title: Bangla Parts-of-Speech tagging using Bangla stemmer and rule based analyzer

abstract: Parts-of-Speech (POS) tagging plays vital roles in the field of Natural Language Processing (NLP), such as — machine translation, spell checker, information retrieval, speech processing, emotion analysis and so on. Bangla is a very inflectional language that induces many variants from a single word. Although there is a few POS Tagger in Bangla language, very small of them address the essence of suffices to identify tag of the words. In this regard, we propose an automated POS Tagging system for Bangla language based on word-suffixes. In our system, we use our own stemming technique to retrieve a possible minimum root words and apply rules according to different forms of suffixes. Moreover, we incorporate a Bangla vocabulary that contains more than 45,000 words with their default tag and a patterned based verb-data-set. These facilitate to improve tagging efficiency of Bangla POS Tagger. We experiment our proposed system on a Bangla text corpus. The result shows that our proposed Bangla POS Tagger has outperformed the known related tagging systems.


title: Ensuring operating system kernel integrity with OSck

abstract: Kernel rootkits that modify operating system state to avoid detection are a dangerous threat to system security. This paper presents OSck, a system that discovers kernel rootkits by detecting malicious modifications to operating system data. OSck integrates and extends existing techniques for detecting rootkits, and verifies safety properties for large portions of the kernel heap with minimal overhead. We deduce type information for verification by analyzing unmodified kernel source code and in-memory kernel data structures. High-performance integrity checks that execute concurrently with a running operating system create data races, and we demonstrate a deterministic solution for ensuring kernel memory is in a consistent state. We introduce two new classes of kernel rootkits that are undetectable by current systems, motivating the need for the OSck API that allows kernel developers to conveniently specify arbitrary integrity properties.


title: RF-Beep: A light ranging scheme for smart devices

abstract: In this paper, we design, implement and evaluate RF-Beep - a high-accuracy, one-way sensing, energy efficient and light-weight ranging scheme for smart devices. RF-Beep is based on the well known Time-Difference-of-Arrival (TDoA) scheme that utilizes the different propagation speeds of both the acoustic and the radio-frequency (RF) signals. Unlike the previous works, RF-Beep utilizes both the audio interface (i.e., microphone, speaker and sound driver) and the RF interface (i.e., WiFi) at the kernel-level of commercial-off-the-shelf smart devices. Implementing the scheme at lower levels enables us to understand and address the challenges related to the timing uncertainties in transmitting and receiving the acoustic signal. Moreover, RF-Beep does not require any special hardware or infrastructure support. In this paper, we describe the complete implementation of RF-Beep at the kernel space of Linux OS. We evaluate RF-Beep under different indoor and outdoor real scenarios. Results show that the error in the estimated range is less than 50cm for more than 93% of the time.


title: Context Sensitive Article Ranking with Citation Context Analysis

abstract: It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of the referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term. This technique can be used in other linked documents including web pages, legal documents, and patents as well as scientific papers.


title: Data-driven modeling and simulation of PV array

abstract: This paper presents a technique to build a simulation model of a Photovoltaic (PV) array using Solar PV data. The Solar PV data is fetched from a physical solar panel operating at a solar power generating station. PV arrays are generally modeled using mathematical equations that describe the behavior of PV system and simulated in MATLAB with mathematical operator library and functions. The proposed modeling and simulation technique does not require formulation and implementation of mathematical equations. To carry out the data-driven (DD) modeling of PV array, information regarding the actual voltage and current generated by the same array with reference to the actual irradiance and temperature is to be known. This can be obtained from experimental results, PV simulator, excel/data sheets of PV plant, etc. Authors have carried out data-driven modeling of PV array by first gathering PV test data from a solar power plant and then directly importing it into Simulink curve fitting tool to obtain a surface-fit object. Using this object a 2-dimensional lookup table is modeled to represent a PV array. The model can be integrated in any power system or power electronics application network for simulation studies regarding performance analysis of the solar PV system. The modeling and simulation with the proposed technique is simple and easy and can be done with any set of PV data, acquired using any data acquisition method or tool.


Human Sleep and Sleep EEG


title: Predictive Analysis of Controllers' Cognitive Errors Using the TRACEr Technique: A Case Study in an Airport Control Tower

abstract: BACKGROUND: In complex socio-technical systems like aviation systems, human error is said to be the main cause of air transport incidents, accounting for about 75 percent of these incidents and events. air traffic management (ATM) is considered a highly reliable industry; however, there is a persistent need to identify safety vulnerabilities and reduce them or their effects, as ATM is very human-centered and will remain so, at least in the mid-term (e.g., until 2025). Objectives: The current study aimed to conduct a predictive analysis of controllers' cognitive errors using the TRACEr technique in an airport control tower. Materials and Methods: This paper was done as a qualitative case study to identify controllers' errors in an airport control tower. First, the controllers' tasks were described by means of interviews and observation, and then the most critical tasks, which were more likely to have more errors, were chosen to be examined. In the next step, the tasks were broken down into sub-tasks using the hierarchical analysis method and presented as HTA charts. Finally, for all the sub-tasks, different error modes and mechanisms of their occurrence were identified and the results were recorded on TRACEr worksheets. Results: The analysis of TRACEr worksheets showed that of a total 315 detected errors, perception and memory errors are the most important errors in tower control controllers' tasks, and perceptual and spatial confusion is the most important psychological factor related to their occurrence. Conclusions: The results of this study led to the identification of many of the errors and conditions that affect the performance of controllers, providing the ability to define safety and ergonomic interventions to reduce the risk of human error. Therefore, the results of this study can be a basis for planning ATM to prioritize prevention programs and safety enhancement Keywords: Airport Control Tower; Human Error; Controller; TRACEr Language: en


title: Significant antiferromagnetic exchange in copper(II) and nickel(II) dinuclear complexes of the substituted pyrazine ligand 2,3,5,6-tetra(2-pyridyl)pyrazine (TPPZ): magnetic properties, synthesis and crystal structure

abstract: Abstract The dinuclear compounds [Cu 2 (TPPZ)Cl 4 ]∗d5H 2 O ( 1 ), [Ni 2 (TPPZ)(H 2 O) 6 ](NO 3 ) 4 ∗d2.5H 2 O ( 3 ) and [Ni 2 (TPPZ)(H 2 O) 6 [-(squarate) 2 ∗d5H 2 O ( 4 ), where TPPZ is the ligand 2,3,5,6-tetra(2-pyridyl)pyrazine, have been synthesised and characterised crystallographically. [Cu 2 (TPPZ)Cl 4 ]∗d5H 2 O ( 1 ) crystallises in the monoclinic system, space group P2 1 /c, a = 15.030(3), b = 13.224(2), c = 15.320(2) A , β = 105.40(1)°, V = 2935.62(8) A 3 , Z = 4, R1 = 0.035 (observed data). [Ni 2 (TPPZ)(H 2 O) 6 ](NO 3 ) 4 ∗d2.5H 2 O ( 3 ) crystallises in the monoclinic system, space group I2/a, a = 20.660(2), b = 13.724(1), c = 25.436(3) A , β = 91.89(1)°, V = 7208.1(12) a 3 , Z = 8, R1 = 0.070 (observed data). [Ni 2 (TPPZ)(H 2 O) 6 ] (squarate) 2 ∗d5H 2 O ( 4 ) crystallises in the monoclinic system, space group A2/n, a = 19.014(2), b = 10.518(1), c = 19.131(2) A , β = 94.22(1)°, V = 3815.6(7) A 3 , Z = 4, R1 = 0.035 (observed data). Magnetic measurements show significant antiferromagnetic coupling for pyrazine bridged compounds: 2 J = −34.1 cm −1 , g = 2.05 for 1 ; 2 J = −7.6 cm −1 , D = −13.3 cm −1 , g = 2.16 for 3 ; 2 J = −7.5 cm −1 , D = −10.4 cm −1 , g = 2.17 for 4 . The magnetic behaviour of the related published compound [Cu 2 (TPPZ)(H 2 O) 4 ](ClO 4 ) 4 ∗d2H 2 O ( 2 ) has also been measured: 2 J = −61.6 cm −1 , g = 2.13. The possibility of a σ superexchange mechanism in these compounds is discussed.


title: 3D geoscience models and their delivery to customers

abstract: Over the past few years, the creation of systematic three dimensional (3D) geological subsurface models has become standard practice of national Geological Surveys. The methods for construction of 3D models are well documented (e.g. Hinze et al., 1999; Sobisch, 2000; Kessler et al., 2004) and software tools like GSI3D (Geological Surveying and Investigation in 3 Dimensions; ©INSIGHT Geologische Softwaresysteme GmbH) use section-building techniques that are familiar to all geologists. (Kessler and Mathers, 2004)


title: STOCHASTIC EVOLUTION OF RULES FOR PLAYING FINITE NORMAL FORM GAMES

abstract: The evolution of boundedly rational rules for playing normal form games is studied within stationary environments of stochastically changing games. Rules are viewed as algorithms prescribing strategies for the different normal form games that arise. It is shown that many of the â€œfolk resultsâ€\x9D of evolutionary game theory, typically obtained with a fixed game and fixed strategies, carry over to the present environments. The results are also related to some recent experiments on rules and games. Copyright Springer Science+Business Media, LLC 2007


abstract: The mechanism of action of acupuncture remains largely unknown. The reaction to acupuncture needling known as ‘de qi’, widely viewed as essential to the therapeutic effect of acupuncture, may be a key to understanding its mechanism of action. De qi includes a characteristic needling sensation, perceived by the patient, and ‘needle grasp’ perceived by the acupuncturist. During needle grasp, the acupuncturist feels pulling and increased resistance to further movement of the inserted needle. We hypothesize that 1) needle grasp is due to mechanical coupling between the needle and connective tissue with winding of tissue around the needle during needle rotation and 2) needle manipulation transmits a mechanical signal to connective tissue cells via mechanotransduction. Such a mechanism may explain local and remote, as well as long-term effects of acupuncture.—Langevin, H. M., Churchill, D. L., Cipolla, M. J. Mechanical signaling through connective tissue: a mechanism for the therapeutic effect of acupuncture.


Husserl's Refutation of Psychologism and the Possibility of a Phenomenological Psychology


title: A Comparative Review of Data Warehousing ETL Tools with New Trends and Industry Insight

abstract: Data Warehouse is a repository of strategic data from many sources gathered over a long period of time. Traditional DW operations mainly comprise of extracting data from multiple sources, transforming these data into a compatible form and finally loading them to DW schema for further analysis. The extract-transform-load (ETL) functions need to be incorporated into appropriate tools so that organisations can utilise these tools efficiently as required. There is a wide variety of such tools available in market. In this paper, we have compared different aspects of some popular ETL tools (Informatica, Datastage, Ab Initio, Oracle Data Integrator, SSIS) and have analysed their advantages and disadvantages. We have also highlighted some salient features (performance optimisation, data lineage, real time data analysis, cost, language binding etc.) of these tools and represented them with a comparative study. Apart from the review of the ETL tools, the paper also provides feedback from data science industry which narrates the market value and relevance of the tools in practical scenario. However, the traditional DW concept is expanding rapidly with the advent of big data, cloud computing, real time data analysis and the growing need of parsing information from structured and unstructured data sources. In this paper, we have also identified these factors which are transforming the definition of data warehousing.


title: Integrative Analysis of FOXP1 Function Reveals a Tumor-Suppressive Effect in Prostate Cancer

abstract: The transcriptional network of the androgen receptor (AR), a key molecule of prostate cancer, is frequently modulated by interactions with other transcriptional factors such as forkhead box protein A1 (FOXA1). However, global regulatory mechanisms of AR signaling mediated by such factors have not been well investigated. Here we conducted a chromatin immunoprecipitation sequence analysis, which revealed that another FOX family, FOXP1, is specifically regulated by both AR and FOXA1. We also found that FOXP1 acts as a tumor suppressor in prostate cancer through inhibiting cell proliferation and migration. We generated an extensive global map of FOXP1 binding sites and found that FOXP1 is directly involved in AR-mediated transcription. We demonstrated that FOXP1 has a repressive effect on AR-induced transcriptional activity or histone modification in enhancer regions. Moreover, by a global analysis of androgen-mediated transcriptional networks, we observed enrichment of FOXP1 binding genes in the gene cluster...


title: Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

abstract: Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.


title: Security Semantics Modeling with Progressive Distillation

abstract: The prevalence of Android platform has attracted adversaries to craft malicious payloads for illegal profit. Such malicious artifacts are frequently reused and embedded in benign, paid apps to lure victims that the apps have been cracked for free. To discover these fraudulent apps, administrators of app markets desire an automated scanning process to maintain the health of app ecosystem. However, conventional approaches cannot be efficiently applied due to the lack of a scalable, effective approach to malware characteristics aggregation. On the other hand, the vast number of apps significantly increases the analysis complexity. In this paper, we propose Petridish which generates discriminative models against the repacked malicious apps . These representative models of malicious semantics can be progressively distilled with malign and benign samples. These models can further detect repacked malicious apps. Our experiment shows that, after two retraining rounds, Petridish achieved an average of 28 percent progressive detection improvement from 63 to 91.2 percent for the large families, exceeding 38 test samples in size. With noise reduction, it accomplished 88 percent detection rate and 1.7 percent false alarm rate. The characteristics aggregation approach will become critical in the age of app explosion.


title: Software Visualization in the Large

abstract: The invisible nature of software hides system complexity, particularly for large team-oriented projects. The authors have evolved four innovative visual representations of code to help solve this problem: line representation; pixel representation; file summary representation; and hierarchical representation. We first describe these four visual code representations and then discuss the interaction techniques for manipulating them. We illustrate our software visualization techniques through five case studies. The first three focus on software history and static software characteristics; the last two discuss execution behavior. The software library and its implementation are then described. Finally, we briefly review some related work and compare and contrast our different techniques for visualizing software.


title: Dropout Sampling for Robust Object Detection in Open-Set Conditions

abstract: Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling. We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision. We evaluate this approach on a large synthetic dataset with 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment.


title: Co-production under the Financial Crisis and Austerity : a means of democratizing Public Services or a race to the bottom?

abstract: The involvement of users in the co-production of public services is of increasing importance as fiscal and financial crises put pressure on public spending in many countries around the world. The role of co-production in public policy is also important as it creates opportunities for users’ empowerment through their greater involvement in the key aspects of services on which they rely. However, there are practical and conceptual limitations to co-production. Citizens normally lack the training and experience to perform specialized services, and the substitution of paid personnel with voluntary efforts means that some of the costs are transferred to co-producers themselves . Co-production and users taking over public services under the financial crisis and austerity, might usher in a new era of democratizing public services or instigate a race to the bottom. This short article highlights the need for broadening and deepening our knowledge of the various forms of co-production, in order to understand the co...


title: SWYN: A Visual Representation for Regular Expressions

abstract: Publisher Summary Most programming tasks involve the creation of abstractions that can be broadly grouped into two categories: abstractions over context and abstractions over time. An abstraction over context defines some category of situations—objects or data—and allows the programmer to define operations on all members of that category. An abstraction over time defines events that will happen in the future as a result of the present actions of the programmer. Both of these are potentially labor-saving devices. A good abstraction can be used as a kind of mental shorthand for interacting with the world. Abstract tasks are often dealt by using notations that make the structure of the abstraction visible. Programming-by-example (PBE) systems sometimes make it more difficult to create abstractions. The user has to second-guess the results of the inference algorithm and sometimes cannot see any visual representation of the inferred result. SWYN (See What You Need) addresses these issues in the context of constructing regular expressions from examples. It provides a visual representation that has been evaluated in empirical user testing and an induction interface that always allows the user to see and modify the effects of the supplied examples. The results demonstrate the potential advantages of more strictly applying cognitive dimensions analysis and direct manipulation principles when designing systems for PBE.


title: KVM vs. LXC : Comparing Performance and Isolation of Hardware-assisted Virtual Routers

abstract: Concerns have been raised about the performance of PC-based virtual routers as they do packet processing in software. Furthermore, it becomes challenging to maintain isolation among virtual routers due to resource contention in a shared environment. Hardware vendors recognize this issue and PC hardware with virtualization support (SR-IOV and Intel-VTd) has been introduced in recent years. In this paper, we investigate how such hardware features can be integrated with two different virtualization technologies (LXC and KVM) to enhance performance and isolation of virtual routers on shared environments. We compare LXC and KVM and our results indicate that KVM in combination with hardware support can provide better trade-offs between performance and isolation. We notice that KVM has slightly lower throughput, but has superior isolation properties by providing more explicit control of CPU resources. We demonstrate that KVM allows defining a CPU share for a virtual router, something that is difficult to achieve in LXC, where packet forwarding is done in a kernel shared by all virtual routers.


title: HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

abstract: Abstract Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present “HDIdx”, an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity.


title: Depression or menopause? Presentation and management of major depressive disorder in perimenopausal and postmenopausal women.

abstract: OBJECTIVE ::: The purpose of this review was to examine the risk of depression onset in perimenopausal and postmenopausal women, discuss the importance and rationale for screening for major depressive disorder (MDD) in women in the menopausal transition, and review therapeutic options for management of MDD in perimenopausal and postmenopausal women. ::: ::: ::: DATA SOURCES ::: PubMed was searched (1970 to 2008) using combinations of the following terms: major depressive disorder, perimenopause, postmenopause, mood disorder, risk factors, reproductive period, family practice, differential diagnosis, hormone, estrogen replacement therapy, reuptake inhibitors, and neurotransmitter. ::: ::: ::: STUDY SELECTION ::: All relevant articles identified via the search terms reporting original data and published in English were considered for inclusion. Twenty-two cross-sectional and longitudinal studies were utilized to evaluate the relationship between the menopausal transition and risk of mood disorders and to formulate recommendations for screening and management of MDD in perimenopausal and postmenopausal women. ::: ::: ::: DATA EXTRACTION ::: RESEARCH STUDIES UTILIZED THE FOLLOWING MEASURES: postal questionnaires, Women's Health Questionnaire, Beck Depression Inventory, Center for Epidemiologic Studies-Depression scale, Modified Menopause Symptom Inventory, 12-item symptom questionnaire, or Structured Clinical Interview for DSM-IV. ::: ::: ::: DATA SYNTHESIS ::: Menopause is a normal, and for most women largely uneventful, part of life. For some women, however, the menopausal transition is a period of biologic vulnerability with noticeable physiologic, psychological, and somatic symptoms. The perimenopausal period is associated with a higher vulnerability for depression, with risk rising from early to late perimenopause and decreasing during postmenopause. Women with a history of depression are up to 5 times more likely to have a MDD diagnosis during this time period. ::: ::: ::: CONCLUSIONS ::: Routine screening of this at-risk population followed by careful assessment for depressive symptoms can help identify the presence of MDD in the menopausal transition. Recognition of menopausal symptoms, with or without depression, is important given their potential impact on quality of life.


Information systems as a strategic partner in organizational performance


Polynomial expansions of solutions of .


title: Analysis and Implementation of Workflow-based Supply Chain Management System

abstract: Because of lack of abstraction at the stage of requirement analysis, the traditional Supply Chain Management systems are not suitable to dynamic reengineering when business needs change. This paper will combine Workflow Technologies with Supply Chain Management systems (WSCM). By decomposing work into the corresponding tasks and roles and monitoring the sequence of work activities according to a defined set of rules, we can boost efficiency, reduce cost, and optimize performance of business processes management of Supply Chain.


title: 3D-Aided Face Recognition Robust to Expression and Pose Variations

abstract: Expression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this paper, we aim to endow state of the art face recognition SDKs with robustness to facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from those due to facial expressions. Specifically, given a probe with expression, a novel view of the face is generated where the pose is rectified and the expression neutralized. We present two methods of expression neutralization. The first one uses prior knowledge to infer the neutral expression image from an input image. The second method, specifically designed for verification, is based on the transfer of the gallery face expression to the probe. Experiments using rectified and neutralized view with a standard commercial FR SDK on two 2D face databases, namely Multi-PIE and AR, show significant performance improvement of the commercial SDK to deal with expression and pose variations and demonstrates the effectiveness of the proposed approach.


Distribution and Metabolism of 1-Propyl-1-nitrosourea in Rats 1


title: Cone carving for surface reconstruction

abstract: We present cone carving, a novel space carving technique supporting topologically correct surface reconstruction from an incomplete scanned point cloud. The technique utilizes the point samples not only for local surface position estimation but also to obtain global visibility information under the assumption that each acquired point is visible from a point lying outside the shape. This enables associating each point with a generalized cone, called the visibility cone, that carves a portion of the outside ambient space of the shape from the inside out. These cones collectively provide a means to better approximate the signed distances to the shape specifically near regions containing large holes in the scan, allowing one to infer the correct surface topology. Combining the new distance measure with conventional RBF, we define an implicit function whose zero level set defines the surface of the shape. We demonstrate the utility of cone carving in coping with significant missing data and raw scans from a commercial 3D scanner as well as synthetic input.


title: Design Quality of Websites for Electronic Commerce: Fortune 1000 Webmasters' Evaluation

abstract: This article presents a study of Fortune 1000 webmasters' perceptions of thirtyfive variables that support quality in the design of consumer-oriented web sites used for electronic commerce. The study combines consumer marketing constructs: Learning Capability, Playfulness, Service Quality with information systems constructs of Information Quality, System Quality, and System Use. The subjects have expert level knowledge related to quality issues in the design of web sites. Eighteen percent of the Webmasters responded to two formats of an electronic survey. The overall importance ratings correlated positively and significantly with the marketing and information systems research constructs, thus these constructs were shown as significant to well designed web sites for consumeroriented electronic commerce. Designers of web sites might apply these results.


title: Planning in hybrid relational MDPs

abstract: We study planning in relational Markov decision processes involving discrete and continuous states and actions, and an unknown number of objects. This combination of hybrid relational domains has so far not received a lot of attention. While both relational and hybrid approaches have been studied separately, planning in such domains is still challenging and often requires restrictive assumptions and approximations. We propose HYPE: a sample-based planner for hybrid relational domains that combines model-based approaches with state abstraction. HYPE samples episodes and uses the previous episodes as well as the model to approximate the Q-function. In addition, abstraction is performed for each sampled episode, this removes the complexity of symbolic approaches for hybrid relational domains. In our empirical evaluations, we show that HYPE is a general and widely applicable planner in domains ranging from strictly discrete to strictly continuous to hybrid ones, handles intricacies such as unknown objects and relational models. Moreover, empirical results showed that abstraction provides significant improvements.


title: The Graph Structure in the Web : Analyzed on Different Aggregation Levels

abstract: Knowledge about the general graph structure of theWorldWideWeb is important for understanding the social mechanisms that govern its growth, for designing ranking methods, for devising better crawling algorithms, and for creating accurate models of its structure. In this paper, we analyze a large web graph. The graph was extracted from a large publicly accessible web crawl that was gathered by the Common Crawl Foundation in 2012. The graph covers over 3:5 billion web pages and 128:7 billion hyperlinks. We analyze and compare, among other features, degree distributions, connectivity, average distances, and the structure of weakly/strongly connected components. We conduct our analysis on three different levels of aggregation: page, host, and pay-level domain (PLD) (one “dot level” above public suffixes). Our analysis shows that, as evidenced by previous research (Serrano et al., 2007), some of the features previously observed by Broder et al., 2000 are very dependent on artifacts of the crawling process, whereas other appear to be more structural. We confirm the existence of a giant strongly connected component; we however find, as observed by other researchers (Donato et al., 2005; Boldi et al., 2002; Baeza-Yates and Poblete, 2003), very different proportions of nodes that can reach or that can be reached from the giant component, suggesting that the “bow-tie structure” as described by Broder et al. is strongly dependent on the crawling process, and to the best of our current knowledge is not a structural property of the Web. More importantly, statistical testing and visual inspection of size-rank plots show that the distributions of indegree, outdegree and sizes of strongly connected components of the page and host graph are not power laws, contrarily to what was previously reported for much smaller crawls, although they might be heavy tailed. If we aggregate at pay-level domain, however, a power law emerges. We also provide for the first time accurate measurement of distance-based features, using recently introduced algorithms that scale to the size of our crawl (Boldi and Vigna, 2013).


title: Accurate Resource Prediction for Hybrid IaaS Clouds Using Workload-Tailored Elastic Compute Units

abstract: Cloud computing's pay-per-use model greatly reduces upfront cost and also enables on-demand scalability as service demand grows or shrinks. Hybrid clouds are an attractive option in terms of cost benefit, however, without proper elastic resource management, computational resources could be over-provisioned or under-provisioned, resulting in wasting money or failing to satisfy service demand. In this paper, to accomplish accurate performance prediction and cost-optimal resource management for hybrid clouds, we introduce Workload-tailored Elastic Compute Units (WECU) as a measure of computing resources analogous to Amazon EC2's ECUs, but customized for a specific workload. We present a dynamic programming-based scheduling algorithm to select a combination of private and public resources which satisfy a desired throughput. Using a loosely-coupled benchmark, we confirmed WECUs have 24 (J% better runtime prediction ability than ECUs on average. Moreover, simulation results with a real workload distribution of web service requests show that our WECU-based algorithm reduces costs by 8-31% compared to a fixed provisioning approach.


title: An Adaptive Locally Connected Neuron Model: Focusing Neuron

abstract: We present a new artificial neuron model capable of learning its receptive field in the spatial domain of inputs. The name for the new model is focusing neuron because it can adapt both its receptive field location and size (aperture) during training. A network or a layer formed of such neurons can learn and generate unique connection structures for particular inputs/problems. The new model requires neither heuristics nor additional optimizations. Hence, all parameters, including those controlling the focus could be trained using the stochastic gradient descent optimization. We have empirically shown the capacity and viability of the new model with tests on synthetic and real datasets. We have constructed simple networks with one or two hidden layers; also employed fully connected networks with the same configurations as controls. In noise-added synthetic Gaussian blob datasets, we observed that focusing neurons can steer their receptive fields away from the redundant inputs and focused into more informative ones. Tests on common datasets such as MNIST have shown that a network of two hidden focusing layers can perform better (99.21% test accuracy) than a fully connected dense network with the same configuration.


title: Central Bank Communication and the Yield Curve: A Semi-Automatic Approach using Non-Negative Matrix Factorization

abstract: Communication is now a standard tool in the central bank's monetary policy toolkit. Theoretically, communication provides the central bank an opportunity to guide public expectations, and it has been shown empirically that central bank communication can lead to financial market fluctuations. However, there has been little research into which dimensions or topics of information are most important in causing these fluctuations. We develop a semi-automatic methodology that summarizes the FOMC statements into its main themes, automatically selects the best model based on coherency, and assesses whether there is a significant impact of these themes on the shape of the U.S Treasury yield curve using topic modeling methods from the machine learning literature. Our findings suggest that the FOMC statements can be decomposed into three topics: (i) information related to the economic conditions and the mandates, (ii) information related to monetary policy tools and intermediate targets, and (iii) information related to financial markets and the financial crisis. We find that statements are most influential during the financial crisis and the effects are mostly present in the curvature of the yield curve through information related to the financial theme.


title: Feedforward Neural Networks for Caching: n Enough or Too Much?

abstract: We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.


title: Investigation of a Ka-band Luneburg lens made of a glide-symmetric holey structure

abstract: A Ka-band 2D flat-profiled Luneburg lens antenna implemented with a glide-symmetric holey structure is presented. The required refractive index for the lens design has been investigated via an analysis of the hole depth and the gap between the two metallic layers constituting the lens. The final unit cell is described and applied to create the complete metasurface Luneburg lens showing that a plane wave is obtained when feeding at an opposite arbitrary point with a discrete source.


title: Building and Refining Rhetorical-Semantic Relation Models

abstract: We report results of experiments which build and refine models of rhetoricalsemantic relations such as Cause and Contrast. We adopt the approach of Marcu and Echihabi (2002), using a small set of patterns to build relation models, and extend their work by refining the training and classification process using parameter optimization, topic segmentation and syntactic parsing. Using human-annotated and automatically-extracted test sets, we find that each of these techniques results in improved relation classification accuracy.


title: A Brief Tour of Join Point Interfaces

abstract: In standard AspectJ, aspects and base code are often insufficiently decoupled, as aspects hold pointcuts, which can contain explicit textual references to base code. This hinders aspect evolution and reuse, and may hinder reasoning about aspects on the base-code side. In this demo we present join point interfaces as an extension to the aspect-oriented programming language AspectJ. Opposed to AspectJ, with join point interfaces aspects and base code communicate only through a shared interface abstraction. Aspects themselves go without pointcuts and only reference the interface. Pointcuts are typically defined on the base-code side, or not at all, as join point interfaces also support pure explicit invocation as known from publish-subscribe systems. As a result, users obtain a language which decouples aspects from base code using a modular type-checking algorithm, and which they can use to adopt aspects gradually as they desire. One major undertaking in the design of join point interfaces was to make the language as flexible to use as standard AspectJ, while nevertheless providing interfaces supported by strong type checks that can completely avoid type errors at composition time. In this demo we will discuss this inherent trade-off, we will present JPIs as an extension to the AspectBench Compiler, and will show how the language eases the maintenance of existing AspectJ applications.


The Situated Function-Behaviour-Structure Framework


title: A 4–32 Gb/s Bidirectional Link With 3-Tap FFE/6-Tap DFE and Collaborative CDR in 22 nm CMOS

abstract: This paper details the design of an 8-lane bidirectional link for both within-the-box and external communications in 22 nm CMOS technology. A low profile connector with a high density cable assembly ensure a data rate of up to 32 Gb/s per lane while maintaining channel loss below 25 dB. Channel equalization is performed by a combination of a 3-tap feed-forward equalizer (FFE), single-stage continuous-time linear equalizer (CTLE) and a 6-tap decision-feedback equalizer (DFE). Collaborative timing recovery is used to enable lane characterization without degrading jitter performance. Phase error decimation, with a conditional phase detection scheme, is used to reduce the DFE complexity by 50%. Power consumption over a wide range of data rates from 4 to 32 Gb/s is reduced by using regulated CMOS clocking with lane bundling, low swing transmitter with a source-series terminated (SST) driver and a highly reconfigurable receiver with an active inductor CTLE. At a lane data rate of 32 Gb/s, over a 0.5 m cable with 16 dB of loss, a transceiver lane consumes 205 mW from a 1.07 V supply. The power scales down to 26 mW from a 0.72 V supply at 8 Gb/s, when transmitting over a channel with 8 dB loss. The active silicon area per lane is 0.079 mm ::: 2 ::: .


title: Maximizing the number of worker's self-selected tasks in spatial crowdsourcing

abstract: With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowd-sourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we also propose approximation and progressive algorithms. We conducted a thorough experimental evaluation on both real-world and synthetic data to compare the performance and accuracy of our proposed approaches.


title: ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment

abstract: This paper presents our approach to semantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two questions: (1) Can we solve these two subtasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To address them, we extracted seven types of features including text difference measures proposed in entailment judgement subtask, as well as common text similarity measures used in both subtasks. Then we exploited the same feature set to solve the both subtasks by considering them as a regression and a classification task respectively and performed a study of influence of different features. We achieved the first and the second rank for relatedness and entailment task respectively.


title: A Review of Recent Papers on Online Discussion in Teaching and Learning in Higher Education A REVIEW OF RECENT PAPERS ON ONLINE DISCUSSION IN TEACHING AND LEARNING IN HIGHER EDUCATION

abstract: This paper presents a review of a sample of recent case studies on the use of asynchronous online discussion in higher education. These studies are analysed in terms of curriculum design; assumptions about teaching and learning; claims and reported conditions for using on-line discussion. The claims made for asynchronous online discussion are found to be frequently based on social constructivist principles, in particular the opportunities for interaction between learners, and permanent access to these interactions. Asynchronous online discussion is seen as offering additional value by providing learners with experience of computer communication tools and opportunities for taking part in group work. Several constraints on participation within on-line forums are described. These are discussed in relation to the nature of curriculum design; tutor support; learners’ attitudes and previous experience; software design. The conditions under which asynchronous online discussion may best support learning are set out and avenues for future research are suggested.


Title: Discourse markers as attentional cues at discourse transitions Running head: Discourse transitions


title: Fast Approximate k NN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection

abstract: Nearest neighbor graphs are widely used in data mining and machine learning. A brute-force method to compute the exact kNN graph takes Θ(dn2) time for n data points in the d dimensional Euclidean space. We propose two divide and conquer methods for computing an approximate kNN graph in Θ(dnt) time for high dimensional data (large d). The exponent t ∈ (1,2) is an increasing function of an internal parameter α which governs the size of the common region in the divide step. Experiments show that a high quality graph can usually be obtained with small overlaps, that is, for small values of t. A few of the practical details of the algorithms are as follows. First, the divide step uses an inexpensive Lanczos procedure to perform recursive spectral bisection. After each conquer step, an additional refinement step is performed to improve the accuracy of the graph. Finally, a hash table is used to avoid repeating distance calculations during the divide and conquer process. The combination of these techniques is shown to yield quite effective algorithms for building kNN graphs.


title: Inverse System of a Symbolic Power II. The Waring Problem for Forms

abstract: Abstract The classical Waring problem for forms is to determine the smallest length s of an additive decomposition of a general degree d homogeneous polynomial or form f in r variables as sum of s d th powers of linear forms. We show that its solution is implied by a result of J. Alexander and A. Hirschowitz, concerning the Hilbert functions of the ideal of functions Vanishing to order two at a generic set of s points in P r − 1 . Using Macaulay′s inverse systems, we show that the Alexander-Hirschowitz result is equivalent to determining the number of linear syzygies of s homogeneous forms in r variables that are d th powers of a given set of general linear forms. We also determine the dimension of the family of degree d forms that have additive decompositions of length s . We then study several notions of length for forms f , having to do with the kind of length- s , zero-dimensional schemes Z in P r − 1 whose defining ideal I ( Z ) annihilates the inverse system of f . When Z is to consist of distinct points, we obtain the above length of additive decomposition of f . When Z is smoothable we obtain the "smoothable length" of f ; when Z is arbitrary, we obtain a "scheme length" of f . All these lengths are at least as large as the dimension of the vector space of all order- i partial derivates of f , for each i . The above-mentioned length functions are distinct. Using results about the existence of nonsmoothable Gorenstein point singularities in codimension 4, we show that when r = 5 there are forms f of scheme length s , which are not in the closure of the family of forms having additive decompositions of length s . Finally, we propose a new set of Waring problems for forms, using these lengths.


title: Multi-scale gray level co-occurrence matrices for texture description

abstract: Abstract Texture information plays an important role in image analysis. Although several descriptors have been proposed to extract and analyze texture, the development of automatic systems for image interpretation and object recognition is a difficult task due to the complex aspects of texture. Scale is an important information in texture analysis, since a same texture can be perceived as different texture patterns at distinct scales. Gray level co-occurrence matrices (GLCM) have been proved to be an effective texture descriptor. This paper presents a novel strategy for extending the GLCM to multiple scales through two different approaches, a Gaussian scale-space representation, which is constructed by smoothing the image with larger and larger low-pass filters producing a set of smoothed versions of the original image, and an image pyramid, which is defined by sampling the image both in space and scale. The performance of the proposed approach is evaluated by applying the multi-scale descriptor on five benchmark texture data sets and the results are compared to other well-known texture operators, including the original GLCM, that even though faster than the proposed method, is significantly outperformed in accuracy.


title: The potential of dwell-free eye-typing for fast assistive gaze communication

abstract: We propose a new research direction for eye-typing which is potentially much faster: dwell-free eye-typing. Dwell-free eye-typing is in principle possible because we can exploit the high redundancy of natural languages to allow users to simply look at or near their desired letters without stopping to dwell on each letter. As a first step we created a system that simulated a perfect recognizer for dwell-free eye-typing. We used this system to investigate how fast users can potentially write using a dwell-free eye-typing interface. We found that after 40 minutes of practice, users reached a mean entry rate of 46 wpm. This indicates that dwell-free eye-typing may be more than twice as fast as the current state-of-the-art methods for writing by gaze. A human performance model further demonstrates that it is highly unlikely traditional eye-typing systems will ever surpass our dwell-free eye-typing performance estimate.


title: A Multi-Bit Fully Homomorphic Encryption With Shorter Public Key From LWE

abstract: The efficiency of fully homomorphic encryption is a big question at present. To improve efficiency of fully homomorphic encryption, we use the technique of packed ciphertext to construct a multi-bit fully homomorphic encryption based on Learning with Errors problem. Our scheme has a short public key. Since our fully homomorphic encryption scheme builds on the basic encryption scheme that choose Learning with Errors samples from Gaussian distribution and add Gaussian error to it, which result in that the number of Learning with Errors samples decrease from 2nlogq to n+1. We prove that our fully homomorphic encryption scheme is feasible and its security relies on the hardness of Learning with Errors problem. In addition we adapt the optimization for the process of key switching from GHS13 and formal this new process of key switching for multi-bit fully homomorphic encryption. At last, we analyze the concert parameters and compare these parameters between our scheme and GHS13 scheme. The data show that our scheme has public key smaller by a factor of about logq than it in GHS13 scheme.


title: A regression-based radar-mote system for people counting

abstract: People counting is key to a diverse set of sensing applications. In this paper, we design a mote-scale event-driven solution that uses a low-power pulsed radar to estimate the number of people within the ∼10m radial range of the radar. In contrast to extant solutions, most of which use computer vision, our solution is light-weight and private. It also better tolerates the presence of obstacles that partially or fully impair line of sight; this is achieved by accounting for “small” indirect radio reflections via joint time-frequency domain features. The counter itself is realized using Support Vector Regression; the regression map is learned from a medium sized dataset of 0–∼40 people in various indoor room settings. 10-fold cross validation of our counter yields a mean absolute error of 2.17 between the estimated count and the ground truth and a correlation coefficient of 0.97.We compare the performance of our solution with baseline counters.


title: A flexible neural network for ATM cash demand forecasting

abstract: The paper presents an artificial neural network based approach in support of cash demand forecasting for automatic teller machine (ATM). On the start phase a three layer feed-forward neural network was trained using Levenberg-Marquardt algorithm and historical data sets. Then ANN was retuned every week using the last observations from ATM. The generalization properties of the ANN were improved using regularization term which penalizes large values of the ANN weights. Regularization term was adapted online depending on complexity of relationship between input and output variables. Performed simulation and experimental tests have showed good forecasting capacities of ANN. At current stage the proposed procedure is in the implementing phase for cash management tasks in ATM network.


The Language of Place: Semantic Value from Geospatial Context


title: Optimal reservoir operation using multi-objective evolutionary algorithm

abstract: This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multi-objective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem.


title: The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

abstract: This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to solve several tasks simultaneously. Learning to solve many tasks simultaneously has been a long-standing, core goal of artificial intelligence, inspired by infant development and motivated by the desire to build flexible robot manipulators capable of many diverse behaviours. We show that the IU agent not only learns to solve many tasks simultaneously but it also learns faster than agents that target a single task at-a-time. In some cases, where the single task DDPG method completely fails, the IU agent successfully solves the task. To demonstrate this, we build a playroom environment using the MuJoCo physics engine, and introduce a grounded formal language to automatically generate tasks.


title: An event-triggered finite-time control scheme for unicycle robots

abstract: This paper is concerned with the event-triggered finite-time control scheme for unicycle robots. First, Lagrange method is used to model the unicycle robot at the roll and pitch axis. Second, on the basis of the established model, an event-triggered finite-time control scheme is proposed to balance the unicycle robot in finite time and to determine whether or not control input should be updated. The control input should be only updated when the triggering condition is violated. As a result, the switching energy of actor can be saved. Third, a stability criterion on unicycle robots with the proposed event-trigged finite-time control scheme is derived by using a Lyapunov method. Finally, the effectiveness of the event-triggered finite-time control scheme is illustrated for unicycle robots.


title: Happy guys finish last: The impact of emotional expressions on sexual attraction

abstract: This research examined the relative sexual attractiveness of individuals showing emotion expressions of happiness, pride, and shame compared with a neutral control. Across two studies using different images and samples ranging broadly in age (total N 1041), a large gender difference emerged in the sexual attractiveness of happy displays: happiness was the most attractive female emotion expression, and one of the least attractive in males. In contrast, pride showed the reverse pattern; it was the most attractive male expression, and one of the least attractive in women. Shame displays were relatively attractive in both genders, and, among younger adult women viewers, male shame was more attractive than male happiness, and not substantially less than male pride. Effects were largely consistent with evolutionary and socio-cultural-norm accounts. Overall, this research provides the first evidence that distinct emotion expressions have divergent effects on sexual attractiveness, which vary by gender but largely hold across age.


title: A Framework for Multiple Radar and Multiple 2D/3D Camera Fusion.

abstract: In this paper we present a framework for the fusion of radar and image information. In the case considered here we combine information from multiple closerange radars to one fused radar measurement using the overlap region of the individual radars. This step is performed automatically using a feature based matching technique. Additionally, we use multiple 2D/3D cameras that generate (color) image and distance information. We show how to fuse these heterogeneous sensors in the context of airport runway surveillance. A possible application of this is the automatic detection of midget objects (e.g. screws) on airfields. We outline how to generate an adaptive background model for the situation on the runway from the fused sensor information. Unwanted objects on the airfield can then be detected by change detection.


title: Detecting unmetered taxi rides from trajectory data

abstract: Taxi fraud has become a serious problem in many large cities, where passengers are overcharged by taxi drivers in various ways. Researchers have developed a number of methods to detect taxi frauds with the assumption that fraudulent trips, among normal trips, are recorded by taximeters. In this paper, different from the previous work, we identify a new type of taxi fraud called unmetered taxi rides, where taxi drivers carry passengers without activating the taximeters. Since these fraudulent rides are not recorded by taximeters, previous detection approaches cannot directly apply to them. Hence, we propose a novel fraud detection system specifically designed for unmetered taxi rides. Our system uses a learning model to detect unmetered trajectory segments that are similar to metered rides, and introduces a heuristic algorithm to construct maximum fraudulent trajectories from the trajectory dataset. We have conducted detailed experiments on real-world datasets, and the results show that the proposed system can detect unmetered taxi rides effectively and efficiently.


title: A temperature-compensated low-noise digitally-controlled crystal oscillator for multi-standard applications

abstract: This paper presents an integrated 26-MHz digitally-controlled crystal oscillator (DCXO) with temperature compensation function for multi-standard cellular applications, that achieves a phase noise of -154 and -159 dBc/Hz at 10 kHz and 100 kHz offset, respectively. The frequency instability over temperature is compensated by built-in temperature sensor and compensating capacitor. The frequency instability from -10 to 55 degC is about +/- 1 ppm. The AFC frequency tuning is done by a digitally-controlled metal-oxide-metal capacitor array that is 13-bit thermometer decoded. The DCXO is implemented in a 0.13-mum CMOS technology.


title: Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter

abstract: A two-stage algorithm, called switching-based adaptive weighted mean filter, is proposed to remove salt-and-pepper noise from the corrupted images. First, the directional difference based noise detector is used to identify the noisy pixels by comparing the minimum absolute value of four mean differences between the current pixel and its neighbors in four directional windows with a predefined threshold. Then, the adaptive weighted mean filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its noise-free neighbors in the filtering window. Numerous simulations demonstrate that the proposed filter outperforms many other existing algorithms in terms of effectiveness in noise detection, image restoration and computational efficiency.


title: Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks

abstract: Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4\% which performs better than every previous work on the dataset.


title: Video-based Evanescent, Anonymous, Asynchronous Social Interaction: Motivation and Adaption to Medium

abstract: Danmaku is an emerging socio-digital media paradigm that puts anonymous, asynchronous user-generated scrolling comments on videos. (How) can danmaku afford the illusion and realization of social interactions, if at all possible given its interactional incoherence nature? To answer this question, we collect Chinese danmaku users' reflection on their motivations to use this social service and explore the actual practices that meet the needs. According to a preliminary danmaku usage survey, users consider it as an information seeking and emotion venting channel. Through archival analysis of real-world data, we find that danmaku commentaries are relatively short, video-centric, saturated with emotions, and similar in syntactic and semantic features. Users have developed a set of mechanisms adapted to the medium, to leverage such text-based messages to foster interpersonal and hyperpersonal communication for sharing of facts, thoughts, and feelings.


title: Improved Forms for Controlling the Acrobot with Motors of Atypical Size Using Artificial Intelligence Techniques

abstract: An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.


title: Locating lucrative passengers for taxicab drivers

abstract: In an urban setting, such as the city of Beijing, after a taxi driver drops the previous passenger, he/she needs to decide where to drive to find the next --- preferably lucrative --- passenger. Different drivers follow different strategies that are mostly based on personal experiences. In this work, we analyze large amounts of GPS location data of taxicabs to compute a high-level profit-maximizing strategy for taxi drivers. Formally, we model the problem of finding a passenger as a Markov Decision Process (MDP) whose parameters are estimated from the GPS data. For this MDP, we compute an optimal policy using dynamic programming. We show that the proposed strategy captures meaningful rules for finding a passenger and we demonstrate that taxi drivers whose behaviors agree with our proposal generate more profit than average drivers.


title: Label Partitioning For Sublinear Ranking

abstract: We consider the case of ranking a very large set of labels, items, or documents, which is common to information retrieval, recommendation, and large-scale annotation tasks. We present a general approach for converting an algorithm which has linear time in the size of the set to a sublinear one via label partitioning. Our method consists of learning an input partition and a label assignment to each partition of the space such that precision at k is optimized, which is the loss function of interest in this setting. Experiments on large-scale ranking and recommendation tasks show that our method not only makes the original linear time algorithm computationally tractable, but can also improve its performance.


title: OMNeT++ and mosaik: Enabling Simulation of Smart Grid Communications

abstract: This paper presents a preliminary system architecture of integrating OMNeT++ into the mosaik co-simulation framework. This will enable realistic simulation of communication network protocols and services for smart grid scenarios and on the other side, further development of communication protocols for smart grid applications. Thus, by integrating OMNeT++ and mosaik, both communities will be able to leverage each others's sophisticated simulation models and expertise. The main challenges identified are the external management of the OMNeT++ simulation kernel and performance issues when federating various simulators, including OMNeT++ into the mosaik framework. The purpose of this paper is to bring these challenges up and to gather relevant experience and expertise from the OMNeT++ community. We especially encourage collaboration among all OMNeT++ developers and users.


title: Caravan: Providing location privacy for vanet

abstract: Abstract : In vehicular ad hoc networks (VANET), it is possible to locate and track a vehicle based on its transmissions, during communication with other vehicles or the road-side infrastructure. This type of tracking leads to threats on the location privacy of the vehicle's user. In this paper, we study the problem of providing location privacy in VANET by allowing vehicles to prevent tracking of their broadcast communications. We first, identify the unique characteristics of VANET that must be considered when designing suitable location privacy solutions. Based on these observations, we propose a location privacy scheme called CARAVAN, and evaluate the privacy enhancement achieved under some existing standard constraints of VANET applications, and in the presence of a global adversary.


title: The new EasyLiving Project at Microsoft Research

abstract: EasyLiving is a new project in intelligent environments at Microsoft Research. We are working to make computing more accessible and more pervasive than today’s desktop computer. More specifically, our goal is to develop a prototype architecture and technologies for building intelligent environments that facilitate the unencumbered interaction of people with other people, with computers, and with devices. This paper describes our goals, design decisions, and applications of EasyLiving. 1. What Is Next in PCs?


title: Software unit test coverage and adequacy

abstract: Objective measurement of test quality is one of the key issues in software testing. It has been a major research focus for the last two decades. Many test criteria have been proposed and studied for this purpose. Various kinds of rationales have been presented in support of one criterion or another. We survey the research work in this area. The notion of adequacy criteria is examined together with its role in software dynamic testing. A review of criteria classification is followed by a summary of the methods for comparison and assessment of criteria.


title: Patent-Free Authenticated-Encryption As Fast As OCB

abstract: This paper presents an efficient authenticated encryption construction based on a universal hash function and block cipher. Encryption is achieved via counter-mode while authentication uses the Wegman-Carter paradigm. A single block-cipher key is used for both operations. The construction is instantiated using the hash functions of UMAC and VMAC, resulting in authenticated encryption with peak performance about ten percent slower than encryption alone.


title: Systematically Varying . . . Implications for Product Choice

abstract: Shaun McQuitty is an Assistant Professor in the Department of Marketing at New Mexico State University, Adam Finn is the Ronald K. Banister Professor of Marketing in the Department of Marketing, Business Economics, and Law at the University of Alberta, and, James B. Wiley is Professor of Marketing Science at the Victoria University of Wellington. All correspondence should be addressed to Shaun McQuitty, Department of Marketing / MSC 5280, College of Business Administration and Economics, New Mexico State University, P.O. Box 30001, Las Cruces, New Mexico, 88003-8001. Phone (505) 646-1463. Fax (505) 646-1498. email: mcquitty@nmsu.edu. The authors would like to thank Mike Hyman, the editor, and three anonymous reviewers for their valuable comments regarding earlier drafts of this article.


Performance Analysis of Text and Image Steganography with RSA Algorithm in Cloud Computing


title: Advanced Polarimetric Concepts - Part 1 (Polarimetric Target Description, Speckle filtering and Decomposition Theorems)

abstract: Abstract : There is currently a great deal of interest in the use of polarimetry for radar remote sensing. In this context, different and important objectives are to classify Earth terrain components within a fully polarimetric SAR image and then extract physical information from the observed scattering of microwaves by surface and volume structures. The most important observable measured by such radar systems is the 3x3-coherency matrix. This matrix accounts for local variations in the scattering matrix and is the lowest order operator suitable to extract polarimetric parameters for distributed scatterers in the presence of additive (system) and/or multiplicative (speckle) noise. In the first part of this paper, the most important Target Polarimetry descriptors: Sinclair Matrix, target vectors, coherency matrix and the covariance matrix as well are presented, their interconnections and equivalences will be shown together with the respective transformations. Speckle appearing in synthetic aperture radar (SAR) images is due to the coherent interference of waves reflected from many elementary scatterers and causes degradation and makes automatic image segmentation and scene description difficult. The speckle reduction problem is more complicated for polarimetric SAR than a single polarization SAR, because of the difficulties of preserving polarimetric properties and of dealing with the cross-product terms. The first part of this paper is ended by a presentation and a description of polarimetric speckle filters preserving polarimetric properties and statistical correlation between channels, not introducing crosstalk, and not degrading the image quality. The impact of using this polarimetric speckle filtering on terrain classification is quite dramatic in boosting classification performance.


title: A Comprehensive UAV Indoor Navigation System Based on Vision Optical Flow and Laser FastSLAM

abstract: Abstract This paper presents a comprehensive control, navigation, localization and mapping solution for an indoor quadrotor unmanned aerial vehicle (UAV) system. Three main sensors are used onboard the quadrotor platform, namely an inertial measurement unit, a downward-looking camera and a scanning laser range finder. With this setup, the UAV is able to estimate its own velocity and position robustly, while flying along the internal walls of a room without collisions. After one complete flight, with the collected data the historic UAV path and the indoor environment can be well estimated. The autonomous navigation part of the system does not require any remote sensory information or off-line computational power, while the mapping is done off-line. Complete flight tests have been carried out to verify fidelity and performance the navigation solution.


title: Ontology-driven Conceptual Design of ETL Processes using Graph Transformations ⋆

abstract: One of the main tasks during the early steps of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the source to the target data stores. This is a challenging task, requiring firstly the semantic and secondly the structural reconciliation of the information provided by the available sources. This task is a part of the Extract-Transform-Load (ETL) process, which is responsible for the population of the data warehouse. In this paper, we propose a customizable and extensible ontology-driven approach for the conceptual design of ETL processes. A graph-based representation is used as a conceptual model for the source and target data stores. We then present a method for devising flows of ETL operations by means of graph transformations. In particular, the operations comprising the ETL process are derived through graph transformation rules, the choice and applicability of which are determined by the semantics of the data with respect to an attached domain ontology. Finally, we present our experimental findings that demonstrate the applicability of our approach.


title: Advanced cicatricial retinopathy of prematurity--outcome and complications.

abstract: AIMS: To assess the outcome and complications of patients with advanced retinopathy of prematurity (ROP). METHODS: All patients with eyes achieving stage 4 or 5 retinopathy of prematurity were reviewed. Twenty one eyes were diagnosed during ROP screening in maternity hospitals and 10 eyes were of infants transferred for treatment. RESULTS: Thirty one eyes of 17 patients were included. Thirteen eyes were treated for acute disease but progressed to stage 4 or 5; seven had cryotherapy and six diode laser photocoagulation. Cataract was found in 17 eyes (54.8%), glaucoma in seven eyes (22.6%), microphthalmos in 15 (48.4%), and corneal opacification in four eyes (12.9%). Fifteen eyes had surgical procedures; two (6.5%) had trabeculectomy, four (12.9%) had lensectomy, and nine (29%) retinal detachment repair. Transferred infants had their initial eye examination later than infants in hospitals screened by the authors and 80% of them had progressed beyond threshold ROP by the time they were transferred for treatment. Twenty nine eyes (93.6%) had visual acuities of 3/60 or less and only two eyes (6.5%) achieved 6/18 or less. CONCLUSION: The visual outcome of the eyes undergoing retinal re-attachment surgery was disappointing. Cataract, microphthalmos, and glaucoma were the most frequent complications, and surgical intervention was often required. The need for children who are blind as a result of ROP to have long term follow up is shown.


title: Energy optimisation of hybrid off-grid system for remote telecommunication base station deployment in Malaysia

abstract: Cellular network operators are always seeking to increase the area of coverage of their networks, open up new markets and provide services to potential customers in remote rural areas. However, increased energy consumption, operator energy cost and the potential environmental impact of increased greenhouse gas emissions and the exhaustion of non-renewable energy resources (fossil fuel) pose major challenges to cellular network operators. The specific power supply needs for rural base stations (BSs) such as cost-effectiveness, efficiency, sustainability and reliability can be satisfied by taking advantage of the technological advances in renewable energy. This study investigates the possibility of decreasing both operational expenditure (OPEX) and greenhouse gas emissions with guaranteed sustainability and reliability for rural BSs using a solar photovoltaic/diesel generator hybrid power system. Three key aspects have been investigated: (i) energy yield, (ii) economic factors and (iii) greenhouse gas emissions. The results showed major benefits for mobile operators in terms of both environmental conservation and OPEX reduction, with an average annual OPEX savings of 43% to 47% based on the characteristics of solar radiation exposure in Malaysia. Finally, the paper compares the feasibility of using the proposed approach in a four-season country and compares the results against results obtained in Malaysia, which is a country with a tropical climate.


title: MC64: A web platform to test bioinformatics algorithms in a many-core architecture

abstract: New analytical methodologies, like the so-called “next-generation sequencing” (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (Needleman-Wunsch, Smith-Waterman and ClustalW) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into third-party tools. Furthermore, the results obtained from the MC64 include time-performance statistics that can be compared with other platforms.


title: Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence

abstract: Camera spectral sensitivity plays an important role for various color-based computer vision tasks. Although several methods have been proposed to estimate it, their applicability is severely restricted by the requirement for a known illumination spectrum. In this work, we present a single-image estimation method using fluorescence with no requirement for a known illumination spectrum. Under different illuminations, the spectral distributions of fluorescence emitted from the same material remain unchanged up to a certain scale. Thus, a camera's response to the fluorescence would have the same chromaticity. Making use of this chromaticity invariance, the camera spectral sensitivity can be estimated under an arbitrary illumination whose spectrum is unknown. Through extensive experiments, we proved that our method is accurate under different illuminations. Moreover, we show how to recover the spectra of daylight from the estimated results. Finally, we use the estimated camera spectral sensitivities and daylight spectra to solve color correction problems.


title: Loosecut: Interactive image segmentation with loosely bounded boxes

abstract: One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeling is performed to achieve a refined segmentation. One major issue of the existing algorithms for such interactive image segmentation is their preference of an input bounding box that tightly encloses the foreground object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including a global similarity constraint to better distinguish the foreground and background, and an additional energy term to encourage consistent labeling of similar-appearance pixels. This MRF model is then solved by an iterated max-flow algorithm. In the experiments, we evaluate LooseCut in three publicly-available image datasets, and compare its performance against several state-of-the-art interactive image segmentation algorithms. We also show that LooseCut can be used for enhancing the performance of unsupervised video segmentation and image saliency detection.


title: Fast Random Walk with Restart and Its Applications

abstract: How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the "connection subgraphs", personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block- wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman- Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150x speed up with 90%+ quality preservation.


title: A Comparison of Features for Android Malware Detection

abstract: With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware. We are able to differentiate between benign and malicious apps by applying a machine learning algorithm. The model that is presented here achieved a classification accuracy of around 80% using permissions and 60% using system calls for a relatively small dataset. In the future, different machine learning algorithms will be examined to see if there is a more suitable algorithm. More features will also be taken into account and the training set will be expanded.


title: Designing a mobile language learning system based on lightweight learning objects

abstract: This paper investigates the possibilities in the area of application of mobile technologies for language learning. The primary goal is to design a mobile system for learning of the Japanese language with a clear separation of content and presentation, and to leverage the learners' interest in the Japanese language. Both the technical and the language learning perspectives are discussed. A reusable, lightweight model of learning objects with compact content and a reduced metadata set is presented. These objects are stored in a simple learning object repository that can deliver them to any client over the Internet. A mobile application is designed to use the learning object repository as its content provider, while defining its own method of presentation suitable for smaller screens of mobile devices. For the purpose of evaluation, an experiment was conducted within the e-learning system at the Faculty of Organizational Sciences, University of Belgrade. Research findings have indicated that the developed mobile application has a positive effect on the students' interest in the subject matter.


title: Analysis and Design of Energy Efficient Time Domain Signal Processing

abstract: Time domain signal processing (TDSP) encodes information into time rather than voltage with higher efficiency than conventional digital design. This paper performs systematical analysis on the design principle and energy efficiency of TDSP. Variation impact, which poses significant challenges to TDSP, is evaluated and a variation driven design methodology is proposed to achieve an optimum tradeoff between energy efficiency and design robustness. Several novel circuit level design techniques such as dual encoding strategy and bit-scalable design are also proposed in this work to significantly improve the energy efficiency of TDSP. Design example on a critical building block of facial recognition application was used to demonstrate the potential of the technique. The result in a 45nm technology shows 3.3X energy-delay product reduction and 34% area saving can be achieved using TDSP compared with conventional digital design technique.


title: Software requirements as an application domain for natural language processing

abstract: AbstractMapping functional requirements first to specifications and then to code is one of the most challenging tasks in software development. Since requirements are commonly written in natural language, they can be prone to ambiguity, incompleteness and inconsistency. Structured semantic representations allow requirements to be translated to formal models, which can be used to detect problems at an early stage of the development process through validation. Storing and querying such models can also facilitate software reuse. Several approaches constrain the input format of requirements to produce specifications, however they usually require considerable human effort in order to adopt domain-specific heuristics and/or controlled languages. We propose a mechanism that automates the mapping of requirements to formal representations using semantic role labeling. We describe the first publicly available dataset for this task, employ a hierarchical framework that allows requirements concepts to be annotated, and discuss how semantic role labeling can be adapted for parsing software requirements.


title: Camera/IMU Calibration Revisited

abstract: With growing interest in visual/inertial state estimation and an increasing number of approaches and applications emerging for this technology, camera/IMU calibration can be a valuable tool to increase the performance of these methods and to further the understanding of the involved sensor modalities. In this paper, we assess the impact of two different adjustments to the commonly used sensor models. First, we extend the IMU model to take the displacement of individual accelerometer axes into account. We show that especially high quality devices benefit from this extension, since these IMUs often employ separate sensors for each axis. Second, we propose a novel, direct model for the camera measurements that operates on image intensities rather than corner positions. This formulation is capable of explicitly accounting for motion blur and defocus, but it requires significant modeling efforts. Our results demonstrate that the transformation between camera and IMU can be estimated to a precision exceeding $\tfrac {1}{5}\mathrm { \text {m} \text {m} }$ and $\tfrac {1}{100}\mathrm { ^{\circ}}$ , while temporal offsets are determined to microsecond precision—on data sets of merely 20-s length. At the same time, image exposure time can be inferred to an accuracy of about $\tfrac {2}{100}\mathrm { \text {m} \text {s} }$ from motion blur.


Efficient Classification of Android Malware in the Wild Using Robust Static Features


title: Lyrics-to-Audio Alignment and its Application

abstract: Automatic lyrics-to-audio alignment techniques have been drawing attention in the last years and various studies have been made in this field. The objective of lyrics-to-audio alignment is to estimate a temporal relationship between lyrics and musical audio signals and can be applied to various applications such as Karaoke-style lyrics display. In this contribution, we provide an overview of recent development in this research topic, where we put a particular focus on categorization of various methods and on applications.


title: Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

abstract: Robust and accurate detection of the pupil position is a key building block for head-mounted eye tracking and prerequisite for applications on top, such as gaze-based human---computer interaction or attention analysis. Despite a large body of work, detecting the pupil in images recorded under real-world conditions is challenging given significant variability in the eye appearance (e.g., illumination, reflections, occlusions, etc.), individual differences in eye physiology, as well as other sources of noise, such as contact lenses or make-up. In this paper we review six state-of-the-art pupil detection methods, namely ElSe (Fuhl et al. in Proceedings of the ninth biennial ACM symposium on eye tracking research & applications, ACM. New York, NY, USA, pp 123---130, 2016), ExCuSe (Fuhl et al. in Computer analysis of images and patterns. Springer, New York, pp 39---51, 2015), Pupil Labs (Kassner et al. in Adjunct proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (UbiComp), pp 1151---1160, 2014. doi:10.1145/2638728.2641695), SET (Javadi et al. in Front Neuroeng 8, 2015), Starburst (Li et al. in Computer vision and pattern recognition-workshops, 2005. IEEE Computer society conference on CVPR workshops. IEEE, pp 79---79, 2005), and źwirski (źwirski et al. in Proceedings of the symposium on eye tracking research and applications (ETRA). ACM, pp 173---176, 2012. doi:10.1145/2168556.2168585). We compare their performance on a large-scale data set consisting of 225,569 annotated eye images taken from four publicly available data sets. Our experimental results show that the algorithm ElSe (Fuhl et al. 2016) outperforms other pupil detection methods by a large margin, offering thus robust and accurate pupil positions on challenging everyday eye images.


Perceived control and subjective well-being across nations and across the life-span


title: Septal Batten Graft to Correct Cartilaginous Deformities in Endonasal Septoplasty

abstract: Objective To evaluate the usefulness of septal batten grafts to correct cartilaginous septal deformities in endonasal septoplasty. Design Retrospective study. Setting University medical center. Patients Of 430 patients who underwent endonasal septoplasties from January 2006 to January 2011, 30 received septal batten grafts and were enrolled in the study. Twenty-eight patients were male and 2 were female. Main Outcome Measures Thirty consecutive patients received septal batten grafts and were followed up for more than 6 months. Patterns of septal deformity, materials used for batten graft, surgical results, symptom improvement, findings of acoustic rhinometry, and surgical complications were investigated. Results Among the 30 patients, 5 were revision cases. Most of the deformities were characterized as moderate to severe degrees of curved or angulated deviations of the cartilaginous septum. The batten graft was performed with either septal cartilage (n = 21) or bony septum (n = 9). A straight septum was achieved in 90% of all procedures. Subjective symptoms of nasal obstruction were improved in all patients, as evaluated by the Nasal Obstruction Symptom Evaluation scale. Acoustic rhinometry revealed that after surgery the mean minimal cross-sectional area changed from 0.33 cm 2 to 0.42 cm 2 (P = .02) and the nasal volume from 4.71 mL to 6.28 mL (P = .02). There were no major complications, eg, septal perforation or saddle nose, and no revision surgery was needed. Conclusion Endonasal septal batten graft is a safe, useful, and effective technique to straighten moderate to severe septal cartilage deformities that are otherwise not correctable via conventional septoplasty techniques.


title: Structural and textural classification of erythrocytes in anaemic cases: A scanning electron microscopic study

abstract: Abstract The objective of this study is to address quantitative microscopic approach for automated screening of erythrocytes in anaemic cases using scanning electron microscopic (SEM) images of unstained blood cells. Erythrocytes were separated from blood samples and processed for SEM imaging. Thereafter, erythrocytes were segmented using marker controlled watershed transformation technique. Total 47 structural and textural features of erythrocytes were extracted using various mathematical measures for six types of anaemic cases as compared to the control group. These features were statistically evaluated at 1% level of significance and subsequently ranked using Fisher's F -statistic describing the group discriminating potentiality. Amongst all extracted features, twenty nine features were found to be statistically significant ( p


title: Methodical Errors of the SWOT-Analysis in Applied Research

abstract: The article discusses the methodology is widely known the SWOT-analysis and suggests approaches to increase confidence in the results of the analysis. Some typical methodological errors are Illustrated by examples and discussed. Recommendations that increase the value of the method in strategic analysis and increase its effectiveness are given. The recommendations could be useful for companies’ managers of different levels in the strategy development and advancement of strategic alternatives, Diploma Thesis supervisors as well as for students and graduates of MBA programs.


title: Posttraumatic stress disorder and fMRI activation patterns of traumatic memory in patients with borderline personality disorder

abstract: Background: Early traumatization and additional posttraumatic stress disorder are frequent in patients with borderline personality disorder (BPD). The purpose of this study was to investigate neural correlates of traumatic memory in BPD with and without posttraumatic stress disorder (PTSD) using functional magnetic resonance imaging (fMRI). Methods: We studied 12 traumatized female patients BPD, 6 of them with and 6 without PTSD. According to an autobiographical interview key words (cues) were defined for traumatic and for negative but nontraumatic episodes. In a block-designed fMRI task patients recalled these episodes. Contrasts between trauma condition and nontrauma condition were analyzed. Results: Analyses for all subjects revealed activation of orbitofrontal cortex areas in both hemispheres, anterior temporal lobes, and occipital areas. In the subgroup without PTSD, activation of orbitofrontal cortex on both sides and Broca’s area predominated. In the subgroup with additional PTSD, we observed right more than left activation of anterior temporal lobes, mesiotemporal areas, amygdala, posterior cingulate gyrus, occipital areas, and cerebellum. Conclusions: Dependent on absence or presence of additional PTSD different neural networks seem to be involved in the traumatic memory of patients with BPD. Biol Psychiatry 2003;00:000 – 000 © 2003 Society of Biological Psychiatry


title: End-user debugging strategies: A sensemaking perspective

abstract: Despite decades of research into how professional programmers debug, only recently has work emerged about how end-user programmers attempt to debug programs. Without this knowledge, we cannot build tools to adequately support their needs. This article reports the results of a detailed qualitative empirical study of end-user programmers' sensemaking about a spreadsheet's correctness. Using our study's data, we derived a sensemaking model for end-user debugging and categorized participants' activities and verbalizations according to this model, allowing us to investigate how participants went about debugging. Among the results are identification of the prevalence of information foraging during end-user debugging, two successful strategies for traversing the sensemaking model, potential ties to gender differences in the literature, sensemaking sequences leading to debugging progress, and sequences tied with troublesome points in the debugging process. The results also reveal new implications for the design of spreadsheet tools to support end-user programmers' sensemaking during debugging.