title: Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

abstract: Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.


title: Process mining and security: visualization in database intrusion detection

abstract: Nowadays, more and more organizations keep their valuable and sensitive data in Database Management Systems (DBMSs). The traditional database security mechanisms such as access control mechanisms, authentication, data encryption technologies do not offer a strong enough protection against the exploitation of vulnerabilities (e.g. intrusions) in DBMSs from insiders. Intrusion detection systems recently proposed in the literature focus on statistical approaches, which are not intuitive. Our research is the first ever effort to use process mining modeling low-level event logs for database intrusion detection. We have proposed a novel approach for visualizing database intrusion detection using process mining techniques. Our experiments showed that intrusion detection visualization will be able to help security officers who might not know deeply the complex system, identify the true positive detection and eliminate the false positive results.


title: 3-D Model-Based Tracking for UAV Indoor Localization

abstract: This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights.


title: Evolved Machines Shed Light on Robustness and Resilience

abstract: In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.


title: Targeting amyloid-β in glaucoma treatment

abstract: The development of the devastating neurodegenerative condition, Alzheimer's disease, is strongly associated with amyloid-β (Aβ) deposition, neuronal apoptosis, and cell loss. Here, we provide evidence that implicates these same mechanisms in the retinal disease glaucoma, a major cause of irreversible blindness worldwide, previously associated simply with the effects of intraocular pressure. We show that Aβ colocalizes with apoptotic retinal ganglion cells (RGC) in experimental glaucoma and induces significant RGC apoptosis in vivo in a dose- and time-dependent manner. We demonstrate that targeting different components of the Aβ formation and aggregation pathway can effectively reduce glaucomatous RGC apoptosis in vivo, and finally, that combining treatments (triple therapy) is more effective than monotherapy. Our work suggests that targeting the Aβ pathway provides a therapeutic avenue in glaucoma management. Furthermore, our work demonstrates that the combination of agents affecting multiple stages in the Aβ pathway may be the most effective strategy in Aβ-related diseases.


title: Decision-Making Framework for Automated Driving in Highway Environments

abstract: This paper presents a decision-making framework for automated driving in highway environments. The framework is capable of reliably, robustly assessing a given highway situation (with respect to the possibility of collision) and of automatically determining an appropriate maneuver for the situation. It consists of two main components: situation assessment and strategy decision. The situation assessment component utilizes multiple complementary “threat measures” and Bayesian networks in its calculations of “threat levels” at the car and lane level to evaluate the possibility of collisions for a given highway traffic situation. The strategy decision component, designed to generate goal-directed and collision-free behaviors, automatically determines an appropriate maneuver in a given highway situation via a hierarchical state machine—such a machine both reduces the complexity of and extends a strategy model. The types of maneuver determined by the component include both simple maneuvers, such as slowing down to avoid collision with a vehicle in front, and complex maneuvers, such as lane changes and overtaking. The presented decision-making framework is tested and evaluated—both on a closed high-speed test track in simulated traffic with various driving scenarios and on public highways in real traffic through in-vehicle testing—to verify that it can provide sufficiently reliable performance for automated driving in highway environments in terms of safety, reliability, and robustness.


title: Mode-finding for mixtures of Gaussian distributions

abstract: Gradient-quadratic and fixed-point iteration algorithms and appropriate values for their control parameters are derived for finding all modes of a Gaussian mixture, a problem with applications in clustering and regression. The significance of the modes found is quantified locally by Hessian-based error bars and globally by the entropy as sparseness measure.


title: Design and performance analysis of a high-speed air-cored axial-flux permanent-magnet generator with circular magnets and coils

abstract: Air-cored, axial flux permanent-magnet (AFPM) machines have magnetic and mechanical characteristics considered to be ideal for compact high speed electric power generation applications. To date, research on high speed AFPM machines is primarily from a mechanical perspective, with emphasis on higher power level applications such as in hybrid vehicles. Recently, there has been an increasing interest in man-portable mobile power sources with a power envelope of around 1kW, for mission critical applications. In this paper, a high speed air-cored surface mounted AFPM machine with circular magnets and coils is proposed. Comprehensive theoretical analysis and three-dimensional (3-D) electromagnetic finite element analysis (FEA) are developed to evaluate the performance of the machine. In addition, the rotor's mechanical stresses developed by high rotational speed are evaluated to ensure mechanical integrity. Finally, a prototype machine is developed for validation. Both the experimental and predicted results demonstrate that the proposed generator possesses distinct advantages over other systems, such as high efficiency, high power factor, and a simple and robust structure that offers a high degree of technology readiness.


title: 5G Millimeter-Wave Antenna Array: Design and Challenges

abstract: As there has been an explosive increase in wireless data traffic, mmw communication has become one of the most attractive techniques in the 5G mobile communications systems. Although mmw communication systems have been successfully applied to indoor scenarios, various external factors in an outdoor environment limit the applications of mobile communication systems working at the mmw bands. In this article, we discuss the issues involved in the design of antenna array architecture for future 5G mmw systems, in which the antenna elements can be deployed in the shapes of a cross, circle, or hexagon, in addition to the conventional rectangle. The simulation results indicate that while there always exists a non-trivial gain fluctuation in other regular antenna arrays, the circular antenna array has a flat gain in the main lobe of the radiation pattern with varying angles. This makes the circular antenna array more robust to angle variations that frequently occur due to antenna vibration in an outdoor environment. In addition, in order to guarantee effective coverage of mmw communication systems, possible solutions such as distributed antenna systems and cooperative multi-hop relaying are discussed, together with the design of mmw antenna arrays. Furthermore, other challenges for the implementation of mmw cellular networks, for example, blockage, communication security, hardware development, and so on, are discussed, as are potential solutions.


title: Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

abstract: Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.


title: Testing the impact and durability of a group malleability intervention in the context of the Israeli–Palestinian conflict

abstract: Fostering perceptions of group malleability (teaching people that groups are capable of change and improvement) has been shown to lead to short-term improvements in intergroup attitudes and willingness to make concessions in intractable conflicts. The present study, a field intervention involving 508 Israelis from three locations in Israel, replicated and substantially extended those findings by testing the durability of a group malleability intervention during a 6-month period of frequent violence. Three different 5-hour-long interventions were administered as leadership workshops. The group malleability intervention was compared with a neutral coping intervention and, importantly, with a state-of-the-art perspective-taking intervention. The group malleability intervention proved superior to the coping intervention in improving attitudes, hope, and willingness to make concessions, and maintained this advantage during a 6-month period of intense intergroup conflict. Moreover, it was as good as, and in some respects superior to, the perspective-taking intervention. These findings provide a naturalistic examination of the potential of group malleability interventions to increase openness to conflict resolution.


title: Multi-task convolutional neural network for car attribute recognition

abstract: This paper explores a multi-task Convolutional Neural Network (CNN) for vehicle attributes recognition. It can not only reduce the consumption of resources, but also reduce the total classification time. Besides, a single-task CNN for vehicle attributes recognition is proposed. We experiment with this different networks and try to understand their performance on my dataset. Vehicle attributes recognition are generally severely affected by environmental or contextual factors, such as occlusions and illuminations. While existing attribute datasets barely care about these factors. To tackle these problems, we build a Car Surveillance Scenarios (CSS) dataset from real multi-camera surveillance scenarios with long term collection. Data samples of CSS are annotated with not only fine-grained vehicle attributes but also environmental and contextual factors. The car's bounding box attaches to the contour of the car. The CSS dataset include 19,039 vehicle samples, each of which is annotated with 2 attributes. Experiments show that the multi-task CNN can be effectively used for vehicle attributes.


Learning distributed event representations with a multi-task approach


title: Massive pre-placental and subchorionic haematoma

abstract: We report an unusual case of massive pre-placental and subchorionic haematoma occurring In a 26-year-old woman who presented with antepartum haemorrhage at 24 weeks gestation. Ultrasonography showed a subchorionic haematoma in the lower posterior uterine wall measuring 5.0 cm in largest diameter. There was also a separate irregular multiloculated structure measuring 4.3 cm In largest diameter on the surface of the placenta, due to a pre-placental haematoma. The subchorionic haematoma diminished in size over time, while the pre-placental haematoma continued to grow, measuring 9.0 cm at 28 weeks, and 9.3 cm at 32 weeks. At 32 weeks, the patient presented with premature rupture of membranes and four days later, an emergency caesarean section was performed when the patient had another episode of severe antepartum haemorrhage. Both mother and child recovered well. The current literature on such haematomas is reviewed.


title: Tonsillectomy and risk of Parkinson's disease: A danish nationwide population‐based cohort study

abstract: BACKGROUND ::: We hypothesized that tonsillectomy modifies the risk of PD. ::: ::: ::: OBJECTIVES ::: To test the hypothesis in a nationwide population-based cohort study. ::: ::: ::: METHODS ::: We used Danish medical registries to construct a cohort of all patients in Denmark with an operation code of tonsillectomy 1980-2010 (n = 195,169) and a matched age and sex general population comparison cohort (n = 975,845). Patients were followed until PD diagnosis, death, censoring, or end of follow-up 30 November 2013. Using Cox regression, we computed hazard ratios for PD and corresponding 95% confidence intervals, adjusting for age and sex by study design, and potential confounders. ::: ::: ::: RESULTS ::: We identified 100 and 568 patients diagnosed with PD among the tonsillectomy and general population comparison cohort, respectively, finding similar risks of PD (adjusted hazard ratio = 0.95 [95% confidence interval: 0.76-1.19]; for > 20 years' follow-up (adjusted hazard ratio = 0.96 [95% confidence interval: 0.64-1.41]). ::: ::: ::: CONCLUSION ::: Tonsillectomy is not associated with risk of PD, especially early-onset PD. © 2017 International Parkinson and Movement Disorder Society.


title: Deep learning architecture for air quality predictions

abstract: With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.


title: Playing violent video games, desensitization, and moral evaluation in children

abstract: Relationships between short- and long-term exposure to violent video games and desensitization, as measured through components of moral evaluation, were examined. Sixty-six children aged 5–12 years old completed questionnaires assessing video game experience and preferences and empathy and attitudes toward violence. The children played a violent or nonviolent video game and then responded to vignettes about everyday occurrences. Vignette responses were coded for aggression and empathy. Preexisting empathy and attitudes towards violence were positively related to the corresponding vignette scores. Long-term exposure to violent video games contributed to lower empathy vignette scores. Playing a violent versus a nonviolent game did not affect vignette responses. Results suggest that long-term exposure to violent video games may be associated with desensitization as reflected in lower empathy, although the direction of causality remains unclear.


title: Integrated anchor and social link predictions across multiple social networks

abstract: In recent years, various online social networks offering specific services have gained great popularity and success. To enjoy more online social services, some users can be involved in multiple social networks simultaneously. A challenging problem in social network studies is to identify the common users across networks to gain better understanding of user behavior. This is referred to as the anchor link prediction problem. Meanwhile, across these partially aligned social networks, users can be connected by different kinds of links, e.g., social links among users in one single network and anchor links between accounts of the shared users in different networks. Many different link prediction methods have been proposed so far to predict each type of links separately. In this paper, we want to predict the formation of social links among users in the target network as well as anchor links aligning the target network with other external social networks. The problem is formally defined as the "collective link identification" problem. Predicting the formation of links in social networks with traditional link prediction methods, e.g., classification-based methods, can be very challenging. The reason is that, from the network, we can only obtain the formed links (i.e., positive links) but no information about the links that will never be formed (i.e., negative links). To solve the collective link identification problem, a unified link prediction framework, collective link fusion (CLF) is proposed in this paper, which consists of two phases: step (1) collective link prediction of anchor and social links with positive and unlabeled learning techniques, and step (2) propagation of predicted links across the partially aligned "probabilistic networks" with collective random walk. Extensive experiments conducted on two real-world partially aligned networks demonstrate that CLF can perform very well in predicting social and anchor links concurrently.


title: Design of Compact Branch-Line Coupler with Coupled Resonators

abstract: A compact branch-line coupler is proposed in this paper. The quarter-wavelength transmission lines are equivalent to the couplings between resonators. Thus, the conventional branch-line coupler can be transformed into a coupled-resonator network. With the proposed miniaturized resonators and coupling structure, the circuit size can be reduced by 50% and a 4% fractional bandwidth can be achieved.


title: Examining the impact of off-task multi-tasking with technology on real-time classroom learning

abstract: The purpose of the present study was to examine the impact of multi-tasking with digital technologies while attempting to learn from real-time classroom lectures in a university setting. Four digitally-based multi-tasking activities (texting using a cell-phone, emailing, MSN messaging and Facebook(TM)) were compared to 3 control groups (paper-and-pencil note-taking, word-processing note-taking and a natural use of technology condition) over three consecutive lectures. Comparisons indicated that participants in the Facebook(TM) and MSN conditions performed more poorly than those in the paper-and-pencil use control. Follow-up analyses were required to accommodate the substantial number of students who failed to comply with the limited use of technology specified by their assigned conditions. These analyses indicated that participants who did not use any technologies in the lectures outperformed students who used some form of technology. Consistent with the cognitive bottleneck theory of attention (Welford, 1967) and contrary to popular beliefs, attempting to attend to lectures and engage digital technologies for off-task activities can have a detrimental impact on learning.


title: Resource Sharing in 5G mmWave Cellular Networks

abstract: In this paper, we discuss resource sharing, a key dimension in mmWave network design in which spectrum, access and/or network infrastructure resources can be shared by multiple operators. It is argued that this sharing paradigm will be essential to fully exploit the tremendous amounts of bandwidth and the large number of antenna degrees of freedom available in these bands, and to provide statistical multiplexing to accommodate the highly variable nature of the traffic. In this paper, we investigate and compare various sharing configurations in order to capture the enhanced potential of mmWave communications. Our results reflect both the technical and the economical aspects of the various sharing paradigms. We deliver a number of key insights, corroborated by detailed simulations, which include an analysis of the effects of the distinctive propagation characteristics of the mmWave channel, along with a rigorous multi-antenna characterization. Key findings of this study include (i) the strong dependence of the comparative results on channel propagation and antenna characteristics, and therefore the need to accurately model them, and (ii) the desirability of a full spectrum and infrastructure sharing configuration, which may result in increased user rate as well as in economical advantages for both service provider.


title: The ODD protocol : a review and first update

abstract: The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.


Automatic Colorization with Deep Convolutional Generative Adversarial Networks


title: Compact Broadband Dual Coupling-Feed Circularly Polarized RFID Microstrip Tag Antenna Mountable on Metallic Surface

abstract: A square-shaped microstrip RFID tag antenna designed for 915 MHz band is proposed. The antenna is capacitively excited by a dual-offset coupling feed network with one of the feed lines a quarter-wavelength longer than the other. By selecting appropriate dimensions for the coupled lines and feed-lines, along with desirable coupling distance between the coupled line and square patch, the input impedance of the proposed tag antenna can be easily controlled, which results in excellent impedance matching. Moreover, by incorporating the dual-offset feed technique with the method of embedding a cross slot of unequal arm lengths into the square patch, the bandwidth of the circularly polarized (CP) operation can be enhanced. The measured 6-dB return-loss bandwidth of the tag antenna is 25 MHz (902-927 MHz), and its corresponding 3-dB axial-ratio (AR) bandwidth is 20 MHz (903-923 MHz). Further experimental results show that the proposed tag antenna can provide slightly better reading range and gain when mounted on a large metallic surface.


title: Knowledge management practices in a public sector organisation: the role of leaders' cognitive styles

abstract: Purpose – The purpose of this paper is to investigate the impact of the cognitive styles of leaders on knowledge management practices in a public sector organisation in India.Design/methodology/approach – The data were gathered from 210 middle and senior managers who were employed in different projects across the country. Self‐reported questionnaires were administered to collect the data on the cognitive styles of leaders and knowledge management practices.Findings – The results of exploratory factor analysis showed three significant factors of cognitive styles – i.e. radical, innovative‐collaborator, and adaptor. The knowledge management questionnaire had five dimensions – i.e. KM process, KM leadership, KM culture, KM technology, and KM measurement. The results of regression analysis showed a negative impact of the radical and innovative‐collaborator styles, while the adaptor style had a positive impact on knowledge management practices.Research limitations/implications – This study was conducted in a l...


title: Design of Electro-Hydraulic Active Suspension System for Four Wheel Vehicles

abstract: Suspension systems have been widely applied to vehicles, from the horse-drawn carriage with flexible leaf springs fixed in the four corners, to the modern automobile with complex control algorithms. The suspension of a road vehicle is usually designed with two objectives; to isolate the vehicle body from road irregularities and to maintain contact of the wheels with


title: Detecting malicious activities with user-agent-based profiles

abstract: Hypertext transfer protocol (HTTP) has become the main protocol to carry out malicious activities. Attackers typically use HTTP for communication with command-and-control servers, click fraud, phishing and other malicious activities, as they can easily hide among the large amount of benign HTTP traffic. The user-agent (UA) field in the HTTP header carries information on the application, operating system (OS), device, and so on, and adversaries fake UA strings as a way to evade detection. Motivated by this, we propose a novel grammar-guided UA string classification method in HTTP flows. We leverage the fact that a number of ‘standard' applications, such as web browsers and iOS mobile apps, have well-defined syntaxes that can be specified using context-free grammars, and we extract OS, device and other relevant information from them. We develop association heuristics to classify UA strings that are generated by ‘non-standard' applications that do not contain OS or device information. We provide a proof-of-concept system that demonstrates how our approach can be used to identify malicious applications that generate fake UA strings to engage in fraudulent activities


title: Sansevieria roxburghiana Schult. & Schult. F. (Family: Asparagaceae) Attenuates Type 2 Diabetes and Its Associated Cardiomyopathy

abstract: Background ::: Sansevieria roxburghiana Schult. & Schult. F. (Family: Asparagaceae) rhizome has been claimed to possess antidiabetic activity in the ethno-medicinal literature in India. Therefore, present experiments were carried out to explore the protective role of edible (aqueous) extract of S. roxburghiana rhizome (SR) against experimentally induced type 2 diabetes mellitus (T2DM) and its associated cardiomyopathy in Wistar rats. ::: Methods ::: SR was chemically characterized by GC-MS analysis. Antidiabetic activity of SR (50 and 100 mg/kg, orally) was measured in high fat diets (ad libitum) + low-single dose of streptozotocin (35 mg/kg, intraperitoneal) induced type 2 diabetic (T2D) rat. Fasting blood glucose level was measured at specific intermissions. Serum biochemical and inflammatory markers were estimated after sacrificing the animals. Besides, myocardial redox status, expressions of signal proteins (NF-κB and PKCs), histological and ultrastructural studies of heart were performed in the controls and SR treated T2D rats. ::: Results ::: Phytochemical screening of the crude extract revealed the presence of phenolic compounds, sugar alcohols, sterols, amino acids, saturated fatty acids within SR. T2D rats exhibited significantly (p < 0.01) higher fasting blood glucose level with respect to control. Alteration in serum lipid profile (p < 0.01) and increased levels of lactate dehydrogenase (p < 0.01) and creatine kinase (p < 0.01) in the sera revealed the occurrence of hyperlipidemia and cell destruction in T2D rats. T2DM caused significant (p < 0.05–0.01) alteration in the biochemical markers in the sera. T2DM altered the redox status (p < 0.05–0.01), decreased (p < 0.01) the intracellular NAD and ATP concentrations in the myocardial tissues of experimental rats. While investigating the molecular mechanism, activation PKC isoforms was observed in the selected tissues. T2D rats also exhibited an up-regulation in nuclear NF-κB (p65) in the cardiac tissues. So, oral administration of SR (50 and 500 mg/kg) could reduce hyperglycemia, hyperlipidemia, membrane disintegration, oxidative stress, vascular inflammation and prevented the activation of oxidative stress induced signaling cascades leading to cell death. Histological and ultra-structural studies of cardiac tissues supported the protective characteristics of SR. ::: Conclusions ::: From the present findings it can be concluded that, SR could offer protection against T2DM and its associated cardio-toxicity via multiple mechanisms viz. hypoglycemic, antioxidant and anti-inflammatory actions.


title: Square annular-ring antenna with circular polarization for UHF RFID reader

abstract: A square annular-ring antenna with circular polarization (CP) for ultra-high-frequency (UHF) radio frequency identification (RFID) reader is presented. The proposed annularring antenna is composed of square-shaped annular-ring radiator, which is fed by four equal amplitude signals with 90° phase difference, and reflector suspended below the radiator. The proposed antenna is matched and optimized using the 4-port antenna matching condition. Experimental results show that the proposed antenna of size 200 × 200 × 11 mm3 has the peak gain of 7.0 dBic at 915 MHz, front-to-back ratio (F/B) of 30.0 dB, axial ratio under 1.5 dB.


title: Rapid and reliable DNA assembly via ligase cycling reaction.

abstract: Assembly of DNA parts into DNA constructs is a foundational technology in the emerging field of synthetic biology. An efficient DNA assembly method is particularly important for high-throughput, automated DNA assembly in biofabrication facilities and therefore we investigated one-step, scarless DNA assembly via ligase cycling reaction (LCR). LCR assembly uses single-stranded bridging oligos complementary to the ends of neighboring DNA parts, a thermostable ligase to join DNA backbones, and multiple denaturation-annealing-ligation temperature cycles to assemble complex DNA constructs. The efficiency of LCR assembly was improved ca. 4-fold using designed optimization experiments and response surface methodology. Under these optimized conditions, LCR enabled one-step assembly of up to 20 DNA parts and up to 20 kb DNA constructs with very few single-nucleotide polymorphisms (<1 per 25 kb) and insertions/deletions (<1 per 50 kb). Experimental comparison of various sequence-independent DNA assembly methods showed that circular polymerase extension cloning (CPEC) and Gibson isothermal assembly did not enable assembly of more than four DNA parts with more than 50% of clones being correct. Yeast homologous recombination and LCR both enabled reliable assembly of up to 12 DNA parts with 60-100% of individual clones being correct, but LCR assembly provides a much faster and easier workflow than yeast homologous recombination. LCR combines reliable assembly of many DNA parts via a cheap, rapid, and convenient workflow and thereby outperforms existing DNA assembly methods. LCR assembly is expected to become the method of choice for both manual and automated high-throughput assembly of DNA parts into DNA constructs.


An Introduction to Particle Filtering


title: Football Match Prediction Using Players Attributes

abstract: Analysis of results of sport matches has always been a hot topic for both data analysts and general public. Modern methods for processing data, in combination with strong computational power of computers, allow us to predict the results of the next matches by applying machine learning algorithms on collected data. This paper suggests innovative approach of processing input data to predict results from input features based on players of the match with limited information about match history itself. Such a model could be used to find optimal players based on their attributes for the specific match or team.


Development of multi degrees-of-freedom bilateral forceps robot system using FPGA


title: Impact Of Mobile Phone Usage On Academic Performance Among Secondary School Students In Taraba State, Nigeria

abstract: This study employed survey design in investigating the influence of mobile phone usage on academic performance among secondary school student in Jalingo, Taraba State, Nigeria. The sample for the study was 300 respondents selected from the total population of 6,482 respondents. Stratified sampling technique was employed to select the sample. The Mobile Phone Usage Questionnaire (MPUQ) adapted from Twum (2011), Mathematic Achievement Test (MAT) and English Language Achievement test (ELAT), were the instrument used for this study. Data collected were analyzed using mean, standard deviation, t-test and ANOVA to answer the research questions and hypotheses. The finding of this study, revealed that mobile phone usage significantly influence academic performance among male and female senior secondary school students (t = 6.113, P = 0.02), age difference was not a significant factor in mobile phone usage on academic performance among senior secondary school students (f = 6.431, P = 0.022), parent’s occupation was not a significant factor in mobile phone usage on academic performance among senior secondary school students (f = 9.005, p= 0.031) and that the frequency of mobile phone usage does not significantly influence academic performance among male and female senior secondary school students (t = 8.131, p = 0.02). It was recommended that, School psychologists, teachers, school administrators, parents and students should be sensitized on the influence of mobile phone usage on academic performance among secondary school students irrespective of gender and age differences.


title: Systematic Thinking Fostered by Illustrations in Scientific Text

abstract: In 2 experiments, students who lacked prior knowledge about car mechanics read a passage about vehicle braking systems that either contained labeled illustrations of the systems, illustrations without labels, labels without illustrations, or no labeled illustrations. Students who received passages that contained labeled illustrations of braking systems recalled more explanative than nonexplanative information as compared to control groups, and performed better on problem solving transfer but not on verbatim recognition as compared to control groups. Results support a model of meaningful learning in which illustrations can help readers to focus their attention on explanative information in text and to reorganize the information into useful mental models. What can be done to improve the understandability of expository text? In particular, how can we design text so that readers will be able to use the information creatively to solve problems? To answer these questions, my colleagues and I have been engaged in a series of studies investigating the effects of advanced organizers (Mayer, 1975a, 1976, 1978, 1979, 1980, 1983; Mayer & Bromage, 1980), signaling (Loman & Mayer, 1983; Mayer, Dyck, & Cook, 1984), and adjunct questions (Mayer, 1975b; Sagerman & Mayer, 1987). This work has been based on the idea that different instructional manipulations may have effects on different aspects of cognitive processing in different learners. In particular, assimilation theory—the idea that learning involves integrating new information with existing knowledge—suggests three primary functions of cognitive processes: to guide selective attention towards certain information in the text, to foster the building of internal connections among ideas from the text, and to foster building external connections between ideas in the text and the learner's existing knowledge (Mayer, 1982, 1984, 1985, 1987). The present study opens a new line of attack by investigating the role of illustrations as potential vehicles for helping students understand expository text.


title: A 500W Push-Pull AlGaN/GaN HEMT Amplifier for L-Band High Power Application

abstract: We have successfully developed a 500W AlGaN/GaN HEMT power amplifier with a frequency of 1.5GHz in L-Band, operating at 65V drain bias voltage. This amplifier consists of 4-chips of HEMT die developed for L-band frequency operation with push-pull configuration. The developed amplifier has an output power of 500W and a high linear gain of 17.8dB at the frequency of 1.5GHz under pulsed conditions at a duty of 10% with a pulse width of 100musec. To the best of our knowledge, this is the highest power ever reported for L-band GaN-related amplifier


title: Design and test of vacuum circuit breaker with hybrid fast operating mechanism

abstract: To satisfy the requirement of controlling and protection of modern power grid, it is necessary to develop the fast switchgear switching quickly as the executive device. The advances of repulsion and permanent magnetic mechanism are combined fully, the hybrid fast operating mechanism constructed with electromagnetic repulsion and permanent magnetic mechanism was designed in the paper. The switching characteristics of fast mechanism were simulated. The mechanical structures of coil skeleton, shell and T-shaped armature were optimized for the mechanism, and the design to avoid closing bounce was conducted. Based on these, the fast mechanism was developed and the strength of vacuum interrupter was promoted. The prototypes of 12/40.5kV fast vacuum circuit breaker were assembled and the factory performance debugging was carried out. The technical specifications of manufacture were formulated according the current standard of circuit breaker. The technical parameters of devices were provided. Two prototypes have passed the performance verification tests which including insulation, machinery, temperature rise, short-time current endurance, breaking and making ability. All results have shown that the performance parameters of two prototypes both meet the requirements of the technical specifications.


title: LIDAR and vision-based real-time traffic sign detection and recognition algorithm for intelligent vehicle

abstract: Real-time traffic sign detection and recognition are essential and challenging tasks for intelligent vehicles. The previous works mainly focus on detecting and recognizing traffic signs based on images captured by onboard cameras. Visual features of traffic signs such as color, shape, and appearance, however, are often sensitive to illumination condition, angle of view, etc. Except for a camera, LIDAR also provides important and alternative features of traffic signs. The fusion of complementary data acquired from both sensors can improve the robustness of the algorithm, especially when data from either of them are of low quality. For this reason, the authors propose a new traffic sign detection and recognition algorithm based on the fusion of camera and LIDAR data. Specifically, position prior, color, laser reflectivity, and 3D geometric features are integrated to detect traffic signs in a 3D space. In most of previous works, different colors of traffic signs are individually handled in a specific color space, which generally results in the use of many thresholds or multiple classifiers. In this paper, the authors use combined color spaces (CCS) such that traffic sign colors can be entirely treated as one class. For traffic sign recognition, in order to improve the robustness to any viewpoint variation, regions of interest (ROIs), which suffer from perspective deformation, are rectified first by fusing LIDAR and camera data. Then the histogram of oriented gradient (HOG) feature and linear support vector machines (SVMs) are used to classify traffic signs. Finally, extensive experimental results in challenging conditions show that the algorithm is real-time and robust.


title: Data Lifecycle Challenges in Production Machine Learning: A Survey

abstract: Machine learning has become an essential tool for gleaning knowledge from data and tackling a diverse set of computationally hard tasks. However, the accuracy of a machine learned model is deeply tied to the data that it is trained on. Designing and building robust processes and tools that make it easier to analyze, validate, and transform data that is fed into large-scale machine learning systems poses data management challenges. Drawn from our experience in developing data-centric infrastructure for a production machine learning platform at Google, we summarize some of the interesting research challenges that we encountered, and survey some of the relevant literature from the data management and machine learning communities. Specifically, we explore challenges in three main areas of focus - data understanding, data validation and cleaning, and data preparation. In each of these areas, we try to explore how different constraints are imposed on the solutions depending on where in the lifecycle of a model the problems are encountered and who encounters them.


title: The IBM Attila speech recognition toolkit

abstract: We describe the design of IBM's Attila speech recognition toolkit. We show how the combination of a highly modular and efficient library of low-level C++ classes with simple interfaces, an interconnection layer implemented in a modern scripting language (Python), and a standardized collection of scripts for system-building produce a flexible and scalable toolkit that is useful both for basic research and for construction of large transcription systems for competitive evaluations.


title: Transferability of Object-Oriented Image Analysis Methods for Slum Identification

abstract: Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.


DSP BASED READ OUT SYSTEM FOR CONTACTLESS HIGH – FREQUENCY INDUCTIVE POSITION SENSORS


title: Monolithically Integrated 4H-SiC MOSFET and JBS Diode (JBSFET) Using a Single Ohmic/Schottky Process Scheme

abstract: This letter reports a new 900 V 4H-SiC JBSFET containing an MOSFET with an integrated JBS diode in the center area of the chip. Both MOSFET and JBS diode structures utilize the same edge termination structure,which results in 30% reduction in SiC wafer area consumption in case of 10 A rating device. In order to form a Schottky contact for the JBS diode as well as ohmic contacts for n+ source and p+ body of the MOSFET,a simple metal process flow has been newly developed. It was found that Ni can simultaneously form ohmic contacts on n+ and p+ implanted regions while it remains a Schottky contact on the n-epitaxial drift layer when it is annealed at moderate temperature (900°C for 2 min). The proposed JBSFET was successfully fabricated using a nine-mask on 6-in 4H-SiC wafers.


title: Towards CNN Map Compression for camera relocalisation

abstract: This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate response to different data inputs -- namely, depth, grayscale, RGB, spatial position and combinations of these. We use a CNN map representation and introduce the notion of CNN map compression by using a smaller CNN architecture. We evaluate our proposal in a series of publicly available datasets. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.


title: Microstrip low-pass filter using modified log periodic radial stub

abstract: This paper presents a novel compact microstrip lowpass filter (LPF) configuration employing modified log periodic radial stubs. The proposed LPF includes three radial stubs of different dimensions to generate three different resonating frequencies at the desired location over the specified band to attain ultra-wide stopband. The operating mechanism of the proposed filter is investigated based on equivalent circuit model and the S-parameter characteristics of the proposed filter are obtained using the simulation tools. A prototype model of the proposed filter is developed and measured. Measurement and simulation results show good agreement. The size of the proposed LPF is only 20.33 × 10.38 mm2.


title: An Integrated On-Chip Flux Concentrator for Galvanic Current Sensing

abstract: On-chip integration of a magnetic flux concentrator with a galvanic current sensor is proposed. Our layout utilizes a discontinuity in a magnetic via, resulting in the penetration of the magnetic field into the substrate. A conversion factor of 96 mT/A is predicted via simulations corresponding to a magnetic gain $1.8\times $ in comparison with air. The permeability of the magnetic core required is 500, much lower than that reported in off-chip concentrators, resulting in a significant easing of the specifications of the material properties of the core.


title: Automating Generation of Low Precision Deep Learning Operators

abstract: State of the art deep learning models have made steady progress in the fields of computer vision and natural language processing, at the expense of growing model sizes and computational complexity. Deploying these models on low power and mobile devices poses a challenge due to their limited compute capabilities and strict energy budgets. One solution that has generated significant research interest is deploying highly quantized models that operate on low precision inputs and weights less than eight bits, trading off accuracy for performance. These models have a significantly reduced memory footprint (up to 32x reduction) and can replace multiply-accumulates with bitwise operations during compute intensive convolution and fully connected layers. Most deep learning frameworks rely on highly engineered linear algebra libraries such as ATLAS or Intel's MKL to implement efficient deep learning operators. To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries. In this paper we introduce a work flow to quickly generate high performance low precision deep learning operators for arbitrary precision that target multiple CPU architectures and include optimizations such as memory tiling and vectorization. We present an extensive case study on low power ARM Cortex-A53 CPU, and show how we can generate 1-bit, 2-bit convolutions with speedups up to 16x over an optimized 16-bit integer baseline and 2.3x better than handwritten implementations.


title: Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

abstract: Measuring similarities between unlabeled time series trajectories is an important problem in domains as diverse as medicine, astronomy, finance, and computer vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g. different sampling rates or outliers). Domain experts typically hand-craft or manually select a specific metric, such as dynamic time warping (DTW), to apply on their data. In this paper, we propose Autowarp, an end-to-end algorithm that optimizes and learns a good metric given unlabeled trajectories. We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Euclidean, and edit distance. Autowarp then leverages the representation power of sequence autoencoders to optimize for a member of this warping distance family. The output is a metric which is easy to interpret and can be robustly learned from relatively few trajectories. In systematic experiments across different domains, we show that Autowarp often outperforms hand-crafted trajectory similarity metrics.


Voice conversion with smoothed GMM and MAP adaptation.


title: An Adaptive Pre-Distortion Technique to Mitigate the DTC Nonlinearity in Digital PLLs

abstract: Digital fractional-N phase-locked loops (PLLs) are an attractive alternative to analog PLLs in the design of frequency synthesizers for wireless applications. However, the main obstacle to their full acceptance in the wireless-systems arena is their higher content of output spurious tones, whose level is ultimately set by the nonlinearity of the time-to-digital converter (TDC). The known methods to improve the linearity of the TDC either increase its dissipation and phase noise or require slow foreground calibrations. By contrast, the class of digital PLLs based on a one-bit TDC driven by a multibit digital-to-time converter (DTC) substantially reduces power dissipation and eliminates the TDC nonlinearity issues. Although its spur performance depends on DTC linearity, the modified architecture enables the application of a background adaptive pre-distortion which does not compromise the PLL phase-noise level and power consumption and is much faster than other calibration techniques. This paper presents a 3.6-GHz digital PLL in 65-nm CMOS, with in-band fractional spurs dropping from -39 to -52 dBc when the pre-distortion is enabled, in-band phase noise of -103 dBc/Hz and power consumption of 4.2 mW.


title: Incremental effort prediction models in Agile Development using Radial Basis Functions

abstract: One of the impediments to the wide dissemination of software estimation and measurement practices is the significant overhead imposed by these practices on the project and development team. Despite significant investment in research, the lightweight estimation of development effort is still an unsolved problem in software engineering. This study proposes a new, lightweight effort estimation model aimed at iterative development environments, as Agile Processes. The model is based on Radial Basis Functions. It is experimented in two semi-industrial projects carried out using a customized version of Extreme Programming (XP). The results are promising and evidence that the proposed model can be developed incrementally and from scratch for new projects without resorting itself to historic data.


title: EMG Responses to Maintain Stance During Multidirectional Surface Translations

abstract: Henry, Sharon M., Joyce Fung, and Fay B. Horak. EMG responses to maintain stance during multidirectional surface translations. J. Neurophysiol. 80: 1939–1950, 1998. To characterize muscle synergy organization underlying multidirectional control of stance posture, electromyographic activity was recorded from 11 lower limb and trunk muscles of 7 healthy subjects while they were subjected to horizontal surface translations in 12 different, randomly presented directions. The latency and amplitude of muscle responses were quantified for each perturbation direction. Tuning curves for each muscle were examined to relate the amplitude of the muscle response to the direction of surface translation. The latencies of responses for the shank and thigh muscles were constant, regardless of perturbation direction. In contrast, the latencies for another thigh [tensor fascia latae (TFL)] and two trunk muscles [rectus abdominis (RAB) and erector spinae (ESP)] were either early or late, depending on the perturbation direction. These three muscles with direction-specific latencies may play different roles in postural control as prime movers or as stabilizers for different translation directions, depending on the timing of recruitment. Most muscle tuning curves were within one quadrant, having one direction of maximal activity, generally in response to diagonal surface translations. Two trunk muscles (RAB and ESP) and two lower limb muscles (semimembranosus and peroneus longus) had bipolar tuning curves, with two different directions of maximal activity, suggesting that these muscle can play different roles as part of different synergies, depending on translation direction. Muscle tuning curves tended to group into one of three regions in response to 12 different directions of perturbations. Two muscles [rectus femoris (RFM) and TFL] were maximally active in response to lateral surface translations. The remaining muscles clustered into one of two diagonal regions. The diagonal regions corresponded to the two primary directions of active horizontal force vector responses. Two muscles (RFM and adductor longus) were maximally active orthogonal to their predicted direction of maximal activity based on anatomic orientation. Some of the muscles in each of the synergic regions were not anatomic synergists, suggesting a complex central organization for recruitment of muscles. The results suggest that neither a simple reflex mechanism nor a fixed muscle synergy organization is adequate to explain the muscle activation patterns observed in this postural control task. Our results are consistent with a centrally mediated pattern of muscle latencies combined with peripheral influence on muscle magnitude. We suggest that a flexible continuum of muscle synergies that are modifiable in a task-dependent manner be used for equilibrium control in stance.


title: Continuity editing for 3D animation

abstract: We describe an optimization-based approach for automatically creating well-edited movies from a 3D animation. While previous work has mostly focused on the problem of placing cameras to produce nice-looking views of the action, the problem of cutting and pasting shots from all available cameras has never been addressed extensively. In this paper, we review the main causes of editing errors in literature and propose an editing model relying on a minimization of such errors. We make a plausible semi-Markov assumption, resulting in a dynamic programming solution which is computationally efficient. We also show that our method can generate movies with different editing rhythms and validate the results through a user study. Combined with state-of-the-art cinematography, our approach therefore promises to significantly extend the expressiveness and naturalness of virtual movie-making.


title: An Automated Attendance System based on NFC&X-Bee Technologies with a Remote Database

abstract: The main aim of this research was to automate attendance registration, thereby reducing human involvement in the whole process. Typically, the system works by storing vital staff personable information, such as Name, Job specification, etc. into a MySQL database upon engagement. The staff is identified with a unique key associated with an NFC based ID card within the database. So on typical work day, an employee scans his/her ID card on the PN532 reader in close proximity. The exact time and date, together with the unique identifier of the scanned card are stored locally on a storage media, before the data is relayed via the XBee to the remote database. The captured data is then authenticated by comparing with the pre-entered data to give access or authorization to the corporate resources, as well as recorded for attendance purposes. Our experiment shows that the automated attendance system is more effective, efficient and reliable, due to its real time capability, remote monitoring and attendance reports that it provides to the institution.


Automatic Correction of Lens Distortion by Using Digital Image Processing


title: Improving Quality-of-Service in Wireless Sensor Networks by Mitigating “Hidden-Node Collisions”

abstract: Wireless sensor networks (WSNs) emerge as underlying infrastructures for new classes of large-scale networked embedded systems. However, WSNs system designers must fulfill the quality-of-service (QoS) requirements imposed by the applications (and users). Very harsh and dynamic physical environments and extremely limited energy/computing/memory/communication node resources are major obstacles for satisfying QoS metrics such as reliability, timeliness, and system lifetime. The limited communication range of WSN nodes, link asymmetry, and the characteristics of the physical environment lead to a major source of QoS degradation in WSNs-the ldquohidden node problem.rdquo In wireless contention-based medium access control (MAC) protocols, when two nodes that are not visible to each other transmit to a third node that is visible to the former, there will be a collision-called hidden-node or blind collision. This problem greatly impacts network throughput, energy-efficiency and message transfer delays, and the problem dramatically increases with the number of nodes. This paper proposes H-NAMe, a very simple yet extremely efficient hidden-node avoidance mechanism for WSNs. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes that scales to multiple clusters via a cluster grouping strategy that guarantees no interference between overlapping clusters. Importantly, H-NAMe is instantiated in IEEE 802.15.4/ZigBee, which currently are the most widespread communication technologies for WSNs, with only minor add-ons and ensuring backward compatibility with their protocols standards. H-NAMe was implemented and exhaustively tested using an experimental test-bed based on ldquooff-the-shelfrdquo technology, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. H-NAMe effectiveness was also demonstrated in a target tracking application with mobile robots over a WSN deployment.


title: Techniques in Cosmetic Surgery Three-Dimensional Endoscopic Midface Enhancement : A Personal Quest for the Ideal Cheek Rejuvenation

abstract: Standard face-lift techniques are excellent for the treatment of the jawline and neck. Treatment of the area between the lower eyelid and the corner of the mouth required the development of techniques in the intermediate lamella of the face. Alternative techniques of subperiosteal dissection by means of lower eyelid incisions were described with good aesthetic results but at the expense of increased morbidity and complications. All these techniques were also two-dimensional manipulations of the soft tissues of the face. The author presents a different approach that he believes is close to the ideal in terms of safety, morbidity, and complications. Although midface rejuvenation may be performed alone, it is more commonly done as a component of total facial rejuvenation. The midface is approached by means of a combination of a temporal slit incision and an upper oral sulcus incision; no eyelid access is used. Fifty percent of the midface dissection is performed under direct visualization, and 50 percent is performed under endoscopic control. Dissection of the temporal area is done under the temporoparietal fascia down to the zygomatic arch. The anterior two-thirds of the zygomatic arch periosteum is elevated along with a few millimeters of the intermediate temporal fascia and the fascia of the masseter muscle. The subperiosteal dissection of the zygoma and maxilla is completed with the medial extension of the dissection just medial to the infraorbital nerve. The orbital fat pads are released by means of intraoral route, and the lateral and middle fat pads are advanced over the orbital rim and fixed to the masseter tendon and the periosteum of the maxillary shelf at the intraoral incision. Three suspension points are typically used on the midface, each one with a different action. All are anchored to the temporal fascia proper. The vascularized Bichat's fat pad is mobilized and fixed with 4-0 polydioxanone sutures. This provides a volumetric cheek augmentation and improvement of the jowl. The inferior malar periosteum and fascia is used for malar imbrication with 4-0 polydioxanone sutures. This provides an anterior projection of the cheek and elevates the corner of the mouth. The suborbicularis oculi fat is used for en bloc vertical suspension of the cheek. This also improves the infraorbital V deformity. This technique has been used in close to 200 patients over the last 5 years. The complications have been minimal: two cases of temporary paresis of the levator of the upper lip, one case of paresis of the orbicularis oris (unilateral), one case of buccinator muscle dysfunction, and two moderate infections that were treated with simple drainage. The degree of facial edema has been minimal compared with the open or the transblepharoplasty approach. Typically, patients can return to work 2 weeks after surgery.The three-dimensional endoscopic midface enhancement provides a technique of midface remodeling that provides the missing dimension (volume) to the rejuvenation of the midface. This can be done with a minimal rate of complications, and the aesthetic results surpass by far the results of other midface techniques previously described by the author.


title: Juvéderm® Volbella™ in the perioral area: a 12-month prospective, multicenter, open-label study

abstract: Background: ::: Hyaluronic acid dermal fillers are frequently used for lip augmentation, and a new filler has been developed with characteristics especially suited for the lips.


title: A research on vulnerability discovering for router protocols based on fuzzing

abstract: How to discover router vulnerabilities effectively and automatically is a critical problem to ensure network and information security. Previous research on router security is mostly about the technology of exploiting known flaws of routers. Fuzzing is a famous automated vulnerability finding technology, however, traditional Fuzzing tools are designed for testing network applications or other software. These tools are not or partly not suitable for testing routers. This paper designs a framework of discovering router protocol vulnerabilities, and proposes a mathematical model Two-stage Fuzzing Test Cases Generator (TFTCG) that improves previous methods to generate test cases. We have developed a tool called RPFuzzer based on TFTCG. RPFuzzer monitors routers by sending normal packets, keeping watch on CPU utilization and checking system logs, which can detect DoS, router reboot and so on. RPFuzzer'debugger based on modified Dynamips, which can record register values when an exception occurs. Finally, we experiment on the SNMP protocol, find 8 vulnerabilities,of which there are five unreleased vulnerabilities. The experiment has proved the effectiveness of RPFuzzer.


title: Intelligent smart home energy efficiency model using artificial TensorFlow engine

abstract: Smart home and IoT-related technologies are developing rapidly, and various smart devices are being developed to help users enjoy a more comfortable lifestyle. However, the existing smart homes are limited by a scarcity of operating systems to integrate the devices that constitute the smart home environment. This is because these devices use independent IoT platforms developed by the brand or company that developed the device, and they produce these devices based on self-service modules. A smart home that lacks an integrated operating system becomes an organizational hassle because the user must then manage each device individually. Furthermore, this leads to problems such as excessive traffic on the smart home network and energy wastage. To overcome these problems, it is necessary to build an integrated management system that connects IoT devices to each other. To efficiently manage IoT, we propose three intelligent models as IoT platform application services for a smart home. The three models are intelligence awareness target as a service (IAT), intelligence energy efficiency as a service (IE2S), and intelligence service TAS (IST). IAT manages the "things" stage. IAT uses intelligent learning to acquire a situational awareness of the data values generated by things (sensors) to collect data according to the environment. IE2S performs the role of a server (IoT platform) and processes the data collected by IAT. The server uses Mobius, which is an open-source platform that follows international standards, and an artificial TensorFlow engine is used for data learning. IE2S analyzes and learns the users' usage patterns to help provide service automatically. IST helps to provide, control, and manage the service stage. These three intelligent models allow the IoT devices in a smart home to mutually cooperate with each other. In addition, these intelligent models can resolve the problems of network congestion and energy wastage by reducing unnecessary network tasks to systematically use energy according to the IoT usage patterns in the smart home.


title: Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network

abstract: Current credit card detection methods usually utilize the idea of classification, requiring a balanced training dataset which should contain both positive and negative samples. However, we often get highly skewed datasets with very few frauds. In this paper, we want to apply deep learning techniques to help handle this situation. We firstly use sparse autoencoder (SAE) to obtain representations of normal transactions and then train a generative adversarial network (GAN) with these representations. Finally, we combine the SAE and the discriminator of GAN and apply them to detect whether a transaction is genuine or fraud. The experimental results show that our solution outperforms the other state-of-the-art one-class methods.


title: Diffeomorphic Atlas Estimation using Geodesic Shooting on Volumetric Images

abstract: In this paper, we propose a new algorithm to compute intrinsic means of organ shapes from 3D medical images. More specifically, we explore the feasibility of Karcher means in the framework of the large deformations by diffeomorphisms (LDDMM). This setting preserves the topology of the averaged shapes and has interesting properties to quantitatively describe their anatomical variability. Estimating Karcher means requires to perform multiple registrations between the averaged template image and the set of reference 3D images. Here, we use a recent algorithm based on an optimal control method to satisfy the geodesicity of the deformations at any step of each registration. We also combine this algorithm with organ specific metrics. We demonstrate the efficiency of our methodology with experimental results on different groups of anatomical 3D images. We also extensively discuss the convergence of our method and the bias due to the initial guess. A direct perspective of this work is the computation of 3D+time atlases.


title: Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto

abstract: Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level and travel-time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in Downtown Toronto.


title: 13 Adapting Morphology for Arabic Information Retrieval*

abstract: This chapter presents an adaptation of existing techniques in Arabic morphology by leveraging corpus statistics to make them suitable for Information Retrieval (IR). The adaptation resulted in the development of Sebawai, an shallow Arabic morphological analyzer, and Al-Stem, an Arabic light stemmer. Both were used to produce Arabic index terms for Arabic IR experimentation. Sebawai is concerned with generating possible roots and stems of given Arabic word along with probability estimates of deriving the word from each of the possible roots. The probability estimates were used a guide to determine which prefixes and suffixes should be used to build the light stemmer Al-Stem. The use of the Sebawai generated roots and stems as index terms along with the stems from Al-Stem are evaluated in an information retrieval application and the results are compared.


title: Task-dependent and cell-type-specific Fos enhancement in rat sensory cortices during audio-visual discrimination

abstract: Attention modulates neural activities in sensory cortices. Because cortical neurons are composed of many types of neurons, the activities of these different types of cells can exhibit different modifications depending on whether an animal pays attention to a particular sensory stimulus or not. In the present study, we examined which types of cortical neurons change their activities in rats during one of two types of audio-visual discrimination (AVD) tasks by using Fos immunohistochemistry. In the tasks, both auditory and visual stimuli were simultaneously presented but only one of the two modalities was task-relevant. Once the rats had learned one of the AVD tasks, presenting only relevant sensory stimuli was sufficient for them to perform the task correctly. These results suggest that the rats indeed attended to the relevant stimuli during the performance of the tasks. We found that Fos expression in the primary auditory and visual cortices was enhanced in a task-dependent manner during the performance of the AVD tasks. The enhancement of Fos expression depended on the behavioural significance of the stimulus in the tasks. Moreover, using double immunohistochemistry of Fos and a cell type-specific marker protein (phosphate-activated glutaminase, nonphosphorylated neurofilament protein, parvalbumin, calretinin or somatostatin), the task-dependent Fos expression was observed preferentially in excitatory neurons but not in inhibitory interneurons. These results suggest that modulation in cortical excitatory neurons might have critical roles in selecting and processing behaviourally relevant sensory stimuli.


title: Noncoherent mmWave Path Tracking

abstract: Millimeter (mm) wave picocellular networks have the potential for providing the 1000X capacity increase required to keep up with the explosive growth of mobile data. However, maintaining beams towards mobile users and adapting to frequent blockage, requires efficient, dynamic path tracking algorithms. In this paper, we develop and experimentally demonstrate a novel noncoherent compressive strategy for this problem, and compare it with conventional hierarchical and exhaustive beam scanning. To the best of our knowledge, this is the first experimental demonstration of practical, scalable path estimation for mmWave/60GHz picocells. Our results indicate the feasibility of sub-second path tracking with low overhead on today's mmWave hardware, and open up a rich space for design of 5G mmWave networks.


title: C4Corpus: Multilingual Web-size corpus with free license

abstract: Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks. In this article we present the construction of 12 million-pages Web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs. Our highly-scalable Hadoop-based framework is able to process the full CommonCrawl corpus on 2000+ CPU cluster on the Amazon Elastic Map/Reduce infrastructure. The processing pipeline includes license identification, state-of-the-art boilerplate removal, exact duplicate and near-duplicate document removal, and language detection. The construction of the corpus is highly configurable and fully reproducible, and we provide both the framework (DKPro C4CorpusTools) and the resulting data (C4Corpus) to the research community.


title: Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification

abstract: Batch normalization (BN) has become a de facto standard for training deep convolutional networks. However, BN accounts for a significant fraction of training run-time and is difficult to accelerate, since it is a memory-bandwidth bounded operation. Such a drawback of BN motivates us to explore recently proposed weight normalization algorithms (WN algorithms), i.e. weight normalization, normalization propagation and weight normalization with translated ReLU. These algorithms don't slow-down training iterations and were experimentally shown to outperform BN on relatively small networks and datasets. However, it is not clear if these algorithms could replace BN in practical, large-scale applications. We answer this question by providing a detailed comparison of BN and WN algorithms using ResNet-50 network trained on ImageNet. We found that although WN achieves better training accuracy, the final test accuracy is significantly lower ($\approx 6\%$) than that of BN. This result demonstrates the surprising strength of the BN regularization effect which we were unable to compensate for using standard regularization techniques like dropout and weight decay. We also found that training of deep networks with WN algorithms is significantly less stable compared to BN, limiting their practical applications.


title: Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks

abstract: In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks. The extrapolation scheme is a classical approach for accelerating stochastic gradient descent for convex optimization, but it does not work well for nonconvex optimization typically. Alternatively, we propose an interpolation scheme to accelerate nonconvex optimization and call the method Interpolatron. We explain motivation behind Interpolatron and conduct a thorough empirical analysis. Empirical results on DNNs of great depths (e.g., 98-layer ResNet and 200-layer ResNet) on CIFAR-10 and ImageNet show that Interpolatron can converge much faster than the state-of-the-art methods such as the SGD with momentum and Adam. Furthermore, Anderson's acceleration, in which mixing coefficients are computed by least-squares estimation, can also be used to improve the performance. Both Interpolatron and Anderson's acceleration are easy to implement and tune. We also show that Interpolatron has linear convergence rate under certain regularity assumptions.


title: Modality-convolutions: Multi-modal gesture recognition based on convolutional neural network

abstract: We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition. For extracting the features in RGB-D images, the modality-CNN is adopted in the gesture recognition framework. The framework use DBN to present the skeleton data. Then, the probability obtained by the two networks are fused and put into the HMM to carry out dynamic gestures classification. We use the Jaccar Index to calculate the accuracy of gesture recognition. A comparative experiment on ChaLearn LAP 2014 gesture datasets shows that the modality-convolution is able to extract the inter- and intra-modality information effectively, which is helpful to improve the accuracy.


title: Learning movement primitive libraries through probabilistic segmentation

abstract: Movement primitives are a well-established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received similar attention. Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.


title: WORKING PAPER Predicting Online Purchasing Behavior

abstract: This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. In doing so, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information.


title: Identifying neural predictors of response to cortical or striatal deep brain stimulation in a rodent model of alcohol drinking: Towards developing individualized therapies for alcohol use disorders

abstract: Alcohol use disorders (AUDs) affect a large proportion of individuals in the United States. Unfortunately, the current therapies to treat AUDs are largely ineffective. Deep brain stimulation (DBS) has been proposed as a promising therapy for AUDs, but the high individual variability in treatment response limits broader implementation of this approach. The corticostriatal circuit is key in processing the rewarding properties of alcohol and we hypothesize that we can identify neural features within this circuit that will predict individual response to DBS in order to maximize treatment outcomes. In the current experiment, local field potentials (LFPs) were recorded from the nucleus accumbens shell (NAcSh) and medial prefrontal cortex (mPFC) of male Sprague-Dawley rats (n=13). The impact of NAcSh and mPFC DBS on levels of alcohol consumption was subsequently evaluated. On a population level, DBS in either region failed to significantly alter alcohol drinking behavior due to high individual variability in response. However, when individual features of corticostriatal LFPs were used as predictors of alcohol drinking outcomes (decreasers, increasers, or nonresponders to DBS) for each rat using a machine-learning approach (lasso), response to DBS could be predicted with greater accuracy than expected by chance. These data suggest that individual differences in corticostriatal circuit activity prior to stimulation likely contributes to the variable responses to DBS therapy. Ultimately, we hope to use knowledge gained from these data to tailor DBS therapies to individuals, further enhancing treatment efficacy.


title: The Natural Product Domain Seeker NaPDoS: A Phylogeny Based Bioinformatic Tool to Classify Secondary Metabolite Gene Diversity

abstract: New bioinformatic tools are needed to analyze the growing volume of DNA sequence data. This is especially true in the case of secondary metabolite biosynthesis, where the highly repetitive nature of the associated genes creates major challenges for accurate sequence assembly and analysis. Here we introduce the web tool Natural Product Domain Seeker (NaPDoS), which provides an automated method to assess the secondary metabolite biosynthetic gene diversity and novelty of strains or environments. NaPDoS analyses are based on the phylogenetic relationships of sequence tags derived from polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes, respectively. The sequence tags correspond to PKS-derived ketosynthase domains and NRPS-derived condensation domains and are compared to an internal database of experimentally characterized biosynthetic genes. NaPDoS provides a rapid mechanism to extract and classify ketosynthase and condensation domains from PCR products, genomes, and metagenomic datasets. Close database matches provide a mechanism to infer the generalized structures of secondary metabolites while new phylogenetic lineages provide targets for the discovery of new enzyme architectures or mechanisms of secondary metabolite assembly. Here we outline the main features of NaPDoS and test it on four draft genome sequences and two metagenomic datasets. The results provide a rapid method to assess secondary metabolite biosynthetic gene diversity and richness in organisms or environments and a mechanism to identify genes that may be associated with uncharacterized biochemistry.


title: Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems

abstract: The optimization of conductance change behavior in synaptic devices based on analog resistive memory is studied for the use in neuromorphic systems. Resistive memory based on Pr1– x Ca x MnO3 (PCMO) is applied to a neural network application (classification of Modified National Institute of Standards and Technology handwritten digits using a multilayer perceptron trained with backpropagation) under a wide variety of simulated conductance change behaviors. Linear and symmetric conductance changes (e.g., self-similar response during both increasing and decreasing device conductance) are shown to offer the highest classification accuracies. Further improvements can be obtained using nonidentical training pulses, at the cost of requiring measurement of individual conductance during training. Such a system can be expected to achieve, with our existing PCMO-based synaptic devices, a generalization accuracy on a previously-unseen test set of 90.55%. These results are promising for hardware demonstration of high neuromorphic accuracies using existing synaptic devices.


Automated Pothole Detection Using Wireless Sensor Motes


title: A survey of types of text noise and techniques to handle noisy text

abstract: Often, in the real world noise is ubiquitous in text communications. Text produced by processing signals intended for human use are often noisy for automated computer processing. Automatic speech recognition, optical character recognition and machine translation all introduce processing noise. Also digital text produced in informal settings such as online chat, SMS, emails, message boards, newsgroups, blogs, wikis and web pages contain considerable noise. In this paper, we present a survey of the existing measures for noise in text. We also cover application areas that ingest this noisy text for various tasks like Information Retrieval and Information Extraction.


title: KEWI: A Knowledge Engineering Tool for Modelling AI Planning Tasks

abstract: This paper introduces the Knowledge Engineering Web Interface (KEWI) which primarily aims to be used for ::: modelling automated planning tasks in a semi-formal framework. The conceptual model used to represent the ::: declarative and procedural knowledge in KEWI is described formally. The model consists of three layers: a ::: rich ontology, a model of basic actions, and more complex methods. It is this structured conceptual model ::: based on the rich ontology that facilitates knowledge engineering. The focus of this paper is to show how ::: the central knowledge model used in KEWI differs from a model directly encoded in PDDL, the language ::: accepted by most existing planning engines. Specifically, the rich ontology enables a more concise and natural ::: style of representation. For operational use, KEWI automatically generates PDDL. Initial experiments show ::: that the generated PDDL can be processed by a planner without incurring significant drawbacks.


title: Searching Solitaire in Real Time

abstract: This article presents a new real-time heuristic search method for planning problems with distinct stages. Our multistage nested rollout algorithm allows the user to apply separate heuristics at each stage of the search process and tune the search magnitude for each stage. We propose a searchtree compression that reveals a new state representation for the games of Klondike Solitaire and Thoughtful Solitaire, a version of Klondike Solitaire in which the location of all cards is known. Moreover, we present a Thoughtful Solitaire solver based on these methods that can determine over 80% of Thoughtful Solitaire games in less than 4 seconds. Finally, we demonstrate empirically that no less than 82% and no more than 91.44% of Klondike Solitaire games have winning solutions, leaving less than 10% of games unresolved.


title: Challenges of Enterprise Resource Planning implementation in Iran large organizations

abstract: An Enterprise Resource Planning (ERP) system is a fully integrated business management system covering functional areas of an enterprise like Logistics, Production, Finance, Accounting and Human Resources. The implementation of this system is a difficult and high cost proposition that places tremendous demands on corporate time and resources. Most of the ERP implementations have been classified as failures because they did not achieve predetermined corporate goals. The main goal of this research is determining the most important challenges of ERP implementation in Iran large organizations and our case study was the Isfahan Telecommunication. The population of this study consists of the 1500 employees of this organization from which 40 experts and employees were selected randomly and uniformly as a sample. We used questionnaire and interviews to collect data and analyzed them by SPSS using one sample t-test. The result of the study shows that the most important challenges of ERP implementation are organizational barriers, especially lack of human resources with the weighted average of 267.33. The next important issues of ERP implementation are technological factors such as unbalanced combination in team projects and then individual factors like lack of senior executives' involvement with the weighted average of 48.8 are the least important challenges in ERP implementation. We selected Isfahan Telecommunication as a large organization in Iran to identify the ERP challenges.We used questionnaire and interviews to test our hypothesis.Organizational barriers such as Lack of human resources are the main ERP implementing challenge.Technological factors and individual factors are the next important challenges to implement ERP.ERP system should be easy to learn, software and hardware requirement have to be sufficient, appropriate and up to date and managers' commitment and support have to be high.


title: Gaussian Process Regression Stochastic Volatility Model for Financial Time Series

abstract: Traditional economic models have rigid-form transition functions when modeling time-varying volatility of financial time series data and cannot capture other time-varying dynamics in the financial market. In this paper, combining the Gaussian process state-space model framework and the stochastic volatility (SV) model, we introduce a new Gaussian process regression stochastic volatility (GPRSV) model building procedures for financial time series data analysis and time-varying volatility modeling. The GPRSV extends the SV model. The flexible stochastic nature of the Gaussian process state description allows the model to capture more time-varying dynamics of the financial market. We also present the model estimation methods for the GPRSV model. We demonstrate the superior volatility prediction performance of our model with both simulated and empirical financial data.


PERFECTION AND AGGLOMERATION OF CRYSTALLINE PRECIPITATES ON AGING


title: Networking for Big Data: A Survey

abstract: Complementary to the fancy big data applications, networking for big data is an indispensable supporting platform for these applications in practice. This emerging research branch has gained extensive attention from both academia and industry in recent years. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. We are therefore motivated to solicit the latest works in this area, aiming to pave a comprehensive and solid starting ground for interested readers. We first clarify the definition of networking for big data based on the cross disciplinary nature and integrated needs of the domain. Second, we present the current understanding of big data from different levels, including its formation, networking features, mathematical representations, and the networking technologies. Third, we discuss the challenges and opportunities from various perspectives in this hopeful field. We further summarize the lessons we learned based on the survey. We humbly hope this paper will shed light for forthcoming researchers to further explore the uncharted part of this promising land.


title: Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion

abstract: Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. Many providers of bike sharing systems are ready to expand their bike stations from the existing service area to surrounding regions. A key to success for a bike sharing systems expansion is the bike demand prediction for expansion areas. There are two major challenges in this demand prediction problem: First. the bike transition records are not available for the expansion area and second. station level bike demand have big variances across the urban city. Previous research efforts mainly focus on discovering global features, assuming the station bike demands react equally to the global features, which brings large prediction error when the urban area is large and highly diversified. To address these challenges, in this paper, we develop a hierarchical station bike demand predictor which analyzes bike demands from functional zone level to station level. Specifically, we first divide the studied bike stations into functional zones by a novel Bi-clustering algorithm which is designed to cluster bike stations with similar POI characteristics and close geographical distances together. Then, the hourly bike check-ins and check-outs of functional zones are predicted by integrating three influential factors: distance preference, zone-to-zone preference, and zone characteristics. The station demand is estimated by studying the demand distributions among the stations within the same functional zone. Finally, the extensive experimental results on the NYC Citi Bike system with two expansion stages show the advantages of our approach on station demand and balance prediction for bike sharing system expansions.


title: Decentralized Trusted Timestamping using the Crypto Currency Bitcoin

abstract: Trusted timestamping is a process for proving that certain information existed at a given point in time. This paper presents a trusted timestamping concept and its implementation in form of a web-based service that uses the decentralized Bitcoin block chain to store anonymous, tamper-proof timestamps for digital content. The service allows users to hash files, such as text, photos or videos, and store the created hashes in the Bitcoin block chain. Users can then retrieve and verify the timestamps that have been committed to the block chain. The non-commercial service enables anyone, e.g., researchers, authors, journalists, students, or artists, to prove that they were in possession of certain information at a given point in time. Common use cases include proving that a contract has been signed, a photo taken, a video recorded, or a task completed prior to a certain date. All procedures maintain complete privacy of the user’s data.