title: Climbing the “Stairway to Heaven”: Evolving From Agile Development to Continuous Deployment of Software

abstract: Software-intensive systems companies need to evolve their practices continuously in response to competitive pressures. Based on a conceptual model presented as the “Stairway to Heaven,” we present the transition process when moving from traditional development towards continuous deployment of software. Our research confirms that the transition towards agile development requires a careful introduction of agile practices into the organization, a shift to small development teams, and a focus on features rather than components. The transition towards continuous integration requires an automated test suite, a main branch to which code is continually delivered, and a modularized architecture. The move towards continuous deployment requires internal and external stakeholders to be fully involved and a proactive customer with whom to explore the concept. Finally, the transition towards R&D as an innovation system requires careful ecosystem management in order to align internal business strategies with the dynamics of a competitive business ecosystem. Characteristic for all transitions is the critical alignment of internal and external processes in order to fully maximize the benefits as provided by the business ecosystem of which a company is part.


title: Machine learning and games

abstract: The history of the interaction of machine learning and computer game-playing goes back to the earliest days of Artificial Intelligence, when Arthur Samuel worked on his famous checker-playing program, pioneering many machine-learning and game-playing techniques (Samuel, 1959, 1967). Since then, both fields have advanced considerably, and research in the intersection of the two can be found regularly in conferences in their respective fields and in general AI conferences. For surveys of the field we refer to Ginsberg (1998), Schaeffer (2000), Furnkranz (2001); edited volumes have been compiled by Schaeffer and van den Herik (2002) and by Furnkranz and Kubat (2001). In recent years, the computer games industry has discovered AI as a necessary ingredient to make games more entertaining and challenging and, vice versa, AI has discovered computer games as an interesting and rewarding application area. The industry’s perspective is witnessed by a plethora of recent books on gentle introductions to AI techniques for game programmers (Collins, 2002; Champanard, 2003; Bourg & Seemann, 2004; Schwab, 2004) or a series of edited collections of articles (Rabin, 2002, 2003, 2006). AI research on computer games began to follow developments in the games industry early on, but since John Laird’s keynote address at the AAAI 2000 conference, in which he advocated Interactive Computer Games as a challenging and rewarding application area for AI (Laird & van Lent, 2001), numerous workshops (Fu & Orkin, 2004; Aha et al., 2005), conferences, and special issues of journals (Forbus & Laird, 2002) demonstrate the growing importance of game-playing applications for Artificial Intelligence.


Artificial Intelligence as a Positive and Negative Factor in Global Risk


title: Manipulation and Transportation With Cooperative Underwater Vehicle Manipulator Systems

abstract: Autonomous underwater manipulation has been a topic of interest since the early 1990s. In the past few years, several milestone projects such as SAUVIM and TRIDENT have demonstrated autonomy capabilities for a single underwater vehicle manipulator system (UVMS) in performing simple manipulation tasks, e.g., the recovery of an object from the seafloor. The Italian funded MARIS project aims to extend some of these results to multiple UVMSs performing a cooperative transportation task of a long object such as a pipe. This paper presents the results achieved in developing a unifying architecture for the control of both individually and cooperatively operating UVMSs which explicitly makes use of a limited amount of information exchange between the agents, which is needed due to the severe bandwidth limitations of the underwater acoustic communications. A complete execution of the reference transportation mission is presented to support the proposed distributed algorithm. Furthermore, hydrodynamic simulations of the cooperative transportation phase are presented and an analysis of the achievable performances as different communication schemes are employed is given.


title: Wireless Sensor Network System Design using Raspberry Pi and Arduino for Environmental Monitoring Applications

abstract: This paper from the 9th International Conference on Future Networks and Communications, FNC 2014 conference proceedings describes a wireless sensor network system developed using open-source hardware platforms, Arduino and Raspberry Pi.


title: A Magnetic-Geared Outer-Rotor Permanent-Magnet Brushless Machine for Wind Power Generation

abstract: This paper presents a new permanent-magnet (PM) brushless machine for wind power generation. This machine adopts an outer-rotor topology, aiming at capturing wind power directly. In order to achieve high power density, a high-speed PM brushless generator is artfully integrated with a coaxial magnetic gear. The design details, with emphasis on the special constraints of wind power generation, are elaborated. By using the time-stepping finite element method, the static characteristics as well as no-load and on-load operations are simulated. A prototype is also built for experimentation. Both simulation and experimental results are given to verify the validity of the proposed machine. Finally, a quantitative comparison is made to justify that the proposed machine is of smaller size, lighter weight, and lower cost than its counterparts.


title: Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition

abstract: DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often used for clothes recognition and although it provides comprehensive annotations, the attributes distribution is unbalanced and repetitive specially for training fine-grained attribute recognition models. In this work, we tailored DeepFashion for fine-grained attribute recognition task by focusing on each category separately. After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes. We use a bilinear convolutional neural network with pairwise ranking loss function for multi-label fine-grained attribute recognition and show that the new annotations improve the results for such a task. The detailed annotations for each of the selected categories are provided for public use.


Using Big Data Analytics for Money Laundering Detection – A Case Study


Nucleotide interconversions. IX. The regulatory influence of deoxycytidine 5'-triphosphate and deoxythymidine 5'-triphosphate on deoxycytidylate deaminase.


title: An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure

abstract: Focusing on woody vegetation in Queensland, Australia, the study aimed to establish whether the relationship between Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) HH and HV backscattering coefficients and above ground biomass (AGB) was consistent within and between structural formations (forests, woodlands and open woodlands, including scrub). Across these formations, 2781 plot-based measurements (from 1139 sites) of tree diameters by species were collated, from which AGB was estimated using generic allometric equations. For Queensland, PALSAR fine beam dual (FBD) 50 m strip data for 2007 were provided through the Japanese Space Exploration Agency's (JAXA) Kyoto and Carbon (K&C) Initiative, with up to 3 acquisitions available for each Reference System for Planning (RSP) paths. When individual strips acquired over Queensland were combined, `banding' was evident within the resulting mosaics, with this attributed to enhanced L-band backscatter following rainfall events in some areas. Reference to Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data indicated that strips with enhanced L-band backscatter corresponded to areas with increased effective vegetation water content (kg m-2) and, to a lesser extent, soil moisture (g cm-3). Regardless of moisture conditions, L-band HV topographically normalized backscattering intensities backscatter (σfo) increased asymptotically with AGB, with the saturation level being greatest for forests and least for open woodlands. However, under conditions of relative maximum surface moisture, L-band HV and HH σfo was enhanced by as much as 2.5 and 4.0 dB respectively, particularly for forests of lower AGB, with this resulting in an overall reduction in dynamic range. The saturation level also reduced at L-band HH for forests and woodlands but remained similar for open woodlands. Differences in the rate of increase in both L-band HH and HV σfo with AGB were observed between forests and the woodland categories (for both relatively wet and dry conditions) with these attributed, in part, to differences in the size class distribution and stem density between non-remnant (secondary) forests and remnant woodlands of lower AGB. The study concludes that PALSAR data acquired when surface moisture and rainfall are minimal allow better estimation of the AGB of woody vegetation and that retrieval algorithms ideally need to consider differences in surface moisture conditions and vegetation structure.


title: Magnet leakage flux of axial flux PM synchronous machines for EV

abstract: This paper presents the study of an axial flux permanent magnet synchronous machine specially designed for electric vehicle (EV) applications. Based on the analysis of the physical machine a 3D simulation model is created. The goal of the simulations is to investigate the influence of certain input design parameters on the machine's PM leakage flux. The paper shows how much, and under what conditions specific parameters affect the resulting PM leakage flux.


title: Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOM

abstract: Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naive Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.


title: Delegated persist ordering

abstract: Systems featuring a load-store interface to persistent memory (PM) are expected soon, making in-memory persistent data structures feasible. Ensuring persistent data structure recoverability requires constraints on the order PM writes become persistent. But, current memory systems reorder writes, providing no such guarantees. To complement their upcoming 3D XPoint memory, Intel has announced new instructions to enable programmer control of data persistence. We describe the semantics implied by these instructions, an ordering model we call synchronous ordering. Synchronous ordering (SO) enforces order by stalling execution when PM write ordering is required, exposing PM write latency on the execution critical path. It incurs an average slowdown of 7.21x over volatile execution without ordering in PM-write-intensive benchmarks. SO tightly couples enforcing order and flushing writes to PM, but this tight coupling is unneeded in many recoverable software systems. Instead, we propose delegated ordering, wherein ordering requirements are communicated explicitly to the PM controller, fully decoupling PM write ordering from volatile execution and cache management. We demonstrate that delegated ordering can bring performance within 1.93x of volatile execution, improving over SO by 3.73x.


title: Fact Checking in Community Forums

abstract: Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.


title: Relaxed Dominance Relations among Female Moor Macaques (Macaca maurus) in Their Natural Habitat, South Sulawesi, Indonesia

abstract: Field observations were made of the social behaviour of moor macaques (Macaca maurus), one of several species of macaques that live in Sulawesi, Indonesia. The present study was des


title: Design of an arduino based voice-controlled automated wheelchair

abstract: This paper represents the design of an Arduino based voice controlled automated wheelchair. The design is developed with a voice recognition system, which allows the physically disabled person to control the wheelchair by voice command who have issues in hand movement due to ageing or paralysis for joystick controlled wheelchairs. The design also provide some additional features such as obstacle detection for the safe movement and a GSM based navigation system for tracking and sending notifications to increase the usability of the automated wheelchair system. To implement the design, Arduino Mega2560, Easy VR3 speech recognition module, SIM900A GSM module and relay based motor controller circuits are used along with the wheelchair. The designed wheelchair system does not require any wearable sensors for using other biomedical signals to control wheelchair movement (i.e. EEG, EMG, EOG sensors) as reported in several research studies which require complex signal processing techniques done with an extra bulky computing system attached with the wheelchair. In the proposed design, the speech processing is done solely with the available integrated speech processing module (Easy VR3) which removes the necessity of any bulky complex extra computing device. Moreover, the proposed technique is relatively simple and cheaper to implement with the widely used available electronic devices in comparison to other existing techniques which will have a great impact on the societies of developing and under developing countries.


title: Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud

abstract: The recent cloud computing paradigm represents a trend of moving business applications to platforms run by parties located in different administrative domains. A cloud platform is often highly scalable and cost-effective through its pay-as-you-go pricing model. However, being shared by a large number of users, the running of applications in the platform faces higher performance uncertainty compared to a dedicated platform. Existing Service Level Agreements (SLAs) cannot sufficiently address the performance variation issue. In this paper, we use utility theory leveraged from economics and develop a new utility model for measuring customer satisfaction in the cloud. Based on the utility model, we design a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them. We consider an infrastructure-as-a-service type cloud platform (e.g., Amazon EC2), where a business service provider leases virtual machine (VM) instances with spot prices from the cloud and gains revenue by serving its customers. Particularly, we investigate the interaction of service profit and customer satisfaction. In addition, we present two scheduling algorithms that can effectively bid for different types of VM instances to make tradeoffs between profit and customer satisfaction. We conduct extensive simulations based on the performance data of different types of Amazon EC2 instances and their price history. Our experimental results demonstrate that the algorithms perform well across the metrics of profit, customer satisfaction and instance utilization.


title: Detection of semantically similar code

abstract: The traditional similar code detection approaches are limited in detecting semantically similar codes, impeding their applications in practice. In this paper, we have improved the traditional metrics-based approach as well as the graph-based approach and presented a metrics-based and graph-based combined approach. First, source codes are represented as augmented system dependence graphs. Then, metrics-based candidate similar code extraction is performed to filter out most of the dissimilar code pairs so as to lower the computational complexity. After that, code normalization is performed on the candidate similar codes to remove code variations so as to detect similar code at the semantic level. Finally, program matching is performed on the normalized control dependence trees to output semantically similar codes. Experiment results show that our approach can detect similar codes with code variations, and it can be applied to large software.


title: A Visual Analytics Framework for Exploring Theme Park Dynamics

abstract: In 2015, the top 10 largest amusement park corporations saw a combined annual attendance of over 400 million visitors. Daily average attendance in some of the most popular theme parks in the world can average 44,000 visitors per day. These visitors ride attractions, shop for souvenirs, and dine at local establishments; however, a critical component of their visit is the overall park experience. This experience depends on the wait time for rides, the crowd flow in the park, and various other factors linked to the crowd dynamics and human behavior. As such, better insight into visitor behavior can help theme parks devise competitive strategies for improved customer experience. Research into the use of attractions, facilities, and exhibits can be studied, and as behavior profiles emerge, park operators can also identify anomalous behaviors of visitors which can improve safety and operations. In this article, we present a visual analytics framework for analyzing crowd dynamics in theme parks. Our proposed framework is designed to support behavioral analysis by summarizing patterns and detecting anomalies. We provide methodologies to link visitor movement data, communication data, and park infrastructure data. This combination of data sources enables a semantic analysis of who, what, when, and where, enabling analysts to explore visitor-visitor interactions and visitor-infrastructure interactions. Analysts can identify behaviors at the macro level through semantic trajectory clustering views for group behavior dynamics, as well as at the micro level using trajectory traces and a novel visitor network analysis view. We demonstrate the efficacy of our framework through two case studies of simulated theme park visitors.


title: Inductive coupling system for E-bike wireless charging

abstract: The paper deals with construction design and laboratory measurement of magnetic inductive coupling system suitable for E-bike wireless chargers. It briefly elaborates key design issues relating to the system configuration, material used and given operational properties. The paper also describes fully automatic laboratory equipment for the experimental testing of the system.


title: Phylogenetic analysis of the vertebrate Excitatory/Neutral Amino Acid Transporter (SLC1/EAAT) family reveals lineage specific subfamilies

abstract: BackgroundThe composition and expression of vertebrate gene families is shaped by species specific gene loss in combination with a number of gene and genome duplication events (R1, R2 in all vertebrates, R3 in teleosts) and depends on the ecological and evolutionary context. In this study we analyzed the evolutionary history of the solute carrier 1 (SLC1) gene family. These genes are supposed to be under strong selective pressure (purifying selection) due to their important role in the timely removal of glutamate at the synapse.ResultsIn a genomic survey where we manually annotated and analyzing sequences from more than 300 SLC1 genes (from more than 40 vertebrate species), we found evidence for an interesting evolutionary history of this gene family. While human and mouse genomes contain 7 SLC1 genes, in prototheria, sauropsida, and amphibia genomes up to 9 and in actinopterygii up to 13 SLC1 genes are present. While some of the additional slc1 genes in ray-finned fishes originated from R3, the increased number of SLC1 genes in prototheria, sauropsida, and amphibia genomes originates from specific genes retained in these lineages.Phylogenetic comparison and microsynteny analyses of the SLC1 genes indicate, that theria genomes evidently lost several SLC1 genes still present in the other lineage. The genes lost in theria group into two new subfamilies of the slc1 gene family which we named slc1a8/eaat6 and slc1a9/eaat7.ConclusionsThe phylogeny of the SLC1/EAAT gene family demonstrates how multiple genome reorganization and duplication events can influence the number of active genes. Inactivation and preservation of specific SLC1 genes led to the complete loss of two subfamilies in extant theria, while other vertebrates have retained at least one member of two newly identified SLC1 subfamilies.


title: 3D Shape Reconstruction from 2D Landmarks: A Convex Formulation

abstract: We investigate the problem of reconstructing the 3D shape of an object, given a set of landmarks in a single image. To alleviate the reconstruction ambiguity, a widely-used approach is to confine the unknown 3D shape within a shape space built upon existing shapes. While this approach has proven to be successful in various applications, a challenging issue remains, i.e. the joint estimation of shape parameters and camera-pose parameters requires to solve a nonconvex optimization problem. The existing methods often adopt an alternating minimization scheme to locally update the parameters, and consequently the solution is sensitive to initialization. In this paper, we propose a convex formulation to address this issue and develop an efficient algorithm to solve the proposed convex program. We demonstrate the exact recovery property of the proposed method, its merits compared to the alternative methods, and the applicability in human pose, car and face reconstruction.


title: Psychophysical Characterization and Testbed Validation of a Wearable Vibrotactile Glove for Telemanipulation

abstract: This paper describes and evaluates a high-fidelity, low-cost haptic interface for teleoperation. The interface is a wearable vibrotactile glove containing miniature voice coils that provides continuous, proportional force information to the user's fingertips. In psychophysical experiments, correlated variations in the frequency and amplitude of the stimulators extended the user's perceptual response range compared to varying amplitude or frequency alone. In an adaptive, force-limited, pick-and-place manipulation task, the interface allowed users to control the grip forces more effectively than no feedback or binary feedback, which produced equivalent performance. A sorting experiment established that proportional tactile feedback enhances the user's ability to discriminate the relative properties of objects, such as weight. We conclude that correlated amplitude and frequency signals, simulating force in a remote environment, substantially improve teleoperation.


title: ATSC 3.0 implementation in GNU Radio Companion

abstract: High quality television services entail more complex systems and higher costs of prototyping, development and testing methods. The concept of Software-Defined Radio, which addresses flexibility and adaptability to new systems as they are researched, is an option to study the behavior of some technologies that will be used in future systems. This paper presents an overview of the new Advanced Television Systems Committee (ATSC) system, named ATSC 3.0. Finally, it proposes an implementation of ATSC 3.0 modulator using the GNU Radio Companion (GRC) software and validates the implementation transmitting and receiving the signal with a third party hardware and software.


title: Advances in All-Neural Speech Recognition

abstract: This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology.


title: Minimum Height Packing for Layered Manufacturing Using an Extended Pattern Search Algorithm

abstract: This paper introduces an approach to solve the minimum height layout problem for layered manufacturing using a pattern search based algorithm called Extended Pattern Search. In the batch processing of several components in a typical layered manufacturing run, it is efficient in terms of cost and time to build the components after packing their 3D models such that the maximum height of the components is minimized. 3D component layout based on a pattern search algorithm provides a framework to accomplish such minimum height packing. A method to determine the objective function weights for a class of problems is also provided. Finally error minimizing build directions are incorporated in the 3D layout framework by imposing suitable spatial orientation constraints on individual components.Copyright © 2003 by ASME


title: Automatic keyword extraction from historical document images

abstract: This paper presents an automatic keyword extraction method from historical document images. The proposed method is language independent because it is purely appearance based, where neither lexical information nor any other statistical language models are required. Moreover, since it does not need word segmentation, it can be applied to Eastern languages where they do not put clear spacing between words. The first half of the paper describes the algorithm to retrieve document image regions which have similar appearance to the given query image. The algorithm was evaluated in recall-precision manner, and showed its performance of over 80–90% average precision. The second half of the paper describes the keyword extraction method which works even if no query word is explicitly specified. Since the computational cost was reduced by the efficient pruning techniques, the system could extract keywords successfully from relatively large documents.


title: Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models

abstract: This paper investigates speaker adaptation techniques for bidirectional long short term memory (BLSTM) recurrent neural network ::: based acoustic models (AMs) trained with the connectionist temporal classification (CTC) objective function. ::: BLSTM-CTC AMs play an important role in end-to-end automatic speech recognition systems. ::: However, there is a lack of research in speaker adaptation algorithms for these models. We explore three different feature-space adaptation approaches for CTC AMs: feature-space maximum linear regression, i-vector based adaptation, and maximum a posteriori adaptation using GMM-derived features. ::: Experimental results on the TED-LIUM corpus demonstrate that speaker adaptation, applied in combination with data augmentation techniques, provides, in an unsupervised adaptation mode, for different test sets, up to 11--20% of relative word error rate reduction over the baseline model built on the raw filter-bank features. In addition, the adaptation behavior is compared for BLSTM-CTC AMs and time-delay neural network AMs trained with the cross-entropy criterion.


title: Mental contamination and mental correction: unwanted influences on judgments and evaluations.

abstract: We define mental contamination as the process whereby a person has an unwanted response because of mental processing that is unconscious or uncontrollable. This type of bias is distinguishable from the failure to know or apply normative rules of inference and can be further divided into the unwanted consequences of automatic processing and source confusion, which is the confusion of 2 or more causes of a response. Mental contamination is difficult to avoid because it results from both fundamental properties of human cognition (e.g., a lack of awareness of mental processes) and faulty lay beliefs about the mind (e.g., incorrect theories about mental biases)


title: EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems

abstract: Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems. EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results. EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.


title: Three-dimensional printing of porous ceramic scaffolds for bone tissue engineering

abstract: This article reports a new process chain for custom-made three-dimensional (3D) porous ceramic scaffolds for bone replacement with fully interconnected channel network for the repair of osseous defects from trauma or disease. Rapid prototyping and especially 3D printing is well suited to generate complex-shaped porous ceramic matrices directly from powder materials. Anatomical information obtained from a patient can be used to design the implant for a target defect. In the 3D printing technique, a box filled with ceramic powder is printed with a polymer-based binder solution layer by layer. Powder is bonded in wetted regions. Unglued powder can be removed and a ceramic green body remains. We use a modified hydroxyapatite (HA) powder for the fabrication of 3D printed scaffolds due to the safety of HA as biocompatible implantable material and efficacy for bone regeneration. The printed ceramic green bodies are consolidated at a temperature of 1250°C in a high temperature furnace in ambient air. The polymeric binder is pyrolysed during sintering. The resulting scaffolds can be used in tissue engineering of bone implants using patient-derived cells that are seeded onto the scaffolds.This article describes the process chain, beginning from data preparation to 3D printing tests and finally sintering of the scaffold. Prototypes were successfully manufactured and characterized. It was demonstrated that it is possible to manufacture parts with inner channels with a dimension down to 450 μm and wall structures with a thickness down to 330 μm. The mechanical strength of dense test parts is up to 22 MPa. © 2005 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 2005


Non-local Unsupervised Variational Image Segmentation Models


title: A new Taxonomy of Continuous Global Optimization Algorithms

abstract: Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field. ::: This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.


title: MRL — Memristor Ratioed Logic

abstract: Memristive devices are novel structures, developed primarily as memory. Another interesting application for memristive devices is logic circuits. In this paper, MRL (Memristor Ratioed Logic) — a hybrid CMOS-memristive logic family — is described. In this logic family, OR and AND logic gates are based on memristive devices, and CMOS inverters are added to provide a complete logic structure and signal restoration. Unlike previously published memristive-based logic families, the MRL family is compatible with standard CMOS logic. A case study of an eight-bit full adder is presented and related design considerations are discussed.


title: Semantic highlight retrieval

abstract: Finding highlights relevant to a text query in unedited videos has become increasingly important due to their unprecedented growth. We refer this task as semantic highlight retrieval and propose a query-dependent video representation for retrieving a variety of highlights. Our method consist of two parts: (1) “viralets”, a mid-level representation bridging between visual and semantic spaces; (2) a novel Semantically-Modulation (SM) procedure to make viralets query-dependent (referred to as SM viralets). Given SM viralets, we train a single highlight ranker to predict the highlightness of clips with respect to a variety of queries, whereas existing approaches can be applied only in a few predefined domains. We collect a viral video dataset1 including users' comments, highlights, and/or original videos. Among a database with 1189 (13% highlights) clips, our highlight ranker achieves 41.2% recall at top-10 retrieved clips. It is significantly higher than a state-of-the-art domain-specific highlight ranker and its extension. Similarly, our method also outperforms all baseline methods on the video highlight dataset.


title: A globally stable adaptive congestion control scheme for Internet-style networks with delay

abstract: In this paper, we develop, analyze and implement a congestion control scheme in a noncooperative game framework, where each user's cost function is composed of a pricing function proportional to the queueing delay experienced by the user, and a fairly general utility function which captures the user demand for bandwidth. Using a network model based on fluid approximations and through a realistic modeling of queues, we establish the existence of a unique equilibrium as well as its global asymptotic stability for a general network topology, where boundary effects are also taken into account. We also provide sufficient conditions for system stability when there is a bottleneck link shared by multiple users experiencing nonnegligible communication delays. In addition, we study an adaptive pricing scheme using hybrid systems concepts. Based on these theoretical foundations, we implement a window-based, end-to-end congestion control scheme, and simulate it in ns-2 network simulator on various network topologies with sizable propagation delays.


title: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

abstract: We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes.We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.


title: Approximate String Joins in a Database (Almost) for Free

abstract: String data is ubiquitous, and its management has taken on particular importance in the past few years. Approximate queries are very important on string data especially for more complex queries involving joins. This is due, for example, to the prevalence of typographical errors in data, and multiple conventions for recording attributes such as name and address. Commercial databases do not support approximate string joins directly, and it is a challenge to implement this functionality efficiently with user-defined functions (UDFs). In this paper, we develop a technique for building approximate string join capabilities on top of commercial databases by exploiting facilities already available in them. At the core, our technique relies on matching short substrings of length , called -grams, and taking into account both positions of individual matches and the total number of such matches. Our approach applies to both approximate full string matching and approximate substring matching, with a variety of possible edit distance functions. The approximate string match predicate, with a suitable edit distance threshold, can be mapped into a vanilla relational expression and optimized by conventional relational optimizers. We demonstrate experimentally the benefits of our technique over the direct use of UDFs, using commercial database systems and real data. To study the I/O and CPU behavior of approximate string join algorithms with variations in edit distance and -gram length, we also describe detailed experiments based on a prototype implementation.


DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output.


title: How to Activate a Plant Gravireceptor. Early Mechanisms of Gravity Sensing Studied in Characean Rhizoids during Parabolic Flights

abstract: Early processes underlying plant gravity sensing were investigated in rhizoids of Chara globularis under microgravity conditions provided by parabolic flights of the A300-Zero-G aircraft and of sounding rockets. By applying centrifugal forces during the microgravity phases of sounding rocket flights, lateral accelerations of 0.14 g, but not of 0.05 g, resulted in a displacement of statoliths. Settling of statoliths onto the subapical plasma membrane initiated the gravitropic response. Since actin controls the positioning of statoliths and restricts sedimentation of statoliths in these cells, it can be calculated that lateral actomyosin forces in a range of 2 x 10(-14) n act on statoliths to keep them in place. These forces represent the threshold value that has to be exceeded by any lateral acceleration stimulus for statolith sedimentation and gravisensing to occur. When rhizoids were gravistimulated during parabolic plane flights, the curvature angles of the flight samples, whose sedimented statoliths became weightless for 22 s during the 31 microgravity phases, were not different from those of in-flight 1g controls. However, in ground control experiments, curvature responses were drastically reduced when the contact of statoliths with the plasma membrane was intermittently interrupted by inverting gravistimulated cells for less than 10 s. Increasing the weight of sedimented statoliths by lateral centrifugation did not enhance the gravitropic response. These results provide evidence that graviperception in characean rhizoids requires contact of statoliths with membrane-bound receptor molecules rather than pressure or tension exerted by the weight of statoliths.


title: When will it happen?: relationship prediction in heterogeneous information networks

abstract: Link prediction, i.e., predicting links or interactions between objects in a network, is an important task in network analysis. Although the problem has attracted much attention recently, there are several challenges that have not been addressed so far. First, most existing studies focus only on link prediction in homogeneous networks, where all objects and links belong to the same type. However, in the real world, heterogeneous networks that consist of multi-typed objects and relationships are ubiquitous. Second, most current studies only concern the problem of whether a link will appear in the future but seldom pay attention to the problem of when it will happen. In this paper, we address both issues and study the problem of predicting when a certain relationship will happen in the scenario of heterogeneous networks. First, we extend the link prediction problem to the relationship prediction problem, by systematically defining both the target relation and the topological features, using a meta path-based approach. Then, we directly model the distribution of relationship building time with the use of the extracted topological features. The experiments on citation relationship prediction between authors on the DBLP network demonstrate the effectiveness of our methodology.


title: Graph-of-word and TW-IDF: new approach to ad hoc IR

abstract: In this paper, we introduce novel document representation (graph-of-word) and retrieval model (TW-IDF) for ad hoc IR. Questioning the term independence assumption behind the traditional bag-of-word model, we propose a different representation of a document that captures the relationships between the terms using an unweighted directed graph of terms. From this graph, we extract at indexing time meaningful term weights (TW) that replace traditional term frequencies (TF) and from which we define a novel scoring function, namely TW-IDF, by analogy with TF-IDF. This approach leads to a retrieval model that consistently and significantly outperforms BM25 and in some cases its extension BM25+ on various standard TREC datasets. In particular, experiments show that counting the number of different contexts in which a term occurs inside a document is more effective and relevant to search than considering an overall concave term frequency in the context of ad hoc IR.


title: 3D self-portraits

abstract: We develop an automatic pipeline that allows ordinary users to capture complete and fully textured 3D models of themselves in minutes, using only a single Kinect sensor, in the uncontrolled lighting environment of their own home. Our method requires neither a turntable nor a second operator, and is robust to the small deformations and changes of pose that inevitably arise during scanning. After the users rotate themselves with the same pose for a few scans from different views, our system stitches together the captured scans using multi-view non-rigid registration, and produces watertight final models. To ensure consistent texturing, we recover the underlying albedo from each scanned texture and generate seamless global textures using Poisson blending. Despite the minimal requirements we place on the hardware and users, our method is suitable for full body capture of challenging scenes that cannot be handled well using previous methods, such as those involving loose clothing, complex poses, and props.


title: A new class of high speed disconnect switch based on piezoelectric actuators

abstract: With the adoption of power electronic converters in shipboard power systems and associated novel fault management concepts, the ability to isolate electric faults quickly from the power system is becoming more important than breaking high magnitude fault currents and the corresponding arcing between opening contacts within a switch. This allows for the design of substantially faster, as well as potentially lighter and more compact, mechanical disconnect switches. Herein, we are proposing a new class of mechanical disconnect switches that utilize piezoelectric actuators to isolate within less than one millisecond. This technology may become a key enabler for future all-electric ships.


title: Dance motion pattern planning for K. Mei as dancing humanoid robot

abstract: As continuation of our last research about dancing humanoid robot in 33 degree of freedom, in this paper we describe how to build dance pattern planning of Indonesia traditional dance movement in complete degree of freedom. We discuss the primitive pose in that commonly happened in traditional dance and build the transition pattern among the pose. The motion pattern method between the poses is based on the ability of robot to reach zero moment point position, and also the system to synchronize timing for dance motion is built. In this research zero moment point is our most concern problem because the worst thing in humanoid research is when the robot cannot maintain the balance itself. The computation considers that the zero moment point stays in a support polygon area.


title: SAIL: single access point-based indoor localization

abstract: This paper presents SAIL, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a single commodity WiFi AP to avoid these restrictions. SAIL computes the distance between the client and an AP using the propagation delay of the signal traversing between the two, combines the distance with smartphone dead-reckoning techniques, and employs geometric methods to ultimately yield the client's location using a single AP. SAIL combines physical layer (PHY) information and human motion to compute the propagation delay of the direct path by itself, eliminating the adverse effect of multipath and yielding sub-meter distance estimation accuracy. Furthermore, SAIL systematically addresses some of the common challenges towards dead-reckoning using smartphone sensors and achieves 2-5x accuracy improvements over existing techniques. We have implemented SAIL on commodity wireless APs and smartphones. Evaluation in a large-scale enterprise environment with 10 mobile users demonstrates that SAIL can capture the user's location with a mean error of 2.3m using just a single AP.


title: A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards

abstract: The resurgence of effort within computational semantics has led to increased interest in various types of relation extraction and semantic parsing. While various manually annotated resources exist for enabling this work, these materials have been developed with different standards and goals in mind. In an effort to develop better general understanding across these resources, we provide a summary overview of the standards underlying ACE, ERE, TAC-KBP Slot-filling, and FrameNet. 1 Overview ACE and ERE are comprehensive annotation standards that aim to consistently annotate Entities, Events, and Relations within a variety of documents. The ACE (Automatic Content Extraction) standard was developed by NIST in 1999 and has evolved over time to support different evaluation cycles, the last evaluation having occurred in 2008. The ERE (Entities, Relations, Events) standard was created under the DARPA DEFT program as a lighter-weight version of ACE with the goal of making annotation easier, and more consistent across annotators. ERE attempts to achieve this goal by consolidating some of the annotation type distinctions that were found to be the most problematic in ACE, as well as removing some more complex annotation features. This paper provides an overview of the relationship between these two standards and compares them to the more restricted standard of the TACKBP slot-filling task and the more expansive standard of FrameNet. Sections 3 and 4 examine Relations and Events in the ACE/ERE standards, section 5 looks at TAC-KBP slot-filling, and section 6 compares FrameNet to the other standards.


title: AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

abstract: New types of compute hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon. In particular, models that exploit structured input via complex and instance-dependent control flow are difficult to accelerate using existing algorithms and hardware that typically rely on minibatching. We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices. Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently, even for small minibatch sizes, resulting in shorter overall training times. Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.


title: Comparative study of self-organizing map and deep self-organizing map using MATLAB

abstract: We are interested in testing the efficiency of different algorithm which are traditional SOM (self-organizing map) and new DSOM (deep self-organizing map) for image cataloging. DSOM consist of SOM and the sampling operator are arranged in the way to get the better result than the SOM which also include generating another 2D map, then it is fed into the next self-organizing layer this manner confined in order is gained, is made better in the higher layers. The making of DSOM is unique and we will test the accuracy and other parameters which are shown in this document. Experiment were carried out to see how DSOM influence the presentation.


title: Tin dioxide sol–gel derived thin films deposited on porous silicon

abstract: SnO/sub 2/ and SnO/sub 2/:Sb sol-gel derived thin films have been prepared from tin (II) ethylhexanoate and tin (IV) ethoxide precursors (the later not reported before) in order to be used for gas sensing applications where porous silicon is used as a substrate. Transparent, crack-free and adherent layers were obtained on different types of substrates (Si, SiO/sub 2//Si). By energy dispersive X-ray spectroscopy (EDXS) performed on the cross section of the porosified silicon (PS) coupled with XTEM analysis, the penetration of the SnO/sub 2/ sol-phase in the nanometric pores followed by the SnO/sub 2/ consolidation film in the pores of the PS, during subsequent annealing at 500/spl deg/C has been experimentally proved.


title: Stylistic Analysis of The Great Gatsby from Lexical and Grammatical Category

abstract: The thesis tries to adopt the method used by Leech and Short in their book Style in Fiction to make a relatively overall and objective analysis of the novel’s language from lexical and grammatical Category. As far as the lexical features are concerned, the author employs special lexical items and lexical clusters. The lexical deviation and word connotations are mainly used for characterization and theme revelation. In terms of the syntactical aspects, narrative sentence type and the contrast of registers are employed, and the author’s sentence endings with elaborate appositions and prepositional phrases provide an effective way to describe the surroundings and evoke moods, serving to generate suspense as well as to create interest and expectation on the part of the reader.


title: Impact of load shedding on frequency and voltage system

abstract: System ability to continuously supply load is very important. One of the factors that influence that system ability is frequency and voltage stability. Frequency and voltage stability need to be considered during electrical power planning and operating to avoid system instability that can cause system blackout. Load shedding need to be done to restore the system into normal condition after interference or system instability. This research discusses impact of load shedding on frequency and voltage system.


title: From Data Mining to Knowledge Mining

abstract: Support for the Laboratory's research related to the presented results has been provided in part by the National Science Foundation under Grants No. DMI-9496192, IRI-9020266, IIS-9906858 and IIS-0097476; in part by the UMBC/LUCITE #32 grant; in part by the Office of Naval Research under Grant No. N00014-91-J-1351; in part by the Defense Advanced Research Projects Agency under Grant No. N00014-91-J-1854 administered by the Office of Naval Research; and in part by the Defense Advanced Research Projects Agency under Grants No. F49620-92-J-0549 and F49620-95-1-0462 administered by the Air Force Office of Scientific Research.


title: Visual analytics in healthcare--opportunities and research challenges.

abstract: As medical organizations modernize their operations, they are increasingly adopting electronic health records (EHRs) and deploying new health information technology systems that create, gather, and manage their information. As a result, the amount of data available to clinicians, administrators, and researchers in the healthcare system continues to grow at an unprecedented rate.1 However, despite the substantial evidence showing the benefits of EHR adoption, e-prescriptions, and other components of health information exchanges, healthcare providers often report only modest improvements in their ability to make better decisions by using more comprehensive clinical information.2,3 ::: ::: The large volume of clinical data now being captured for each patient poses many challenges to (a) clinicians trying to combine data from different disparate systems and make sense of the patient’s condition within the context of the patient’s medical history, (b) administrators trying to make decisions grounded in data, (c) researchers trying to understand differences in population outcomes, and (d) patients trying to make use of their own medical data. In fact, despite the many hopes that access to more information would lead to more informed decisions, access to comprehensive and large-scale clinical data resources has instead made some analytical processes even more difficult.4 ::: ::: Visual analytics is an emerging discipline that has shown significant promise in addressing many of these information overload challenges. Visual analytics is the science of analytical reasoning facilitated by advanced interactive visual interfaces.5 In order to facilitate …


title: Road region estimation using a sequence of monocular images

abstract: In advanced driving assistance systems, it is important to be able to detect the region covered by the road in the images. This paper proposes a method for estimating the road region in images captured by a vehicle-mounted monocular camera. Our proposed method first estimates all of relevant parameters for the camera motion and the 3D road plane from correspondence points between successive images. By calculating a homography matrix from the estimated camera motion and the estimated road plane parameters, and then warping the image at the previous frame, the road region can be determined. To achieve robustness in various road scenes, our method selects the threshold for determining the road region adaptively and incorporates the assumption of a simple road region boundary. In our experiments, it has been shown that the proposed method is able to estimate the road region in real road environments.


title: Automated Decision Support for Financial Regulatory/Policy Compliance, using Textual Rulelog

abstract: We present a novel technological approach, based on Textual Rulelog, to automated decision support for financial regulatory/policy compliance, via a case study on banking Regulation W from the US Federal Reserve. Legal regulations and related bank operational policies in English documents are en- coded relatively inexpensively by authors into Rulelog, a highly expressive log- ical knowledge representation. Key compliance queries are automatically an- swered accurately and fully explained in English, understandable to non-IT com- pliance staff and auditors. The prospective business impact of our approach over the next decade or two is significantly increased productivity and systemic stabil- ity, industry-wide, worth many billions of dollars.


title: Hyper/J/sup TM/: multi-dimensional separation of concerns for Java/sup TM/

abstract: Hyper/J supports a new approach to constructing, integrating and evolving software, called multi-dimensional separation of concerns. Developers can decompose and organize code and other artifacts according to multiple arbitrary criteria (concerns) simultaneously - even after the software has been implemented - and synthesize or integrate the pieces into larger-scale components and systems. Hyper/J facilitates several common development and evolution activities non-invasively, including: adaptation and customization, mix-and-match of features, reconciliation and integration of multiple domain models, reuse, product line management, extraction or replacement of existing parts of software, and on-demand remodularization. Hyper/J works with standard Java software, not requiring special compilers or environments. This demonstration shows it in action in a number of software engineering scenarios at different stages of the software life-cycle.


title: A Fast and Accurate Dependency Parser using Neural Networks

abstract: Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2% improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.


title: Movie Recommendation in Context

abstract: The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real-world online movie recommendation communities and competed against each other for obtaining the highest accuracy of recommendations. The datasets contained contextual features, such as tags, annotation, social relationsips, and comments, normally not available in public recommendation datasets. More than 40 teams from 21 countries participated in the challenge. Their participation was summarized by 10 papers published by the workshop, which have been extended and revised for this special section. In this preface we overview the challenge datasets, tasks, evaluation metrics, and the obtained outcomes.


title: The dark side of information: overload, anxiety and other paradoxes and pathologies

abstract: This review article identifies and discusses some of main issues and potential problems - paradoxes and pathologies - around the communication of recorded information, and points to some possible solutions. The article considers the changing contexts of information communication, with some caveats about the identification of `pathologies of information', and analyses the changes over time in the way in which issues of the quantity and quality of information available have been regarded. Two main classes of problems and issues are discussed. The first comprises issues relating to the quantity and diversity of information available: information overload, information anxiety, etc. The second comprises issues relating to the changing information environment with the advent of Web 2.0: loss of identity and authority, emphasis on micro-chunking and shallow novelty, and the impermanence of information. A final section proposes some means of solution of problems and of improvements to the situation.


title: Controlling Kuka Industrial Robots: Flexible Communication Interface JOpenShowVar

abstract: JOpenShowVar is a Java open-source cross-platform communication interface to Kuka industrial robots. This novel interface allows for read-write use of the controlled manipulator variables and data structures. JOpenShowVar, which is compatible with all the Kuka industrial robots that use KUKA Robot Controller version 4 (KR C4) and KUKA Robot Controller version 2 (KR C2), runs as a client on a remote computer connected with the Kuka controller via TCP/IP. Even though only soft real-time applications can be implemented, JOpenShowVar opens up to a variety of possible applications, making the use of various input devices and sensors as well as the development of alternative control methods possible.


title: The language that gets people to give: phrases that predict success on kickstarter

abstract: Crowdfunding sites like Kickstarter--where entrepreneurs and artists look to the internet for funding--have quickly risen to prominence. However, we know very little about the factors driving the 'crowd' to take projects to their funding goal. In this paper we explore the factors which lead to successfully funding a crowdfunding project. We study a corpus of 45K crowdfunded projects, analyzing 9M phrases and 59 other variables commonly present on crowdfunding sites. The language used in the project has surprising predictive power accounting for 58.56% of the variance around successful funding. A closer look at the phrases shows they exhibit general persuasion principles. For example, also receive two reflects the principle of Reciprocity and is one of the top predictors of successful funding. We conclude this paper by announcing the release of the predictive phrases along with the control variables as a public dataset, hoping that our work can enable new features on crowdfunding sites--tools to help both backers and project creators make the best use of their time and money.


A General Knowledge Representation Model of Concepts


Comparative study of anabolic activity of substances of plant origin: Ekdisten, leveton, and Prime Plus


title: A benchmark for surface reconstruction

abstract: We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation. We use implicit surfaces for modeling shapes which are capable of representing details of varying size and sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data produced by an optical triangulation scanner. We validate our synthetic sampling scheme by comparing against scan data produced by a commercial optical laser scanner, where we scan a 3D-printed version of the original surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformly distributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for comparison, they lack a fine-grain analysis. To complement this, the second set of experiments measures specific properties of surface reconstruction, in terms of sampling characteristics and surface features. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms.


title: Data-Driven Innovation through Open Government Data

abstract: The exponentially growing production of data and the social trend towards openness and sharing are powerful forces that are changing the global economy and society. Governments around the world have become active participants in this evolution, opening up their data for access and re-use by public and private agents alike. The phenomenon of Open Government Data has spread around the world in the last four years, driven by the widely held belief that use of Open Government Data has the ability to generate both economic and social value. However, a cursory review of the popular press, as well as an investigation of academic research and empirical data, reveals the need to further understand the relationship between Open Government Data and value. In this paper, we focus on how use of Open Government Data can bring about new innovative solutions that can generate social and economic value. We apply a critical realist approach to a case study analysis to uncover the mechanisms that can explain how data is transformed to value. We explore the case of Opower, a pioneer in using and transforming data to induce a behavioral change that has resulted in a considerable reduction in energy use over the last six years.


title: Topology and size optimization of an adaptive compliant gripper to maximize the geometric advantage

abstract: This study presents a systematic optimal design procedure to develop an adaptive compliant gripper (ACG) for grasping objects with various sizes and shapes. A soft-add topology optimization algorithm is developed to synthesize the optimal layout of the ACG. One special characteristic of the proposed soft-add scheme is that the elements are equivalent to be numerically added into the analysis domain. A size optimization procedure by using a mixed method combing Augmented Lagrange Multiplier (ALM) method and Simplex method is also proposed to maximize the geometric advantage (GA, which is defined as output displacement divided by input displacement) of the ACG. After the optimal design is obtained, the explicit dynamic Unite element analysis and experimental tests are carried out to analyze the design. The results show the developed ACG is with good adaptability and highest GA. The average GA can improve 58% comparing to the previous design.


Compiling Little Languages in Python


title: DelDroid: An automated approach for determination and enforcement of least-privilege architecture in android

abstract: Abstract Android is widely used for the development and deployment of autonomous and smart systems, including software targeted for IoT and mobile devices. Security of such systems is an increasingly important concern. Android relies on a permission model to secure the system’s resources and apps. In Android, since the permissions are granted at the granularity of apps, and all components in an app inherit those permissions, an app’s components are over-privileged, i.e., components are granted more privileges than they actually need. Systematic violation of least-privilege principle in Android is the root cause of many security vulnerabilities. To mitigate this issue, we have developed DelDroid , an automated system for determination of least privilege architecture in Android and its enforcement at runtime. A key contribution of DelDroid is the ability to limit the privileges granted to apps without modifying them. DelDroid utilizes static analysis techniques to extract the exact privileges each component needs. A Multiple-Domain Matrix representation of the system’s architecture is then used to automatically analyze the security posture of the system and derive its least-privilege architecture. Our experiments on hundreds of real-world apps corroborate DelDroid ’s ability in effectively establishing the least-privilege architecture and its benefits in alleviating the security threats.


title: MAC SoC hardware implementation for fast industrial WLAN communication systems

abstract: This paper presents a System on a Chip (SoC) Field-Programmable Gate Array (FPGA) implementation for Media Access Control (MAC) layer of a fast industrial Wireless Local Area Network (iWLAN) protocol, which is used to control industrial robots in factory automation environments. This protocol is a potential technique for industrial wireless communication system by using the Low overhead Packet Division Multiple Access (PDMA) scheme controlled by MAC layer. In our previous research, the MAC layer hardware was implemented and tested on simulation software, but the SoC design for the MAC layer has not been implemented and real tested on the FPGA. In this paper, we have successfully implemented the SoC system for MAC layer of this high-speed wireless communication protocol on the FPGA.


title: Advanced web analytics tool for mouse tracking and real-time data processing

abstract: Web analytic tools offer important support for better recognition of the web user's behavior, identification of bottlenecks and errors in user interface design, performance measurement of web environment, monitoring of website availability or recommendation of appropriate website content. These tools are based on tracking techniques and sophisticated algorithms that process and evaluate large volumes of captured data. In this paper, we propose a new solution to capture mouse movements of web users, to identify their area of interest. This solution is based on real-time data transformation, which converts discrete position data with high sample period to predefined functions. This transformation has a high degree of accuracy, which is exemplified by case scenarios. The result of this solution is a significant saving in data, transmitted from the client to the server, which leads to significant savings in system resources on the server side.


title: A reconfigurable bandpass filter for RF/microwave multifunctional systems

abstract: A network prototype suitable for reconfigurable filters with imaginary-axis transmission zeros is presented. The prototype is synthesized using classical cascade synthesis. Reconfiguration of the transmission zeros is implemented entirely by tunable capacitors. The measured performance of a narrow-band reconfigurable bandpass filter realized in a planar combline using varactor diodes demonstrates the principle.


title: Fall-prediction algorithm using a neural network for safety enhancement of elderly

abstract: Among the elderly, falls are a well-known safety hazard, often resulting in major injury, hospitalization and death. To reduce the injuries caused by falls, it is first necessary to predict a fall as early as possible and then to provide protection for the person who is falling. This paper proposes a fall-prediction algorithm (FPA) that can predict whether the person will fall within one-walking-step. The fall prediction is different from the fall detection, and it is intended to predict a fall before it occurs and provide sufficient time to enable a safety mechanism. The proposed FPA adopts a neural network to perform prediction in which the inputs are accelerations and angular rates of upper trunk and the output presents fall or no fall. A wearable inertial sensor package with a triple axis accelerometer and a triple axis gyroscope is developed to measure the required motion data. Five subjects were asked to wear the inertial sensor package and perform a number of simulated falls. The experimental results show that the FPA could predict a fall 0.4 seconds prior to the beginning of the fall. The time interval is sufficient to inflate an airbag covering the head, trunk, and hip, an intervention that would reduce fall-related injuries among older people.


Prediction and Interpretation for Machine Learning Regression Methods


title: Immersive Augmented Reality Training for Complex Manufacturing Scenarios

abstract: In the complex manufacturing sector a considerable amount of resources are focused on developing new skills and training workers. In that context, increasing the effectiveness of those processes and reducing the investment required is an outstanding issue. In this paper we present an experiment that shows how modern Human Computer Interaction (HCI) metaphors such as collaborative mixed-reality can be used to transmit procedural knowledge and could eventually replace other forms of face-to-face training. We implement a real-time Immersive Augmented Reality (IAR) setup with see-through cameras that allows for collaborative interactions that can simulate conventional forms of training. The obtained results indicate that people who took the IAR training achieved the same performance than people in the conventional face-to-face training condition. These results, their implications for future training and the use of HCI paradigms in this context are discussed in this paper.


title: Evaluation of the potential immunotoxicity of chlorinated drinking water in mice

abstract: Abstract Recent epidemiological studies have reported associations between the consumption of chlorinated drinking water and various types of human cancer; in addition, exposure to chlorine (Cl − ) in drinking water has been reported to suppress certain immune functions in laboratory animals. The current studies were conducted to extend our knowledge of the effects of drinking water exposure to Cl − . Female C57BL/6 mice were administered hyperchlorinated drinking water (7.5, 15, or 30 ppm Cl − ) for 2 weeks prior to sacrifice for evaluation of spleen and thymus weights, the plaque-forming cell (PFC) response, hemagglutination (HA) titer, and lymphocyte proliferation (LP). Significant reductions in organ weights and immune response were observed in the positive control groups (i.e. dexamethasone- or cyclophosphamide-exposed mice). No consistent differences were observed between the Cl − -exposed animals and vehicle control mice for the evaluated parameters. Thus, under the conditions of these experiments, 2 weeks of exposure to hyperchlorinated drinking water had no apparent adverse effects on immune function.


title: Project-service Solutions in the Yacht Industry: A Value-Chain Analysis

abstract: In recent years, the increased provision of bundled products and services has become an increasingly relevant economic trend for manufacturers in achieving competitive advantage. The project environment has not been left untouched by this economic trend, in particular throughout the delivery of integrated project-service solutions in all project life cycle stages. Innovative value offerings encompass a complex network of suppliers and subcontractors that is not stable and is arranged in a sporadic and unpredictable manner. Multiple case studies in the yacht industry were conducted to explore the configuration of project-service solutions. The research constitutes an original contribution to studies on servitization adoption in an industrial project context from an inter-organizational perspective. It emerged that SMEs reorganize themselves, in order to provide flexible on-demand solutions to customers, by including all the capabilities within their network. Newly arising professional roles are oriented to the implementation of smart networks and are focused on service infusion in order to provide increased customer value.


title: Interactive deep learning method for segmenting moving objects

abstract: The first machine learning method for ground truthing a video is proposed.We devise different sampling strategies for training different categories.Our results can be considered within the error margin of a human. With the increasing number of machine learning methods used for segmenting images and analyzing videos, there has been a growing need for large datasets with pixel accurate ground truth. In this letter, we propose a highly accurate semi-automatic method for segmenting foreground moving objects pictured in surveillance videos. Given a limited number of user interventions, the goal of the method is to provide results sufficiently accurate to be used as ground truth. In this paper, we show that by manually outlining a small number of moving objects, we can get our model to learn the appearance of the background and the foreground moving objects. Since the background and foreground moving objects are highly redundant from one image to another (videos come from surveillance cameras) the model does not need a large number of examples to accurately fit the data. Our end-to-end model is based on a multi-resolution convolutional neural network (CNN) with a cascaded architecture. Tests performed on the largest publicly-available video dataset with pixel accurate groundtruth (changdetection.net) reveal that on videos from 11 categories, our approach has an average F-measure of 0.95 which is within the error margin of a human being. With our model, the amount of manual work for ground truthing a video gets reduced by a factor of up to 40. Code is made publicly available at: https://github.com/zhimingluo/MovingObjectSegmentation


title: WITHDRAWN: Resilience: The emergence of a perspective for social–ecological systems analyses

abstract: The resilience perspective is increasingly used as an approach for understanding the dynamics of social–ecological systems. This article presents the origin of the resilience perspective and provides an overview of its development to date. With roots in one branch of ecology and the discovery of multiple basins of attraction in ecosystems in the 1960–1970s, it inspired social and environmental scientists to challenge the dominant stable equilibrium view. The resilience approach emphasizes non-linear dynamics, thresholds, uncertainty and surprise, how periods of gradual change interplay with periods of rapid change and how such dynamics interact across temporal and spatial scales. The history was dominated by empirical observations of ecosystem dynamics interpreted in mathematical models, developing into the adaptive management approach for responding to ecosystem change. Serious attempts to integrate the social dimension is currently taking place in resilience work reflected in the large numbers of sciences involved in explorative studies and new discoveries of linked social–ecological systems. Recent advances include understanding of social processes like, social learning and social memory, mental models and knowledge–system integration, visioning and scenario building, leadership, agents and actor groups, social networks, institutional and organizational inertia and change, adaptive capacity, transformability and systems of adaptive governance that allow for management of essential ecosystem services


title: Measuring test anxiety in children: Scale development and internal construct validation

abstract: Given the increased testing of school-aged children in the United States there is a need for a current and valid scale to measure the effects of test anxiety in children. The domain of children's test anxiety was theorized to be comprised of three dimensions: thoughts, autonomic reactions, and off-task behaviors. Four stages are described in the evolution of the Children's Test Anxiety Scale (CTAS): planning, construction, quantitative evaluation, and validation. A 50-item scale was administered to a development sample (N=230) of children in grades 3–6 to obtain item analysis and reliability estimates which resulted in a refined 30-item scale. The reduced scale was administered to a validation sample (N=261) to obtain construct validity evidence. A three-factor structure fit the data reasonably well. Recommendations for future research with the scale are described.


title: Natural Language Processing (almost) from Scratch

abstract: We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.


title: Secure and imperceptible digital image steganographic algorithm based on diamond encoding in DWT domain

abstract: This paper presents a new efficient embedding algorithm in the wavelet domain of digital images based on the diamond encoding (DE) scheme. Current discrete wavelet transform (DWT) steganography adds an unacceptable distortion to the images and is considered as an ineffective in terms of security. Applying the DE scheme to the current DWT steganographic methods solves the problems of these methods, and reduces the distortion added to the images, and thus improves the embedding efficiency. The proposed algorithm first converts the secret image into a sequence of base-5 digits. After that, the cover image is transformed into the DWT domain and segmented into 2 × 1 coefficient pairs. The DE scheme is used then to change at most one coefficient of each coefficient pair to embed the base-5 digits. Experimental results depict that the proposed algorithm is more efficient in embedding compared to other methods in terms of embedding payload and image quality. Moreover, the proposed algorithm is attacked by well-known steganalysis software. Results are showing that the proposed algorithm is secure against the powerful universal steganalyzer “ensemble classifier” and the histogram attack. The results also reveal that the proposed algorithm is robust against different image processing attacks such as compression, added noise, and cropping attacks.


title: A Wearable Sensor for Measuring Sweat Rate

abstract: Wearable sensors present a new frontier in the development of monitoring techniques. They are of great importance in sectors such as sport and healthcare as they enable physiological signals and biological fluids, such as human sweat, to be continuously monitored. Until recently this could only be carried out in specialized laboratories using cumbersome and often expensive devices. Sweat monitoring sensors integrated onto textile substrates are not only innovative but they also represent the first attempt to use such an idea in a system that will be worn directly on the body. This study outlines the development of a wearable sweat-rate sensor integrated onto a textile.


title: Analysis of SIGMOD's co-authorship graph

abstract: In this paper we investigate the co-authorship graph obtained from all papers published at SIGMOD between 1975 and 2002. We find some interesting facts, for instance, the identity of the authors who, on average, are "closest" to all other authors at a given time. We also show that SIGMOD's co-authorship graph is yet another example of a small world---a graph topology which has received a lot of attention recently. A companion web site for this paper can be found at http://db.cs.ualberta.ca/coauthorship.


title: Enhanced direct conversion of fibroblasts into hepatocyte-like cells by Kdm2b.

abstract: Cell fate conversion of terminally differentiated cells by defined transcription factors between different lineages is a new approach to produce new cells that have the capability to repair or replace diseased and damaged tissues. Previous studies have demonstrated that this inefficient process can be facilitated by the inclusion of additional factors. Here we report that Kdm2b, a histone demethylase, has the capability to promote conversion of fibroblasts to functional induced hepatocyte-like (iHep) cells in combination with previously reported hepatic lineage transcription factors, Hnf4α and Foxa3. This approach led to increased numbers of epithelial-like colonies, hepatic markers and functionality which included periodic acid-Schiff (PAS) positive colonies, CYP450 activity, low-density lipoprotein and indocyanine green (ICG) uptake, as well as Albumin secretion. Additionally, the transplanted iHep cells were engraftable, expressed Albumin, and contributed to the recovery of a carbon tetrachloride-injured mouse model. These results have not only identified an important epigenetic factor for iHep generation, but also brought new insight into the molecular nature of hepatogenesis and future biomedical applications for liver diseases.


title: Foundations and Tools for the Static Analysis of Ethereum Smart Contracts

abstract: The recent growth of the blockchain technology market puts its main cryptocurrencies in the spotlight. Among them, Ethereum stands out due to its virtual machine (EVM) supporting smart contracts, i.e., distributed programs that control the flow of the digital currency Ether. Being written in a Turing complete language, Ethereum smart contracts allow for expressing a broad spectrum of financial applications. The price for this expressiveness, however, is a significant semantic complexity, which increases the risk of programming errors. Recent attacks exploiting bugs in smart contract implementations call for the design of formal verification techniques for smart contracts. This, however, requires rigorous semantic foundations, a formal characterization of the expected security properties, and dedicated abstraction techniques tailored to the specific EVM semantics. This work will overview the state-of-the-art in smart contract verification, covering formal semantics, security definitions, and verification tools. We will then focus on EtherTrust [1], a framework for the static analysis of Ethereum smart contracts which includes the first complete small-step semantics of EVM bytecode, the first formal characterization of a large class of security properties for smart contracts, and the first static analysis for EVM bytecode that comes with a proof of soundness.


title: Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis

abstract: Nowadays many people use smartphones with built-in accelerometers which makes these smartphones capable of recognizing daily activities. However, mobile phones are carried along freely instead of a firm attachment to a body part. Since the output of any body-worn triaxial accelerometer varies for the same physical activity at different positions on a subject's body, the acceleration data thus could vary significantly for the same activity which could result in high within-class variance. Therefore, realization of activity-aware smartphones requires a recognition method that could function independent of phone's position along subjects' bodies. In this study, we present a method to address this problem. The proposed method is validated using five daily physical activities. Activity data is collected from five body positions using a smartphone with a built-in triaxial accelerometer. Features including autoregressive coefficients and signal magnitude area are calculated. Kernel Discriminant Analysis is then employed to extract the significant non-linear discriminating features which maximize the between-class variance and minimize the within-class variance. Final classification is performed by means of artificial neural nets. The average accuracy of about 96\% illustrates the effectiveness of the proposed method.


title: Physical Activity Motivating Games: Be Active and Get Your Own Reward

abstract: People’s daily lives have become increasingly sedentary, with extended periods of time being spent in front of a host of electronic screens for learning, work, and entertainment. We present research into the use of an adaptive persuasive technology, which introduces bursts of physical activity into a traditionally sedentary activity: computer game playing. Our game design approach leverages the playfulness and addictive nature of computer games to motivate players to engage in mild physical activity. The design allows players to gain virtual in-game rewards in return for performing real physical activity captured by sensory devices. This article presents a two-stage analysis of the activity-motivating game design approach applied to a prototype game. Initially, we detail the overall acceptance of active games discovered when trialing the technology with 135 young players. Results showed that players performed more activity without negatively affecting their perceived enjoyment of the playing experience. The analysis did discover, however, a lack of balance between the amounts of physical activity carried out by players with various gaming skills, which prompted a subsequent investigation into adaptive techniques for balancing the amount of physical activity performed by players. An evaluation of additional 90 players showed that adaptive techniques successfully overcame the gaming skills dependence and achieved more balanced activity levels. Overall, this work positions activity-motivating games as an approach that can potentially change the way players interact with computer games and lead to healthier lifestyles.


title: Complex software project development: agile methods adoption

abstract: The Agile Software Development paradigm has become increasingly popular in the last few years, since it claims lower costs, better productivity, better quality and better business satisfaction. Supply chain management (SCM) is a complex software development project. Owing to its scope and uncertain, complex and unstable requirements, it is not possible to develop it with predictable software development process models. Agile methodologies are targeted toward such kinds of problems that involve change and uncertainty, and are adaptive rather than predictive. How an agile process is introduced will significantly impact the implementation success of the process change. The objective of this paper is to analyze the agile development methodologies and management approach used in developing a complex software project. This further demonstrates how to overcome risks and barriers in each development phase of such complex inventive software projects. It also provides a set of guidelines regarding how the agile methodologies can be adopted, combined and used in these kinds of complex software projects. These findings have implications for software engineers and managers developing software by agile methods. Copyright © 2011 John Wiley & Sons, Ltd. ::: ::: (The objective of this paper is to analyze the agile development methodologies and management approach used in developing a complex software project. This further demonstrates how to overcome risks and barriers in each development phase of such complex inventive software projects. It also provides a set of guidelines regarding how the agile methodologies can be adopted, combined and used in these kinds of complex software projects. These findings have implications for software engineers and managers developing software by agile methods.)


title: GPU-accelerated Hungarian algorithms for the Linear Assignment Problem

abstract: Linear Assignment is one of the most fundamental problems in operations research.A creative parallelization of a Hungarian-like algorithm on GPU cluster.Efficient parallelization of the augmenting path search step.Large problems with 1.6 billion variables can be solved.It is probably the fastest LAP solver using a GPU. In this paper, we describe parallel versions of two different variants (classical and alternating tree) of the Hungarian algorithm for solving the Linear Assignment Problem (LAP). We have chosen Compute Unified Device Architecture (CUDA) enabled NVIDIA Graphics Processing Units (GPU) as the parallel programming architecture because of its ability to perform intense computations on arrays and matrices. The main contribution of this paper is an efficient parallelization of the augmenting path search phase of the Hungarian algorithm. Computational experiments on problems with up to 25 million variables reveal that the GPU-accelerated versions are extremely efficient in solving large problems, as compared to their CPU counterparts. Tremendous parallel speedups are achieved for problems with up to 400 million variables, which are solved within 13 seconds on average. We also tested multi-GPU versions of the two variants on up to 16 GPUs, which show decent scaling behavior for problems with up to 1.6 billion variables and dense cost matrix structure.


title: Bi-level Locality Sensitive Hashing for k-Nearest Neighbor Computation

abstract: We present a new Bi-level LSH algorithm to perform approximate $k$-nearest neighbor search in high dimensional spaces. Our formulation is based on a two-level scheme. In the first level, we use a RP-tree that divides the dataset into sub-groups with bounded aspect ratios and is used to distinguish well-separated clusters. During the second level, we compute a single LSH hash table for each sub-group along with a hierarchical structure based on space-filling curves. Given a query, we first determine the sub-group that it belongs to and perform $k$-nearest neighbor search within the suitable buckets in the LSH hash table corresponding to the sub-group. Our algorithm also maps well to current GPU architectures and can improve the quality of approximate KNN queries as compared to prior LSH-based algorithms. We highlight its performance on two large, high-dimensional image datasets. Given a runtime budget, Bi-level LSH can provide better accuracy in terms of recall or error ration. Moreover, our formulation reduces the variation in runtime cost or the quality of results.


title: Spoken English Grading: Machine Learning with Crowd Intelligence

abstract: In this paper, we address the problem of grading spontaneous speech using a combination of machine learning and crowdsourcing. Traditional machine learning techniques solve the stated problem inadequately as automatic speaker-independent speech transcription is inaccurate. The features derived from it are also inaccurate and so is the machine learning model developed for speech evaluation. We propose a framework that combines machine learning with crowdsourcing. This entails identifying human intelligence tasks in the feature derivation step and using crowdsourcing to get them completed. We post the task of speech transcription to a large community of online workers (crowd). We also get spoken English grades from the crowd. We achieve 95% transcription accuracy by combining transcriptions from multiple crowd workers. Speech and prosody features are derived by force aligning the speech samples on these highly accurate transcriptions. Additionally, we derive surface and semantic level features directly from the transcription. We demonstrate the efficacy of our approach by predicting expert graded speech sample of 566 adult non-native speakers across two different countries - India and Philippines. Using the regression modeling technique, we are able achieve a Pearson correlation of 0.79 on the Philippines set and 0.74 on the Indian set with expert grades, an accuracy much higher than any previously reported machine learning approach. Our approach has an accuracy that rivals that of expert agreement. We show the value of the system through a case study in a real-world industrial deployment. This work is timely given the huge requirement of spoken English training and assessment.