Query: Machine Learning Approaches for Failure Type Detection and Predictive Maintenance

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A tutorial on support vector regression <=SEP=> In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention somemodifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

ORTHONORMAL BASES OF COMPACTLY SUPPORTED WAVELETS <=SEP=> We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.

An introduction to hidden Markov models <=SEP=> The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

The Nature of Statistical Learning Theory <=SEP=> 

State-of-the-Art Predictive Maintenance Techniques* <=SEP=> This paper discusses the limitations of time-based equipment maintenance methods and the advantages of predictive or online maintenance techniques in identifying the onset of equipment failure. The three major predictive maintenance techniques, defined in terms of their source of data, are described as follows: 1) the existing sensor-based technique; 2) the test-sensor-based technique (including wireless sensors); and 3) the test-signal-based technique (including the loop current step response method, the time-domain reflectrometry test, and the inductance-capacitance-resistance test). Examples of detecting blockages in pressure sensing lines using existing sensor-based techniques and of verifying calibration using existing-sensor direct current output are given. Three Department of Energy (DOE)-sponsored projects, whose aim is to develop online and wireless hardware and software systems for performing predictive maintenance on critical equipment in nuclear power plants, DOE research reactors, and general industrial applications, are described.

Image Steganography based on a Parameterized Canny Edge Detection Algorithm <=SEP=> Steganography is the science of hiding digital information in such a way that no one can suspect its existence. Unlike cryptography which may arouse suspicions, steganography is a stealthy method that enables data communication in total secrecy. Steganography has many requirements, the foremost one is irrecoverability which refers to how hard it is for someone apart from the original communicating parties to detect and recover the hidden data out of the secret communication. A good strategy to guaranteeirrecoverability is to cover the secret data not usinga trivial method based on a predictable algorithm, but using a specific random pattern based on a mathematical algorithm. This paper proposes an image steganography technique based on theCanny edge detection algorithm. It is designed to hide secret data into a digital image within the pixels that make up the boundaries of objects detected in the image. More specifically, bits of the secret data replace the three LSBs of every color channel of the pixels detected by the Canny edge detection algorithm as part of the edges in the carrier image. Besides, the algorithm is parameterized by three parameters: The size of the Gaussian filter, a low threshold value, and a high threshold value. These parameters can yield to different outputs for the same input image and secret data. As a result, discovering the inner-workings of the algorithm would be considerably ambiguous, misguiding steganalysts from the exact location of the covert data. Experiments showed a simulation tool codenamed GhostBit, meant to cover and uncover secret data using the proposed algorithm. As future work, examining how other image processing techniques such as brightness and contrast adjustment can be taken

A sample size calculator for SMART pilot studies <=SEP=> In clinical practice, as well as in other areas where interventions are provided, a sequential individualized approach to treatment is often necessary, whereby each treatment is adapted based on the object ′s response. An adaptive intervention is a sequence of decision rules which formalizes the provision of treatment at critical decision points in the care of an individual. In order to inform the development of an adaptive intervention, scientists are increasingly interested in the use of sequential multiple assignment randomized trials (SMART), which is a type of multistage randomized trial where individuals are randomized repeatedly at critical decision points to a set treatment options. While there is great interest in the use of SMART and in the development of adaptive interventions, both are relatively new to themedical and behavioral sciences. As a result, many clinical researchers will first implement a SMART pilot study (i.e., a small-scale version of a SMART) to examine feasibility and acceptability considerations prior to conducting a full-scale SMART study. A primary aim of this paper is to introduce a new methodology to calculate minimal sample size necessary for conducting a SMART pilot.

A Quantitative and Qualitative Assessment of Automatic Text Summarization Systems <=SEP=> Text summarization is the process of automatically creating a shorter version of one or more text documents. This paper presents a qualitative and quantitative assessment of the 22 state-of-the-art extractive summarization systems using the CNN corpus, a dataset of 3,000 news articles.

A foundation for representing and querying moving objects <=SEP=> Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.

Free Form based active contours for image segmentation and free space perception <=SEP=> In this paper we present a novel approach for representing and evolving deformable active contours. The method combines piecewise regular Bézier models and curve evolution defined by local Free Form Deformation. The contour deformation is locally constrained which allows contour convergence with almost linear complexity while adapting to various shape settings and handling topology changes of the active contour. We demonstrate the effectiveness of the new active contour scheme for visual free space perception and segmentation using omnidirectional images acquired by a robot exploring unknown indoor and outdoor environments. Several experiments validate the approach with comparison to state-of-the art parametric and geometric active contours and provide fast and real-time robot free space segmentation and navigation.

Neon: A (Big) (Fast) Single-Chip 3D Workstation Graphics Accelerator <=SEP=> High-performance 3D graphics accelerators traditionally require multiple chips on multiple boards. Specialized chips perform geometry transformations and lighting computations, rasterizing, pixel processing, and texture mapping. Multiple chip designs are often scalable: they can increase performance by using more chips. Scalability has obvious costs: a minimal configuration needs several chips, and some configurations must replicate texture maps. A less obvious cost is the almost irresistible temptation to replicate chips to increase performance, rather than to design individual chips for higher performance in the first place. In contrast, Neon is a single chip that performs like a multichip design. Neon accelerates OpenGL 3D rendering, as well as X11 and Windows/NT 2D rendering. Since our pin budget limited peak memory bandwidth, we designed Neon from the memory system upward in order to reduce bandwidth requirements. Neon has no special-purpose memories; its eight independent 32-bit memory controllers can access color buffers, Z depth buffers, stencil buffers, and texture data. To fit our gate budget, we shared logic among different operations with similar implementation requirements, and left floating point calculations to Digital's Alpha CPUs. Neon’s performance is between HP’s Visualize fx and fx, and is well above SGI’s MXE for most operations. Neon-based boards cost much less than these competitors, due to a small part count and use of commodity SDRAMs.

Boosting for transfer learning <=SEP=> Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.

Publicity Trends in Arrests of " Online Predators " How the National Juvenile Online Victimization (n‐jov) Study Was Conducted <=SEP=> about " online predators " * – sex of‐ fenders who use the Internet to meet juvenile victims – has raised considerable alarm about the extent to which Internet use may be put‐ ting children and adolescents at risk for sexual abuse and exploitation. Media stories and Internet safety messages have raised fears by describing violent offenders who use the Inter‐ net to prey on naïve children by tricking them into face‐to‐face meetings or tracking them down through information posted online. Law enforcement has mobilized on a number of fronts, setting up task forces to identify and prosecute online predators, developing under‐ cover operations, and urging social networking sites to protect young users. Unfortunately, however, reliable information on the scope and nature of the online predator problem remains scarce. Established criminal justice data collection systems do not gather detailed data on such crimes that could help inform public policy and education. To remedy this information vacuum, the Crimes against Children Research Center at the University of New Hampshire conducted two waves of a The N‐JOV Study collected information from a national sample of law en‐ forcement agencies about the prevalence of arrests for and characteristics of online sex crimes against minors during two 12 month periods: July 1, 2000 through June 30, 2001 (Wave 1) and calendar year 2006 (Wave 2). For both Waves, we used a two‐phase process of mail surveys followed by telephone interviews to collect data from a national sample of the same lo‐ cal, county, state, and federal law enforcement agencies. First, we sent the mail surveys to a national sample of more than 2,500 agencies. These sur‐ veys asked if agencies had made arrests for online sex crimes against minors during the respective one‐year timeframes. Then we conducted detailed telephone interviews with law enforcement investigators about a random sample of arrest cases reported in the mail surveys. For the telephone interviews, we designed a sampling procedure that took into account the number of arrests reported by an agency, so that we would not unduly burden respondents in agencies with many cases. If an agency reported between one and three arrests for online sex crimes, we conducted follow‐up interviews for every case. For agencies that reported more than three arrests, we conducted interviews for all cases that involved youth vic‐ tims (victims who were located and contacted during the investigation), and sampled other arrest cases (i.e., …

AutoBrief: an experimental system for the automatic generation of briefings in integrated text and information graphics <=SEP=> This paper describes AutoBrief, an experimental intelligent multimedia presentation system that generates presentations in text and information graphics in the domain of transportation scheduling. Acting as an intelligent assistant, AutoBrief creates a presentation to communicate its analysis of alternative schedules. In addition, the multimedia presentation facilitates data exploration through its complex information visualizations and support for direct manipulation of presentation elements. AutoBrief’s research contributions include (1) a design enabling a new human–computer interaction style in which intelligent multimedia presentation objects (textual or graphic) can be used by the audience in direct manipulation operations for data exploration, (2) an application-independent approach to multimedia generation based on the representation of communicative goals suitable for both generation of text and of complex information graphics, and (3) an application-independent approach to intelligent graphic design based upon communicative goals. This retrospective overview paper, aimed at a multidisciplinary audience from the fields of human–computer ARTICLE IN PRESS *Corresponding author. Tel.: +1-336-2561133; fax: +1-336-3345949. E-mail addresses: nlgreen@uncg.edu (N.L. Green), carenini@cs.ubc.ca (G. Carenini), kerpedjiev@maya.com (S. Kerpedjiev), mattis@mayaviz.com (J. Mattis), j.moore@ed.ac.uk (J.D. Moore), roth+@cs.cmu.edu (S.F. Roth). 1071-5819/$ see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2003.10.007 interaction and natural language generation, presents AutoBrief’s design and design rationale. r 2003 Elsevier Ltd. All rights reserved.

BodyAvatar: creating freeform 3D avatars using first-person body gestures <=SEP=> BodyAvatar is a Kinect-based interactive system that allows users without professional skills to create freeform 3D avatars using body gestures. Unlike existing gesture-based 3D modeling tools, BodyAvatar centers around a first-person "you're the avatar" metaphor, where the user treats their own body as a physical proxy of the virtual avatar. Based on an intuitive body-centric mapping, the user performs gestures to their own body as if wanting to modify it, which in turn results in corresponding modifications to the avatar. BodyAvatar provides an intuitive, immersive, and playful creation experience for the user. We present a formative study that leads to the design of BodyAvatar, the system's interactions and underlying algorithms, and results from initial user trials.

Failure Analysis of SGI XFS File System <=SEP=> Commodity file systems expect a fail stop disk. But todays disks fail in unexpected ways. Disks exhibit latent sector errors, silent corruption and transient failures to name a few. In this paper we study the behavior of SGI XFS to such errors. File systems play a key role in handling most data and hence the failure handling policy of the file system plays a major role in ensuring the integrity of the data. XFS is a highly scalable journaling file system developed by SGI. We analyze the failure handling policy of XFS file system. We fail reads and writes of various blocks that originate from the file system and analyze the file system behavior. Some such blocks include super block, journal header block, commit block, data block, index blocks. We classify our errors according to the extended IRON Taxonomy. We see that XFS is vulnerable to these failures and does not possess a uniform failure handling policy. Further the file system at times silently corrupts data. We also see that XFS has very little internal redundancy even for critical structures such as the super block and B+Tree root. Finally we see that XFS, like most commodity file systems, fails to recover from these failures putting the data at risk.

The Wnt signaling pathway in development and disease. <=SEP=> Tight control of cell-cell communication is essential for the generation of a normally patterned embryo. A critical mediator of key cell-cell signaling events during embryogenesis is the highly conserved Wnt family of secreted proteins. Recent biochemical and genetic analyses have greatly enriched our understanding of how Wnts signal, and the list of canonical Wnt signaling components has exploded. The data reveal that multiple extracellular, cytoplasmic, and nuclear regulators intricately modulate Wnt signaling levels. In addition, receptor-ligand specificity and feedback loops help to determine Wnt signaling outputs. Wnts are required for adult tissue maintenance, and perturbations in Wnt signaling promote both human degenerative diseases and cancer. The next few years are likely to see novel therapeutic reagents aimed at controlling Wnt signaling in order to alleviate these conditions.

Magnetic Optimization Algorithms a new synthesis <=SEP=> A novel optimization algorithm is proposed here that is inspired by the principles of magnetic field theory. In the proposed Magnetic Optimization Algorithm (MOA) the possible solutions are magnetic particles scattered in the search space. Each magnetic particle has a measure of mass and magnetic field according to its fitness. The fitter magnetic particles are those with higher magnetic field and higher mass. These particles are located in a lattice-like environment and apply a force of attraction to their neighbors. The proposed cellular structure allows a better exploitation of local neighborhoods before they move towards the global best, hence it increases population diversity. Experimental results on 14 numerical benchmark functions show that MOA in some benchmark functions can work better than GA and PSO.

Learning Filter Banks Using Deep Learning For Acoustic Signals <=SEP=> Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urbansound8K dataset [1], the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.

Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code <=SEP=> Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.

SpatialHadoop: towards flexible and scalable spatial processing using mapreduce <=SEP=> Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.

Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies <=SEP=> The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.

Automated Verification of Electrum Wallet <=SEP=> We introduce a formal modeling in ASLan++ of the twofactor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify i) new intruder deduction rules with clauses and ii) non-deducibility constraints.

A Semi-Supervised Approach for Kernel-Based Temporal Clustering <=SEP=> Temporal clustering refers to the partitioning of a time series into multiple nonoverlapping segments that belong to k temporal clusters, in such a way that segments in the same cluster are more similar to each other than to those in other clusters. Temporal clustering is a fundamental task in many fields, such as computer animation, computer vision, health care, and robotics. The applications of temporal clustering in those areas are diverse, and include human-motion imitation and recognition, emotion analysis, human activity segmentation, automated rehabilitation exercise analysis, and human-computer interaction. However, temporal clustering using a completely unsupervised method may not produce satisfactory results. Similar to regular clustering, temporal clustering also benefits from some expert knowledge that may be available. The type of approach that utilizes a small amount of knowledge to “guide” the clustering process is known as “semi-supervised clustering.” Semi-supervised temporal clustering is a strategy in which extra knowledge, in the form of pairwise constraints, is incorporated into the temporal data to help with the partitioning problem. This thesis proposes a process to adapt and transform two kernel-based methods into semi-supervised temporal clustering methods. The proposed process is exclusive to kernel-based clustering methods, and is based on two concepts. First, it uses the idea of instance-level constraints, in the form of must-link and cannot-link, to supervise the clustering methods. Second, it uses a dynamic-programming method to search for the optimal temporal clusters. The proposed process is applied to two algorithms, aligned cluster analysis (ACA) and spectral clustering. To validate the advantages of the proposed temporal semi-supervised clustering methods, a comparative analysis was performed, using the original versions of the algorithm and another semi-supervised temporal cluster. This evaluation was conducted with both synthetic data and two real-world applications. The first application includes two naturalistic audio-visual human emotion datasets, and the second application focuses on human-motion segmentation. Results show substantial improvements in accuracy, with minimal supervision, compared to unsupervised and other temporal semi-supervised approaches, without compromising time performance.

Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length <=SEP=> 3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112  $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).

BRAINIAC: Bringing reliable accuracy into neurally-implemented approximate computing <=SEP=> Applications with large amounts of data, real-time constraints, ultra-low power requirements, and heavy computational complexity present significant challenges for modern computing systems, and often fall within the category of high performance computing (HPC). As such, computer architects have looked to high performance single instruction multiple data (SIMD) architectures, such as accelerator-rich platforms, for handling these workloads. However, since the results of these applications do not always require exact precision, approximate computing may also be leveraged. In this work, we introduce BRAINIAC, a heterogeneous platform that combines precise accelerators with neural-network-based approximate accelerators. These reconfigurable accelerators are leveraged in a multi-stage flow that begins with simple approximations and resorts to more complex ones as needed. We employ high-level, application-specific light-weight checks (LWCs) to throttle this multi-stage acceleration flow and reliably ensure user-specified accuracy at runtime. Evaluation of the performance and energy of our heterogeneous platform for error tolerance thresholds of 5%-25% demonstrates an average of 3× gain over computation that only includes precise acceleration, and 15×-35× gain over software-based computation.

The folly of forecasting : The effects of a disaggregated sales forecasting system on sales forecast error , sales forecast positive bias , and inventory levels <=SEP=> In this study we provide field evidence of the role that sales forecasts play as the coordination mechanism between sales managers and production managers. An extensive body of operations research documents the negative consequences of sales forecast error and investigates how to respond to sales forecast error by optimizing inventory “buffer” stocks. In contrast, we focus on whether a change in the sales forecasting information environment implemented at our research site reduces forecast error and, hence, the need for those buffer stocks. The newly implemented sales forecast “contingency system” disaggregated the sales forecast into two components: (i) an official sales forecast that reflected relatively more certain expected demand, and (ii) a separate report that provided the probability of a contingent demand “event” occurring and the expected volume impact of that event. We predict and find that the system had the intended effect of a reduction in inventory levels, both through better timing of production via a production “postponement” strategy and through a decrease in absolute forecast error. We further consider the incentives of the self-interested sales managers and predict and find that the inventory reduction benefits of the sales forecast system gained through postponement and a decline in absolute forecast error were partially offset by an increase in positive sales forecast bias. Our study provides novel insights regarding the role of forecasting within the organizational context. While the operations literature uses analytic and simulation methods to examine sales and operations planning in a very mechanistic way, we examine the role that changes in the sales forecast information environment play and the opportunistic responses of self-interested managers. (266 words)

Creating people-aware IoT applications by combining design thinking and user-centered design methods <=SEP=> This article presents a methodology based on design thinking and user experience design methods for creating what we call `people-aware' IoT applications, where user needs, not technological opportunities, drive the development. This methodology is divided into 7 steps: discovery, capturing, research, design, prototype, evaluate and refine. The tools used include conventional user experience procedures such as problem identification, group brainstorming, surveys, or interviews, mixed with more IoT-specific design specificities. The results of the methodology include well-described and user-oriented scenarios meeting user's needs and also a complete toolbox to assist the implementation and the testing of abovementioned scenarios in an IoT perspective. The article describes the methodology in detail with the help of a use case conducted in a business environment available for the project that leads to the identification and partial design of concrete people-aware IoT applications in the context of a smart meeting room.

A Survey of Surface-Based Illustrative Rendering for Visualization <=SEP=> In this paper, we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub-area of visualization. For the remainder of the survey, we then focus on surface-based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods’ implementations. We then provide an overview of low-level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.

Recovering the interleaver of an unknown turbo-code <=SEP=> We give here an efficient algorithm for recovering the permutation of an unknown turbo-code when several noisy codewords are given. The algorithm presented here uses the same information as some other algorithms given previously for this problem but in an optimal fashion. This paper also clarifies the link between this problem and the BCJR decoding algorithm.