title: Time-optimal trajectory planning for tractor-trailer vehicles via simultaneous dynamic optimization

abstract: Trajectory planning is a critical aspect of autonomous tractor-trailer vehicle design. Trajectory planning algorithms usually compute paths first, trajectories are obtained thereafter. This multi-step feature makes those planners inefficacious to handle time-dependent constraints. In this study, we consider the original trajectory planning mission directly, which is described as an optimal control problem containing the kinematics, mechanical/physical constraints, environmental requirements as well as an optimization criterion. In this formulation, only the fundamental driving principles with no special issues (e.g., backing-up maneuver and jackknife) are considered. For example, the prevailing “small-angle assumption” is not utilized to prevent jackknifing. Instead, we only require that different parts of a tractor-trailer vehicle should not collide, since the emergence of jackknife does not physically violate the kinematics. An interior-point method based simultaneous approach is adopted to solve the formulated optimal control problem. Simulation results verify our proposal is capable of handling scenarios with various user-specified requirements.


title: ChipWhisperer: An Open-Source Platform for Hardware Embedded Security Research

abstract: This paper introduces a complete side channel analysis toolbox, inclusive of the analog capture hardware, target device, capture software, and analysis software. The highly modular design allows use of the hardware and software with a variety of existing systems. The hardware uses a synchronous capture method which greatly reduces the required sample rate, while also reducing the data storage requirement, and improving synchronization of traces. The synchronous nature of the hardware lends itself to fault injection, and a module to generate glitches of programmable width is also provided. The entire design (hardware and software) is open-source, and maintained in a publicly available repository. Several long example capture traces are provided for researchers looking to evaluate standard cryptographic implementations.


title: A Methodological Approach to Model-Driven Design and Development of Automation Systems

abstract: The growing complexity of industrial automation demands the adoption of software engineering principles for improving the development process of control systems. This paper presents a methodological approach to the design and development of complex automation systems relying on model-driven engineering (MDE). A benefit of this approach is the integration of methods and techniques widespread within the automation discipline with modern MDE techniques guiding the designer through the development phases. A second advantage is to add flexibility enough to adapt the steps to the needs of the system under design. Finally, the architecture presented is prepared to be adapted to methodology extensions to cover other aspects of automation systems. The framework is based on domain models that are defined through the development phases using the terminology of the automation field. Using model transformations both documentation about system analysis and design and the skeleton of software units are automatically generated. A proof-of-concept tool has been developed that has been tested on the design of medium-complexity projects to assess the impact of its use with respect to project documentation and maintenance. Note to Practitioners —Control software development can be considered one of the challenges in automation field for achieving leadership in the future economic market. This work presents a model-driven engineering-based approach making use of both automation and software engineering methods and techniques for developing automation control systems. The framework implements the methodology for industrial automation systems ( ${\rm MeiA}_{\bullet }$ ) for guiding developers through the development phases and generates the analysis and design documentation using domain terminology, the design documentation that involves the minimal units of design, and the program organization units in one-to-one correspondence with the minimal units of design. From a practical point of view, it should be highly emphasized that developers of automation projects benefit from more structured designs, reduced number of errors, and improved project documentation.


title: 3D facial model construction and animation from a single frontal face image

abstract: In this paper we present a system which automatically generates a 3D face model from a single frontal image of a face with the help of generic 3D model. Our system consists of three components. The first component detects the features like eyes, mouth, eyebrow and contour of face. After detecting features the second component automatically adapts the generic 3d model into face specific 3D model using geometric transformations. Our system allows the rotation and zooming of 3D model and generation of texture. Animation is produced using 3D shape morphing between the corresponding face models and blending the corresponding textures. Our system has the advantage that it is fully automatic, robust and fast. It can be used in a variety of applications for which the accuracy of depths are not critical such as games, avatars, face recognition etc. We have tested and evaluated our system using database BU-3DFE.


title: Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention

abstract: In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for potential interactions. The method can recognize objects from arbitrary viewpoints and generalizes to instances that have never been observed during training, even if they are partially occluded and appear against cluttered backgrounds. Our approach builds on the implicit shape model of Leibe et al. We extend it to couple recognition to the provision of meta-data useful for a task and to the case of multiple viewpoints by integrating it with the dense multi-view correspondence finder of Ferrari et al. Meta-data can be part labels but also depth estimates, information on material types, or any other pixelwise annotation. We present experimental results on wheelchairs, cars, and motorbikes.


title: Active Learning for Breast Cancer Identification

abstract: Breast cancer is the second most common malignancy among women and has become a major public health problem in current society. Traditional breast cancer identification requires experienced pathologists to carefully read the breast slice, which is laborious and suffers from inter-observer variations. Consequently, an automatic classification framework for breast cancer identification is worthwhile to develop. Recent years witnessed the development of deep learning technique. Increasing number of medical applications start to use deep learning to improve diagnosis accuracy. In this paper, we proposed a novel training strategy, namely reversed active learning (RAL), to train network to automatically classify breast cancer images. Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images. We evaluate the CNN trained with RAL on publicly available ICIAR 2018 Breast Cancer Dataset (IBCD). The experimental results show that our RAL increases the slice-based accuracy of CNN from 93.75% to 96.25%.


title: Heart Rate Responses During Small-Sided Games and Short Intermittent Running Training in Elite Soccer Players: A Comparative Study

abstract: Dellal, A, Chamari, K, Pintus, A, Girard, O, Cotte, T, and Keller, D. Heart rate responses during small-sided games and short intermittent running training in elite soccer players: a comparative study. J Strength Cond Res 22(5): 1449–1457, 2008—The purpose of this study was to compare heart rate (HR) responses within and between physical controlled (shortduration intermittent running) and physical integrated (sided games) training methods in elite soccer players. Ten adult male elite soccer players (age, 26 6 2.9 years; body mass, 78.3 6 4.4 kg; maximum HR [HRmax], 195.4 6 4.9 bmin 21 and velocity at maximal aerobic speed (MAS), 17.1 6 0.8 kmh 21 ) performed different short-duration intermittent runs, e.g., 30– 30 (30 seconds of exercise interspersed with 30 seconds of recovery) with active recovery, and 30–30, 15–15, 10–10, and 5–20 seconds with passive recovery, and different sided games (1 versus 1, 2 versus 2, 4 versus 4, 8 versus 8 with and without a goalkeeper, and 10 versus 10). In both training methods, HR was measured and expressed as a mean percentage of HR reserve (%HRres). The %HRres in the 30–30-second intermittent run at 100% MAS with active recovery (at 9 kmh 21 with corresponding distance) was significantly higher than that with passive recovery (85.7% versus 77.2% HRres, respectively, p , 0.001) but also higher than the 1 versus 1 (p , 0.01), 4 versus 4 (p # 0.05), 8 versus 8 (p , 0.001), and 10 versus 10 (p , 0.01) small-sided games. The %HRres was 2-fold less homogeneous during the different small-sided games than during the short-duration intermittent running (intersubjects coefficient of variation [CV] = 11.8% versus 5.9%, respectively). During the 8 versus 8 sided game, the presence of goalkeepers induced an ~11% increase in %HRres and reduced homogeneity when compared to games without goalkeepers (intersubject CV = 15.6% versus 8.8%). In conclusion, these findings showed that some small-sided games allow the HR to increase to the same level as that in short-duration intermittent running. The sided game method can be used to bring more variety during training, mixing physical, technical, and tactical training approaching the intensity of short-duration intermittent running but with higher intersubject variability.


title: Wireless Communication and Security Issues for Cyber–Physical Systems and the Internet-of-Things

abstract: Wireless sensors and actuators connected by the Internet-of-Things (IoT) are central to the design of advanced cyber–physical systems (CPSs). In such complex, heterogeneous systems, communication links must meet stringent requirements on throughput, latency, and range, while adhering to tight energy budget and providing high levels of security. In this paper, we first summarize wireless communication principles from the perspective of the connectivity needs of IoT and CPS. Based on these principles, we then review the most relevant wireless communication standards before focusing on the key security issues and features of such systems. In particular, the gap between the security features in the communication standards used in CPSs and IoT and their actual vulnerabilities are pointed out with practical examples and recent attacks. We emphasize the need for a more in-depth study of the security issues across all the protocol layers, including both logical layer security and physical layer security.


Efficient and Robust Retrieval by Shape Content through Curvature Scale Space


title: Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories

abstract: Abstract The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed.


title: Selection of the number of clusters via the bootstrap method

abstract: Here the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation. In this paper, an estimation scheme for clustering instability is developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering instability is minimized. The proposed selection criterion's effectiveness is demonstrated on simulations and real examples.


title: Open Access Automated identification of animal species in camera trap images

abstract: Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identification method for wildlife pictures captured by remote camera traps. Our process starts with images that are cropped out of the background. We then use improved sparse coding spatial pyramid matching (ScSPM), which extracts dense SIFT descriptor and cell-structured LBP (cLBP) as the local features, that generates global feature via weighted sparse coding and max pooling using multi-scale pyramid kernel, and classifies the images by a linear support vector machine algorithm. Weighted sparse coding is used to enforce both sparsity and locality of encoding in feature space. We tested the method on a dataset with over 7,000 camera trap images of 18 species from two different field cites, and achieved an average classification accuracy of 82%. Our analysis demonstrates that the combination of SIFT and cLBP can serve as a useful technique for animal species recognition in real, complex scenarios.


title: Double JPEG compression detection based on block statistics

abstract: In this paper, a novel method is proposed to detect the aligned double JPEG compression with different quantization matrix. The proposed method is based on the theory that the correlation among adjacent coefficients of frequency spectrum in DCT blocks is enhanced after DCT transformation, and the correlation among same locations in adjacent DCT blocks is strong. Classification features are divided into two types, the intra-block frequency domain features and the inter-block frequency domain features. The intra-block frequency domain features are used to catch the strong correlation among adjacent coefficients in DCT blocks, and the inter-block frequency domain features are used to catch the correlation among same locations of adjacent DCT blocks. Then the intra-block frequency domain features with inter-block frequency domain features are combined as the classification features. Finally, the classification features are used to train classifiers to detect double JPEG compression. The results of extensive experiments demonstrate the effectiveness of the proposed method.


title: Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base

abstract: We present a new perspective on neural knowledge base (KB) embeddings, from which we build a framework that can model symbolic knowledge in the KB together with its learning process. We show that this framework well regularizes previous neural KB embedding model for superior performance in reasoning tasks, while having the capabilities of dealing with unseen entities, that is, to learn their embeddings from natural language descriptions, which is very like human's behavior of learning semantic concepts.


title: Learning Dilation Factors for Semantic Segmentation of Street Scenes

abstract: Contextual information is crucial for semantic segmentation. However, finding the optimal trade-off between keeping desired fine details and at the same time providing sufficiently large receptive fields is non trivial. This is even more so, when objects or classes present in an image significantly vary in size. Dilated convolutions have proven valuable for semantic segmentation, because they allow to increase the size of the receptive field without sacrificing image resolution. However, in current state-of-the-art methods, dilation parameters are hand-tuned and fixed. In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.


Raghavendra B.K., & S.K. Srivatsa Evaluation of Logistic Regression and Neural Network Model With Sensitivity Analysis on Medical Datasets


title: Opportunities and Obligations for Libraries in a Social Networking Age: A Survey of Web 2.0 and Networking Sites

abstract: ABSTRACT This article considers tools for communicating and collaborating among scientists and examines the roles emerging software in terms of how librarians can use it to support the faculty. The current use of Web 2.0 technology by researchers, as well as by librarians, is discussed. An annotated sampling of tools now available is included.


title: Architectural implications on the performance and cost of graph analytics systems

abstract: Graph analytics systems have gained significant popularity due to the prevalence of graph data. Many of these systems are designed to run in a shared-nothing architecture whereby a cluster of machines can process a large graph in parallel. In more recent proposals, others have argued that a single-machine system can achieve better performance and/or is more cost-effective. There is however no clear consensus which approach is better. In this paper, we classify existing graph analytics systems into four categories based on the architectural differences, i.e., processing infrastructure (centralized vs distributed), and memory consumption (in-memory vs out-of-core). We select eight open-source systems to cover all categories, and perform a comparative measurement study to compare their performance and cost characteristics across a spectrum of input data, applications, and hardware settings. Our results show that the best performing configuration can depend on the type of applications and input graphs, and there is no dominant winner across all categories. Based on our findings, we summarize the trends in performance and cost, and provide several insights that help to illuminate the performance and resource cost tradeoffs across different graph analytics systems and categories.


title: Distributional Advantage Actor-Critic

abstract: In traditional reinforcement learning, an agent maximizes the reward collected during its interaction with the environment by approximating the optimal policy through the estimation of value functions. Typically, given a state s and action a, the corresponding value is the expected discounted sum of rewards. The optimal action is then chosen to be the action a with the largest value estimated by value function. However, recent developments have shown both theoretical and experimental evidence of superior performance when value function is replaced with value distribution in context of deep Q learning [1]. In this paper, we develop a new algorithm that combines advantage actor-critic with value distribution estimated by quantile regression. We evaluated this new algorithm, termed Distributional Advantage Actor-Critic (DA2C or QR-A2C) on a variety of tasks, and observed it to achieve at least as good as baseline algorithms, and outperforming baseline in some tasks with smaller variance and increased stability.


title: All Your GPS Are Belong To Us: Towards Stealthy Manipulation of Road Navigation Systems

abstract: Mobile navigation services are used by billions of users around globe today. While GPS spoofing is a known threat, it is not yet clear if spoofing attacks can truly manipulate road navigation systems. Existing works primarily focus on simple attacks by randomly setting user locations, which can easily trigger a routing instruction that contradicts with the physical road condition (i.e., easily noticeable). ::: ::: In this paper, we explore the feasibility of a stealthy manipulation attack against road navigation systems. The goal is to trigger the fake turn-by-turn navigation to guide the victim to a wrong destination without being noticed. Our key idea is to slightly shift the GPS location so that the fake navigation route matches the shape of the actual roads and trigger physically possible instructions. To demonstrate the feasibility, we first perform controlled measurements by implementing a portable GPS spoofer and testing on real cars. Then, we design a searching algorithm to compute the GPS shift and the victim routes in real time. We perform extensive evaluations using a trace-driven simulation (600 taxi traces in Manhattan and Boston), and then validate the complete attack via real-world driving tests (attacking our own car). Finally, we conduct deceptive user studies using a driving simulator in both the US and China. We show that 95% of the participants follow the navigation to the wrong destination without recognizing the attack. We use the results to discuss countermeasures moving forward.


title: The Right to Basic Education in South Africa: Lessons from Section 27 and Others v Minister of Education and Another (2012) ZAGPPHC 114

abstract: This article proffers a critical discussion on the right to basic education in South Africa. It argues that access to basic education is society’s crucial developmental aspect. Further that minimal or lack of commitment and respect for education largely impact adversely on the realization of Millennium Development Goals, and constitute a recipe for disaster in any country. The article is a product of distress subsequent to a dismal experience resultant from a failure by Limpopo Department of Basic Education to honour and carry out its obligations of providing and delivering textbooks. Provision of textbooks to schools constitute state’s non-derogable duty which is indispensable in reinforcing the transformative ideals of the Constitution, that aid the commitment of realizing the right to basic education and uplifting societal literacy. It is argued that nationwide workshops and campaigns ought to be carried out in view of propagating for respect, care and commitment to deliver quality basic education to the populace. Further that there exists a desperate need for a reinforced system that ensures quality leadership and accountability for the progressive realization of this essential right. The South African Constitution, other legislation, international instruments, articles and case law were utilized in the completion of this article. DOI: 10.5901/mjss.2014.v5n27p1120


title: Compensation of CMOS op-amps using split-length transistors

abstract: Theoretical and experimental results are presented for op-amp compensation using split-length transistors. By using split-length devices the right-half plane zero which plagues op-amp performance can be eliminated. Experimental results indicate substantial enhancements in speed while reducing power consumption and layout area. Further, these techniques can be used to compensate op-amps when using small supply voltage (VDD).


title: Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

abstract: Emergence of peer-to-peer lending has opened an appealing option for micro-financing and is growing rapidly as an option in the financial industry. However, peer-to-peer lending possesses a high risk of investment failure due to the lack of expertise on the borrowers' creditworthiness. In addition, information asymmetry, the unsecured nature of loans as well as lack of rigid rules and regulations increase the credit risk in peer-to-peer lending. This paper proposes a credit scoring model using artificial neural networks in classifying peer-to-peer loan applications into default and non-default groups. The results indicate that the neural network-based credit scoring model performs effectively in screening default applications.


title: Normal values of the electrocardiogram for ages 16-90 years

abstract: Introduction: To establish an up-to-date and comprehensive set of normal values for the clinically current measurements in the adult ECG, covering all ages for both sexes. Methods: The study population included 13,354 individuals, taken from four population studies in The Netherlands, ranging in age from 16 to 90 years (55% men) and cardiologically healthy by commonly accepted criteria. Standard 12-lead ECGs were available for all participants. The ECGs were processed by a well-validated computer program. Normal limits were taken as the 2nd and 98th percentiles of the measurement distribution per age group. Results: Our study corroborates many findings of previous studies, but also provides more differentiated results, in particular for the older age groups. Age trends were apparent for the QTc interval, QRS axis, and indices of left ventricular hypertrophy. Amplitudes in the left precordial leads showed a substantial increase in the older age groups for women, but not for men. Sex-dependent differences were apparent for most ECG parameters. All results are available on the Website www.normalecg.org, both in tabular and in graphical format. Conclusions: We determined age- and sex-dependent normal values of the adult ECG. Our study distinguishes itself from other studies by the large size of the study population, comprising both sexes, the broad range of ages, and the exhaustive set of measurements. Our results emphasize that most diagnostic ECG criteria should be age- and sex-specific.


title: Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis

abstract: OBJECTIVE ::: The distinct trajectories of patients with autism spectrum disorders (ASDs) have not been extensively studied, particularly regarding clinical manifestations beyond the neurobehavioral criteria from the Diagnostic and Statistical Manual of Mental Disorders. The objective of this study was to investigate the patterns of co-occurrence of medical comorbidities in ASDs. ::: ::: ::: METHODS ::: International Classification of Diseases, Ninth Revision codes from patients aged at least 15 years and a diagnosis of ASD were obtained from electronic medical records. These codes were aggregated by using phenotype-wide association studies categories and processed into 1350-dimensional vectors describing the counts of the most common categories in 6-month blocks between the ages of 0 to 15. Hierarchical clustering was used to identify subgroups with distinct courses. ::: ::: ::: RESULTS ::: Four subgroups were identified. The first was characterized by seizures (n = 120, subgroup prevalence 77.5%). The second (n = 197) was characterized by multisystem disorders including gastrointestinal disorders (prevalence 24.3%) and auditory disorders and infections (prevalence 87.8%), and the third was characterized by psychiatric disorders (n = 212, prevalence 33.0%). The last group (n = 4316) could not be further resolved. The prevalence of psychiatric disorders was uncorrelated with seizure activity (P = .17), but a significant correlation existed between gastrointestinal disorders and seizures (P < .001). The correlation results were replicated by using a second sample of 496 individuals from a different geographic region. ::: ::: ::: CONCLUSIONS ::: Three distinct patterns of medical trajectories were identified by unsupervised clustering of electronic health record diagnoses. These may point to distinct etiologies with different genetic and environmental contributions. Additional clinical and molecular characterizations will be required to further delineate these subgroups.


title: Liquid Crystal polymer (LCP): a new organic material for the development of multilayer dual-frequency/dual-polarization flexible antenna arrays

abstract: The development of dual-frequency (14 and 35 GHz), dual-polarization microstrip antenna arrays is presented for the first time on liquid crystal polymer (LCP) multilayer technology. Some of the properties of LCP, such as multilayer (three-dimensional) vertical integration capability, good electrical and mechanical properties, and near-hermetic nature, make this substrate a practical choice for the design of low-cost antenna arrays that can be integrated with remote sensing applications operating in the Ku and millimeter-wave frequency bands. This work illustrates the potential of LCP as a low-cost, "all-package" solution for developing compact, flexible, antenna arrays that can be used in future communication and remote sensing systems.


title: Stylized caustics: progressive rendering of animated caustics

abstract: In recent years, much work was devoted to the design of light editing methods such as relighting and light path editing. So far, little work addressed the target-based manipulation and animation of caustics, for instance to a differently-shaped caustic, text or an image. The aim of this work is the animation of caustics by blending towards a given target irradiance distribution. This enables an artist to coherently change appearance and style of caustics, e.g., for marketing applications and visual effects. Generating a smooth animation is nontrivial, as photon density and caustic structure may change significantly. Our method is based on the efficient solution of a discrete assignment problem that incorporates constraints appropriate to make intermediate blends plausibly resemble caustics. The algorithm generates temporally coherent results that are rendered with stochastic progressive photon mapping. We demonstrate our system in a number of scenes and show blends as well as a key frame animation.


title: Image Manipulation with Perceptual Discriminators

abstract: Systems that perform image manipulation using deep convolutional networks have achieved remarkable realism. Perceptual losses and losses based on adversarial discriminators are the two main classes of learning objectives behind these advances. In this work, we show how these two ideas can be combined in a principled and non-additive manner for unaligned image translation tasks. This is accomplished through a special architecture of the discriminator network inside generative adversarial learning framework. The new architecture, that we call a perceptual discriminator, embeds the convolutional parts of a pre-trained deep classification network inside the discriminator network. The resulting architecture can be trained on unaligned image datasets while benefiting from the robustness and efficiency of perceptual losses. We demonstrate the merits of the new architecture in a series of qualitative and quantitative comparisons with baseline approaches and state-of-the-art frameworks for unaligned image translation.


title: DevOps Reference Architecture for Multi-cloud IOT Applications

abstract: There is a growing interest among organizations in adopting DevOps approach for IoT (Internet of Things) applications. However, the challenge is: how to apply DevOps when a multi-cloud heterogeneous environment is required for IoT application. This paper aims to addresses this important challenge and proposes a DevOps Reference Architecture (DRA) to deploy IoT-applications on multi-cloud. The proposed architecture is evaluated by the means of a case study, which involves deploying an IoT application on the chosen set of clouds. The results of this initial evaluation indicate that the proposed architecture would help practitioners and researchers to understand the usefulness and applicability of DevOps approach on multi-cloud platform for automating IoT application deployment.


title: Single-pin dual-band patch antenna for GPS and SDARS applications

abstract: A novel low-cost reduced-size single-feed single-layer microstrip patch antenna is presented that receives right-hand circularly polarized satellite signals for the Global Positioning System's L1-Band (1575.42 MHz) and left-hand circularly polarized satellite signals and vertically linearly polarized terrestrial signals for the Satellite Digital Audio Radio System band (2320 MHz – 2345 MHz). The proposed antenna is constructed of a main central patch and a ring surrounding it, both with truncated corners to achieve the desired circular polarization characteristics. The two patches are connected to each other using 4 long and thin microstrip transmission lines. This antenna has acceptable circularly polarized gain at high elevation angles and acceptable vertically linearly polarized gain at the horizon.


title: Joint Probabilistic Data Association Revisited

abstract: In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.


title: Smooth Parametric Surfaces and n-Sided Patches

abstract: The theory of ‘geometric continuity’ within the subject of CAGD is reviewed. In particular, we are concerned with how parametric surface patches for CAGD can be pieced together to form a smooth Ck surface. The theory is applied to the problem of filling an n-sided hole occurring within a smooth rectangular patch complex. A number of solutions to this problem are surveyed.


Ethics As a Competitive Edge


title: Automating test automation

abstract: Mention “test case”, and it conjures up the image of a script or a program that exercises a system under test. In industrial practice, however, test cases often start out as steps described in natural language. These are essentially directions a human tester needs to follow to interact with an application, exercising a given scenario. Since tests need to be executed repeatedly, such manual tests then have to go through test automation to create scripts or programs out of them. Test automation can be expensive in programmer time. We describe a technique to automate test automation. The input to our technique is a sequence of steps written in natural language, and the output is a sequence of procedure calls with accompanying parameters that can drive the application without human intervention. The technique is based on looking at the natural language test steps as consisting of segments that describe actions on targets, except that there can be ambiguity in identifying segments, in identifying the action in a segment, as well as in the specification of the target of the action. The technique resolves this ambiguity by backtracking, until it can synthesize a successful sequence of calls. We present an evaluation of our technique on professionally created manual test cases for two open-source web applications as well as a proprietary enterprise application. Our technique could automate over 82% of the steps contained in these test cases with no human intervention, indicating that the technique can reduce the cost of test automation quite effectively.


title: Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction

abstract: To overcome the limitations of Neural Programmer-Interpreters (NPI) in its universality and learnability, we propose the incorporation of combinator abstraction into neural programing and a new NPI architecture to support this abstraction, which we call Combinatory Neural Programmer-Interpreter (CNPI). Combinator abstraction dramatically reduces the number and complexity of programs that need to be interpreted by the core controller of CNPI, while still allowing the CNPI to represent and interpret arbitrary complex programs by the collaboration of the core with the other components. We propose a small set of four combinators to capture the most pervasive programming patterns. Due to the finiteness and simplicity of this combinator set and the offloading of some burden of interpretation from the core, we are able construct a CNPI that is universal with respect to the set of all combinatorizable programs, which is adequate for solving most algorithmic tasks. Moreover, besides supervised training on execution traces, CNPI can be trained by policy gradient reinforcement learning with appropriately designed curricula.


title: Autoencoding topology

abstract: The problem of learning a manifold structure on a dataset is framed in terms of a generative model, to which we use ideas behind autoencoders (namely adversarial/Wasserstein autoencoders) to fit deep neural networks. From a machine learning perspective, the resulting structure, an atlas of a manifold, may be viewed as a combination of dimensionality reduction and"fuzzy"clustering.


title: Predicting Topic Participation by Jointly Learning User Intrinsic and Extrinsic Preference

abstract: Understanding the preferences of social media participants plays a crucial role in many business applications. A specific aspect of interest is to predict which topics a particular user is more likely to be involved in. Existing efforts on such topic participation forecasting mainly focus on learning from historical user-generated texts to infer their preferred topics, or leverage on an information propagation theory on networks to predict the topics of potential interest. However, jointly utilizing both the sources of data to provide a holistic prediction for such a task has not been exploited. We present a novel joint learning framework that takes advantage of both users’ intrinsic (learned from their past social media postings) and extrinsic preference (learned from the social network influence) and that embeds them into low-dimensional space vectors. To facilitate effective learning, we encode latent continuous embedded vectors into binary ones via locality-sensitive hashing. Furthermore, to explain the predictions made by our “black-box” model, we investigate the importance of each training sample on the topic prediction performance of a testing instance to demonstrate its interpretability. The experiments conducted on datasets collected from several popular social media platforms demonstrate the effectiveness of our proposed method when compared with existing baselines.


title: Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text

abstract: Biomedical named entity recognition (bio-NER) is a crucial and basic step in many biomedical information extraction tasks. However, traditional NER systems are mainly based on complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper, we construct Recurrent Neural Network to identify entity names with word embeddings input rather than hand-designed features. Our contributions mainly include three aspects: (1) we adapt a deep learning architecture Recurrent Neural Network (RNN) to entity names recognition; (2) based on the original RNNs such as Elman-type and Jordan-type model, an improved RNN model is proposed; (3) considering that both past and future dependencies are important information, we combine bidirectional recurrent neural networks based on information entropy at the top layer. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF and deep neural networks (DNN), furthermore, the improved RNN model performs better than two original RNN models and the combined method is effective.


title: Nonlinear Behavior Immunity Modeling of an LDO Voltage Regulator Under Conducted EMI

abstract: This paper presents a nonlinear behavior modeling method committed to the immunity of low-dropout (LDO) voltage regulator to conducted electromagnetic interference (EMI). A nonlinear behavior model is proposed to predict the immunity of LDOs, where the nonlinear characteristics of an LDO voltage regulator are described as a cascade of Taylor series and linear filter system. A test printed circuit board (PCB), using the direct power injection (DPI) method, is designed up to 2 GHz to measure the immunity of LDOs. A nonlinear behavior model, lump-parameter models of discrete components on PCB extracted from S -parameter measurement, and DPI method are used to predict the immunity of an LDO voltage regulator. The comparison of immunity between simulation and measurement shows that the model has a good fit up to 2 GHz.


title: iSAM: An iPhone Stealth Airborne Malware

abstract: Modern and powerful mobile devices comprise an attractive target for any potential intruder or malicious code. The usual goal of an attack is to acquire user’s sensitive data or compromise the device so as to use it as a stepping stone (or bot) to unleash a number of attacks to other targets. In this paper, we focus on the popular iPhone device. We create a new stealth and airborne malware namely iSAM able to wirelessly infect and self-propagate to iPhone devices. iSAM incorporates six different malware mechanisms, and is able to connect back to the iSAM bot master server to update its programming logic or to obey commands and unleash a synchronized attack. Our analysis unveils the internal mechanics of iSAM and discusses the way all iSAM components contribute towards achieving its goals. Although iSAM has been specifically designed for iPhone it can be easily modified to attack any iOS-based device.


title: BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

abstract: In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations. We employ recursive autoencoders to generate tree structures of phrases with embeddings at different levels of granularity (e.g., words, sub-phrases and phrases). Over these embeddings on the source and target side, we introduce a bidimensional attention network to learn their interactions encoded in a bidimensional attention matrix, from which we extract two soft attention weight distributions simultaneously. These weight distributions enable BattRAE to generate compositive phrase representations via convolution. Based on the learned phrase representations, we further use a bilinear neural model, trained via a max-margin method, to measure bilingual semantic similarity. To evaluate the effectiveness of BattRAE, we incorporate this semantic similarity as an additional feature into a state-of-the-art SMT system. Extensive experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.63 BLEU points on average over the baseline.


title: Empathic Concern and Perspective Taking: Linear and Quadratic Effects of Age Across the Adult Life Span

abstract: Results. For both measures and in all three samples, we found evidence for an inverse-U-shaped pattern across age: Middle-aged adults reported higher empathy than both young adults and older adults. We also found a consistent gender difference: Women reported more empathy than men. We did not find systematic differences by ethnicity. However, neither gender nor ethnicity interacted with age effects.


title: Development of a Compact Switched-Reluctance Motor Drive for EV Propulsion With Voltage-Boosting and PFC Charging Capabilities

abstract: This paper presents a compact battery-powered switched-reluctance motor (SRM) drive for an electric vehicle with voltage-boosting and on-board power-factor-corrected-charging capabilities. Although the boost-type front-end DC/DC converter is externally equipped, the on-board charger is formed by the embedded components of SRM windings and converter. In the driving mode, the DC/DC boost converter is controlled to establish boostable well-regulated DC-link voltage for the SRM drive from the battery. During demagnetization of each stroke, the winding stored energy is automatically recovered back to the battery. Moreover, the regenerative braking is achieved by properly setting the SRM in the regenerating mode. The controls of two power stages are properly designed and digitally realized using a common digital signal processor. Good winding current and speed dynamic responses of the SRM drive are obtained. In addition, the static operation characteristics are also improved. In the charging mode, the power devices that were embedded in the SRM converter and the three motor-phase windings are used to form a buck-boost switch-mode rectifier to charge the battery from utility with good power quality. All constituted circuit components of the charger are placed on board, and only the insertion of power cords is needed.


title: Promoting Healthy Eating Attitudes Among Uninsured Primary Care Patients

abstract: Obesity is associated with a number of chronic health problems such as cardiovascular disease, diabetes and cancer. While common prevention and treatment strategies to control unhealthy weight gain tend to target behaviors and lifestyles, the psychological factors which affect eating behaviors among underserved populations also need to be further addressed and included in practice implementations. The purpose of this study is to examine positive and negative emotional valence about food among underserved populations in a primary care setting. Uninsured primary care patients (N = 621) participated in a self-administered survey from September to December in 2015. Higher levels of perceived benefits of healthy food choice were associated with lower levels of a negative emotional valence about food while higher levels of perceived barriers to healthy food choice are related to higher levels of a negative emotional valence about food. Greater acceptance of motivation to eat was associated with higher levels of positive and negative emotional valence about food. Spanish speakers reported greater acceptance of motivation to eat and are more likely to have a negative emotional valence about food than US born or non-US born English speakers. The results of this study have important implications to promote healthy eating among underserved populations at a primary care setting. Healthy food choice or healthy eating may not always be achieved by increasing knowledge. Psychological interventions should be included to advance healthy food choice.


title: URL tree: efficient unsupervised content extraction from streams of web documents

abstract: The Web represents the largest, and an increasingly growing, source of information. Extracting meaningful content from Web pages presents a challenging problem, already extensively addressed in the offline setting. In this work, we focus on content extraction from streams of HTML documents. We present an infrastructure that converts continuously acquired HTML documents into a stream of plain text documents. The presented pipeline consists of RSS readers for data acquisition from different Web sites, a duplicate removal component, and a novel content extraction algorithm which is efficient, unsupervised, and language-independent. Our content extraction approach is based on the observation that HTML documents from the same source normally share a common template. The core of the proposed content extraction algorithm is a simple data structure called URL Tree. The performance of the algorithm was evaluated in a stream setting on a time-stamped semi-automatically annotated dataset which was made publicly available. We compared the performance of URL Tree with that of several open source content extraction algorithms. The evaluation results show that our stream-based algorithm already starts outperforming the other algorithms after only 10 to 100 documents from a specific domain.


title: The Nature of Self-Improving Artificial Intelligence Contents

abstract: An electronic still camera indicates the available capacity of a recording medium and the number of frames remaining that may be photographed, as well as warning by stages in at least two forms, based on the available capacity of the recording medium. The electronic still camera includes a photo lens, mirror, CCD, signal executing circuit, A/D converter, buffer memory, compression circuit, CPU, memory card, and indicator device. A CPU reads in the gross capacity and the written-on portion from a memory card and calculates the available capacity. When the available capacity becomes small, the indicator device that indicates the available capacity or the number of frames remaining that can be photographed begins to flash the display slowly on and off. When the available capacity grows even smaller and is almost gone, the display flashes rapidly on and off. In this way, the photographer can easily detect how many more frames he or she may photograph on the memory card.


title: Object Captioning and Retrieval with Natural Language

abstract: We address the problem of jointly learning vision and language to understand the object in a fine-grained manner. The key idea of our approach is the use of object descriptions to provide the detailed understanding of an object. Based on this idea, we propose two new architectures to solve two related problems: object captioning and natural language-based object retrieval. The goal of the object captioning task is to simultaneously detect the object and generate its associated description, while in the object retrieval task, the goal is to localize an object given an input query. We demonstrate that both problems can be solved effectively using hybrid end-to-end CNN-LSTM networks. The experimental results on our new challenging dataset show that our methods outperform recent methods by a fair margin, while providing a detailed understanding of the object and having fast inference time. The source code will be made available.


title: Provision of Docker and InfiniBand in High Performance Computing

abstract: High Performance Computing (HPC) is playing an important role in a variety of domains with the demand of high-level computational capacity. Besides, HPC provides services for a huge range of different users as well as multiple environments. Hence, the performance of the network is also one of the important criteria. The advent of InfiniBand (IB) aims at the improvement of computer-networking. IB technology has been used and expanded on virtualization environments, especially in virtual machines (VMs). Recently, another virtualization technique known as Docker platform is being popularly considered. Docker promises to bring higher performance, but it also poses some challenging problems. Concretely, while VMs are combined with IB by standard virtualization modules such as SR-IOV, Docker containers are still being examined on feasible solutions with IB. An important question is the advantages and disadvantages of both architectures, namely VM and Docker. In this paper, we deploy Docker on IB infrastructure and evaluate their performance with VMs. Remarkably, we highlight the benefits and the drawbacks of Docker in the conflict of resources when its architecture shares the same kernel with the host. Our evaluations emphasize the potential of Docker containers in HPC field, simultaneously, we propose experiences when using Docker for running parallel applications.


title: The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research

abstract: Collective and swarm robotics explores scenarios involving many robots running at the same time. A good platform for collective-robotic experiments should provide certain features among others: it should have a large battery life, it should be able to perceive its peers, and it should be capable of interacting with them. This paper presents the marXbot, a miniature mobile robot that addresses these needs. The marXbot uses differential-drive treels to provide rough-terrain mobility. The marXbot allows continuous experiments thanks to a sophisticated energy management and a hotswap battery exchange mechanism. The marXbot can self-assemble with peers using a compliant attachment mechanism. The marXbot provides high-quality vision, using two cameras directly interfaced with an ARM processor. Compared to the related work, the marXbot has better energy management, vision, and interaction capabilities. By allowing complex tasks in large environments for long durations, the marXbot opens new perspectives for the collective-robotic research.


title: Household food insecurity associated with stunting and underweight among preschool children in Antioquia, Colombia

abstract: OBJECTIVE: To assess criterion validity of a household food security scale through its associations with child health status in participants of the Colombian Plan for Improving Food and Nutrition in Antioquia (Mejoramiento Alimentario y Nutricional de Antioquia (MANA)). METHODS: A 12-item household food security survey (Colombian Household Food Security Scale, CHFSS) was applied to a cross-sectional stratified random sample of 2 784 low-income households with preschool children receiving MANA food supplements in Antioquia, Colombia. Anthropometrics and health status of the children were also assessed. Chi-square tests were used to initially compare child health status and household food security status. Logistic regression models were further developed to assess this relationship in bivariate and multiple regression models. RESULTS: Statistically significant associations were found between household food insecurity and diagnoses of children's diarrhea, respiratory infections, and parasitosis (P < 0.0001). The risk for child stunting and underweight increased in a dose-response way as food insecurity became more severe. CONCLUSIONS: Our research establishes an important link between household food insecurity and child nutritional status in participants of a food assistance program. The results affirm the criterion validity of the CHFSS, establishing the proposed instrument as a valid measure for food insecurity with high-risk populations.


title: A multilevel deep learning method for big data analysis and emergency management of power system

abstract: The general focus of this study is to design a multilevel deep learning model that provides big data analytics and emergency management knowledge. A big data covariance analysis approach has been used to find multilevel representations of data based on prior knowledge from large scale power systems. For purpose of meeting requirements of incremental knowledge discovery, an adaptive regression algorithm is presented. Given the multilevel operating status and development trend of power system, the emergency management techniques are then proposed to produce intelligent decision making support. In this paper, a multilevel clustered hidden Markov model based global optimization approach is considered for power system emergency management problem, which is an extension of the conventional optimal power flow problem. The objective is defined to generate operation mode that minimizes multilevel cost while satisfying different constraints. To demonstrate the effectiveness of the presented approach, this paper carefully compared the discriminatory power of knowledge discovery models that utilize deep learning with dimensionality reduction based method and machine learning without dimensionality reduction based method. The experimental results showed that the proposed multilevel deep learning approach consistently outperformed the traditional machine learning method. The emergency management of large scale power system may also benefit from the modified hidden Markov model and global optimization.


title: Algorithms for coplanar camera calibration

abstract: Abstract. Coplanar camera calibration is the process of determining the extrinsic and intrinsic camera parameters from a given set of image and world points, when the world points lie on a two-dimensional plane. Noncoplanar calibration, on the other hand, involves world points that do not lie on a plane. While optimal solutions for both the camera-calibration procedures can be obtained by solving a set of constrained nonlinear optimization problems, there are significant structural differences between the two formulations. We investigate the computational and algorithmic implications of such underlying differences, and provide a set of efficient algorithms that are specifically tailored for the coplanar case. More specifically, we offer the following: (1) four algorithms for coplanar calibration that use linear or iterative linear methods to solve the underlying nonlinear optimization problem, and produce sub-optimal solutions. These algorithms are motivated by their computational efficiency and are useful for real-time low-cost systems. (2) Two optimal solutions for coplanar calibration, including one novel nonlinear algorithm. A constraint for the optimal estimation of extrinsic parameters is also given. (3) A Lyapunov type convergence analysis for the new nonlinear algorithm. We test the validity and performance of the calibration procedures with both synthetic and real images. The results consistently show significant improvements over less complete camera models.


title: Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence

abstract: Increasing size of datasets is challenging for machine learning, and Big Data frameworks, such as Apache Spark, have shown promise for facilitating model building on distributed resources. Conformal prediction is a mathematical framework that allows to assign valid confidence levels to object-specific predictions. This contrasts to current best-practices where the overall confidence level for predictions on unseen objects is estimated based on previous performance, assuming exchangeability. Here we report a Spark-based distributed implementation of conformal prediction, which introduces valid confidence estimation in predictive modeling for Big Data analytics. Experimental results on two large-scale datasets show the validity and the scalabilty of the method, which is freely available as open source.


title: Characterization of interconnect coupling noise using in-situ delay-change curve measurements

abstract: The delay-change curve (DCC) characterizes the variation of the interconnect delay due to coupling noise. This paper describes a set of novel models that relate the DCC to the coupling noise waveform. These models are targeted for use in the timing margin design and accurate experimental determination of sub-nanosecond coupling noise. The circuit structure, a set of measurements, the model equations, and the waveform extraction procedures are newly proposed. Evaluation results using a 0.25 /spl mu/m test chip are presented showing good agreement with SPICE simulations.


title: Sliding mode control of the DC-DC flyback converter with zero steady-state error

abstract: In this paper a novel approach for controlling the output voltage of the isolated flyback converter is presented using sliding mode controller. Due to non-minimum-phase nature of the converter and presence of a right-half-plan-zero in voltage transfer function, indirect regulation of the output voltage is applied. In the designed controller, simultaneous use of output voltage and transformer current feedbacks improves dynamic response of the controller. The sliding mode controller is developed so that in the final control law, integral of the output voltage error is present and consequently the output voltage error will be zero in the steady-state. Also, in order to measure transformer magnetizing current, simple and efficient method is proposed. Finally, in order to evaluate response of the controller, flyback converter is a simulated using MATLAB / Simulink.


title: Investigating Information Security Awareness: Research and Practice Gaps

abstract: The aim of this survey is largely exploratory, namely, to discover patterns and trends in the way that practitioners and academics alike tackle the security awareness issue and to have a better understanding of the reasons why security awareness practice remains an unsolved problem. Open coding analysis was performed on numerous publications (articles, surveys, standards, reports and books). A classification scheme of six categories of concern has emerged from the content analysis (e.g., terminology ambiguity), and the chosen publications were classified based on it. The paper identifies ambiguous aspects of current security awareness approaches and the proposed classification provides a guide to identify the range of options available to researchers and practitioners when they design their research and practice on information security awareness.


title: More than you ever wanted to know about synchronization: synchrobench, measuring the impact of the synchronization on concurrent algorithms

abstract: In this paper, we present the most extensive comparison of synchronization techniques. We evaluate 5 different synchronization techniques through a series of 31 data structure algorithms from the recent literature on 3 multicore platforms from Intel, Sun Microsystems and AMD. To this end, we developed in C/C++ and Java a new micro-benchmark suite, called Synchrobench, hence helping the community evaluate new data structures and synchronization techniques. The main conclusion of this evaluation is threefold: (i) although compare-and-swap helps achieving the best performance on multicores, doing so correctly is hard; (ii) optimistic locking offers varying performance results while transactional memory offers more consistent results; and (iii) copy-on-write and read-copy-update suffer more from contention than any other technique but could be combined with others to derive efficient algorithms.


title: WATER EVAPORATION FROM POROUS MEDIA

abstract: Drying is one of the fundamental unit operations in manufacturing, agriculture, and food production. Interestingly, evaporation of water from a porous material can be faster than from the bulk liquid. Motivation for this work arises from the manufacturing of porous materials and the engineering use of matrix-assisted transport. A special core analysis technique, low-field nuclear magnetic resonance (NMR) relaxometry, observed water drying from consolidated and unconsolidated porous samples. The amplitude response provided water content and drying rate, and the T2 relaxation distributions provided the water pore distribution during drying and insight into the internal transport mechanism. In accord with current drying models, three major drying regimes were evident. An early constant rate period (CRP) declined linearly with water saturation Sw. Subsequently, the first falling rate period (FRP1) ultimately declined as the square root of time. Following this, for Sw < Swir, the irreducible water saturation, a second falling rate period (FRP2) had an even slower square-root drying dependence. Measurements of sample-averaged T2 distributions support the concept of capillarity-driven thinning-film pore water transport in CRP mode and stochastic, discontinuous transport of pore-filling blobs in FRP mode. The relation of drying to sample geometry, pore size, and pore structure was determined using seven consolidated porous media and three unconsolidated samples (silica sand, kaolinite clay, and montmorillonite clay). Due to a fast 1 H exchange rate, accurate NMR total water measurement was possible for the swelling montmorillonite, which had significant interlamellar water. In addition, the montmorillonite interlamellar-interstitial mass transfer step did not slow its drying rate compared to the kaolinite clay rate. The sand dried in CRP mode until Swir, but both clay rates lessened for Sw < 0.5. However, the T2-evidenced pore water transport mechanism in the clays appeared to stay in CRP mode.


title: Analysis of Retinal Vessel Segmentation with Deep Learning and its Effect on Diabetic Retinopathy Classification

abstract: The success of deep learning methodologies draws a huge attention to their applications in medical image analysis. One of the applications of deep learning is in segmentation of retinal vessel and severity classification of diabetic retinopathy (DR) from retinal funduscopic image. This paper studies U-Net model performance in segmenting retinal vessel with different settings of dropout and batch normalization and use it to investigate the effect of retina vessel in DR classification. Pre-trained Inception V1 network was used to classify the DR severity. Two sets of retinal images, with and without the presence of vessel, were created from MESSIDOR dataset. The vessel extraction process was done using the best trained U-Net on DRIVE dataset. Final analysis showed that retinal vessel is a good feature in classifying both severe and early cases of DR stage.


title: Arabic speech recognition using MFCC feature extraction and ANN classification

abstract: This research addresses a challenging issue that is to recognize spoken Arabic letters, that are three letters of hijaiyah that have indentical pronounciation when pronounced by Indonesian speakers but actually has different makhraj in Arabic, the letters are sa, sya and tsa. The research uses Mel-Frequency Cepstral Coefficients (MFCC) based feature extraction and Artificial Neural Network (ANN) classification method. The result shows the proposed method obtain a good accuracy with an average acuracy is 92.42%, with recognition accuracy each letters (sa, sya, and tsa) prespectivly 92.38%, 93.26% and 91.63%.


title: Curved Folded Plate Timber Structures

abstract: This work investigates the development of a Curved Origami Prototype made with timber panels. In the last fifteen years the timber industry has developed new, large size, timber panels. Composition and dimensions of these panels and the possibility of milling them with Computer Numerical Controlled machines shows great potential for folded plate structures. To generate the form of these structures we were inspired by Origami, the Japanese art of paper folding. Common paper tessellations are composed of straight creases. First curved paper models were studied by Joseph Albers and his students in the preliminary course of the Bauhaus. Later, David Huffman explored the form finding possibilities offered by curved creased origami. To form curved creases, we use a model developed to create doubly corrugated surfaces with straight creases and planar faces. This model has been developed to build folded plate structures by assembly of planar timber panels. Doubly corrugated surfaces are defined by the corrugation profile and the cross section profile, where the corrugation profile defines the geometry of a simply corrugated surface and the cross section profile it’s bending in space. However, using undulating corrugation profiles, curved creased origami structures can be generated. Within the scope of this work, curved origami figures are used to develop a structural and load bearing timber component. The proposed element is composed of multiple layers, in order to bend the timber panels and to procure the necessary structural stiffness of the element. Our investigations examine the joint geometry of the bent multi-layer surface and its. Experimental investigations are conducted on a simple geometry (see fig.1), which is composed of three simply curved timber panels. A series of prototypes analyse the feasibility of curved folded plate structures with timber panels in terms of design, manufacturing and montage. Further investigations have to show the structural behaviour of such elements and how multiple elements can be used for roof structures.


title: Cluster-based topologies for 3D Networks-on-Chip using advanced inter-layer bus architecture

abstract: Three-dimensional integrated circuits (3D ICs) have emerged as a viable candidate to achieve better performance and packaging density as compared to traditional two-dimensional (2D) ICs. In addition, combining the benefits of 3D ICs and Networks-on-Chip (NoCs) schemes provides a significant performance gain for 3D architectures. In recent years, through-silicon-via (TSV), employed for inter-layer connectivity (vertical channel), has attracted a lot of interest since it enables faster and more power efficient inter-layer communication across multiple stacked layers. The router-based and bus-based organizations are the two dominant architectures for utilizing TSVs as inter-layer communication channel in 3D architectures. Both approaches have some disadvantages. The former suffers from poor scalability and deteriorates the performance at high injection rates, and the latter consumes more area and power. The area overhead of TSVs reduces wafer utilization and yield, which can impact designing 3D architectures with a large number of TSVs. In this paper, two mesh-based topologies for 3D architectures are introduced to mitigate TSV footprint and power dissipation on each layer with a small performance penalty. On top of that, we propose a novel pipeline bus structure for inter-layer communication to improve the performance by reducing the delay and complexity of traditional bus arbitration.


title: Securing smart home: Technologies, security challenges, and security requirements

abstract: Smart homes are gaining vast popularity as the most promising application of the emerging Internet of Things (IoT) technology. Exploiting the high level of connectivity present in current electronic devices (such as smartphones, tablets, and multimedia systems), smart homes provide innovative, automated and interactive services for residential customers through distributed and collaborative operations. As these types of networks become enormously popular, it is fundamental to provide the adequate level of protection against cyber-attacks for the residential customers. However, the resource-constrained nature of many of the devices present in a smart home environment, does not permit to implement the standard security solutions and therefore smart homes currently present security vulnerabilities. In this paper the security challenges and threats to the existing solutions suited for smart homes are examined in detail with the objective of fostering the development of practical solutions to secure the smart homes.


title: Enhanced Temperature Monitoring System for Sinter in a Rotatory Cooler

abstract: Temperature monitoring of moving material is a complex procedure that provides relevant information about thermal behavior. Temperature monitoring requires accurate tracking of moving material to calculate its temperature so temperature curves can be calculated. This work proposes a temperature monitoring system for sinter material, with multiple potential applications in a number of different areas. Firstly, a calibration procedure that translates images to real world units is developed. Calibration enables the calculation of images without perspective projection that provide reliable geometric information. Moreover, with these transformed images the model used to estimate movement is greatly simplified. Secondly, a tracking procedure consisting of feature detection, feature matching and model estimation is applied to the rectified images. Finally, multiple temperature decay curves are calculated simultaneously, providing relevant information about cooling behavior. Features extracted from these curves can be used to detect abnormal cooling behaviors, and to control and regulate the sintering process. Tests of the proposed system indicate excellent performance in terms of calibration accuracy, temperature measurement, description of the cooling patterns, and detection of abnormal conditions.


Multi-task Learning for Predicting Health , Stress , and Happiness


title: Streaming feature selection using alpha-investing

abstract: In Streaming Feature Selection (SFS), new features are sequentially considered for addition to a predictive model. When the space of potential features is large, SFS offers many advantages over traditional feature selection methods, which assume that all features are known in advance. Features can be generated dynamically, focusing the search for new features on promising subspaces, and overfitting can be controlled by dynamically adjusting the threshold for adding features to the model. We describe α-investing, an adaptive complexity penalty method for SFS which dynamically adjusts the threshold on the error reduction required for adding a new feature. α-investing gives false discovery rate-style guarantees against overfitting. It differs from standard penalty methods such as AIC, BIC or RIC, which always drastically over- or under-fit in the limit of infinite numbers of non-predictive features. Empirical results show that SFS is competitive with much more compute-intensive feature selection methods such as stepwise regression, and allows feature selection on problems with over a million potential features.


title: Smartphone sensor data as digital evidence

abstract: The proliferation of smartphones introduces new opportunities in digital forensics. One of the reasons is that smartphones are usually equipped with sensors (e.g. accelerometer, proximity sensor, etc.), hardware which can be used to infer the user's context. This context may be useful in a digital investigation, as it can aid in the rejection or acceptance of an alibi, or even reveal a suspect's actions or activities. Nonetheless, sensor data are volatile, thus are not available in post-mortem analysis. Thus, the only way to timely acquire them, in case such a need arises during a digital investigation, is by software that collects them when they are generated by the suspect's actions. In this paper we examine the feasibility of ad-hoc data acquisition from smartphone sensors by implementing a device agent for their collection in Android, as well as a protocol for their transfer. Then, we discuss our experience regarding the data collection of smartphone sensors, as well as legal and ethical issues that arise from their collection. Finally, we describe scenarios regarding the agent's preparation and use in a digital investigation.


title: Computational design of mechanical characters

abstract: We present an interactive design system that allows non-expert users to create animated mechanical characters. Given an articulated character as input, the user iteratively creates an animation by sketching motion curves indicating how different parts of the character should move. For each motion curve, our framework creates an optimized mechanism that reproduces it as closely as possible. The resulting mechanisms are attached to the character and then connected to each other using gear trains, which are created in a semi-automated fashion. The mechanical assemblies generated with our system can be driven with a single input driver, such as a hand-operated crank or an electric motor, and they can be fabricated using rapid prototyping devices. We demonstrate the versatility of our approach by designing a wide range of mechanical characters, several of which we manufactured using 3D printing. While our pipeline is designed for characters driven by planar mechanisms, significant parts of it extend directly to non-planar mechanisms, allowing us to create characters with compelling 3D motions.


title: Financial Accounting Fraud Detection Using Business Intelligence

abstract: The paper investigates the inherent problems of financial fraud detection and proposes a forensic accounting framework using business intelligence as a plausible means of addressing them. The paper adopts an empirical case study approach to present how business intelligence could be used effectively in the detection of financial accounting fraud. The proposed forensic accounting framework using business intelligence (BI) provides a three-phase model via novel knowledge discovery technique to perform the financial analysis such as ratio analysis for a business case scenario. The implementation of the framework practically demonstrates by using their accounting data how the technologies and the investigative methods of trend analysis could be adopted in order to investigate fraudulent financial reporting unlike traditional methods of vertical and horizontal analysis for the business case study. Finally, the results justify the effectiveness of the proposed BI model in proactively identifying, classifying and evaluating financial fraud in the organisation. This research further leads to practical follow-up steps that would serve as guidelines for the forensic accounting auditors and management to focus on the prime areas of financial fraud present in the case study. Overall, the proposed model caters to detecting various types of accounting fraud as well as aids in continuous improvement of an organisation?s accounting, audit, systems and policies through the feedback loop.


title: Machine learning and pattern matching in physical design

abstract: Machine learning (ML) and pattern matching (PM) are powerful computer science techniques which can derive knowledge from big data, and provide prediction and matching. Since nanometer VLSI design and manufacturing have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning and pattern matching techniques in VLSI physical design (including physical verification), e.g., lithography hotspot detection and data/pattern-driven physical design, as ML and PM can raise the level of abstraction from detailed physics-based simulations and provide reasonably good quality-of-result. In this paper, we will discuss key techniques and recent results of machine learning and pattern matching, with their applications in physical design.


SLA application with Big Data on WSQ Taxonomy


title: Neuronal population coding of movement direction.

abstract: Although individual neurons in the arm area of the primate motor cortex are only broadly tuned to a particular direction in three-dimensional space, the animal can very precisely control the movement of its arm. The direction of movement was found to be uniquely predicted by the action of a population of motor cortical neurons. When individual cells were represented as vectors that make weighted contributions along the axis of their preferred direction (according to changes in their activity during the movement under consideration) the resulting vector sum of all cell vectors (population vector) was in a direction congruent with the direction of movement. This population vector can be monitored during various tasks, and similar measures in other neuronal populations could be of heuristic value where there is a neural representation of variables with vectorial attributes.


Mechanism Design for Total Quality Management: Using the Bootstrap Algorithm for Changing the Control Game


title: Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil

abstract: This paper studies the introduction of electronic voting technology in Brazilian elections. Estimates exploiting a regression discontinuity design indicate that electronic voting reduced residual (error‐ridden and uncounted) votes and promoted a large de facto enfranchisement of mainly less educated citizens. Estimates exploiting the unique pattern of the technology's phase‐in across states over time suggest that, as predicted by political economy models, it shifted government spending toward health care, which is particularly beneficial to the poor. Positive effects on both the utilization of health services (prenatal visits) and newborn health (low‐weight births) are also found for less educated mothers, but not for the more educated.


title: Examining Attitudes toward Information Security Behaviour using Mixed Methods.

abstract: This paper reports on a mixed-method research project that examined the attitudes of computer users toward accidental/naive information security (InfoSec) behaviour. The aim of this research was to investigate the extent to which attitude data elicited from repertory grid technique (RGT) interviewees support their responses collected via an online survey questionnaire. Twenty five university students participated in this two-stage project. Individual attitude scores were calculated for each of the research methods and were compared across seven behavioural focus areas using Spearman product-moment correlation coefficient. The two sets of data exhibited a small-to-medium correlation when individual attitudes were analysed for each of the focus areas. In summary, this exploratory research indicated that the two research approaches were reasonably complementary and the RGT interview results tended to triangulate the attitude scores derived from the online survey questionnaire, particularly in regard to attitudes toward Incident Reporting behaviour, Email Use behaviour and Social Networking Site Use behaviour. The results also highlighted some attitude items in the online questionnaire that need to be reviewed for clarity, relevance and non-ambiguity.


title: Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation

abstract: User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users’ interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user’s interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users’ ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.


title: Multiple human detection in images based on differential evolution and HOG-LBP

abstract: In this paper a method for multiple human detection in the image has been presented. This method uses differential evolution (DE) algorithm to improve window position detection speed and HOG-LBP algorithm for feature extraction. Fitness function for DE algorithm is SVM and in the final state, a postprocessing on detected windows by DE algorithm is performed. This method has been tested on INRIA datasets and its precision for detecting humans in the image is 92% which is better than state of the art methods.


title: An adoption model of Software As a Service (SAAS) in SMEs

abstract: Software as Service (SaaS) model of cloud computing is currently one of the most advanced technology innovations. Due to its scalability and network-based operation, SaaS offers opportunities for small and medium enterprises (SMEs) to take advantage of the latest technology without costly investment on IT infrastructure and expertise. Despite its benefits, SMEs' adoption of SaaS is not prevalent. In this paper, by synthesizing Diffusion of Innovation theory, Technology-OrganizationEnvironment theory and Protection Motivation theory, we propose a conceptual model which explains adoption factors of SaaS in SMEs. Our proposed model extends focus on protection motivation of SMEs in assessing SaaS adoption risks in addition to technology, organisation, and environment factors.


title: An Examination of Verbal Working Memory Capacity in Children With Specific Language Impairment

abstract: This study investigated verbal working memory capacity in children with specific language impairment (SLI). The task employed in this study was the Competing Language Processing Task (CLPT) developed by Gaulin and Campbell (1994). A total of 40 school-age children participated in this investigation, including 20 with SLI and 20 normal language (NL) age-matched controls. Results indicated that the SLI and NL groups performed similarly in terms of true/false comprehension items, but that the children with SLI evidenced significantly poorer word recall than the NL controls, even when differences in nonverbal cognitive scores were statistically controlled. Distinct patterns of word-recall errors were observed for the SLI and NL groups, as well as different patterns of associations between CLPT word recall and performance on nonverbal cognitive and language measures. The findings are interpreted within the framework of a limited-capacity model of language processing.


title: Investigation on transformer design of high frequency high efficiency dc-dc converters

abstract: This paper studies the high-frequency high-efficiency transformer design. Several novel concepts are proposed to reveal the essence of the transformer design. In order to minimize the winding loss, several winding structure are proposed and compared. The planar transformer winding with PCB or spiral windings are discussed. The key factors to achieve the low winding losses are presented. The transformer design of the LLC resonant converter is investigated. The impacts on integration of magnetizing inductance and leakage inductance are studied.


title: MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing

abstract: This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018. Based on training data and systems from the WMT2017 shared task, we re-implement our own models from the last shared task and introduce improvements based on extensive parameter sharing. Next we experiment with our implementation of dual-source transformer models and data selection for the IT domain. Our submissions decisively wins the SMT post-editing sub-task establishing the new state-of-the-art and is a very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on the rather weak results in the NMT sub-task, we hypothesize that neural-on-neural APE might not be actually useful.


title: Evaluation of Neural Networks for Image Recognition Applications: Designing a 0-1 MILP Model of a CNN to create adversarials

abstract: Image Recognition is a central task in computer vision with applications ranging across search, robotics, self-driving cars and many others. There are three purposes of this document: 1. We follow up on (Fischetti & Jo, December, 2017) and show how standard convolutional neural network can be optimized to a more sophisticated capsule architecture. 2. We introduce a MILP model based on CNN to create adversarials. 3. We compare and evaluate each network for image recognition tasks.


title: Next-Term Student Performance Prediction: A Recommender Systems Approach

abstract: An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood. To further this goal, we develop a system to predict students’ grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them. We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain. In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Multi-Linear Regression model achieve the lowest prediction error. Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance. We also discover key differences between transfer and non-transfer students. Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses. Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.


title: Think like a vertex, behave like a function! a functional DSL for vertex-centric big graph processing

abstract: The vertex-centric programming model, known as “think like a vertex”, is being used more and more to support various big graph processing methods through iterative supersteps that execute in parallel a user-defined vertex program over each vertex of a graph. However, the imperative and message-passing style of existing systems makes defining a vertex program unintuitive. In this paper, we show that one can benefit more from “Thinking like a vertex” by “Behaving like a function” rather than “Acting like a procedure” with full use of side effects and explicit control of message passing, state, and termination. We propose a functional approach to vertex-centric graph processing in which the computation at every vertex is abstracted as a higher-order function and present Fregel, a new domain-specific language. Fregel has clear functional semantics, supports declarative description of vertex computation, and can be automatically translated into Pregel, an emerging imperative-style distributed graph processing framework, and thereby achieve promising performance. Experimental results for several typical examples show the promise of this functional approach.


title: Unraveling Go gaming nature by Ising Hamiltonian and common fate graphs: tactics and statistics

abstract: Go gaming is a struggle between adversaries, black and white simple stones, and aim to control the most Go board territory for success. Rules are simple but Go game fighting is highly intricate. Stones placement and interaction on board is random-appearance, likewise interaction phenomena among basic elements in physics thermodynamics, chemistry, biology, or social issues. We model the Go game dynamic employing an Ising model energy function, whose interaction coefficients reflect the application of rules and tactics to build long-term strategies. At any step of the game, the energy function of the model assesses the control and strength of a player over the board. A close fit between predictions of the model with actual game's scores is obtained. AlphaGo computer is the current top Go player, but its behavior does not wholly reveal the Go gaming nature. The Ising function allows for precisely model the stochastic evolutions of Go gaming patterns, so, to advance the understanding on Go own-dynamic -beyond the player`s abilities. The analysis of the frequency and combination of tactics shows the formation of patterns in the groups of stones during a game, regarding the turn of each player, or if human or computer adversaries are confronted.


title: Smart digital door lock for the home automation

abstract: In this paper, we propose a smart digital door lock system for home automation. A digital door lock system is equipment that uses the digital information such as a secret code, semi-conductors, smart card, and finger prints as the method for authentication instead of the legacy key system. In our proposed system, a ZigBee module is embedded in digital door lock and the door lock acts as a central main controller of the overall home automation system. Technically, our proposed system is the network of sensor nodes and actuators with digital door lock as base station. A door lock system proposed here consists of RFID reader for user authentication, touch LCD, motor module for opening and closing of the door, sensor modules for detecting the condition inside the house, communication module, and control module for controlling other modules. Sensor nodes for environment sensing are deployed at appropriate places at home. Status of individual ZigBee module can be monitored and controlled by the centralized controller, digital door lock. As the door lock is the first and last thing people come across in entering and leaving the home respectively, the home automation function in digital door lock system enables user to conveniently control and monitor home environment and condition all at once before entering or leaving the house. Furthermore, it also allows users to remotely monitor the condition inside the house through Internet or any other public network. The biggest advantage of our proposed system over existing ones is that it can be easily installed when and where necessary without requirement of any infrastructures and proper planning.


title: Spatio-Temporal Attention Models for Grounded Video Captioning

abstract: Automatic video captioning is challenging due to the complex interactions in dynamic real scenes. A comprehensive system would ultimately localize and track the objects, actions and interactions present in a video and generate a description that relies on temporal localization in order to ground the visual concepts. However, most existing automatic video captioning systems map from raw video data to high level textual description, bypassing localization and recognition, thus discarding potentially valuable information for content localization and generalization. In this work we present an automatic video captioning model that combines spatio-temporal attention and image classification by means of deep neural network structures based on long short-term memory. The resulting system is demonstrated to produce state-of-the-art results in the standard YouTube captioning benchmark while also offering the advantage of localizing the visual concepts (subjects, verbs, objects), with no grounding supervision, over space and time.


title: A novel speaker clustering algorithm via supervised affinity propagation

abstract: This paper addresses the problem of speaker clustering in telephone conversations. Recently, a new clustering algorithm named affinity propagation (AP) is proposed. It exhibits fast execution speed and finds clusters with low error. However, AP is an unsupervised approach which may make the resulting number of clusters different from the actual one. This deteriorates the speaker purity dramatically. This paper proposes a modified method named supervised affinity propagation (SAP), which automatically reruns the AP procedure to make the final number of clusters converge to the specified number. Experiments are carried out to compare SAP with traditional k-means and agglomerative hierarchical clustering on 4-hour summed channel conversations in the NIST 2004 Speaker Recognition Evaluation. Experiment results show that the SAP method leads to a noticeable speaker purity improvement with slight cluster purity decrease compared with AP.


title: DW-AES: A Domain-Wall Nanowire-Based AES for High Throughput and Energy-Efficient Data Encryption in Non-Volatile Memory

abstract: Big-data storage poses significant challenges to anonymization of sensitive information against data sniffing. Not only will the encryption bandwidth be limited by the I/O traffic, the transfer of data between the processor and the memory will also expose the input-output mapping of intermediate computations on I/O channels that are susceptible to semi-invasive and non-invasive attacks. Limited by the simplistic cell-level logic, existing logic-in-memory computing architectures are incapable of performing the complete encryption process within the memory at reasonable throughput and energy efficiency. In this paper, a block-level in-memory architecture for advanced encryption standard (AES) is proposed. The proposed technique, called DW-AES, maps all AES operations directly to the domain-wall nanowires. The entire encryption process can be completed within a homogeneous, high-density, and standby-power-free non-volatile spintronic-based memory array without exposing the intermediate results to external I/O interface. Domain-wall nanowire-based pipelining and multi-issue pipelining methods are also proposed to increase the throughput of the baseline DW-AES with an insignificant area overhead and negligible difference on leakage power and energy consumption. The experimental results show that DW-AES can reduce the leakage power and area by the orders of magnitude compared with existing CMOS ASIC accelerators. It has an energy efficiency of 22 pJ/b, which is $5\times $ and $3\times $ better than the CMOS ASIC and memristive CMOL-based implementations, respectively. Under the same area budget, the proposed DW-AES achieves $4.6\times $ higher throughput than the latest CMOS ASIC AES with similar power consumption. The throughput improvement increases to $11\times $ for pipelined DW-AES at the expense of doubling the power consumption.


Pretreatment of feedstock for enhanced biogas production