title: Motivations, work–family enrichment and job satisfaction: an indirect effects model

abstract: Self Determination Theory (SDT) recognises that people may experience their motivations as either coming from within themselves (autonomous) or from outside of themselves (controlled). Unlike traditional motivation processes, SDT makes distinctions between underling regulatory motivational processes. On one side, amotivation reflects a lack of any motivation towards work, whereas external regulation and introjected regulation reflect controlled motivation where they are controlled by external forces, such as work for pay or prestige. At the other end of the continuum, identified regulation, integrated regulation and intrinsic motivation reflect autonomous motivation where an employee is internally motivated towards their work (e.g. work activities being viewed as meaningful). The following study explores the six dimensions of motivation on a sample of 386 New Zealand managers towards work-family and family-work enrichment and job satisfaction using SEM. ::: ::: The data fit a partial mediation model best. The model showed that autonomous motivation dimensions influenced job satisfaction only indirectly through enrichment rather than as a direct predictor. Intrinsic motivation was found to be significantly and positively related to family-work enrichment, while integrated regulation and identified regulation was also significantly and positively related to work-family enrichment. However, controlled motivation dimensions were found to both directly influence job satisfaction and enrichment dimensions. Amotivation was significantly and negatively related to job satisfaction, while external regulation was significantly and negatively related to job satisfaction and family-work enrichment. Both work-family and family-work enrichment positively influenced job satisfaction. Overall, the six motivation dimensions accounted for moderate amounts of variance towards family-work enrichment (12%) and work-family enrichment (15%), while a sizeable 25% job satisfaction. When enrichment is included, the model accounts for 30% of the variance towards job satisfaction. Overall, motivation influenced enrichment and job satisfaction as expected, with motivation influencing job satisfaction both directly and indirectly through enrichment dimensions. This study shows the importance that motivation can play towards enhancing enrichment and job satisfaction at least amongst managers.


title: A Theoretical Framework for Information Systems Portfolio Management

abstract: Information systems portfolio management is a tool that is increasingly being used by large organizations to keep track of IT projects and align those projects with organizational strategy. This paper is an initial attempt to provide a theoretical lens with which to view the IS portfolio management phenomenon. Contingency theory, modern portfolio theory, and absorptive capacity serve as theoretical perspectives from which to examine IS portfolio management.


title: Adaptive Large Margin Training for Multilabel Classification

abstract: Multilabel classification is a central problem in many areas of data analysis, including text and multimedia categorization, where individual data objects need to be assigned multiple labels. A key challenge in these tasks is to learn a classifier that can properly exploit label correlations without requiring exponential enumeration of label subsets during training or testing. We investigate novel loss functions for multilabel training within a large margin framework—identifying a simple alternative that yields improved generalization while still allowing efficient training. We furthermore show how co-variances between the label models can be learned simultaneously with the classification model itself, in a jointly convex formulation, without compromising scalability. The resulting combination yields state of the art accuracy in multilabel webpage classification.


title: Grounded theory research: literature reviewing and reflexivity.

abstract: AIM ::: This paper is a report of a discussion of the arguments surrounding the role of the initial literature review in grounded theory. ::: ::: ::: BACKGROUND ::: Researchers new to grounded theory may find themselves confused about the literature review, something we ourselves experienced, pointing to the need for clarity about use of the literature in grounded theory to help guide others about to embark on similar research journeys. ::: ::: ::: DISCUSSION ::: The arguments for and against the use of a substantial topic-related initial literature review in a grounded theory study are discussed, giving examples from our own studies. The use of theoretically sampled literature and the necessity for reflexivity are also discussed. Reflexivity is viewed as the explicit quest to limit researcher effects on the data by awareness of self, something seen as integral both to the process of data collection and the constant comparison method essential to grounded theory. ::: ::: ::: CONCLUSION ::: A researcher who is close to the field may already be theoretically sensitized and familiar with the literature on the study topic. Use of literature or any other preknowledge should not prevent a grounded theory arising from the inductive-deductive interplay which is at the heart of this method. Reflexivity is needed to prevent prior knowledge distorting the researcher's perceptions of the data.


title: MLaaS: Machine Learning as a Service

abstract: The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time.


title: Malware detection on Android smartphones using API class and machine learning

abstract: This paper proposes a (new) method to detect malware in Android smartphones using API (application programming interface) classes. We use machine learning to classify whether an application is benign or malware. Furthermore, we compare classification precision rate from machine learning. This research uses 51 APIs package classes from 16 APIs classes and employs cross validation and percentage split test to classify benign and malware using Random Forest, J48, and Support Vector Machine algorithms. We use 412 total application samples (205 benign, 207 malware). We obtain that the classification precision average is 91.9%.


title: Prefrontal space ratio: comparison between trisomy 21 and euploid fetuses in the second trimester

abstract: Objective To evaluate a novel ultrasound measurement, the prefrontal space ratio (PFSR), in second-trimester trisomy 21 and euploid fetuses. Methods Stored three-dimensional volumes of fetal profiles from 26 trisomy 21 fetuses and 90 euploid fetuses at 15–25 weeks’ gestation were examined. A line was drawn between the leading edge of the mandible and the maxilla (MM line) and extended in front of the forehead. The ratio of the distance between the leading edge of the skull and that of the skin (d1) to the distance between the skin and the point where the MM line was intercepted (d2) was calculated (d2/d1). The distributions of PFSR in trisomy 21 and euploid fetuses were compared, and the relationship with gestational age in each group was evaluated by Spearman’s rank correlation coefficient (rs). Results The PFSR in trisomy 21 fetuses (mean, 0.36; range, 0–0.81) was significantly lower than in euploid fetuses (mean, 1.48; range, 0.85–2.95; P < 0.001 (Mann–Whitney U-test)). There was no significant association between PFSR and gestational age in either trisomy 21 (rs = 0.25; 95% CI, −0.15 to 0.58) or euploid (rs = 0.06; 95% CI, −0.15 to 0.27) fetuses. Conclusion The PFSR appears to be a highly sensitive and specific marker of trisomy 21 in the second trimester of pregnancy. Copyright  2012 ISUOG. Published by John Wiley & Sons, Ltd.


title: A retrospective audit of the outcome of composites used to restore worn teeth

abstract: Objectives The aim of this audit was to monitor the outcome of composite restorations placed at an increased vertical dimension in patients with severe tooth wear. Methods This convenience sample of patients were treated by 11 specialist trainees in prosthodontics, and restored with direct composites. Exclusion criteria included bruxism, poor medical health and a preference for monitoring rather than intervention. The restorations were placed between 2012 and 2016 and were placed over more than one appointment and the outcome monitored for up to 14 months. Failure was assessed at a binary level, either success or failure (minor or major). Results A total of 35 patients with a mean age of 45 years (range 24–86), 27 of whom were male, received 251 restorations placed from November 2012 to November 2016. The patients had a mean of 11.51 (range 4 to 16) occluding pairs of teeth. There was a total of 40 restoration failures (17%) which was an 83% success rate based on the total number of restorations. For the patient-based data, 14 patients (39%) had no chips or bulk factures while 22 (61%) patients had failures, of which 60% were chips and 40% bulk fractures. Conclusion Restoration of worn teeth with composites is associated with a high incidence of fractures. Clinical significance The restoration of worn teeth with composite can involve regular maintenance following fractures and patients need to be aware of this when giving consent.


Chapter 1 Bufferless and Minimally-Buffered Deflection Routing


title: Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization

abstract: The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that clinical researchers need or use. Existing phenotyping approaches typically require labor intensive supervision from medical experts. We propose Marble, a novel sparse non-negative tensor factorization method to derive phenotype candidates with virtually no human supervision. Marble decomposes the observed tensor into two terms, a bias tensor and an interaction tensor. The bias tensor represents the baseline characteristics common amongst the overall population and the interaction tensor defines the phenotypes. We demonstrate the capability of our proposed model on both simulated and patient data from a publicly available clinical database. Our results show that Marble derived phenotypes provide at least a 42.8% reduction in the number of non-zero element and also retains predictive power for classification purposes. Furthermore, the resulting phenotypes and baseline characteristics from real EHR data are consistent with known characteristics of the patient population. Thus it can potentially be used to rapidly characterize, predict, and manage a large number of diseases, thereby promising a novel, data-driven solution that can benefit very large segments of the population.


title: Rumor Propagation in Online Social Networks Like Twitter -- A Simulation Study

abstract: Rumor has drawn much of the attention of researchers, considering its importance and influence, as well as its complexity. Under different circumstances, the process and results of rumor spreading are also different. The method of agent-based modeling is considered a good way to look into the process of rumor propagation. In this study, we put the theme under the circumstances of online social networks like twitter, which is different from traditional social network websites. Assuming that the structure of these websites is a kind of scale free network, we build a specific agent-based model using Net Logo based on the propagation model of SIR. Taking consideration of the unique property of twitter-like websites in information flow, max spread time and the mechanism of people believing the rumor, we draw the conclusion that compared to the traditional online social networks, the rumor propagation in twitter-like websites is of more efficiency, unpredictability, and a tendency to start the rumor again.


title: DoS attack-tolerant TESLA-based broadcast authentication protocol in Internet of Things

abstract: The Internet of Things (IoTs) is an emerging concept referring to networked everyday objects that interconnect to each other via wireless sensors attached to them. TESLA is a source authentication protocol for the broadcast network. Scalability of TESLA is limited by distribution of its unicast-based initial parameter. Low energy consumption version of TESLA is μTESLA, which is designed for wireless sensor network (WSN), while cannot tolerate DoS attack. TESLA++ is the DoS-tolerant version and is designed for VANET. TESLA++ cannot be accepted by WSN because of its higher consumption of power. To realize secure and robust DoS attack in the hybrid-vehicle-sensor network, we provide a TESLA-based protocol against DoS attack with a lower consumption of power. Analysis results demonstrate that using our protocol is better than using μTESLA or TELSA++, respectively.


title: Mutual Interference of Millimeter-Wave Radar Systems

abstract: This paper examines the probability that any millimeter-wave radar systems will interfere mutually by considering spatial, temporal, and operational frequency-related overlaps. It examines the nature and magnitude of the interference under different conditions and for different sensor types before concluding that in an overlapping frequency band, the probability that interference will occur is high. It goes on to demonstrate that, though there are some forms of interference that can be identified and controlled, there are others which are impossible to isolate, resulting in degraded target detection performance and tracking


title: Description of the thermo-mechanical properties of a Sn-based solder alloy by a unified viscoplastic material model for Finite Element calculations

abstract: Automotive electronic devices are exposed to substantially harsher thermo-mechanical loads compared to commercial consumer electronic products. As a consequence, solder joints carrying out the electrical interconnection between the components undergo deformation and degradation under thermal cycling, which can determine the lifetime of the electronic assembly in long term operation. In the past decade, lifetime prediction methods for solder joints based on finite element (FE) simulations are increasingly employed in the process of product design. However, constitutive FE models for solder alloys capable of describing their mechanical behavior at the relevant conditions of automotive applications are still not widely established. Here, we employ a unified viscoplastic material model initially proposed by Chaboche et al. in order to address the mechanical properties of an as-casted Sn-based solder alloy under a cyclic mechanical load. Extensive experimental investigations at temperatures from −40°C up to +150°C reveal a complex nonlinear interplay between hardening, recovery and thermally activated inelastic deformation processes in the material studied. We identified the necessary constitutive model terms and performed parameter calibration according to their specific functionality. A very good agreement between the numerical calculations and experimental data is achieved, which renders the constitutive model used a very promising approach for a wide use in FE simulations of lead-free solder alloys.


title: Analysis and control of multi-level dual active bridge DC-DC converter

abstract: Dual Active Bridge (DAB) dc-dc converter finds applications in the intermediate high frequency link power conversion stage of a high-power solid state transformer. In this paper, a multilevel DAB dc-dc converter is proposed. A three-level neutral point clamped (NPC) inverter topology has been used in the high voltage bridge which enables semiconductor switches to be operated in higher voltage range without using cascaded bridges and multiple DAB converters. Modulation strategy to control power flow between the active bridges with one of the bridges switching at multi-level is proposed in this paper. Steady-state analysis of the converter with the proposed modulation scheme is presented along with simulation results to augment the analysis.


title: Use of opposition method in the test of high-power electronic converters

abstract: The test and the characterization of medium or high-power electronic converters, under nominal operating conditions, are made difficult by the requirement of high-power electrical source and load. In addition, the energy lost during the test may be very significant. The opposition method, which consists of an association of two identical converters supplied by the same source, one operating as a generator, the other as a receptor, can be a better way to do these test. Another advantage is the possibility to realize accurate measurements of the different losses in the converters under test. In the first part of this paper, the characteristics of the method concerning loss measurements are compared to those of the electrical or calorimetric methods, then it is shown how it can be applied to different types of power electronic converters, choppers, switched mode power supplies, and pulsewidth modulation inverters. In the second part, different examples of studies conducted by the authors, and using this method, are presented. They have varying goals, from the test of soft-switching inverters to the characterization of integrated gate-commutated thyristor (IGCT) devices mounted into 2-MW choppers.


title: Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing

abstract: This paper presents a comprehensive performance comparison of energy detection, matched-filter detection, and cyclostationarity-based detection, the three popular choices for spectrum sensing by cognitive radios. Analytical expressions for the false alarm and detection probability achieved by all the detectors are derived. For cyclostationarity-based detection, two architectures that exploit cyclostationarity are proposed: the Spectral Correlation Density (SCD) detector, and the Magnitude Squared Coherence (MSC) detector. The MSC detector offers improved performance compared to existing detectors, and this is demonstrated using the 802.22 RF capture database. It is also shown that the cyclostationarity-based detectors are naturally insensitive to uncertainty in the noise variance, as the decision statistic is based on the noise rejection property of the cyclostationary spectrum. Simulation results plotting the receiver operating characteristics corroborate the theoretical results, and enable visual comparison of the performance.


title: Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

abstract: A K-means clustering approach is proposed for the automated diagnosis of defective rolling element bearings. Since K-means clustering is an unsupervised learning procedure, the method can be directly implemented to measured vibration data. Thus, the need for training the method with data measured on the specific machine under defective bearing conditions is eliminated. This fact consists the major advantage of the method, especially in industrial environments. Critical to the success of the method is the feature set used, which consists of a set of appropriately selected frequency-domain parameters, extracted both from the raw signal, as well as from the signal envelope, as a result of the engineering expertise, gained from the understanding of the physical behavior of defective rolling element bearings. Other advantages of the method are its ease of programming, simplicity and robustness. In order to overcome the sensitivity of the method to the choice of the initial cluster centers, the initial centers are selected using features extracted from simulated signals, resulting from a well established model for the dynamic behavior of defective rolling element bearings. Then, the method is implemented as a two-stage procedure. At the first step, the method decides whether a bearing fault exists or not. At the second step, the type of the defect (e.g. inner or outer race) is identified. The effectiveness of the method is tested in one literature established laboratory test case and in three different industrial test cases. Each test case includes successive measurements from bearings under different types of defects. In all cases, the method presents a 100% classification success. Contrarily, a K-means clustering approach, which is based on typical statistical time domain based features, presents an unstable classification behavior.


title: Ultra-deep sequencing for the analysis of viral populations.

abstract: Next-generation sequencing allows for cost-effective probing of virus populations at an unprecedented level of detail. The massively parallel sequencing approach can detect low-frequency mutations and it provides a snapshot of the entire virus population. However, analyzing ultra-deep sequencing data obtained from diverse virus populations is challenging because of PCR and sequencing errors and short read lengths, such that the experiment provides only indirect evidence of the underlying viral population structure. Recent computational and statistical advances allow for accommodating some of the confounding factors, including methods for read error correction, haplotype reconstruction, and haplotype frequency estimation. With these methods ultra-deep sequencing can be more reliably used to analyze, in a quantitative manner, the genetic diversity of virus populations.


title: How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

abstract: Endowing robots with the capability of assessing risk and making risk-aware decisions is widely considered a key step toward ensuring safety for robots operating under uncertainty. But, how should a robot quantify risk? A natural and common approach is to consider the framework whereby costs are assigned to stochastic outcomes–an assignment captured by a cost random variable. Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping from the cost random variable to a real number. Yet, the question of what constitutes a “good” risk metric has received little attention within the robotics community. The goal of this paper is to explore and partially address this question by advocating axioms that risk metrics in robotics applications should satisfy in order to be employed as rational assessments of risk. We provide instantiations of the class of risk metrics that satisfy these axioms (referred to as distortion risk metrics) and also discuss pitfalls of commonly used risk metrics in robotics that do not satisfy these axioms. Our hope is that the ideas presented here will lead to a foundational framework for quantifying risk (and hence safety) in robotics applications.


title: EMG-driven model-based knee torque estimation on a variable impedance actuator orthosis

abstract: Development of movement assistance devices used in rehabilitation must take into account the variable human-machine interaction. To this end, electromyography (EMG) sensors detecting the muscle activation are regarded as a feasible method. In this paper, we focus on torque estimation of the human knee joint. The joint motion analysis was captured through the developed EMG sensor, and subsequently torque estimation by using an EMG-driven model. For calibrating the EMG-driven model, the experimental torque was computed by employing the inverse dynamics that have been ensured within a well-established leg-orthosis system. Here, an impedance-controlled variable stiffness actuator has been implemented. To realize a wide range of calibration tasks, we compared the knee torque by the EMG-driven model and the inverse dynamics through setting different impedance controller gains. The proposed calibration results will provide a support towards the applications in the bio-feedback based impedance control.


title: High gain printed antenna array for FMCW radar at 17 GHz

abstract: We are presenting the design of a high gain printed antenna array consisting of 420 identical patch antennas intended for FMCW radar at Ku band. The array exhibits 3 dB-beamwidths of 2° and 10° in H and E plane, respectively, side lobe suppression better than 20 dB, gain about 30 dBi and VSWR less than 2 in the frequency range 17.1 – 17.6 GHz. Excellent antenna efficiency that is between 60 and 70 % is achieved by proper impedance matching throughout the array and by using series feeding architecture with both resonant and traveling-wave feed. Enhanced cross polarization suppression is obtained by anti-phase feeding of the upper and the lower halves of the antenna. Overall antenna dimensions are 31 λ0 × 7.5 λ0.


title: Scaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect

abstract: The crossbar array architecture with resistive synaptic devices is attractive for on-chip implementation of weighted sum and weight update in the neuro-inspired learning algorithms. This paper discusses the design challenges on scaling up the array size due to non-ideal device properties and array parasitics. Circuit-level mitigation strategies have been proposed to minimize the learning accuracy loss in a large array. This paper also discusses the peripheral circuits design considerations for the neuro-inspired architecture. Finally, a circuit-level macro simulator is developed to explore the design trade-offs and evaluate the overhead of the proposed mitigation strategies as well as project the scaling trend of the neuro-inspired architecture.


title: Guided Open Vocabulary Image Captioning with Constrained Beam Search

abstract: Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels.


title: Feature-cost sensitive learning with submodular trees of classifiers

abstract: During the past decade, machine learning algorithms have become commonplace in large-scale real-world industrial applications. In these settings, the computation time to train and test machine learning algorithms is a key consideration. At training-time the algorithms must scale to very large data set sizes. At testing-time, the cost of feature extraction can dominate the CPU runtime. Recently, a promising method was proposed to account for the feature extraction cost at testing time, called Cost-sensitive Tree of Classifiers (CSTC). Although the CSTC problem is NP-hard, the authors suggest an approximation through a mixed-norm relaxation across many classifiers. This relaxation is slow to train and requires involved optimization hyperparameter tuning. We propose a different relaxation using approximate submodularity, called Approximately Submodular Tree of Classifiers (ASTC). ASTC is much simpler to implement, yields equivalent results but requires no optimization hyperparameter tuning and is up to two orders of magnitude faster to train.


title: A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms

abstract: Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.


title: Automatic Interlinking of Music Datasets on the Semantic Web

abstract: In this paper, we describe current efforts towards interlinking music-related datasets on the Web. We first explain some initial interlinking experiences, and the poor results obtained by taking a naive approach. We then detail a particular interlinking algorithm, taking into account both the similarities of web resources and of their neighbours. We detail the application of this algorithm in two contexts: to link a Creative Commons music dataset to an editorial one, and to link a personal music collection to corresponding web identifiers. The latter provides a user with personally meaningful entry points for exploring the web of data, and we conclude by describing some concrete tools built to generate and use such links.


title: Comparison of deep learning based concept representations for biomedical document clustering

abstract: In this research, document representations based on distributed representations of the concepts along with new weighting schemes for the documents are explored. The baseline weighting scheme is the traditional Term Frequency-Inverse Document Frequency (TF-IDF) of the concepts, whereas, the other two newly proposed ones consider both local content using the TF-IDF and associations between concepts. The distributed representations of the concepts are measured using a deep learning algorithm. The evaluation of the proposed document representations is based on the k-means clustering results. The results show that document representation based on TF-IDF in combination with the term based distributed representations for concepts outperforms the other two based on the returned evaluation metrics — F1-measure (80.21%) and Purity (77.1%).


title: The Body Extender: A Full-Body Exoskeleton for the Transport and Handling of Heavy Loads

abstract: This article introduces and describes an electrically powered full-body (FB) exoskeleton, called the body extender (BE), intended as a research platform for the study of the transport and handling of heavy loads up to 50 kg, with one hand at worst-load conditions (WLCs). The machine features a 22-degrees-of-freedom (DoF) quasi-anthropomorphic kinematic scheme and a modular hardware/software architecture that made it possible to manage the complexity of the system design. Besides providing a context and some general guidelines, which have driven the design of the BE, this article presents the hardware and software developments that have been achieved and implemented in the machine. The experimental results are shown that prove the functionalities of the BE in common operating conditions such as walking, squatting, and handling loads. The one-of-a-kind system demonstrates, in relevant laboratory settings, the feasibility of a complex, electrically powered full-body exoskeleton with such a target payload.


title: Low-drift and real-time lidar odometry and mapping

abstract: Here we propose a real-time method for low-drift odometry and mapping using range measurements from a 3D laser scanner moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation (especially without an external reference such as GPS) cause mis-registration of the resulting point cloud. To date, coherent 3D maps have been built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift in motion estimation and low-computational complexity. The key idea that makes this level of performance possible is the division of the complex problem of Simultaneous Localization and Mapping, which seeks to optimize a large number of variables simultaneously, into two algorithms. One algorithm performs odometry at a high-frequency but at low fidelity to estimate velocity of the laser scanner. Although not necessary, if an IMU is available, it can provide a motion prior and mitigate for gross, high-frequency motion. A second algorithm runs at an order of magnitude lower frequency for fine matching and registration of the point cloud. Combination of the two algorithms allows map creation in real-time. Our method has been evaluated by indoor and outdoor experiments as well as the KITTI odometry benchmark. The results indicate that the proposed method can achieve accuracy comparable to the state of the art offline, batch methods.


title: Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

abstract: One of the defining properties of deep learning is that models are chosen to have many more parameters than available training data. In light of this capacity for overfitting, it is remarkable that simple algorithms like SGD reliably return solutions with low test error. One roadblock to explaining these phenomena in terms of implicit regularization, structural properties of the solution, and/or easiness of the data is that many learning bounds are quantitatively vacuous when applied to networks learned by SGD in this "deep learning" regime. Logically, in order to explain generalization, we need nonvacuous bounds. We return to an idea by Langford and Caruana (2001), who used PAC-Bayes bounds to compute nonvacuous numerical bounds on generalization error for stochastic two-layer two-hidden-unit neural networks via a sensitivity analysis. By optimizing the PAC-Bayes bound directly, we are able to extend their approach and obtain nonvacuous generalization bounds for deep stochastic neural network classifiers with millions of parameters trained on only tens of thousands of examples. We connect our findings to recent and old work on flat minima and MDL-based explanations of generalization.


title: Gla-rich Protein (GRP), A New Vitamin K-dependent Protein Identified from Sturgeon Cartilage and Highly Conserved in Vertebrates

abstract: We report the isolation of a novel vitamin K-dependent protein from the calcified cartilage of Adriatic sturgeon (Acipenser nacarii). This 10.2-kDa secreted protein contains 16 γ-carboxyglutamic acid (Gla) residues in its 74-residue sequence, the highest Gla percent of any known protein, and we have therefore termed it Gla-rich protein (GRP). GRP has a high charge density (36 negative + 16 positive = 20 net negative) yet is insoluble at neutral pH. GRP has orthologs in all taxonomic groups of vertebrates, and a paralog (GRP2) in bony fish; no GRP homolog was found in invertebrates. There is no significant sequence homology between GRP and the Gla-containing region of any presently known vitamin K-dependent protein. Forty-seven GRP sequences were obtained by a combination of cDNA cloning and comparative genomics: all 47 have a propeptide that contains a γ-carboxylase recognition site and a mature protein with 14 highly conserved Glu residues, each of them being γ-carboxylated in sturgeon. The protein sequence of GRP is also highly conserved, with 78% identity between sturgeon and human GRP. Analysis of the corresponding gene structures suggests a highly constrained organization, particularly for exon 4, which encodes the core Gla domain. GRP mRNA is found in virtually all rat and sturgeon tissues examined, with the highest expression in cartilage. Cells expressing GRP include chondrocytes, chondroblasts, osteoblasts, and osteocytes. Because of its potential to bind calcium through Gla residues, we suggest that GRP may regulate calcium in the extracellular environment.


title: On-the-fly Table Generation

abstract: Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query. We introduce and address the task of on-the-fly table generation: given a query, generate a relational table that contains relevant entities (as rows) along with their key properties (as columns). This problem is decomposed into three specific subtasks: (i) core column entity ranking, (ii) schema determination, and (iii) value lookup. We employ a feature-based approach for entity ranking and schema determination, combining deep semantic features with task-specific signals. We further show that these two subtasks are not independent of each other and can assist each other in an iterative manner. For value lookup, we combine information from existing tables and a knowledge base. Using two sets of entity-oriented queries, we evaluate our approach both on the component level and on the end-to-end table generation task.


title: Defending Against Adversarial Attacks by Leveraging an Entire GAN

abstract: Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending against adversarial attacks by using both the discriminator and generator of a GAN trained on the same dataset. We show that the discriminator consistently scores the adversarial samples lower than the real samples across multiple attacks and datasets. We provide empirical evidence that adversarial samples lie outside of the data manifold learned by the GAN. Based on this, we propose a cleaning method which uses both the discriminator and generator of the GAN to project the samples back onto the data manifold. This cleaning procedure is independent of the classifier and type of attack and thus can be deployed in existing systems.


title: Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks

abstract: This paper proposes a novel method in order to obtain voxel-level segmentation for three fluid lesion types (IR-F/SRF/PED) in OCT images provided by the ReTOUCH challenge [1]. The method is based on a deep neural network consisting of encoding and de-coding blocks connected with skip-connections which was trained using a combined cost function comprising of cross-entropy, dice and adversarial loss terms. The segmentation results on a held-out validation set shows that the network architecture and the loss functions used has resulted in improved retinal fluid segmentation. Our method was ranked fourth in the ReTOUCH challenge.


title: Combining Content and Sentiment Analysis on Lyrics for a Lightweight Emotion-Aware Chinese Song Recommendation System

abstract: Traditional music recommendation systems (RS) rely on collaborative filtering technique (CF) to recommend songs or artists in which recommendations are made based on the neighboring analysis of items/users. It is computationally efficient and performs well when the data is ideally full, when there are limited user inputs or few user/item inputs, it immediately lost its competitive advantage. Additionally, traditional RS techniques including content-based one heavily rely on explicit user feedback (e.g. user rating) to generate recommendations. In music/song recommendation, however, implicit feedbacks such as play frequency, play list prevail. Making recommendation on such implicit feedbacks requires efficient and accurate latent factor learning techniques to construct user or item feature space, which is inherently computationally costly. This paper presents a new and lightweight classification model for Chinese song RS based on computational analysis of the lingual part of song lyrics. Through extracting and combining the term frequency and inverse document frequency (tf*idf) from song lyrics, we construct a composite emotion point matrix for each song which can then be used to further classify songs based on its inherent emotion and make recommendation accordingly.


title: Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq

abstract: Transcriptome reconstruction is an important application of RNA-Seq, providing critical information for further analysis of transcriptome. Although RNA-Seq offers the potential to identify the whole picture of transcriptome, it still presents special challenges. To handle these difficulties and reconstruct transcriptome as completely as possible, current computational approaches mainly employ two strategies: de novo assembly and genome-guided assembly. In order to find the similarities and differences between them, we firstly chose five representative assemblers belonging to the two classes respectively, and then investigated and compared their algorithm features in theory and real performances in practice. We found that all the methods can be reduced to graph reduction problems, yet they have different conceptual and practical implementations, thus each assembly method has its specific advantages and disadvantages, performing worse than others in certain aspects while outperforming others in anther aspects at the same time. Finally we merged assemblies of the five assemblers and obtained a much better assembly. Additionally we evaluated an assembler using genome-guided de novo assembly approach, and achieved good performance. Based on these results, we suggest that to obtain a comprehensive set of recovered transcripts, it is better to use a combination of de novo assembly and genome-guided assembly.


title: Transcriptome profiling of primary murine monocytes, lung macrophages and lung dendritic cells reveals a distinct expression of genes involved in cell trafficking

abstract: BackgroundPeripheral blood monocytes (PBMo) originate from the bone marrow, circulate in the blood and emigrate into various organs where they differentiate into tissue resident cellular phenotypes of the mononuclear phagocyte system, including macrophages (Mϕ) and dendritic cells (DC). Like in other organs, this emigration and differentiation process is essential to replenish the mononuclear phagocyte pool in the lung under both inflammatory and non-inflammatory steady-state conditions. While many studies have addressed inflammation-driven monocyte trafficking to the lung, the emigration and pulmonary differentiation of PBMo under non-inflammatory conditions is much less understood.MethodsIn order to assess the transcriptional profile of circulating and lung resident mononuclear phagocyte phenotypes, PBMo, lung Mϕ and lung DC from naïve mice were flow-sorted to high purity, and their gene expression was compared by DNA microarrays on a genome-wide scale. Differential regulation of selected genes was validated by quantitative PCR and on protein level by flow cytometry.ResultsDifferentially-expressed genes related to cell traffic were selected and grouped into the clusters (i) matrix metallopeptidases, (ii) chemokines/chemokine receptors, and (iii) integrins. Expression profiles of clustered genes were further assessed at the mRNA and protein levels in subsets of circulating PBMo (GR1- vs GR1+) and lung resident macrophages (alveolar vs interstitial Mϕ). Our data identify differentially activated genetic programs in circulating monocytes and their lung descendents. Lung DC activate an extremely diverse set of gene families but largely preserve a mobile cell profile with high expression levels of integrin and chemokine/chemokine receptors. In contrast, interstitial and even more pronounced alveolar Mϕ, stepwise downregulate gene expression of these traffic relevant communication molecules, but strongly upregulate a distinct set of matrix metallopetidases potentially involved in tissue invasion and remodeling.ConclusionOur data provide new insight in the changes of the genetic profiles of PBMo and their lung descendents, namely DC and Mϕ under non-inflammatory, steady-state conditions. These findings will help to better understand the complex relations within the mononuclear phagocyte pool of the lung.


title: Probabilistic planning via determinization in hindsight

abstract: This paper investigates hindsight optimization as an approach for leveraging the significant advances in deterministic planning for action selection in probabilistic domains. Hindsight optimization is an online technique that evaluates the one-step-reachable states by sampling future outcomes to generate multiple non-stationary deterministic planning problems which can then be solved using search. Hindsight optimization has been successfully used in a number of online scheduling applications; however, it has not yet been considered in the substantially different context of goal-based probabilistic planning. We describe an implementation of hindsight optimization for probabilistic planning based on deterministic forward heuristic search and evaluate its performance on planning-competition benchmarks and other probabilistically interesting problems. The planner is able to outperform a number of probabilistic planners including FF-Replan on many problems. Finally, we investigate conditions under which hindsight optimization is guaranteed to be effective with respect to goal achievement, and also illustrate examples where the approach can go wrong.


title: The Influence of Experiential and Dispositional Factors in Phishing: An Empirical Investigation of the Deceived

abstract: Phishing has been a major problem for information systems managers and users for several years now. In 2008, it was estimated that phishing resulted in close to $50 billion in damages to U.S. consumers and businesses. Even so, research has yet to explore many of the reasons why Internet users continue to be exploited. The goal of this paper is to better understand the behavioral factors that may increase one's susceptibility for complying with a phisher's request for personal information. Using past research on deception detection, a research model was developed to help explain compliant phishing responses. The model was tested using a field study in which each participant received a phishing e-mail asking for sensitive information. It was found that four behavioral factors were influential as to whether the phishing e-mails were answered with sensitive information. The paper concludes by suggesting that the behavioral aspect of susceptible users be integrated into the current tools and materials used in antiphishing efforts.


title: PARHELIA: Particle Filter-Based Heart Rate Estimation From Photoplethysmographic Signals During Physical Exercise

abstract: The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact of the subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the best-known conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS.


title: An end-to-end network slicing framework for 5G wireless communication systems

abstract: Wireless industry nowadays is facing two major challenges: 1) how to support the vertical industry applications so that to expand the wireless industry market and 2) how to further enhance device capability and user experience. In this paper, we propose a technology framework to address these challenges. The proposed technology framework is based on end-to-end vertical and horizontal slicing, where vertical slicing enables vertical industry and services and horizontal slicing improves system capacity and user experience. The technology development on vertical slicing has already started in late 4G and early 5G and is mostly focused on slicing the core network. We envision this trend to continue with the development of vertical slicing in the radio access network and the air interface. Moving beyond vertical slicing, we propose to horizontally slice the computation and communication resources to form virtual computation platforms for solving the network capacity scaling problem and enhancing device capability and user experience. In this paper, we explain the concept of vertical and horizontal slicing and illustrate the slicing techniques in the air interface, the radio access network, the core network and the computation platform. This paper aims to initiate the discussion on the long-range technology roadmap and spur development on the solutions for E2E network slicing in 5G and beyond.


title: FPC-based compact 25-Gb/s optical transceiver module for optical interconnect utilizing novel high-speed FPC connector

abstract: A compact (22.6 × 9.1 × 0.86 mm), 25-Gb/s, flexible printed circuit based optical transceiver module was developed. The module consists of a 25-μm-thick FPC substrate on which a vertical-cavity surface-emitting laser array, driver, photodiode array, and a transimpedance amplifier are mounted by flip-chip bonding. The polymer waveguide is laminated from the backside of the FPC via microlens-imprinted film for the optical coupling. Its compactness and high-speed characteristics were realized by using a 0.35-mm-pitch novel FPC connector accurately designed by using a three-dimensional electromagnetic simulation. It was confirmed that the bandwidth of the FPC connector was more than 40 GHz. Also the measured results of the frequency response corresponded with the simulation results. Clear eye openings and error-free operation of all eight channels at 25 Gb/s were obtained with the fabricated optical transceiver.


title: Promoting transparency and accountability through ICTs, social media, and collaborative e‐government

abstract: Purpose – The purpose of this paper is to examine the ways in which governments build social media and information and communication technologies (ICTs) into e‐government transparency initiatives, to promote collaboration with members of the public and the ways in members of the public are able to employ the same social media to monitor government activities.Design/methodology/approach – This study used an iterative strategy that involved conducting a literature review, content analysis, and web site analysis, offering multiple perspectives on government transparency efforts, the role of ICTs and social media in these efforts, and the ability of e‐government initiatives to foster collaborative transparency through embedded ICTs and social media.Findings – The paper identifies key initiatives, potential impacts, and future challenges for collaborative e‐government as a means of transparency.Originality/value – The paper is one of the first to examine the interrelationships between ICTs, social media, and c...


title: Interactive Editing of GigaSample Terrain Fields

abstract: Previous terrain rendering approaches have addressed the aspect of data compression and fast decoding for rendering, but applications where the terrain is repeatedly modified and needs to be buffered on disk have not been considered so far. Such applications require both decoding and encoding to be faster than disk transfer. We present a novel approach for editing gigasample terrain fields at interactive rates and high quality. To achieve high decoding and encoding throughput, we employ a compression scheme for height and pixel maps based on a sparse wavelet representation. On recent GPUs it can encode and decode up to 270 and 730 MPix/s of color data, respectively, at compression rates and quality superior to JPEG, and it achieves more than twice these rates for lossless height field compression. The construction and rendering of a height field triangulation is avoided by using GPU ray-casting directly on the regular grid underlying the compression scheme. We show the efficiency of our method for interactive editing and continuous level-of-detail rendering of terrain fields comprised of several hundreds of gigasamples. © 2012 Wiley Periodicals, Inc.


title: Scalable lock-free dynamic memory allocation

abstract: Dynamic memory allocators (malloc/free) rely on mutual exclusion locks for protecting the consistency of their shared data structures under multithreading. The use of locking has many disadvantages with respect to performance, availability, robustness, and programming flexibility. A lock-free memory allocator guarantees progress regardless of whether some threads are delayed or even killed and regardless of scheduling policies. This paper presents a completely lock-free memory allocator. It uses only widely-available operating system support and hardware atomic instructions. It offers guaranteed availability even under arbitrary thread termination and crash-failure, and it is immune to deadlock regardless of scheduling policies, and hence it can be used even in interrupt handlers and real-time applications without requiring special scheduler support. Also, by leveraging some high-level structures from Hoard, our allocator is highly scalable, limits space blowup to a constant factor, and is capable of avoiding false sharing. In addition, our allocator allows finer concurrency and much lower latency than Hoard. We use PowerPC shared memory multiprocessor systems to compare the performance of our allocator with the default AIX 5.1 libc malloc, and two widely-used multithread allocators, Hoard and Ptmalloc. Our allocator outperforms the other allocators in virtually all cases and often by substantial margins, under various levels of parallelism and allocation patterns. Furthermore, our allocator also offers the lowest contention-free latency among the allocators by significant margins.


title: The anatomy and clinical implications of perioral submuscular fat.

abstract: BACKGROUND ::: One of the goals of lip augmentation is to recreate the natural shape and contour of the lips. The literature describes numerous fillers and multiple techniques for this procedure. However, there is a paucity of information regarding the anatomy of the fat layers of the upper and lower lips. While prosecting cadavers, the authors noted the presence of fat deep to the orbicularis muscle of the lip and a separate fat compartment deep to the mentalis muscle. This anatomy has not been previously described. ::: ::: ::: METHODS ::: The authors dissected a total of 10 cadaver specimens. Dissection of the upper and lower lips and chin was performed. Latex injection of the arterial circuit aided the identification of boundaries. Histologic examination was performed on full-thickness specimens using hematoxylin and eosin stains. ::: ::: ::: RESULTS ::: Fat was noted deep to the orbicularis oris and mentalis muscles. This fat is distinct from the more superficial fat of the cutaneous lip. The wet-dry border of the lip appears to be the most anterior boundary of the submuscular lip fat. The lateral boundary is the zygomaticus muscle. Histologic examination confirms this macroscopic anatomy. ::: ::: ::: CONCLUSIONS ::: If volume loss is a significant component of facial aging, it becomes important for the clinician to know the precise anatomy of the subcutaneous tissue. Greater precision is possible in rejuvenating specific characteristics of aging. Augmentation at the vermilion cutaneous junction defines the vermilion border and improves perioral rhytides. Deep fat augmentation of the vermilion restores volume, shape, and lip eversion. Augmentation of submuscular fat beneath the cutaneous lip improves the convex shape. Submentalis fat augmentation specifically reduces the depth of the labiomental hollow. These findings are in agreement with previous observations that volume loss of deep fat--temporal, buccal, and sub-orbicularis oculi fat --contributes to the aging process. Knowledge of this anatomy and site-specific augmentation make it possible for the clinician to approach perioral rejuvenation in an algorithmic fashion.


title: Learning Determinantal Point Processes with Moments and Cycles

abstract: Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where returning a diverse set of objects is important. While there are fast algorithms for sampling, marginalization and conditioning, much less is known about learning the parameters of a DPP. Our contribution is twofold: (i) we establish the optimal sample complexity achievable in this problem and show that it is governed by a natural parameter, which we call the \emph{cycle sparsity}; (ii) we propose a provably fast combinatorial algorithm that implements the method of moments efficiently and achieves optimal sample complexity. Finally, we give experimental results that confirm our theoretical findings.


title: Who Owns the Data? Open Data for Healthcare

abstract: Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual's characteristics, known as personalized or "stratified medicine." Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become "data donors" and the "quantified self" movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of -healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals' NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health.


title: Antibodies to Tissue Transglutaminase as Serologic Markers in Patients with Dermatitis Herpetiformis

abstract: Dermatitis herpetiformis is a gluten-sensitive disease with a symmetrically distributed blistering over extensor surfaces. The association with celiac disease is further supported by the high rate of immunoglobulin A autoantibodies to endomysium in patients with dermatitis herpetiformis, which are highly specific and sensitive indicators of celiac disease. Therefore, we determined immunoglobulin A antibodies to tissue transglutaminase, the recently discovered endomysial autoantigen in celiac disease, in patients with dermatitis herpetiformis and controls. Sera of 61 patients with dermatitis herpetiformis, as characterized by granular immunoglobulin A deposits in the subepidermal basement membrane and known endomysial antibody titers (determined by indirect immunofluorescence) as well as 84 control sera of patients with dermal or intestinal diseases unrelated to dermatitis herpetiformis, were analyzed for circulating immunoglobulin A antibodies to tissue transglutaminase by enzyme-linked immunosorbent assay. Immunoglobulin A anti-tissue transglutaminase titers in patients with dermatitis herpetiformis were significantly elevated above the controls. Furthermore, the immunoglobulin A anti-tTG titers showed a positive correlation with semiquantitative endomysial antibody data. Compared with endomysial antibodies, determination of immunoglobulin A anti-tissue transglutaminase reached a specificity and sensitivity of 97.6% and 89.1%. Patients with dermatitis herpetiformis have elevated immunoglobulin A autoantibodies to tissue transglutaminase, confirming its pathogenic relation with celiac disease and further supporting the usefulness of this novel assay for screening and therapy control.


title: What the orbitofrontal cortex does not do

abstract: In the last 25 years, explosive interest has implicated the orbitofrontal cortex in nearly every function known to cognitive neuroscience. Yet scientific progress comes as much from questioning existing ideas as proposing new ones. This review discusses major theories of orbitofrontal function and the data that invalidate these ideas.


title: Ensemble Learning using Decorrelated Neural Networks

abstract: We describe a decorrelation network training method for improving the quality of regression learning in 'ensemble' neural networks NNs that are composed of linear combinations of individual NNs. In this method, individual networks are trained by backpropogation not only to reproduce a desired output, but also to have their errors linearly decorrelated with the other networks. Outputs from the individual networks are then linearly combined to produce the output of the ensemble network. We demonstrate the performances of decorrelated network training on learning the 'three-parity' logic function, a noisy sine function and a one-dimensional non-linear function, and compare the results with the ensemble networks composed of independently trained individual networks without decorrelation training . Empirical results show than when individual networks are forced to be decorrelated with one another the resulting ensemble NNs have lower mean squared errors than the ensemble networks having independently trained i...


title: Investigation of a photodefinable glass substrate for millimeter-wave radios on package

abstract: Photodefinable glass has been investigated for applications at mm-wave frequencies. Test structures including through-glass vias, transmission lines and microstrip patch antennas have been fabricated and fully characterized up to 67 GHz. Good correlation was obtained between simulation and measurements. The antenna exhibited return loss better than 10 dB in the frequency band of interest. Via transition losses were less than 0.15 dB/via and the transmission line insertion loss was ~ 3.3 dB/cm at 60 GHz.


title: Approximation of the plastic zone by circular arcs during the sliding of a cylinder on the rigid-plastic half-space

abstract: An analytical method for constructing a plastic zone during the sliding of a smooth circular cylinder on the rigid-plastic half-space is proposed under plane strain conditions. The method is based on an approximation of the slip lines by circular arcs. The results are used as an initial approximation for determining the radius of curvature of the slip lines and the velocity field into the plastic zone in a double-power series.


title: Detection and Segmentation of Clustered Objects by Using Iterative Classification, Segmentation, and Gaussian Mixture Models and Application to Wood Log Detection

abstract: There have recently been advances in the area of fully automatic detection of clustered objects in color images. State of the art methods combine detection with segmentation. In this paper we show that these methods can be significantly improved by introducing a new iterative classification, statistical modeling, and segmentation procedure. The proposed method used a detect-and-merge algorithm, which iteratively finds and validates new objects and subsequently updates the statistical model, while converging in very few iterations.


title: Integrating Arduino-based educational mobile robots in ROS

abstract: This article presents the full integration of compact educational mobile robotic platforms built around an Arduino controller board in the Robot Operating System (ROS). To decrease the development time, a driver interface in ROS was created so as to provide hardware abstraction and intuitive operation mode, thus allowing researchers to focus essentially in their main research motivation, e.g., search and rescue, multi-robot surveillance or swarm robotics. Moreover, the full integration in ROS provided by the driver enables the use of several tools for data analysis, easiness of interaction between multiple robots, use of different sensors and teleoperation devices, thereby targeting engineering education. To validate the approach, diverse experimental tests were conducted using different Arduino-based robotic platforms.


title: An Efficient Method For Dark License Plate Detection

abstract: License plate detection from images is an important step in vehicle license plate recognition for automated transport system. Dark images and complex background make license plate detection problems challenging. In this paper, a novel method of based on rectangle window was proposed to resolve license plate detection problems. The proposed method firstly enhances images using traditional method. Secondly input enhanced images are converted from RGB color space to HSI color space, then we extract I component from HSI color space and get vertical edge images using Soble operator. Thirdly a novel method was proposed to remove most of the background curves and noise edges. Fourthly, we propose an effective algorithm search the license plate region by a rectangle window in the residual edge. Finally segment the license plate detection from the original dark license plate images. Experimental results show that the license plate detection by our method is better than other conventional methods. The proposed method also shows the effectiveness and robustness.


title: The effect of the performance appraisal system on trust for management: A field quasi-experiment.

abstract: Recent theoretical developments have enabled the empirical study of trust for specific referents in organizations. The authors conducted a 14-month field study of employee trust for top management. A 9-month quasi-experiment found that the implementation of a more acceptable performance appraisal system increased trust for top management. The 3 proposed factors of trustworthiness (ability, benevolence, and integrity) mediated the relationship between perceptions of the appraisal system and trust.


title: Proportions of Hand Segments

abstract: The analysis of proportions of hand segments has been carried out on the basis of X-ray films of 66 adult patients without developmental abnormalities. There were hand joint topographies determined proceeding from the data obtained. There were suggested methods of practical utilization of the obtained data in the field of medicine. Se analizaron en radiografias de 66 pacientes sin anomalias del desarrollo las proporciones de los diferentes segmentos de la mano. De acuerdo a los datos obtenidos se determino una topografia de las articulaciones. Se proponen modos de empleo en la medicina de los datos obtenidos en este estudio.


title: Stereo vision based road surface preview

abstract: Road surface preview is an application where road surface elevation profile in front of the host vehicle is estimated, whereby bumps and potholes can be detected for active suspension control and roadway maintenance. In this paper, the authors describe a stereo vision system that performs road surface preview along the host vehicle path. Real-time three dimensional (3D) road surface reconstruction was calculated from stereo range images at centimeter scale to provide elevation profiles along predicted tire tracks. Camera pitch angle is dynamically estimated, which is used to correct elevation errors caused by host vehicle pitching up and down on the roadway. Having been implemented in embedded hardware and tested in passenger vehicles, the described system showed good performance for various road conditions and on different road surfaces (asphalt, concrete, and dirt roads).


title: Joint inference for cross-document information extraction

abstract: Previous information extraction (IE) systems are typically organized as a pipeline architecture of separated stages which make independent local decisions. When the data grows beyond some certain size, the extracted facts become inter-dependent and thus we can take advantage of information redundancy to conduct reasoning across documents and improve the performance of IE. We describe a joint inference approach based on information network structure to conduct cross-fact reasoning with an integer linear programming framework. Without using any additional labeled data this new method obtained 13.7%-24.4% user browsing cost reduction over a state-of-the-art IE system which extracts various types of facts independently.


title: Joint Millimeter Wave and Microwave Resources Allocation in Cellular Networks With Dual-Mode Base Stations

abstract: The use of dual-mode base stations that can jointly exploit millimeter wave (mmW) and microwave ( $\mu \text{W}$ ) resources is a promising solution for overcoming the uncertainty of the mmW environment. In this paper, a novel dual-mode scheduling framework is proposed that jointly performs user applications (UAs) selection and scheduling over $\mu \text{W}$ and mmW bands. The proposed scheduling framework allows multiple UAs to run simultaneously on each user equipment (UE) and utilizes a set of context information , including the channel state information per UE, the delay tolerance and required load per UA, and the uncertainty of mmW channels, to maximize the quality-of-service (QoS) per UA. The dual-mode scheduling problem is then formulated as an optimization problem with minimum unsatisfied relations problem, which is shown to be challenging to solve. Consequently, a long-term scheduling framework, consisting of two stages, is proposed. Within this framework, first, the joint UA selection and scheduling over the $\mu \text{W}$ band is formulated as a one-to-many matching game between the $\mu \text{W}$ resources and UAs. To solve this problem, a novel scheduling algorithm is proposed and shown to yield a two-sided stable resource allocation. Second, over the mmW band, the joint context-aware UA selection and scheduling problem is formulated as a 0-1 Knapsack problem and a novel algorithm that builds on the Q-learning algorithm is proposed to find a suitable mmW scheduling policy while adaptively learning the UEs’ line-of-sight probabilities. Furthermore, it is shown that the proposed scheduling framework can find an effective scheduling solution, over both $\mu \text{W}$ and mmW, in polynomial time. Simulation results show that, compared with conventional scheduling schemes, the proposed approach significantly increases the number of satisfied UAs while improving the statistics of QoS violations and enhancing the overall users’ quality-of-experience.


title: Violent and nonviolent video games produce opposing effects on aggressive and prosocial outcomes

abstract: Experimental studies routinely show that participants who play a violent game are more aggressive immediately following game play than participants who play a nonviolent game. The underlying assumption is that nonviolent games have no effect on aggression, whereas violent games increase it. The current studies demonstrate that, although violent game exposure increases aggression, nonviolent video game exposure decreases aggressive thoughts and feelings (Exp 1) and aggressive behavior (Exp 2). When participants assessed after a delay were compared to those measured immediately following game play, violent game players showed decreased aggressive thoughts, feelings and behavior, whereas nonviolent game players showed increases in these outcomes. Experiment 3 extended these findings by showing that exposure to nonviolent puzzle-solving games with no expressly prosocial content increases prosocial thoughts, relative to both violent game exposure and, on some measures, a no-game control condition. Implications of these findings for models of media effects are discussed.


Internal uncertainty, rather than expected performance, determines visual confidence


title: Understanding Leaky-Wave Structures: A Special Form of Guided-Wave Structure

abstract: Guided-wave structures are the foundation for the design and development of RF and microwave circuits and systems. Whether it is planar or nonplanar, periodic or straight, a guided-wave structure, which consists of metallic and/or dielectric composite building blocks, is used to support signal propagation and processing. Except for free-space propagation, diffraction and scattering in an open medium or space, microwave energy is usually transferred by means of specially designed guided-wave structures with modal propagation behavior that are fundamentally characterized by a propagation constant and transmission loss with respect to specific guided-wave modes. Those modes are structure and frequency dependent, and satisfy the boundary conditions of a guided-wave structure. Guided-wave structures can be divided into two kinds, open and closed. The completely closed structures are known to support only the wave guided therein, while the open structure, including semi-open or partially open structures, are subject to a potential gradual wave leakage along the propagation path. Wave leakage is always related to wave guidance, which is very much dependent on a number of factors including structural geometry, fill materials, operating frequency and guided mode. Such leakage can be used positively to develop a leaky-wave structure called a leaky-wave antenna, which enjoys some distinctive properties such as beam-scanning with frequency. Leaky-wave radiating structures are easily fabricated at millimeter wave frequencies compared with other antennas. Although leaky-wave and guided-wave structures have similar characteristics and can be designed by common methods, the simulation and design of leaky-wave structures are usually much more complicated. This is because leaky-wave structures are not closed and their attenuation constant related to leakage needs to be considered. In this article, we focus on the presentation of basic operating principles and special features of positive leaky-wave structures (leaky-wave antennas) ranging from straight to periodic geometries.


title: Forms of Formative Assessment in Virtual Learning Environments

abstract: One of the challenges in the contemporary education is improving learner’s learning capability during the learning period through formative assessment in virtual learning environments. The formative assessment process requires learner to be active participants in the learning process by communicating and interacting with the instructor. The formative assessment process accommodates learner responses that give an opportunity to support the learner in learning through feedback. In a virtual learning environment, formative assessment can be significantly improved by the use of information systems. In this research we explore forms of formative assessment in virtual leaning environments with a specific focus on the design dimension. Our analysis improves the understanding of formative assessment and the design dimension specific to forms of formative assessment in virtual learning environments. Our work informs the design of online learning environments to improve the effectiveness of formative assessment.


title: Ku Band Continuously Tunable Circular Cavity SIW Filter With One Parameter

abstract: This letter introduces a Ku band continuously tunable filter with one parameter. The proposed filter is implemented using substrate integrated waveguide (SIW) technology and has a simple and symmetrical structure. The tunability of the circular resonator is a kind of mechanical tuning, achieved by the different distance between moving copper column and the center of the circular cavity. Location of moving distance has an approximately linear relation with the dominant cavity resonance frequency. The single circular cavity has a tuning range of 12.6–15.0 GHz. The fabricated filter is made up of two cavities and has a frequency tuning ratio percentage of 19.2% compared with the center frequency 13.825 GHz. The insertion loss is between 1.7 and 2.8 dB.


title: Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

abstract: Abstract Lithium-ion batteries are widely used as power sources in commercial products, such as laptops, electric vehicles (EVs) and unmanned aerial vehicles (UAVs). In order to ensure a continuous power supply, the functionality and reliability of lithium-ion batteries have received considerable attention. In this paper, a battery capacity prognostic method is developed to estimate the remaining useful life of lithium-ion batteries. This capacity prognostic method consists of a relevance vector machine and a conditional three-parameter capacity degradation model. The relevance vector machine is used to derive the relevance vectors that can be used to find the representative training vectors containing the cycles of the relevance vectors and the predictive values at the cycles of the relevance vectors. The conditional three-parameter capacity degradation model is developed to fit the predictive values at the cycles of the relevance vectors. Extrapolation of the conditional three-parameter capacity degradation model to a failure threshold is used to estimate the remaining useful life of lithium-ion batteries. Three instance studies were conducted to validate the developed method. The results show that the developed method is able to predict the future health condition of lithium-ion batteries.


title: Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

abstract: In this work, we present an application of domain randomization and generative adversarial networks (GAN) to train a near real-time object detector for industrial electric parts, entirely in a simulated environment. Large scale availability of labelled real world data is typically rare and difficult to obtain in many industrial settings. As such here, only a few hundred of unlabelled real images are used to train a Cyclic-GAN network, in combination with various degree of domain randomization procedures. We demonstrate that this enables robust translation of synthetic images to the real world domain. We show that a combination of the original synthetic (simulation) and GAN translated images, when used for training a Mask-RCNN object detection network achieves greater than 0.95 mean average precision in detecting and classifying a collection of industrial electric parts. We evaluate the performance across different combinations of training data.


title: GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

abstract: Deep neural networks (DNNs) are vulnerable to adversarial examples, even in the black-box case, where the attacker is limited to solely query access. Existing black-box approaches to generating adversarial examples typically require a significant number of queries, either for training a substitute network or estimating gradients from the output scores. We introduce GenAttack, a gradient-free optimization technique which uses genetic algorithms for synthesizing adversarial examples in the black-box setting. Our experiments on the MNIST, CIFAR-10, and ImageNet datasets show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than existing approaches. For example, in our CIFAR-10 experiments, GenAttack required roughly 2,568 times less queries than the current state-of-the-art black-box attack. Furthermore, we show that GenAttack can successfully attack both the state-of-the-art ImageNet defense, ensemble adversarial training, and non-differentiable, randomized input transformation defenses. GenAttack's success against ensemble adversarial training demonstrates that its query efficiency enables it to exploit the defense's weakness to direct black-box attacks. GenAttack's success against non-differentiable input transformations indicates that its gradient-free nature enables it to be applicable against defenses which perform gradient masking/obfuscation to confuse the attacker. Our results suggest that evolutionary algorithms open up a promising area of research into effective gradient-free black-box attacks.


title: An optimized system to solve text-based CAPTCHA

abstract: CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently utilize text-based scheme because of most convenience and user-friendly way \cite{bursztein2011text}. Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways. Our goal is to defeat the CAPTCHA, thus firstly the CAPTCHAs need to be split into character by character. There isn't a regular segmentation algorithm to obtain the divided characters in all kinds of examples, which means that we have to treat the segmentation individually. In this paper, we build a whole system to defeat the CAPTCHAs as well as achieve state-of-the-art performance. In detail, we present our self-adaptive algorithm to segment different kinds of characters optimally, and then utilize both the existing methods and our own constructed convolutional neural network as an extra classifier. Results are provided showing how our system work well towards defeating these CAPTCHAs.


title: Obesity relationships with community design, physical activity, and time spent in cars.

abstract: BACKGROUND ::: Obesity is a major health problem in the United States and around the world. To date, relationships between obesity and aspects of the built environment have not been evaluated empirically at the individual level. ::: ::: ::: OBJECTIVE ::: To evaluate the relationship between the built environment around each participant's place of residence and self-reported travel patterns (walking and time in a car), body mass index (BMI), and obesity for specific gender and ethnicity classifications. ::: ::: ::: METHODS ::: Body Mass Index, minutes spent in a car, kilometers walked, age, income, educational attainment, and gender were derived through a travel survey of 10,878 participants in the Atlanta, Georgia region. Objective measures of land use mix, net residential density, and street connectivity were developed within a 1-kilometer network distance of each participant's place of residence. A cross-sectional design was used to associate urban form measures with obesity, BMI, and transportation-related activity when adjusting for sociodemographic covariates. Discrete analyses were conducted across gender and ethnicity. The data were collected between 2000 and 2002 and analysis was conducted in 2004. ::: ::: ::: RESULTS ::: Land-use mix had the strongest association with obesity (BMI >/= 30 kg/m(2)), with each quartile increase being associated with a 12.2% reduction in the likelihood of obesity across gender and ethnicity. Each additional hour spent in a car per day was associated with a 6% increase in the likelihood of obesity. Conversely, each additional kilometer walked per day was associated with a 4.8% reduction in the likelihood of obesity. As a continuous measure, BMI was significantly associated with urban form for white cohorts. Relationships among urban form, walk distance, and time in a car were stronger among white than black cohorts. ::: ::: ::: CONCLUSIONS ::: Measures of the built environment and travel patterns are important predictors of obesity across gender and ethnicity, yet relationships among the built environment, travel patterns, and weight may vary across gender and ethnicity. Strategies to increase land-use mix and distance walked while reducing time in a car can be effective as health interventions.


title: Adjustable Real-time Style Transfer

abstract: Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output. Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum" for different input images. Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization. In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters. These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters. We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.


title: Prosocial behavior in an online game community: an ethnographic study

abstract: We report an ethnographic study of prosocial behavior inconnection to League of Legends, one of the most popular games in the world. In this game community, the game developer, Riot Games, implemented a system that allowed players to volunteer their time to identify unacceptable player behaviors and punish players associated with these behaviors. With the prosocial goal of improving the community and promoting sportsmanship with in the competitive culture, a small portion of players worked diligently in the system with little reward. In this paper, we use interviews and analysis of forum discussions to examine how players themselves explain their participation in the system situated in the game culture of League of Legends. We show a myriad of social and technical factors that facilitated or hindered players' prosocial behavior. We discuss how our findings might provide generalizable insights for player engagement and community-building in online games.


title: What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA

abstract: This paper presents a syntax-driven approach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than using syntactic features to augment existing statistical classifiers (as in previous work), we build on the idea that questions and their (correct) answers relate to each other via loose but predictable syntactic transformations. We propose a probabilistic quasi-synchronous grammar, inspired by one proposed for machine translation (D. Smith and Eisner, 2006), and parameterized by mixtures of a robust nonlexical syntax/alignment model with a(n optional) lexical-semantics-driven log-linear model. Our model learns soft alignments as a hidden variable in discriminative training. Experimentalresultsusing theTRECdataset are shown to significantly outperform strong state-of-the-art baselines.


title: Automatically learning semantic features for defect prediction

abstract: Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models. To bridge the gap between programs' semantics and defect prediction features, this paper proposes to leverage a powerful representation-learning algorithm, deep learning, to learn semantic representation of programs automatically from source code. Specifically, we leverage Deep Belief Network (DBN) to automatically learn semantic features from token vectors extracted from programs' Abstract Syntax Trees (ASTs). Our evaluation on ten open source projects shows that our automatically learned semantic features significantly improve both within-project defect prediction (WPDP) and cross-project defect prediction (CPDP) compared to traditional features. Our semantic features improve WPDP on average by 14.7% in precision, 11.5% in recall, and 14.2% in F1. For CPDP, our semantic features based approach outperforms the state-of-the-art technique TCA+ with traditional features by 8.9% in F1.


title: Denial-of-Service in Wireless Sensor Networks : attacks and Defenses This survey of denial-of-service threats and countermeasures considers wireless sensor platforms ’ resource constraints as well as the denial-of-sleep attack , which targets a battery-powered device ’ s energy supply

abstract: This survey of denial-of-service threats and countermeasures considers wireless sensor platforms' resource constraints as well as the denial-of-sleep attack, which targets a battery-powered device's energy supply. Here, we update the survey of denial-of-service threats with current threats and countermeasures.In particular, we more thoroughly explore the denial-of-sleep attack, which specifically targets the energy-efficient protocols unique to sensor network deployments. We start by exploring such networks' characteristics and then discuss how researchers have adapted general security mechanisms to account for these characteristics.


KIL: An Abstract Intermediate Language for Symbolic Execution and Test Generation of C++ Programs


title: Measuring the Similarity between Automatically Generated Topics

abstract: Previous approaches to the problem of measuring similarity between automatically generated topics have been based on comparison of the topics’ word probability distributions. This paper presents alternative approaches, including ones based on distributional semantics and knowledgebased measures, evaluated by comparison with human judgements. The best performing methods provide reliable estimates of topic similarity comparable with human performance and should be used in preference to the word probability distribution measures used previously.


title: A unifying computational framework for motor control and social interaction

abstract: Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.


title: Solar Powered Wheel Chair for Physically Challenged People Using Surface EMG Signal

abstract: This paper presents the design of low cost solar powered wheel chair for physically challenged people. The signals necessary to maneuver the wheel chair are acquired from different muscles of the hand using surface Electromyography (sEMG) technique. The raw sEMG signals collected from upper limb muscles are processed to extract the desired direction of movement for the wheel chair. The accuracy of the extracted EMG signals is found to be relatively high. A prototype is developed and tests verified the objective functionalities. The analysis of life cycle cost of the solar powered wheel chair is presented. It is found that the proposed wheel chair is financially feasible for developing countries.


title: Pokémon Go: Impact on Yelp Restaurant Reviews

abstract: Pokemon Go, the popular Augmented Reality based mobile application, launched in July of 2016. The game's meteoric rise in usage since that time has had an impact on not just the mobile gaming industry, but also the physical activity of players, where they travel, where they spend their money, and possibly how they interact with other social media applications. In this paper, we studied the impact of Pokemon Go on Yelp reviews. For restaurants near PokeStops, we found a slight drop in the number of online reviews.


The Role of Information Theory in Cryptography


title: Assessment of maturity models for smart cities supported by maturity model design principles

abstract: The concept of smart cities has gained relevance over the years. City leaders plan investments with the aim of evolving the city towards a smart city. Several models and frameworks, of which maturity models, provide directions or support such investment decisions. Nevertheless, it is not always clear whether the maturity models developed so far are able to fulfil their proposed objectives. This paper identifies smart city maturity models and assesses them, taking into account an approach based on the design principles framework proposed by Poppelbus & Roglinger [1]. The main objective of this paper is to infer on the relevance of current maturity models for smart cities, taking into account their purpose. Furthermore, it aims at creating awareness towards the need for completeness when developing a maturity model.


title: Transfer Learning in Decision Trees

abstract: Most research in machine learning focuses on scenarios in which a learner faces a single learning task, independently of other learning tasks or prior knowledge. In reality, however, learning is not performed in isolation, starting from scratch with every new task. Instead, it is a lifelong activity during which a learner encounters many learning tasks, and usefully transfers to new tasks knowledge acquired from earlier related tasks. We propose a novel approach to transfer learning with decision trees. Our system learns a new task semi-incrementally from a partial decision tree model which captures knowledge from a previous task. Empirical results on several UCI data sets show that our approach is generally more effective and accurate than the base approach.


Heterogeneous multiprocessor for the management of real-time video and graphics streams


title: Practical modbus flooding attack and detection

abstract: The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.