title: The Impact of Business Intelligence Systems on Management Accounting Systems: The Consultant’s Perspective

abstract: The use of Business Intelligence (BI) and Business Analytics for supporting decision-making is widespread in the world of praxis and their relevance for Management Accounting (MA) has been outlined in non-academic literature. Nonetheless, current research on Business Intelligence systems’ implications for the Management Accounting System is still limited. The purpose of this study is to contribute to understanding how BI system implementation and use affect MA techniques and Management Accountants’ role. An explorative field study, which involved BI consultants from Italian consulting companies, was carried out. We used the qualitative field study method since it permits dealing with complex “how” questions and, at the same time, taking into consideration multiple sites thus offering a comprehensive picture of the phenomenon. We found that BI implementation can affect Management Accountants’ expertise and can bring about not only incremental changes in existing Management Accounting techniques but also more relevant ones, by supporting the introduction of new and advanced MA techniques. By identifying changes in the Management Accounting System as well as factors which can prevent or favor a virtuous relationship between BI and Management Accounting Systems this research can be useful both for consultants and for client-companies in effectively managing BI projects.


title: Robots testing robots: ALAN-Arm, a humanoid arm for the testing of robotic rehabilitation systems

abstract: Robotics is increasing in popularity as a method of providing rich, personalized and cost-effective physiotherapy to individuals with some degree of upper limb paralysis, such as those who have suffered a stroke. These robotic rehabilitation systems are often high powered, and exoskeletal systems can attach to the person in a restrictive manner. Therefore, ensuring the mechanical safety of these devices before they come in contact with individuals is a priority. Additionally, rehabilitation systems may use novel sensor systems to measure current arm position. Used to capture and assess patient movements, these first need to be verified for accuracy by an external system. We present the ALAN-Arm, a humanoid robotic arm designed to be used for both accuracy benchmarking and safety testing of robotic rehabilitation systems. The system can be attached to a rehabilitation device and then replay generated or human movement trajectories, as well as autonomously play rehabilitation games or activities. Tests of the ALAN-Arm indicated it could recreate the path of a generated slow movement path with a maximum error of 14.2mm (mean = 5.8mm) and perform cyclic movements up to 0.6Hz with low gain (<1.5dB). Replaying human data trajectories showed the ability to largely preserve human movement characteristics with slightly higher path length and lower normalised jerk.


title: VLSI design of sequential minimal optimization algorithm for SVM learning

abstract: The sequential minimal optimization (SMO) algorithm has been widely used for training the support vector machine (SVM). In this paper, we present the first chip design for sequential minimal optimization. This chip is implemented as an intellectual property (IP) core, suitable to be utilized in an SVM-based recognition system on a chip. The proposed SMO chip has been tested to be fully functional, using a prototype system based on the Altera DE2 board with Cyclone II 2C70 FPGA (field-programmable gate array).


title: TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

abstract: There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms -- such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) -- requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.


Towards an empirical typology of collegiate leadership development programs: Examining effects on student self -efficacy and leadership for social change


title: Learning Entity Type Embeddings for Knowledge Graph Completion

abstract: Missing data is a severe problem for algorithms that operate over knowledge graphs (KGs). Most previous research in KG completion has focused on the problem of inferring missing entities and missing relation types between entities. However, in addition to these, many KGs also suffer from missing entity types (i.e. the category labels for entities, such as /music/artist). Entity types are a critical enabler for many NLP tasks that use KGs as a reference source, and inferring missing entity types remains an important outstanding obstacle in the field of KG completion. Inspired by recent work to build a contextual KG embedding model, we propose a novel approach to address the entity type prediction problem. We compare the performance of our method with several state-of-the-art KG embedding methods, and show that our approach gives higher prediction accuracy compared to baseline algorithms on two real-world datasets. Our approach also produces consistently high accuracy when inferring entities and relation types, as well as the primary task of inferring entity types. This is in contrast to many of the baseline methods that specialize in one prediction task or another. We achieve this while preserving linear scalability with the number of entity types. Source code and datasets from this paper can be found at (https://github.ncsu.edu/cmoon2/kg).


title: A Printed Log-Periodic Koch-Dipole Array (LPKDA)

abstract: Koch-shaped dipoles are introduced for the first time in a wideband antenna design and evolve the traditional Euclidean log-periodic dipole array into the log-periodic Koch-dipole array (LPKDA). Antenna size can be reduced while maintaining its overall performance characteristics. Observations and characteristics of both antennas are discussed. Advantages and disadvantages of the proposed LPKDA are validated through a fabricated proof-of-concept prototype that exhibited approximately 12% size reduction with minimal degradation in the impedance and pattern bandwidths. This is the first application of Koch prefractal elements in a miniaturized wideband antenna design.


title: Sensor-based globally asymptotically stable range-only simultaneous localization and mapping

abstract: Range-only simultaneous localization and mapping is addressed in this paper, through the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter. A nonlinear sensor-based system is designed and its dynamics augmented so that the proposed formulation can be considered as linear time-varying for the purpose of observability analysis. This allows the establishment of observability results related to the original nonlinear system that naturally lead to the design of a Kalman filter with GAS error dynamics. The performance of the proposed algorithm is assessed resorting to a set of realistic simulations and to the results obtained from experimental tests.


title: Eye Tracking in Neuromarketing: A Research Agenda for Marketing Studies

abstract: This article investigates the potential use of Eye Tracking as a neuromarketing tool and its potential for marketing in general. We sought to identify some of the main applications within the mainstream of marketing. The objective of this research was achieved by means of a conceptual literature review. The results of our research indicate important potential uses for Eye Tracking in practical marketing applications, such as brand equity, segmentation, new product development, pricing decisions, place decisions, promotion decisions, and social marketing studies. It is believed that in the near future, neuromarketing tools such as Eye Tracking will be part of mainstream marketing studies.


title: Variational Regularization and Fusion of Surface Normal Maps

abstract: In this work we propose an optimization scheme for variational, vectorial denoising and fusion of surface normal maps. These are common outputs of shape from shading, photometric stereo or single image reconstruction methods, but tend to be noisy and request post-processing for further usage. Processing of normals maps, which do not provide knowledge about the underlying scene depth, is complicated due to their unit length constraint which renders the optimization non-linear and non-convex. The presented approach builds upon a linearization of the constraint to obtain a convex relaxation, while guaranteeing convergence. Experimental results demonstrate that our algorithm generates more consistent representations from estimated and potentially complementary normal maps.


title: Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media

abstract: Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output"transmission matrix"for a fixed medium. However, this"one-to-one"mapping is highly susceptible to speckle decorrelations - small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical"one-to-all"deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.


title: Metric-induced optimal embedding for intrinsic 3D shape analysis

abstract: For various 3D shape analysis tasks, the Laplace-Beltrami(LB) embedding has become increasingly popular as it enables the efficient comparison of shapes based on intrinsic geometry. One fundamental difficulty in using the LB embedding, however, is the ambiguity in the eigen-system, and it is conventionally only handled in a heuristic way. In this work, we propose a novel and intrinsic metric, the spectral l2-distance, to overcome this difficulty. We prove mathematically that this new distance satisfies the conditions of a rigorous metric. Using the resulting optimal embedding determined by the spectral l2-distance, we can perform both local and global shape analysis intrinsically in the embedding space. We demonstrate this by developing a template matching approach in the optimal embedding space to solve the challenging problem of identifying major sulci on vervet cortical surfaces. In our experiments, we validate the robustness of our method by the successful identification of major sulcal lines on a large data set of 698 cortical surfaces and illustrate its potential in brain mapping studies.


title: Mapping wildland fuels for fire management across multiple scales : Integrating remote sensing , GIS , and biophysical modeling

abstract: Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.


title: Measuring Motivations of Crowdworkers: The Multidimensional Crowdworker Motivation Scale

abstract: Crowd employment is a new form of short-term and flexible employment that has emerged during the past decade. To understand this new form of employment, it is crucial to illuminate the underlying motivations of the workforce involved in it. This article introduces the Multidimensional Crowdworker Motivation Scale (MCMS), a scale for measuring the motivation of crowdworkers on microtask platforms. The MCMS is theoretically grounded in self-determination theory and tailored specifically to the context of paid crowdsourced microlabor. The scale measures the motivation of crowdworkers along six motivational dimensions, ranging from amotivation to intrinsic motivation. We validated the MCMS on data collected in ten countries and three income groups. Factor analyses demonstrated that the MCMS’s six dimensions showed good model fit, validity, and reliability. Furthermore, our measurement invariance tests showed that motivations measured with the MCMS are comparable across countries and income groups, and we present a first cross-country comparison of crowdworker motivations. This work constitutes an important first step toward understanding the motivations of the international crowd workforce.


title: PriBots: Conversational Privacy with Chatbots

abstract: Traditional mechanisms for delivering notice and enabling choice have so far failed to protect users’ privacy. Users are continuously frustrated by complex privacy policies, unreachable privacy settings, and a multitude of emerging standards. The miniaturization trend of smart devices and the emergence of the Internet of Things (IoTs) will exacerbate this problem further. In this paper, we propose Conversational Privacy Bots (PriBots) as a new way of delivering notice and choice through a two-way dialogue between the user and a computer agent (a chatbot). PriBots improve on state-of-the-art by offering users a more intuitive and natural interface to inquire about their privacy settings, thus allowing them to control their privacy. In addition to presenting the potential applications of PriBots, we describe the underlying system needed to support their functionality. We also delve into the challenges associated with delivering privacy as an automated service. PriBots have the potential for enabling the use of chatbots in other related fields where users need to be informed or to be put in control.


title: Location-based recommendation system using Bayesian user's preference model in mobile devices

abstract: As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user's physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.


title: Fast rendering of fabric micro-appearance models under directional and spherical gaussian lights

abstract: Rendering fabrics using micro-appearance models---fiber-level microgeometry coupled with a fiber scattering model---can take hours per frame. We present a fast, precomputation-based algorithm for rendering both single and multiple scattering in fabrics with repeating structure illuminated by directional and spherical Gaussian lights. Precomputed light transport (PRT) is well established but challenging to apply directly to cloth. This paper shows how to decompose the problem and pick the right approximations to achieve very high accuracy, with significant performance gains over path tracing. We treat single and multiple scattering separately and approximate local multiple scattering using precomputed transfer functions represented in spherical harmonics. We handle shadowing between fibers with precomputed per-fiber-segment visibility functions, using two different representations to separately deal with low and high frequency spherical Gaussian lights. Our algorithm is designed for GPU performance and high visual quality. Compared to existing PRT methods, it is more accurate. In tens of seconds on a commodity GPU, it renders high-quality supersampled images that take path tracing tens of minutes on a compute cluster.


Prime Numbers and Cryptography


title: Intelligent Techniques for E-Commerce

abstract: There is no review paper available on Artificial Intelligence (AI) applications to e-commerce to the best of knowledge. This paper is a survey of various AI techniques that are used in the design and development of ecommerce systems. The techniques are categorized into three groups namely, AI techniques for B2B ecommerce, AI techniques for B2C e-commerce, and AI techniques for both B2B and B2C e-commerce. In addition, this paper presents how the AI techniques are used in e -commerce.


title: Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing

abstract: The aim of this paper is to propose a computation offloading st rategy, to be used in mobile cloud computing, in order to minimize the energy expenditure at the mobile handset necessary to run an application under a latency constraint. We exploit the concept of call graph, which models a generic computer program as a set of procedures related to each other through a weighted directed graph. Our goal is to derive the partition of the call graph establishin g which procedures are to be executed locally or remotely. The main novelty of our work is that the optimal partition is obtained jointly with the selection of the transmit power and constellation size, in order to minimize the energy consumption at the mobile handset, under a latency constraint taking into account tra nsmit time, packet drops, and execution time. We consider both a single channel and a multi-channel transmission strategy, thus proving that a globally optimal solution can be achieved in both cases with affordable complexity. The theoretical findings are corroborated by numerical results and are aimed to show under what conditions, in terms of call graph topology, communication strategy, and computation parameters, the proposed offloading strategy can provide a significant performance gain.


title: A survey of music similarity and recommendation from music context data

abstract: In this survey article, we give an overview of methods for music similarity estimation and music recommendation based on music context data. Unlike approaches that rely on music content and have been researched for almost two decades, music-context-based (or contextual) approaches to music retrieval are a quite recent field of research within music information retrieval (MIR). Contextual data refers to all music-relevant information that is not included in the audio signal itself. In this article, we focus on contextual aspects of music primarily accessible through web technology. We discuss different sources of context-based data for individual music pieces and for music artists. We summarize various approaches for constructing similarity measures based on the collaborative or cultural knowledge incorporated into these data sources. In particular, we identify and review three main types of context-based similarity approaches: text-retrieval-based approaches (relying on web-texts, tags, or lyrics), co-occurrence-based approaches (relying on playlists, page counts, microblogs, or peer-to-peer-networks), and approaches based on user ratings or listening habits. This article elaborates the characteristics of the presented context-based measures and discusses their strengths as well as their weaknesses.


title: Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness

abstract: We consider a global optimization problem of a deterministic function f in a semi-metric space, given a finite budget of n evaluations. The function f is assumed to be locally smooth (around one of its global maxima) with respect to a semi-metric l We describe two algorithms based on optimistic exploration that use a hierarchical partitioning of the space at all scales. A first contribution is an algorithm, DOO, that requires the knowledge of l. We report a finite-sample performance bound in terms of a measure of the quantity of near-optimal states. We then define a second algorithm, SOO, which does not require the knowledge of the semi-metric l under which f is smooth, and whose performance is almost as good as DOO optimally-fitted.


title: Faster Ridge Regression via the Subsampled Randomized Hadamard Transform

abstract: We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations (p ≫ n). The standard way to solve ridge regression in this setting works in the dual space and gives a running time of O(n2p). Our algorithm Subsampled Randomized Hadamard Transform- Dual Ridge Regression (SRHT-DRR) runs in time O(np log(n)) and works by preconditioning the design matrix by a Randomized Walsh-Hadamard Transform with a subsequent subsampling of features. We provide risk bounds for our SRHT-DRR algorithm in the fixed design setting and show experimental results on synthetic and real datasets.


title: A solution to the binding problem for compositional connectionism

abstract: Achieving compositional connectionism means finding a way to represent role-filler bindings in a connectionist system without sacrificing role-filler independence. Role-filler binding schemes based on varieties of conjunctive coding (the most common approach in the connectionist literature) fail to preserve role-filler independence. At the same time, dynamic binding of roles to fillers (e.g., by synchrony of firing) represents bindings without sacrificing independence, but is inadequate for storing bindings in long-term memory. An appropriate combination of dynamic binding (for representation in working memory) and conjunctive coding (for long-term storage and token formation) provides a platform for compositional connectionism, and has proven successful in simulating numerous aspects of human perception and cognition. Compositional Systems, Role-Filler Independence and Binding Compositional systems combine and recombine a finite number of representational elements (symbols or subsymbols) into a much larger (infinite, if recursion is permitted) number of specific structures. Examples include natural languages and formal symbol systems (e.g., propositional notation, mathematical notation, and computer programming languages). An important property of a compositional system is that the meaning and representation of individual elements in a structure do not vary as a function of their position in the structure as a whole. For example, “loves,” “John” and “Mary” can be combined to form two different propositions, loves (John, Mary) and loves (Mary, John). The meaning and representation of these elements (which can be objects, relations or relational roles) is fundamentally independent of each element’s place in the compositional structure as a whole (e.g.,, Mary is represented in the same way whether she is a lover or a beloved). At the same time, the meaning of the compositional structure does depend on the elements’ configuration—i.e., the bindings of arguments (here, John and Mary) to relational roles (lover and beloved). Copyright © 2004, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. Role-Filler Independence This property of formal compositional systems is so fundamental that it is easy to overlook. Yet it is essential: If the representation of an element (role or object) varied as a function the argument or role to which it was bound, then the resulting system would not be properly described as “compositional”: Different expressions consisting of the “same” symbols would not consist of the same symbols; they would be different expressions with different symbols. They would simply be different, with nothing systematic in common. The importance of role-filler independence is apparent in human relational reasoning. Relational generalization—inferences and generalizations that depend on relations between objects rather than just the features of those objects—is only possible if relational roles are represented independently of their fillers (Hummel & Holyoak, 1997, 2003). For example, suppose someone knows that John loves Mary, Mary loves Zack, and John is jealous of Zack. This person then observes that Sally loves Tom, and Tom loves Cindy. A plausible analogical inference is that Sally will be jealous of Cindy. This inference is based on the relational correspondences (i.e., participation in corresponding roles) of John to Sally, Mary to Tom, and Zack to Cindy. Making the inference requires the reasoner to discover these correspondences and to use them to guide inferences about the second (novel) situation (e.g., it is not a plausible inference that Tom will be jealous of Sally). Importantly, it is only possible to discover and use the correspondences if the loves relation is represented in the same way regardless of who loves whom (i.e., independently of its fillers), and the people involved are represented independently of their role bindings (e.g., the same Mary is both a lover and beloved; see Hummel & Holyoak, 2003).


title: Invited commentary: How big is that interaction (in my community)--and in which direction?

abstract: In an accompanying article, Turner et al. (Am J Epidemiol. 2014;180(12):1145-1149) compare the joint effects of smoking and air pollution to make inferences about the reduction in lung cancer mortality achieved when reducing each exposure separately and when reducing both together. In this commentary, we use first principles to quantify the difference between the risk or mortality reduction obtained from reducing each of 2 exposures together and the sum of the risk differences obtained from reducing the 2 exposures separately. Metrics of the impact of joint effects or comparisons of joint effects presented in units of absolute risk, such as Rothman's I, can provide more meaningful quantitative measures of public health impact than unitless metrics (e.g., ratios) and standardized metrics (e.g., the population attributable fraction) of potential interventions for reducing smoking and air pollution exposure. In particular, the venerable attributable community risk metric can provide an estimate of the community impact of such interventions in units of absolute risk. A spreadsheet we provide demonstrates the calculation of the various metrics for hypothetical data similar to those reported by Turner et al. Using algebra, graphics, and examples, we show that positive interaction, or synergy, on the additive scale implies that the impact on risk reduction from a program that applies both interventions will be lesser than the sum of the impacts of the separate interventions.


title: Improved Protocol and Data Analysis for Accelerated Shelf-Life Estimation of Solid Dosage Forms

abstract: Purpose ::: To propose and test a new accelerated aging protocol for solid-state, small molecule pharmaceuticals which provides faster predictions for drug substance and drug product shelf-life.


title: Comparative thermal analysis of IPMSMs with integral-slot distributed-winding (ISDW) and fractional-slot concentrated-winding (FSCW) for electric vehicle application

abstract: Fractional-slot concentrated-winding (FSCW) interior permanent magnet synchronous motors (IPMSMs) have been attracting considerable attention due to their high power density, high efficiency, short end-winding, high slot fill factor, low cogging torque, excellent flux-weakening and fault tolerance capability. However, compared to integer-slot distributed-winding (ISDW) IPMSMs, the key challenge of using FSCW configurations is the significant rotor losses, particularly at high speed. To figure out the IPMSM with which winding configuration is more suitable for electric vehicle (EV) application, the thermal behaviors of four IPMSMs with ISDW, single-, double- and four-layer FSCWs are comprehensively and comparatively investigated in this paper. Firstly, electromagnetic design and loss analysis of the four IPMSMs are investigated to meet the EV traction motor requirements. Secondly, finite element method (FEM) is employed to investigate the thermal performance of the IPMSMs under different rotational speeds and torque overload capacities. Besides, the influence of magnet segmentation on the temperature is also taken into account. Finally, some conclusions are drawn for the suitable winding configuration selection and the PM traction motor design.


title: An Entity-Centric Approach for Privacy and Identity Management in Cloud Computing

abstract: Entities (e.g., users, services) have to authenticate themselves to service providers (SPs) in order to use their services. An entity provides personally identifiable information (PII) that uniquely identifies it to an SP. In the traditional application-centric Identity Management (IDM) model, each application keeps trace of identities of the entities that use it. In cloud computing, entities may have multiple accounts associated with different SPs, or one SP. Sharing PIIs of the same entity across services along with associated attributes can lead to mapping of PIIs to the entity. We propose an entity-centric approach for IDM in the cloud. The approach is based on: (1) active bundles—each including a payload of PII, privacy policies and a virtual machine that enforces the policies and uses a set of protection mechanisms to protect themselves, (2) anonymous identification to mediate interactions between the entity and cloud services using entity’s privacy policies. The main characteristics of the approach are: it is independent of third party, gives minimum information to the SP and provides ability to use identity data on untrusted hosts.


title: EasyLexia: A Mobile Application for Children with Learning Difficulties

abstract: Abstract Dyslexia is one of the most common learning disabilities experienced by children and adults. A large amount of research is currently being conducted in exploring the benefits of using Information & Communication Technologies as a learning platform for individuals and especially children with such learning difficulties. Given the potential benefits, we focused on developing a mobile application which could foster learning and help children improve some of their fundamental skills, such as reading comprehension, orthographic coding, short-term memory and mathematical problem solving. We attempted to design a stimulating and interactive experience for children, which could encourage the learning process. Our main focus was to assess the usability of the technology, evaluate how it affects the learning experience, its consequences and the benefits it offers to each user. In this paper we present the methodology, environment setup, design choices, implementation and the results of our preliminary evaluation and assessment of “EasyLexia”, a mobile application for children with learning difficulties. Preliminary results show the promising prospects mobile learning holds in such contexts.


title: A bilevel optimization approach to automated parameter tuning

abstract: Many of the modern optimization algorithms contain a number of parameters that require tuning before the algorithm can be applied to a particular class of optimization problems. A proper choice of parameters may have a substantial effect on the accuracy and efficiency of the algorithm. Until recently, parameter tuning has mostly been performed using brute force strategies, such as grid search and random search. Guesses and insights about the algorithm are also used to find suitable parameters or suggest strategies to adjust them. More recent trends include the use of meta-optimization techniques. Most of these approaches are computationally expensive and do not scale when the number of parameters increases. In this paper, we propose that the parameter tuning problem is inherently a bilevel programming problem. Based on this insight, we introduce an evolutionary bilevel algorithm for parameter tuning. A few commonly used optimization algorithms (Differential Evolution and Nelder-Mead) have been chosen as test cases, whose parameters are tuned on a number of standard test problems. The bilevel approach is found to quickly converge towards the region of efficient parameters. The code for the proposed algorithm can be accessed from the website http://bilevel.org.


A survey on Data Mining Techniques for Crop Yield Prediction


title: The Introduction of Technical Debt Tracking in Large Companies

abstract: Large software companies need to support continuous and fast delivery of customer value both in the short and long term. However, this can be hindered if both evolution and maintenance of existing systems are hampered by Technical Debt. Although a lot of theoretical work on Technical Debt has been recently produced, its practical management lacks empirical studies. In this paper we investigate the state of practice in several companies in order to understand how they start tracking Technical Debt. We combined different methodologies: we conducted a survey, involving 226 respondents from 15 organizations and a more in-depth multiple case-study in three organizations, where Technical Debt was tracked: we involved 13 interviews and 79 Technical Debt issues analysis. We found that the development time dedicated to manage Technical Debt is substantial (around 25% of the overall development) but not systematic: only a few participants methodically track Technical Debt. By studying the approaches in the companies participating in the case-study, we understood how companies start tracking Technical Debt and what are the initial benefits and challenges. Finally, we propose a Strategic Adoption Model based to define and adopt a dedicated process for tracking Technical Debt


title: Skin barrier and microbiome in acne

abstract: Acne is an immune-mediated chronic inflammatory disease. Although several factors are involved in its pathophysiology, this process is not completely understood. Androgen hormone activity increases sebum production inside the pilosebaceous follicle, adjusting the environment for the development of Propionibacterium acnes which triggers inflammation. Knowing how others factors such as the skin barrier and microbiome are involved in acne, can help in understanding more about the disease and may help to conduct a better treatment.


title: Monitoring Wind Turbine Vibration Based on SCADA Data

abstract: Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data.


title: How Best-Self Activation Influences Emotions, Physiology and Employment Relationships

abstract: It may be possible to offer people a new understanding of their best-self concepts, leading to positive personal and social change. We developed theory about how best-self activation can lead to both immediate and long-term outcomes through recursion, interaction, and subjective construal between the self concept and the social system. In two lab experiments and a field experiment in a global consulting firm, we tested the hypotheses by offering people reflections on times they were at their best. Results confirmed that best-self activation inspired improvements in people’s emotions, resistance to disease, resilience to stress and burnout, creative problem solving, performance under pressure, and relationships with their employer. Results also revealed that best-self activations are more effective in creating improvements when they feature information from one’s social network rather than personal reflections.


title: The designment of student information management system based on B/S architecture

abstract: The amount of high-schools and the students expands rapidly, so high-schools have faced great challenge on management of student information. Aimed at real situation of high-schools, a student information management system is designed and it can meet the requirement.


title: Federated Over-the-Air Subspace Learning from Incomplete Data

abstract: Federated learning refers to a distributed learning scenario in which users/nodes keep their data private but only share intermediate locally computed iterates with the master node. The master, in turn, shares a global aggregate of these iterates with all the nodes at each iteration. In this work, we consider a wireless federated learning scenario where the nodes communicate to and from the master node via a wireless channel. Current and upcoming technologies such as 5G (and beyond) will operate mostly in a non-orthogonal multiple access (NOMA) mode where transmissions from the users occupy the same bandwidth and interfere at the access point. These technologies naturally lend themselves to an "over-the-air" superposition whereby information received from the user nodes can be directly summed at the master node. ::: However, over-the-air aggregation also means that the channel noise can corrupt the algorithm iterates at the time of aggregation at the master. This iteration noise introduces a novel set of challenges that have not been previously studied in the literature. It needs to be treated differently from the well-studied setting of noise or corruption in the dataset itself. In this work, we first study the subspace learning problem in a federated over-the-air setting. Subspace learning involves computing the subspace spanned by the top $r$ singular vectors of a given matrix. We develop a federated over-the-air version of the power method (FedPM) and show that its iterates converge as long as (i) the channel noise is very small compared to the $r$-th singular value of the matrix; and (ii) the ratio between its $(r+1)$-th and $r$-th singular value is smaller than a constant less than one. The second important contribution of this work is to show how over-the-air FedPM can be used to obtain a provably accurate federated solution for subspace tracking in the presence of missing data.


title: Theano: new features and speed improvements

abstract: Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.


Hippocampal Neurogenesis and Memory Clearance


title: Towards a design generation methodology

abstract: Inspired by the recent advances of deep learning and computer graphics techniques in generating sophisticated artistic styles and architectural designs, we envision a general design generation methodology that uses various specification and generative methods and tools in an integral design cycle.


A Multi-Agent Extension of Hierarchical Task Network


title: Citation-Based Bootstrapping for Large-Scale Author Disambiguation

abstract: We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first “bootstrap” stage, a collection of high-precision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our self-supervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B3 F1 of.807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from.748 to.767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1. © 2012 Wiley Periodicals, Inc.


title: Automating spoken dialogue management design using machine learning: An industry perspective

abstract: In designing a spoken dialogue system, developers need to specify the actions a system should take in response to user speech input and the state of the environment based on observed or inferred events, states, and beliefs. This is the fundamental task of dialogue management. Researchers have recently pursued methods for automating the design of spoken dialogue management using machine learning techniques such as reinforcement learning. In this paper, we discuss how dialogue management is handled in industry and critically evaluate to what extent current state-of-the-art machine learning methods can be of practical benefit to application developers who are deploying commercial production systems. In examining the strengths and weaknesses of these methods, we highlight what academic researchers need to know about commercial deployment if they are to influence the way industry designs and practices dialogue management.


title: Silent speech interfaces

abstract: The possibility of speech processing in the absence of an intelligible acoustic signal has given rise to the idea of a 'silent speech' interface, to be used as an aid for the speech-handicapped, or as part of a communications system operating in silence-required or high-background-noise environments. The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telecommunications privacy issues, and then follows with a presentation of demonstrator systems based on seven different types of technologies. A concluding section underlining some of the common challenges faced by silent speech interface researchers, and ideas for possible future directions, is also provided.


title: Whole exome sequencing identifies three novel mutations in ANTXR1 in families with GAPO syndrome.

abstract: GAPO syndrome (OMIM#230740) is the acronym for growth retardation, alopecia, pseudoanodontia, and optic atrophy. About 35 cases have been reported, making it among one of the rarest recessive conditions. Distinctive craniofacial features including alopecia, rarefaction of eyebrows and eyelashes, frontal bossing, high forehead, mid-facial hypoplasia, hypertelorism, and thickened eyelids and lips make GAPO syndrome a clinically recognizable phenotype. While this genomic study was in progress mutations in ANTXR1 were reported to cause GAPO syndrome. In our study we performed whole exome sequencing (WES) for five affected individuals from three Turkish kindreds segregating the GAPO trait. Exome sequencing analysis identified three novel homozygous mutations including; one frame-shift (c.1220_1221insT; p.Ala408Cysfs*2), one splice site (c.411A>G; p.Gln137Gln), and one non-synonymous (c.1150G>A; p.Gly384Ser) mutation in the ANTXR1 gene. Our studies expand the allelic spectrum in this rare condition and potentially provide insight into the role of ANTXR1 in the regulation of the extracellular matrix.


title: Three-phase grid-connected photovoltaic system with active and reactive power control using dq0 transformation

abstract: This paper presents a three-phase grid-connected photovoltaic generation system with unity power factor for any situation of solar radiation. The model of the PWM inverter and a control strategy using dq0 transformation are proposed the system operates as an active filter capable of compensate harmonic components and reactive power, generated by the other loads connected to the system. A input voltage clamping technique is proposed to control the power between the grid and photovoltaic system, where it is intended to achieve the maximum power point operation. Simulation results and analyses are presented to validate the proposed methodology for grid connected photovoltaic generation system.


title: SEP-Nets: Small and Effective Pattern Networks

abstract: While going deeper has been witnessed to improve the performance of convolutional neural networks (CNN), going smaller for CNN has received increasing attention recently due to its attractiveness for mobile/embedded applications. It remains an active and important topic how to design a small network while retaining the performance of large and deep CNNs (e.g., Inception Nets, ResNets). Albeit there are already intensive studies on compressing the size of CNNs, the considerable drop of performance is still a key concern in many designs. This paper addresses this concern with several new contributions. First, we propose a simple yet powerful method for compressing the size of deep CNNs based on parameter binarization. The striking difference from most previous work on parameter binarization/quantization lies at different treatments of $1\times 1$ convolutions and $k\times k$ convolutions ($k>1$), where we only binarize $k\times k$ convolutions into binary patterns. The resulting networks are referred to as pattern networks. By doing this, we show that previous deep CNNs such as GoogLeNet and Inception-type Nets can be compressed dramatically with marginal drop in performance. Second, in light of the different functionalities of $1\times 1$ (data projection/transformation) and $k\times k$ convolutions (pattern extraction), we propose a new block structure codenamed the pattern residual block that adds transformed feature maps generated by $1\times 1$ convolutions to the pattern feature maps generated by $k\times k$ convolutions, based on which we design a small network with $\sim 1$ million parameters. Combining with our parameter binarization, we achieve better performance on ImageNet than using similar sized networks including recently released Google MobileNets.


title: A Review of Reinforcement Learning

abstract: Review of "Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, The MIT Press, Cambridge, Massachusetts, 1998, 322 pp., ISBN 0-262-19398-1.


title: A Unified Framework for Generalizable Style Transfer: Style and Content Separation

abstract: Image style transfer has drawn broad attention recently. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is often not generalizable to new styles. Based on the idea of style and content separation, we here propose a unified style transfer framework that consists of style encoder, content encoder, mixer and decoder. The style encoder and the content encoder are used to extract the style and content representations from the corresponding reference images. The two representations are integrated by the mixer and fed to the decoder, which generates images with the target style and content. Assuming the same encoder could be shared among different styles/contents, the style/content encoder explores a generalizable way to represent style/content information, i.e. the encoders are expected to capture the underlying representation for different styles/contents and generalize to new styles/contents. Training simultaneously with a number of styles and contents, the framework enables building one single transfer network for multiple styles and further leads to a key merit of the framework, i.e. its generalizability to new styles and contents. To evaluate the proposed framework, we apply it to both supervised and unsupervised style transfer, using character typeface transfer and neural style transfer as respective examples. For character typeface transfer, to separate the style features and content features, we leverage the conditional dependence of styles and contents given an image. For neural style transfer, we leverage the statistical information of feature maps in certain layers to represent style. Extensive experimental results have demonstrated the effectiveness and robustness of the proposed methods. Furthermore, models learned under the proposed framework are shown to be better generalizable to new styles and contents.


title: A 1.2V 10b 20MSample/s non-binary successive approximation ADC in 0.13/spl mu/m CMOS

abstract: A successive-approximation ADC with non-binary code achieves 55dB SNR at sampling frequencies up to 20MHz. The converter, with on-chip driver for analog input and reference input, measures 0.08mm/sup 2/ in a standard 0.13/spl mu/m CMOS process and consumes 12mW from a single 1.2V supply.


Jean Piaget and the child psychologist


title: Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

abstract: We consider an assortment optimization problem where a retailer chooses an assortment of products that maximizes the profit subject to a capacity constraint. The demand is represented by a multinomial logit choice model. We consider both the static and dynamic optimization problems. In the static problem, we assume that the parameters of the logit model are known in advance; we then develop a simple algorithm for computing a profit-maximizing assortment based on the geometry of lines in the plane and derive structural properties of the optimal assortment. For the dynamic problem, the parameters of the logit model are unknown and must be estimated from data. By exploiting the structural properties found for the static problem, we develop an adaptive policy that learns the unknown parameters from past data and at the same time optimizes the profit. Numerical experiments based on sales data from an online retailer indicate that our policy performs well.


The complexity of flat origami


title: SRST: A Storytelling Model Using Rhetorical Relations

abstract: Storytelling models are usually constrained to the applications they are implemented in because of the particular characteristics of the data used to define story events and the way those events are linked. In order to develop a more generic model to create storytelling applications, we need to focus the solution not on the data itself, but on the manner this data, in the form of events, is organized and conveyed to the user. In this paper, we present SRST (Storytelling RST), our proposal for a generic storytelling ontology model based on the organization of events using the relations proposed by the Rhetorical Structure Theory (RST) and how narrative principles are applied to these RST relations to generate coherent stories.


title: Topologically based decision support tools for aircraft routing

abstract: This paper considers the problem of rapidly generating aircraft trajectories and providing visual tools for analyzing airspace in the presence of convective weather. When strong convective weather elements are present within an airspace, nominal operations are disrupted. Often, such disruptions result in the closure of arrival and departure routes, or major jetways. Partial recovery from disturbances can take the form of ground delay programs and diversions, re-routing of flights to adjacent airspaces, or utilization of other standard arrival and departure fixes. The work presented here proposes two decision support tools to aid air traffic controllers and managers in re-routing traffic. Starting from weather information, partitions of the airspace are constructed whose boundaries and junctions form a Voronoi graph. The graph is used for path planning and provides information on optimal routing policies. Furthermore, the partitions are also used to identify the set of reachable space by airplanes. A key feature of the tool is that a set of optimal solutions and/or informational content is provided, as opposed to simply providing a single optimal trajectory solution or output with limited informative content. The sets of optimal solutions are obtained by finding homotopy classes for flight routing, leading to greater flexibility in decision making. The central thesis of this paper is that graph-based and continuous trajectory-based path planning algorithms can provide the necessary information for strategic and tactical path planning through weather, and are thus well suited as decision support tools for enhancing air traffic management.


title: Quantized Kernel Least Mean Square Algorithm

abstract: In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the "redundant" data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.


title: Crowd Counting Using Multiple Local Features

abstract: In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.


title: A Decade of Lattice Cryptography.

abstract: Lattice-based cryptography is the use of conjectured hard problems on point lattices in Rn as the foundation for secure cryptographic systems. Attractive features of lattice cryptography include apparent resistance to quantum attacks in contrast with most number-theoretic cryptography, high asymptotic efficiency and parallelism, security under worst-case intractability assumptions, and solutions to long-standing open problems in cryptography. This work surveys most of the major developments in lattice cryptography over the past ten years. The main focus is on the foundational short integer solution SIS and learning with errors LWE problems and their more efficient ring-based variants, their provable hardness assuming the worst-case intractability of standard lattice problems, and their many cryptographic applications.


title: Total, Direct, and Indirect Effects in Structural Equation Models

abstract: Decomposing the total effects of one variable on another into direct and indirect effects has long been of interest to researchers who use path analysis. In this paper, I review the decomposition of effects in general structural equation models with latent and observed variables. I present the two approaches to defining total effects. One is based on sums of powers of coefficient matrices. The other defines total effects as reducedform coefficients. I show the conditions under which these two definitions are equivalent. I also compare the different types of specific indirect effects. These are the influences that are transmitted through particular


title: Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations

abstract: In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time. Neuromorphic systems realize the function of biological neural systems by emulating their structure. Designers of such systems face three major design choices: 1) whether to emulate the four neural elementsVaxonal arbor, synapse, dendritic tree, and somaVwith dedicated or shared electronic circuits; 2) whether to implement these electronic circuits in an analog or digital manner; and 3) whether to interconnect arrays of these silicon neurons with a mesh or a tree network. The choices we made were: 1) we emulated all neural elements except the soma with shared electronic circuits; this choice maximized the number of synaptic connections; 2) we realizedallelectroniccircuitsexcept those foraxonal arbors in an analog manner; this choice maximized energy efficiency; and


title: Multigait soft robot.

abstract: This manuscript describes a unique class of locomotive robot: A soft robot, composed exclusively of soft materials (elastomeric polymers), which is inspired by animals (e.g., squid, starfish, worms) that do not have hard internal skeletons. Soft lithography was used to fabricate a pneumatically actuated robot capable of sophisticated locomotion (e.g., fluid movement of limbs and multiple gaits). This robot is quadrupedal; it uses no sensors, only five actuators, and a simple pneumatic valving system that operates at low pressures (< 10 psi). A combination of crawling and undulation gaits allowed this robot to navigate a difficult obstacle. This demonstration illustrates an advantage of soft robotics: They are systems in which simple types of actuation produce complex motion.


title: A Compact Rotary Series Elastic Actuator for Human Assistive Systems

abstract: Precise and large torque generation, back drivability, low output impedance, and compactness of hardware are important requirements for human assistive robots. In this paper, a compact rotary series elastic actuator (cRSEA) is designed considering these requirements. To magnify the torque generated by an electric motor in the limited space of the compact device, a worm gear is utilized. However, the actual torque amplification ratio provided by the worm gear is different from the nominal speed reduction ratio due to friction, which makes the controller design challenging. In this paper, the friction effect is considered in the model of cRSEA, and a robust control algorithm is designed to precisely control the torque output in the presence of nonlinearities such as the friction. The mechanical design and dynamic model of the proposed device and the design of a robust control algorithm are discussed, and actuation performance is verified by experiments. Experimental results with a human subject are also presented to show the performance of the cRSEA while interacting with humans.


title: An Integrated Microwave Imaging Radar With Planar Antennas for Breast Cancer Detection

abstract: The system design of an integrated microwave imaging radar for the diagnostic screening of breasts cancer is presented. A custom integrated circuit implemented in a 65-nm CMOS technology and a pair of patch antennas realized on a planar laminate are proposed as the basic module of the imaging antenna array. The radar operates on the broad frequency range from 2 to 16 GHz with a dynamic range of 107 dB. Imaging experiments carried out on a realistic breast phantom show that the system is capable of detecting tumor targets with a resolution of 3 mm.


title: Eunomia: Scaling Concurrent Search Trees under Contention Using HTM

abstract: While hardware transactional memory (HTM) has recently been adopted to construct efficient concurrent search tree structures, such designs fail to deliver scalable performance under contention. In this paper, we first conduct a detailed analysis on an HTM-based concurrent B+Tree, which uncovers several reasons for excessive HTM aborts induced by both false and true conflicts under contention. Based on the analysis, we advocate Eunomia, a design pattern for search trees which contains several principles to reduce HTM aborts, including splitting HTM regions with version-based concurrency control to reduce HTM working sets, partitioned data layout to reduce false conflicts, proactively detecting and avoiding true conflicts, and adaptive concurrency control. To validate their effectiveness, we apply such designs to construct a scalable concurrent B+Tree using HTM. Evaluation using key-value store benchmarks on a 20-core HTM-capable multi-core machine shows that Eunomia leads to 5X-11X speedup under high contention, while incurring small overhead under low contention.


EXPRESSION OF PLACENTAL ALKALINE PHOSPHATASE IN ESOPHAGEAL CANCER CELL LINE Eca109


title: Tracking Multiple People under Occlusion using Multiple Cameras

abstract: We describe a system for tracking multiple people with multiple cameras based on fusion of multiple cues. Face trackers are used to self-calibrate our system. Epipolar geometry and landmarks are employed to disambiguate the tracking problem. The correlation of visual information between different cameras is learnt using Support Vector Regression and Hierarchical Principal Component Analysis to estimate the subject appearance across cameras. The joint features of subjects extracted from multiple cameras are tracked and used as a model to re-track people once the subjects are lost tracking in the system. Results demonstrate that our system can deal with the occlusion.


title: Modeling and percept of transcorneal electrical stimulation in humans

abstract: Retinal activation via transcorneal electrical stimulation (TcES) in normal humans was investigated by comparing subject perception, model predictions, and brain activation patterns. The preferential location of retinal stimulation was predicted from 3-D admittance modeling. Visual cortex activation was measured using positron emission tomography (PET) and 18F-fluorodeoxyglucose (FDG). Two different corneal electrodes were investigated: DTL-Plus and ERG-Jet. Modeling results predicted preferential stimulation of the peripheral, inferior, nasal retina during right eye TcES using DTL-Plus, but more extensive activation of peripheral, nasal hemiretina using ERG-Jet. The results from human FDG PET study using both corneal electrodes showed areas of visual cortex activation that consistently corresponded with the reported phosphene percept and modeling predictions. ERG-Jet was able to generate brighter phosphene percept than DTL-Plus and elicited retinotopically mapped primary visual cortex activation. This study demonstrates that admittance modeling and PET imaging consistently predict the perceived location of electrically elicited phosphenes produced during TcES.


title: The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS

abstract: Nowadays, the performance of processors is primarily bound by a fixed energy budget, the power wall. This forces hardware vendors to optimize processors for specific tasks, which leads to an increasingly heterogeneous hardware landscape. Although efficient algorithms for modern processors such as GPUs are heavily investigated, we also need to prepare the database optimizer to handle computations on heterogeneous processors. GPUs are an interesting base for case studies, because they already offer many difficulties we will face tomorrow.


title: Software bertillonage: finding the provenance of an entity

abstract: Deployed software systems are typically composed of many pieces, not all of which may have been created by the main development team. Often, the provenance of included components -- such as external libraries or cloned source code -- is not clearly stated, and this uncertainty can introduce technical and ethical concerns that make it difficult for system owners and other stakeholders to manage their software assets. In this work, we motivate the need for the recovery of the provenance of software entities by a broad set of techniques that could include signature matching, source code fact extraction, software clone detection, call flow graph matching, string matching, historical analyses, and other techniques. We liken our provenance goals to that of Bertillonage, a simple and approximate forensic analysis technique based on bio-metrics that was developed in 19th century France before the advent of fingerprints. As an example, we have developed a fast, simple, and approximate technique called anchored signature matching for identifying library version information within a given Java application. This technique involves a type of structured signature matching performed against a database of candidates drawn from the Maven2 repository, a 150GB collection of open source Java libraries. An exploratory case study using a proprietary e-commerce Java application illustrates that the approach is both feasible and effective.


Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games


title: Carbon-Double-Bond-Free Printed Solar Cells from TiO2/CH3NH3PbI3/CuSCN/Au: Structural Control and Photoaging Effects

abstract: Carbon double bond-free printed solar cells have been fabricated with the structure <F-doped SnO2 (FTO)/dense TiO2/nanocrystalline TiO2/CH3NH3PbI3/Au> and <FTO/dense TiO2/nanocrystalline TiO2/CH3NH3PbI3/CuSCN/Au>, in which CuSCN acts as a hole conductor. The thickness of the CH3NH3PbI3 layer is controlled by a hot air flow during spin coating. The best conversion efficiency (4.86%) is obtained with <FTO/dense TiO2/nanocrystalline TiO2/thin CH3NH3PbI3 (hot-air dried)/CuSCN/Au>. However, a thick CH3NH3PbI3 layer on CuSCN is better for light-exposure stability (100 mW cm(-2) AM 1.5) when not encapsulated. Without the CuSCN coverage, the black CH3NH3PbI3 crystal changes to yellow during the light-exposure stability test, which is due to the transformation of the CH3NH3PbI3 perovskite crystal into hexagonal PbI2.


title: Repetitive High-Voltage All-solid-state Marx Generator for Excimer DBD UV Sources

abstract: In order to develop an efficient and reliable pulsed power supply for excimer dielectric barrier discharge (DBD) ultraviolet (UV) sources, a pulse generator using Marx topology is adopted. MOSFETs are used as switches. The 12-stage pulse generator operates with a voltage amplitude in the range of 0–5.5 kV. The repetition rate and pulsewidth can be adjusted from 0.1 to 50 kHz and 2 to 20 $\mu \text{s}$ , respectively. It is used to excite KrCl* excilamp, a typical DBD UV source. In order to evaluate the performance of the pulse generator, a sinusoidal voltage power supply dedicated for DBD lamp is also used to excite the KrCl* excilamp. It shows that the lamp excited by the pulse generator has better performance with regard to radiant power and system efficiency. The influence of voltage amplitude, repetition rate, pulsewidth, and rise and fall times on radiant power and system efficiency is investigated using the pulse generator. An inductor is inserted between the pulse generator and the KrCl* excilamp to reduce electromagnetic interference and enhance system reliability. The experimental results show that it also helps to improve radiant power and system efficiency.


title: Adversarial Training for Cross-Domain Universal Dependency Parsing

abstract: We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique. Our approach is an extension to the recently proposed adversarial training technique for domain adaptation, which we apply on top of a graph-based neural dependency parsing model on bidirectional LSTMs. In our ::: experiments, we find our baseline graph-based parser already outperforms the official baseline model (UDPipe) by a large margin. Further, by applying our technique to the treebanks of the same lan- ::: guage with different domains, we observe an additional gain in the performance, in particular for the domains with less training data


title: A 2.1 MHz Crystal Oscillator Time Base with a Current Consumption under 500 nA

abstract: A micro-power circuit is encapsulated with a 2.1 MHz ZT-cut quartz in a vacuum package. The oscillator core has 2 complementary active MOSFETS and amplitude stabilization. New coupling and biasing circuits, and dynamic frequency dividers allow to achieve ±2 ppm frequency stability down to 1.8 V with a current under 0.5 ?A.


title: A novel refinement approach for text categorization

abstract: In this paper we present a novel strategy, DragPushing, for improving the performance of text classifiers. The strategy is generic and takes advantage of training errors to successively refine the classification model of a base classifier. We describe how it is applied to generate two new classification algorithms; a Refined Centroid Classifier and a Refined Naive Bayes Classifier. We present an extensive experimental evaluation of both algorithms on three English collections and one Chinese corpus. The results indicate that in each case, the refined classifiers achieve significant performance improvement over the base classifiers used. Furthermore, the performance of the Refined Centroid Classifier implemented is comparable, if not better, to that of state-of-the-art support vector machine (SVM)-based classifier, but offers a much lower computational cost.


title: Split Renal Function Outcome after Renal Angioplasty in Patients with Unilateral Renal Artery Stenosis

abstract: Abstract . The general use of bilateral rather than separate renal function evaluation has led to the publication of conflicting results concerning the effect of percutaneous transluminal renal angioplasty (PTRA) on renal function, especially in patients with atherosclerotic renal artery stenosis. The aim of this study was to evaluate prospectively, in standardized conditions, split renal function (SRF) and GFR outcome after successful PTRA, by measuring single kidney GFR with synchronous inulin or 51 Cr-ethylenediaminetetraacetic acid clearance and 99m Tc-diethylenetriamine pentaacetic acid scintigraphy, in a well-defined population of patients with unilateral renal artery stenosis. Thirty-two consecutive hypertensive patients (18 with atherosclerotic and 14 with dysplastic disease) with significant unilateral stenosis of the main native renal artery (≥60%) and normal renal function were included in the study. Renal and angiographic follow-up evaluations were performed 6 mo after PTRA. PTRA alone or combined with stenting ( n = 2) was technically successful in all patients. Repeat PTRA was necessary in two patients, evaluated 6 mo after the second PTRA. Six mo after PTRA, total GFR had increased slightly but significantly in the 29 patients with positive lateralization indices. SRF and single-kidney GFR of the stenotic kidney increased significantly, whereas concurrently the GFR and SRF of the nonstenotic kidney decreased significantly. Six mo after successful PTRA reducing renal ischemia, a reversal of both the hypoperfusion of the stenotic side and the hyperperfusion of the nonstenotic side was observed, which was accompanied by a slight increase in total GFR.


title: High-Efficiency 60-GHz Printed Yagi Antenna Array

abstract: A low-cost and highly efficient printed Yagi antenna structure with high radiation efficiency is presented. The proposed prototype consists of two stacked planar layers. The first layer electromagnetically couples energy to the printed Yagi array parasitic elements located on the second layer. A reduced-size 60-GHz prototype (1.0687 × 0.8015 cm2) is designed to operate over the Industrial, Scientific, and Medical (ISM) band. The measured impedance bandwidth is more than 5 GHz with a gain of 10 dB measured at 60 GHz. With these features, this antenna achieves a low profile that makes it suitable for millimeter-wave monolithic microwave integrated circuits (MMICs) packaging as well as for short-range wireless communications and imaging applications.


title: Efficient C-space and cost function updates in 3D for unmanned aerial vehicles

abstract: When operating in partially-known environments, autonomous vehicles must constantly update their maps and plans based on new sensor information. Much focus has been placed on developing efficient incremental planning algorithms that are able to efficiently replan when the map and associated cost function changes. However, much less attention has been placed on efficiently updating the cost function used by these planners, which can represent a significant portion of the time spent replanning. In this paper, we present the Limited Incremental Distance Transform algorithm, which can be used to efficiently update the cost function used for planning when changes in the environment are observed. Using this algorithm it is possible to plan paths in a completely incremental way starting from a list of changed obstacle classifications. We present results comparing the algorithm to the Euclidean distance transform and a mask-based incremental distance transform algorithm. Computation time is reduced by an order of magnitude for a UAV application. We also provide example results from an autonomous micro aerial vehicle with on-board sensing and computing.


title: Is 3D technique superior to 2D in Down syndrome screening? Evaluation of six second and third trimester fetal profile markers

abstract: OBJECTIVE ::: The objective of this article is to investigate whether in the clinical setting of second trimester ultrasound (US) investigations, 3D multiplanar correction prior to the measurement of Down syndrome (DS) facial markers (nasal bone length, prenasal thickness, fetal profile line, maxilla-nasion-mandible angle, prenasal thickness to nasal bone length ratio, and prefrontal space ratio) is superior to subjective judgment of a correct midsagittal plane by 2D technique. ::: ::: ::: METHODS ::: Measurements were performed on 2D images and 3D volumes (corrected to the midsagittal plane), acquired during the same scanning session. ::: ::: ::: RESULTS ::: All six markers were measured in 105 datasets (75 of euploid fetuses and 30 of DS fetuses). The maxilla-nasion-mandible angle measured on 2D images was significantly larger than on 3D volumes (p < 0.01). In all other markers, there was no significant difference between measurements performed on 2D images or 3D volumes. No statistical difference was found for any marker between measurements performed on images acquired by either 2D or 3D US in their ability to discriminate between normal and DS fetuses. ::: ::: ::: CONCLUSIONS ::: Nasal bone length, prenasal thickness, fetal profile line, prenasal thickness to nasal bone length ratio, and prefrontal space ratio can be confidently used as DS markers in second trimester US examinations performed by 2D US. © 2014 John Wiley & Sons, Ltd.


title: Randomized requantization with local differential privacy

abstract: In this paper we study how individual sensors can compress their observations in a privacy-preserving manner. We propose a randomized requantization scheme that guarantees local differential privacy, a strong model for privacy in which individual data holders must mask their information before sending it to an untrusted third party. For our approach, the problem becomes an optimization over discrete mem-oryless channels between the sensor observations and their compressed version. We show that for a fixed compression ratio, finding privacy-optimal channel subject to a distortion constraint is a quasiconvex optimization problem that can be solved by the bisection method. Our results indicate interesting tradeoffs between the privacy risk, compression ratio, and utility, or distortion. For example, in the low distortion regime, we can halve the bit rate at little cost in distortion while maintaining the same privacy level. We illustrate our approach for a simple example of privatizing and recompressing lowpass signals and show that it yields better tradeoffs than existing approaches based on noise addition. Our approach may be useful in several privacy-sensitive monitoring applications envisioned for the Internet of Things (IoT).


title: Measuring good governance for complex ecosystems: Perceptions of coral reef-dependent communities in the Caribbean

abstract: Abstract Good governance is widely seen as a prerequisite for effective natural resources management in the context of environmental decline and increasing anthropogenic pressures. Few studies quantitatively examine governance principles, or explore links between perceptions of community members and the governance that shapes their behaviour. Comparative work, spanning multiple sites and contexts, is rare. This paper measures community members’ perceptions of governance in twelve coral reef-dependent communities across four countries in the Wider Caribbean Region. In relation to established principles of ‘good governance’, multiple correspondence analysis indicates that perceptions can be reliably described using two themes, institutional acceptance and engagement. These explain over 50% of variation in individual perceptions. These measurable themes provide an indication of the social fit of governance arrangements, and have implications for expected outcomes, including support for management and compliance with regulations. Cluster analysis provides unique empirical evidence linking structural characteristics of governance to community perceptions; four of five good governance indicators were present in communities with positive perceptions. Results suggest a combination of supportive structures and processes are necessary to achieve governance systems positively perceived by community members. Findings are relevant to those seeking to design management systems and governance structures that are appropriate to local circumstances and will engender stakeholder support.


title: Dynamically Balanced Folded-Beam Suspensions for Resonators

abstract: The standard folded-beam suspension is often used in electrostatic comb-drive resonators with the intention of achieving a system with a linear response. However, we show that the harmonic response of the standard folded-beam suspension is not linear at the fundamental resonance frequency of the system. Specifically, we show that at the fundamental resonance frequency, the stiffness of the suspension increases monotonically with increasing motion amplitude. We show that even though the suspension is intended to respond as a linear spring, it is actually designed to do so only in static loading. We present a solution to the problem in the form of a new dynamically balanced folded-beam suspension. This suspension is designed such that at the fundamental frequency of the system, its response is linear, and its stiffness is unaffected by motion. The methodology for designing this suspension is fully detailed. We experimentally demonstrate the viability of the new design. [2015-0128]


title: How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

abstract: Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Who-did-What datasets, finding that question- and passage-only models often perform surprisingly well. On $14$ out of $20$ bAbI tasks, passage-only models achieve greater than $50\%$ accuracy, sometimes matching the full model. Interestingly, while CBT provides $20$-sentence stories only the last is needed for comparably accurate prediction. By comparison, SQuAD and CNN appear better-constructed.


title: System-Level Behavioral Model of a 12-Bit 1.5-Bit Per Stage Pipelined ADC Based on Verilog®=-AMS

abstract: This document presents a system-level behavioral model of a 12-bit 1.5-bit/stage pipelined ADC based on Verilog-AMS that can be used in the CMOS circuit design activities. It was developed with one of the currently most used EDA tools, Cadence Virtuoso, the versatility of this software allowed to create a model that consist of subsystems at circuit level described in Verilog, Verilog-A and SPICE, in others words, Verilog-AMS. The model was tested in time and frequency, the results exhibit a SQNR of 73.7 dB and an ENoB of 11.5 bits when ideal parameters are present.


title: Ego-network probabilistic graphical model for discovering on-line communities

abstract: Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.


title: Characterizing Multi-Agent Team Behavior from Partial Team Tracings: Evidence from the English Premier League

abstract: Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i.e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player's plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into "discretized" plays (i.e. plans), making it difficult to obtain a library that characterizes a team's behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time. Using these representations we can characterize team behavior from entropy maps, which give a measure of predictability of team behaviors across the field. We show the efficacy and applicability of our method on the 2010-2011 English Premier League soccer data.


title: Jonathan Schler 1 2 Effects of Age and Gender on Blogging

abstract: Analysis of a corpus of tens of thousands of blogs – incorporating close to 300 million words – indicates significant differences in writing style and content between male and female bloggers as well as among authors of different ages. Such differences can be exploited to determine an unknown author’s age and gender on the basis of a blog’s vocabulary.


title: Technological and nutritional properties of gluten-free snacks based on brown rice and chestnut flour

abstract: Abstract Effect of different ratios of chestnut flour substitution on brown rice flour based snack was investigated for technological and nutritional properties. Pasting properties significantly varied among the flour ratios and the higher levels of chestnut flour led to the increase in pasting properties. Colour parameters, L∗, a∗ and b∗ values of snacks decreased with increase in chestnut flour level. Expansion ratio of snacks is increased while as hardness decreased with progressive increase of chestnut flour. Mineral analysis showed that snacks were rich in potassium, phosphorus, sulphur and magnesium etc. Scanning electronic microscopy showed the porous structure of fried snack which leads to increase in expansion ratio. Flavour value of snacks was increased with the increase in chestnut flour level in the blend. Based on present study, brown rice-chestnut flour based snacks have good potential for consumer acceptance and are regarded as health promoting functional food, especially for celiac disease patients.


title: Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management

abstract: A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).


title: A prefrontal–thalamo–hippocampal circuit for goal-directed spatial navigation

abstract: Spatial navigation requires information about the relationship between current and future positions. The activity of hippocampal neurons appears to reflect such a relationship, representing not only instantaneous position but also the path towards a goal location. However, how the hippocampus obtains information about goal direction is poorly understood. Here we report a prefrontal-thalamic neural circuit that is required for hippocampal representation of routes or trajectories through the environment. Trajectory-dependent firing was observed in medial prefrontal cortex, the nucleus reuniens of the thalamus, and the CA1 region of the hippocampus in rats. Lesioning or optogenetic silencing of the nucleus reuniens substantially reduced trajectory-dependent CA1 firing. Trajectory-dependent activity was almost absent in CA3, which does not receive nucleus reuniens input. The data suggest that projections from medial prefrontal cortex, via the nucleus reuniens, are crucial for representation of the future path during goal-directed behaviour and point to the thalamus as a key node in networks for long-range communication between cortical regions involved in navigation.


title: Evolution of the cancer stem cell model.

abstract: Genetic analyses have shaped much of our understanding of cancer. However, it is becoming increasingly clear that cancer cells display features of normal tissue organization, where cancer stem cells (CSCs) can drive tumor growth. Although often considered as mutually exclusive models to describe tumor heterogeneity, we propose that the genetic and CSC models of cancer can be harmonized by considering the role of genetic diversity and nongenetic influences in contributing to tumor heterogeneity. We offer an approach to integrating CSCs and cancer genetic data that will guide the field in interpreting past observations and designing future studies.


title: An Empirical Study of ADMM for Nonconvex Problems

abstract: The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems. We provide an empirical study of the practical performance of ADMM on several nonconvex applications, including l0 regularized linear regression, l0 regularized image denoising, phase retrieval, and eigenvector computation. Our experiments suggest that ADMM performs well on a broad class of non-convex problems. Moreover, recently proposed adaptive ADMM methods, which automatically tune penalty parameters as the method runs, can improve algorithm efficiency and solution quality compared to ADMM with a non-tuned penalty.


title: Reverse engineering: a roadmap

abstract: By the early 1990s the need for reengineering legacy systems was already acute, but recently the demand has increased significantly with the shift toward web-based user interfaces. The demand by all business sectors to adapt their information systems to the Web has created a tremendous need for methods, tools, and infrastructures to evolve and exploit existing applications efficiently and cost-effectively. Reverse engineering has been heralded as one of the most promising technologies to combat this legacy systems problem. This paper presents a roadmap for reverse engineering research for the first decade of the new millennium, building on the program comprehension theories of the 1980s and the reverse engineering technology of the 1990s.


title: Real-time prediction of battery power requirements for electric vehicles

abstract: A battery management system (BMS) is responsible for protecting the battery from damage, predicting battery life, and maintaining the battery in an operational condition. In this paper, we propose an efficient way of predicting the power requirements of electric vehicles (EVs) based on a history of their power consumption, speed, and acceleration, as well as the road information from a pre-downloaded map. The predicted power requirement is then used by the BMS to prevent the damage of battery cells that might result from high discharge rates. This prediction also helps BMS efficiently schedule and allocate battery cells in real time to meet an EV's power demands. For accurate prediction of power requirements, we need an accurate model for the power requirement of each given application. We generate this model in real time by collecting and using historical data of power consumption, speed, acceleration, and road information such as slope and speed limit. By using this information and the operator's driving pattern, the model extracts the vehicle's history of speed and acceleration, which, in turn, enables the prediction of the vehicle's (immediate) future power requirements. That is, the power requirement prediction is achieved by combining a real-time power requirement model and the estimation of the vehicle's acceleration and speed. The proposed approach predicts closer to the actual required power than a widely-used heuristic approach that uses measured power demand, by up to 69.2%.


title: Risk assessment in ERP projects

abstract: Conducting Risk Management of Enterprise Resource Planning (ERP) projects is an ambitious task. ERP projects are complex undertakings for business enterprises, as the associated risk factors include myriad technological, managerial, psychological and sociological aspects. Moreover, such factors are often tightly interconnected and can have indirect effects on projects. Such features make Risk Management more difficult, uncertain and important than in traditional projects, especially in the Assessment stage. The aim of this paper is to propose an innovative technique to support Risk Analysis in order to provide a better, more structured and systematic understanding of the major relations amongst various risk factors, on the one hand, and between risk factors and the specific effects associated with ERP projects, on the other. A real case study regarding a multinational company and involving a panel of experts and practitioners is presented to illustrate application of the method.


title: Web application security vulnerabilities detection approaches: A systematic mapping study

abstract: Number of security vulnerabilities in web application has grown with the tremendous growth of web application in last two decades. As the domain of Web Applications is maturing, large number of empirical studies has been reported in web applications to address the solution of vulnerable web application. However, before advancing towards finding new approaches of web applications security vulnerability detection, there is a need to analyze and synthesize existing evidence based studies in web applications area. To do this, we have planned to conduct a systematic mapping study to view and report the state-of-the-art of empirical work in existing research of web applications. In this paper, we aimed at providing a description of mapping study for synthesizing the reported empirical research in the area of web applications security vulnerabilities detection approaches. The proposed solutions are mapped against: (1) the software development stages for which the solution has been proposed and (2) the web application vulnerabilities mapping according to OWASP Top 10 security vulnerabilities. To do this, existing literature has been surveyed using a systematic mapping study by phrasing two research questions. In the mapping study, a total of 41 studies dating from 1994 to 2014 were evaluated and mapped against the aforementioned categories. The outcome of this mapping study is current state-of-the-art of empirical research in web application area, strength and weaknesses of existing empirical work, best practices and possible directions for future research.