title: An Ontology Based Collaborative Recommender System for Security Requirements Elicitation

abstract: Security requirements elicitation is considered a "wicked" problem. Open issues such as determining relevant set of secure requirements, uncertainty and poor decision-making by developers deserves the needed attention. Ontologies and recommender systems have been used in the requirements elicitation. The goals of this dissertation are to 1) develop an ontology-based collaborative recommender system to help with security requirements elicitation and conduct a system performance evaluation and 2) conduct user-centric study of stakeholders using the recommender system. This system will help recommend CAPEC/CWE that should be considered in a given system to be built based on the use case description and so doing will reduce the workload of eliciting relevant security requirements. An analysis of the system performance and user-centric effects will be used to evaluate usefulness of the recommender system for developers.


title: Sequential Compressed Sensing With Progressive Signal Reconstruction in Wireless Sensor Networks

abstract: This paper considers sequential compressed acquisition and progressive reconstruction of spatially and temporally correlated sensor data streams in wireless sensor networks (WSNs) via compressed sensing (CS). We develop a sequential framework based on sliding window processing, in which the sink can efficiently reconstruct the current sensors' readings from a sequence of periodically delivered CS measurements by exploiting the joint compressibility via Kronecker sparsifying bases. Specifically, we derive a recursive CS recovery method which utilizes the estimates from the preceding decoding instants via a regularization and reweighted $\ell_{1} $ -minimization to improve the reconstruction accuracy of sensor data streams while reducing the necessary communications. As beneficial features, the method produces estimates for the current sensors' readings without additional decoding delay, and, via adjusting the window size, it can dynamically trade-off between the CS recovery performance and decoding complexity. Numerical results show that our proposed method achieves higher reconstruction accuracy with a smaller number of required transmissions, and with lower decoding delay and complexity as compared to those of the state of the art CS methods.


title: Bandwidth extension of hybrid-reluctance-force-based tip/tilt system by reduction of eddy currents

abstract: This paper investigates the influence of eddy currents on the dynamic behavior of a novel hybrid-reluctance-force actuator for tip/tilt systems. The effects of eddy currents on the mechatronic system dynamics are identified and modeled by a first order system approximation. Their influence on the achievable performance of a closed loop controlled system is studied by investigating two configurations of the ferromagnetic actuator yoke. An initial solid steel configuration of the yoke is compared to a layered yoke structure with isolated steel sheets, with the aim to improve the system bandwidth, which is limited by the eddy currents. Robust PID feedback controllers are designed for controlling the mover position of both system configurations, resulting in comparable phase margins. Experiments demonstrate that the closed loop bandwidth of the prototype setup can be increased from 370 Hz to 1 kHz by changing the yoke structure, which equals a bandwidth improvement by a factor of 2.8.


title: 3D reconstruction of medical images from slices automatically landmarked with growing neural models

abstract: In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.


title: Structure mapping in the comparison process

abstract: Carrying out similarity and analogy comparisons can be modeled as the alignment and mapping of structured representations. In this article we focus on three aspects of comparison that are central in structure-mapping theory. All three are controversial. First, comparison involves structured representations. Second, the comparison process is driven by a preference for connected relational structure. Third, the mapping between domains is rooted in semantic similarity between the relations that characterize the domains. For each of these points, we review supporting evidence and discuss some challenges raised by other researchers. We end with a discussion of the role of structure mapping in other cognitive processes.


Extending the Technology Acceptance Model and the Task-Technology Task Technology Fit Model to Consumer E-Commerce


Detecting Bias : Does an Algorithm Have to Be Transparent in Order to Be Fair ?


title: CLS: A Cross-user Learning based System for Improving QoE in 360-degree Video Adaptive Streaming

abstract: Viewport adaptive streaming is emerging as a promising way to deliver high quality 360-degree video. It is still a critical issue to predict user's viewpoint and deliver partial video within the viewport. Current widely-used motion-based or content-saliency methods have low precision, especially for long-term prediction. In this paper, benefiting from data-driven learning, we propose a Cross-user Learning based System (CLS) to improve the precision of viewport prediction. Since users have similar region-of-interest (ROI) when watching a same video, it is possible to exploit cross-users' ROI behavior to predict viewport. We use a machine learning algorithm to group users according to historical fixations, and predict the viewing probability by the class. Additionally, we present a QoE-driven rate allocation to minimize the expected streaming distortion under bandwidth constraint, and give a Multiple-Choice Knapsack solution. Experiments demonstrate that CLS provides 2dB quality improvement than full-image streaming and 1.5 dB quality improvement than linear regression (LR) method. On average, the precision of viewpoint prediction improve 15% compared with LR.


title: Do Deep Generative Models Know What They Don't Know?

abstract: A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are widely viewed to be robust to such mistaken confidence as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption. We find that the density learned by flow-based models, VAEs, and PixelCNNs cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. Moreover, we find evidence of this phenomenon when pairing several popular image data sets: FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN. To investigate this curious behavior, we focus analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find such behavior persists even when we restrict the flow models to constant-volume transformations. These transformations admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature. Our results caution against using the density estimates from deep generative models to identify inputs similar to the training distribution until their behavior for out-of-distribution inputs is better understood.


title: Sequential Context Encoding for Duplicate Removal

abstract: Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks. Albeit simple and effective, most previous algorithms utilized a greedy process without making sufficient use of properties of input data. In this work, we design a new two-stage framework to effectively select the appropriate proposal candidate for each object. The first stage suppresses most of easy negative object proposals, while the second stage selects true positives in the reduced proposal set. These two stages share the same network structure, an encoder and a decoder formed as recurrent neural networks (RNN) with global attention and context gate. The encoder scans proposal candidates in a sequential manner to capture the global context information, which is then fed to the decoder to extract optimal proposals. In our extensive experiments, the proposed method outperforms other alternatives by a large margin.


title: GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing

abstract: SUMMARY Clusters, Grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving largescale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers’ or owners’ requirements, and continuously adapt to changes in resource availability. The management of resources and scheduling of applications in such large-scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java-based discrete-event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time- and space-shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod-G


title: Novel Method to Minimize the Air-Gap MMF Spatial Harmonic Content in Three-Phase Windings

abstract: Most of industrial induction motors currently used employ simple winding patterns, which commonly are designed to fulfil the fundamental magnetizing flux and torque requirements, disregarding the spatial harmonic content of the air-gap magnetomotive force (MMF). However, it is well known that the lower-order MMF spatial harmonics have a negative impact on the motor efficiency, vibration, noise, and torque production. The use of different turns per coil in the winding design is a possible solution to mitigate the problem. In this paper, a novel winding optimizing algorithm is fully described. The air-gap is modelled as a linear function of the current-sheet created by the conductors in the slots. Several winding patterns with different poles for stators with different slots are optimized, and the turns per coil pattern is presented in tables for single and double layer windings with optimal coil pitch shortening. These tables can be used, as reference, in winding design projects. An application example of winding optimization is also presented.


title: Cut to the Chase: A Context Zoom-in Network for Reading Comprehension

abstract: In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To ::: show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset ‘NarrativeQA’. The proposed architecture outperforms state-of-the-art results (Tay et al., 2018) by 12.62% (ROUGE-L) relative improvement.


title: Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem

abstract: We study the $K$-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. We introduce a tight asymptotic regret lower bound that is based on the information divergence. An algorithm that is inspired by the Deterministic Minimum Empirical Divergence algorithm (Honda and Takemura, 2010) is proposed, and its regret is analyzed. The proposed algorithm is found to be the first one with a regret upper bound that matches the lower bound. Experimental comparisons of dueling bandit algorithms show that the proposed algorithm significantly outperforms existing ones.


The Role of Magnetizing and Leakage Inductance in Transformer Coupling Circuitry


A Hybrid Approach for Intrusion Detection System


title: What makes us click like on Facebook? Examining psychological, technological, and motivational factors on virtual endorsement

abstract: Enjoyment and interpersonal relationship as most salient motives.Those with higher self-esteem, more diligence, more emotional stability, and less subjective norm clicked "like" to express enjoyment.Those with lower self-esteem, less diligence, less emotional stability, and higher subjective norm clicked "like" for pleasing others.Enjoyment, interpersonal relationship, and perceived ease of "like" positively predicted the attitude toward "like."Subjective norm and the passing time motive positively predicted clicking "like." The pleasing others motivation negatively predicted "like" behavior. This study examines motives for virtually endorsing others on social media, focusing on the Facebook "like" function. Motives are studied in terms of uses and gratifications, Theory of Reasoned Action, and personality and technology factors. Data from an online survey of 213 respondents were examined using factor- and hierarchical-regression analyses. Findings showed enjoyment and interpersonal relationship as most salient motives. Two types of user profiles emerged. Those with higher self-esteem, more diligence, more emotional stability, and less subjective norm clicked "like" to express enjoyment. Those with lower self-esteem, less diligence, less emotional stability, and higher subjective norm clicked "like" for pleasing others.


title: PAAS: A system level simulator for heterogeneous computing architectures

abstract: Heterogeneous computing with hardware accelerators is a promising direction to overcome the power and performance walls in traditional computing systems. CPU-accelerator integrated architectures, such as CPU with ASIC or FPGA based accelerators, are able to provide customized processing according to application requirements and are thus particularly attractive to speed up computation-intensive applications. Therefore, system level simulation showing the interaction among CPUs, hardware accelerators and memory system precisely is important for performing design space exploration leading to architecture and design optimization. In this work, we present PAAS (Processor Accelerator Architecture Simulator), a system level simulator to enable cycle-accurate full system simulation of CPU-accelerator heterogeneous systems. PAAS can easily support flexible architectural configurations, such as different on-chip interconnection topologies, memory hierarchy, etc. Using PAAS, this paper also presents the analysis of the impact of different architectural configurations on the performance of benchmark applications with different execution characteristics using FPGA based accelerators. Furthermore, as an example showing the research capability of PAAS, this paper proposes and investigates a cache-partitioning scheme for improving the performance of shared-cache based CPU-FPGA systems.


title: Beam selection for performance-complexity optimization in high-dimensional MIMO systems

abstract: Millimeter-wave (mm-wave) communications systems offer a promising solution to meeting the increasing data demands on wireless networks. Not only do mm-wave systems allow orders of magnitude larger bandwidths, they also create a high-dimensional spatial signal space due to the small wavelengths, which can be exploited for beamforming and multiplexing gains. However, the complexity of digitally processing the entire high-dimensional signal is prohibitive. By exploiting the inherent channel sparsity in beamspace due to highly directional propagation at mm-wave, it is possible to design near-optimal transceivers with dramatically lower complexity. In such beamspace MIMO systems, it is first necessary to determine the set of beams which define the low-dimensional communication subspace. In this paper, we address this beam selection problem and introduce a simple power-based classifier for determining the beamspace sparsity pattern that characterizes the communication subspace. We first introduce a physical model for a small cell which will serve as the setting for our analysis. We then develop a classifier for the physical model, and show its optimality for a class of ideal signals. Finally, we present illustrative numerical results and show the feasibility of the classifier in mobile settings.


title: A Driver for the Single-Phase Brushless DC Fan Motor With Hybrid Winding Structure

abstract: This paper mainly proposes a novel driver for a single-phase brushless dc fan motor with a hybrid series/parallel winding structure. The winding symbols and directions of the hybrid motor stator structure are defined, and the winding steps for the proposed series/parallel winding are explained. An adequate inverter driving circuit, which is capable to simultaneously obtain the advantages of the hybrid structure, is also discussed. At last, the overall system of this hybrid brushless dc motor with the proposed driving circuit is then implemented to verify the performance of the proposed driver and structure.


title: Test-retest reliability of seven common clinical tests for assessing lower extremity muscle flexibility in futsal and handball players.

abstract: OBJECTIVE ::: To determine the inter-session reliability of seven lower limb muscle flexibility measures obtained from the passive hip extension test (PHE), passive hip flexion test (PHF), passive hip abduction test (PHA), passive straight leg raise test (PSLR), modified Thomas test (MTh), the ankle dorsi-flexion with knee extended (ADFKE) and flexed (ADFKF) tests. ::: ::: ::: DESIGN ::: Repeated measures design. ::: ::: ::: SETTING ::: Controlled laboratory environment. ::: ::: ::: PARTICIPANTS ::: 60 futsal and 30 handball players. ::: ::: ::: MAIN OUTCOME MEASURES ::: Reliability was examined through the change in the mean (ChM), standard error of measurement expressed in absolute values (SEM) and as a percentage of the mean score (%SEM), minimal detectable change (MDC95), and intraclass correlation coefficient (ICC2,k). ::: ::: ::: RESULTS ::: The findings showed negligible ChM values for all the flexibility measures analysed (<1°). Furthermore, the SEM and MDC95 for each flexibility measure ranged from 1.3° to 2.5° and from 3.8° to 6.9°, respectively, with %SEM scores lower than 10% and ICC scores higher than 0.88. ::: ::: ::: CONCLUSIONS ::: Clinicians can be 95% confident that an observed change between 2 measures larger than 3.7°, 6.2°, 5.5°, 6.1°, 6.9°, 4.7°, and 5.0° for the flexibility measures obtained from the PHE, PHF, PHA, PSLR, MTh, ADFKE and ADFKF, respectively, likely indicates a real change in muscle flexibility.


title: Object Partitioning Using Local Convexity

abstract: The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated data-sets. As an alternative to this, we present a new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which--with high accuracy--represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to state-of-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.


title: Evaluating the use of word embeddings for part-of-speech tagging in Bahasa Indonesia

abstract: This paper studies the use of word embeddings for POS tagging in Bahasa Indonesia. The experiments are conducted with an architecture based on neural network model, that is a simple feed forward neural network with one hidden layer. The word embeddings (i.e., CBOW, skip-gram, and GloVe) are trained on unlabelled text corpus created from Wikipedia Bahasa Indonesia. The results show that word embeddings can be used for POS tagging in Bahasas Indonesia with good performance. The F1 score of all word embeddings types are roughly 80% and the accuracies are higher than 93% on a manually tagged corpus of about 250.000 tokens (12,775 unique tokens).


title: Update on the Hybrid Wireless Mesh Protocol of IEEE 802.11s

abstract: HWMP, the default routing protocol of IEEE 802.11s has been revised during the comment resolution of the first letter ballot. The paper gives an update on three areas where major changes have been introduced to HWMP: support for interworking with other networks; revised concept for proactive routing trees to root MPs; and a better aligned extensible path selection framework.


title: A new grid-connected PV system based on cascaded H-bridge quasi-Z source inverter

abstract: A new scheme for grid-connected photovoltaic (PV) interface by combination of a quasi-Z source inverter (qZSI) into cascaded H-bridge (CHB) is proposed in this paper. The proposed scheme enables PV string voltage boost to a higher level, and solves the imbalance problem of DC-link voltage in traditional CHB inverters. A multilevel voltage waveform of inverter output is generated by an improved phase shifted sinusoidal pulse width modulation (PS-SPWM) algorithm, which introduces shoot-through states into the conventional zero states to control qZS-CHB module. The effective control schemes are proposed to regulate the maximum power point tracking (MPPT) of each string, and control the DC-link voltage of each H-bridge, respectively. Grid injected power is controlled corresponding to the proportionality factors of each PV string output power. A 1.5 kW system is built in MATLAB/SIMULINK, and the simulation results verify the proposed novel multilevel PV interface inverter and its control principles.


title: Enhanced Security for Cloud Storage using File Encryption

abstract: Cloud computing is a term coined to a network that offers incredible processing power, a wide array of storage space and unbelievable speed of computation. Social media channels, corporate structures and individual consumers are all switching to the magnificent world of cloud computing. The flip side to this coin is that with cloud storage emerges the security issues of confidentiality, data integrity and data availability. Since the cloud is a mere collection of tangible super computers spread across the world, authentication and authorization for data access is more than a necessity. Our work attempts to overcome these security threats. The proposed methodology suggests the encryption of the files to be uploaded on the cloud. The integrity and confidentiality of the data uploaded by the user is ensured doubly by not only encrypting it but also providing access to the data only on successful authentication.


title: A kinematic approach to Kokotsakis meshes

abstract: A Kokotsakis mesh is a polyhedral structure consisting of an n-sided central polygon P"0 surrounded by a belt of quadrangles or triangles in the following way: Each side a"i of P"0 is shared by an adjacent polygon P"i, and the relative motion between cyclically consecutive neighbor polygons is a spherical coupler motion. Hence, each vertex of P"0 is the meeting point of four faces. In the case n=3 the mesh is part of an octahedron. These structures with rigid faces and variable dihedral angles were first studied in the thirties of the last century. However, in the last years there was a renaissance: The question under which conditions such meshes are infinitesimally or continuously flexible gained high actuality in discrete differential geometry. The goal of this paper is to revisit the well-known continuously flexible examples (Bricard, Graf, Sauer, Kokotsakis) from the kinematic point of view and to extend their list by a new family.


title: SVM based models for predicting foreign currency exchange rates

abstract: Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e.g., neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and /spl epsiv/-insensitive loss function. We investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented.


title: Interpretation of Semantic Tweet Representations

abstract: Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc. Although the performance of the representation learning models has been better than the traditional baselines for such tasks, little is known about the elementary properties of a tweet encoded within these representations, or why particular representations work better for certain tasks. Our work presented here constitutes the first step in opening the black-box of vector embeddings for tweets. Traditional feature engineering methods for high-level applications have exploited various elementary properties of tweets. We believe that a tweet representation is effective for an application because it meticulously encodes the application-specific elementary properties of tweets. To understand the elementary properties encoded in a tweet representation, we evaluate the representations on the accuracy to which they can model each of those properties such as tweet length, presence of particular words, hashtags, mentions, capitalization, etc. Our systematic extensive study of nine supervised and four unsupervised tweet representations against most popular eight textual and five social elementary properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors (STV) best encode the textual and social properties of tweets respectively. FastText is the best model for low resource settings, providing very little degradation with reduction in embedding size. Finally, we draw interesting insights by correlating the model performance obtained for elementary property prediction tasks with the high-level downstream applications.


title: Sensorless control of hybrid stepper motor

abstract: Today thanks to low cost and high performance DSP's, Kalman filtering (KF) becomes an efficient candidate to avoid mechanical sensors in motor control. We present in this work experimental results by using a steady state KF method to estimate the speed and rotor position for hybrid stepper motor. With this method the computing time is reduced. The Kalman gain is pre-computed from numerical simulation and introduced as a constant in the real time algorithm. The load torque is also on-line estimated by the same algorithm. At start-up the initial rotor position is detected by the impulse current method.


title: Active Learning Strategies to Promote Critical Thinking

abstract: Stacy E. Walker, PhD, ATC, provided conception and design; acquisition and analysis and interpretation of the data; anddrafting, critical revision, and ﬁnal approval of the article.Address correspondence to Stacy E. Walker, PhD, ATC, Department of EMS, 300 Pompton Road, William Paterson University,Wayne, NJ 07470. Address e-mail to walkers@wpunj.edu.Objective: To provide a brief introduction to the deﬁnitionand disposition to think critically along with active learning strat-egies to promote critical thinking.Data Sources: I searched MEDLINE and Educational Re-sources Information Center (ERIC) from 1933 to 2002 for lit-erature related to critical thinking, the disposition to think criti-cally, questioning, and various critical-thinking pedagogictechniques.Data Synthesis: The development of critical thinking hasbeen the topic of many educational articles recently. Numerousinstructional methods exist to promote thought and active learn-ing in the classroom, including case studies, discussion meth-ods, written exercises, questioning techniques, and debates.Three methods—questioning, written exercises, and discussionand debates—are highlighted.Conclusions/Recommendations:The deﬁnition of criticalthinking, the disposition to think critically, and different teach-ing strategies are featured. Although not appropriate for allsubject matter and classes, these learning strategies can beused and adapted to facilitate critical thinking and active par-ticipation.Key Words:athletic training education


title: Programming Online Judge System

abstract: With the development of online education, the pattern of education which based on the Internet obtained the general recognition of the society. People prefer to learn and share the open courses on the Internet in universities or colleges all over the world. However, the courses opened online can support the massive peoples to learn online, but couldn't support they to program on the network, and judge the program on the network. In order to support more and more students to complete the experiments of the online courses, automatic judging system about software experiments and hardware experiments are needed to develop to ensure the correctness of the design of experiments. The Background Evaluation System is in MOOC (massive online open courses) environment, it can connect different experiment systems to finish different courses. It also can simulate the hardware experiments online and test hardware experiments automatically. Programming online judge system, mentioned in this paper, can solve much of the problems. With the help of Programming online judge system, different universities can share their resources and save investment. Users can look up for the information conveniently and get experience similar to local experiment.


title: Narcissists are approach-oriented toward their money and their friends

abstract: According to the unmitigated approach model (UAM) of narcissism, narcissists possess strong approach motivation coupled with weak avoidance motivation. The present research tests the UAM in two independent contexts: social and financial. In Study 1, narcissists report having social goals that emphasize the promotion of positive outcomes (e.g., having fun). This predilection is mediated by strong approach motivation. In Study 2, narcissists report a preference for aggressive financial investment strategies (e.g., investing in volatile stocks rather than stable bonds). This preference is mediated by both strong approach and weak avoidance motivation. Discussion focuses on how these results support the UAM and more broadly on how the UAM provides a theoretical framework for understanding the costs and benefits of narcissistic personality.


title: Bridging the divide in financial market forecasting: machine learners vs. financial economists

abstract: An extensive benchmark in financial time series forecasting is performed.Best machine learning(ML) methods out-perform best econometric methods.The ML methodology employed significantly affects forecasting accuracy.Market maturity, forecast horizon & model-assessment method affect forecast accuracy.Evidence against the informational value of technical indicators. Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.


title: Applications of Entropy in Finance: A Review

abstract: Although the concept of entropy is originated from thermodynamics, its concepts and relevant principles, especially the principles of maximum entropy and minimum cross-entropy, have been extensively applied in finance. In this paper, we review the concepts and principles of entropy, as well as their applications in the field of finance, especially in portfolio selection and asset pricing. Furthermore, we review the effects of the applications of entropy and compare them with other traditional and new methods.


An evaluation of the Pomodoro Technique for stopping procrastination and behaviour change


title: Early operative outcomes of endoscopic (eTEP access) robotic-assisted retromuscular abdominal wall hernia repair

abstract: BACKGROUND ::: The enhanced-view totally extraperitoneal (eTEP) hernia repair technique was first described for laparoscopic inguinal hernia repair and later applied to laparoscopic ventral and incisional hernia repair. We present our center's early operative outcomes utilizing principles of this technique during robotic ventral and incisional hernia repair for implementation of the robotic eTEP Rives-Stoppa (eRS) and eTEP transversus abdominis release (eTAR) techniques. ::: ::: ::: METHODS ::: A review of a prospectively maintained database of hernia patients was conducted identifying 37 patients who underwent robotic eTEP for ventral, incisional, flank or parastomal hernia repair between March and October 2017. All patients underwent retrorectus dissection with selective utilization of transversus abdominis release (TAR) as indicated. ::: ::: ::: RESULTS ::: 37 patients including 13 male and 24 female with mean age, body mass index, and ASA score of 54, 35.5, and 2.4, respectively, underwent a mean operation room time of 198 min. Mean length of stay was 0.7 days. There were no intraoperative complications. Two patients developed subcutaneous seromas requiring interventional radiology drainage. One patient was readmitted at 30 days for PO intolerance that was managed expectantly. Mean postoperative follow-up visit occurred at 36 days with no sign of early hernia recurrences. ::: ::: ::: CONCLUSION ::: The enhanced-view totally extraperitoneal approach is both safe and feasible in robotic-assisted repair of ventral and incisional hernias. Although long-term outcomes and patient selection criteria require further study, we believe this technique will become an important tool in the armamentarium of minimally invasive hernia surgeons.


title: Occlusion in augmented reality

abstract: Augmented reality in video-based display simply overlay virtual objects on real environment. In many cases this does not represent the actual situation in AR scene. When a real object supposes to occlude a virtual object, the augmented image may cause confusion in users' perception. This incorrect display contributes to misconceptions and wrong operations of task amongst users. The scenario of occlusion in AR application is discussed in this paper. Next, the main method used by other researchers to resolve occlusion problem was discussed. This is followed by a discussion about dependency factors and characteristics that influence the selection of occlusion handling approach and a comparison between a model-based and depth-based approach. Finally, a conclusion and proposed work are presented.


title: Bigdata Oriented Multimedia Mobile Health Applications

abstract: In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process.


title: One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling

abstract: We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models.


title: Modelling and control of a Tilt-Wing Unmanned Aerial Vehicle

abstract: In this article a Tilt-Wing Unmanned Aerial Vehicle (TW-UAV) and the preliminary evaluation of its hovering characteristics in extended simulation studies are presented. In the beginning, an overview of the TW-UAV's design properties are established, highlighting the novelties of the proposed structure and the overall merits. The TW-UAV's design and structural properties are mathematically modeled and utilized for the synthesis of a cascaded P-PI and PID based control structure for the regulation of its hovering performance. In addition, extensive simulation trials are performed in order to evaluate the structure's efficiency in controlling the TW-UAV's attitude and position under various noise and disturbance scenarios.


title: Mel Frequency Cepstral Coefficients for Music Modeling

abstract: We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spectral vectors. We examine the first assumption in the context of speech/music discrimination. Our results show that the use of the Mel scale for modeling music is at least not harmful for this problem, although further experimentation is needed to verify that this is the optimal scale in the general case. We investigate the second assumption by examining the basis vectors of the theoretically optimal transform to decorrelate music and speech spectral vectors. Our results demonstrate that the use of the DCT to decorrelate vectors is appropriate for both speech and music spectra. MFCCs for Music Analysis Of all the human generated sounds which influence our lives, speech and music are arguably the most prolific. Speech has received much focused attention and decades of research in this community have led to usable systems and convergence of the features used for speech analysis. In the music community however, although the field of synthesis is very mature, a dominant paradigm has yet to emerge to solve other problems such as music classification or transcription. Consequently, many representations for music have been proposed (e.g. (Martin1998), (Scheirer1997), (Blum1999)). In this paper, we examine some of the assumptions of Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and examine whether these assumptions are valid for modeling music. MFCCs have been used by other authors to model music and audio sounds (e.g. (Blum1999)). These works however use cepstral features merely because they have been so successful for speech recognition without examining the assumptions made in great detail. MFCCs (e.g. see (Rabiner1993)) are short-term spectral features. They are calculated as follows (the steps and assumptions made are explained in more detail in the full paper): 1. Divide signal into frames. 2. For each frame, obtain the amplitude spectrum. 3. Take the logarithm. 4. Convert to Mel (a perceptually-based) spectrum. 5. Take the discrete cosine transform (DCT). We seek to determine whether this process is suitable for creating features to model music. We examine only steps 4 and 5 since, as explained in the full paper, the other steps are less controversial. Step 4 calculates the log amplitude spectrum on the so-called Mel scale. This transformation emphasizes lower frequencies which are perceptually more meaningful for speech. It is possible however that the Mel scale may not be optimal for music as there may be more information in say higher frequencies. Step 5 takes the DCT of the Mel spectra. For speech, this approximates principal components analysis (PCA) which decorrelates the components of the feature vectors. We investigate whether this transform is valid for music spectra. Mel vs Linear Spectral Modeling To investigate the effect of using the Mel scale, we examine the performance of a simple speech/music discriminator. We use around 3 hours of labeled data from a broadcast news show, divided into 2 hours of training data and 40 minutes of testing data. We convert the data to ‘Mel’ and ‘Linear’ cepstral features and train mixture of Gaussian classifiers for each class. We then classify each segment in the test data using these models. This process is described in more detail in the full paper. We find that for this speech/music classification problem, the results are (statistically) significantly better if Mel-based cepstral features rather than linear-based cepstral features are used. However, whether this is simply because the Mel scale models speech better or because it also models music better is not clear. At worst, we can conclude that using the Mel cepstrum to model music in this speech/music discrimination problem is not harmful. Further tests are needed to verify that the Mel cepstrum is appropriate for modeling music in the general case. Using the DCT to Approximate Principal Components Analysis We additionally investigate the effectiveness of using the DCT to decorrelate Mel spectral features. The mathematically correct way to decorrelate components is to use PCA (or equivalently the KL transform). This transform uses the eigenvalues of the covariance matrix of the data to be modeled as basis vectors. By investigating how closely these vectors approximate cosine functions we can get a feel for how well the DCT approximates PCA. By inspecting the eigenvectors for the Mel log spectra for around 3 hours of speech and 4 hours of music we see that the DCT is an appropriate transform for decorrelating music (and speech) log spectra. Future Work Future work should focus on a more thorough examination the parameters used to generate MFCC features such as the sampling rate of the signal, the frequency scaling (Mel or otherwise) and the number of bins to use when smoothing. Also worthy of investigation is the windowing size and frame rate. Suggested Readings Blum, T, Keislar, D., Wheaton, J. and Wold, E., 1999, Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information, U.S. Patent 5, 918, 223. Martin, K.. 1998, Toward automatic sound source recognition: identifying musical instruments, Proceedings NATO Computational Hearing Advanced Study Institute. Rabiner, L. and Juang, B., 1993, Fundamentals of Speech Recognition, Prentice-Hall. Scheirer, E. and Slaney, M., 1997, Construction and evaluation of a robust multifeature speech/music discriminator, Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing.


title: Object Classification in a High-Level Sensor Data Fusion Architecture for Advanced Driver Assistance Systems

abstract: Reliable estimation of an object's type is an important aspect of advanced driver assistance systems (ADAS) and automated driving applications. A type-specific ADAS reaction or object prediction can therefore be realized, improving the performance of the system. Object detection research usually focuses strongly on the state and existence estimation of detected objects. In this paper, an approach is presented for estimating an the class type of an object within the framework of a high-level sensor data fusion architecture. A novel classification fusion approach using the Dempster-Shafer evidence theory is presented. The performance of the algorithms are evaluated using a test vehicle with 12 sensors for surround environment perception in an overtaking scenario on a closed test track and on the highway in real traffic.


title: IL-33-dependent group 2 innate lymphoid cells promote cutaneous wound healing

abstract: Breaches in the skin barrier initiate an inflammatory immune response that is critical for successful wound healing. Innate lymphoid cells (ILCs) are a recently identified population of immune cells that reside at epithelial barrier surfaces such as the skin, lung, and gut, and promote proinflammatory or epithelial repair functions after exposure to allergens, pathogens, or chemical irritants. However, the potential role of ILCs in regulating cutaneous wound healing remains undefined. Here, we demonstrate that cutaneous injury promotes an IL-33-dependent group 2 ILC (ILC2) response and that abrogation of this response impairs re-epithelialization and efficient wound closure. In addition, we provide evidence suggesting that an analogous ILC2 response is operational in acute wounds of human skin. Together, these results indicate that IL-33-responsive ILC2s are an important link between the cutaneous epithelium and the immune system, acting to promote the restoration of skin integrity after injury.


title: Texture overlay for virtual clothing based on PCA of silhouettes

abstract: In this paper, we propose a method for overlaying an arbitrary texture image onto a surface of a plain T-shirt worn by a user. For overlaying arbitrary textures onto the surface of the T-shirt, we need to know the deformation of the surface. For estimating the deformation of the surface from the input images, we use a two-phase process: learning and searching. In the learning phase, the system learns the relationship between the deformation of the surface and the silhouette of the T-shirt region in the image. A database of a number of training images in which a person wearing a T-shirt with markers moves through a variety of positions is used for this learning. Using the database, the system can learn the relationship between the shape of the silhouette and the surface deformation that is provided by the 2D positions of the markers on the surface of the T-shirt. In the searching phase, the silhouette of the user's T-shirt is extracted from the input image, and then, a search for a similar silhouette in the database is conducted in the subspace of the silhouette, which is computed using a PCA of the database. By using the proposed method for estimating the deformation of the surface of the T-shirt, we perform experiments for overlaying virtual clothing.


title: Full-bridge current-fed inverter with automatic frequency control for forging application

abstract: The paper describes a full-bridge current-fed inverter for forging applications. Inverter is implemented by using IGBT in series with diodes as its switching devices. The operating frequency is automatically tracked to maintain a small constant leading phase angle when load parameters change. The load voltage is controlled to protect the switches. The output power can be adjusted by varying the input current from phase controlled rectifiers which is a part of current source. The system has been operated at 15.5-16.8 kHz. The output power transferred to the load is 1,595 watts. It can heat the steel work pieces with 15 mm diameter and 120 mm long from room temperature to approximately 1100/spl deg/C within 30 seconds with 0.97 leading power factors on the input side.


Broadband bow-tie antenna with tapered balun


title: A learning algorithm for Boltzmann Machines

abstract: The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in a very short time. One kind of computation for which massively parallel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a general parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. We describe some simple examples in which the learning algorithm creates internal representations that are demonstrably the most efficient way of using the preexisting connectivity structure.


title: Today ’ s State-Owned Enterprises of China : Are They Dying Dinosaurs or Dynamic Dynamos ?

abstract: This paper raises the question and provides empirical evidence regarding the status of the evolution of the state-owned enterprises (SOEs) in China today. In this study, we compare the SOEs to domestic private-owned enterprises (POEs) and foreign-controlled businesses (FCBs) in the context of their organizational cultures. While a new ownership form, many of the POEs evolved from former collectives that reflect the traditional values of Chinese business. Conversely, the FCBs are much more indicative of the large global MNCs. Therefore, we look at the SOEs in the context of these two reference points. We conclude that the SOEs of today have substantially transformed to approximate a configuration desired by the Chinese government when it began the SOE transformation a couple of decades ago to make them globally competitive. The SOEs of today appear to be appropriately described as China's economic dynamic dynamo for the future. Copyright © 2006 John Wiley & Sons, Ltd.


IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis


Chapter 17 Phenolic Compounds in Water : Sources , Reactivity , Toxicity and Treatment Methods


Evaluation of Error Detection Strategies for an FPGA-Based Self-Checking Arithmetic and Logic Unit


title: Regression modeling of competing risk using R: an in depth guide for clinicians

abstract: We describe how to conduct a regression analysis for competing risks data. The use of an add-on package for the R statistical software is described, which allows for the estimation of the semiparametric proportional hazards model for the subdistribution of a competing risk analysis as proposed by Fine and Gray. J Am Stat Assoc 1999; 94: 496–509.


title: Mining triadic closure patterns in social networks

abstract: A closed triad is a group of three people who are connected with each other. It is the most basic unit for studying group phenomena in social networks. In this paper, we study how closed triads are formed in dynamic networks. More specifically, given three persons, what are the fundamental factors that trigger the formation of triadic closure? There are various factors that may influence the formation of a relationship between persons. Can we design a unified model to predict the formation of triadic closure? Employing a large microblogging network as the source in our study, we formally define the problem and conduct a systematic investigation. The study uncovers how user demographics and network topology influence the process of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in dynamic networks. The experimental results on the microblogging data demonstrate the efficiency of our proposed model for the prediction of triadic closure formation.


title: Discovering routines from large-scale human locations using probabilistic topic models

abstract: In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.


title: Inductorless DC-AC Cascaded H-Bridge Multilevel Boost Inverter for Electric/Hybrid Electric Vehicle Applications

abstract: This paper presents an inductorless cascaded H- bridge multilevel boost inverter for EV and HEV applications. Currently available power inverter systems for HEVs use a DC- DC boost converter to boost the battery voltage for a traditional 3-phase inverter. The present HEV traction drive inverters have low power density, are expensive, and have low efficiency because they need a bulky inductor. An inductorless cascaded H-bridge multilevel boost inverter for EV and HEV applications is proposed in this paper. Traditionally, each H-bridge needs a DC power supply. The proposed inductorless cascaded H-bridge multilevel boost inverter uses a standard 3-leg inverter (one leg for each phase) and an H-bridge in series with each inverter leg which uses a capacitor as the DC power source. Fundamental switching scheme is used to do modulation control and to produce a 5-level phase voltage. Experiments show that the proposed inductorless DC-AC cascaded H-bridge multilevel boost inverter can output a boosted AC voltage.


title: Tracking-based deer vehicle collision detection using thermal imaging

abstract: Deer vehicle collision (DVC) is constantly a major safety issue for the driving on rural road. It is estimated that there are over 35,000 DVCs yearly in the US resulting in about 200 deaths and close to 4,000 reported property damages of one thousand dollars or more. This justifies many attempts trying to detect deer on road. However, very little success has been achieved. In order to reduce the number of DVCs, this work focused on the study of using an infrared thermal camera with tracking system to detect the presence of deer to avoid DVCs. The prototype consists of an infrared thermal temperature image grabbing and processing system, which includes an infrared thermal camera, a frame grabber, an image processing system and a motion tracking system, which includes two motors with their motion control system. By analyzing the infrared thermal images which are independent of visible light, the presence of an animal can be determined in either night or day time through pattern recognition and matching.


title: H.263+: Video Coding at Low Bit Rates

abstract: We discuss the ITU-T H.263+ (or H.263 Version 2) low-bit-rate video coding standard. We first describe, briefly, the H.263 standard including its optional modes. We then address the 12 new negotiable modes of H.263+. Next, we present experimental results for these modes, based on our public-domain implementation (http:ilspmg.ece.ubc.ca). Tradeoffs among compression performance, complexity, and memory requirements for the H.263+ optional modes are discussed. Finally, results for mode combinations are presented.


title: Preliminary genetic analysis of koaro (Galaxias brevipinnis) in New Zealand lakes: Evidence for allopatric differentiation among lakes but little population subdivision within lakes

abstract: Abstract Lake systems are often notable for the diversification of freshwater fish species. This has yet to be documented in New Zealand, although a possible case has been described in Galaxias brevipinnis from Lake Okareka, where lake‐ and stream‐resident fish show morphological and ecological differences. Samples of G. brevipinnis collected from three New Zealand lakes (Wakatipu, Wanaka, and Okareka) were examined using microsatellite DNA analysis to look for evidence of genetic structuring among samples within lakes (possibly indicative of incipient sympatric speciation or natal stream loyalty). Comparison among lakes was made to provide a context for genetic structuring within lakes. There was evidence of genetic differentiation among lake populations, reflecting original founder effect and subsequent genetic drift. However, there was little evidence of restricted gene flow among samples from different parts of lakes and their tributaries. Namely, there is no genetic evidence of incipient speciation i...


title: Whisper to normal speech conversion using pitch estimated from spectrum

abstract: Abstract We can perceive pitch in whispered speech, although fundamental frequency (F0) does not exist physically or phonetically due to the lack of vocal-fold vibration. This study was carried out to determine how people generate such an unvoiced pitch. We conducted experiments in which speakers uttered five whispered Japanese vowels in accordance with the pitch of a guide pure tone. From the results, we derived a multiple regression function to convert the outputs of a mel-scaled filter bank of whispered speech into the perceived pitch value. Next, using this estimated pitch value as F0, we constructed a system for conversion of whispered speech to normal speech. Since the pitch varies with time according to the spectral shape, it was expected that the pitch accent would be kept by this conversion. Indeed, auditory experiments demonstrated that the correctly perceived rate of Japanese word accent was increased from 55.5% to 72.0% compared with that when a constant F0 was used.


title: Hepcidin the link between inflammation and anemia in chronic renal failure.

abstract: A chronic inflammatory state characterized the patients with chronic renal failure. The causes of this situation are multiple and inter-connected. The break of the equilibrium between pro-inflammatory agents and anti-inflammatory ones in haemodialysis patients leads to anemia of chronic inflammation, characterized by a functional iron deficiency. Very recently it was discovered a peptide produced by the liver, named hepcidin, that provides a good link between inflammation and metabolism of iron, rendering more understandable the interdependence of inflammation and anemia.


title: Structured Learning Modulo Theories

abstract: Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model this kind of domains is crucial in"learning to design"tasks, that is, learning applications where the goal is to learn from examples how to perform automatic {\em de novo} design of novel objects. In this paper we present Structured Learning Modulo Theories, a max-margin approach for learning in hybrid domains based on Satisfiability Modulo Theories, which allows to combine Boolean reasoning and optimization over continuous linear arithmetical constraints. The main idea is to leverage a state-of-the-art generalized Satisfiability Modulo Theory solver for implementing the inference and separation oracles of Structured Output SVMs. We validate our method on artificial and real world scenarios.


title: ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation

abstract: We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time. Generalization to new objects never observed during training is known to be a hard task for supervised approaches that would need to be retrained. To tackle this problem, we propose a more efficient solution that learns spatio-temporal features self-adapting to the object of interest via conditional affine transformations. This approach is simple, can be trained end-to-end and does not necessarily require extra training steps at inference time. Our method shows competitive results on DAVIS2016 with respect to state-of-the art approaches that use online fine-tuning, and outperforms them on DAVIS2017. ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the $10$-th position.


title: A novel statistical measure for sequence comparison on the basis of k-word counts.

abstract: Numerous efficient methods based on word counts for sequence analysis have been proposed to characterize DNA sequences to help in comparison, retrieval from the databases and reconstructing evolutionary relations. However, most of them seem unrelated to any intrinsic characteristics of DNA. In this paper, we proposed a novel statistical measure for sequence comparison on the basis of k-word counts. This new measure removed the influence of sequences' lengths and uncovered bulk property of DNA sequences. The proposed measure was tested by similarity search and phylogenetic analysis. The experimental assessment demonstrated that our similarity measure was efficient.


title: A Comparative Study of Metastatic Renal Cell Carcinoma with Correlation to Subtype and Primary Tumor

abstract: Summary Clear cell (CRCC), papillary (PRCC) and chromophobe (CHRC) renal cell carcinoma (RCC) are the three most frequent subtypes of RCC. The rate and distribution of their metastatic lesions have not been well documented. We compared metastatic RCC according to subtype and primary tumor characteristics to better understand their behavior and to aid in the diagnosis of metastatic RCC. Pathology reports and clinical charts related to 283 CRCC, 48 PRCC and 13 CHRCC, including their respective sarcomatoid variants, were reviewed. A hundred and thirty-seven CRCC, 5 PRCC and 1 CHRCC with metastases were identified. CRCC and non-CRCC (PRCC and CHRCC) had different patterns of metastasis and primary tumor growth. CRCC metastases were predominantly distributed in lungs, bone, brain, lymph nodes, and adrenal glands. The associated primary CRCC measured 1.5 to 15 cm, were of all grades and stages, and were often associated with invasion of small or large veins. Three PRCC had regional lymph node metastases, 1 PRCC had both regional and mediastinal lymph node metastases. Bone metastasis was present in 1 case each of PRCC and CHRCC. One PRCC with metastasis solely to regional nodes measured 4 cm. The other 4 cases of PRCC with regional lymph node and/or distant metastases as well as the CHRCC with distant metastases were greater than 8 cm in diameter. In metastasizing and non-metastasizing non-CRCC, invasion of small veins was rare and invasion of renal veins was not seen. We cannot comment with any certainty on the metastatic behavior of CHRCC. In our experience, PRCC tend to loco-regional invasion with lymph node spread. They have a low potential for vascular invasion and distant metastases that likely occur only at late stages of the disease. CRCC has a propensity for vascular invasion and may be associated with distant metastasis at an early stage. Therefore, metastatic RCC at a distant location are most likely to be of CRCC origin than PRCC origin.


title: Large vocabulary continuous speech recognition with context-dependent DBN-HMMS

abstract: The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a context-dependent DBN-HMM system that dramatically outperforms strong Gaussian mixture model (GMM)-HMM baselines on a challenging, large vocabulary, spontaneous speech recognition dataset from the Bing mobile voice search task. Our system achieves absolute sentence accuracy improvements of 5.8% and 9.2% over GMM-HMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively, which translate to relative error reductions of 16.0% and 23.2%.


title: Unsupervised object learning from dense equivariant image labelling

abstract: One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations. Starting from the recent idea of viewpoint factorization, we propose a new approach that, given a large number of images of an object and no other supervision, can extract a dense object-centric coordinate frame. This coordinate frame is invariant to deformations of the images and comes with a dense equivariant labelling neural network that can map image pixels to their corresponding object coordinates. We demonstrate the applicability of this method to simple articulated objects and deformable objects such as human faces, learning embeddings from random synthetic transformations or optical flow correspondences, all without any manual supervision.


title: New cardinality estimation algorithms for HyperLogLog sketches

abstract: This paper presents new methods to estimate the cardinalities of data sets recorded by HyperLogLog sketches. A theoretically motivated extension to the original estimator is presented that eliminates the bias for small and large cardinalities. Based on the maximum likelihood principle a second unbiased method is derived together with a robust and efficient numerical algorithm to calculate the estimate. The maximum likelihood approach can also be applied to more than a single HyperLogLog sketch. In particular, it is shown that it gives more precise cardinality estimates for union, intersection, or relative complements of two sets that are both represented by HyperLogLog sketches compared to the conventional technique using the inclusion-exclusion principle. All the new methods are demonstrated and verified by extensive simulations.


title: Laser-based obstacle avoidance algorithm for four driving/steering wheels autonomous vehicle

abstract: In this paper an algorithm for trajectory-tracking with obstacle avoidance for autonomous vehicles is presented. The algorithm tracks a trajectory composed of an intended global trajectory that was previously generated and local obstacle avoidance trajectories that are created when an obstacle is detected by the laser. A discrete-time sliding-mode controller is used by the autonomous vehicle to follow the global trajectory and the local obstacle avoidance trajectories. Quintic equations are used to generate the paths used by the vehicle to avoid the obstacles. The velocity, acceleration, angular velocity and angular acceleration profiles are calculated for the generated paths and fed to the sliding-mode controller.


title: Unsupervised feature selection for linked social media data

abstract: The prevalent use of social media produces mountains of unlabeled, high-dimensional data. Feature selection has been shown effective in dealing with high-dimensional data for efficient data mining. Feature selection for unlabeled data remains a challenging task due to the absence of label information by which the feature relevance can be assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, (e.g., part of social media data is linked, which makes invalid the independent and identically distributed assumption), bringing about new challenges to traditional unsupervised feature selection algorithms. In this paper, we study the differences between social media data and traditional attribute-value data, investigate if the relations revealed in linked data can be used to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We perform experiments with real-world social media datasets to evaluate the effectiveness of the proposed framework and probe the working of its key components.


title: Discrete Event Dyn Syst (2007) 17:1–21 DOI 10.1007/s10626-006-0005-9 A Location-free Algorithm of Energy-Efficient Connected Coverage for High Density Wireless Sensor Networks

abstract: One of the most serious concerns for wireless sensor networks (WSN) is energy. To obtain long lifetime, one potential method is deploying redundant sensors in the WSN and let each sensor switch its state between ACTIVE and OFF. At the same time, the WSN should meet various requirements of quality of service (QoS). This paper focuses on two important measurements of OoS: sensing coverage and network connectivity. Existing researches have provided many algorithms to schedule the nodes' states for coverage preserving, including location-free ones. Our work differs from existing location-free algorithms in several key ways: (1). We propose a novel principle on coverage preserving that is suitable for location-free schemes; (2). Following the principle, a distributed, localized and location-free node scheduling algorithm, Stand Guard Algorithm (StanGA), is proposed for treating coverage and connectivity in a unified scheme. Under certain conditions, StanGA can guarantee network connectivity and any degree of sensing coverage. Simulation results show that StanGA is scalable and robust, and also show that StanGA outperforms existing location-free algorithms even when only coverage issue is considered.


title: Evaluation of games for teaching computer science

abstract: Well-designed educational games (both online and offline) can be a valuable way to engage students with a topic. However, games can also be a time-consuming distraction, so it is useful to be able to identify which are suitable. This paper provides guidelines for evaluating games, and applies them to identify promising games. We begin by defining what is meant by a game, ruling out simple animations or quizzes, and focusing on a more playful approach to education. Based on this definition, we have identified games that are available for teaching topics in computer science, and classified them. We have developed criteria for evaluating games based on existing research on educational games, combined with educational theory and considering them in the context of CS. The criteria developed cover the topics taught, how easy it is to install and run, how engaging the game is, and how much time students might invest to use the game. We find that although there are a number of worth-while games in the field of CS and most are free, the coverage of topics is very patchy, with most focused on programming concepts and binary numbers.


title: Multi-lane detection based on accurate geometric lane estimation in highway scenarios

abstract: Multi-lane detection algorithms have been required for various vehicle safety-related applications. In most of the previous study, visual features are fundamental clues for multi-lane detection. However, since visual features vary with the illumination, weather condition and distance of the region, feature-based algorithm is restricted to the illuminant variation and laterally adjacent regions. On the other hand, conventional geometric estimation-based approaches, not relying on visual features, are inaccurate and susceptible to pitch or lateral movement. In this paper, we propose a robust multi-lane detection algorithm based on the accurate geometric estimation in highway scenarios. With the steps of adjacent lanes hypothesis generation (HG) and hypothesis verification (HV), the algorithm detects successfully independent of environmental changes. For accurate adjacent lane HG, we adopt the `cross ratio' and propose `dynamic homography matrix estimation.' Our approach is independent of the calibration, pitch angle changes and additional vehicle sensors. In addition, the proposed algorithm can covers six lanes including the driving lane and adjacent lanes that two-lanes away from the driving lane. We demonstrate robustness on the illumination variance including daytime, rainy and sunset by using trough simulations and video sequences with a resolution of 752 × 480.


title: 3D Grand Tour for Multidimensional Data and Clusters

abstract: Grand tour is a method for viewing multidimensional data via linear projections onto a sequence of two dimensional subspaces and then moving continuously from one projection to the next. This paper extends the method to 3D grand tour where projections are made onto three dimensional subspaces. 3D cluster-guided tour is proposed where sequences of projections are determined by cluster centroids. Cluster-guided tour makes inter-cluster distance-preserving projections under which clusters are displayed as separate as possible. Various add-on features, such as projecting variable vectors together with data points, interactive picking and drill down, and cluster similarity graphs, help further the understanding of data. A CAVE virtual reality environment is at our disposal for 3D immersive display. This approach of multidimensional visualization provides a natural metaphor to visualize clustering results and data at hand by mapping the data onto a time-indexed family of 3D natural projections suitable for human eye's exploration.


title: A fast regular expression indexing engine

abstract: In this paper; we describe the design, architecture, and lessons learned from the implementation of a fast regular-expression indexing engine FREE. FREE uses a prebuilt index to identify the text data units which may contain a matching string and only examines these further. In this way, FREE shows orders of magnitude performance improvement in certain cases over standard regular expression matching systems, such as lex, awk and grep.


title: Finding expert users in community question answering

abstract: Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.


Natural Language Processing techniques in Text-To-Speech synthesis and Automatic Speech Recognition


Multitask learning for mental health conditions with limited social media data


title: Java Simulation Library (JSL): an open-source object-oriented library for discrete-event simulation in Java

abstract: This paper describes the design and functionality of an open-source object-oriented library for executing discrete-event simulation models in the Java programming language. The structure of the library is described in terms of its packages, class structure, and functionalities. The purpose of this paper is to provide an understanding of the library so that practitioners and researchers can better use the library for simulation modelling. In addition, the expository nature of this paper can help new users or students learn the basics of discrete-event simulation from the library's design. The capabilities of the library are illustrated through a number of source-code examples.


title: Extended Isomap for Pattern Classification

abstract: The Isomap method has demonstrated promising results in finding low dimensional manifolds from data points in the high dimensional input space. While classical subspace methods use Euclidean or Manhattan metrics to represent distances between data points and apply Principal Component Analysis to induce linear manifolds, the Isomap method estimates geodesic distances between data points and then uses Multi-Dimensional Scaling to induce low dimensional manifolds. Since the Isomap method is developed based on reconstruction principle, it may not be optimal from the classification viewpoint. In this paper, we present an extended Isomap method that utilizes Fisher Linear Discriminant for pattern classification. Numerous experiments on image data sets show that our extension is more effective than the original Isomap method for pattern classification. Furthermore, the extended Isomap method shows promising results compared with best methods in the face recognition literature.


title: DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

abstract: Life science is entering a new era of petabyte-level sequencing data. Converting such big data to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed ( 100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.


title: Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

abstract: Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.


title: Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features

abstract: The European standard for access control imposes stringent performance requirements on commercial biometric technologies that few existing recognition systems are able to meet. In this correspondence paper, we present the first mouse dynamics biometric recognition system that fulfills this standard. The proposed system achieves notable performance improvement by developing separate models for separate feature groups involved. The improvements are achieved through the use of a fuzzy classification based on the Learning Algorithm for Multivariate Data Analysis and using a score-level fusion scheme to merge corresponding biometric scores. Evaluation of the proposed framework using mouse data from 48 users achieves a false acceptance rate of 0% and a false rejection rate of 0.36%.


title: A General PWM Strategy for Four-Switch Three-Phase Inverters

abstract: A general pulsewidth modulation (PWM) method for control of four-switch three-phase inverters is presented. The proposed vector PWM offers a simple method to select three or four vectors that effectively synthesize the desired output voltage, even in presence of voltage oscillations across the two dc-link capacitors. The method utilizes the so called space vector modulation, and includes its scalar version. Different vector combinations are compared. The effect of Wye and delta motor winding connections over the pulse width modulator is also considered. The common mode voltage generated by the four-switch three-phase converter is evaluated and compared to that provided by the standard six-switch three-phase inverter. Simulation and experimental results are presented to demonstrate the feasibility of the proposed approach


title: A New Rectenna With All-Polarization-Receiving Capability for Wireless Power Transmission

abstract: This letter presents a new rectenna with all-polarization-receiving capability for wireless power transmission applications. By utilizing a dual linearly-polarized antenna, the incident wave of arbitrary polarization can be totally collected at its two ports. To be connected with the antenna as a rectenna, a dual-input rectifier is designed so that the RF power supplied by the two ports of the antenna can be efficiently rectified. With a proper polarization of the incident wave, the proposed rectenna exhibits a maximum efficiency of 78% under the input power density of 295.3 $~\mu\hbox{W/cm}^{2}$ . Moreover, under the same power density, the rectenna’s efficiency can always stay higher than 61% regardless of the incident wave’s polarization.


A New Approach to Animacy Detection


title: Multichannel-Sniffing-System for Real-World Analysing of Wi-Fi-Packets

abstract: Wireless technologies like Wi-Fi send their data using multiple channels. To analyze an environment and all Wi-Fi packets inside, a sniffing system is needed, which can sniff on all used channels of the wireless technology at the same time. This allows catching most packets on each channel. In this paper, a way to build up a multi-channel-sniffing-system (MCSS) is described. The test system uses several single board computers (SBC) with an external Wi-Fi adapter (USB), 19 SBCs are sniffing nodes (SFN) and one SBC as sending node (SN). The sniffing SBCs are placed in a cycle around the sender so that every node has the same chance to receive the simulated packets from the SN. For the control of all 20 SBCs, a self-developed software is used, which connects from the host to the clients and is used for configuring the experiments. The configuration is sent to each client and will initiate their start, so that their times are also synchronized, for this all clients are synchronised using a time


title: Using decision tree for diagnosing heart disease patients

abstract: Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. Decision Tree is one of the successful data mining techniques used. However, most research has applied J4.8 Decision Tree, based on Gain Ratio and binary discretization. Gini Index and Information Gain are two other successful types of Decision Trees that are less used in the diagnosis of heart disease. Also other discretization techniques, voting method, and reduced error pruning are known to produce more accurate Decision Trees. This research investigates applying a range of techniques to different types of Decision Trees seeking better performance in heart disease diagnosis. A widely used benchmark data set is used in this research. To evaluate the performance of the alternative Decision Trees the sensitivity, specificity, and accuracy are calculated. The research proposes a model that outperforms J4.8 Decision Tree and Bagging algorithm in the diagnosis of heart disease patients.


title: Wormhole: A Fast Ordered Index for In-memory Data Management

abstract: In-memory data management systems, such as key-value stores, have become an essential infrastructure in today's big-data processing and cloud computing. They rely on efficient index structures to access data. While unordered indexes, such as hash tables, can perform point search with O(1) time, they cannot be used in many scenarios where range queries must be supported. Many ordered indexes, such as B+ tree and skip list, have a O(log N) lookup cost, where N is number of keys in an index. For an ordered index hosting billions of keys, it may take more than 30 key-comparisons in a lookup, which is an order of magnitude more expensive than that on a hash table. With availability of large memory and fast network in today's data centers, this O(log N) time is taking a heavy toll on applications that rely on ordered indexes. In this paper we introduce a new ordered index structure, named Wormhole, that takes O(log L) worst-case time for looking up a key with a length of L. The low cost is achieved by simultaneously leveraging strengths of three indexing structures, namely hash table, prefix tree, and B+ tree, to orchestrate a single fast ordered index. Wormhole's range operations can be performed by a linear scan of a list after an initial lookup. This improvement of access efficiency does not come at a price of compromised space efficiency. Instead, Wormhole's index space is comparable to those of B+ tree and skip list. Experiment results show that Wormhole outperforms skip list, B+ tree, ART, and Masstree by up to 8.4x, 4.9x, 4.3x, and 6.6x in terms of key lookup throughput, respectively.


title: Gait algorithm of personal mobility vehicle for negotiating obstacles.

abstract: PURPOSE ::: Personal mobility vehicles (PMVs) are under active development. Most PMVs are wheel-driven, a mode of transport notable for its efficiency. However, wheeled PMVs tend to have poor mobility against negotiating obstacles. The four-wheeled vehicle RT-Mover PType 3 has been developed featuring wheeled legs capable of leg motion. This allows the PMV to overcome uneven terrains, including a step approached at an angle, which ordinary wheelchairs cannot negotiate. ::: ::: ::: METHOD ::: This article discusses a gait algorithm in which a leg executes the necessary leg motion when optionally presented with obstacles on a road. In order to lift a wheel off the ground and perform a leg motion, the support wheels must be moved to support points to ensure that the vehicle remains stable on three wheels. When moving towards the target support point, a wheel may encounter another obstacle, and a response method for this case is also described. ::: ::: ::: RESULTS ::: To assess the gait algorithm, several configurations of obstacles were used for performance tests with a passenger. The capabilities of the PMV were demonstrated through experiments. ::: ::: ::: CONCLUSION ::: We proposed a novel gait algorithm for our PMV and realised the proposed motion pattern for PMV-based negotiating obstacles. ::: ::: ::: IMPLICATIONS FOR REHABILITATION ::: Our single-seat personal mobility vehicle, RT-Mover PType 3 features wheels attached on legs capable of performing leg motion, which allows the vehicle to traverse rough terrains in urban areas. We proposed a gait algorithm for RT-Mover PType 3 consisting of a series of leg motions in response to rough terrain. With this algorithm, the vehicle can traverse not only randomly placed obstacles, but also a step approached at an oblique angle, which conventional powered wheelchairs cannot navigate. Experiments with a passenger demonstrated the effectiveness of the proposed gait algorithm, suggesting that RT-Mover PType 3 can expand the mobility and range of activities of wheelchair users.


title: A study of IoT-based post-disaster management

abstract: In recent decades, serious natural or man-made disasters such as earthquakes, floods, tsunamis, and 9/11 have occurred with catastrophic consequences. To minimize the damage or loss of lives in the aftermath of a disaster, it is important that rescuers are able to track the trapped victims and perform coordinated relief efforts immediately. The traditional telecommunication system (e.g. a landline or cellular network) may be either partially or entirely damaged by a disaster incident. Internet of Things (IoT) is a promising technology that can be used to solve some of the problems mentioned above. To date, the application of IoT in post-disaster management is still an unexplored problem. The objective of this paper is to study the IoT-based proposals for post-disaster management framework. It discusses the key enabling technologies for IoT and provides an insight into IoT-based post-disaster response and recovery. A new algorithm (flowchart) for an ad hoc network formation linked to device-to-device communications towards a robust post-disaster management framework is presented.


title: A New Distance Measure for Face Recognition System

abstract: This paper proposes a new powerful distance measure called Normalized Unmatched Points (NUP). This measure can be used in a face recognition system to discriminate facial images. It works by counting the number of unmatched pixels between query and database images. A face recognition system has been proposed which makes use of this proposed distance measure for taking the decision on matching. This system has been tested on four publicly available databases, viz. ORL, YALE, BERN and CALTECH databases. Experimental results show that the proposed measure achieves recognition rates more than 98.66% for the first five likely matched faces. It is observed that the NUP distance measure performs better than other existing similar variants on these databases.


Advanced Persistent Threat Detection using Malware Infection


title: The Role of the Hippocampus in Prediction and Imagination

abstract: Traditionally, the hippocampal system has been studied in relation to the goal of retrieving memories about the past. Recent work in humans and rodents suggests that the hippocampal system may be better understood as a system that facilitates predictions about upcoming events. The hippocampus and associated cortical structures are active when people envision future events, and damage that includes the hippocampal region impairs this ability. In rats, hippocampal ensembles preplay and replay event sequences in the absence of overt behavior. If strung together in novel combinations, these sequences could provide the neural building blocks for simulating upcoming events during decision-making, planning, and when imagining novel scenarios. Moreover, in both humans and rodents, the hippocampal system is spontaneously active during task-free epochs and sleep, further suggesting that the system may use idle moments to derive new representations that set the context for future behaviors.


title: Property Asset Bubbles: Evidence from the Sydney Office Market

abstract: The cyclical variation in office construction, vacancies, rents, and values over the last decade has been enormous throughout the world. Reasons advanced for this enormity include prolifigate lenders, egotistical developers, and even rational behavior in the face of uncertainly and long construction periods. Our analysis of the Sydney office market suggests a fourth contributing factor: the failure of investors to understand the workings of property markets. Given the incentives of developers to build when value rises substantially above replacement cost and not to build when value is low relative to replacement cost, the property market has to be mean reverting. We provide direct evidence that Sydney investors did not incorporate mean reversion into their vacancy rate forecasts at the cyclical trough and as a result under valued properties. We provide indirect evidence that mean reversion of cash flows was not incorporated at the cyclical peak and that this triggered excessive construction and vacancies. That is, the Sydney office market in the late 1980s is another example of “excess price volatility” or an asset price bubble.


title: CONCEPT LEVEL NAVAL SURFACE COMBATANT DESIGN IN THE AXIOMATIC APPROACH TO DESIGN FRAMEWORK

abstract: The design of ships is an inherently complex process. This complexity is significantly increased when the particular ship being designed is a naval surface combatant. Naval combatant designers, or more appropriately, design teams, must not only address the factors common to all seagoing vessels such as hull form, propulsion, and maneuverability, but additionally must consider the selection, placement, and interaction of sophisticated weapons systems and sensors. The ship design process is traditionally viewed as a highly coupled collection of interrelated physical attributes often determined in an ad hoc fashion. Therefore, lack of understanding and documenting the design progression frequently necessitates modification of a completely developed, functionally acceptable portion of the ship because of its adverse effect on other functionally unrelated parameters. This paper proposes a methodology based on axiomatic design principles that strives to eliminate the currently accepted iterative nature of concept level ship design. By implementing this approach, the ship design process follows a repeatable structured format in which functional relationships between physical parameters are mapped, documented, and controlled. The AAD method is applied to a ship synthesis model, and a new ship design process is defined and coded to illustrate the utility of the method.