query: Transforming GIS Data into Functional Road Models for Large-Scale Traffic Simulation

positive: Procedural Generation of Roads In this paper, we propose an automatic method for generating roads based on a weighted anisotropic shortest path algorithm. Given an input scene, we automatically create a path connecting an initial and a final point. The trajectory of the road minimizes a cost function that takes into account the different parameters of the scene including the slope of the terrain, natural obstacles such as rivers, lakes, mountains and forests. The road is generated by excavating the terrain along the path and instantiating generic parameterized models.

positive: Arc-Length Parameterized Spline Curves for Real-Time Simulation Parametric curves are frequently used in computer animation and virtual environments to control the movements of synthetic objects. In many of these applications it is essential to efficiently relate parameter values to the arc length of the curve. Current approaches to compute arc length or to construct an arc-length parameterized curve are impractical to use in real-time applications. This paper presents a simple and efficient technique to generate approximately arc-length parameterized spline curves that closely match spline curves typically used to model roads in high-fidelity driving simulators.

positive: Congested traffic states in empirical observations and microscopic simulations We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber, Phys. Rev. Lett. 82, 4360 (1999)]. We simulate these situations with a continuous microscopic single-lane model, the "intelligent driver model," using empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has essentially the same effect as an on-ramp.

positive: Computing the arc length of parametric curves Specifying constraints on motion is simpler if the curve is parameterized by arc length, but many parametric curves of practical interest cannot be parameterized by arc length. An approximate numerical reparameterization technique that improves on a previous algorithm by using a different numerical integration procedure that recursively subdivides the curve and creates a table of the subdivision points is presented. The use of the table greatly reduces the computation required for subsequent arc length calculations. After table construction, the algorithm takes nearly constant time for each arc length calculation. A linear increase in the number of control points can result in a more than linear increase in computation. Examples of this type of behavior are shown.<<ETX>>

positive: Approximate Arc Length Parametrization Current approaches to compute the arc length of a parametric curve rely on table lookup schemes. We present an approximate closed-form solution to the problem of computing an arc length parametrization for any given parametric curve. Our solution outputs a one or two-span Bézier curve which relates the length of the curve to the parametric variable. The main advantage of our approach is that we obtain a simple continuous function relating the length of the curve and the parametric variable. This allows the length to be easily computed given the parametric values. Tests with our algorithm on several thousand curves show that the maximum error in our approximation is 8.7% and that the average of maximum errors is 1.9%. Our algorithm is fast enough to compute the closed-form solution in a fraction of a second. After that a user can interactively get an approximation of the arc length for an arbitrary parameter value.

negative: The optimal training load for the development of dynamic athletic performance. This study was performed to determine which of three theoretically optimal resistance training modalities resulted in the greatest enhancement in the performance of a series of dynamic athletic activities. The three training modalities included 1) traditional weight training, 2) plyometric training, and 3) explosive weight training at the load that maximized mechanical power output. Sixty-four previously trained subjects were randomly allocated to four groups that included the above three training modalities and a control group. The experimental groups trained for 10 wk performing either heavy squat lifts, depth jumps, or weighted squat jumps. All subjects were tested prior to training, after 5 wk of training and at the completion of the training period. The test items included 1) 30-m sprint, 2) vertical jumps performed with and without a countermovement, 3) maximal cycle test, 4) isokinetic leg extension test, and 5) a maximal isometric test. The experimental group which trained with the load that maximized mechanical power achieved the best overall results in enhancing dynamic athletic performance recording statistically significant (P < 0.05) improvements on most test items and producing statistically superior results to the two other training modalities on the jumping and isokinetic tests.

negative: NIH Image to ImageJ: 25 years of image analysis For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

negative: Graph Database Applications and Concepts with Neo4j Graph databases (GDB) are now a viable alternative to Relational Database Systems (RDBMS). Chemistry, biology, semantic web, social networking and recommendation engines are all examples of applications that can be represented in a much more natural form. Comparisons will be drawn between relational database systems (Oracle, MySQL) and graph databases (Neo4J) focusing on aspects such as data structures, data model features and query facilities. Additionally, several of the inherent and contemporary limitations of current offerings comparing and contrasting graph vs. relational database implementations will be explored.

negative: EigenTrustp++: Attack resilient trust management This paper argues that trust and reputation models should take into account not only direct experiences (local trust) and experiences from the circle of “friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrust, one of the most popular reputation systems to date, and identify the inherent vulnerabilities of EigenTrust in terms of its local trust vector, its global aggregation of local trust values, and its eigenvector based reputation propagating model. Then we present EigenTrust++, an attack resilient trust management scheme. EigenTrust++ extends the eigenvector based reputation propagating model, the core of EigenTrust, and counters each of vulnerabilities identified with alternative methods that are by design more resilient to dishonest feedbacks and sparse network connectivity under four known attack models. We conduct extensive experimental evaluation on EigenTrust++, and show that EigenTrust++ can significantly outperform EigenTrust in terms of both performance and attack resilience in the presence of dishonest feedbacks and sparse network connectivity against four representative attack models.

negative: Fully Convolutional Networks for Semantic Segmentation Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.

negative: Computational Decomposition of Style for Controllable and Enhanced Style Transfer Neural style transfer has been demonstrated to be powerful in creating artistic image with help of Convolutional Neural Networks (CNN). However, there is still lack of computational analysis of perceptual components of the artistic style. Different from some early attempts which studied the style by some pre-processing or post-processing techniques, we investigate the characteristics of the style systematically based on feature map produced by CNN. First, we computationally decompose the style into basic elements using not only spectrum based methods including Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) but also latent variable models such Principal Component Analysis (PCA), Independent Component Analysis (ICA). Then, the decomposition of style induces various ways of controlling the style elements which could be embedded as modules in state-of-the-art style transfer algorithms. Such decomposition of style brings several advantages. It enables the computational coding of different artistic styles by our style basis with similar styles clustering together, and thus it facilitates the mixing or intervention of styles based on the style basis from more than one styles so that compound style or new style could be generated to produce styled images. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also sketch style transfer which indicates possible applications on picture-to-sketch problems.

negative: A CMOS low-power low-offset and high-speed fully dynamic latched comparator This paper presents a novel dynamic latched comparator that demonstrates lower offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage is improved. The complementary version of the regenerative latch stage, which provides larger output drive current than the conventional one at a limited area, is implemented. The proposed circuit is designed using 90nm CMOS technology and 1V power supply voltage, and it demonstrates up to 19% less offset voltage and 62% less sensitivity of the delay to the input voltage difference (17ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption.

negative: A DYNAMIC FUZZY-COGNITIVE-MAP APPROACH BASED ON RANDOM NEURAL NETWORKS A fuzzy cognitive map is a graphical means of representing arbitrarily complex models of interrelations between concepts. The purpose of this paper is to describe a dynamic fuzzy cognitive map based on the random neural network model. Previously, we have developed a random fuzzy cognitive map and illustrated its application in the modeling of processes. The adaptive fuzzy cognitive map changes its fuzzy causal web as causal patterns change and as experts update their causal knowledge. Our model carries out inferences via numerical calculation instead of symbolic deduction. We show how the dynamic random fuzzy cognitive map can reveal implications of models composed of dynamic processes. Copyright c ©2003 Yang’s Scientific Research Institute, LLC. All rights reserved.

negative: Eclectic extraction of propositional rules from Neural Networks Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt to curb this limitation by extracting comprehensible rules from a trained Network. Many such extraction algorithms have been developed over the years with their respective strengths and weaknesses. They have been broadly categorized into three types based on their approach to use internal model of the Network. Eclectic Methods are hybrid algorithms that combine the other approaches to attain more performance. In this paper, we present an Eclectic method called HERETIC. Our algorithm uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules. Experiments and theoretical analysis show HERETIC to be better in terms of speed and performance.

negative: The Role of Smart Meters in Enabling Real-Time Energy Services for Households : The Italian Case The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.

negative: High Efficiency AC – AC Power Electronic Converter Applied To Domestic Induction Heating Induction heating is a well-known technique to produce very high temperature for applications. A large number of topologies have been developed in this area such as voltage and current source inverter. This project presents the analysis and design of a new ac–ac resonant converter applied to domestic induction heating. The proposed topology, based on the halfbridge series resonant inverter, uses only two diodes to rectify the mains voltage.The proposed converter can operate with zerovoltage switching during both switches-on and switch-off transitions. This topology doubles the output voltage, and therefore, the current in the load is reduced for the same output power. As a consequence, the converter efficiency is significantly improved.The output power is controlled using the PWM technique. This results in an increase of the net efficiency of the induction heating system. The circuit is simulated using MATLAB. The circuit is implemented using PIC controller. Both simulation and hardware results are compared.

negative: Censoring Representations with an Adversary In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying information. We address these two related issues by learning flexible representations that minimize the capability of an adversarial critic. This adversary is trying to predict the relevant sensitive variable from the representation, and so minimizing the performance of the adversary ensures there is little or no information in the representation about the sensitive variable. We demonstrate this adversarial approach on two problems: making decisions free from discrimination and removing private information from images. We formulate the adversarial model as a minimax problem, and optimize that minimax objective using a stochastic gradient alternate min-max optimizer. We demonstrate the ability to provide discriminant free representations for standard test problems, and compare with previous state of the art methods for fairness, showing statistically significant improvement across most cases. The flexibility of this method is shown via a novel problem: removing annotations from images, from separate training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model.

negative: EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing healthrelated data—the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients’ health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70% to 72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level—Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1+2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that ‘‘learn’’ over time. & 2012 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved. 2211-8837/$ see front matter & 2012 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.hlpt.2012.03.001 Corresponding author at: Department of Informatics, Centerstone Research Institute, 365 South Park Ridge Road, Suite 103, Bloomington, IN 47401, United States. Tel.: +1 812 337 2302. E-mail addresses: Casey.Bennett@CenterstoneResearch.org (C.C. Bennett), Tom.Doub@CenterstoneResearch.org (T.W. Doub), RebeccaSelove@CenterstoneResearch.org (R. Selove). Health Policy and Technology (2012) 1, 105–114 Author's personal copy

negative: Full-wave design of H-plane contiguous manifold output multiplexers using the fictitious reactive load concept In this paper, a new procedure for the design of contiguous band manifold output multiplexers in H-plane configuration is presented. Such a configuration eliminates the slots between the T-junctions and the filters allowing high power handling, as well as dramatically reducing the risk of multipactor. This paper pursues the division of the design into tasks with low computational effort. The approach comprises: 1) the singly terminated synthesis of the channel filters and their corresponding full-wave responses; 2) the manifold design using fictitious reactive loads simulating the phase response of every channel out of its corresponding band; and 3) the final full-wave optimization of the whole structure using the simulated-annealing method. In order to validate the above procedure, a five-channel Ku-band multiplexer has been designed, manufactured, and measured.

negative: Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.

negative: STRIP: A Defence Against Trojan Attacks on Deep Neural Networks Recent trojan attacks on deep neural network (DNN) models are one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger input. Trojan attacks are easy to craft; survive even in adverse conditions such as different viewpoints, and lighting conditions on images; and threaten real world applications such as autonomous vehicles and robotics. The trojan trigger is a secret guarded and exploited by the attacker. Therefore, detecting such trojaned inputs is a challenge, especially at run-time when models are in active operation. We focus on vision systems and build the STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of the predicted classes for the perturbed inputs for a given model—malicious or benign—under deployment. A low entropy in the predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on two popular and contrasting datasets: MNIST and CIFAR10. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for four different tested trojan trigger types—three triggers are identified in previous attack works and one dedicated trigger is crafted by us to demonstrate the trigger-size insensitivity of the STRIP detection approach. In particular, on the dataset of natural images in CIFAR10, we have empirically achieved the desired result of 0% for both FRR and FAR.

negative: A formal ontological perspective on the behaviors and functions of technical artifacts In this paper we present a formal characterization of the engineering concepts of behavior and function of technical artifacts. We capture the meanings that engineers attach to these concepts by formalizing, within the formal ontology DOLCE, the five meanings of artifact behavior and the two meanings of function that Chandrasekaran and Josephson identified in 2000 within the Functional Representation approach. We begin our formalization by reserving the term ‘behavior’ of a technical artifact as ‘the specific way in which the artifact occurs in an event.’ This general notion is characterized formally, and used to provide definitions of actual behaviors of artifacts, and the physically possible and physically impossible behaviors that rational agents believe that artifacts have. We also define several other notions, e.g., input and output behaviors of artifacts, and then show that these ontologically characterizedconcepts give a general framework in which Chandrasekaran and Josephson’s meanings of behavior can be explicitly formalized. Finally we show how Chandrasekaran and Josephson’s two meanings of artifact functions, namely, device-centric and environmental-centric functions, can be captured in DOLCE via the concepts of behavioral constraint and mode of deployment of an artifact. A more general goal of this work is to show that foundational ontologies are suited to the engineering domain: they can facilitate information sharing and exchange in the various engineering domains by providing concept structures and clarifications that make explicit and precise important engineering notions. The meanings of the terms ‘behavior’ and ‘function’ in domains like designing, redesigning, reverse engineering, product architecture, and engineering knowledge bases are often ambiguous or overloaded. Our results show that foundational ontologies can accommodate the variety of denotations these terms have and can explain their relationships.

negative: THE DEVELOPMENT OF THE BALANCED SCORECARD AS A STRATEGIC MANAGEMENT TOOL The Balanced Scorecard is a widely adopted performance management framework first described in the early 1990s. More recently it has been proposed as the basis for a ‘strategic management system’. This paper describes its evolution, recognising three distinct generations of Balanced Scorecard design. The paper relates the empirically driven developments in Balanced Scorecard thinking with literature concerning strategic management within organisations. It concludes that developments to date have been worthwhile, highlights potential areas for further refinement, and sets out some possible topics for future research into the field. The Balanced Scorecard and its development The Balanced Scorecard was first introduced in the early 1990s through the work of Robert Kaplan and David Norton of the Harvard Business School. Since then, the concept has become well known and its various forms widely adopted across the world (Rigby, 2001). By combining financial measures and non-financial measures in a single report, the Balanced Scorecard aims to provide managers with richer and more relevant information about activities they are managing than is provided by financial measures alone. To aid clarity and utility, Kaplan and Norton proposed that the number of measures on a Balanced Scorecard should also be constrained in number, and clustered into four groups (Kaplan and Norton, 1992, 1993). Beyond this, the original definition of Balanced Scorecard was sparse. But from the outset it was clear that the selection of measures, both in terms of filtering (organisations typically had access to many more measures than were needed to populate the Balanced Scorecard) and clustering (deciding which measures should appear in which perspectives) would be a key activity. Kaplan and Norton proposed that measure selection should focus on information relevant to the implementation of strategic plans, and that simple attitudinal questions be used to help determine the appropriate allocation of measures to perspectives (Kaplan and Norton, 1992). In essence the Balanced Scorecard has remained unchanged since these early papers, having at its core a limited number of measures clustered into groups, and an underlying strategic focus. But modern Balanced Scorecard designs also have a number of features that clearly differentiate them from earlier examples. This paper describes these changes as an evolution through three distinct ‘generations’ of Balanced Scorecard design. 1 Generation Balanced Scorecard Balanced Scorecard was initially described as a simple, “4 box” approach to performance measurement (Kaplan and Norton, 1992). In addition to financial measures, managers were encouraged to look at measures drawn from three other “perspectives” of the business: Learning and Growth; Internal Business Process; and Customer, chosen to represent the major stakeholders in a business (Mooraj et al, 1999). Definition of what comprised a Balanced Scorecard was sparse and focused on the high level structure of the device. Simple ‘causality’ between the four perspectives was illustrated but not used for specific purpose. Kaplan and Norton’s original paper’s focus was on the selection and reporting of a limited number of measures in each of the four perspectives (Kaplan and Norton, 1992). The paper suggested use of attitudinal questions relating to the vision and goals of the organisation to help in the selection of measures to be used, and also encouraged the consideration of ‘typical’ areas of interest in this process. Kaplan and Norton’s original work makes no specific observations concerning how the Balanced Scorecard might improve the performance of organisations; the implication is that the provision of accessible relevant measurement data itself will trigger improved organisational performance. However, they do imply that the source of these improvements is changes in behaviour: “It establishes goals but assumes that people will adopt whatever behaviours and take whatever actions are necessary to arrive at those goals”. In the light of this, the basis for selecting the goals represented by the Balanced Scorecard is of some importance. But in their first paper Kaplan and Norton say little about how a Balanced Scorecard could be developed in practice beyond a general assertion that design involved “putting vision and strategy at the centre of the measurement system” (1992). Later writing includes increasing amounts of proscription about development methods, concluding with a lengthy description of one such process in their first book on the subject published in 1996. Figure 1 – 1 Generation Balanced Scorecard Figure 1 shows a diagrammatic representation of Kaplan and Norton’s original Balanced Scorecard design. We will subsequently refer to this type of Balanced Scorecard design as a ‘1 Generation’ Balanced Scorecard. Key attributes of the design and suggested development process for this type of Balanced Scorecard are summarised in the table below. Practical Experiences with 1 Generation Balanced Scorecards The authors’ professional experience suggests that 1 Generation Balanced Scorecards are still being developed, and that they probably still form the large majority of Balanced Scorecard designs introduced into organisations. This is reflected in the literature, where books and articles that use more advanced representations of Balanced Scorecard are only recently appearing (Olve et al, 1999, Kaplan and Norton, 2000, Niven, 2002). But despite its huge popularity as a concept, and apparently widespread adoption, relatively few detailed case studies concerning Balanced Scorecard implementation experiences appear to exist in the academic literature. Those few that do focus primarily on the architecture of the Balanced Scorecard design (e.g. Butler et al, 1997), and associated organisational experiences (e.g. Ahn, 2001). Commercial / practitioner writing on Balanced Scorecard is more extensive (e.g. Schneiderman, 1999), but often more partisan (e.g. Lingle et al 1996). But in general the literature endorses the utility of the approach (Epstein et al, 1997) but notes weaknesses in the initial design proposition, and recommends improvements (e.g. Eagleson et al, 2000, Kennerley et al, 2000). 2 Generation Balanced Scorecard The practical difficulties associated with the design of 1 Generation Balanced Scorecards are significant, in part because the definition of a Balanced Scorecard was initially vague, allowing for considerable interpretation. Two significant areas of concern were filtering (the process of choosing specific measures to report), and clustering (deciding how to group measures into ‘perspectives’). Discussions relating to clustering continue to be rehearsed in the literature (e.g. Butler et al, 1997, Kennerley et al, 2000), but discussions relating to filtering are less common, and usually appear as part of descriptions of methods of Balanced Scorecard design (e.g. Kaplan and Norton, 1996, Olve et al, 1999). Perhaps the most significant early change translated the attitudinal approach to measure selection proposed initially be Kaplan and Norton (e.g. “To succeed financially, how should we appear to our shareholders?”) into a process that yielded a few appropriate key measures of performance in each perspective. A solution was the introduction of the concept of ‘strategic objectives’ (Kaplan and Norton, 1993). Initially these were represented as short sentences attached to the four perspectives, and were used to capture the essence of the organisation’s strategy material to each of the areas: measures were then selected that reflected achievement of these strategic objectives. Although subtle, this approach to measure selection quite different from that initially proposed, since strategic objectives were developed directly from strategy statements based on a corporate vision or a strategic plan. Another key development concerned causality. Causality between the perspectives had been introduced in early ‘1st Generation’ Balanced Scorecard thinking (see Figure 1). ‘2nd Generation’ Balanced Scorecard saw the idea of causality developed further. Instead of simply highlighting causal links between perspectives, internal documents from one consulting firm’s work in 1993 shows an early attempt to indicate linkages between the measures themselves. This improvement was also proposed later by others (Newing, 1995). Measure based linkages provided a richer model of causality than before, but presented conceptual problems – for example, the use of measures encouraged attempts to ‘prove’ the causality between measures using various forms of analysis (indeed this is still the case – e.g. Brewer, 2002). Collectively the changes in design described here represent a materially different definition of what comprises a Balanced Scorecard compared to Kaplan and Norton’s original work we will refer to Balanced Scorecards that incorporate these developments as ‘2 Generation Balanced Scorecards’. The impact of these changes were characterised by Kaplan and Norton in 1996 as enabling the Balanced Scorecard to evolve from “an improved measurement system to a core management system” (Kaplan and Norton 1996). Maintaining the focus that Balanced Scorecard was intended to support the management of strategic implementation, Kaplan and Norton further described the use of this development of the Balanced Scorecard as the central element of “a strategic management system”. One consequence of this change in emphasis was to increase the pressure on the design process to accurately reflect the organisation’s strategic goals. Over time the idea of strategic linkage became an increasingly important element of Balanced Scorecard design methodology, and in the mid 1990’s Balanced Scorecard documentation began to show graphically linkages between the strategic objectives themselves (rather than the measures) with causality linking across the perspectives toward key objectives re

negative: Detection of Ellipses by a Modified Hough Transformation The Hough transformation can detect straight lines in an edge-enhanced picture, however its extension to recover ellipses requires too long a computing time. This correspondence proposes a modified method which utilizes two properties of an ellipse in such a way that it iteratively searches for clusters in two different parameter spaces to find almost complete ellipses, then evaluates their parameters by the least mean squares method.

negative: An agent-based decision support system for wholesale electricity market Application software has been developed for analyzing and understanding a dynamic price change in the US wholesale power market. Traders can use the software as an effective decision-making tool by modeling and simulating a power market. The software uses different features of a decision support system by creating a framework for assessing new trading strategies in a competitive electricity trading environment. The practicality of the software is confirmed by comparing its estimation accuracy with those of other methods (e.g., neural network and genetic algorithm). The software has been applied to a data set regarding the California electricity crisis in order to examine whether the learning (convergence) speed of traders is different between the two periods (before and during the crisis). Such an application confirms the validity of the proposed software. © 2007 Elsevier B.V. All rights reserved.

negative: Tuning of PID controller using Ziegler-Nichols method for speed control of DC motor In this paper, a weighted tuning methods of a PID speed controller for separately excited Direct current motor is presented, based on Empirical Ziegler-Nichols tuning formula and modified Ziegler-Nichol PID tuning formula. Both these methods are compared on the basis of output response, minimum settling time, and minimum overshoot for speed demand application of DC motor. Computer simulation shows that the performance of PID controller using Modified Ziegler-Nichols technique is better than that of traditional Ziegler-Nichols technique.

negative: Comparative analysis of RADAR-IR sensor fusion methods for object detection This paper presents the Radar and IR sensor fusion method for objection detection. The infrared camera parameter calibration with Levenberg-Marquardt (LM) optimization method is proposed based on the Radar ranging data represented by Cartesian coordinate compared with 6 fusion methods. The proposed method firstly performs the estimation of the intrinsic parameter matrix of infrared camera with some optical trick. Then the method searches the extrinsic parameters using the generative approach. The initial angle and translation of the extrinsic parameters are optimized by the LM method with the geometrical cost function. In the experiments, the performance of proposed method outperforms by a maximum 13 times the performance of the other baseline methods on the averaged Euclidian distance error. In future work, the angular noise of the Radar information will be improved and the proposed method will provide the effective proposals for the deep neural network.

negative: Noise-Immune Embedded NAND-ROM Using a Dynamic Split Source-Line Scheme for VDDmin and Speed Improvements Embedded NAND-type read-only-memory (NAND-ROM) provides large-capacity, high-reliability, on-chip non-volatile storage. However, NAND-ROM suffers from code-dependent read noises and cannot survive at low supply voltages (VDDs). These code-dependent read noises are primarily due to the charge-sharing effect, bitline leakage current, and crosstalk between bitlines, which become worse at lower VDD. This study proposes a dynamic split source-line (DSSL) scheme for NAND-ROM. The proposed scheme overcomes code-dependent read noises while improving the read access time and suppressing the active-mode gate leakage current, with only a 1% area penalty in the cell array. Experiments on a fabricated 256 Kb macro using a 90 nm industrial logic process demonstrate that the proposed DSSL scheme achieves 100% code-pattern coverage under a small sensing margin. Additionally, the DSSL NAND-ROM works with a wide range of supply voltages (1-0.31 V) with a 38%, 45.8%, and 37% improvement in speed, power, and standby current, respectively, at VDD = 1 V.

negative: FlowQA: Grasping Flow in History for Conversational Machine Comprehension Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce FLOW, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to shallow approaches that concatenate previous questions/answers as input, FLOW integrates the latent semantics of the conversation history more deeply. Our model, FLOWQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of FLOW also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FLOWQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.

negative: VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.

negative: Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety. Problem Statement The costs of fatalities and injuries due to road traffic accidents (RTAs) have a tremendous impact on societal well-being and socioeconomic development. RTAs are among the leading causes of death and injury worldwide, causing an estimated 1.2 million deaths and 50 million injuries each year (World Health Organization, 2004). Ethiopia has the highest rate of RTAs, owing to the fact that road transport is the major transportation system in the country. The Ethiopian traffic control system archives data on various aspects of the traffic system, such as traffic volume, concentration, and vehicle accidents. With more vehicles and traffic, the capital city of Addis Ababa takes the lion’s share of the risk, with an average of 20 accidents being recorded every day and even more going unreported. The basic hypothesis of this research is that accidents are not randomly scattered along the road network, and that drivers are not involved in accidents at random. There are complex circumstantial relationships between several characteristics (driver, road, car, etc.) and the accident occurrence. As such, one cannot improve safety without successfully relating accident frequency and severity to the causative variables (Kononov and Janson 2002). We will attempt to extend the authors’ previous work in this area by generating additional attributes and focusing on the contribution of road-related factors to accident severity in Ethiopia. This will help to identify the parts of a road that are risky, thus supporting traffic accident data analysis in decision-making processes. The general objective of the research is to investigate the role of road-related factors in accident severity, using RTA data from Ethiopia and predictive models. Our three specific objectives include: 1) exploring the underlying variables (especially road-related ones) that impact car accident severity, 2) predicting accident severity using different data mining techniques, and 3) comparing standard classification models for this task. Literature Review Various studies have addressed the different aspects of RTAs, with most focusing on predicting or establishing the critical factors influencing injury severity (Chong, A. et al. 2005). Numerous data mining-related studies have been undertaken to analyze RTA data locally and globally, with results frequently varying depending on the socio-economic conditions and infrastructure of a given location. Ossenbruggen, Pendharkar et al. (2001) used a logistic regression model to identify the prediction factors of crashes and crash-related injuries, using models to perform a risk assessment of a given region. These models included attributes describing a site by its land use activity, roadside design, use of traffic control devices, and traffic exposure. Their study illustrated that village sites were less hazardous than residential or shopping sites. Abdalla et al. (1997) also studied the relationship between casualty frequency and the distance of an accident from residential zones. Not surprisingly, casualty frequencies were higher in accidents that occurred nearer to residential zones, possibly due to higher exposure. The

negative: Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithmby considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes. ARTICLE HISTORY Received 21 March 2016 Accepted 28 September 2016

negative: Fast Road Sign Detection Using Hough Transform for Assisted Driving of Road Vehicles A system for real-time traffic sign detection is described in this paper. The system uses restricted Hough transform for circumferences in order to detect circular signs, and for straight lines for triangular ones. Some results obtained from a set of real road images captured under both normal and adverse weather conditions are presented as well in order to illustrate the robustness of the detection system. The average processing time is 30 ms per frame, what makes the system a good approach to work in real time conditions.

negative: Towards Automated Driving: Unmanned Protective Vehicle for Highway Hard Shoulder Road Works Mobile road works on the hard shoulder of German highways bear an increased accident risk for the crew of the protective vehicle which safeguards road works against moving traffic. The project “Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works” aims at the unmanned operation of the protective vehicle in order to reduce this risk. Simultaneously, this means the very first unmanned operation of a vehicle on German roads in public traffic. This contribution introduces the project by pointing out the main objectives and demonstrates the current state of the system design regarding functionality, modes of operation, as well as the functional system architecture. Pivotal for the project, the scientific challenges raised by the unmanned operation - strongly related to the general challenges in the field of automated driving - are presented as well. The results of the project shall serve as a basis to stimulate an advanced discussion about ensuring safety for fully automated vehicles in public traffic operating at higher speeds and in less defined environments. Thus, this contribution aims at contacting the scientific community in an early state of the project.

negative: Density Based Smart Traffic System with Real Time Data Analysis Using IoT This paper aims to develop a convenient traffic system that allows a smooth movement of cars which will help build a smarter city. The traffic system currently implemented in many areas is not based on the density of traffic and every road is allotted a preset time. This results in traffic congestion due to large red-light delays and timings allotted for roads in a city that should vary during peak on-off hours, but in reality don't. These traditional systems are not adaptable and fail to support traffic during an unexpected situation or during an accident, and this makes them inefficient. In order to calculate the density of traffic various sensors can be used, each having their merits and demerits. In our proposed system Ultrasound Sensors are used along with Image Processing(using live feed from a camera) that works on a Raspberry Pi platform and calculates the vehicle density and dynamically allots time for different levels of traffic. This in turn allows better signal control and effective management of traffic thereby reducing the probability of a collision. By using Internet Of Things(IoT) real time data from the system can be collected, stored and managed on a cloud. This data can be used to interpret the signal duration in-case any of the sensing equipment fail, and also for future analysis.

negative: Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS. Keywords—Data mining, K-means, road traffic accidents, Waze, Weka.