title: An automated approach to specification animation for validation

abstract: Formal specification has been increasingly adopted for the development of software systems of the highest integrity. However, the readability of specifications for large-scale and complex systems can be so poor that even the developers may not easily understand whether their specifications define the ''intended behaviors''. In this paper, we describe a software tool that supports the animation of specifications by simulating their functional scenarios using the Message Sequence Chart (MSC). The tool extracts automatically functional scenarios from a specification and generates a message sequence chart for each of them for a syntactic level analysis. The tool can also execute a functional scenario with test cases for a semantic level analysis if all the processes involved in the scenario are defined using explicit specifications. With the tool support the animation of a specification can be carried out incrementally to assist its user to review the adequacy of the specification. We present a case study applying the tool to animate a formal specification for a library system and evaluate its result.


title: Viral Marketing for Smart Cities: Influencers in Social Network Communities

abstract: Social networks are used by cities primarily for announcing local-area events, but also for increasing engagement of citizens in votes and elections. Given the current plethora of heterogeneous social networks, city administrators can benefit from social networks to promote initiatives, which are important to a current smart city as well use them to discover future needs in order to manage resources more efficiently. Our focus in this paper is how we can adapt commercial and viral marketing techniques to smart city systems to influence the behavior, opinion and choices of citizens in order to improve their well being and that of the whole society as well as predicting future trends and events.


title: Knowledge transfer for cross domain learning to rank

abstract: Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training and test data are drawn from the same data distribution. However, this assumption does not always hold true in real world applications. For example, it can be violated when the labeled training data become outdated or originally come from another domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments, we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level transfer method comes out with varying performance depending on the dataset used.


title: Self-supervised calibration for robotic systems

abstract: We present a generic algorithm for self calibration of robotic systems that utilizes two key innovations. First, it uses information theoretic measures to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a truncated QR decomposition of the Gauss-Newton system. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess. Through an extensive set of simulated and real-world experiments, we demonstrate that our method outperforms state-of-the-art algorithms in terms of stability, accuracy, and computational efficiency.


title: Anomalous video event detection using spatiotemporal context

abstract: Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency-based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic regulations.


Predicting US Primary Elections with Twitter


Envisioning transitions A design process for Healthcare and Wellbeing


title: On Improving User Response Times in Tableau

abstract: The rapid increase in data volumes and complexity of applied analytical tasks poses a big challenge for visualization solutions. It is important to keep the experience highly interactive, so that users stay engaged and can perform insightful data exploration. Query processing usually dominates the cost of visualization generation. Therefore, in order to achieve acceptable response times, one needs to utilize backend capabilities to the fullest and apply techniques, such as caching or prefetching. In this paper we discuss key data processing components in Tableau: the query processor, query caches, Tableau Data Engine [1, 2] and Data Server. Furthermore, we cover recent performance improvements related to the number and quality of remote queries, broader reuse of cached data, and application of inter and intra query parallelism.


title: Optimal Execution of Portfolio Transactions

abstract: We consider the execution of portfolio transactions with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. For a simple linear cost model, we explicitly construct the efficient frontier in the space of time-dependent liquidation strategies, which have minimum expected cost for a given level of uncertainty. We may then select optimal strategies either by minimizing a quadratic utility function, or by minimizing Value at Risk. The latter choice leads to the concept of Liquidity-adjusted VAR, or L-VaR, that explicitly considers the best tradeoff between volatility risk and liquidation costs. ∗We thank Andrew Alford, Alix Baudin, Mark Carhart, Ray Iwanowski, and Giorgio De Santis (Goldman Sachs Asset Management), Robert Ferstenberg (ITG), Michael Weber (Merrill Lynch), Andrew Lo (Sloan School, MIT), and George Constaninides (Graduate School of Business, University of Chicago) for helpful conversations. This paper was begun while the first author was at the University of Chicago, and the second author was first at Morgan Stanley Dean Witter and then at Goldman Sachs Asset Management. †University of Toronto, Departments of Mathematics and Computer Science; almgren@math.toronto.edu ‡ICor Brokerage and Courant Institute of Mathematical Sciences; Neil.Chriss@ICorBroker.com


title: On Batcher's Merge Sorts as Parallel Sorting Algorithms

abstract: We examine the average running times of Batcher's bitonic merge and Batcher's odd-even merge when they are used as parallel merging algorithms. It has been shown previously that the running time of odd-even merge can be upper bounded by a function of the maximal rank difference for elements in the two input sequences. Here we give an almost matching lower bound for odd-even merge as well as a similar upper bound for (a special version of) bitonic merge. From this follows that the average running time of odd-even merge (bitonic merge) is Θ((n/p)(1 + log(1 + p 2 ))) (O((n/p)(1 + log(1 + p 2 /n))), resp.) where n is the size of the input and p is the number of processors. Using these results we then show that the average running times of odd-even merge sort and bitonic merge sort are O((n/p)(log n + (log(1 + p 2 /n)) 2 )), that is, the two algorithms are optimal on the average if n ≥ p 2 /2 √ log p .


title: Simulated annealing algorithms: an overview

abstract: A brief introduction is given to the actual mechanics of simulated annealing, and a simple example from an IC layout is used to illustrate how these ideas can be applied. The complexities and tradeoffs involved in attacking a realistically complex design problem are illustrated by dissecting two very different annealing algorithms for VLSI chip floorplanning. Several current research problems aimed at determining more precisely how and why annealing algorithms work are examined. Some philosophical issues raised by the introduction of annealing are discussed. >


title: Soft-Tissue Lesions: When Can We Exclude Sarcoma?

abstract: OBJECTIVE. A wide spectrum of space-occupying soft-tissue lesions may be discovered on MRI studies, either as incidental findings or as palpable or symptomatic masses. Characterization of a lesion as benign or indeterminate is the most important step toward optimal treatment and avoidance of unnecessary biopsy or surgical intervention. CONCLUSION. The systemic MRI interpretation approach presented in this article enables the identification of cases in which sarcoma can be excluded.


title: Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression

abstract: Chronic deep brain stimulation (DBS) of subgenual cingulate white matter results in dramatic remission of symptoms in some previously treatment-resistant depression patients. The effects of stimulation may be mediated locally or via corticocortical or corticosubcortical connections. We use tractography to define the likely connectivity of cingulate regions stimulated in DBSresponsive patients using diffusion imaging data acquired in healthy control subjects. We defined 2 distinct regions within anterior cingulate cortex based on anatomical connectivity: a pregenual region strongly connected to medial prefrontal and anterior midcingulate cortex and a subgenual region with strongest connections to nucleus accumbens, amygdala, hypothalamus, and orbitofrontal cortex. The location of electrode contact points from 9 patients successfully treated with DBS lies within this subgenual region. The anatomical connectivity of the subgenual cingulate region targeted with DBS for depression supports the hypothesis that treatment efficacy is mediated via effects on a distributed network of frontal, limbic, and visceromotor brain regions. At present, targeting of DBS for depression is based on landmarks visible in conventional magnetic resonance imaging. Preoperatively acquired diffusion imaging for connectivity-based cortical mapping could improve neurosurgical targeting. We hypothesize that the subgenual region with greatest connectivity across the distributed network described here may prove most effective.


A Literature Survey on Domain Adaptation of Statistical Classifiers


title: Prognosis for mild traumatic brain injury: results of the who collaborating centre task force on mild traumatic brain injury

abstract: Prognosis for mild traumatic brain injury: Results of the WHO collaborating centre task force on mild traumatic brain injury


title: Measuring Cultural Awareness in Nursing Students

abstract: Recognizing the need for a valid and reliable way to measure outcomes of a program to promote multicultural awareness among nursing faculty and students, the authors developed a cultural awareness scale. In the first phase of the study, a scale consisting of 37 items was generated from a literature review on cultural awareness, sensitivity, and competence in nursing. A Cronbach’s alpha reliability coefficient of .91 was obtained from a sample of 72 student nurses. In the second phase, the items were presented to a panel of experts in nursing and culture to determine content validity. A content validity index of .88 was calculated, and the total number of items on the scale was reduced to 36. The scale then was administered to 118 nursing students. Data from the two samples then were combined, and factor analysis was conducted to support construct validity. Cronbach’s alpha for the combined samples was .82.


title: Finding twitter communities with common interests using following links of celebrities

abstract: One important problem in target advertising and viral marketing on online social networking sites is the efficient identification of communities with common interests in large social networks. Existing methods involve large scale community detection on the entire social network before determining the interests of individuals within these communities. This approach is both computationally intensive and may result in communities without a common interest. We propose an efficient approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the characteristics of these communities and the effects of deepening or specialization of interest.


Fraud Detection in E-Transactions using Deep Neural Networks-A Case of Financial Institutions in Zimbabwe


A muscle-driven approach to restore stepping with an exoskeleton for individuals with paraplegia


title: Spectral shape classification: A deep learning approach

abstract: We introduce low-level shape descriptors using area-weighted spectral graph wavelets.We design mid-level features using the bag-of-features model.We use deep belief networks to learn high-level features for 3D shape classification.Experimental results on 3D shape benchmarks demonstrate the feasibility of our approach. In this paper, we propose a deep learning approach to 3D shape classification using spectral graph wavelets and the bag-of-features paradigm. In order to capture both the local and global geometry of a 3D shape, we present a three-step feature description strategy. Local descriptors are first extracted via the spectral graph wavelet transform having the Mexican hat wavelet as a generating kernel. Then, mid-level features are obtained by embedding local descriptors into the visual vocabulary space using the soft-assignment coding step of the bag-of-features model. A global descriptor is subsequently constructed by aggregating mid-level features weighted by a geodesic exponential kernel, resulting in a matrix representation that describes the frequency of appearance of nearby codewords in the vocabulary. Experimental results on two standard 3D shape benchmarks demonstrate the much better performance of the proposed approach in comparison with state-of-the-art methods.


title: Shielding Effectiveness of Protective Metallic Wire Meshes: EM Modeling and Validation

abstract: An effective-layer model is proposed to predict the EM shielding performances of metallic wire grids typically used as protective layers in carbon fiber-reinforced composite laminates for aeronautical applications. The model is valid over a wide frequency range and it takes into account the field penetration through the grating. The shielding performance of the wire grid is estimated by means of the average shielding effectiveness, which represents the response of the material to an incident plane wave having both transverse-magnetic and transverse-electric polarizations. The proposed model is validated by comparison with experimental data, which demonstrates its validity over a wide frequency range, up to 18 GHz.


title: Fast on-line index construction by geometric partitioning

abstract: Inverted index structures are the mainstay of modern text retrieval systems. They can be constructed quickly using off-line merge-based methods, and provide efficient support for a variety of querying modes. In this paper we examine the task of on-line index construction -- that is, how to build an inverted index when the underlying data must be continuously queryable, and the documents must be indexed and available for search as soon they are inserted. When straightforward approaches are used, document insertions become increasingly expensive as the size of the database grows. This paper describes a mechanism based on controlled partitioning that can be adapted to suit different balances of insertion and querying operations, and is faster and scales better than previous methods. Using experiments on 100GB of web data we demonstrate the efficiency of our methods in practice, showing that they dramatically reduce the cost of on-line index construction.


title: A Neurolinguistic Model of Grammatical Construction Processing

abstract: One of the functions of everyday human language is to communicate meaning. Thus, when one hears or reads the sentence, “John gave a book to Mary,” some aspect of an event concerning the transfer of possession of a book from John to Mary is (hopefully) transmitted. One theoretical approach to language referred to as construction grammar emphasizes this link between sentence structure and meaning in the form of grammatical constructions. The objective of the current research is to (1) outline a functional description of grammatical construction processing based on principles of psycholinguistics, (2) develop a model of how these functions can be implemented in human neurophysiology, and then (3) demonstrate the feasibility of the resulting model in processing languages of typologically diverse natures, that is, English, French, and Japanese. In this context, particular interest will be directed toward the processing of novel compositional structure of relative phrases. The simulation results are discussed in the context of recent neurophysiological studies of language processing.


title: Local Directional Ternary Pattern for Facial Expression Recognition

abstract: This paper presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features (i.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two-level grid to construct the face descriptor while sampling expression-related information at different scales. We use a coarse grid for stable codes (highly related to non-expression), and a finer one for active codes (highly related to expression). This multi-level approach enables us to do a finer grain description of facial motions while still characterizing the coarse features of the expression. Moreover, we learn the active LDTP codes from the emotion-related facial regions. We tested our method by using person-dependent and independent cross-validation schemes to evaluate the performance. We show that our approaches improve the overall accuracy of facial expression recognition on six data sets.


title: Optimal Design of Remote Terminal Unit (RTU) for Wireless SCADA System for Energy Management

abstract: For energy deficit countries, the design of Supervisory Control and Data Acquisition (SCADA) based energy management systems for optimal distribution is of high interest. Such design involves development of Remote Terminal Unit (RTU) which is considered as an essential component of any high scale SCADA system and it is functioning as remote field data interface. Considering various generations of SCADA, two different designs of an RTU are proposed for third generation (Networked Approach) SCADA in view of energy management applications. One design includes Programmable Logic Controller (PLC) as CPU of RTU and other would involve Field Programmable Gate Array (FPGA) instead of PLC. This paper results in comparative study of two different selections of CPUs for designing an RTU based on performance measurement. PLC based RTU exhibits limited features where as FPGA based RTU possesses unique features like encryption support, radio support and large memory area. Suitable simulation tools are needed in order to determine the best approach. The main objective of this study is to propose a design outline considering significant parameters that facilitates optimized development and low cost implementation of an RTU, also featured with wireless communication. In addition to the optimized design of an RTU by means of comparative study, a brief discussion on optimization of wireless link for Remote Terminal Unit is also presented. This phase involves detailed comparison among various options considering the RF spectrum for optimal solution. This segment of research results in design of optimized wireless link for the planned low cost Remote Terminal Unit (RTU). A scenario depicting multiple RTUs communicating with one Tele-Control Interface (TCI) is discussed to address optimized implementation of wireless SCADA.


title: Visual tracking with online Multiple Instance Learning

abstract: In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.


title: Development of a Microcontroller-Controlled Security Door System

abstract: Over the years, several security measures have been employed to combat the menace of insecurity of lives and property. This is done by preventing unauthorized entrance into buildings through entrance doors using conventional and electronic locks, discrete access code, and biometric methods such as the finger prints, thumb prints, the iris and facial recognition. In this paper, a prototyped door security system is designed to allow a privileged user to access a secure keyless door where valid smart card authentication guarantees an entry. The model consists of hardware module and software which provides a functionality to allow the door to be controlled through the authentication of smart card by the microcontroller unit. (


title: A novel mixed conventional microstrip and composite right/left-handed backward-wave directional coupler with broadband and tight coupling characteristics

abstract: A novel backward directional coupler, composed of a conventional microstrip (C/spl mu/S) line and composite right/left-handed (CRLH) line, is proposed. This coupler is functionally backward but it is based on a forward-type coupling phenomenon. The coupler is shown to exhibit broad bandwidth and tight coupling characteristics. The theoretical circuit-model results are supported by full-wave and experimental evidence. A quasi 0-dB prototype with more than 50% bandwidth is demonstrated.


title: Characteristics of Serial Peripheral Interfaces (SPI) Timing Parameters for Optical Mouse Sensor

abstract: In this paper we report the characterizations results of serial peripheral interface (SPI) timing parameters for optical mouse sensor. SPI is an interface that facilitates the transfer of synchronous serial data. It supports two-way communication between mouse sensor and microcontroller. The test setups were used consist of digital oscilloscope, power supply, mouse sensor, test board and personal computer. The SPI timing parameters that were evaluated are read- address data delay, the timing between read and subsequent commands, rise and fall time, hold time, setup time, NCS to SCLK active, SCLK to NCS inactive (read operation) and SCLK to NCS inactive (write operation). The timing parameters results were within the limit as specified in product datasheet.


title: Analysis on Load-Adaptive Phase-Shift Control for High Efficiency Full-Bridge LLC Resonant Converter Under Light-Load Conditions

abstract: Recently, the full-bridge (FB) LLC resonant converter is getting more attention due to its zero-voltage switching of the primary switches, zero-current switching of rectifier diodes, and high power capability. However, the conversion efficiency is degraded under light-load conditions due to the core loss in magnetic components and the switching loss in semiconductor devices. In this paper, a new control method is proposed for the FB LLC resonant converter. In the proposed method, the frequency control is basically used to regulate the output voltage over entire load conditions. Moreover, the proposed method utilizes phase-shifted gate signals between switch legs, based on predetermined optimal duty ratio under light-load conditions. At this point, the optimal duty ratio is determined to get minimum power loss under each light-load condition through the loss analysis of all components. Therefore, the proposed method makes high efficiency under light-load conditions. To confirm the validity of this paper, the prototype of the network power supply with 320–385 V dc input and 48 V/720 W dc output is tested.


title: Filling the periorbital hollows with hyaluronic acid gel: Long‐term review of outcomes and complications

abstract: INTRODUCTION ::: Hyaluronic acid gel has been used for rejuvenation of the periorbital tissues since 2004. The in-office, nonsurgical procedural nature has resulted in the growing popularity of the use of fillers. The periorbital region poses unique challenges to the cosmetic surgeon. Malar edema, blue-gray dyschromia, and contour irregularities are well reported short-term complications. We present a long-term follow-up review of complications associated with periocular injection of hyaluronic acid gel fillers. ::: ::: ::: METHODS ::: Retrospective review of 147 patients with at least a 5-year follow-up period since the first injection. Five senior masked reviewers were asked to grade photographs of patients in comparison with preinjection photographs. Grading was based on a reference photographic key looking specifically at the presence and severity of malar edema, blue-gray dyschromia, and contour irregularity. ::: ::: ::: RESULTS ::: Malar edema (11%), blue-gray dyschromia (31.3%), and contour irregularities (30.5%) continue to be problems in longer term follow-up of patients. The vast majority (90%) of these complications were mild and required no intervention. There is no statistically significant difference in the mean volumes injected by severity grading of each measured complication. ::: ::: ::: CONCLUSION ::: Hyaluronic acid gel fillers of the periorbital region are well tolerated in our long-term follow-up experience. The vast majority of cases of malar edema, blue-gray dyschromia, and contour irregularities are mild and do not require intervention. Conservative treatment and tailored treatment approaches, in terms of volume and frequency of injections, may decrease the tendency of these adverse effects to occur.


title: Design of Division Circuits for Stochastic Computing

abstract: Stochastic computing (SC) encodes data in the signal probabilities associated with pseudo-random bit-streams. It enables very low-area and low-power arithmetic operations using standard VLSI circuits, it is also highly error-tolerant. While addition, subtraction and multiplication have extremely simple SC implementations, this is not true for division. Known stochastic dividers employ sequential logic circuits whose accuracy, convergence properties, etc., are unsatisfactory or not well under-stood. As a result, division is usually avoided or approximated in SC design. We first review and analyze in depth the existing design approaches to stochastic division. We then propose a novel division technique called CORDIV that exploits correlation between the input parameters. CORDIV not only has lower cost than previous stochastic dividers, but is also significantly more accurate. Area is reduced mainly because CORDIV requires less overhead for stochastic number conversion. We provide experimental data showing a typical 3x reduction in area and about a 10x improvement in accuracy.


title: A Hierarchical Matcher using Local Classifier Chains

abstract: This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture. A hierarchical matcher is proposed that builds chains of local binary neural networks after one global neural network over all the class labels, named as Local Classifier Chains based Convolutional Neural Network (LCC-CNN). The signature of each sample as two components: global component based on the global network; local component based on local binary networks. The local networks are built based on label pairs created by a similarity matrix and confusion matrix. During matching, each sample travels through one global network and a chain of local networks to obtain its final matching to avoid error propagation. The proposed matcher has been evaluated with image recognition, character recognition and face recognition datasets. The experimental results indicate that the proposed matcher achieves better performance when compared with methods using only a global deep network. Compared with the UR2D system, the accuracy is improved significantly by 1% and 0.17% on the UHDB31 dataset and the IJB-A dataset, respectively.


title: Changes of myocardial lipidomics profiling in a rat model of diabetic cardiomyopathy using UPLC/Q-TOF/MS analysis

abstract: BACKGROUND ::: Diabetic cardiomyopathy (DCM) is a serious cardiac dysfunction induced by changes in the structure and contractility of the myocardium that are initiated in part by alterations in energy substrates. The underlying mechanisms of DCM are still under controversial. The observation of lipids, especially lipidomics profiling, can provide an insight into the know the biomarkers of DCM. The aim of our research was to detect changes of myocardial lipidomics profiling in a rat model of diabetic cardiomyopathy. ::: ::: ::: METHODS ::: Diabetic cardiomyopathy was induced by feeding a high-sucrose/fat diet (HSFD) for 28 weeks and streptozotocin (30 mg/kg, intraperitoneally). The ultra-high-performance liquid chromatography (UPLC) coupled to quadruple time-of flight (QTOF) mass spectrometer was used to acquire and analyze the lipidomics profiling of myocardial tissue. Meanwhile, parameters of cardiac function were collected using cardiac catheterization, and the cardiac index was calculated, and fasting blood glucose and lipid levels were measured by an ultraviolet spectrophotometric method. ::: ::: ::: RESULTS ::: We detected 3023 positive ion peaks and 300 negative ion peaks. Levels of phosphatidylcholine (PC) (22:6/18:2), PC (22:6/18:1), PC (20:4/16:1), PC (16:1/18:3), phosphatidylethanolamine (PE) (20:4/18:2), and PE (20:4/16:0) were down-regulated, and PC (20:2/18:2), PC (18:0/16:0), and PC (20:4/18:0) were up-regulated in DCM model rats, when compared with control rats. Cardiac functions signed as values of left ventricular systolic pressure, maximal uprising velocity of left ventricular pressure and maximal decreasing velocity of left ventricular pressure were injured by 21-44%, and the cardiac index was increased by 25%, and fasting blood glucose and lipids were increased by 34-368%. Meanwhile, the cardiac lipid-related biomarkers have significant correlation with changes of cardiac function and cardiac index. ::: ::: ::: CONCLUSIONS ::: UPLC/Q-TOF/MS analysis data suggested changes of some potential lipid biomarkers in the development of cardiac dysfunction and hypertrophy of diabetic cardiomyopathy, which may serve as potential important targets for clinical diagnosis and therapeutic intervention of DCM in the future.


title: High efficiency high step-up DC/DC converters - a review

abstract: The renewable energy sources such as PV modules, fuel cells or energy storage devices such as super capacitors or batteries deliver output voltage at the range of around 12 to 70 VDC. In order to connect them to the grid the voltage level should be adjusted according to the electrical network standards in the countries. First of all the voltage should be stepped up to sufficient level at which the DC/AC conversion can be performed to AC mains voltage requirements. Overall performance of the renewable energy system is then affected by the efficiency of step-up DC/DC converters, which are the key parts in the system power chain. This review is focused on high efficiency step-up DC/DC converters with high voltage gain. The differentiation is based on the presence or lack of galvanic isolation. A comparison and discussion of different DC/DC step-up topologies will be performed across number of parameters and presented in this paper.


Joint Beat and Downbeat Tracking with Recurrent Neural Networks


title: Enabling strategic transformations with enterprise systems : beyond operational efficiency

abstract: The last few decades have seen the widespread adoption of large-scale, integrated, packaged software suites collectively called Enterprise Systems. However, most studies done so far have largely focused on the operational efficiency achieved by using these systems. This paper reports on research-in-progress that seeks to explore the strategic potential of Enterprise Systems to enable business transformations. In doing so, a new model is proposed that places the emphasis on the potential of Enterprise Systems to achieve innovation and reshape business strategy, rather than merely focusing on operational benefits. This model proposes that Enterprise Systems, when used to integrate, optimise and informate, can help firms achieve alliance innovation, process innovation and reshaped business strategy. The paper also provides preliminary empirical evidence that supports the propositions. This model will be tested further through primary case studies and a survey. This research, once completed, will assess the strategic role of Enterprise Systems in enabling business transformations beyond operational benefits.


title: StaticGreedy: solving the scalability-accuracy dilemma in influence maximization

abstract: Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracy In this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.


title: IQM3: Information Quality Management Maturity Model

abstract: In order to enhance their global business performance, organizations must be careful with the quality of their information since it is one of their main assets. Analogies to quality management of classical products demonstrate that Information Quality is also preferably attainable through management by integrating some corresponding Information Quality management activities into the organizational processes. To achieve this goal we have developed an Information Quality Management Framework (IQMF). It is articulated on the concept of Information Management Process (IMP), based on the idea of Software Process. An IMP is a combination of two sub-processes: the first, a production process, aimed to manufacture information from raw data, and the second to adequately manage the required Information Quality level of the first. IQMF consists of two main components: an Information Quality Management Maturity Model (IQM3), and a Methodology for the Assessment and Improvement of Information Quality Management (MAIMIQ), which uses IQM3 as a reference model for the assessment and for the improvement goal of an IMP. Therefore, as a result of an assessment with MAIMIQ, an IMP can be said to have raised one of the maturity levels described in IQM3, and as improvement goal, it would be desirable to achieve a higher maturity level. Since an Information System can be seen as a set of several IMPs sharing several resources, it is possible to improve the Information Quality level of the entire Information System by improving the most critical IMPs. This paper is focused only on describing the foundations and structure of IQM3, which is based on staged CMMI.


title: Individualism and Collectivism: Cross-Cultural Perspectives on Self-Ingroup Relationships

abstract: The individualism and collectivism constructs are theoretically analyzed and linked to certain hypothesized consequences (social behaviors, health indices). Study 1 explores the meaning of these constructs within culture (in the United States), identifying the individual-differences variable, idiocentrism versus allocentrism, that corresponds to the constructs. Factor analyses of responses to items related to the constructs suggest that UrS. individualism is reflected in (a) Self-Reliance With Competition, (b) Low Concern for Ingroups, and (c) Distance from Ingroups. A higher order factor analysis suggests that Subordination oflngroup Goals to Personal Goals may be the most important aspect of U.S. individualism. Study 2 probes the limits of the constructs with data from two collectivist samples (Japan and Puerto Rico) and one individualist sample (Illinois) of students. It is shown that responses depend on who the other is (i.e., which ingroup), the context, and the kind of social behavior (e.g., feel similar to other, attentive to the views of others). Study 3 replicates previous work in Puerto Rico indicating that allocentric persons perceive that they receive more and a better quality of social support than do idiocentric persons, while the latter report being more lonely than the former. Several themes, such as self-reliance, achievement, and competition, have different meanings in the two kinds of societies, and detailed examinations of the factor patterns show how such themes vary across cultures.


title: Increasing emitter efficiency in 3.3-kV enhanced trench IGBTs for higher short-circuit capability

abstract: In this paper, a 3.3-kV Enhanced Trench IGBT has been designed with a high emitter efficiency, for improving its short-circuit robustness. The carrier distribution profile has been shaped in a way that it is possible to increase the electric field at the surface of the IGBT, and thereby, counteract the Kirk Effect onset. This design approach is beneficial for mitigating high-frequency oscillations, typically observed in IGBTs under short-circuit conditions. The effectiveness of the proposed design rule is validated by means of mixed-mode device simulations. Then, two IGBTs have been fabricated with different emitter efficiencies and tested under short circuit, validating that the high-frequency oscillations can be mitigated, with higher emitter efficiency IGBT designs.


Iris Feature Extraction and Recognition using Unbalanced Haar Wavelets & Modified Multi Texton Histogram


title: Auditory displays in human-machine interfaces

abstract: Auditory displays are described for several application domains: transportation, industrial processes, health care, operation theaters, and service sectors. Several types of auditory displays are compared, such as warning, state, and intent displays. Also, the importance for blind people in a visualized world is considered with suitable approaches. The service robot domain has been chosen as an example for the future use of auditory displays within multimedia process supervision and control applications in industrial, transportation, and medical systems. The design of directional sounds and of additional sounds for robot states, as well as the design of more complicated robot sound tracks, are explained. Basic musical elements and robot movement sounds have been combined. Two exploratory experimental studies, one on the understandability of the directional sounds and the robot state sounds as well as another on the auditory perception of intended robot trajectories in a simulated supermarket scenario, are described. Subjective evaluations of sound characteristics such as urgency, expressiveness, and annoyance have been carried out by nonmusicians and musicians. These experimental results are briefly compared with time-frequency analyses.


title: Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review

abstract: Context: The exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century. Methods: This article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We didanexhaustivereviewofpublishedarticlesindexedinthedatabasesPubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance. Findings: Our literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done


title: Over-exposure image correction with automatic texture synthesis

abstract: A novel over-exposure image correction algorithm combining synthesized texture information in over-exposed regions is presented. In this algorithm, intensity image is decomposed into structure and detail layers by weighted least squares method. Modified exemplar-based texture synthesis method is applied to the detail layer in order to synthesize texture details in over-exposed regions. The synthesized texture is combined with the original texture and fed to the lightness and color correction modules to get the final result. An efficient diffusion-like color correction method is presented. Qualitative and quantitative experimental results show that the proposed method produces vivid details which are absent in previous methods without human assistance in over-exposed regions. This improves the overall appearance of the recovered image.


title: Instance-based Domain Adaptation via Multiclustering Logistic Approximation

abstract: With the explosive growth of the Internet online texts, we could nowadays easily collect a large amount of labeled training data from different source domains. However, a basic assumption in building statistical machine learning models for sentiment analysis is that the training and test data must be drawn from the same distribution. Directly training a statistical model usually results in poor performance, when the training and test data have different distributions. Faced with the massive labeled data from different domains, it is therefore important to identify the source-domain training instances that are closely relevant to the target domain, and make better use of them. In this work, we propose a new approach, called multiclustering logistic approximation (MLA), to address this problem. In MLA, we adapt the source-domain training data to the target domain via a framework of multiclustering logistic approximation. Experimental results demonstrate that MLA has significant advantages over the state-of-the-art instance adaptation methods, especially in the scenario of multidistributional training data.


title: Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion

abstract: Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed triplets could be costly. Hence, a manually designed procedure is often used when training the models. In this paper, we propose Implicit ReasoNets (IRNs), which is designed to perform multi-step inference implicitly through a controller and shared memory. Without a human-designed inference procedure, IRNs use training data to learn to perform multi-step inference in an embedding neural space through the shared memory and controller. While the inference procedure does not explicitly operate on top of observed triplets, our proposed model outperforms all previous approaches on the popular FB15k benchmark by more than 5.7%.


title: Revisiting Multiple Instance Neural Networks

abstract: Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In this paper, we revisit the problem of solving multiple instance learning problems using neural networks. Neural networks are appealing for solving multiple instance learning problem. The multiple instance neural networks perform multiple instance learning in an end-to-end way, which take a bag with various number of instances as input and directly output bag label. All of the parameters in a multiple instance network are able to be optimized via back-propagation. We propose a new multiple instance neural network to learn bag representations, which is different from the existing multiple instance neural networks that focus on estimating instance label. In addition, recent tricks developed in deep learning have been studied in multiple instance networks, we find deep supervision is effective for boosting bag classification accuracy. In the experiments, the proposed multiple instance networks achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, e.g., it takes only 0.0003 second to predict a bag and a few seconds to train on a MIL datasets on a moderate CPU.


Additive lattice kirigami


title: Observations on Insect Damage to Pigeonpea in Nigeria

abstract: During a survey of insect pests of pigeon pea in Nigeria in 1993 a large number of species was recorded, but relatively little damage to the crop. The species causing the greatest damage was Clavigralla tomentosicollis. Ratoon crops suffered the greatest damage, followed by pigeon pea intercropped with maize and sorghum. Seed samples collected from a market were infested with Callosobruchus maculatus. Some hymenopteran parasitoids of the pests were recorded.


title: Bayesian optimization with robust Bayesian neural networks

abstract: Bayesian optimization is a prominent method for optimizing expensive-to-evaluate black-box functions that is widely applied to tuning the hyperparameters of machine learning algorithms. Despite its successes, the prototypical Bayesian optimization approach - using Gaussian process models - does not scale well to either many hyperparameters or many function evaluations. Attacking this lack of scalability and flexibility is thus one of the key challenges of the field. We present a general approach for using flexible parametric models (neural networks) for Bayesian optimization, staying as close to a truly Bayesian treatment as possible. We obtain scalability through stochastic gradient Hamiltonian Monte Carlo, whose robustness we improve via a scale adaptation. Experiments including multi-task Bayesian optimization with 21 tasks, parallel optimization of deep neural networks and deep reinforcement learning show the power and flexibility of this approach.


title: A human-robot interaction interface for mobile and stationary robots based on real-time 3D human body and hand-finger pose estimation

abstract: This paper presents a real-time gesture-based human-robot interaction (HRI) interface for mobile and stationary robots. A human detection approach is used to estimate the entire 3D point cloud of a human being inside the field of view of a moving camera. Afterwards, the pose of the human body is estimated using an efficient self-organizing map approach. Furthermore, a hand-finger pose estimation approach based on a self-scaling kinematic hand skeleton is presented and evaluated. A trained support vector machine is used to classify 29 hand-finger gestures based on the angles of the finger joints. The HRI interface is integrated into the ROS framework and qualitatively evaluated in a first test scenario on a mobile robot equipped with an RGB-D camera for gesture interaction. Since the hand-finger pose, the hand-finger gesture, as well as the whole body pose are estimated, the interface allows a flexible implementation of various applications.


title: A View of Fog Computing from Networking Perspective

abstract: With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people's daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective.


title: Maximizers versus satisficers: Decision-making styles, competence, and outcomes

abstract: Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007). Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002), we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions, more avoidance of decision making, and greater tendency to experience regret. Contrary to predictions, self-reported maximizers were more likely to report spontaneous decision making. However, the relationship between self-reported maximizing and worse life outcomes is largely unaffected by controls for measures of other decision-making styles, decision-making competence, and demographic variables.


title: Arthroscopy of the acute traumatic knee in children. Prospective study of 138 cases.

abstract: We performed diagnostic arthroscopy for acute knee trauma in 138 children and adolescents aged 13 (1-15) years. The compatibility between the clinical examination and the arthroscopic findings was 59 percent. Ligament injuries were found in 32 cases, 14 of which had rupture of the anterior cruciate ligament. The compatibility in the case of ligament injuries was 31 percent. 48 patients had dislocation of the patella, and a displaced osteochondral fragment was seen in 19, 14 of which were radiographically silent. In 37 cases of distortion of the knee a correct diagnosis would have been missed unless arthroscopy had been performed. Arthroscopy is therefore indicated in children with severe distortion of the knee, hemarthrosis and with dislocation of the patella.


title: BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

abstract: Incorporating prior knowledge like lexical constraints into the model’s output to generate meaningful and coherent sentences has many applications in dialogue system, machine translation, image captioning, etc. However, existing auto-regressive models incrementally generate sentences from left to right via beam search, which makes it difficult to directly introduce lexical constraints into the generated sentences. In this paper, we propose a new algorithmic framework, dubbed BFGAN, to address this challenge. Specifically, we employ a backward generator and a forward generator to generate lexically constrained sentences together, and use a discriminator to guide the joint training of two generators by assigning them reward signals. Due to the difficulty of BFGAN training, we propose several training techniques to make the training process more stable and efficient. Our extensive experiments on three large-scale datasets with human evaluation demonstrate that BFGAN has significant improvements over previous methods.


title: Depression detection using emotion artificial intelligence

abstract: Depression is a leading cause of mental ill health, which has been found to increase risk of early death. Moreover it is a major cause of suicidal ideation and leads to significant impairment in daily life. Emotion artificial intelligence is a field of ongoing research in emotion detection, specifically in the field of text mining. The advent of internet based media sources has resulted in significant user data being available for sentiment analysis of text and images. This paper aims to apply natural language processing on Twitter feeds for conducting emotion analysis focusing on depression. Individual tweets are classified as neutral or negative, based on a curated word-list to detect depression tendencies. In the process of class prediction, support vector machine and Naive-Bayes classifier have been used. The results have been presented using the primary classification metrics including F1-score, accuracy and confusion matrix.


title: Forward forecast of stock prices using LSTM neural networks with statistical analysis of published messages

abstract: The article presents a new approach that combines two separate fields of stock exchange analysis. The aim of proposed solution is to support investors in their decisions and recommend to buy the assets which provide the greatest profits. To achieve this goal, decisive algorithms have been developed using artificial neural networks and technical analysis, which were used along with statistics that refer to the occurrence of single words in the fundamental analysis. Based on this, a model was prepared that in response gives a recommendation for future increases. The system consists of two algorithms. The first of them uses the LSTM (Long Short-Term Memory) artificial neural network. As inputs, information about the current closing price as well as technical analysis indicators along with the value of the current volume were used. The output has been specified as the closing price on the following day. In order to improve the response from the ANN (Artificial Neural Network), statistics of the occurrence of words in publications from last week were used. Subsequent signals gained much more importance if the volume of all transactions was much larger than the moving average of the last 15 periods and if the words that appeared in the last publication caused earlier increases. Additional information for the system are also data that come from Google Trends. This allows to verify the trend of interest and whether the published messages are important.


title: Better human computation through principled voting

abstract: Designers of human computation systms often face the need to aggregate noisy information provided by multiple people. While voting is often used for this purpose, the choice of voting method is typically not principled. We conduct extensive experiments on Amazon Mechanical Turk to better understand how different voting rules perform in practice. Our empirical conclusions show that noisy human voting can differ from what popular theoretical models would predict. Our short-term goal is to motivate the design of better human computation systems; our long-term goal is to spark an interaction between researchers in (computational) social choice and human computation.


title: A tri-directional spotlight filter for object contour extraction

abstract: In this study, an object contour extraction using local structure information (LSI) captured by tri-directional spotlight (TDSL) filter is proposed. The proposed TDSL filter is designed by using three directional spotlight filters with overlapping areas in capturing local structure information around a discontinuity along object contour. The TDSL filter for object contour extraction is consisted of three steps. First, the TDSL filter is placed on an initial contour point to calculate the local structure information in determining and storing the edge candidates between any two pre-selected initial contour points. Next, edge points with weak evidence will be filtered out to obtain an object contour wideband. By performing a thinning approach on the object contour wideband image, a closed one-pixel-width object contour will be finally obtained. Experimental results of the proposed method for manmade and real medical images exhibit that the proposed TDSL filter can enhance the accuracy of contour detection and extraction much better than other existing methods for object contour extraction.


Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments


title: A PCB-Embedding Scheme for LCP Ribbon Waveguide at D-band

abstract: This paper reports on a D-band polymer waveguide PCB integration concept whereby the waveguide and wave launcher are embedded in the flex layer(s) of a rigid-flex PCB buildup. We here report on the design, fabrication and measurement of a first prototype that launches the $\pmb{E}_{\mathbf{11}}^{\pmb{x}}$ mode in a ribbon waveguide using a Vivaldi-type differential structure. Measurements were done using GSG on-wafer probes via an intermediate planar balun. At 140 GHz, the attenuation of two back-to-back launchers with an 8.5cm section of waveguide was measured to be 10 dB, to which the waveguide itself contributed 2 dB.


title: Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images

abstract: Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.


title: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.


title: Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory

abstract: Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, etc.) can occur. The two most important issues of FMEA are the acquirement of FMEA team members' diversity opinions and the determination of risk priorities of the failure modes that have been identified. First, the FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information because of its cross-functional and multidisciplinary nature. These different types of information are very hard to incorporate into the FMEA by the traditional model and fuzzy logic approach. Second, the traditional FMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. In this paper, we present an FMEA using the fuzzy evidential reasoning (FER) approach and grey theory to solve the two problems and improve the effectiveness of the traditional FMEA. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members' diversity opinions and prioritize failure modes under different types of uncertainties.


title: Implementing a Photorealistic Rendering System using GLSL

abstract: Ray tracing on GPUs is becoming quite common these days. There are many publicly available documents on how to implement basic ray tracing on GPUs for spheres and implicit surfaces. We even have some general frameworks for ray tracing on GPUs. We however hardly find details on how to implement more complex ray tracing algorithms themselves that are commonly used for photorealistic rendering. This paper explains an implementation of a stand-alone rendering system on GPUs which supports the bounding volume hierarchy and stochastic progressive photon mapping. The key characteristic of the system is that it uses only GLSL shaders without relying on any platform dependent feature. The system can thus run on many platforms that support OpenGL, making photorealistic rendering on GPUs widely accessible. This paper also sketches practical ideas for stackless traversal and pseudorandom number generation which both fit well with the limited system configuration.


title: Extraction of Visual Features for Lipreading

abstract: The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information. We integrate speech cues from many sources and this improves intelligibility, especially when the acoustic signal is degraded. The paper shows how this additional, often complementary, visual speech information can be used for speech recognition. Three methods for parameterizing lip image sequences for recognition using hidden Markov models are compared. Two of these are top-down approaches that fit a model of the inner and outer lip contours and derive lipreading features from a principal component analysis of shape or shape and appearance, respectively. The third, bottom-up, method uses a nonlinear scale-space analysis to form features directly from the pixel intensity. All methods are compared on a multitalker visual speech recognition task of isolated letters.


title: Finding and Optimizing Solvable Priority Schemes for Decoupled Path Planning Techniques for Teams of Mobile Robots

abstract: Abstract Coordinating the motion of multiple mobile robots is one of the fundamental problems in robotics. The predominant algorithms for coordinating teams of robots are decoupled and prioritized, thereby avoiding combinatorially hard planning problems typically faced by centralized approaches. While these methods are very efficient, they have two major drawbacks. First, they are incomplete, i.e. they sometimes fail to find a solution even if one exists, and second, the resulting solutions are often not optimal. In this paper, we present a method for finding and optimizing priority schemes for such prioritized and decoupled planning techniques. Existing approaches apply a single priority scheme which makes them overly prone to failure in cases where valid solutions exist. By searching in the space of prioritization schemes, our approach overcomes this limitation. It performs a randomized search with hill-climbing to find solutions and to minimize the overall path length. To focus the search, our algorithm is guided by constraints generated from the task specification. To illustrate the appropriateness of this approach, this paper discusses experimental results obtained with real robots and through systematic robot simulation. The experimental results illustrate the superior performance of our approach, both in terms of efficiency of robot motion and in the ability to find valid plans.


title: Sleep Apnea Detection via Depth Video and Audio Feature Learning

abstract: Obstructive sleep apnea, characterized by repetitive obstruction in the upper airway during sleep, is a common sleep disorder that could significantly compromise sleep quality and quality of life in general. The obstructive respiratory events can be detected by attended in-laboratory or unattended ambulatory sleep studies. Such studies require many attachments to a patient's body to track respiratory and physiological changes, which can be uncomfortable and compromise the patient's sleep quality. In this paper, we propose to record depth video and audio of a patient using a Microsoft Kinect camera during his/her sleep, and extract relevant features to correlate with obstructive respiratory events scored manually by a scientific officer based on data collected by Philips system Alice6 LDxS that is commonly used in sleep clinics. Specifically, we first propose an alternating-frame H.264 video encoding scheme and bit recovery scheme at the decoder. Next, we perform depth video temporal denoising using a motion vector graph smoothness prior. Then, we build a dual-ellipse model and track a patient's chest and abdominal movements in the denoised videos. Finally, we extract features from both depth video and audio for classifier training and respiratory event detection. Experimental results show 1) that our depth video compression scheme outperforms a competitor that records only the 8 most significant bits, 2) our graph-based temporal denoising scheme reduces the flickering effect without over-smoothing, and 3) our trained classifiers can deduce respiratory events scored manually based on data collected by system Alice6 LDxS with high accuracy.


title: An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

abstract: Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics. In the proposed model, the parameters are compressed significantly by converting the weight matrices to the canonical polyadic format. Furthermore, an efficient learning algorithm is designed to train the parameters. Finally, the proposed efficient deep learning model is applied to the workload prediction of virtual machines on cloud. Experiments are conducted on the datasets collected from PlanetLab to validate the performance of the proposed model by comparing with other machine-learning-based approaches for workload prediction of virtual machines. Results indicate that the proposed model achieves a higher training efficiency and workload prediction accuracy than state-of-the-art machine-learning-based approaches, proving the potential of the proposed model to provide predictive services for industry informatics.


title: Scheduling in time sensitive networks (TSN) for mixed-criticality industrial applications

abstract: IEEE 802.1 Time-Sensitive Networking (TSN) is a set of IEEE standards that extend Ethernet for safety-critical and real-time applications. TSN is envisioned to be widely used in several applications areas, from industrial automation to in-vehicle networking. TSN supports mixed-criticality applications via multiple traffic classes: Time-Triggered (TT) communication, Audio-Video-Bridging (AVB) streams with bounded end-to-end latency as well as Best-Effort messages. TT traffic is scheduled via Gate Control Lists (GCLs) specified for each queue of an egress port. Although researchers have started to propose approaches for the GCL synthesis, all the work so far has ignored lower priority real-time traffic such as AVB, resulting in GCLs that increase the worst-case delays of AVB traffic rendering it unschedulable. In this paper, we propose a GCL synthesis approach based on a Greedy Randomized Adaptive Search Procedure, which takes into account the AVB traffic, such that both TT and the AVB traffic are schedulable. Our approach is evaluated on several test cases.


title: Neural Machine Translation with External Phrase Memory

abstract: In this paper, we propose phraseNet, a neural machine translator with a phrase memory which stores phrase pairs in symbolic form, mined from corpus or specified by human experts. For any given source sentence, phraseNet scans the phrase memory to determine the candidate phrase pairs and integrates tagging information in the representation of source sentence accordingly. The decoder utilizes a mixture of word-generating component and phrase-generating component, with a specifically designed strategy to generate a sequence of multiple words all at once. The phraseNet not only approaches one step towards incorporating external knowledge into neural machine translation, but also makes an effort to extend the word-by-word generation mechanism of recurrent neural network. Our empirical study on Chinese-to-English translation shows that, with carefully-chosen phrase table in memory, phraseNet yields 3.45 BLEU improvement over the generic neural machine translator.


title: Embedding Covert Channels into TCP/IP

abstract: It is commonly believed that steganography within TCP/IP is easily achieved by embedding data in header fields seemingly filled with “random” data, such as the IP identifier, TCP initial sequence number (ISN) or the least significant bit of the TCP timestamp. We show that this is not the case; these fields naturally exhibit sufficient structure and non-uniformity to be efficiently and reliably differentiated from unmodified ciphertext. Previous work on TCP/IP steganography does not take this into account and, by examining TCP/IP specifications and open source implementations, we have developed tests to detect the use of naive embedding. Finally, we describe reversible transforms that map block cipher output onto TCP ISNs, indistinguishable from those generated by Linux and OpenBSD. The techniques used can be extended to other operating systems. A message can thus be hidden so that an attacker cannot demonstrate its existence without knowing a secret key.


title: Business Intelligence (BI) Critical Success Factors

abstract: Companies are increasingly focussing their information systems efforts around Business Intelligence (BI) ::: solutions. The benefits realised from BI vary significantly from company to company. BI systems are now being ::: used as extensions of Enterprise Resource Planning (ERP) systems as they consolidate, transform and analyse ::: the vast amounts of data generated by the firm. Much attention has been given to the identification of critical ::: success factors (CSF) associated with the adoption of ERP systems. However, there is only limited research that ::: has focussed on the CSF associated with BI implementations as part of an ERP system environment. Hence, this ::: research documents BI specific critical success factors that industry partners, venders or systems users have ::: identified in their presentations at conferences, education forms or formal user group meetings. --21st Australasian Conference on Information Systems, 1-3 Dec 2010, Brisbane


title: Triple Bottom Line Accounting: A Conceptual Expose

abstract: This paper is centered on Triple Bottom line Accounting (TBL) which has now become an important issue in the business accounting. The objective of this study is to review the concept TBL, explain how it can be useful to business, policy makers and economic development practitioners and highlights some current examples on how TBL reporting can be practicalized. To achieve the above stated objectives, the researcher explains the concept of TBL, measures of TBL, its advantages and disadvantages and the Operationalization of TBL. The study was concluded in favour of TBL because by adopting TBL reporting, businesses understand that they are held to specific principles that are developed by internal and external forces. For this reason, they will need to focus on the impact that their operations have on the community. This change of mindset will, typically, be followed by changes in ordinary, everyday operations to increase transparency.


Editor's Comments: Health Information Systems – Opportunities and Challenges in a Global Health Ecosystem


title: Long-Range Battery-Free UHF RFID With a Single Wire Transmission Line

abstract: We present a long-range passive (battery-free) radio frequency identification (RFID) and distributed sensing system using a single wire transmission line (SWTL) as the communication channel. A SWTL exploits guided surface wave propagation along a single conductor, which can be formed from existing infrastructure, such as power lines, pipes, or steel cables. Guided propagation along a SWTL has far lower losses than a comparable over-the-air (OTA) communication link; so much longer read distances can be achieved compared with the conventional OTA RFID system. In a laboratory-scale experiment with an ISO18000–6C (EPC Gen 2) passive tag, we demonstrate an RFID system using an 8 mm diameter, 5.2 m long SWTL. This SWTL has 30 dB lower propagation loss than a standard OTA RFID system at the same read range. We further demonstrate that the SWTL can tolerate extreme temperatures far beyond the capabilities of coaxial cable, by heating an operating SWTL conductor with a propane torch having a temperature of nearly 2000 °C. Extrapolation from the measured results suggest that a SWTL-based RFID system is capable of read ranges of over 70 m assuming a reader output power of +32.5 dBm and a tag power-up threshold of −7 dBm.


Legal Compliance by Design (LCbD) and through Design (LCtD): Preliminary Survey


Multiple Objective Optimization with Vector Evaluated Genetic Algorithms


title: Improving speech recognition by revising gated recurrent units

abstract: Speech recognition is largely taking advantage of deep learning, showing that substantial benefits can be obtained by modern Recurrent Neural Networks (RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which typically reach state-of-the-art performance in many tasks thanks to their ability to learn long-term dependencies and robustness to vanishing gradients. Nevertheless, LSTMs have a rather complex design with three multiplicative gates, that might impair their efficient implementation. An attempt to simplify LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just two multiplicative gates. ::: This paper builds on these efforts by further revising GRUs and proposing a simplified architecture potentially more suitable for speech recognition. The contribution of this work is two-fold. First, we suggest to remove the reset gate in the GRU design, resulting in a more efficient single-gate architecture. Second, we propose to replace tanh with ReLU activations in the state update equations. Results show that, in our implementation, the revised architecture reduces the per-epoch training time with more than 30% and consistently improves recognition performance across different tasks, input features, and noisy conditions when compared to a standard GRU.


Musical Preferences are Linked to Cognitive Styles


Alter-CNN : An Approach to Learning from Label Proportions with Application to Ice-Water Classification


title: Design and Implementation of Low-Profile Contactless Battery Charger Using Planar Printed Circuit Board Windings as Energy Transfer Device

abstract: This paper paper presents the practical details involved in the design and implementation of a contactless battery charger that employs a pair of neighboring printed circuit board (PCB) windings as a contactless energy transfer device. A prototype contactless battery charger developed for application with cellular phones is used as an example to address the design considerations for the PCB windings and energy transfer circuit, plus demonstrates the performance of the contactless charger adapted to a practical application system.


title: Anycast-aware transport for content delivery networks

abstract: Anycast-based content delivery networks (CDNs) have many properties that make them ideal for the large scale distribution of content on the Internet. However, because routing changes can result in a change of the endpoint that terminates the TCP session, TCP session disruption remains a concern for anycast CDNs, especially for large file downloads. In this paper we demonstrate that this problem does not require any complex solutions. In particular, we present the design of a simple, yet efficient, mechanism to handle session disruptions due to endpoint changes. With our mechanism, a client can continue the download of the content from the point at which it was before the endpoint change. Furthermore, CDN servers purge the TCP connection state quickly to handle frequent switching with low system overhead. We demonstrate experimentally the effectiveness of our proposed mechanism and show that more complex mechanisms are not required. Specifically, we find that our mechanism maintains high download throughput even with a reasonably high rate of endpoint switching, which is attractive for load balancing scenarios. Moreover, our results show that edge servers can purge TCP connection state after a single timeout-triggered retransmission without any tangible impact on ongoing connections. Besides improving server performance, this behavior improves the resiliency of the CDN to certain denial of service attacks.


title: Intelligent classification of electronic music

abstract: Electronic Dance Music (EDM) is a class of music produced primarily using electronic instruments and computers comprising several sub-genres. Disc jockeys (DJs) mix EDM songs from similar sub-genres for live performances in concerts and festivals. Given a large database of electronic music, if a system can classify songs into their respective sub-genres, it can greatly help producers and DJs while making mixes. We describe an approach to classify four major sub-genres of EDM using artificial neural networks. We extracted several rhythmic and statistical features from a database of 400 songs, evenly distributed among the four sub-genres, and used these features to train a Back Propagation Network (BPN) and a Probabilistic Neural Network (PNN). In addition to classification, we also employed the RELIEFF feature selection algorithm to reduce the number of features and improve our results. Comparing the performances of the two networks on the basis of their correct classifications, we observed that BPN combined with the features proposed yielded a better result of 94.16%.


title: Posterolateral transfibular approach to tibial plateau fractures: technique, results, and rationale.

abstract: Summary:We describe a posterolateral transfibular neck approach to the proximal tibia. This approach was developed as an alternative to the anterolateral approach to the tibial plateau for the treatment of two fracture subtypes: depressed and split depressed fractures in which the comminution and de


title: Evaluation of bronchoalveolar cells in pulmonary paracoccidioidomycosis

abstract: To investigate the local immune response, the cellular infiltrate and cytokine levels were analysed in bronchoalveolar lavage (BAL) from patients with pulmonary paracoccidioidomycosis. The group consisted of 19 patients aged 34–65 yrs. The diagnosis was confirmed by demonstration of the fungus in the sputum or BAL fluid and by serological tests. Cytospin preparations showed an increased number of lymphocytes and neutrophils in BAL. A higher number of CD8+ lymphocytes were observed in BAL compared with peripheral blood. Alveolar macrophages (AM) expressed approximately three-fold more major histocompatibility class II, intercellular adhesion molecule-1 and B7-2 molecules on their surfaces than their circulating counterparts, indicating that they had differentiated into activated macrophages inside the lungs. Cultured AM produced higher levels of interleukin (IL)‐6, tumour necrosis factor (TNF)‐α and macrophage inflammatory protein (MIP)‐1α than peripheral blood monocytes. BAL fluid contained low but detectable amounts of IL‐6, TNF‐α and MIP‐1α, and specific antibodies to Paracoccidioides brasiliensis , mainly of the immunoglobulin G 2 isotype. As macrophage inflammatory protein‐1α was shown to selectively attract CD8+ T‐cells and this population was elevated in bronchoalveolar lavage, the data suggest that, besides macrophages, CD8+ T‐cells may have an important role in the pathogenesis of pulmonary paracoccidioidomycosis.


title: Recent advances in 3D printing of biomaterials

abstract: Abstract3D Printing promises to produce complex biomedical devices according to computer design using patient-specific anatomical data. Since its initial use as pre-surgical visualization models and tooling molds, 3D Printing has slowly evolved to create one-of-a-kind devices, implants, scaffolds for tissue engineering, diagnostic platforms, and drug delivery systems. Fueled by the recent explosion in public interest and access to affordable printers, there is renewed interest to combine stem cells with custom 3D scaffolds for personalized regenerative medicine. Before 3D Printing can be used routinely for the regeneration of complex tissues (e.g. bone, cartilage, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with intricate 3D microarchitecture (e.g. liver, lymphoid organs), several technological limitations must be addressed. In this review, the major materials and technology advances within the last five years for each of the common 3D Printing technologies (Three Dimensional Printing, Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, and 3D Plotting/Direct-Write/Bioprinting) are described. Examples are highlighted to illustrate progress of each technology in tissue engineering, and key limitations are identified to motivate future research and advance this fascinating field of advanced manufacturing.


title: Hybrid learning of direct neural controller using approximated potential function

abstract: A hybrid learning algorithm that combines the genetic algorithm and steepest decent method is proposed for neural networks. Also described is a method for overcoming the Jacobian problem when the hybrid learning algorithm is applied to a neural network controller. In the proposed algorithm, a parallel search by the genetic algorithm and a continuous search by the steepest descent method are carried out sequentially. This may improve the convergence speed and solve the local minimum problem. In this approach, many neural network controllers are generated in the search process of the genetic algorithm. Potential functions, defined by both the control performance of each neural network controller and the reciprocal actions between the neural network controllers, are introduced into the neural network's learning. This allows application of the steepest descent method without either the Jacobian information or the neural network identifier. Simulations were performed using a discrete-time SISO nonlinear system as the objective system. The results confirm the feasibility of the proposed method for the learning of the neural network controller.


title: Top-N recommendations from implicit feedback leveraging linked open data

abstract: The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.


title: What Is the Blockchain?

abstract: Blockchain is a new technology, based on hashing, which is at the foundation of the platforms for trading cryptocurrencies and executing smart contracts. This article reviews the basic ideas of this technology and provides a sample minimalist implementation in Python.


title: Margin Loss: Making Faces More Separable

abstract: The key point of face recognition is creating a discriminative feature representation to ensure intraclass compactness and interclass separability. Softmax loss is widely used in deep learning networks, but it is indirect for face verification. Center loss is effective to improve intraclass compactness, while interclass distances are ignored. In this letter, we propose a novel loss function, termed margin loss, to enlarge distances of interclass and reduce intraclass variations simultaneously. Margin loss aims to focus on samples hard to classify by a distance margin. Different from Softmax loss, margin loss is based on Euclidean distances that can directly measure face similarity. Experiments on different datasets have demonstrated the effectiveness of our method.


title: Music Mood and Theme Classification - a Hybrid Approach

abstract: Music perception is highly intertwined with both emotions and context. Not surprisingly, many of the users’ information seeking actions aim at retrieving music songs based on these perceptual dimensions ‐ moods and themes, expressing how people feel about music or which situations they associate it with. In order to successfully support music retrieval along these dimensions, powerful methods are needed. Still, most existing approaches aiming at inferring some of the songs’ latent characteristics focus on identifying musical genres. In this paper we aim at bridging this gap between users’ information needs and indexed music features by developing algorithms for classifying music songs by moods and themes. We extend existing approaches by also considering the songs’ thematic dimensions and by using social data from the Last.fm music portal, as support for the classification tasks. Our methods exploit both audio features and collaborative user annotations, fusing them to improve overall performance. Evaluation performed against the AllMusic.com ground truth shows that both kinds of information are complementary and should be merged for enhanced classification accuracy.


title: Tetranortriterpenoids from Cipadessa baccifera.

abstract: Five new mexicanolide-type tetranortriterpenoids, tigloylseneganolide A (1), 2'R-methylbutanoylproceranolide (2), 2'S-methylbutanoylproceranolide (3), 2'R-cipadesin A (4), and 2'R-cipadesin (6), as well as the known 2'S-epimers of 4 and 6 (5 and 7), together with six other known limonoids, were isolated from the seeds of Cipadessa baccifera. The structures of these compounds were elucidated on the basis of spectroscopic analyses and chemical methods. 1H NMR-based conformational analysis was applied to establish the absolute configuration of the sterically hindered 2-methylbutanoyl in three epimeric pairs (2-7). A general rule for the determination of the absolute configurations of 2R- and 2S-methylbutanoyl groups at C-3 of a limonoid in a mixture is proposed.


title: Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks

abstract: We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks. ::: ::: This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. ::: ::: This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.


title: NextSlidePlease: Authoring and delivering agile multimedia presentations

abstract: Presentation support tools, such as Microsoft PowerPoint, pose challenges both in terms of creating linear presentations from complex data and fluidly navigating such linear structures when presenting to diverse audiences. NextSlidePlease is a slideware application that addresses these challenges using a directed graph structure approach for authoring and delivering multimedia presentations. The application combines novel approaches for searching and analyzing presentation datasets, composing meaningfully structured presentations, and efficiently delivering material under a variety of time constraints. We introduce and evaluate a presentation analysis algorithm intended to simplify the process of authoring dynamic presentations, and a time management and path selection algorithm that assists users in prioritizing content during the presentation process. Results from two comparative user studies indicate that the directed graph approach promotes the creation of hyperlinks, the consideration of connections between content items, and a richer understanding of the time management consequences of including and selecting presentation material.


title: Outliers, level shifts, and variance changes in time series

abstract: Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.


title: Analytical Modeling of Partially Shaded Photovoltaic Systems

abstract: As of today, the considerable influence of select environmental variables, especially irradiance intensity, must still be accounted for whenever discussing the performance of a solar system. Therefore, an extensive, dependable modeling method is required in investigating the most suitable Maximum Power Point Tracking (MPPT) method under different conditions. Following these requirements, MATLAB-programmed modeling and simulation of photovoltaic systems is presented here, by focusing on the effects of partial shading on the output of the photovoltaic (PV) systems. End results prove the reliability of the proposed model in replicating the aforementioned output characteristics in the prescribed setting. The proposed model is chosen because it can, conveniently, simulate the behavior of different ranges of PV systems from a single PV module through the multidimensional PV structure.


title: Data mining techniques for optimizing inventories for electronic commerce

abstract: of their strategy for incorporating electronic commerce capabilities, many organizations are involved in the development of information systems that will establish effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. These linkages have given birth to comprehensive data warehouses that integrate operational data with supplier, customer, channel partners and market information. Data mining techniques can now provide the technological leap needed to structure and prioritize information from these data warehouses to address specific end-user problems. Emerging data mining techniques permit the semi-automatic discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. Very significant business benefits have been attained through the integration of data mining techniques with current information systems aiding electronic commerce. This paper explains key data mining principles that can play a pivotal role in an electronic commerce environment. The paper also highlights two case studies in which neural network-based data mining techniques were used for inventory optimization. The results from the data mining prototype in a large medical distribution company provided the rationale for the strategy to reduce the total level of inventory by 50% (from a billion dollars to half a billion dollars) in the particular organization, while maintaining the same level of probability that a particular customer's demand will be satisfied. The second case study highlights the use of neural network based data mining techniques for forecasting hot metal temperatures in a steel mill blast furnace.