title: A Model of Corticostriatal Plasticity for Learning Oculomotor Associations and Sequences

abstract: We present models that learn context-dependent oculomotor behavior in (1) conditional visual discrimination and (2) sequence reproduction tasks, based on the following three principles: (1) Visual input and efferent copies of motor output produce patterns of activity in cortex. (2) Cortex influences the saccade system in part via corticostriatal projections. (3) A reinforcement learning mechanism modifies corticostriatal synapses to link patterns of cortical activity to the correct saccade responses during trial-and-error learning. Our conditional visual discrimination model learns to associate visual cues with the corresponding saccades to one of two left-right targets. A visual cue produces patterns of neuronal activity in inferotemporal cortex (IT) that projects to the oculomotor region of the striatum. Initially random saccadic "guesses," when directed to the correct target for the current cue, result in increased synaptic strength between the cue-related IT cells and the striatal cells that participate in the correct saccade, increasing the probability that this cue will later elicit the correct saccade. We show that the model generates "inhibitory gradients" on the striatum as the substrate for spatial generalization. Our sequence reproduction model learns, when presented with temporal sequences of spatial targets, to reproduce the corresponding sequence of saccades. At any point in the execution of a saccade sequence, the current pattern of activity in pre-frontal cortex (PFC), combined with visual input and the motor efferent copy of the previous saccade, produces a new pattern of activity in PFC to be associated with the next saccade. Like IT, PFC also projects to the oculomotor region of the striatum. Correct guesses for the subsequent saccade in the sequence results in strengthening of corticostriatal synapses between active PFC cells and striatal cells involved in the correct saccade. The sequence is thus reproduced as a concatenation of associations. We compare the results of this model with data previously obtained in the monkey and discuss the nature of cortical representations of spatiotemporal information.


title: Intended or Unintended Doping? A Review of the Presence of Doping Substances in Dietary Supplements Used in Sports

abstract: INTRODUCTION ::: The use of dietary supplements is increasing among athletes, year after year. Related to the high rates of use, unintentional doping occurs. Unintentional doping refers to positive anti-doping tests due to the use of any supplement containing unlisted substances banned by anti-doping regulations and organizations, such as the World Anti-Doping Agency (WADA). The objective of this review is to summarize the presence of unlabeled doping substances in dietary supplements that are used in sports. ::: ::: ::: METHODOLOGY ::: A review of substances/metabolites/markers banned by WADA in ergonutritional supplements was completed using PubMed. The inclusion criteria were studies published up until September 2017, which analyzed the content of substances, metabolites and markers banned by WADA. ::: ::: ::: RESULTS ::: 446 studies were identified, 23 of which fulfilled all the inclusion criteria. In most of the studies, the purpose was to identify doping substances in dietary supplements. ::: ::: ::: DISCUSSION ::: Substances prohibited by WADA were found in most of the supplements analyzed in this review. Some of them were prohormones and/or stimulants. With rates of contamination between 12 and 58%, non-intentional doping is a point to take into account before establishing a supplementation program. Athletes and coaches must be aware of the problems related to the use of any contaminated supplement and should pay special attention before choosing a supplement, informing themselves fully and confirming the guarantees offered by the supplement.


title: Binarization Techniques used for Grey Scale Images

abstract: ABSTARCT Image binarization is important step in the OCR (Optical Character Recognition). There are several methods used for image binarization recently, but there is no way to select single or best method which is used for all images. The main objective of this paper is to present the study on various existing binarization algorithms and compared their measurements. This paper will act as guide for fresher’s to start their work on binarization.


title: RURAL AND URBAN ADULT KNOWLEDGE AND PERCEPTIONS OF AGRICULTURE

abstract: This study was conducted to assess the knowledge and perception of rural and urban adults in a midwestern state regarding agriculture, food, and natural resources. Since this study was a pilot project, these two subgroups of the United States population were assessed to provide baseline data reflecting the knowledge and perceptions of United States residents regarding agriculture and the food industry. Data were collected from 886 respondents but did not constitute a representative sample of all United States citizens. However, the results of this study may have implications for other groups on a practical basis. Three objectives were specified for this study as follows: 1) To assess the level of agricultural knowledge among rural and urban adults in a midwestern state; 2) To assess the level of agricultural perceptions among rural and urban adults in a midwestern state; 3) To describe the demographic variables of both subgroups that may influence their knowledge or perceptions of agriculture. Both groups of adult respondents were most knowledgeable about the Animals concept and least knowledgeable about the Plants in Agriculture concept. Respondents were most positive about the Natural Resources concept and were the least positive about the Agricultural Policy concept. However, rural adults were also very positive about the Animals concept. Respondents living on farms were more knowledgeable about agriculture than their rural non-farm neighbors, who were more knowledgeable than their urban counterparts. Respondents with high levels of education were more knowledgeable about agriculture than those with less education. The notion of agricultural literacy, since its inception, has been based on the premise that every person should possess a minimum level of knowledge of the industry which produces and markets food needed for human survival. The rationale to support the development of agricultural literacy is based on the assumption that as societal awareness of problems and issues facing agriculture and food production increases, public pressure will increase for the development of policies which are mutually beneficial for both consumers and producers. Mawby (1990, p. 72) noted that by "...educating Americans in the wise management of food supplies and related renewable resources, we can anticipate more knowledgeable decisionmaking about agriculture in the future."


title: Neural Circuits Involved in the Recognition of Actions Performed by Nonconspecifics: An fMRI Study

abstract: Functional magnetic resonance imaging was used to assess the cortical areas active during the observation of mouth actions performed by humans and by individuals belonging to other species (monkey and dog). Two types of actions were presented: biting and oral communicative actions (speech reading, lip-smacking, barking). As a control, static images of the same actions were shown. Observation of biting, regardless of the species of the individual performing the action, determined two activation foci (one rostral and one caudal) in the inferior parietal lobule and an activation of the pars opercularis of the inferior frontal gyrus and the adjacent ventral premotor cortex. The left rostral parietal focus (possibly BA 40) and the left premotor focus were very similar in all three conditions, while the right side foci were stronger during the observation of actions made by conspecifics. The observation of speech reading activated the left pars opercularis of the inferior frontal gyrus, the observation of lip-smacking activated a small focus in the pars opercularis bilaterally, and the observation of barking did not produce any activation in the frontal lobe. Observation of all types of mouth actions induced activation of extrastriate occipital areas. These results suggest that actions made by other individuals may be recognized through different mechanisms. Actions belonging to the motor repertoire of the observer (e.g., biting and speech reading) are mapped on the observer's motor system. Actions that do not belong to this repertoire (e.g., barking) are essentially recognized based on their visual properties. We propose that when the motor representation of the observed action is activated, the observer gains knowledge of the observed action in a "personal" perspective, while this perspective is lacking when there is no motor activation.


title: A Unified Algorithm for Boolean Shape Operations

abstract: Most algorithms for Boolean shape operations are complicated, slow, and of limited applicability. This one, based on a triangulation of potentially intersecting faces, is relatively fast and simple.


title: Compressing pattern databases

abstract: A pattern database (PDB) is a heuristic function implemented as a lookup table that stores the lengths of optimal solutions for subproblem instances. Standard PDBs have a distinct entry in the table for each subproblem instance. In this paper we investigate compressing PDBs by merging several entries into one, thereby allowing the use of PDBs that exceed available memory in their uncompressed form. We introduce a number of methods for determining which entries to merge and discuss their relative merits. These vary from domain-independent approaches that allow any set of entries in the PDB to be merged, to more intelligent methods that take into account the structure of the problem. The choice of the best compression method is based on domain-dependent attributes. We present experimental results on a number of combinatorial problems, including the four-peg Towers of Hanoi problem, the sliding-tile puzzles, and the Top-Spin puzzle. For the Towers of Hanoi, we show that the search time can be reduced by up to three orders of magnitude by using compressed PDBs compared to uncompressed PDBs of the same size. More modest improvements were observed for the other domains.


title: A self-powered UV photodetector based on TiO2 nanorod arrays

abstract: Large-area vertical rutile TiO2 nanorod arrays (TNAs) were grown on F/SnO2 conductive glass using a hydrothermal method at low temperature. A self-powered ultraviolet (UV) photodetector based on TiO2 nanorod/water solid–liquid heterojunction is designed and fabricated. These nanorods offer an enlarged TiO2/water contact area and a direct pathway for electron transport simultaneously. By connecting this UV photodetector to an ammeter, the intensity of UV light can be quantified using the output short-circuit photocurrent without a power source. A photosensitivity of 0.025 A/W and a quick response time were observed. At the same time, a high photosensitivity in a wide range of wavelength was also demonstrated. This TNA/water UV detector can be a particularly suitable candidate for practical applications for its high photosensitivity, fast response, excellent spectral selectivity, uncomplicated low-cost fabrication process, and environment-friendly feature.


title: Integrity tests: Critical issues

abstract: Abstract This paper reviews the issues related to the use of overt integrity tests as a pre-employment screening technique. We offer a critical examination of the research on integrity testing; and we come to the conclusion that there are serious consequences to the application of these tests. First, the tests raise validity issues on what these tests actually measure. Second, the tests seem to lead to bad consequences such as the high percentage of false positives. Third, there is a body of literature on faking, coaching, and retaking tests which should result in lower criterion-related validities and an increase in decision errors. Finally there is a discussion on fairness and privacy concerns.


title: Formal analysis of SAML 2.0 web browser single sign-on: breaking the SAML-based single sign-on for google apps

abstract: Single-Sign-On (SSO) protocols enable companies to establish a federated environment in which clients sign in the system once and yet are able to access to services offered by different companies. The OASIS Security Assertion Markup Language (SAML) 2.0 Web Browser SSO Profile is the emerging standard in this context. In this paper we provide formal models of the protocol corresponding to one of the most applied use case scenario (the SP-Initiated SSO with Redirect/POST Bindings) and of a variant of the protocol implemented by Google and currently in use by Google's customers (the SAML-based SSO for Google Applications). We have mechanically analysed these formal models with SATMC, a state-of-the-art model checker for security protocols. SATMC has revealed a severe security flaw in the protocol used by Google that allows a dishonest service provider to impersonate a user at another service provider. We have also reproduced this attack in an actual deployment of the SAML-based SSO for Google Applications. This security flaw of the SAML-based SSO for Google Applications was previously unknown.


title: Effect of Carouseling on Angular Rate Sensor Error Processes

abstract: Carouseling is an efficient method to mitigate the measurement errors of inertial sensors, particularly microelectromechanical systems (MEMS) gyroscopes. In this paper, the effect of carouseling on the most significant stochastic error processes of an MEMS gyroscope, i.e., additive bias, white noise, 1/f noise, and rate random walk (RRW) is investigated. Variance propagation equations for these processes under averaging and carouseling are defined. Furthermore, a novel approach to generating 1/f noise is presented. The experimental results show that carouseling reduces the contributions of additive bias, 1/f noise, and RRW significantly compared with plain averaging, which can be used to improve the accuracy of dead reckoning systems.


title: An Efficient Noma V2X Communication Scheme in the Internet of Vehicles

abstract: This paper proposes a new graph-based practical encoding and joint belief propagation (BP) decoding techniques for two-vehicle communicating simultaneously with another vehicle or a roadside unit. The proposed scheme achieves any rate pair close to the capacity region without using time sharing. While at the encoders, the corresponding parity check matrices are randomly built from a half-rate matrix, the joint BP decoder employs the associated Tanner graphs of the parity check matrices to iteratively recover the erasures in the received combined messages. Specifically, the joint decoder performs two steps in each decoding iteration: 1) simultaneously and independently runs the BP decoding process at each constituent sub- graph to recover some of the common erasures, 2) update the other sub-graph with newly recovered erasures and vice versa. When the number of erasures in the received message is less than or equal to the number of parity check constraints, the decoder may successfully decode both messages, otherwise the decoder declares decoding failure. Furthermore, the probability of decoding failure and the outage capacity are computed. Additionally, it is shown how the erasure probability evolves with the number of decoding iterations and the maximum tolerable loss. Simulations show that any rate pair close to the capacity boundary is achievable without using time sharing.


title: Twitter sentiment analysis of DKI Jakarta's gubernatorial election 2017 with predictive and descriptive approaches

abstract: The main purpose of this research is to analyze sentiment of DKI Jakarta's gubernatorial election 2017 in social media Twitter with predictive and descriptive approaches. The dataset were collected from Twitter by using each candidate's username as the search query. For the predictive approach, machine learning algorithms such as Multinomial Naive Bayes and Support Vector Machine are used to classify the dataset. As for the descriptive approach, time series graphs and wordclouds are used to get the deeper insights of the dataset and find the connection between Twitter's sentiment and the result of the election itself.


title: An efficient vision-based traffic light detection and state recognition for autonomous vehicles

abstract: Traffic Light Detection(TLD) and understanding their state semantics at intersections plays a pivotal role in driver assistance systems and, by extension, autonomous vehicles. Despite of several reliable traffic light state detection approaches in literature, traffic light state recognition still remains an open problem due to outdoor perception challenge which includes occlusions, illumination and scale variations. This paper presents a vision-based traffic light structure detection and convolutional neural network (CNN) based state recognition method, which is robust under different illumination and weather conditions. In the first step, traffic light candidate regions are generated by performing HSV based color segmentation, which are then filtered out using shape and area analysis. Further, in order to incorporate the structural information of traffic light in diverse background scenarios, Maximally Stable Extremal Region (MSER) approach is employed, which helps to localize the correct traffic light structure in the image. To further validate the traffic light candidate regions, Histogram of Oriented Gradients (HOG) features are extracted for each region and traffic light structures are validated using Support Vector Machine (SVM). The state of the traffic lights are then recognized using CNN. To evaluate the performance of the proposed method, we present several results under a variety of lighting conditions in a real-world environment. Experimental result shows that the proposed method outperforms other vision based conventional methods under varying light and weather conditions.


title: A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

abstract: Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.


title: Multilevel image thresholding by nature-inspired algorithms - A short review.

abstract: Nondeterministic metaheuristic optimization and digital image processing are two very different research fields, both extremely active and applicable. They touch in a very limited area, but that narrow interaction opens new very promising applications for digital image processing and new and different deployment of metaheuristic optimization. Multilevel image thresholding is very important for image segmentation, which in turn is crucial for higher level image analysis. The problem includes exponential combinatorial optimization with complex objective functions which are solvable only by nondeterministic methods. This short review presents successful applications of the natureinspired metaheuristics to multilevel image thresholding.


title: Region-based bas-relief generation from a single image

abstract: Abstract Bas-relief is an art form part way between sculpture and drawing. In this paper, we present an algorithm for generating a bas-relief from a single image, inspired by the process that artists use to create reliefs. We do not aim to recover exact depth values for objects in the image, which is a tricky computer vision problem, requiring assumptions that are rarely satisfied. Instead, we determine layers based on relative depth ordering of objects (and their parts) in the image, and use this information to construct surfaces in the 3D relief model. Feature lines are extracted and used to build a new region-based representation of the input image. During surface construction, a base surface is first generated; it is then augmented using both intensity and gradient information from the original image. To prevent depth errors arising due to augmentation, a feedback process is used to refine the output. Our experimental results show the generated bas-reliefs have smooth boundaries with appropriate height relationships, a key property of bas-reliefs created by artists. We demonstrate that our algorithm works well for a range of input images, including human faces, flowers and animals.


title: A Probabilistic Model for Exteriors of Residential Buildings

abstract: We propose a new framework to model the exterior of residential buildings. The main goal of our work is to design a model that can be learned from data that is observable from the outside of a building and that can be trained with widely available data such as aerial images and street-view images. First, we propose a parametric model to describe the exterior of a building (with a varying number of parameters) and propose a set of attributes as a building representation with fixed dimensionality. Second, we propose a hierarchical graphical model with hidden variables to encode the relationships between building attributes and learn both the structure and parameters of the model from the database. Third, we propose optimization algorithms to generate three-dimensional models based on building attributes sampled from the graphical model. Finally, we demonstrate our framework by synthesizing new building models and completing partially observed building models from photographs.


title: FingerShadow: an OLED power optimization based on smartphone touch interactions

abstract: Despite that OLED screen has been increasingly adopted in smartphones to save power; screen is still one of the most energy-consuming modules in smartphones. Techniques such as local dimming are proposed to further reduce the power consumption of OLED screen, but it is hard to decide which part of the screen could be dimmed, and it often results in compromised user experience. Intuitively, when a user interacts with a smartphone via the touch screen, the screen areas are covered by the user's fingers and even some of the neighboring areas could be safely dimmed. Thus, in this paper, we propose FingerShadow, a new technique which does local dimming for the screen areas covered by user fingers to save more power, without compromising the user visual experience. We have studied 10 users' touch interaction behaviors and found that on average 11.14% of the screen were covered by fingers. For these 10 users, we estimate that FingerShadow can achieve 5.07%-22.32% power saving, averaging 12.96%, with negligible overhead. We discuss the challenges and future research work to implement Finger-Shadow in existing smartphone systems.


title: Forensic Taxonomy of Popular Android mHealth Apps

abstract: Mobile health applications (or mHealth apps, as they are commonly known) are increasingly popular with both individual end users and user groups such as physicians. Due to their ability to access, store and transmit personally identifiable and sensitive information (e.g. geolocation information and personal details), they are potentially an important source of evidentiary materials in digital investigations. In this paper, we examine 40 popular Android mHealth apps. Based on our findings, we propose a taxonomy incorporating artefacts of forensic interest to facilitate the timely collection and analysis of evidentiary materials from mobile devices involving the use of such apps. Artefacts of forensic interest recovered include user details and email addresses, chronology of user locations and food habits. We are also able to recover user credentials (e.g. user password and four-digit app login PIN number), locate user profile pictures and identify timestamp associated with the location of a user.


title: Product availability in competitive and cooperative dual-channel distribution with stock-out based substitution

abstract: This paper investigates the impact of customers' stock-out based substitution on the product availability and the channel efficiency of a dual-channel supply chain, which consists of a supplier distributing a single product to customers through both its wholly owned direct channel and an independent retailer. The supplier and its retailer, with the objective of optimizing their own profit, simultaneously choose their own base-stock level to satisfy the stochastic demand from the customers whose channel preferences are heterogeneous and may be affected by each channel's product availability. The customers dynamically substitute between the two channels in the event of a stock-out. The result shows that the effect of the stock-out based substitution may increase or decrease the efficiency of a decentralized supply chain. It is found that while the integrated supplier-retailer may consolidate the base-stock levels to benefit from stock-out based substitution, the independent supplier and retailer are more inattentive to customers' stock-out based substitution. Thus, the competitive base-stock levels of the decentralized dual-channel supply chain rarely agree with the system optimal levels. Various contracts are examined to shed light on channel coordination mechanisms. In addition, it is shown that the channel efficiency of the dual-channel distribution can be improved by the emergence of Stackelberg leadership from either the supplier or the retailer.


title: Contextual Influences on Superintendents ' Time Usage

abstract: This report examines effects of structural and process features of professional development programs on teachers' knowledge, practice and efficacy. It is based on four recent (2002-2003) studies undertaken through the Australian Government Quality Teacher Programme, designed to enhance teacher quality. The total data set for the survey study includes 3,250 teachers who had participated in eighty individual professional development1 activities within these studies. Teachers were surveyed at least three months after participating in an activity, which provided them with the opportunity to gauge the impact of programs on their practice. To investigate factors affecting impact, a theoretical model was developed based on recent research into the characteristics of effective professional development and tested using blockwise regression analysis. The model included contextual factors (e.g., school support), structural features of programs (e.g. ,length), process features (e.g., emphasis on content; active learning; examination of student work; feedback; follow-up), a mediating variable (level of professional community generated), and four outcome measures (knowledge; practice; student learning and efficacy). Consistent significant direct effects were found across the four studies for the impact of content focus, active learning, and follow-up on knowledge and professional community. Feedback was rarely incorporated into program design. Impact on efficacy was strongly related to the perceived impact of activities on teachers' practice and student learning outcomes.


title: Comparative Analysis of the PIN Auxin Transporter Gene Family in Different Plant Species: A Focus on Structural and Expression Profiling of PINs in Solanum tuberosum

abstract: Plant growth and morphogenesis largely benefit from cell elongation and expansion and are normally regulated by environmental stimuli and endogenous hormones. Auxin, as one of the most significant plant growth regulators, controls various phases of plant growth and development. The PIN-FORMED (PIN) gene family of trans-membrane proteins considered as auxin efflux carriers plays a pivotal role in polar auxin transport and then mediates the growth of different plant tissues. In this study, the phylogenetic relationship and structural compositions of the PIN gene family in 19 plant species covering plant major lineages from algae to angiosperms were identified and analyzed by employing multiple bioinformatics methods. A total of 155 PIN genes were identified in these species and found that representative of the PIN gene family in algae came into existence and rapidly expanded in angiosperms (seed plants). The phylogenetic analysis indicated that the PIN proteins could be divided into 14 distinct clades, and the origin of PIN proteins could be traced back to the common ancestor of green algae. The structural analysis revealed that two putative types (canonical and noncanonical PINs) existed among the PIN proteins according to the length and the composition of the hydrophilic domain of the protein. The expression analysis of the PIN genes exhibited inordinate responsiveness to auxin (IAA) and ABA both in shoots and roots of Solanum tuberosum. While the majority of the StPINs were up-regulated in shoot and down-regulated in root by the two hormones. The majority of PIN genes had one or more putative auxin responses and ABA-inducible response elements in their promoter regions, respectively, implying that these phytohormones regulated the expression of StPIN genes. Our study emphasized the origin and expansion of the PIN gene family and aimed at providing useful insights for further structural and functional exploration of the PIN gene family in the future.


title: Activity-Sensitive Flip-Flop and Latch Selection for Reduced Energy

abstract: This article presents new techniques to evaluate the energy and delay of flip-flop and latch designs and shows that no single existing design performs well across the wide range of operating regimes present in complex systems. We prepose the use of a selection of flip-flop latch designs, each timed for different activation patterns and speed requirements. We illustrate the use of our technique on a pipelined MIPS processor datapath running SPECint95 benchmarks, where we reduce total flip-flop and latch energy by 60% without increasing cycle time.


title: Detecting rumor patterns in streaming social media

abstract: Rumor detection in streaming social media is a significant but challenging problem. In this paper, we present a method to identify rumor patterns in the streaming social media environment. Patterns which combine both structural and behavioral properties of rumor are firstly proposed to distinguish false rumors from valid news. A novel graph-based pattern matching algorithm is also described to detect rumor patterns from streaming social media data. Compared within Twitter data of rumors and non-rumors, our selected rumor patterns contain distinct properties of rumors in short-term series.


title: Design of a New High-Gain Circularly Polarized Antenna for Inmarsat Communications

abstract: A novel circularly polarized (CP) antenna with 1357-1997 MHz impedance bandwidth, 8 dB high gain, 3-dB axial-ratio bandwidth (ARBW) of 12.9% and ±45° half-power beamwidth (HPBW) is proposed. Compared to other reported Inmarsat antennas, the proposed antenna possesses much higher gain and larger CP bandwidth and is more suitable for high-latitude satellite communication systems.


title: Integrating Multi-Purpose Natural Language Understanding, Robot's Memory, and Symbolic Planning for Task Execution in Humanoid Robots

abstract: Abstract We propose an approach for instructing a robot using natural language to solve complex tasks in a dynamic environment. In this study, we elaborate on a framework that allows a humanoid robot to understand natural language, derive symbolic representations of its sensorimotor experience, generate complex plans according to the current world state, and monitor plan execution. The presented development supports replacing missing objects and suggesting possible object locations. It is a realization of the concept of structural bootstrapping developed in the context of the European project Xperience. The framework is implemented within the robot development environment ArmarX. We evaluate the framework on the humanoid robot ARMAR-III in the context of two experiments: a demonstration of the real execution of a complex task in the kitchen environment on ARMAR-III and an experiment with untrained users in a simulation environment.


Functional Partitioning of Ontologies for Natural Language Query Completion in Question Answering Systems


title: IoT Edge Device Based Key Frame Extraction for Face in Video Recognition

abstract: Following the development of computing and communication technologies, the idea of Internet of Things (IoT) has been realized not only at research level but also at application level. Among various IoT-related application fields, biometrics applications, especially face recognition, are widely applied in video-based surveillance, access control, law enforcement and many other scenarios. In this paper, we introduce a Face in Video Recognition (FivR) framework which performs real-time key-frame extraction on IoT edge devices, then conduct face recognition using the extracted key-frames on the Cloud back-end. With our key-frame extraction engine, we are able to reduce the data volume hence dramatically relief the processing pressure of the cloud back-end. Our experimental results show with IoT edge device acceleration, it is possible to implement face in video recognition application without introducing the middle-ware or cloud-let layer, while still achieving real-time processing speed.


title: Employees’ Intended Information Security Behaviour in Real Estate Organisations: a Protection Motivation Perspective

abstract: Due to the amount of identifiable customer personal, financial and other information stored by real estate organisations in their information systems, the threats are real. Challenges to secure the organisational (and customer) data are compounded by the nature of the industry (e.g. the core business and employees’ qualifications are non-security-related). To investigate the factors that influence real estate employees’ intended information security behaviour, we propose a research model based on Protection Motivation Theory (PMT) where we also include previous incidents as constituting threat appraisal components. Our findings from a survey of 105 real estate business employees in Australia reveal that perceived vulnerability, perceived severity, previous incidents, and response efficacy have a positive impact on real estate employees’ information security behavioural intention whereas self-efficacy does not. Our study also determines that response cost has a negative significant effect on intended information security behaviour.


title: Comparison of three DNA extraction methods for polymerase chain reaction (PCR) analysis of bacterial genomic DNA

abstract: Rapid isolation of high-purity microbial genomic DNA is necessary for genome analysis. In this study, the authors compared a one-hour procedure using a microwave with enzymatic and boiling methods of genomic DNA extraction from Gram-negative and Gram-positive bacteria. High DNA concentration and purity were observed for both MRSA and ESBL strains (80.1 and 91.1 µg/ml; OD260/280, 1.82 and 1.70, respectively) when the extraction protocol included microwave pre-heating. DNA quality was further confirmed by PCR detection of mecA and CTX-M. In conclusion, the microwave-based procedure was rapid, efficient, cost-effective, and applicable for both Gram-positive and Gram-negative bacteria. ::: ::: Key words: DNA, MRSA, ESBL, gene, enzymatic, boiling, microwave.


title: E-Commerce in Saudi Arabia: adoption and perspectives

abstract: Among one of the most far-reaching influences of the Internet, especially from the perspective of business and market models, is the concept and application of eCommerce. Today, this erstwhile ‘experimental’ commerce model has become a standard platform for streamlining business flows and broadening product outreach. This is validated by the significant emphasis by a number of countries in developing robust eCommerce architectures tailored to suit their economical-social systems. An interesting case study in this perspective is the Kingdom of Saudi Arabia, especially given its emphasis on local, regional and religious traditions. Presently, with a tremendous growth rate in Internet connectivity, the Kingdom is actively moving towards large-scale adoption of eCommerce systems. In 2001, a two year project was initiated to study the present influence of Internet on social, educational and business systems within the Kingdom. One of the major aspects was the analysis of user responses collected over the two-year period through a number of surveys, which were designed based on two prominent behavioural models: Theory of Planned Behaviour and Roger’s Theory of Diffusion of Innovations. The consequent analysis of the results was supported and in some cases verified with linear regression models. This paper reports on the consequent findings, which identify factors that may significantly affect the adoption of eCommerce in the Kingdom.


title: Standalone OPC UA Wrapper for Industrial Monitoring and Control Systems

abstract: OPC unified architecture (UA), a communication standard for the manufacturing industry, enables exchanging control and management data among distributed entities in industrial automation systems. OPC UA wrapper is a migration strategy that provides UA clients with seamless access to legacy servers having OPC classic interfaces. This paper presents the design of a standalone OPC UA wrapper and discusses its performance through extensive experiments using a prototype implementation. The wrapper consists of two main components, i.e., UA server and classic client, which communicate with each other via shared memory and semaphore. One important feature of the design is that it employs a distributed component object model runtime library implemented in Java for platform independence. This makes it possible to build a cost-competitive wrapper system by using commercial off-the-shelf non-Windows solutions with low-cost microprocessors. Another key feature is the event-driven update interface between the UA and classic components, which we propose as an alternative to the sampling-based mechanism for the reduced delay. Through experiments using workloads from an industrial monitoring system, we present a systematic approach of identifying the system parameters having a direct impact on the wrapper performance and eventually tuning them such that the read and subscription services of OPC UA exhibit the best performance.


title: Structural Studies of (Ca,Sr)F2 Single Crystals with Raman and NMR Spectroscopies

abstract: Single crystal CaF2 is the material of choice for optics in the vacuum UV, due in part to its high transmission at these wavelengths. Unfortunately, as the wavelengths decrease, intrinsic birefringence (also referred to as spatial dispersion) can become larger than the target birefringence for precision optics at 157 nm, even for a cubic material such as CaF2. One elegant solution to this problem is to capitalize on opposite signs for this intrinsic birefringence, such as mixing SrF2 or BaF2 with CaF2. If the proper fluorides are mixed with the correct concentration, the intrinsic birefringence of the mixed single crystals can be minimized. It is also important that the resulting mixed single crystal maintain the high symmetry of CaF2, which requires that the cations mix randomly in the CaF2 structure. We show through Raman and NMR spectroscopic studies that this random mixing of cations can be achieved for mixed (Ca,Sr)F2 single crystals. A single Raman band is observed for all mixed crystals, which changes in both position and width upon addition of SrF2 to CaF2. 19F NMR spectra also reflect a random distribution of cations around the different fluorine environments in the mixed crystals.


title: Dynamic Simulation-Based Action Planner for a Reconfigurable Hybrid Leg–Wheel Planetary Exploration Rover

abstract: In this paper, an action planning algorithm is presented for a reconfigurable hybrid leg–wheel mobile robot. Hybrid leg–wheel robots have recently receiving growing interest from the space community to explore planets, as they offer a solution to improve speed and mobility on uneven terrain. One critical issue connected with them is the study of an appropriate strategy to define when to use one over the other locomotion mode, depending on the soil properties and topology. Although this step is crucial to reach the full hybrid mechanism's potential, little attention has been devoted to this topic. Given an elevation map of the environment, we developed an action planner that selects the appropriate locomotion mode along an optimal path toward a point of scientific interest. This tool is helpful for the space mission team to decide the next move of the robot during the exploration. First, a candidate path is generated based on topology and specifications' criteria functions. Then, switching actions are defi...


title: UAVs as aviators: Environment skills capability for UAVs

abstract: To reduce the workload of flying an Unmanned Areal Vehicle (UAV), some of the tasks of the human operator can be automated. In the Levels Of Sophistication (LOS) structure, the capabilities of a UAV system are subdivided. Using this structure, the capabilities concerned with the aviating of an UAV are selected to be automated for this research. The task of the aviating capabilities of an UAV is to integrate all the higher level mission and navigation constraints with constraints from the environment, in order to find a route for the UAV to fly which is safe and efficient. This is done by expressing the constraints in cost maps. The A* pathfinding algorithm then uses these cost maps to find a route for the UAV to fly, taking into account these constraints. The UAV aviating automation has been implemented in a simulation program. The system is capable of finding the best route in the cost map representing the constraints from different levels of sophistication, and let the UAV fly this route in the simulation. The complete automation system was tested in the 2007 Micro UAV flight competition, where the UAV was required to execute some autonomous tasks.


title: Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review.

abstract: BACKGROUND ::: Systematic literature reviews identify, select, appraise, and synthesize relevant literature on a particular topic. Typically, these reviews examine primary studies based on similar methods, e.g., experimental trials. In contrast, interest in a new form of review, known as mixed studies review (MSR), which includes qualitative, quantitative, and mixed methods studies, is growing. In MSRs, reviewers appraise studies that use different methods allowing them to obtain in-depth answers to complex research questions. However, appraising the quality of studies with different methods remains challenging. To facilitate systematic MSRs, a pilot Mixed Methods Appraisal Tool (MMAT) has been developed at McGill University (a checklist and a tutorial), which can be used to concurrently appraise the methodological quality of qualitative, quantitative, and mixed methods studies. ::: ::: ::: OBJECTIVES ::: The purpose of the present study is to test the reliability and efficiency of a pilot version of the MMAT. ::: ::: ::: METHODS ::: The Center for Participatory Research at McGill conducted a systematic MSR on the benefits of Participatory Research (PR). Thirty-two PR evaluation studies were appraised by two independent reviewers using the pilot MMAT. Among these, 11 (34%) involved nurses as researchers or research partners. Appraisal time was measured to assess efficiency. Inter-rater reliability was assessed by calculating a kappa statistic based on dichotomized responses for each criterion. An appraisal score was determined for each study, which allowed the calculation of an overall intra-class correlation. ::: ::: ::: RESULTS ::: On average, it took 14 min to appraise a study (excluding the initial reading of articles). Agreement between reviewers was moderate to perfect with regards to MMAT criteria, and substantial with respect to the overall quality score of appraised studies. ::: ::: ::: CONCLUSION ::: The MMAT is unique, thus the reliability of the pilot MMAT is promising, and encourages further development.


title: Learning to detect natural image boundaries using local brightness, color, and texture cues

abstract: The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.


Odin's Runes: A rule language for information extraction


title: Video capture of clinical care to enhance patient safety.

abstract: Experience from other domains suggests that videotaping and analyzing actual clinical care can provide valuable insights for enhancing patient safety through improvements in the process of care. Methods are described for the videotaping and analysis of clinical care using a high quality portable multi-angle digital video system that enables simultaneous capture of vital signs and time code synchronization of all data streams. An observer can conduct clinician performance assessment (such as workload measurements or behavioral task analysis) either in real time (during videotaping) or while viewing previously recorded videotapes. Supplemental data are synchronized with the video record and stored electronically in a hierarchical database. The video records are transferred to DVD, resulting in a small, cheap, and accessible archive. A number of technical and logistical issues are discussed, including consent of patients and clinicians, maintaining subject privacy and confidentiality, and data security. Using anesthesiology as a test environment, over 270 clinical cases (872 hours) have been successfully videotaped and processed using the system.


title: Multilingual Twitter Sentiment Classification: The Role of Human Annotators

abstract: What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered.


title: Make, Buy, or Ally? Choice of and Payoff from Announcements of Alternate Strategies for Innovations

abstract: Firms constantly grapple with the question of whether to make, buy, or ally for innovations. The literature has not, to our knowledge, analyzed the choice of and payoff from these alternate routes to innovation for the same firm. To address this issue, we collect, code, and analyze the choice of and payoff from 3,522 announcements of make, buy, and ally for 192 firms across 108 industries over five years. ::: ::: We find that announcements to make or ally generate positive and higher payoffs than announcements to buy, which generate negative payoffs. Nevertheless, firms continue to buy for two reasons. First, firms seem to have no memory of the payoff from buy, even though they have a memory of the payoff from make. Second, firms tend to buy when they lack commercializations, even though this strategy does not always seem to pay off. These results suggest that firms see buy as a signal to investors that they have a solution for what may be a deep strategic problem. Nevertheless, the negative returns to a buy can be mitigated if the acquirer is experienced, and the target is related and offers high customer benefit. We offer explanations for and implications of the results.


title: An evaluation of second-generation ebook readers

abstract: Purpose – This study seeks to evaluate a selection of second‐generation ebook readers in order to determine which devices deliver the best experience for the user, in terms of functionality and overall experience. The technical and physical elements of the devices are also compared.Design/methodology/approach – This paper starts with a brief discussion of the current ebook marketplace, and previous studies that evaluate ebook readers. It then reports on a study in which 33 Master's students from an Information and Library Studies course were each given an ebook reader and asked to complete a task designed to engage them with the device. The participants then evaluated the devices by completing a questionnaire. A discussion of the results of the study and implications for the development of ebook reading devices follows.Findings – Although some issues, specifically size, weight, and screen quality, have been addressed in the new generation of ebook readers, some residual dissatisfaction remains. The partic...


title: A model-based approach for RFID data stream cleansing

abstract: In recent years, RFID technologies have been used in many applications, such as inventory checking and object tracking. However, raw RFID data are inherently unreliable due to physical device limitations and different kinds of environmental noise. Currently, existing work mainly focuses on RFID data cleansing in a static environment (e.g. inventory checking). It is therefore difficult to cleanse RFID data streams in a mobile environment (e.g. object tracking) using the existing solutions, which do not address the data missing issue effectively. In this paper, we study how to cleanse RFID data streams for object tracking, which is a challenging problem, since a significant percentage of readings are routinely dropped. We propose a probabilistic model for object tracking in a mobile environment. We develop a Bayesian inference based approach for cleansing RFID data using the model. In order to sample data from the movement distribution, we devise a sequential sampler that cleans RFID data with high accuracy and efficiency. We validate the effectiveness and robustness of our solution through extensive simulations and demonstrate its performance by using two real RFID applications of human tracking and conveyor belt monitoring.


title: Chapter 9 – Cognitive Computing Applications in Education and Learning

abstract: Abstract Education and learning stand out among many application areas of cognitive computing due to their practical appeal as well as their research challenge. We can gauge the level of research challenge once we recognize the broad spectrum of human learning, the complex and not fully understood human learning process, and various influencing factors of learning, such as pedagogy, technology, and social elements. We survey some of the key research problems and recent efforts to solve them. In particular, we examine the important roles played by the Educational Data Mining and Learning Analytics communities in these research efforts. We find evidence of significant progress over the last few years. We also find evidence of concerted efforts by academia and industry in bringing some of these research results to the public in the form of learning and educational software.


title: ROOT: replaying multithreaded traces with resource-oriented ordering

abstract: We describe ROOT, a new method for incorporating the nondeterministic I/O behavior of multithreaded applications into trace replay. ROOT is the application of Resource-Oriented Ordering to Trace replay: actions involving a common resource are replayed in an order similar to that of the original trace. ROOT is based on the idea that how a program manages resources, as seen in a trace, provides hints about an application's internal dependencies. Inferring these dependencies allows us to partially constrain trace replay in a way that reflects the constraints of the original program. We make three contributions: (1) we describe the ROOT approach, (2) we release ARTC, a new ROOT-based tool for replaying I/O traces, and (3) we create Magritte, a file-system benchmark suite generated by applying ARTC to 34 Apple desktop application traces. When collecting traces on one platform and replaying on another, ARTC achieves an average timing inaccuracy of 10.6% on our benchmark workloads, halving the 21.3% achieved by the next-best replay method we evaluate.


title: A comparative study of social network analysis tools

abstract: Social networks have known an important development since the appearance of web 2.0 platforms. This leads to a growing need for social network mining and social network analysis (SNA) methods and tools in order to provide deeper analysis of the network but also to detect communities in view of various applications. For this reason, a lot of works have focused on graph characterization or clustering and several new SNA tools have been developed over these last years. The purpose of this article is to compare some of these tools which implement algorithms dedicated to social network analysis.


title: EMG controlled low cost prosthetic arm

abstract: Electromyography (EMG) signals have been extensively used as a control signal in robotics, rehabilitation and health care. In this paper, cost effective design of prosthetic hand using EMG control is presented. Signal amplification and filtering is the primary step in surface EMG signal processing and application systems. Quality of the acquired EMG signal depends on the amplifiers and filters employed. Single channel continuous EMG signal has been acquired from the users arm for various hand movements. The acquired signal is passed through various stages of filters and amplifiers for amplification and noise reduction. The conditioned analog signal is converted into digital samples. After the signal acquisition process, features are extracted from the acquired signal and the extracted features are reduced to minimize the number of computations. These reduced feature parameters are used to classify the signal for different hand movements. Once the classifier identifies the intended motion, the control signal will be generated and given to the motors in the prosthetic hand to perform the intended movements. Experiments were done to find the efficiency of the developed system and it is found that this system can give basic movements at a very low cost.


title: Model-based kernel for efficient time series analysis

abstract: We present novel, efficient, model based kernels for time series data rooted in the reservoir computation framework. The kernels are implemented by fitting reservoir models sharing the same fixed deterministically constructed state transition part to individual time series. The proposed kernels can naturally handle time series of different length without the need to specify a parametric model class for the time series. Compared with most time series kernels, our kernels are computationally efficient. We show how the model distances used in the kernel can be calculated analytically or efficiently estimated. The experimental results on synthetic and benchmark time series classification tasks confirm the efficiency of the proposed kernel in terms of both generalization accuracy and computational speed. This paper also investigates on-line reservoir kernel construction for extremely long time series.


title: Detecting the Hate Code on Social Media

abstract: Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google's Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.


title: Sentiment analysis using product review data

abstract: Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis.


title: Optimal solid waste collection routes identified by the ant colony system algorithm

abstract: In the present paper, the Ant Colony System (ACS) algorithm is used for the identification of optimal routes in the case of municipal solid waste (MSW) collection. The proposed MSW management system is based on a geo-referenced spatial database supported by a geographic information system (GIS). The GIS takes into account all the required parameters for solid waste collection. These parameters include static and dynamic data, such as the positions of waste bins, the road network and the related traffic, as well as the population density in the area under study. In addition, waste collection schedules, truck capacities and their characteristics are also taken into consideration. Spatio-temporal statistical analysis is used to estimate inter-relations between dynamic factors, like network traffic changes in residential and commercial areas. The user, in the proposed system, is able to define or modify all of the required dynamic factors for the creation of alternative initial scenarios. The objective of the system is to identify the most cost-effective scenario for waste collection, to estimate its running cost and to simulate its application. Finally, the results of the ACS algorithm are compared with the empirical method currently used by the Municipality of Athens.


title: Stable Proportional-Derivative Controllers

abstract: In computer animation, the proportional-derivative (PD) controller is a common technique for tracking characters' motion. A new formulation of the PD controller-stable PD (SPD)-allows arbitrarily high gains, even at large time steps. The key is to determine joint forces and torques while taking into account the character's positions and velocities in the next time step. SPD is stable even when combined with a physics simulator that uses simple Euler integration. SPD controllers have a variety of uses, including motion tracking in a physics simulator, keyframe interpolation with secondary motion, and constraint satisfaction for simulation.


title: Variational mesh segmentation via quadric surface fitting

abstract: We present a new variational method for mesh segmentation by fitting quadric surfaces. Each component of the resulting segmentation is represented by a general quadric surface (including plane as a special case). A novel energy function is defined to evaluate the quality of the segmentation, which combines both L^2 and L^2^,^1 metrics from a triangle to a quadric surface. The Lloyd iteration is used to minimize the energy function, which repeatedly interleaves between mesh partition and quadric surface fitting. We also integrate feature-based and simplification-based techniques in the segmentation framework, which greatly improve the performance. The advantages of our algorithm are demonstrated by comparing with the state-of-the-art methods.


title: Scalable SPARQL querying using path partitioning

abstract: The emerging need for conducting complex analysis over big RDF datasets calls for scale-out solutions that can harness a computing cluster to process big RDF datasets. Queries over RDF data often involve complex self-joins, which would be very expensive to run if the data are not carefully partitioned across the cluster and hence distributed joins over massive amount of data are necessary. Existing RDF data partitioning methods can nicely localize simple queries but still need to resort to expensive distributed joins for more complex queries. In this paper, we propose a new data partitioning approach that takes use of the rich structural information in RDF datasets and minimizes the amount of data that have to be joined across different computing nodes. We conduct an extensive experimental study using two popular RDF benchmark data and one real RDF dataset that contain up to billions of RDF triples. The results indicate that our approach can produce a balanced and low redundant data partitioning scheme that can avoid or largely reduce the cost of distributed joins even for very complicated queries. In terms of query execution time, our approach can outperform the state-of-the-art methods by orders of magnitude.


title: Semantic Web of Things: an analysis of the application semantics for the IoT moving towards the IoT convergence

abstract: The Internet of Things IoT is being applied for stovepipe solutions, since it presents a semantic description limited to a specific domain. IoT needs to be pushed towards a more open, interoperable and collaborative IoT. The first step has been the Web of Things WoT. WoT evolves the IoT with a common stack based on web services. But, even when a homogeneous access is reached through web protocols, a common understanding is not yet acquired. For this purpose, the Semantic Web of Things SWoT is proposed for the integration of the semantic web on the WoT. This work analyses the SWoT, presenting its different levels to offer an IoT convergence. Specifically, we analyse the trends for capillary networks and for cellular networks with standards such as IPSO, ZigBee, OMA, and the oneM2M initiative. This work also analyses the impact of the semantic-annotations/metadata in the performance of the resources.


title: Beyond the Lab: Using Technology Toys to Engage South African Youth in Computational Thinking

abstract: We present a two-part case study to explore how technology toys can promote computational thinking for young children. First, we conducted a formal study using littleBits, a commercially available technology toy, to explore its potential as a learning tool for computational thinking in three different educational settings. Our findings revealed differences in learning indicators across settings. We applied these insights during a teaching project in Cape Town, South Africa, where we partnered with an educational NGO, ORT SA CAPE, to offer enriching learning opportunities for both privileged and impoverished children. We describe our methods, observations, and lessons learned using littleBits to teach computational thinking to children in early elementary school, and discuss how our lab study informed practical work in the developing world.


title: Named entity recognition in vietnamese text using label propagation

abstract: This paper presents our named entity recognition system for Vietnamese text using labeled propagation. In here we propose: (i) a method of choosing noun phrases as the named entity candidates; (ii) a method to measure the word similarity; and (iii) a method of decreasing the effect of high frequency labels in labeled documents. Experimental results show that our labeled propagate method achieves higher accuracy than the old one [12]. In addition, when the number of the labeled data is small, its accuracy is higher than when using conditional random fields.


title: VIP, Vagina, Clitoral and Periurethral Glans — an Update on Human Female Genital Arousal

abstract: The sexually quiescent human vagina is a just moist, potential space with a minimal blood flow and very low luminal oxygen tension. The first measurable sign of sexual arousal is an increase in the blood flow. This creates the engorged condition, elevates the luminal oxygen tension and stimulates the production of surface vaginal fluid by an increased plasma transudation that saturates the fluid reabsorptive capacity of the epithelium. The vaginal lubrication created allows painless penile penetration and coital movements. The mechanisms underlying the changes appear to be mediated by Vasoactive Intestinal Peptide (VIP). VIP is present in nerves closely applied to blood vessels in the vaginal wall. Administration of VIP either intravenously, or by subepithelial injection in the vaginal wall, increases vaginal blood flow and induces vaginal fluid production. Increases in vaginal blood flow by sexual arousal are not blocked by atropine injection indicating that cholinergic mechanisms are unimportant. All the present evidence suggests that the local vaginal release of VIP induces the vaginal changes of arousal. Discourse on vaginal and pudendal anatomy (Sevely, 1987) has proposed that the female glans of the clitoris is not the true homologue of the penile glans because it has no urethral opening. The speculative suggestion is that the true female glans is the area surrounding the urethral opening (which has no specific anatomical name). Preliminary studies indicate that the area of this tissue (periurethral glans) decreases on vaginal penile insertion and reappears on withdrawal indicating that it is moved during coitus. How important such movement is to stimulate erotic sensation and how sensitive this area is to erotic stimulation are unanswered questions.


MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review


title: A low-power 8-PAM serial transceiver in 0.5-μm digital CMOS

abstract: A CMOS multi-level (8-PAM) transceiver is described. Preemphasis is implemented without an increase in DAC resolution or digital computation. The receiver oversamples with three fully differential 3-bit ADCs. The device transmits at up to 1.3Gb/s and has a measured BER of for an 81OMb/s PRBS transmission. The device, packaged in a 68 pin CLCC, is implemented k 0.5pm digital CMOS, occupies 2”’ and dissipates 400mW from a 3.3V supply.


title: Reconciling the different faces of hippocampal theta: the role of theta oscillations in cognitive, emotional and innate behaviors

abstract: The theta oscillation (5–10 Hz) is a prominent behavior-specific brain rhythm. This review summarizes studies showing the multifaceted role of theta rhythm in cognitive functions, including spatial coding, time coding and memory, exploratory locomotion and anxiety-related behaviors. We describe how activity of hippocampal theta rhythm generators − medial septum, nucleus incertus and entorhinal cortex, links theta with specific behaviors. We review evidence for functions of the theta-rhythmic signaling to subcortical targets, including lateral septum. Further, we describe functional associations of theta oscillation properties − phase, frequency and amplitude – with memory, locomotion and anxiety, and outline how manipulations of these features, using optogenetics or pharmacology, affect associative and innate behaviors. We discuss work linking cognition to the slope of the theta frequency to running speed regression, and emotion-sensitivity (anxiolysis) to its y-intercept. Finally, we describe parallel emergence of theta oscillations, theta-mediated neuronal activity and behaviors during development. This review highlights a complex interplay of neuronal circuits and synchronization features, which enables an adaptive regulation of multiple behaviors by theta-rhythmic signaling.


title: Managing effective knowledge transfer: an integrative framework and some practice implications

abstract: One of the major challenges an organization faces is to manage its knowledge assets. Increasingly, the use of knowledge is seen as a basis for competitive advantage. This paper explores the key factors that have been cited as significant influences on the ability to transfer knowledge, an important area of knowledge management. Each of these factors is discussed separately and then integrated into a conceptual framework to explain how effective knowledge transfer can be managed in an organization. A set of managerial implications, or a qualitative assessment approach, is also discussed. It is framed as organizational characteristics and managerial practices required to establish an effective knowledge transfer process in an organization. Conclusions are drawn about the complexity of managing knowledge transfer and the need to take a balanced approach to the process.


title: Life cycle assessment of potential municipal solid waste management strategies for Mumbai, India

abstract: Dumping of municipal solid waste into uncontrolled dumpsites is the most common method of waste disposal in most cities of India. These dumpsites are posing a serious challenge to environmental quality and sustainable development. Mumbai, which generates over 9000 t of municipal solid waste daily, also disposes of most of its waste in open dumps. It is important to analyse the impact of municipal solid waste disposal today and what would be the impact under integrated waste management schemes. In this study, life cycle assessment methodology was used to determine the impact of municipal solid waste management under different scenarios. Six different scenarios were developed as alternatives to the current practice of open dumping and partially bioreactor landfilling. The scenarios include landfill with biogas collection, incineration and different combinations of recycling, landfill, composting, anaerobic digestion and incineration. Global warming, acidification, eutrophication and human toxicity were assessed as environmental impact categories. The sensitivity analysis shows that if the recycling rate is increased from 10% to 90%, the environmental impacts as compared with present scenario would reduce from 998.43 kg CO2 eq t-1 of municipal solid waste, 0.124 kg SO2 eq t-1, 0.46 kg PO4-3 eq t-1, 0.44 kg 1,4-DB eq t-1 to 892.34 kg CO2 eq t-1, 0.121 kg SO2 eq t-1, 0.36 kg PO4-3 eq t-1, 0.40 kg 1,4-DB eq t-1, respectively. An integrated municipal solid waste management approach with a mix of recycling, composting, anaerobic digestion and landfill had the lowest overall environmental impact. The technologies, such as incineration, would reduce the global warming emission because of the highest avoided emissions, however, human toxicity would increase.


title: Perceptual Organization in User-Generated Graph Layouts

abstract: Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were ldquomaskedrdquo by extraneous edges we were able to measure observerspsila sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithms.


title: Brand Portfolio Reengineering: The Gruppo Coin Case

abstract: Brand portfolio reengineering is the typical process a company uses to set the organization in a complete, harmonious, and consistent manner; the target is to maximize the value of its brand equity. The more complex and varied are brand portfolio and product portfolio of a company, the more difficult is the competitive settlement that normally deals in conjunction with other epochal moments of its life (acquisitions, mergers, change of shareholders or top management). In the Gruppo Coin the process of store brand portfolio reengineering has entailed a major revision of the organizational model to support themselves store brands, actually it is still evolving.


A Comprehensive Approach Towards Data Preprocessing Techniques & Association Rules


title: Dynamic fictitious play, dynamic gradient play, and distributed convergence to Nash equilibria

abstract: We consider a continuous-time form of repeated matrix games in which player strategies evolve in reaction to opponent actions. Players observe each other's actions, but do not have access to other player utilities. Strategy evolution may be of the best response sort, as in fictitious play, or a gradient update. Such mechanisms are known to not necessarily converge. We introduce a form of "dynamic" fictitious and gradient play strategy update mechanisms. These mechanisms use derivative action in processing opponent actions and, in some cases, can lead to behavior converging to Nash equilibria in previously nonconvergent situations. We analyze convergence in the case of exact and approximate derivative measurements of the dynamic update mechanisms. In the ideal case of exact derivative measurements, we show that convergence to Nash equilibrium can always be achieved. In the case of approximate derivative measurements, we derive a characterization of local convergence that shows how the dynamic update mechanisms can converge if the traditional static counterparts do not. We primarily discuss two player games, but also outline extensions to multiplayer games. We illustrate these methods with convergent simulations of the well known Shapley and Jordan counterexamples.


title: Application of interval valued fuzzy matrices in medical diagnosis via a new approach

abstract: The concept of interval valued fuzzy matrices (IVFM), which is a generalization of fuzzy matrix and still in its infant stage, plays a crucial role in networks, document retrieval system, medical diagnosis, database management system, decision-making theory, control theory and dynamical systems. In this paper, we propose a new approach, denoted as <R|V>, for the multiplication of IVFM, and we use this technique for predicting the stages (mild, moderate or severe) of diarrhea. This technique is more suitable than the composition (max. min.) to diagnose a disease and enable the physicians to have a better analytic understanding of present diagnostic process of diarrhea.


title: Identifying Expectation Errors in Value/Glamour Strategies: A Fundamental Analysis Approach

abstract: It is well established that value stocks outperform glamour stocks, yet considerable debate exists about whether the return differential reflects compensation for risk or mispricing. Under mispricing explanations, prices of glamour (value) firms reflect systematically optimistic (pessimistic) expectations; thus, the value/glamour effect should be concentrated (absent) among firms with (without) ex ante identifiable expectation errors. Classifying firms based upon whether expectations implied by current pricing multiples are congruent with the strength of their fundamentals, we document that value/glamour returns and ex post revisions to market expectations are predictably concentrated (absent) among firms with ex ante biased (unbiased) market expectations. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.


Observability-constrained Vision-aided Inertial Navigation


A Survey on Scalable Big Data Analytics Platform


title: On the robustness of power systems: Optimal load-capacity distributions and hardness of attacking

abstract: We consider a power system with N transmission lines whose initial loads (i.e., power flows) L 1 ,…, L N and capacities C1,…, C N are independent and identically distributed with the joint distribution P LC (x, y) = P [L ≤ x, C ≤ y]; the capacity C i defines the maximum flow allowed on line i. We survey some results on the robustness of this power system against random attacks (or, failures) that target a p-fraction of the lines, under a democratic fiber bundle-like model. Namely, when a line fails, the load it was carrying is redistributed equally among the remaining lines. We then consider the case where an adversary can launch a targeted attack, and present several results on the hardness of attacking optimally.


title: A lightweight framework for efficient M2M device management in oneM2M architecture

abstract: Recent years have witnessed an explosion in the number and types of physical devices connected to the Internet. This exponential growth in the volume of objects poses challenges in terms of managing the connected M2M devices. A unified approach for efficient management of the M2M devices while preserving scalability is necessary. This paper proposes an M2M device management framework that can be deployed in a cloud system, M2M gateway or even inside a mobile application. CoRE Link Format is used for lightweight description of smart M2M devices. The capabilities of CoRE Link are extended to describe legacy M2M devices as a part of Internet of Things ecosystem. Open Mobile Alliance Lightweight M2M (OMA LwM2M) Technical Specifications are used in the framework to provide M2M service enablement for end users. Self-management of the M2M device configurations is outlined. The capabilities of the framework are exposed using RESTful web services. oneM2M architecture for device management is described which integrates the proposed framework. Its software implementation is examined to be ultra-lightweight. Utilization of CoRE Link settles the heterogeneity of the managed devices and promotes interoperability. Finally the paper summarizes the contributions and concludes with future directions.


title: Online Topic-Aware Influence Maximization

abstract: Influence maximization, whose objective is to select k users (called seeds) from a social network such that the number of users influenced by the seeds (called influence spread) is maximized, has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. However, in real-world social networks, users have their own interests (which can be represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. We can increase the influence spread by taking into consideration topics. To address this problem, we study topic-aware influence maximization, which, given a topic-aware influence maximization (TIM) query, finds k seeds from a social network such that the topic-aware influence spread of the k seeds is maximized. Our goal is to enable online TIM queries. Since the topic-aware influence maximization problem is NP-hard, we focus on devising efficient algorithms to achieve instant performance while keeping a high influence spread. We utilize a maximum influence arborescence (MIA) model to approximate the computation of influence spread. To efficiently find k seeds under the MIA model, we first propose a best-effort algorithm with 1 − 1/e approximation ratio, which estimates an upper bound of the topic-aware influence of each user and utilizes the bound to prune large numbers of users with small influence. We devise effective techniques to estimate tighter upper bounds. We then propose a faster topic-sample-based algorithm with e · (1 − 1/e) approximation ratio for any e ∈ (0, 1], which materializes the influence spread of some topic-distribution samples and utilizes the materialized information to avoid computing the actual influence of users with small influences. Experimental results show that our methods significantly outperform baseline approaches.


title: Conceptual Projection and Middle Spaces

abstract: Conceptual projection from one mental space to another always involves projection to "middle" spaces-abstract "generic" middle spaces or richer "blended" middle spaces. Projection to a middle space is a general cognitive process, operating uniformly at different levels of abstraction and under superficially divergent contextual circumstances. Middle spaces are indispensable sites for central mental and linguistic work. The process of blending is in particular a fundamental and general cognitive process, running over many (conceivably all) cognitive phenomena, including categorization, the making of hypotheses, inference, the origin and combining of grammatical constructions, analogy, metaphor, and narrative. Blending is not secondary to these phenomena but prerequisite, and its operation is not restricted to any one of these phenomena. We give evidence for blending from a wide range of data that includes everyday language, idioms, literary metaphor, non-verbal conceptualization of action, creative thought in mathematics, evolution of socio-cultural models, jokes, and advertising. Blending is in general invisible to consciousness and detectable only on analysis. Blended spaces are routinely necessary for constructing central meanings, inferences, and structures, and for motivating emotions. We show that the blending of highly schematic spaces yields the fusion of grammatical constructions and functional assemblies studied in Cognitive Grammar and Construction Grammar. Finally, recognizing the cognitive import of middle spaces allows us to propose a generalized four-space model of conceptual projection that subsumes a variety of previous models. We explore the consequences of this model for the theory of concept formation.Introduction 3I. The four space model 4II. The phenomenon of blended spaces 51. Dante's Inferno 52. Regatta 73. The riddle of the Buddhist monk 84. Getting ahead of oneself 95. Tuning in, and other actions 116. Complex numbers 12III. Prototypes of blending and mistaken reductions 14IV. Further evidence for conceptual projection into a blended space 171. President Bush on third base 182. President Nixon in France 193. Personification 21V. Category extension 22VI. Generic spaces 24VII. Parameters and subschemes 25VIII. Blending and grammar 30IX. The concept of a concept 33Notes 35References 38


title: A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006

abstract: In this paper we introduce a multilingual Named Entity Recognition (NER) system that uses statistical modeling techniques. The system identifies and classifies NEs in the Hungarian and English languages by applying AdaBoostM1 and the C4.5 decision tree learning algorithm. We focused on building as large a feature set as possible, and used a split and recombine technique to fully exploit its potentials. This methodology provided an opportunity to train several independent decision tree classifiers based on different subsets of features and combine their decisions in a majority voting scheme. The corpus made for the CoNLL 2003 conference and a segment of Szeged Corpus was used for training and validation purposes. Both of them consist entirely of newswire articles. Our system remains portable across languages without requiring any major modification and slightly outperforms the best system of CoNLL 2003, and achieved a 94.77% F measure for Hungarian. The real value of our approach lies in its different basis compared to other top performing models for English, which makes our system extremely successful when used in combination with CoNLL modells.


title: Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns

abstract: In our study, we investigate the effectiveness of different models to the purchasing behaviour at YOOCHOOSE website. This paper provide a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and show that the result is comparable to using more massive feature engineering that requires session summarizing. Our proposed method requires much lesser feature engineering and more natural modeling of the click events directly in a typical purchasing session in e-commerce.


title: Modeling value propositions in e-Business

abstract: In this paper we provide a conceptual approach to modelling value propositions. We argue that rigorous modelling in an ontological style could improve several aspects of business. Modelling and mapping value propositions helps better understanding the value a company wants to offer its customers and makes it communicable between various stakeholders. Using a common language (ontology) in defining a company's offering brings manager's mental models into a common form. Further, conceptually seized value propositions are comparable to the value propositions of a firm's competitors because the follow a rigid framework and make it possible to identify the competitive position of a firm's value proposition.


title: CHANGE DETECTION OF REMOTE SENSING IMAGES BY DT-CWT AND MRF

abstract: Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.


title: Automatic Mapping Clinical Notes to Medical Terminologies

abstract: Automatic mapping of key concepts from clinical notes to a terminology is an important task to achieve for extraction of the clinical information locked in clinical notes and patient reports. The present paper describes a system that automatically maps free text into a medical reference terminology. The algorithm utilises Natural Language Processing (NLP) techniques to enhance a lexical token matcher. In addition, this algorithm is able to identify negative concepts as well as performing term qualification. The algorithm has been implemented as a web based service running at a hospital to process real-time data and demonstrated that it worked within acceptable time limits and accuracy limits for them. However broader acceptability of the algorithm will require comprehensive evaluations.


title: Visual illusions can facilitate sport skill learning.

abstract: Witt, Linkenauger, and Proffitt (Psychological Science, 23, 397–399, 2012) demonstrated that golf putting performance was enhanced when the hole was surrounded by small circles, making it look larger, relative to when it was surrounded by large circles, making it look smaller. In the present study, we examined whether practicing putting with small or large surrounding circles would have not only immediate effects on performance, but also longer-lasting effects on motor learning. Two groups of nongolfers practiced putting golf balls to a 10.4-cm circle (“hole”) from a distance of 2 m. Small or large circles were projected around the hole during the practice phase. Perception of hole size was affected by the size of the surrounding circles. Also, self-efficacy was higher in the group with the perceived larger hole. One day after practice, participants performed the putting task, but without visual illusions (i.e., a retention test). Putting accuracy in retention was greater for the group that had practiced with the perceived larger hole. These findings suggest that the apparently larger target led to the more effective learning outcome.


Modeling Situations in Neural Chat Bots


title: A novel 0.35µm 800V BCD technology platform for offline applications

abstract: Here is presented the development of the new BCD800 platform which conjugates 3.3V CMOS logic, 5/25/30V power devices and a 800V nLDMOS in a 0.35µm technology node. LV components can be placed into a floating pocket which can be referred up to 650V, furthermore this process features an innovative lateral junction isolation module obtained by a boron-doped poly-filled deep trench with great advantages in terms of performances and area saving. The process industrialization has been demonstrated and a fully functional test vehicle is available in the form of a single-chip High Voltage Smart Gate Driver for half bridges.


title: Propranolol treatment for infantile hemangioma: a case series of sixty-two patients

abstract: Infantile hemangiomas (IH) complicated by ulceration, disfigurement, functional impairment or life-threatening conditions need early, safe and effective treatment. This study explores the impact of propranolol on complicated IH. We report our experience of 62 patients treated with oral propranolol for complicated IH. The effect of propranolol was assessed using a score on a visual analogue scale integrated with echo, magnetic resonance or endoscopic findings. The average age at the beginning of the treatment was seven months [standard deviation (SD)±8.9], with a median of four months (range 1-53 months). The average age at the end of the treatment was 15 months (SD±8.4), with a median of 13 months (range 7-59 months). The mean treatment length was eight months (SD±3.2). Oral propranolol was successful in 95.2% of the patients in reducing the volume, the intensity of color and the elevation of IH. Statistically significant improvement of IH volume was observed in the first two months of therapy (P≤0.001), and between the second month and the end of the treatment (P<0.05). No significant bradycardia or hypotension occurred. Severe hypoglycemia occurred in one patient. Mild adverse effects were observed in seven patients. Our study demonstrates that propranolol administered orally at 2 to 3 mg/kg/day has a rapid therapeutic effect leading to remarkable shortening of the natural course of IH and it is safe in the majority of patients.


title: A Successive Linear Programming Approach to Solving the IV-ACOPF

abstract: Improved formulations of and solution techniques for the alternating current optimal power flow (ACOPF) problem are critical to improving current market practices in economic dispatch. We introduce the IV-ACOPF formulation that unlike canonical ACOPF formulations–which represent network balancing through nonlinear coupling–is based on a current injections approach that linearly couple the quadratic constraints at each bus; yet, the IV-ACOPF is mathematically equivalent to the canonical ACOPF formulation. We propose a successive linear programming (SLP) approach to solve the IV-ACOPF, which we refer to as the SLP IV-ACOPF algorithm. The SLP IV-ACOPF leverages commercial LP solvers and can be readily extended and integrated into more complex decision processes, e.g., unit commitment and transmission switching. We demonstrate with the standard MATPOWER test suite an acceptable quality of convergence to a best-known solution and linear scaling of computational time in proportion to network size.


title: Screen Printing of Highly Loaded Silver Inks on Plastic Substrates Using Silicon Stencils

abstract: Screen printing is a potential technique for mass-production of printed electronics; however, improvement in printing resolution is needed for high integration and performance. In this study, screen printing of highly loaded silver ink (77 wt %) on polyimide films is studied using fine-scale silicon stencils with openings ranging from 5 to 50 μm wide. This approach enables printing of high-resolution silver lines with widths as small as 22 μm. The printed silver lines on polyimide exhibit good electrical properties with a resistivity of 5.5×10(-6) Ω cm and excellent bending tolerance for bending radii greater than 5 mm (tensile strains less than 0.75%).


title: CSRR loaded Sierpinski gasket fractal antenna for multiband applications

abstract: The CSRR loaded Sierpinski fractal antenna for multiband applications are presented. This model fairs that the multiband presence of the Sierpinski fractal antenna is a fallout of its fractal nature. The antenna consists of iterations of sierpinski gasket with a complementary split ring resonator. In plan to provoke multi resonant frequency band aspect, these CSRR are implanted back side of the substrate. The simulated results offers a three operating bands, covering C band at 5.72 GHz, Ku band at 14.3 GHz and 16.06 GHz. The size of the CSRR loaded Gasket antenna is 14 mm × 12 mm × 1.6 mm3 printed on FR4 substrate. The parametric review and simulated radiation component are presented.


title: A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios

abstract: Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.


The social approach of knowledge management: the effect on the organizational learning capacity influences


title: NewSQL: Towards Next-Generation Scalable RDBMS for Online Transaction Processing (OLTP) for Big Data Management

abstract: One of the key advances in resolving the big-data problem has been the emergence of an alternative database technology. Today, classic RDBMS are complemented by a rich set of alternative Data Management Systems (DMS) specially designed to handle the volume, variety, velocity and variability ofBig Data collections; these DMS include NoSQL, NewSQL and Search-based systems. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. This paper discusses about NewSQL data management system; and compares with NoSQL and with traditional database system. This paper covers architecture, characteristics, classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list ofpopular NoSQL as well as NewSQL databases in separate categorized tables. This paper compares SQL based RDBMS, NoSQL and NewSQL databases with set of metrics; as well as, addressed some research issues ofNoSQL and NewSQL.