title: Efficient Crawling Through URL Ordering

abstract: In this paper we study in what order a crawler should visit the URLs it has seen, in order to obtain more "important" pages first. Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Web in a reasonable amount of time. We define several importance metrics, ordering schemes, and performance evaluation measures for this problem. We also experimentally evaluate the ordering schemes on the Stanford University Web. Our results show that a crawler with a good ordering scheme can obtain important pages significantly faster than one without.


title: Semantic WordRank: Generating Finer Single-Document Summarizations

abstract: We present Semantic WordRank (SWR), an unsupervised method for generating an extractive summary of a single document. Built on a weighted word graph with semantic and co-occurrence edges, SWR scores sentences using an article-structure-biased PageRank algorithm with a Softplus function adjustment, and promotes topic diversity using spectral subtopic clustering under the Word-Movers-Distance metric. We evaluate SWR on the DUC-02 and SummBank datasets and show that SWR produces better summaries than the state-of-the-art algorithms over DUC-02 under common ROUGE measures. We then show that, under the same measures over SummBank, SWR outperforms each of the three human annotators (aka. judges) and compares favorably with the combined performance of all judges.


title: SAFE: A Neural Survival Analysis Model for Fraud Early Detection

abstract: Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-theart fraud early detection approaches.


title: Determinants of maximal whole‐body fat oxidation in elite cross‐country skiers: Role of skeletal muscle mitochondria

abstract: Elite endurance athletes possess a high capacity for whole-body maximal fat oxidation (MFO). The aim was to investigate the determinants of a high MFO in endurance athletes. The hypotheses were that augmented MFO in endurance athletes is related to concomitantly increments of skeletal muscle mitochondrial volume density (MitoVD ) and mitochondrial fatty acid oxidation (FAOp ), that is, quantitative mitochondrial adaptations as well as intrinsic FAOp per mitochondria, that is, qualitative adaptations. Eight competitive male cross-country skiers and eight untrained controls were compared in the study. A graded exercise test was performed to determine MFO, the intensity where MFO occurs (FatMax ), and V ˙ O 2 Max . Skeletal muscle biopsies were obtained to determine MitoVD (electron microscopy), FAOp , and OXPHOSp (high-resolution respirometry). The following were higher (P < 0.05) in endurance athletes compared to controls: MFO (mean [95% confidence intervals]) (0.60 g/min [0.50-0.70] vs 0.32 [0.24-0.39]), FatMax (46% V ˙ O 2 Max [44-47] vs 35 [34-37]), V ˙ O 2 Max (71 mL/min/kg [69-72] vs 48 [47-49]), MitoVD (7.8% [7.2-8.5] vs 6.0 [5.3-6.8]), FAOp (34 pmol/s/mg muscle ww [27-40] vs 21 [17-25]), and OXPHOSp (108 pmol/s/mg muscle ww [104-112] vs 69 [68-71]). Intrinsic FAOp (4.0 pmol/s/mg muscle w.w/MitoVD [2.7-5.3] vs 3.3 [2.7-3.9]) and OXPHOSp (14 pmol/s/mg muscle ww/MitoVD [13-15] vs 11 [10-13]) were, however, similar in the endurance athletes and untrained controls. MFO and MitoVD correlated (r2 = 0.504, P < 0.05) in the endurance athletes. A strong correlation between MitoVD and MFO suggests that expansion of MitoVD might be rate-limiting for MFO in the endurance athletes. In contrast, intrinsic mitochondrial changes were not associated with augmented MFO.


title: Eager aggregation and lazy aggregation

abstract: Efficient processing of aggregation queries is essential for decision support applications. This paper describes a class of query transformations, called eager aggregation and laty aggregation, that allows a query optimizer to move group-by operations up and down the query tree. Eager aggregation partially pushes a groupby past a join. After a group-by is partially pushed down, we still need to perform the original groupby in the upper query block. Eager aggregation reduces the number of input rows to the join and thus may result in a better overall plan. The reverse transformation, lazy aggregation, pulls a group-by above a join and combines two group-by operations into one. This transformation is typically of interest when an aggregation query references a grouped view (a view containing a groupby). Experimental results show that the technique is very beneficial for queries in the TPC-D benchmark.


title: Biomechanics Measurements in Archery

abstract: The purpose of this study is to measure the biomechanics parameter of the archery sports and correlate to the games performance. Archery becomes as one of the prospect sports to contribute medal in Olympics Games 2016. Therefore, the research in this sport is directly affected to the athlete performance during games tournament. This research is considered as preliminary study to measure the biomechanics parameters for applied to the professional athlete. The biomechanics parameter such as muscle activity, heartbeat, balancing and body posture as well as draw force line were chosen as important parameters related to the athletes performance. The subject was shooting the arrow on the force plate meanwhile the high speed video camera capturing the arrow velocity, body posture and elbow angle. Furthermore, those parameters will correlate to the point of target in order to propose the right technique for the player. The finding in this study will help the coach and athlete to improve the performance of the games, especially for the beginner level to become expert as an elite player.


title: The nursing role in out-patient child and adolescent mental health services

abstract: • Whilst nurses in the UK have been working in out-patient settings in the speciality of child, adolescent and family psychiatry for nearly 20 years, they are frequently unclear about what is their nursing contribution to the multidisciplinary team. ::: ::: ::: ::: • This paper reports a qualitative study of perceptions of the nursing role in child and adolescent mental health teams of 11 clinicians from six different clinics. ::: ::: ::: ::: • Whilst other disciplines were each able to define what was unique about their role, there was found to be no consensus as to what nurses brought by virtue of their nursing background. ::: ::: ::: ::: • There is a risk that if nurses cannot develop a clearer rationale for their role, or be better able to articulate what they do in these teams, that role might be lost.


title: A portable, autonomous, urban reconnaissance robot

abstract: Abstract Portable mobile robots, in the size class of 20 kg or less, could be extremely valuable as autonomous reconnaissance platforms in urban hostage situations and disaster relief. We have developed a prototype urban robot on a novel chassis with articulated tracks that enable stair climbing and scrambling over rubble. Autonomous navigation capabilities of the robot include stereo vision-based obstacle avoidance, visual servoing to user-designated goals, and autonomous vision-guided stair climbing. The system was demonstrated in an urban reconnaissance mission scenario at Fort Sam Houston in October 1999. A two-axis scanning laser rangefinder has been developed and integrated into the robot for indoor mapping and position estimation. This paper describes the robot, its performance in field trials, and some of the technical challenges that remain to enable fieldable urban reconnaissance robots.


title: Racial Disparities in Birth Outcomes Increase with Maternal Age: Recent Data from North Carolina

abstract: BACKGROUND ::: Racial disparities in birth outcomes persist in North Carolina and the United States. We examined patterns of birth outcomes and womens health measures in North Carolina by race and age to portray the largest disparities. We wanted to see if our data were consistent with the "weathering hypothesis," which holds that the health of African American women may begin to deteriorate in early adulthood, with negative effects on birth outcomes. ::: ::: ::: METHODS ::: We conducted a descriptive analysis of 1999-2003 North Carolina live birth and infant death records and 2001-2003 Behavioral Risk Factor Surveillance System survey data. Birth outcome measures examined were low birth weight, very low birth weight, infant mortality neonatal mortality and postneonatal mortality. Womens health measures examined were obesity self-reported health status, high blood pressure, high cholesterol, current smoking, and smoking during pregnancy. Rates for whites and African Americans were compared for each of three age groups. ::: ::: ::: RESULTS ::: Racial disparities in birth outcomes increase with increasing maternal age. African American teens often experience better birth outcomes than older African American women. Racial disparities in measures of womens health also increase with increasing age. ::: ::: ::: CONCLUSIONS ::: Health problems among older African American women of reproductive age may contribute substantially to racial disparities in birth outcomes. Improving the health of older African American women may be an effective strategy to reduce the overall racial disparities in birth outcomes.


title: Deep learning for decentralized parking lot occupancy detection

abstract: We propose an effective CNN architecture for visual parking occupancy detection.The CNN architecture is small enough to run on smart cameras.The proposed solution performs and generalizes better than other SotA approaches.We provide a new training/validation dataset for parking occupancy detection. A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.


title: Trajectory Convolution for Action Recognition

abstract: How to leverage the temporal dimension is a key question in video analysis. Recent work suggests an efficient approach to video feature learning, namely, factorizing 3D convolutions into separate components respectively for spatial and temporal convolutions. The temporal convolution, however, comes with an implicit assumption – the feature maps across time steps are well aligned so that the features at the same locations can be aggregated. This assumption may be overly strong in practical applications, especially in action recognition where the motion of people or objects is a crucial aspect. In this work, we propose a new CNN architecture TrajectoryNet, which incorporates trajectory convolution, a new operation for integrating features along the temporal dimension, to replace the standard temporal convolution. This operation explicitly takes into account the location changes caused by deformation or motion, allowing the visual features to be aggregated along the the motion paths. On two very large-scale action recognition datasets, namely, Something-Something and Kinetics, the proposed network architecture achieves notable improvement over strong baselines.


title: A 191μW BPSK demodulator for data and power telemetry in biomedical implants

abstract: This paper presents a fully digital binary-phase-shift keying (BPSK) demodulator for data and power telemetry. This demodulator recovers BPSK signals by detecting the symbol edge of the digitized received carrier. Parameters of the coupling coils, rectifier DC output, and data rate are taken into consideration in the early design stage. Given a limited coil size and quality factor, the demodulator achieves a data rate of 678kb/s with BER


Hardware Architecture Review of Swarm Robotics System: Self-Reconfigurability, Self-Reassembly, and Self-Replication


title: A direct fingerprint minutiae extraction approach based on convolutional neural networks

abstract: Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of minutiae. In this paper, a novel minutiae extraction approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks. Minutiae can be effectively extracted due to the well designed architectures. Furthermore, the accuracy is guaranteed in that the comprehensive estimate is made to eliminate spurious minutiae. Moreover, a number of implement skills are employed both to avoid overfitting and to improve the robustness. This approach makes a good performance because it not only makes all use of information in fingerprint images but also learns the minutiae patterns from large amounts of data. Comparisons are made with previous works and a widely applied commercial fingerprint identification system. Results show that our approach performs better both in accuracy and robustness.


title: Female waist-to-hip and male waist-to-shoulder ratios as determinants of romantic partner desirability

abstract: This study examines the effects of body shape (women's waist-to-hip ratio and men's waist-to-shoulder ratio) on desirability of a potential romantic partner. In judging desirability, we expected male participants to place more emphasis on female body shape, whereas females would focus more on personality characteristics. Further, we expected that relationship type would moderate the extent to which physical characteristics were valued over personality. Specifically, physical characteristics were expected to be most valued in short-term sexual encounters when compared with long-term relationships. Two hundred and thirty-nine participants (134 females, 105 males; 86% Caucasian) rated the desirability of an opposite-sex target for a date, a one-time sexual encounter, and a serious relationship. All key hypotheses were supported by the data.


title: Database foundations for scalable RDF processing

abstract: As more and more data is provided in RDF format, storing huge amounts of RDF data and efficiently processing queries on such data is becoming increasingly important. The first part of the lecture will introduce state-of-the-art techniques for scalably storing and querying RDF with relational systems, including alternatives for storing RDF, efficient index structures, and query optimization techniques. As centralized RDF repositories have limitations in scalability and failure tolerance, decentralized architectures have been proposed. The second part of the lecture will highlight system architectures and strategies for distributed RDF processing. We cover search engines as well as federated query processing, highlight differences to classic federated database systems, and discuss efficient techniques for distributed query processing in general and for RDF data in particular. Moreover, for the last part of this chapter, we argue that extracting knowledge from the Web is an excellent showcase - and potentially one of the biggest challenges - for the scalable management of uncertain data we have seen so far. The third part of the lecture is thus intended to provide a close-up on current approaches and platforms to make reasoning (e.g., in the form of probabilistic inference) with uncertain RDF data scalable to billions of triples.


title: False recognition and the right frontal lobe: A case study

abstract: Abstract We described a patient, BG, who exhibited a striking pattern of false recognition after an infarction of the right frontal lobe. Seven experiments document the existence of the phenomenon, explore its characteristics, and demonstrate how it can be eliminated. BG showed pathologically high false alarm rates when stimuli were visual words (experiments 1 and 4), auditory words (experiment 2), environmental sounds (experiment 3), pseudowords (experiment 5), and pictures (experiment 7). His false alarms were not merely attributable to the semantic or physical similarity of studied and non-studied items (experiments 4 and 5). However, BG's false recognitions were virtually eliminated by presenting him with categorized stimuli and testing him with new stimuli from non-studied categories (experiments 6 and 7). The results suggest that BG's false alarms may be attributable to an over-reliance on memory for general characteristics of the study episode, along with impaired memory for specific items. The damaged right frontal lobe mechanisms may normally support the monitoring and/or retrieval processes that are necessary for item-specific recognition.


title: Duty-cycling buildings aggressively: The next frontier in HVAC control

abstract: Buildings are known to be the largest consumers of electricity in the United States, and often times the dominant energy consumer is the HVAC system. Despite this fact, in most buildings the HVAC system is run using primitive static control algorithms based on fixed work schedules causing wasted energy during periods of low occupancy. In this paper we present a novel control architecture that uses occupancy sensing to guide the operation of a building HVAC system. We show how we can enable aggressive duty-cycling of building HVAC systems — that is, turn them ON or OFF — to save energy while meeting building performance requirements using inexpensive sensing and control methods. We have deployed our occupancy sensor network across an entire floor of a university building and our data shows several periods of low occupancy with significant opportunities to save energy over normal HVAC schedules. Furthermore, by interfacing with the building Energy Management System (EMS) directly and using real-time occupancy data collected by our occupancy nodes, we measure electrical energy savings of 9.54% to 15.73% and thermal energy savings of 7.59% to 12.85% for the HVAC system by controlling just one floor of our four floor building.


title: Qualitative research. Introducing focus groups.

abstract: This paper introduces focus group methodology, gives advice on group composition, running the groups, and analysing the results. Focus groups have advantages for researchers in the field of health and medicine: they do not discriminate against people who cannot read or write and they can encourage participation from people reluctant to be interviewed on their own or who feel they have nothing to say.


RFID Based Traffic Sign Recognition


title: Tutorial: Text Analytics for Security

abstract: Computing systems that make security decisions often fail to take into account human expectations. This failure occurs because human expectations are typically drawn from in textual sources (e.g., mobile application description and requirements documents) and are hard to extract and codify. Recently, researchers in security and software engineering have begun using text analytics to create initial models of human expectation. In this tutorial, we will provide an introduction to popular techniques and tools of natural language processing (NLP) and text mining, and share our experiences in applying text analytics to security problems. We will also highlight the current challenges of applying these techniques and tools for addressing security problems. We conclude with discussion of future research directions.


title: Understanding social interactions with socially assistive robotics in intergenerational family groups

abstract: We present a pilot study of a socially assistive robot interacting with intergenerational groups. The system is designed to improve the social well-being of older adults by supporting interactions within families. Six intergenerational family groups interacted with the robot in four tablet-based games. Users' behavior during the sessions was used to compare the games and understand how members of different generations and different families interact with the robot. Interviews with users provide insight into users' priorities for in-home deployment of socially assistive robots, as well as preferences about the activities, appearance, and behavior of the robot.


title: Selection of reverse engineering methods for relational databases

abstract: The problem of choosing a method for the reverse engineering of relational database systems is not trivial. Methods have different input requirements, and each legacy system has its particular characteristics that restrict information availability. In this paper, we propose a classification framework based on the method's input requirements, namely: attribute semantics, attribute name consistency, data instances, applications source code, candidate keys, 3rd normal form (3NF), inclusion dependencies and human input. We present a short description of seven reverse engineering methods and analyse the applicability of each method to existing database systems, as well as the possibility of applying different methods to different parts of the system.


title: Secure Information Sharing in an Industrial Internet of Things

abstract: This paper investigates how secure information sharing with external vendors can be achieved in an Industrial Internet of Things (IIoT). It also identifies necessary security requirements for secure information sharing based on identified security challenges stated by the industry. The paper then proposes a roadmap for improving security in IIoT which investigates both short-term and long-term solutions for protecting IIoT devices. The short-term solution is mainly based on integrating existing good practices. The paper also outlines a long term solution for protecting IIoT devices with fine-grained access control for sharing data between external entities that would support cloud-based data storage.


title: Regulation of synchronous generators by means of hydrostatic transmissions

abstract: Summary form only given as follows. In many applications within the engineering world, an isolated generator is needed (e.g. in ships). Diesel units (diesel engine and synchronous generator) are the most common solution However, the diesel engine can be eliminated if the energy from another source (e.g. the prime mover in a ship) is used to move the generator. This is the care for the shaft coupled generator, where the coupling between the mover and the generator is made via a hydrostatic transmission so that the mover can have different speeds and the generator is able to keep a constant frequency. The main problem of this system is the design of a speed governor that makes possible the desired behaviour. In this paper a simulation model is presented in order to analyse the behaviour of this kind of systems and to help in the speed governor design. The model is achieved with a parameter identification process also depicted in the paper. A comparison between simulation results and measurements is made to show the model validity.


title: Silhouette Analysis-Based Gait Recognition for Human Identification

abstract: Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost.


title: Commutation Torque Ripple Suppression Strategy for Brushless DC Motors With a Novel Noninductive Boost Front End

abstract: This paper first presents a novel boost front end simply with a diode, a MOSFET, and a DC-link capacitor. Without extra inductors or other power components, the boost font end could boost the capacitor voltage with the motor stator inductances, thus reducing the influence of the limited DC-link voltage on commutation torque ripple reduction to a large extent. A unified commutation torque ripple suppression strategy is further proposed with the front end adopted based on the analysis about the effects of four switching vectors on motor speed regulation and DC-link capacitor voltage regulation. The proposed strategy can boost the DC-link capacitor voltage via properly selection of switching vectors under the premise of guaranteeing normal speed regulation in noncommutation period, and reduce both the commutation torque ripple and commutation time by two consistent switching vectors with the boosted DC-link capacitor voltage in commutation period. Finally, the proposed method is theoretically analyzed with respect to the capacitance selection and the boot capacity of noninductive boost front end. The correctness of the analysis and the effectiveness of the presented method are validated by the experimental results.


title: The devoted actor’s will to fight and the spiritual dimension of human conflict

abstract: Frontline investigations with fighters against the Islamic State (ISIL or ISIS), combined with multiple online studies, address willingness to fight and die in intergroup conflict. The general focus is on non-utilitarian aspects of human conflict, which combatants themselves deem ‘sacred’ or ‘spiritual’, whether secular or religious. Here we investigate two key components of a theoretical framework we call ‘the devoted actor’—sacred values and identity fusion with a group—to better understand people’s willingness to make costly sacrifices. We reveal three crucial factors: commitment to non-negotiable sacred values and the groups that the actors are wholly fused with; readiness to forsake kin for those values; and perceived spiritual strength of ingroup versus foes as more important than relative material strength. We directly relate expressed willingness for action to behaviour as a check on claims that decisions in extreme conflicts are driven by cost–benefit calculations, which may help to inform policy decisions for the common defense. The study by Gomez et al. of frontline fighters and non-combatants shows that a willingness to fight and die in intergroup conflict is associated with the sacrifice of material concerns for sacred values, and the perceived spiritual strength of in-groups and adversaries.


title: Enhancing data security using DES-based cryptography and DCT-based steganography

abstract: The security and privacy of the data transmitted is an important aspect of the exchange of information on the Internet network. Cryptography and Steganography are two of the most commonly used digital data security techniques. In this research, we proposed the combination of the cryptographic method with Data Encryption Standard (DES) algorithm and the steganographic method with Discrete Cosine Transform (DCT) to develop a digital data security application. The application can be used to secure document data in Word, Excel, Powerpoint or PDF format. Data encrypted with DES algorithm and further hidden in image cover using DCT algorithm. The results showed that the quality of the image that has been inserted (stego-image) is still in a good category with an average PSNR value of 46.9 dB. Also, the experiment results show that the average computational time of 0.75 millisecond/byte, an average size increase of 4.79 times and a success rate of 58%. This research can help solve the problem of data and information security that will be sent through a public network like the internet.


title: Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing

abstract: This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.


title: A Survey on the Edge Computing for the Internet of Things

abstract: The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.


Complex Encounters: Trade Unions, Immigration and Racism


title: MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels.

abstract: Recent studies have discovered that deep networks are capable of memorizing the entire data even when the labels are completely random. Since deep models are trained on big data where labels are often noisy, the ability to overfit noise can lead to poor performance. To overcome the overfitting on corrupted training data, we propose a novel technique to regularize deep networks in the data dimension. This is achieved by learning a neural network called MentorNet to supervise the training of the base network, namely, StudentNet. Our work is inspired by curriculum learning and advances the theory by learning a curriculum from data by neural networks. We demonstrate the efficacy of MentorNet on several benchmarks. Comprehensive experiments show that it is able to significantly improve the generalization performance of the state-of-the-art deep networks on corrupted training data.


Adaptive Path-Integral Approach to Representation Learning and Planning for Dynamical Systems


title: $K$ -Band CMOS Differential and Quadrature Voltage-Controlled Oscillators for Low Phase-Noise and Low-Power Applications

abstract: In this paper, modified circuit topologies of a differential voltage-controlled oscillator (VCO) and a quadrature VCO (QVCO) in a standard bulk 90-nm CMOS process are presented for low dc power and low phase-noise applications. By utilizing current-reuse and transformer-feedback techniques, the proposed VCO and QVCO can be operated at reduced dc power consumption while maintaining extraordinary circuit performance in terms of low phase-noise and low amplitude/phase errors. The VCO circuit topology is investigated to obtain the design procedure. The VCO is further applied to the QVCO design with a bottom-series coupling technique. The coupling network between two differential VCOs and device size are properly designed based on our proposed design methodology to achieve low amplitude and phase errors. Moreover, the VCO and the QVCO are fully characterized with amplitude and phase errors via a four-port vector network analyzer. With a dc power of 3 mW, the VCO exhibits a frequency tuning range from 20.3 to 21.3 GHz, a phase noise of - 116.4 dBc/Hz at 1-MHz offset, a figure-of-merit (FOM) of -198 dBc/Hz, a phase error of 3.8° , and an amplitude error of 0.9 dB. With a dc power of 6 mW, the QVCO demonstrates a phase noise of -117.4 dBc/Hz, a FOM of -195.6 dBc/Hz, a phase error of 4° , and an amplitude error of 0.6 dB. The proposed VCO and QVCO can be compared with the previously reported state-of-the-art VCOs and QVCOs in silicon-based technologies.


title: HOW CUSTOMER PARTICIPATION CAN DRIVE REPURCHASE INTENT

abstract: The purpose of this paper is to examine the impact of customer participation in the service delivery process by designing and testing an empirical model with the customers’ point of view in mind. Data are collected in the context of professional financial insurance services. The proposed model is analyzed with partial least squares (PLS) path modeling in SmartPLS 2.0 software. The results of the study show that customer participation produces positive effects on customer satisfaction and affective commitment through the customer relational value. Affective commitment is a strong predictor of repurchase intent, but no relationship between customer satisfaction and repurchase intent was found. This study suggests that customer participation can be a win-win situation for customers and the service firm. Customers who create relational value with their service providers effectively enjoy their services more and are more likely to build and maintain long-term relationships with their service firm. Our findings highlight the roles of the customer and indicate the heuristic value of viewing customer satisfaction and affective commitment as consequences of customer participation. This can enhance the understanding of how encounters should be designed in order to support employees and improve the co-creation of value.


title: A conceptual framework for serious games and its validation

abstract: This research introduces a conceptual framework for the design of serious games and uses the Technology Acceptance Model (TAM) for its validation. An initial study identified 12 attributes of educational serious games that can be used to support effective learning. These attributes are used in the conceptual framework to support learning and pedagogy in combination with the games. The Serious-Games-TAM was used to confirm that serious games, based on the proposed framework, would both be accepted by the learner and be useful for learning. Validation involved the collection and analysis of data from learners using a specially developed serious game that introduced international students to public transport in Southampton. After completing the game, participants completed a short questionnaire and the data was analysed using structural equation modelling (SEM). The results identified the attributes and combinations of attributes that led the learners to accept and to use the serious game for learning. These findings significantly contribute to helping game designers and educational practitioners design serious games for effective learning.


title: Seizure activity selectively reduces 5-HT1A receptor immunoreactivity in CA1 interneurons in the hippocampus of seizure-prone gerbils

abstract: Since the correlation between the serotonin (5-hydroxytryptamine, 5-HT) system and seizure activity remains to be clarified, we investigated the 5-HT system in the hippocampus of seizure-resistant (SR) and seizure-sensitive (SS) gerbils. There was no difference of the 5-HT system in the hippocampi of young animals (predisposed and juvenile gerbils) in both SR and SS gerbils. 5-HT immunoreactivity in the dorsal raphe nucleus and the median raphe nucleus was also similarly detected in both animal groups. As compared to SR adult gerbils, only 5-HT1A receptor immunoreactivity was selectively reduced in CA1 interneurons within SS adult gerbils. (+/-)-8-hydroxy-2-(di-n-propylamino)tetralin (a 5-HT1A receptor agonist, 1 and 2 mg/kg) markedly reduced paired-pulse inhibition in the CA1 region of SS adult gerbils only. These findings suggest that the selective reduction in 5-HT1A receptor expression on CA1 interneurons of SS adult gerbil may not be developmental defects, but be an acquired compensatory change induced by repeated seizure activity.


title: Design of A CW-SF Ground Penetrating Radar

abstract: Most of Ground Penetrating Radars (GPR) are pulsed system, nevertheless there are a number of applications for which Continuous Wave Step Frequency (CWSF) radar can be advantageous. Generally speaking, CWSF systems are more complex then pulse radar, but they can manage larger bandwidth, have larger time efficiency and better control of the band. Furthermore, power, integration time and step of each single frequency can be controlled. In this paper the authors, that work in the CWSF field since several years, are aimed to give some guidelines and a practical example of CWSF designing.


A Survey on Outlier Detection Methods


title: SE-AKA : A secure and efficient group authentication and key agreement protocol for LTE networks

abstract: To support Evolved Packet System (EPS) in the Long Term Evolution (LTE) networks, the 3rd Generation Partnership Project (3GPP) has proposed an authentication and key agreement (AKA) protocol, named EPS-AKA, which has become an emerging standard for fourth-generation (4G) wireless communications. However, due to the requirement of backward compatibility, EPS-AKA inevitably inherits some defects of its predecessor UMTS-AKA protocol that cannot resist several frequent attacks, i.e., redirection attack, man-in-the-middle attack, and DoS attack. Meanwhile, there are additional security issues associated with the EPS-AKA protocol, i.e., the lack of privacy-preservation and key forward/backward secrecy (KFS/KBS). In addition, there are new challenges with the emergence of group-based communication scenarios in authentication. In this paper, we propose a secure and efficient AKA protocol, called SE-AKA, which can fit in with all of the group authentication scenarios in the LTE networks. Specifically, SE-AKA uses Elliptic Curve Diffie-Hellman (ECDH) to realize KFS/KBS, and it also adopts an asymmetric key cryptosystem to protect users' privacy. For group authentication, it simplifies the whole authentication procedure by computing a group temporary key (GTK). Compared with other authentication protocols, SE-AKA cannot only provide strong security including privacy-preservation and KFS/KBS, but also provide a group authentication mechanism which can effectively authenticate group devices. Extensive security analysis and formal verification by using proverif have shown that the proposed SE-AKA is secure against various malicious attacks. In addition, elaborate performance evaluations in terms of communication, computational and storage overhead also demonstrates that SE-AKA is more efficient than those existing protocols.


title: Challenges for efficient communication in underwater acoustic sensor networks

abstract: Ocean bottom sensor nodes can be used for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the underwater channel is characterized. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed at all layers of the protocol stack. Furthermore, open research issues are discussed and possible solution approaches are outlined.


title: Credit scoring using the clustered support vector machine

abstract: This study introduces the use of the clustered support vector machine (CSVM) for credit scoring.The CSVM has been shown to relax size constraints while remaining highly accurate.The results suggest that the CSVM is a useful alternative to kernel SVM approaches when training datasets get large. This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally.


title: The Prediction of Students’ Academic Performance With Fluid Intelligence in Giving Special Consideration to the Contribution of Learning

abstract: The present study provides a new account of how fluid intelligence influences academic performance. In this account a complex learning component of fluid intelligence tests is proposed to play a major role in predicting academic performance. A sample of 2, 277 secondary school students completed two reasoning tests that were assumed to represent fluid intelligence and standardized math and verbal tests assessing academic performance. The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect. Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component. The link from the learning component to math performance was especially strong. These results indicated that fluid intelligence, which has so far been considered as homogeneous, could be decomposed in such a way that the resulting components showed different properties and contributed differently to the prediction of academic performance. Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.


title: Musicians have better memory than nonmusicians: A meta-analysis

abstract: Background ::: Several studies have found that musicians perform better than nonmusicians in memory tasks, but this is not always the case, and the strength of this apparent advantage is unknown. Here, we conducted a meta-analysis with the aim of clarifying whether musicians perform better than nonmusicians in memory tasks. ::: ::: ::: Methods ::: Education Source; PEP (WEB)—Psychoanalytic Electronic Publishing; Psychology and Behavioral Science (EBSCO); PsycINFO (Ovid); PubMed; ScienceDirect—AllBooks Content (Elsevier API); SCOPUS (Elsevier API); SocINDEX with Full Text (EBSCO) and Google Scholar were searched for eligible studies. The selected studies involved two groups of participants: young adult musicians and nonmusicians. All the studies included memory tasks (loading long-term, short-term or working memory) that contained tonal, verbal or visuospatial stimuli. Three meta-analyses were run separately for long-term memory, short-term memory and working memory. ::: ::: ::: Results ::: We collected 29 studies, including 53 memory tasks. The results showed that musicians performed better than nonmusicians in terms of long-term memory, g = .29, 95% CI (.08–.51), short-term memory, g = .57, 95% CI (.41–.73), and working memory, g = .56, 95% CI (.33–.80). To further explore the data, we included a moderator (the type of stimulus presented, i.e., tonal, verbal or visuospatial), which was found to influence the effect size for short-term and working memory, but not for long-term memory. In terms of short-term and working memory, the musicians’ advantage was large with tonal stimuli, moderate with verbal stimuli, and small or null with visuospatial stimuli. ::: ::: ::: Conclusions ::: The three meta-analyses revealed a small effect size for long-term memory, and a medium effect size for short-term and working memory, suggesting that musicians perform better than nonmusicians in memory tasks. Moreover, the effect of the moderator suggested that, the type of stimuli influences this advantage.


Effects of psychological ownership on teachers’ beliefs about a cloud-based virtual learning environment


Auditory feedback blocks memory benefits of cueing during sleep


title: Abstract Real-Time Imaging] (]]]])]]]–]]] Real-time foreground–background segmentation using codebook model

abstract: We present a real-time algorithm for foreground-background segmentation. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. The codebook representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos. We compared our method with other multimode modeling techniques. In addition to the basic algorithm, two features improving the algorithm are presented-layered modeling/detection and adaptive codebook updating. For performance evaluation, we have applied perturbation detection rate analysis to four background subtraction algorithms and two videos of different types of scenes.


title: RMPE: Regional Multi-person Pose Estimation

abstract: Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve a 17% increase in mAP over the state-of-the-art methods on the MPII (multi person) dataset.Our model and source codes are publicly available.


title: Regulation of skeletal muscle growth by the IGF1-Akt/PKB pathway: insights from genetic models

abstract: A highly conserved signaling pathway involving insulin-like growth factor 1 (IGF1), and a cascade of intracellular components that mediate its effects, plays a major role in the regulation of skeletal muscle growth. A central component in this cascade is the kinase Akt, also called protein kinase B (PKB), which controls both protein synthesis, via the kinases mammalian target of rapamycin (mTOR) and glycogen synthase kinase 3β (GSK3β), and protein degradation, via the transcription factors of the FoxO family. In this paper, we review the composition and function of this pathway in skeletal muscle fibers, focusing on evidence obtained in vivo by transgenic and knockout models and by muscle transient transfection experiments. Although this pathway is essential for muscle growth during development and regeneration, its role in adult muscle response to mechanical load is less clear. A full understanding of the operation of this pathway could help to design molecularly targeted therapeutics aimed at preventing muscle wasting, which occurs in a variety of pathologic contexts and in the course of aging.


title: Consumption of milk and calcium in midlife and the future risk of Parkinson disease

abstract: Objective:To examine the relation between milk and calcium intake in midlife and the risk of Parkinson disease (PD). Methods:Findings are based on dietary intake observed from 1965 to 1968 in 7,504 men ages 45 to 68 in the Honolulu Heart Program. Men were followed for 30 years for incident PD. Results:In the course of follow-up, 128 developed PD (7.1/10,000 person-years). Age-adjusted incidence of PD increased with milk intake from 6.9/10,000 person-years in men who consumed no milk to 14.9/10,000 person-years in men who consumed >16 oz/day (p = 0.017). After further adjustment for dietary and other factors, there was a 2.3-fold excess of PD (95% CI 1.3 to 4.1) in the highest intake group (>16 oz/day) vs those who consumed no milk. The effect of milk consumption on PD was also independent of the intake of calcium. Calcium from dairy and nondairy sources had no apparent relation with the risk of PD. Conclusions:Findings suggest that milk intake is associated with an increased risk of Parkinson disease. Whether observed effects are mediated through nutrients other than calcium or through neurotoxic contaminants warrants further study.


title: Mindfulness Meditation and Aromatherapy to Reduce Stress and Anxiety

abstract: Coping with symptoms of stress and anxiety is a challenging experience and continues to tax the resources and limits of modern conventional health care. In the past 25–30 years, western medical science has turned increasing attention to the psychological and physical correlates of meditation, with mindfulness becoming quite popular in recent years. An extensive literature reviewwas performed; themajority of articles were rated at level ll evidence based on a scale of l–Vll (l being the best evidence). Findings of the literature review support the evidence-based practice project of using mindfulness meditation as an intervention to reduce both stress and anxiety levels (Lee et al., 2007; Leite et al., 2010; Malini, Uddip, & Vivek, 2011; Piet & Esben, 2011; Ramel et al., 2004; Schreiner & Malcolm James, 2008; Toneatto & Nguyen, 2007). There was no prior research using a mindfulness intervention combined with aromatherapy to reduce stress and anxiety for hospitalized psychiatric patients. The purpose of this pilot project was to develop and implement a stress management activity to provide in-patient psychiatric patients with self-improvement techniques to help promote healing, wellness, and to better copewith increased levels of stress andanxiety.Mindfulness meditation was merged with aromatherapy to create aromatherapy enhanced meditation as a unique stress management activity.


title: The significance of heavy pornography involvement for romantic partners: research and clinical implications.

abstract: This article presents a paradigm case portrait of female romantic partners of heavy pornography users. Based on a sample of 100 personal letters, this portrait focuses on their often traumatic discovery of the pornography usage and the significance they attach to this usage for (a) their relationships, (b) their own worth and desirability, and (c) the character of their partners. Finally, we provide a number of therapeutic recommendations for helping these women to think and act more effectively in their very difficult circumstances.


title: Energy-Efficient Query Processing on Embedded CPU-GPU Architectures

abstract: Energy efficiency is a major design and optimization factor for query co-processing of databases in embedded devices. Recently, GPUs of new-generation embedded devices have evolved with the programmability and computational capability for general-purpose applications. Such CPU-GPU architectures offer us opportunities to revisit GPU query co-processing in embedded environments for energy efficiency. In this paper, we experimentally evaluate and analyze the performance and energy consumption of a GPU query co-processor on such hybrid embedded architectures. Specifically, we study four major database operators as micro-benchmarks and evaluate TPC-H queries on CARMA, which has a quad-core ARM Cortex-A9 CPU and a NVIDIA Quadro 1000M GPU. We observe that the CPU delivers both better performance and lower energy consumption than the GPU for simple operators such as selection and aggregation. However, the GPU outperforms the CPU for sort and hash join in terms of both performance and energy consumption. We further show that CPU-GPU query co-processing can be an effective means of energy-efficient query co-processing in embedded systems with proper tuning and optimizations.


title: Dynamic Bayesian Networks for Audio-Visual Speaker Recognition

abstract: The use of visual features in audio-visual speech recognition (AVSR) is justified by both the speech generation mechanism, which is essentially bimodal in audio and visual representation, and by the need for features that are invariant to acoustic noise perturbation. As a result, current AVSR systems demonstrate significant accuracy improvements in environments affected by acoustic noise. In this paper, we describe the use of two statistical models for audio-visual integration, the coupled HMM (CHMM) and the factorial HMM (FHMM), and compare the performance of these models with the existing models used in speaker dependent audio-visual isolated word recognition. The statistical properties of both the CHMM and FHMM allow to model the state asynchrony of the audio and visual observation sequences while preserving their natural correlation over time. In our experiments, the CHMM performs best overall, outperforming all the existing models and the FHMM.


title: Automatic classification of web images as UML diagrams

abstract: Our purpose in this research is to develop a methodology to automatically and efficiently classify web images as UML static diagrams, and to produce a computer tool that implements this function. The tool receives as input a bitmap file (in different formats) and tells whether the image corresponds to a diagram. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19000 web images manually classified by experts. In this work we do not consider the textual contents of the images.


title: An Analysis Study of Improving Brand Awareness and Its Impact on Consumer Behavior Via Media in North Cyprus (A Case Study of Fast Food Restaurants)

abstract: The brand awareness has turned into an important variable that impacts customer's perceptions of a brand. Achievement in brand management arises from understanding and overseeing brand image and loyalty correctly to create strong characteristics that will impact consumers when making on their decisions.This thesis concentrates on the importance of these dimensions (brand awareness, brand loyalty, brand image and consumer behavior) of customer built brand equity in light of consumer's perceptions of a brand. This is focused around the assumption that all these dimensions of customer based-brand image and loyalty will have impact on consumer's perceptions of brand. However, this study aims to discover which among these three dimensions (brand image, brand loyalty and consumer behavior) seem to have the slightest brand equity in restaurants and to find out whether customer based-brand equity differ between fast food with respect to each characteristic of brand awareness, brand image, consumer behaviorand brand loyalty. Brand awareness was treated with independently from different dimensions because of the difference in scale, and moreover media and sorts of media affecting on consumer behavior. An organized survey was constructed to provide answers to research question. In this study, one hundred and fifteen questionnaires, on facilities available in the society, and technology were distributed. The research studied four dimensions of consumer's based-brand equity specifically brand awareness, brand image, perceived quality and brand loyalty. Among the three dimensions, brand loyalty seems to have the minimum brand equity rating by consumers than alternate dimensions. Although, the dimension seem to have impact on consumer perceptions of brand. This paper likewise provides a solution to brand awareness via media store sellers which may help the sellers to promote their products in light of consumer behavior.


title: A Survey of Anomaly Detection Techniques and Hidden Markov Model

abstract: malicious activities performed in the network whether in wired or in wireless. Although there are various techniques implemented for the detection of intrusions but still various techniques are yet to be implemented for the accurate detection of intrusion such that the false positive rate can be minimized. Hidden Markov model is a technique which consists of number of states having initial transition of data and at each transition from one state to another a probability is calculated, this technique can be considered for the detection of intrusions. Here in this paper a complete survey of all the technique implemented for the intrusion detection and their various advantages and disadvantages are discussed such that a new technique can be implemented in future.


title: Use alone or in Combination of Red and Infrared Laser in Skin Wounds.

abstract: A systematic review was conducted covering the action of red laser, infrared and combination of both, with emphasis on cutaneous wound therapy, showing the different settings on parameters such as fluency, power, energy density, time of application, frequency mode and even the type of low-power lasers and their wavelengths. It was observed that in general, the lasers brings good clinical and histological results mainly, but there is not a protocol that defines a dosage of use that has predictability of therapeutic success in repairing these wounds.


title: The time dependent defect spectroscopy (TDDS) for the characterization of the bias temperature instability

abstract: We introduce a new method to analyze the statistical properties of the defects responsible for the ubiquitous recovery behavior following negative bias temperature stress, which we term time dependent defect spectroscopy (TDDS). The TDDS relies on small-area metal-oxide-semiconductor field effect transistors (MOSFETs) where recovery proceeds in discrete steps. Contrary to techniques for the analysis of random telegraph noise (RTN), which only allow to monitor the defect behavior in a rather narrow window, the TDDS can be used to study the capture and emission times of the defects over an extremely wide range. We demonstrate that the recoverable component of NBTI is due to thermally activated hole capture and emission in individual defects with a very wide distribution of time constants, consistent with nonradiative multiphonon theory previously applied to the analysis of RTN. The defects responsible for this process show a number of peculiar features similar to anomalous RTN previously observed in nMOS transistors. A quantitative model is suggested which can explain the bias as well as the temperature dependence of the characteristic time constants. Furthermore, it is shown how the new model naturally explains the various abnormalities observed.


title: How can i help you': comparing engagement classification strategies for a robot bartender

abstract: A robot agent existing in the physical world must be able to understand the social states of the human users it interacts with in order to respond appropriately. We compared two implemented methods for estimating the engagement state of customers for a robot bartender based on low-level sensor data: a rule-based version derived from the analysis of human behaviour in real bars, and a trained version using supervised learning on a labelled multimodal corpus. We first compared the two implementations using cross-validation on real sensor data and found that nearly all classifier types significantly outperformed the rule-based classifier. We also carried out feature selection to see which sensor features were the most informative for the classification task, and found that the position of the head and hands were relevant, but that the torso orientation was not. Finally, we performed a user study comparing the ability of the two classifiers to detect the intended user engagement of actual customers of the robot bartender; this study found that the trained classifier was faster at detecting initial intended user engagement, but that the rule-based classifier was more stable.


title: Object characterization from spectral data using nonnegative factorization and information theory

abstract: The identification and classification of non-imaging space objects, and ultimately the determination of their shape, function, and status, is an important but difficult problem still to be resolved. While ground-based telescopes with adaptive optics technology have been able to produce high-resolution images for a variety of spaced-based objects, current telescope technology is limited for objects in orbits beyond 1,000 km altitude, such as geosynchronous satellites (approx. 36,000 km orbit). An interesting and promising approach to circumvent this limitation is to collect wavelength-resolved spectral reflectance data, rather than spatially-resolved data, which can then be used to determine information such as material composition of an object. Current work has been focused on the determination of material composition (fractional abundances) from spectral traces when the types or classes of composing materials are known a priori. In this paper, we extend previous work to determine not only fractional abundances, but also the classes of composing materials that make up the object, called endmembers. Endmembers are selected using information-theoretic methods. We employ non-negative matrix factorization algorithms for unmixing of spectral reflectance data to find endmember candidates and regularized inverse problem methods for determining corresponding fractional abundances. Promising numerical results are presented using laboratory and simulated datasets.


State-of-the-art Design Pattern Approaches - An Overview


title: Genome-wide prediction of cis-regulatory regions using supervised deep learning methods

abstract: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.


VLM 2 : A Very Lightweight Mobile Multicast System For Wireless Sensor Networks


title: Instance-based learning algorithms

abstract: Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.


title: Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients

abstract: In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a small number of agents. When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these methods do not scale well. To address this, we propose casting the multi-agent reinforcement learning problem as a distributed optimization problem. Our algorithm assumes that for multi-agent settings, policies of individual agents in a given population live close to each other in parameter space and can be approximated by a single policy. With this simple assumption, we show our algorithm to be extremely effective for reinforcement learning in multi-agent settings. We demonstrate its effectiveness against existing comparable approaches on co-operative and competitive tasks.


title: Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data

abstract: The tree structure is one of the most powerful structures for data organization. An efficient learning framework for transforming tree-structured data into vectorial representations is presented. First, in attempting to uncover the global discriminative information of child nodes hidden at the same level of all of the trees, a clustering technique can be adopted for allocating children into different clusters, which are used to formulate the components of a vector. Moreover, a locality-sensitive reconstruction method is introduced to model a process, in which each parent node is assumed to be reconstructed by its children. The resulting reconstruction coefficients are reversely transformed into complementary coefficients, which are utilized for locally weighting the components of the vector. A new vector is formulated by concatenating the original parent node vector and the learned vector from its children. This new vector for each parent node is inputted into the learning process of formulating vectorial representation at the upper level of the tree. This recursive process concludes when a vectorial representation is achieved for the entire tree. Our method is examined in two applications: book author recommendations and content-based image retrieval. Extensive experimental results demonstrate the effectiveness of the proposed method for transforming tree-structured data into vectors.


title: Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching

abstract: The scale-invariant feature transform algorithm and its many variants are widely used in feature-based remote sensing image registration. However, it may be difficult to find enough correct correspondences for remote image pairs in some cases that exhibit a significant difference in intensity mapping. In this letter, a new gradient definition is introduced to overcome the difference of image intensity between the remote image pairs. Then, an enhanced feature matching method by combining the position, scale, and orientation of each keypoint is introduced to increase the number of correct correspondences. The proposed algorithm is tested on multispectral and multisensor remote sensing images. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the number of correct correspondences and aligning accuracy.


title: Empirical research in requirements engineering: trends and opportunities

abstract: Requirements engineering (RE) being a foundation of software development has gained a great recognition in the recent era of prevailing software industry. A number of journals and conferences have published a great amount of RE research in terms of various tools, techniques, methods, and frameworks, with a variety of processes applicable in different software development domains. The plethora of empirical RE research needs to be synthesized to identify trends and future research directions. To represent a state-of-the-art of requirements engineering, along with various trends and opportunities of empirical RE research, we conducted a systematic mapping study to synthesize the empirical work done in RE. We used four major databases IEEE, ScienceDirect, SpringerLink and ACM and Identified 270 primary studies till the year 2012. An analysis of the data extracted from primary studies shows that the empirical research work in RE is on the increase since the year 2000. The requirements elicitation with 22 % of the total studies, requirements analysis with 19 % and RE process with 17 % are the major focus areas of empirical RE research. Non-functional requirements were found to be the most researched emerging area. The empirical work in the sub-area of requirements validation and verification is little and has a decreasing trend. The majority of the studies (50 %) used a case study research method followed by experiments (28 %), whereas the experience reports are few (6 %). A common trend in almost all RE sub-areas is about proposing new interventions. The leading intervention types are guidelines, techniques and processes. The interest in RE empirical research is on the rise as whole. However, requirements validation and verification area, despite its recognized importance, lacks empirical research at present. Furthermore, requirements evolution and privacy requirements also have little empirical research. These RE sub-areas need the attention of researchers for more empirical research. At present, the focus of empirical RE research is more about proposing new interventions. In future, there is a need to replicate existing studies as well to evaluate the RE interventions in more real contexts and scenarios. The practitioners’ involvement in RE empirical research needs to be increased so that they share their experiences of using different RE interventions and also inform us about the current requirements-related challenges and issues that they face in their work.


title: Does a presentation’s medium affect its message? PowerPoint, Prezi, and oral presentations

abstract: Despite the prevalence of PowerPoint in professional and educational presentations, surprisingly little is known about how effective such presentations are. All else being equal, are PowerPoint presentations better than purely oral presentations or those that use alternative software tools? To address this question we recreated a real-world business scenario in which individuals presented to a corporate board. Participants (playing the role of the presenter) were randomly assigned to create PowerPoint, Prezi, or oral presentations, and then actually delivered the presentation live to other participants (playing the role of corporate executives). Across two experiments and on a variety of dimensions, participants evaluated PowerPoint presentations comparably to oral presentations, but evaluated Prezi presentations more favorably than both PowerPoint and oral presentations. There was some evidence that participants who viewed different types of presentations came to different conclusions about the business scenario, but no evidence that they remembered or comprehended the scenario differently. We conclude that the observed effects of presentation format are not merely the result of novelty, bias, experimenter-, or software-specific characteristics, but instead reveal a communication preference for using the panning-and-zooming animations that characterize Prezi presentations.


title: CuSha: vertex-centric graph processing on GPUs

abstract: Vertex-centric graph processing is employed by many popular algorithms (e.g., PageRank) due to its simplicity and efficient use of asynchronous parallelism. The high compute power provided by SIMT architecture presents an opportunity for accelerating these algorithms using GPUs. Prior works of graph processing on a GPU employ Compressed Sparse Row (CSR) form for its space-efficiency; however, CSR suffers from irregular memory accesses and GPU underutilization that limit its performance. In this paper, we present CuSha, a CUDA-based graph processing framework that overcomes the above obstacle via use of two novel graph representations: G-Shards and Concatenated Windows (CW). G-Shards uses a concept recently introduced for non-GPU systems that organizes a graph into autonomous sets of ordered edges called shards. CuSha's mapping of GPU hardware resources on to shards allows fully coalesced memory accesses. CW is a novel representation that enhances the use of shards to achieve higher GPU utilization for processing sparse graphs. Finally, CuSha fully utilizes the GPU power by processing multiple shards in parallel on GPU's streaming multiprocessors. For ease of programming, CuSha allows the user to define the vertex-centric computation and plug it into its framework for parallel processing of large graphs. Our experiments show that CuSha provides significant speedups over the state-of-the-art CSR-based virtual warp-centric method for processing graphs on GPUs.


title: Deep Graph Infomax

abstract: We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.


title: Effect of probiotic treatment on the clinical course, intestinal microbiome, and toxigenic Clostridium perfringens in dogs with acute hemorrhagic diarrhea

abstract: INTRODUCTION ::: The impact of probiotics on dogs with acute hemorrhagic diarrhea syndrome (AHDS) has not been evaluated so far. The study aim was to assess the effect of probiotic treatment on the clinical course, intestinal microbiome, and toxigenic Clostridium perfringens in dogs with AHDS in a prospective, placebo-controlled, blinded trial. ::: ::: ::: METHODS ::: Twenty-five dogs with AHDS with no signs of sepsis were randomly divided into a probiotic (PRO; Visbiome, ExeGi Pharma) and placebo group (PLAC). Treatment was administered for 21 days without antibiotics. Clinical signs were evaluated daily from day 0 to day 8. Key bacterial taxa, C. perfringens encoding NetF toxin and enterotoxin were assessed on days 0, 7, 21. ::: ::: ::: RESULTS ::: Both groups showed a rapid clinical improvement. In PRO a significant clinical recovery was observed on day 3 (p = 0.008), while in PLAC it was observed on day 4 (p = 0.002) compared to day 0. Abundance of Blautia (p<0.001) and Faecalibacterium (p = 0.035) was significantly higher in PRO on day 7 compared to day 0, while in PLAC the abundance of Faecalibacterium was not significantly higher on any study day and Blautia (p = 0.016) was only significantly higher on day 21 compared to day 0. Abundance of C. perfringens was significantly lower on day 7 (p = 0.011) compared to day 0 in PRO but not in PLAC. Enterotoxin genes were significantly lower in PRO on day 21 (p = 0.028) compared to PLAC. Fecal samples of 57% of all dogs were positive for netF toxin genes on day 0 and the abundance was significantly lower on day 7 compared to day 0 in PRO (p = 0.016) and PLAC (p = 0.031). ::: ::: ::: CONCLUSION ::: The probiotic treatment was associated with an accelerated normalization of the intestinal microbiome. Dogs with aseptic AHDS showed a rapid decrease of netF toxin genes and fast clinical recovery in both groups under symptomatic treatment without antibiotics.


title: Sensorimotor synchronization: A review of recent research (2006–2012)

abstract: Sensorimotor synchronization (SMS) is the coordination of rhythmic movement with an external rhythm, ranging from finger tapping in time with a metronome to musical ensemble performance. An earlier review (Repp, 2005) covered tapping studies; two additional reviews (Repp, 2006a, b) focused on music performance and on rate limits of SMS, respectively. The present article supplements and extends these earlier reviews by surveying more recent research in what appears to be a burgeoning field. The article comprises four parts, dealing with (1) conventional tapping studies, (2) other forms of moving in synchrony with external rhythms (including dance and nonhuman animals’ synchronization abilities), (3) interpersonal synchronization (including musical ensemble performance), and (4) the neuroscience of SMS. It is evident that much new knowledge about SMS has been acquired in the last 7 years.


title: Small business survival and sample selection bias

abstract: Analyses of small business and the factors affecting their survival are fairly common in the research literature. The level of research interest may stem from the fact that in the US, only about half of all new small businesses survive after 4 years (Headd 2003). However, research attempting to understand the phenomenon that employs data using only information from and about surviving firms may lead to erroneous conclusions regarding the factors that influence firm survival and failure. In this paper, we provide evidence that omitted information about the firms that disappear from the research data over time leads to biased coefficient estimates. Comparing the Heckman two-step estimation approach of switching regression models to a semi-parametric Cox hazard model, the Accelerated Failure Time (AFT) model, we conclude that the Cox ATF approach is the most appropriate model for firm survival analysis.


title: Electronic white cane for blind people navigation assistance

abstract: In modern daily life people need to move, whether in business or leisure, sightseeing or addressing a meeting. Often this is done in familiar environments, but in some cases we need to find our way in unfamiliar scenarios. Visual impairment is a factor that greatly reduces mobility. Currently, the most widespread and used means by the visually impaired people are the white stick and the guide dog; however both present some limitations. With the recent advances in inclusive technology it is possible to extend the support given to people with visual impairment during their mobility. In this context we propose a system, named SmartVision, whose global objective is to give blind users the ability to move around in unfamiliar environments, whether indoor or outdoor, through a user friendly interface that is fed by a geographic information system (GIS). In this paper we propose the development of an electronic white cane that helps moving around, in both indoor and outdoor environments, providing contextualized geographical information using RFID technology.


title: IEEE 802.11ah: A Long Range 802.11 WLAN at Sub 1 GHz

abstract: IEEE 802.11ah is an emerging Wireless LAN (WLAN) standard that defines a WLAN system operating at sub 1 GHz license-exempt bands. Thanks to the favorable propagation characteristics of the low frequency spectra, 802.11ah can provide much improved transmission range compared with the conventional 802.11 WLANs operating at 2.4 GHz and 5 GHz bands. 802.11ah can be used for various purposes including large scale sensor networks, extended range hotspot, and outdoor Wi-Fi for cellular traffic offloading, whereas the available bandwidth is relatively narrow. In this paper, we give a technical overview of 802.11ah Physical (PHY) layer and Medium Access Control (MAC) layer. For the 802.11ah PHY, which is designed based on the down-clocked operation of IEEE 802.11ac’s PHY layer, we describe its channelization and transmission modes. Besides, 802.11ah MAC layer has adopted some enhancements to fulfill the expected system requirements.These enhancements include the improvement of power saving features, support of large number of stations, efficient medium access mechanisms and throughput enhancements by greater compactness of various frame formats. Through the numerical analysis, we evaluate the transmission range for indoor and outdoor environments and the theoretical throughput with newly defined channel access mechanisms.


title: Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints

abstract: In recent years, with the upgrading of mobile positioning and the popularity of smart devices, location related research gets a lot of attentions. One of popular issues is the trip planning problem. Although many related scientific or technical literature have been proposed, most of them focused only on tourist attraction recommendation or arrangement meeting some user demands. In fact, to grasp the huge tourism opportunities, more and more tour operators design tourist packages and provide to users. Generally, tourist packages have many advantages such as cheaper ticket price and higher transportation convenience. However, researches on trip planning combining tourist packages have not been mentioned in the past studies. In this research, we present a new approach named Package-Attraction-based Trip Planner (PAT-Planner) to simultaneously combine tourist packages and tourist attractions for personalized trip planning satisfying users' travel constraints. In PAT-Planner, we first based on user preferences and temporal characteristics to design a Score Inference Model for respectively measuring the score of a tourist package or tourist attraction. Then, we develop the Hybrid Trip-Mine algorithm meeting user travel constraints for personalized trip planning. Besides, we further propose two improvement strategies, namely Score Estimation and Score Bound Tightening, based on Hybrid Trip-Mine to speed up the trip planning efficiency. As far as we know, our study is the first attempt to simultaneously combine tourist packages and tourist attractions on trip planning problem. Through a series of experimental evaluations and case studies using the collected Gowalla datasets, PAT-Planner demonstrates excellent planning effects.


title: Systematic Generalization: What Is Required and Can It Be Learned?

abstract: Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We find that end-to-end methods from prior work often learn inappropriate layouts or parametrizations that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.


title: PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding

abstract: Despite the fact that many 3D human activity benchmarks being proposed, most existing action datasets focus on the action recognition tasks for the segmented videos. There is a lack of standard large-scale benchmarks, especially for current popular data-hungry deep learning based methods. In this paper, we introduce a new large scale benchmark (PKU-MMD) for continuous multi-modality 3D human action understanding and cover a wide range of complex human activities with well annotated information. PKU-MMD contains 1076 long video sequences in 51 action categories, performed by 66 subjects in three camera views. It contains almost 20,000 action instances and 5.4 million frames in total. Our dataset also provides multi-modality data sources, including RGB, depth, Infrared Radiation and Skeleton. With different modalities, we conduct extensive experiments on our dataset in terms of two scenarios and evaluate different methods by various metrics, including a new proposed evaluation protocol 2D-AP. We believe this large-scale dataset will benefit future researches on action detection for the community.


title: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

abstract: We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.


title: Introductions and Questions: Rhetorical Strategies That Elicit Response in Online Communities

abstract: Online communities allow millions of people who would never meet in person to interact. People join web-based discussion boards, email lists, and chat rooms for friendship, social support, entertainment, and information on technical, health, and leisure activities [24]. And they do so in droves. One of the earliest networks of online communities, Usenet, had over nine million unique contributors, 250 million messages, and approximately 200,000 active groups in 2003 [27], while the newer My Space, founded in 2003, attracts a quarter million new members every day [27].


title: Serverless computing: economic and architectural impact

abstract: Amazon Web Services unveiled their "Lambda" platform in late 2014. Since then, each of the major cloud computing infrastructure providers has released services supporting a similar style of deployment and operation, where rather than deploying and running monolithic services, or dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. These technologies are gathered together under the marketing term "serverless" and the providers suggest that they have the potential to significantly change how client/server applications are designed, developed and operated. This paper presents two case industrial studies of early adopters, showing how migrating an application to the Lambda deployment architecture reduced hosting costs - by between 66% and 95% - and discusses how further adoption of this trend might influence common software architecture design practices.


title: A low-voltage low-power Frequency-to-voltage converter for VCO feedback linearization

abstract: A Frequency-to-voltage converter (FVC) for use in a system for feedback linearization of VCOs is presented. The VCO linearization is intended for use in an ADC application where high resolution, low power consumption, and low voltage operation is required. These requirements places stringent demands on the performance of the FVC. The proposed implementation of the FVC is a good fit for digital CMOS technology, and the system is tunable across PVT corners. Results from transistor level simulations using a 90 nm CMOS process are presented, showing a signal-to-noise and distortion ratio (SINAD) of 76.43 dB while consuming 77.6 µW from a 1.2 V supply.


title: An Optimal and Progressive Approach to Online Search of Top-K Influential Communities

abstract: Community search over large graphs is a fundamental problem in graph analysis. Recent studies propose to compute top-k influential communities, where each reported community not only is a cohesive subgraph but also has a high influence value. The existing approaches to the problem of top-k influential community search can be categorized as index-based algorithms and online search algorithms without indexes. The index-based algorithms, although being very efficient in conducting community searches, need to pre-compute a special-purpose index and only work for one built-in vertex weight vector. In this paper, we investigate on-line search approaches and propose an instance-optimal algorithm LocalSearch whose time complexity is linearly proportional to the size of the smallest subgraph that a correct algorithm needs to access without indexes. In addition, we also propose techniques to make LocalSearch progressively compute and report the communities in decreasing influence value order such that k does not need to be specified. Moreover, we extend our framework to the general case of top-k influential community search regarding other cohesiveness measures. Extensive empirical studies on real graphs demonstrate that our algorithms outperform the existing online search algorithms by several orders of magnitude.


title: Magnetic vector potential based model for eddy-current loss calculation in round-wire planar windings

abstract: An analytical development of the magnetic vector potential is used to investigate the losses in round-wire planar windings. The current distribution in wires is affected by the skin effect and the field created by adjacent wires (proximity effect). This field depends on the current distribution in conductors, resulting in a closed-form problem. In this paper, we obtain the vector potential outside a conductor to estimate the effect of induced currents in the field shape over the neighboring conductors. We use the results to calculate the losses in planar windings such as those in domestic induction heaters. We obtain an equivalent resistance representing the losses in windings and compare it with measurements. This solution provides an accurate analytical approach to modeling the losses in close-packed windings


title: Enriching domain ontologies using question-answer datasets

abstract: Ontologies are knowledge representation structures that are used to model a domain in the form of concepts, entities and relations between them. Existing works on domain ontology enrichment in the literature mainly make use of the web corpus. Community generated Question-Answer data, inspite of being a rich source of information on various topics, has not been exploited much for the purpose of ontology enrichment. Such datasets especially provide good scope to extract new entities and relations and hence help to broaden the coverage of the domain ontology under consideration. In this paper, we propose a novel approach to extract triples from Question-Answer pairs for the purpose of ontology enrichment, particularly focussing upon T-Box enrichment. Some initial experiments have been conducted on two domain ontologies and the preliminary results obtained reveal the potential of the system to convert Question-Answer pairs to meaningful triples that can be added to the ontologies, thus enhancing the quality of the ontologies.