beneficial effect of early nimodipine in stroke patients.


title: MONITORING ELDERLY PEOPLE WITH THE ROBOCARE DOMESTIC ENVIRONMENT: INTERACTION SYNTHESIS AND USER EVALUATION

abstract: This article describes aspects of a fully implemented artificial intelligence (AI) system that integrates multiple intelligent components to actively assist an elderly person at home. Specifically, we describe how constraint-based scheduling technology is used to actively monitor a pattern of activities executed by the person and how detected temporal constraint violations are used to trigger meaningful and contextualized proactive interactions. This article also presents a psychological evaluation of the system focusing on elderly people’s attitudes, in which system acceptability, perceived utility, interaction modality, and emotional response are considered.


title: Heterogeneous Hardware/Software Acceleration of the BWA-MEM DNA Alignment Algorithm

abstract: The fast decrease in cost of DNA sequencing has resulted in an enormous growth in available genome data, and hence led to an increasing demand for fast DNA analysis algorithms used for diagnostics of genetic disorders, such as cancer. One of the most computationally intensive steps in the analysis is represented by the DNA read alignment. In this paper, we present an accelerated version of BWA-MEM, one of the most popular read alignment algorithms, by implementing a heterogeneous hardware/software optimized version on the Convey HC2ex platform. A challenging factor of the BWA-MEM algorithm is the fact that it consists of not one, but three computationally intensive kernels: SMEM generation, suffix array lookup and local Smith-Waterman. Obtaining substantial speedup is hence contingent on accelerating all of these three kernels at once. The paper shows an architecture containing two hardware-accelerated kernels and one kernel optimized in software. The two hardware kernels of suffix array lookup and local Smith-Waterman are able to reach speedups of 2.8x and 5.7x, respectively. The software optimization of the SMEM generation kernel is able to achieve a speedup of 1.7x. This enables a total application acceleration of 2.6x compared to the original software version.


title: A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data

abstract: This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data. The framework enables the representation, extraction, and mining of high-order latent event structure and relationships within single and multiple event sequences. The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process. We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures. We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the β-divergence to learn an overcomplete sparse latent factor model. A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures. We validate the framework on synthetic data and on an electronic health record dataset.


title: Efficient Bayesian clustering for reinforcement learning

abstract: A fundamental artificial intelligence challenge is how to design agents that intelligently trade off exploration and exploitation while quickly learning about an unknown environment. However, in order to learn quickly, we must somehow generalize experience across states. One promising approach is to use Bayesian methods to simultaneously cluster dynamics and control exploration; unfortunately, these methods tend to require computationally intensive MCMC approximation techniques which lack guarantees. We propose Thompson Clustering for Reinforcement Learning (TCRL), a family of Bayesian clustering algorithms for reinforcement learning that leverage structure in the state space to remain computationally efficient while controlling both exploration and generalization. TCRL-Theoretic achieves near-optimal Bayesian regret bounds while consistently improving over a standard Bayesian exploration approach. TCRL-Relaxed is guaranteed to converge to acting optimally, and empirically outperforms state-of-the-art Bayesian clustering algorithms across a variety of simulated domains, even in cases where no states are similar.


title: A fast, universal algorithm to learn parametric nonlinear embeddings

abstract: Nonlinear embedding algorithms such as stochastic neighbor embedding do dimensionality reduction by optimizing an objective function involving similarities between pairs of input patterns. The result is a low-dimensional projection of each input pattern. A common way to define an out-of-sample mapping is to optimize the objective directly over a parametric mapping of the inputs, such as a neural net. This can be done using the chain rule and a nonlinear optimizer, but is very slow, because the objective involves a quadratic number of terms each dependent on the entire mapping's parameters. Using the method of auxiliary coordinates, we derive a training algorithm that works by alternating steps that train an auxiliary embedding with steps that train the mapping. This has two advantages: 1) The algorithm is universal in that a specific learning algorithm for any choice of embedding and mapping can be constructed by simply reusing existing algorithms for the embedding and for the mapping. A user can then try possible mappings and embeddings with less effort. 2) The algorithm is fast, and it can reuse N-body methods developed for nonlinear embeddings, yielding linear-time iterations.


title: Occlusal considerations in implant therapy: clinical guidelines with biomechanical rationale

abstract: Abstract: Due to lack of the periodontal ligament, osseointegrated implants, unlike natural teeth, react biomechanically in a different fashion to occlusal force. It is therefore believed that dental implants may be more prone to occlusal overloading, which is often regarded as one of the potential causes for peri-implant bone loss and failure of the implant/implant prosthesis. Overloading factors that may negatively influence on implant longevity include large cantilevers, parafunctions, improper occlusal designs, and premature contacts. Hence, it is important to control implant occlusion within physiologic limit and thus provide optimal implant load to ensure a long-term implant success. The purposes of this paper are to discuss the importance of implant occlusion for implant longevity and to provide clinical guidelines of optimal implant occlusion and possible solutions managing complications related to implant occlusion. It must be emphasized that currently there is no evidence-based, implant-specific concept of occlusion. Future studies in this area are needed to clarify the relationship between occlusion and implant success.


title: Extractive Text Summarization Using Word Vector Embedding

abstract: These days, text summarization is an active research field to identify the relevant information from large documents produced in various domains such as finance, news media, academics, politics, etc. Text summarization is the process of shortening the documents by preserving the important contents of the text. This can be achieved through extractive and abstractive summarization. In this paper, we have proposed an approach to extract a good set of features followed by neural network for supervised extractive summarization. Our experimental results on Document Understanding Conferences 2002 dataset show the effectiveness of the proposed method against various online extractive text summarizers.


title: On the optimality of tree-reweighted max-product message-passing

abstract: Tree-reweighted max-product (TRW) message passing [9] is a modified form of the ordinary max-product algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a configuration that is provably optimal. In this paper, we focus on the case of binary variables with pairwise couplings, and establish stronger properties of TRW fixed points that satisfy only the milder condition of weak tree agreement (WTA). First, we demonstrate how it is possible to identify part of the optimal solution—i.e., a provably optimal solution for a subset of nodes— without knowing a complete solution. Second, we show that for submodular functions, a WTA fixed point always yields a globally optimal solution. We establish that for binary variables, any WTA fixed point always achieves the global maximum of the linear programming relaxation underlying the TRW method.


title: Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation

abstract: Segmentation of medical images using seeded region growing technique is increasingly becoming a popular method because of its ability to involve high-level knowledge of anatomical structures in seed selection process. Region based segmentation of medical images are widely used in varied clinical applications like visualization, bone detection, tumor detection and unsupervised image retrieval in clinical databases. As medical images are mostly fuzzy in nature, segmenting regions based intensity is the most challenging task. In this paper, we discuss about popular seeded region grow methodology used for segmenting anatomical structures in CT Angiography images. We have proposed a gradient based homogeneity criteria to control the region grow process while segmenting CTA images.


title: Adaptive piecewise polynomial estimation via trend filtering

abstract: We study trend filtering, a recently proposed tool of Kim et al. [SIAM Rev. 51 (2009) 339-360] for nonparametric regression. The trend filtering estimate is defined as the minimizer of a penalized least squares criterion, in which the penalty term sums the absolute $k$th order discrete derivatives over the input points. Perhaps not surprisingly, trend filtering estimates appear to have the structure of $k$th degree spline functions, with adaptively chosen knot points (we say ``appear'' here as trend filtering estimates are not really functions over continuous domains, and are only defined over the discrete set of inputs). This brings to mind comparisons to other nonparametric regression tools that also produce adaptive splines; in particular, we compare trend filtering to smoothing splines, which penalize the sum of squared derivatives across input points, and to locally adaptive regression splines [Ann. Statist. 25 (1997) 387-413], which penalize the total variation of the $k$th derivative. Empirically, we discover that trend filtering estimates adapt to the local level of smoothness much better than smoothing splines, and further, they exhibit a remarkable similarity to locally adaptive regression splines. We also provide theoretical support for these empirical findings; most notably, we prove that (with the right choice of tuning parameter) the trend filtering estimate converges to the true underlying function at the minimax rate for functions whose $k$th derivative is of bounded variation. This is done via an asymptotic pairing of trend filtering and locally adaptive regression splines, which have already been shown to converge at the minimax rate [Ann. Statist. 25 (1997) 387-413]. At the core of this argument is a new result tying together the fitted values of two lasso problems that share the same outcome vector, but have different predictor matrices.


title: Supervised Sequence Labelling with Recurrent Neural Networks

abstract: Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.


title: Active Controls in Ground Transportation — A Review of the State-of-the-Art and Future Potential

abstract: SUMMARY Active control systems offer significant functional advantages over passive systems; their introduction into production-line vehicles, however, is cautious and slow. This survey describes the recent progress in the analysis, design and technology of active controls in vehicles. It includes the state-of-the-art of their introduction into operation as well as their future potential in view of recent advances in technology and computer aided design strategies. The survey has been limited to suspensions for vehicles on roads and tracks.


title: Understanding the bias of call detail records in human mobility research

abstract: In recent years, call detail records CDRs have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs ~27% plus other cellphone-related logs e.g., tower pings, cellular handovers generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.