Image-Based Slicing and Tool Path Planning for Hybrid Stereolithography Additive Manufacturing


title: Handling non-functional requirements for big data and IOT projects in Scrum

abstract: Agile methodologies are gaining popularity at a lightning pace in the software industry. Prominent agile methodologies, such as Scrum and Extreme Programming (XP), help quickly deliver a system that meets Functional Requirements (FRs), while adapting to the changes in the customer requirements and feedback. However, Non-Functional Requirements (NFRs) by and large have been either mostly ignored or introduced and retrofit late in the software development cycle, oftentimes leading to project failures. With the recent, increasing shifts towards cloud and emphasis in big data in the software industry, NFRs, such as security and performance, have become more critical than ever before. This research proposes a novel approach to handling security and performance NFRs for projects involving big data and cloud, using Scrum. An industrial case study, conducted over a span of 9 months, shows that the approach helps deal with security and performance requirements individually as well as conflicts between them in an agile methodology.


title: A Collision-Mitigation Cuckoo Hashing Scheme for Large-Scale Storage Systems

abstract: With the rapid growth of the amount of information, cloud computing servers need to process and analyze large amounts of high-dimensional and unstructured data timely and accurately. This usually requires many query operations. Due to simplicity and ease of use, cuckoo hashing schemes have been widely used in real-world cloud-related applications. However, due to the potential hash collisions, the cuckoo hashing suffers from endless loops and high insertion latency, even high risks of re-construction of entire hash table. In order to address these problems, we propose a cost-efficient cuckoo hashing scheme, called MinCounter. The idea behind MinCounter is to alleviate the occurrence of endless loops in the data insertion by selecting unbusy kicking-out routes. MinCounter selects the “cold” (infrequently accessed), rather than random, buckets to handle hash collisions. We further improve the concurrency of the MinCounter scheme to pursue higher performance and adapt to concurrent applications. MinCounter has the salient features of offering efficient insertion and query services and delivering high performance of cloud servers, as well as enhancing the experiences for cloud users. We have implemented MinCounter in a large-scale cloud testbed and examined the performance by using three real-world traces. Extensive experimental results demonstrate the efficacy and efficiency of MinCounter.


title: Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

abstract: We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin's L* algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.


title: The Truth About Baby Boomer Gamers: A study of over-forty computer game players

abstract: This article describes a study conducted in the summer of 2006 aimed at exploring the play patterns and lifestyles of gamers who fall into the loose demographic of “Baby Boomers,” typically defined as people born between 1946 and 1964. This independent study, including more than 300 participants, combined quantitative and qualitative techniques to paint a multifaceted picture of the gaming lifestyles and tastes of this understudied population. The study findings show that Baby Boomers comprise a vibrant video game audience, that they are devoted players, and that they have distinct needs and interests that have gone ignored by both the mainstream game industry and the game press. They also provide some detailed data about their play styles and gaming interests, the role of gaming in their larger media mix, as well as specific case studies that paint a nuanced portrait of this understudied and underserved audience.


title: Curvature-based regularization for surface approximation

abstract: We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noisy) data point by minimizing the squared distances to the points and the irregularity of the surface implicitly defined by the tangent planes. In order to avoid the well-known ”shrinking” bias associated with first-order surface regularization, we choose a robust smoothing term that approximates curvature of the underlying surface. In contrast to a number of recent publications estimating curvature using discrete (e.g. binary) labellings with triple-cliques we use higher-dimensional labels that allows modeling curvature with only pair-wise interactions. Hence, many standard optimization algorithms (e.g. message passing, graph cut, etc) can minimize the proposed curvature-based regularization functional. The accuracy of our approach for representing curvature is demonstrated by theoretical and empirical results on synthetic and real data sets from multiview reconstruction and stereo.


title: Predictive control of fractional state-space model

abstract: In the first part of this paper we present the discretization method of fractional order system. In the second part, predictive control based on a fractional state-space model will be presented. In the third part we will present simulation results.


title: Fully integrated wideband high-current rectifiers for inductively powered devices

abstract: This paper describes the design and implementation of fully integrated rectifiers in BiCMOS and standard CMOS technologies for rectifying an externally generated RF carrier signal in inductively powered wireless devices, such as biomedical implants, radio-frequency identification (RFID) tags, and smartcards to generate an on-chip dc supply. Various full-wave rectifier topologies and low-power circuit design techniques are employed to decrease substrate leakage current and parasitic components, reduce the possibility of latch-up, and improve power transmission efficiency and high-frequency performance of the rectifier block. These circuits are used in wireless neural stimulating microsystems, fabricated in two processes: the University of Michigan's 3-/spl mu/m 1M/2P N-epi BiCMOS, and the AMI 1.5-/spl mu/m 2M/2P N-well standard CMOS. The rectifier areas are 0.12-0.48 mm/sup 2/ in the above processes and they are capable of delivering >25mW from a receiver coil to the implant circuitry. The performance of these integrated rectifiers has been tested and compared, using carrier signals in 0.1-10-MHz range.


title: Deep API learning

abstract: Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bags-of-words and lack a deep understanding of the semantics of the query. We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bag-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches.


title: Non-Orthogonal Multiple Access With Cooperative Full-Duplex Relaying

abstract: We propose a full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) system with dual users, where a dedicated FD relay assists the information transmission to the user with weak channel condition. Under the realistic assumption of imperfect self-interference cancellation, the achievable outage probability of both users and ergodic sum capacity are investigated, and exact analytical expressions are derived. Simulation results demonstrate that the proposed FD cooperative NOMA system attains better performance than the half-duplex cooperative NOMA system in the moderate signal-to-noise ratio regime.


title: Lane Detection Using Catmull-Rom Spline

abstract: In this paper, a Catmull-Rom spline based lane model which described the perspective effect of parallel lines was proposed for generic lane boundary. As Catmull-Rom spline can form arbitrary shapes by control points, it can describe a wider range of lane structures than other lane models such as straight and parabolic model. It formulates the lane detection problem in the form of determining the set of lane model control points. The proposed algorithm uses a Maximum likelihood approach to the lane detection problem. We have employed a multiresolution strategy to rapidly find an accurate solution. This coarse-to-fine matching offers an acceptable solution at an affordable computational cost and speeds up the detection. The proposed method is robust to the noise present in the road image with shadows, variations in illumination, marked and unmarked road.


title: Local optical-flow estimation for forward looking imaging sonar data

abstract: In the underwater environment the use of optical camera technology is limited by the light attenuation and distortions due to turbidity the water column. High frame rate forward looking sonars have been developed as an alternative, however, when compared to optical systems the image is of a lower resolution and suffers from sound imaging related artefacts. This means that there is a need for registration algorithms that are better able to address these specific difficulties. The registration algorithm presented in this paper, uses local phase information to estimate movement within the image for each pixel. A coarse estimate is achieved by using sparse recovery techniques to select from a dictionary of modelled movements. This estimate is then refined using an adaptive filter. The resultant displacement map can then be used to enhance the image quality, or, as the basis for estimating the sensor movement. The method is used to register an example sonar image and the results shown against full image phase based registration.


title: A Privacy Preserving Context-Aware Insider Threat Prediction and Prevention Model Predicated on the Components of the Fraud Diamond

abstract: This paper presents a novel, interdisciplinary insider threat prediction and prevention model which combines several approaches, techniques and tools from computer science, criminology and psychology. It is based on the fraud diamond which comprises four components: pressure (i.e. motive), opportunity, capability and rationalisation. In general, it has been found that a criminal must have the motive and opportunity to commit a crime. However, the criminal must also have the capability to commit crime particularly in the cybercrime domain. Furthermore, criminals tend to rationalise (i.e. excuse) their crimes. The model presented here involves leveraging the fraud diamond to predict and prevent insider threats. Typically insider threat prediction and prevention models tend to overlook the privacy concerns of insiders. Hence the deployment of the model involves gathering information about the insider's possible intent within a privacy-preserving context. This will help to identify high risk insider threats while balancing privacy concerns.


title: Redefining Field Experiences: Virtual Environments in Teacher Education

abstract: The explosion of interest in online gaming and virtual worlds is leading many universities to investigate possible educational applications of the new environments. In this paper we explore the possibilities of 3D online worlds for teacher education, particularly the field experience component. Drawing upon two pedagogical examples, we suggest that virtual simulations may, with certain limitations, create safe spaces that allow preservice teachers to adopt alternate identities and interact safely with the "other." In so doing they may become aware of the constructed nature of social categories and gain the essential pedagogical skill of perspective-taking. We suggest that, ultimately, the ability to be the principal creators of themselves in virtual environments can increase their ability to do the same in the real world. Keywords—field experience, pedagogy, simulation, teacher education


Versatile Protocol Processing for Multimedia Communications


title: Dissociable neural mechanisms supporting visual short-term memory for objects

abstract: Using visual information to guide behaviour requires storage in a temporary buffer, known as visual short-term memory (VSTM)1, that sustains attended information across saccades and other visual interruptions. There is growing debate on whether VSTM capacity is limited to a fixed number of objects2,3 or whether it is variable4,5. Here we report four experiments using functional magnetic resonance imaging that resolve this controversy by dissociating the representation capacities of the parietal and occipital cortices. Whereas representations in the inferior intra-parietal sulcus (IPS) are fixed to about four objects at different spatial locations regardless of object complexity, those in the superior IPS and the lateral occipital complex are variable, tracking the number of objects held in VSTM, and representing fewer than four objects as their complexity increases. These neural response patterns were observed during both VSTM encoding and maintenance. Thus, multiple systems act together to support VSTM: whereas the inferior IPS maintains spatial attention over a fixed number of objects at different spatial locations, the superior IPS and the lateral occipital complex encode and maintain a variable subset of the attended objects, depending on their complexity. VSTM capacity is therefore determined both by a fixed number of objects and by object complexity.


title: Comparison of music genre classification using Nearest Centroid Classifier and k-Nearest Neighbours

abstract: Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching. Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research is conducted to classify music from digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively. The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%. The test results with the k-Nearest Neighbours algorithm to 120 songs for k = 3 accuracy reaches 22.5%, k = 5 accuracy reaches 22.5%, k = 7 accuracy reaching 26.7% and k = 9 accuracy reaches 26.7 %. Results showed that genre classification by matching the shortest distance through the centre of the class, yields better results than using the k-NN algorithm.


title: Speed, Data, and Ecosystems: The Future of Software Engineering

abstract: An evaluation of recent industrial and societal trends revealed three key factors driving software engineering's future: speed, data, and ecosystems. These factors' implications have led to guidelines for companies to evolve their software engineering practices. This article is part of a special issue on the Future of Software Engineering.


title: Efficiency evaluation of open source ETL tools

abstract: Business intelligence (BI) is considered to have a high impact on businesses. Research activity has risen in the last years. An important part of BI systems is a well performing implementation of the Extract, Transform, and Load (ETL) process. In typical BI projects, implementing the ETL process can be the task with the greatest effort. However, little work is published on ETL applications and in particular on open source ETL tools. We have analyzed open source ETL tools especially with regard to their performance. In this paper we present the analysis' background and highlight related work. We then sketch the test setup, show the detailed results for Talend Open Studio and Pentaho Data Integration, and discuss our observations. Eventually, we draw a conclusion and point out future work.


title: Contributions of the ventromedial prefrontal cortex to goal‐directed action selection

abstract: In this article, it will be argued that one of the key contributions of the ventromedial prefrontal cortex (vmPFC) to goal-directed action selection lies both in retrieving the value of goals that are the putative outcomes of the decision process and in establishing a relative preference ranking for these goals by taking into account the value of each of the different goals under consideration in a given decision-making scenario. These goal-value signals are then suggested to be used as an input into the on-line computation of action values mediated by brain regions outside of the vmPFC, such as parts of the parietal cortex, supplementary motor cortex, and dorsal striatum. Collectively, these areas can be considered to be constituent elements of a multistage decision process whereby the values of different goals must first be represented and ranked before the value of different courses of action available for the pursuit of those goals can be computed.


title: Tracking And Object Classification For

abstract: In this paper we discuss the issues that need to be resolved before fully automated outdoor surveillance systems can be developed, and present solutions to some of these problems. Any outdoor surveillance system must be able to track objects moving in its field of view, classify these objects and detect some of their activities. We have developed a method to track and classify these objects in realistic scenarios. Object tracking in a single camera is performed using background subtraction, followed by region correspondence. This takes into account multiple cues including velocities, sizes and distances of bounding boxes. Objects can be classified based on the type of their motion. This property may be used to label objects as a single person, vehicle or group of persons. Our proposed method to classify objects is based upon detecting recurrent motion for each tracked object. We develop a specific feature vector called a 'Recurrent Motion Image' (RMI) to calculate repeated motion of objects. Different types of objects yield very different RMI's and therefore can easily be classified into different categories on the basis of their RMI. The proposed approach is very efficient both in terms of computational and space criteria. RMI's are further used to detect carried objects. We present results on a large number of real world sequences including the PETS 2001 sequences. Our surveillance system works in real time at approximately 15Hz for 320?240 resolution color images on a 1.7 GHz pentium-4 PC.


title: A model for notification systems evaluation—assessing user goals for multitasking activity

abstract: Addressing the need to tailor usability evaluation methods (UEMs) and promote effective reuse of HCI knowledge for computing activities undertaken in divided-attention situations, we present the foundations of a unifying model that can guide evaluation efforts for notification systems. Often implemented as ubiquitous systems or within a small portion of the traditional desktop, notification systems typically deliver information of interest in a parallel, multitasking approach, extraneous or supplemental to a user's attention priority. Such systems represent a difficult challenge to evaluate meaningfully. We introduce a design model of user goals based on blends of three critical parameters---interruption, reaction, and comprehension. Categorization possibilities form a logical, descriptive design space for notification systems, rooted in human information processing theory. This model allows conceptualization of distinct action models for at least eight classes of notification systems, which we describe and analyze with a human information processing model. System classification regions immediately suggest useful empirical and analytical evaluation metrics from related literature. We present a case study that demonstrates how these techniques can assist an evaluator in adapting traditional UEMs for notification and other multitasking systems. We explain why using the design model categorization scheme enabled us to generate evaluation results that are more relevant for the system redesign than the results of the original exploration done by the system's designers.


title: The effectiveness of orthopedic patient education in improving patient outcomes: a systematic review protocol.

abstract: REVIEW OBJECTIVE ::: The objective of this review is to identify the effectiveness of patient education for orthopedic surgery patients. More specifically, the objectives are to identify the effectiveness of patient education on:length of staysatisfactionpain levelcost of carefunctional abilitiesknowledgeanxietyquality of life in orthopedic settings. ::: ::: ::: BACKGROUND ::: Patient education is an essential part of practice for all healthcare professionals. In the orthopedic setting, effective patient education contributes to positive patient outcomes. Patient education is critical to ensure that patients receive appropriate information to assist in the pre-admission, peri-operative treatment and rehabilitation process for the patient. The process of patient education is essentially one where the patient comes to understand his or her physical condition and self-care using the experience and guidance of the multidisciplinary team.With an effective and well-structured patient education program, the cost benefit for health care provider and patient includes a shortened length of stay, and reduced cost of care. According to Huang et al. a simplified pre-operative education program reduced the length of stay and cost of care. Similarly, Jones et al. found that length of stay of a patient who received pre-operative education was reduced. In that study, the mean length of stay was significantly reduced from seven days in patients who did not received pre-operative education to five days in patients who received pre-operative education. These results suggest that pre-operative education programs are an effective method in reducing the length of stay of orthopedic patients.Johansson et al. also described pre-operative education for orthopedic patients in a systematic review published in 2005. They discussed the effect of patient education on the orthopedic patient and found that knowledge, anxiety, pain, length of hospital stay, performance of exercise and mobilization, self-efficacy, patient compliance, adherence and empowerment were all improved as a result of patient education. While Johansson et al. included studies up to 2003, the proposed systematic review will include studies from 2003 to 2013.Kruzik also reported benefits of decreased length of stay, reduced pain medication requested post-operatively together with increased patient and family member satisfaction. Bastable reported benefits of patient education, including increased patient satisfaction, improved quality of life, enhanced continuity of care, decreased anxiety, fewer complications, promotion of adherence to the plan of care, maximized independence, and empowerment.Types of education involved in this systematic review are patient education, pre-operative education, and discharge education. Only one study discussed discharge education and the outcome from this review. The outcomes that have been discussed in this systematic review include length of stay, satisfaction, pain level,, 21 cost of care, functional abilities, knowledge, anxiety, and quality of life.Major outcomes discussed in these studies are length of stay, pain,, 21 functional abilities, and anxiety. Most of these studies found significant results of reduced length of stay on those patients who received patient education compared to patient who did not received any patient education or information. Studies support the positive outcomes around length of stay.Common orthopedic conditions that have been discussed are osteoarthritis with total arthroplasty either hip or knee or joint replacement. A study on spinal surgery patients and the effects of pre-operative education, which stated that although there are many studies on the effectiveness on patient education, there are missing data on spinal surgery. This study found that the implementation of patient education has positive impacts upon patient satisfaction especially in managing pain.This review will look specifically at the effectiveness of orthopedic patient education for length of stay, satisfaction, pain level, cost of care, functional ability, knowledge, anxiety, and quality of life.


title: Developing science teachers' pedagogical content knowledge

abstract: This article discusses the concept of pedagogical content knowledge (PCK) within the context of science teaching. First, an attempt is made to define this concept within the tradition of research on teachers' craft knowledge and to identify possible purposes of research on PCK. From this point of view, recent research on science teaching is investigated. This investigation identifies teaching experience as the major source of PCK, whereas adequate subject-matter knowledge appears to be a prerequisite. Finally, an empirical study is presented which focuses on PCK with respect to a specific topic—that is, chemical equilibrium. The effects on teachers' PCK of participation in an in-service workshop and conducting an experimental course in classroom practice are reported. This leads to the identification of elements of PCK teachers can use to promote student understanding. It is concluded that research on topic-related PCK may complement research on student learning of specific topics. © 1998 John Wiley & Sons, Inc. J Res Sci Teach 35: 673–695, 1998.


title: Why does the spotted UX Ari system get bluer as it gets darker?

abstract: We present simplified calculations of UX Ari B − V colour index and V magnitude for a range of parameter values. The results of our calculations are compared to previously published measurements. We find that our calculations reproduce the well established colour-brightness relation (UX Ari gets bluer as it gets darker) only if the dark spots on the surface of the K star are accompanied by bright facular areas. There is currently an ambiguity between the temperature of the facular areas and the portion of the stellar surface occupied by them.


title: Face recognition using deep multi-pose representations

abstract: We introduce our method and system for face recognition using multiple pose-aware deep learning models. In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features. 3D rendering is used to generate multiple face poses from the input image. Sensitivity of the recognition system to pose variations is reduced since we use an ensemble of pose-specific CNN features. The paper presents extensive experimental results on the effect of landmark detection, CNN layer selection and pose model selection on the performance of the recognition pipeline. Our novel representation achieves better results than the state-of-the-art on IARPA's CS2 and NIST's IJB-A in both verification and identification (i.e. search) tasks.


title: A Programmable Lens-Array Antenna With Monolithically Integrated MEMS Switches

abstract: This paper describes a reconfigurable millimeter-wave lens-array antenna based on monolithically integrated microelectromechanical systems (MEMS) switches. This device is constructed as a planar array of 2-bit programmable MEMS antenna-filter-antenna (AFA) unit cells that are used to provide a 1-D programmable ldquoaperture transfer functionrdquo between the input and output wavefronts. The fully integrated device consists of 484 (22 times 22) AFA elements and 2420 switches. Switches, bias lines, antennas, and the rest of the RF structure are fabricated on two quartz wafers (epsivr = 3.8, tandelta = 0.002) that are subsequently stacked using adhesive bonding to form the tri-layer metal structure of the AFA array. The bonded structure also forms a package for the MEMS switches. This paper investigates the design and fabrication issues and presents the measured data related to yield and frequency response of this lens-array. It also characterizes the performance of this device as a steerable antenna. Measured results show that this lens-array can be used to steer the beam of a low gain horn antenna to plusmn40deg in either the E- or the H-plane. For the fabricated prototype, the yield is estimated to be 50% for the best region of the array, resulting in a relatively high insertion loss and sidelobe level.


title: Using multivariate statistics to analyze body characteristic index for garment supply chain an industrial application

abstract: Garment production standards are essential to successfully designing and sizing clothing for garment production. Numerous approaches utilize approximate range of the girth difference to determine body types for developing garment production standards, but these body types could not be accurately classified. Moreover, few studies have applied the improvement girth difference model to body typing for the garment industry. Accordingly, this study attempts to identify body types using the improvement girth difference model as obtained from anthropometric data for garment production standards development. The proposed approach can reflect fine distinctions between figures. Body types and their distributions will have precisely identify and use in developing garment production standards. Furthermore, new garment production standards forecast production numbers and the proportion of sizes to be produced, leading to accurate production planning and material control. Unnecessary inventory costs and production waste resulting from sizing mismatches between finished garments and consumers thus can be minimized.


title: Fashionable shape switching: explorations in outfit-centric design

abstract: We present a design exercise illustrating how fashion practices and the fashion design process can be used to create new opportunities both in the mobile domain and in product design, as well as in wearable computing. We investigate the concept of outfit-centric design by extending the support for social and visual interaction with digital devices beyond the currently available shells and stickers, and drawing on the ways in which people vary their dress ensembles. We designed a set of mock-up samples in a local fashion style, as a first step in understanding possible applications of the emerging technology of organic interfaces. Initial user feedback shows how fashion-conscious participants creatively experimented with the set's variations of shape and color in outfits created from their personal wardrobes, which revealed the importance of the objects' size and location on the body. It also points out that a lack of integration with the fashion system's processes reduces the attractiveness of the samples.


title: Expertise recommender: a flexible recommendation system and architecture

abstract: Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.


title: A keyword extraction method from twitter messages represented as graphs

abstract: Abstract Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets.


title: A Vision for Next Generation Query Processors and an Associated Research Agenda

abstract: Query processing is one of the most important mechanisms for data management, and there exist mature techniques for effective query optimization and efficient query execution. The vast majority of these techniques assume workloads of rather small transactional tasks with strong requirements for ACID properties. However, the emergence of new computing paradigms, such as grid and cloud computing, the increasingly large volumes of data commonly processed, the need to support data driven research, intensive data analysis and new scenarios, such as processing data streams on the fly or querying web services, the fact that the metadata fed to optimizers are often missing at compile time, and the growing interest in novel optimization criteria, such as monetary cost or energy consumption, create a unique set of new requirements for query processing systems. These requirements cannot be met by modern techniques in their entirety, although interesting solutions and efficient tools have already been developed for some of them in isolation. Next generation query processors are expected to combine features addressing all of these issues, and, consequently, lie at the confluence of several research initiatives. This paper aims to present a vision for such processors, to explain their functionality requirements, and to discuss the open issues, along with their challenges.


title: Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting

abstract: It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.


title: The weighted median filter

abstract: The median filter is well-known [ 1, 2 ]. However, if a user wishes to predefine a set of feature types to remove or retain, the median filter does not necessarily satisfy the requirements. A more general filter, called the Weighted Median Filter, of which the median filter is a special case, is described. It enables filters to be designed with a wide variety of properties. Particular cases of filter requirements are discussed and the corresponding filters are derived. The notion of a minimal weighted median filter, of a subclass that act identically, is introduced and discussed. The question of finding the number of distinct ways a class of filters can act is considered and solved for some classes.


title: Modeling Lithium Ion battery degradation in electric vehicles

abstract: A new aging model for Lithium Ion batteries is proposed based on theoretical models of crack propagation. This provides an exponential dependence of aging on stress such as depth of discharge. A measure of stress is derived from arbitrary charge and discharge histories to include mixed use in vehicles or vehicle to grid operations. This aging model is combined with an empirical equivalent circuit model, to provide time and state of charge dependent charge and discharge characteristics at any rate and temperature. This choice of model results in a cycle life prediction with few parameters to be fitted to a particular cell.


title: Storylines of research in diffusion of innovation: a meta-narrative approach to systematic review

abstract: Producing literature reviews of complex evidence for policymaking questions is a challenging methodological area. There are several established and emerging approaches to such reviews, but unanswered questions remain, especially around how to begin to make sense of large data sets drawn from heterogeneous sources. ::: ::: Drawing on Kuhn's notion of scientific paradigms, we developed a new method—meta-narrative review—for sorting and interpreting the 1024 sources identified in our exploratory searches. We took as our initial unit of analysis the unfolding ‘storyline’ of a research tradition over time. We mapped these storylines by using both electronic and manual tracking to trace the influence of seminal theoretical and empirical work on subsequent research within a tradition. We then drew variously on the different storylines to build up a rich picture of our field of study. We identified 13 key meta-narratives from literatures as disparate as rural sociology, clinical epidemiology, marketing and organisational studies. Researchers in different traditions had conceptualised, explained and investigated diffusion of innovations differently and had used different criteria for judging the quality of empirical work. Moreover, they told very different over-arching stories of the progress of their research. Within each tradition, accounts of research depicted human characters emplotted in a story of (in the early stages) pioneering endeavour and (later) systematic puzzle-solving, variously embellished with scientific dramas, surprises and ‘twists in the plot’. By first separating out, and then drawing together, these different meta-narratives, we produced a synthesis that embraced the many complexities and ambiguities of ‘diffusion of innovations’ in an organisational setting. We were able to make sense of seemingly contradictory data by systematically exposing and exploring tensions between research paradigms as set out in their over-arching storylines. In some traditions, scientific revolutions were identifiable in which breakaway researchers had abandoned the prevailing paradigm and introduced a new set of concepts, theories and empirical methods. We concluded that meta-narrative review adds value to the synthesis of heterogeneous bodies of literature, in which different groups of scientists have conceptualised and investigated the ‘same’ problem in different ways and produced seemingly contradictory findings. Its contribution to the mixed economy of methods for the systematic review of complex evidence should be explored further.


title: Diagnostic tests: Imaging for chronic abdominal pain in adults

abstract: Diagnostic imaging is often not indicated in chronic abdominal pain. In particular, undifferentiated abdominal pain is rarely an indication for a CT scan. ::: ::: CT scanning is overused even when imaging is required. Other modalities may be preferable. A normal CT scan does not rule out cancer. ::: ::: Alarm symptoms, including anaemia, blood in the stool, waking at night with gastrointestinal symptoms, and weight loss, should be investigated. The most appropriate modality depends on the symptoms. ::: ::: Clinical information on request forms for CT scans should be specific and include the suspected condition as this helps the radiologist to determine an appropriate imaging protocol.


title: Person-centered therapy and solution-focused brief therapy: An integration of present and future awareness.

abstract: The authors propose an integration of person-centered therapy, with its focus on the here and now of client awareness of self, and solution-focused therapy, with its future-oriented techniques that also raise awareness of client potentials. Although the two theories hold different assumptions regarding the therapist's role in facilitating client change, it is suggested that solution-focused techniques are often compatible for use within a person-centered approach. Further, solution-focused activities may facilitate the journey of becoming self-aware within the person-centered tradition. This article reviews the two theories, clarifying the similarities and differences. To illustrate the potential integration of the approaches, several types of solution-focused strategies are offered through a clinical example. (PsycINFO Database Record (c) 2011 APA, all rights reserved).


title: Expanded Hemodialysis: A New Therapy for a New Class of Membranes.

abstract: A wide spectrum of molecules is retained in end-stage kidney disease, normally defined as uremic toxins. These solutes have different molecular weights and radii. Current dialysis membranes and techniques only remove solutes in the range of 50-15,000 Da, with limited or no capability to remove solutes in the middle to high molecular weight range (up to 50,000 Da). Improved removal has been obtained with high cut-off (HCO) membranes, with albumin loss representing a limitation to their practical application. Hemodiafiltration (HDF) at high volumes (>23 L/session) has produced some results on middle molecules and clinical outcomes, although complex hardware and high blood flows are required. A new class of membrane has been recently developed with a cut off (MWCO) close to the molecular weight of albumin. While presenting negligible albumin loss, these membranes have a very high retention onset (MWRO), allowing high clearances of solutes in a wide spectrum of molecular weights. These membranes originally defined (medium cut off) are probably better classified as high retention onset. The introduction of such membranes in the clinical routine has allowed the development of a new concept therapy called “expanded hemodialysis” (HDx). The new therapy is based on a special hollow fiber and dialyzer design. Its simple set-up and application offer the possibility to use it even in patients with suboptimal vascular access or even with an indwelling catheter. The system does not require a particular hardware or unusual nursing skill. The quality of dialysis fluid is, however, mandatory to ensure a safe conduction of the dialysis session. This new therapy is likely to modify the outcome of end-stage kidney disease patients, thanks to the enhanced removal of molecules traditionally retained by current dialysis techniques.


title: A horizontal fragmentation algorithm for the fact relation in a distributed data warehouse

abstract: Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a “horizontal” fragmentation algorithm for the fact relation.


title: Multichannel EMG data acquisition system: Design and temporal analysis during human ankle joint movements

abstract: This paper presents the implementation of four channel Electromyography (EMG) signal acquisition system for acquiring the EMG signal of the lower limb muscles during ankle joint movements. Furthermore, some post processing and statistical analysis for the recorded signal were presented. Four channels were implemented using instrumentation amplifier (INA114) for pre-amplification stage then the amplified signal subjected to the band pass filter to eliminate the unwanted signals. Operational amplifier (OPA2604) was involved for the main amplification stage to get the output signal in volts. The EMG signals were detected during movement of the ankle joint of a healthy subject. Then the signal was sampled at the rate of 2 kHz using NI6009 DAQ and Labview used for displaying and storing the acquired signal. For EMG temporal representation, mean absolute value (MAV) analysis algorithm is used to investigate the level of the muscles activity. This data will be used in future as a control input signal to drive the ankle joint exoskeleton robot. I. INTRODUCTION Human body consists of different outstanding and complex functional systems. One of most significant systems is the skeletal muscle system. There are more than 600 hundred muscles in the human body. One and foremost function of these muscles is to produce the movement of the different parts of the body such as walking, standing and more actions throughout the body. These muscles controlled by the nervous system. The human nervous system divided into two parts; Central Nervous System (CNS) and peripheral nervous system (PNS). The CNS consists of brain and spinal cord while the peripheral system found throughout the body. The nerve cell or the neuron is the basic element of the nervous system. It transmits the order received from the brain to the body parts such as muscles through their axon and dendrites. Also this nerve cell called motor neuron and it forms the motor unit with the muscle fibers as depicted in Fig 1. Each muscle contains a certain number of motor units ranging from 100 for small hand muscle to 1000 for the largest muscle in the lower limb (1). Each motor unit produces an action potential and it is called motor unit action potential (MUAP). The composition of these MUAP form the EMG signal. Therefore the EMG signal can be defined as a representation of the electrical activity of the muscles during contraction phase. The idea of measuring the muscle activity


title: How Can Heterogeneous Internet of Things Build Our Future: A Survey

abstract: Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. HetIoT is being employed in increasing number of areas, such as smart home, smart city, intelligent transportation, environmental monitoring, security systems, and advanced manufacturing. Therefore, relaying on strong application fields, HetIoT will be filled in our life and provide a variety of convenient services for our future. The network architectures of IoT are intrinsically heterogeneous, including wireless sensor network, wireless fidelity network, wireless mesh network, mobile communication network, and vehicular network. In each network unit, smart devices utilize appropriate communication methods to integrate digital information and physical objects, which provide users with new exciting applications and services. However, the complexity of application requirements, the heterogeneity of network architectures and communication technologies impose many challenges in developing robust HetIoT applications. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. Then, the state of the art in HetIoT research and applications have been discussed. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including self-organizing, big data transmission, privacy protection, data integration and processing in large-scale HetIoT.


title: Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data

abstract: [1] Despite remarkable successes achieved by Differential InSAR, estimations of low tectonic strain rates remain challenging in areas where deformation and topography are correlated, mainly because of the topography-related atmospheric phase screen (APS). In areas of high relief, empirical removal of the stratified component of the APS may lead to biased estimations of tectonic deformation rates. Here we describe a method to correct interferograms from the effects of the spatial and temporal variations in tropospheric stratification by computing tropospheric delay maps coincident with SAR acquisitions using the ERA-Interim global meteorological model. The modeled phase delay is integrated along vertical profiles at the ERA-I grid nodes and interpolated at the spatial sampling of the interferograms above the elevation of each image pixel. This approach is validated on unwrapped interferograms. We show that the removal of the atmospheric signal before phase unwrapping reduces the risk of unwrapping errors in areas of rough topography.


title: Level Set Methods and Dynamic Implicit Surfaces

abstract: This book is an introduction to level set methods and dynamic implicit surfaces. These are powerful techniques for analyzing and computing moving fronts in a variety of different settings. While it gives many examples of the utility of the methods to a diverse set of applications, it also gives complete numerical analysis and recipes, which will enable users to quickly apply the techniques to real problems. The book begins with a description of implicit surfaces and their basic properties, then devises the level set geometry and calculus toolbox, including the construction of signed distance functions. Part II adds dynamics to this static calculus. Topics include the level set equation itself, Hamilton-Jacobi equations, motion of a surface normal to itself, re-initialization to a signed distance function, extrapolation in the normal direction, the particle level set method and the motion of co-dimension two (and higher) objects. Part III is concerned with topics taken from the fields of Image Processing and Computer Vision. These include the restoration of images degraded by noise and blur, image segmentation with active contours (snakes), and reconstruction of surfaces from unorganized data points. Part IV is dedicated to Computational Physics. It begins with one phase compressible fluid dynamics, then two-phase compressible flow involving possibly different equations of state, detonation and deflagration waves, and solid/fluid structure interaction. Next it discusses incompressible fluid dynamics, including a computer graphics simulation of smoke, free surface flows, including a computer graphics simulation of water, and fully two-phase incompressible flow. Additional related topics include incompressible flames with applications to computer graphics and coupling a compressible and incompressible fluid. Finally, heat flow and Stefan problems are discussed. A student or researcher working in mathematics, computer graphics, science, or engineering interested in any dynamic moving front, which might change its topology or develop singularities, will find this book interesting and useful.


title: Chemical composition and nutritional value of the most widely appreciated cultivated mushrooms: an inter-species comparative study.

abstract: Herein, it was reported and compared the chemical composition and nutritional value of the most consumed species as fresh cultivated mushrooms: Agaricus bisporus (white and brown mushrooms), Pleurotus ostreatus (oyster mushroom), Pleurotus eryngii (King oyster mushroom), Lentinula edodes (Shiitake) and Flammulina velutipes (Golden needle mushroom). Shiitake revealed the highest levels of macronutrients, unless proteins, as also the highest sugars, tocopherols and PUFA levels, and the lowest SFA content. White and brown mushrooms showed similar macronutrients composition, as also similar values of total sugars, MUFA, PUFA and total tocopherols. Oyster and king oyster mushrooms gave the highest MUFA contents with similar contents in PUFA, MUFA and SFA in both samples. They also revealed similar moisture, ash, carbohydrates and energy values. This study contributes to the elaboration of nutritional databases of the most consumed fungi species worldwide, allowing comparison between them. Moreover it was reported that cultivated and the wild samples of the same species have different chemical composition, including sugars, fatty acids and tocopherols profiles.


title: "Do you dare to compare?" Associations between maternal social comparisons on social networking sites and parenting, mental health, and romantic relationship outcomes

abstract: Social comparisons on social networking sites can be problematic for some individuals. However, this has never been examined in a parenting context, where the pressure for mothers to portray themselves as perfect parents may be high. The aim of the current study was to examine associations between making social comparisons on social networking sites with mothers parenting, mental health, and romantic relationship outcomes. In the iMom Project, 721 mothers completed a number of questionnaires regarding their social media use, parenting behaviors, and health outcomes. Results revealed that making social comparisons on social networking sites was related to parenting outcomes (in the form of higher levels of parental role overload, and lower levels of parental competence and perceived social support), relationship outcomes (in the form of more conflict over social networking sites and perceiving less positive coparenting relationships) and higher levels of maternal depression. This study adds to a growing literature suggesting that making social comparisons online may be associated with a number of negative outcomes, and extends it to the context of mothering. This study has implications for the way that mothers use social media, specifically in the use of social comparisons. Social comparisons on social media are common.Such comparisons were associated with negative outcomes for mothers.These extended to the parenting, relationship, and mental health domains.Implications include helping mothers become critical thinkers on social media.


title: Development of a cybercrime investigation simulator for immersive virtual reality

abstract: In recent years, the advances in computer hardware and mobile phones have finally met the demand of high-quality Virtual reality (VR) rendering. This has allowed VR simulator training to be extended into new and exciting fields. One of these new-found areas is Crime scene investigative training for inexperienced police officers. It frees the inexperienced officers from the danger of damaging actual crime scenes during their training. The project aims at creating a technical prototype to prepare for making the current existing desktop based Virtual Crime Scene Simulator work in immersive VR. This demonstration paper reports on the implementation and design of this prototype.


title: CatBoost: unbiased boosting with categorical features

abstract: This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.


title: Architectural support for fast symmetric-key cryptography

abstract: The emergence of the Internet as a trusted medium for commerce and communication has made cryptography an essential component of modern information systems. Cryptography provides the mechanisms necessary to implement accountability, accuracy, and confidentiality in communication. As demands for secure communication bandwidth grow, efficient cryptographic processing will become increasingly vital to good system performance.In this paper, we explore techniques to improve the performance of symmetric key cipher algorithms. Eight popular strong encryption algorithms are examined in detail. Analysis reveals the algorithms are computationally complex and contain little parallelism. Overall throughput on a high-end microprocessor is quite poor, a 600 Mhz processor is incapable of saturating a T3 communication line with 3DES (triple DES) encrypted data.We introduce new instructions that improve the efficiency of the analyzed algorithms. Our approach adds instruction set support for fast substitutions, general permutations, rotates, and modular arithmetic. Performance analysis of the optimized ciphers shows an overall speedup of 59% over a baseline machine with rotate instructions and 74% speedup over a baseline without rotates. Even higher speedups are demonstrated with optimized substitutions (SBOXes) and additional functional unit resources. Our analyses of the original and optimized algorithms suggest future directions for the design of high-performance programmable cryptographic processors.


title: An Efficient Memetic Algorithm for Influence Maximization in Social Networks

abstract: Influence maximization is to extract a small set of nodes from a social network which influences the propagation maximally under a cascade model. In this paper, we propose a memetic algorithm for community-based influence maximization in social networks. The proposed memetic algorithm optimizes the 2-hop influence spread to find the most influential nodes. Problem-specific population initialization and similarity-based local search are designed to accelerate the convergence of the algorithm. Experiments on three realworld datasets demonstrate that our algorithm has competitive performances to the comparing algorithms in terms of effectiveness and efficiency. For example, on a real-world network of 15233 nodes and 58891 edges, the influence spread of the proposed algorithm is 12.5%, 13.2% and 173.5% higher than the three comparing algorithms Degree, PageRank and Random, respectively.


title: Self-Learning Based Image Decomposition With Applications to Single Image Denoising

abstract: Decomposition of an image into multiple semantic components has been an effective research topic for various image processing applications such as image denoising, enhancement, and inpainting. In this paper, we present a novel self-learning based image decomposition framework. Based on the recent success of sparse representation, the proposed framework first learns an over-complete dictionary from the high spatial frequency parts of the input image for reconstruction purposes. We perform unsupervised clustering on the observed dictionary atoms (and their corresponding reconstructed image versions) via affinity propagation, which allows us to identify image-dependent components with similar context information. While applying the proposed method for the applications of image denoising, we are able to automatically determine the undesirable patterns (e.g., rain streaks or Gaussian noise) from the derived image components directly from the input image, so that the task of single-image denoising can be addressed. Different from prior image processing works with sparse representation, our method does not need to collect training image data in advance, nor do we assume image priors such as the relationship between input and output image dictionaries. We conduct experiments on two denoising problems: single-image denoising with Gaussian noise and rain removal. Our empirical results confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art image denoising algorithms.


title: Design of a 9-bit UART module based on Verilog HDL

abstract: Universal Asynchronous Receiver Transmitter (UART) is widely used in data communication process especially for its advantages of high reliability, long distance and low cost. In this paper, we present the design of 9-bit UART modules based on Verilog HDL. This design features automatic address identification in the character itself. We have implemented the VLSI design of the module and pass data between the proposed 9-bit UART module with a host CPU. The design consists of receiver module, transmitter module, prescaler module and asynchronous FIFOs. We have explained the functions of each individual sub-modules and how the design works in simulation.


title: Vulnerability Discovery Technology and Its Applications

abstract: V ulnerability discovery technology becomes more and more important in software development and network security. This paper presents the classification of vulnerability discovery technologies and discusses the advantages, disadvantages and the extent of application of each class. Then we emphasize the procedure and the improvement methods of the Fuzzing test combined with protocol analysis. Furthermore, according to protocol vulnerability discovery methods, we analyze the issues of network vulnerability discovery and propose the directions of future research.


title: Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios

abstract: Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. In order to solve challenging tasks in problem situations, learners not only have to perform cognitive activities, e.g., activating existing cognitive structures or organizing new information, they also have to set specific goals, plan their activities, monitor their performance during the problem-solving process, and evaluate the efficiency of their actions. This paper reports an experimental study with 98 participants where effective instructional interventions for self-regulated learning within problem-solving processes are investigated. Furthermore, an automated assessment and analysis methodology for determining the quality of learning outcomes is introduced. The results indicate that generic prompts are an important aid for developing cognitive structures while solving problems.


title: Feasibility of single wire communication for PCB-level interconnects

abstract: In this paper, the applicability of Single Wire Communication (SWC) is explored for Printed Circuit Board (PCB) interconnects. Traditionally quasi-TEM mode based multi-conductor transmission lines like microstrip or stripline configurations have been employed for PCB applications with a ground plane for current return. At high frequencies, approaching 50GHz and more, the insertion profile of quasi-TEM transmission suffers primarily due to dielectric loss. On the other hand, due to TM mode of propagation SWC presents a viable low-loss communication alternative at such high frequencies. In this work, some of the challenges of practical implementation for SWC, including planar launchers and mitigating cross-talk effects, are studied and the relative advantages over microstrip transmission is demonstrated.


title: Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography

abstract: Automatic identification of abnormalities is a key problem in medical imaging. While the majority of previous work in mammography has focused on classification of abnormalities rather than detection and localization, here we introduce a novel deep learning method for detection of masses and calcifications. The power of this approach comes from generating an ensemble of individual Faster-RCNN models each trained for a specific set of abnormal clinical categories, together with extending a modified two stage Faster-RCNN scheme to a three stage cascade. The third stage being an additional classifier working directly on the image pixels with the handful of sub-windows generated by the first two stages. The performance of the algorithm is evaluated on the INBreast benchmark and on a large internal multi-center dataset. Quantitative results compete well with state of the art in terms of accuracy. Computationally the methods runs significantly faster than current state-of-the art techniques.


title: Security challenges in automotive hardware/software architecture design

abstract: This paper is an introduction to security challenges for the design of automotive hardware/software architectures. State-of-the-art automotive architectures are highly heterogeneous and complex systems that rely on distributed functions based on electronics and software. As cars are getting more connected with their environment, the vulnerability to attacks is rapidly growing. Examples for such wireless communication are keyless entry systems, WiFi, or Bluetooth. Despite this increasing vulnerability, the design of automotive architectures is still mainly driven by safety and cost issues rather than security. In this paper, we present potential threats and vulnerabilities, and outline upcoming security challenges in automotive architectures. In particular, we discuss the challenges arising in electric vehicles, like the vulnerability to attacks involving tampering with the battery safety. Finally, we discuss future automotive architectures based on Ethernet/IP and how formal verification methods might be used to increase their security.


title: Claytronics: An Instance of Programmable Matter

abstract: Programmable matter refers to a technology that will allow one to control and manipulate three-dimensional physical artifacts (similar to how we already control and manipulate two-dimensional images with computer graphics). In other words, programmable matter will allow us to take a (big) step beyond virtual reality, to synthetic reality, an environment in which all the objects in a user’s environment (including the ones inserted by the computer) are physically realized. Note that the idea is not to transport objects nor is it to recreate an objects chemical composition, but rather to create a physical artifact that will mimic the shape, movement, visual appearance, sound, and tactile qualities of the original object. The enabling hardware technology behind synthetic reality is Claytronics, a form of programmable matter that can organize itself into the shape of an object and render its outer surface to match the visual appearance of that object. Claytronics is made up of individual components, called catoms—for Claytronic atoms—that can move in three dimensions (in relation to other catoms), adhere to other catoms to maintain a 3D shape, and compute state information (with possible assistance from other catoms in the ensemble). In our preliminary designs, each catom is a self-contained unit with a CPU, an energy store, a network device, a video output device, one or more sensors, a means of locomotion, and a mechanism for adhering to other catoms. Creating a physical replica of an arbitrary moving 3D object that can be updated in real time involves many challenges. The research involved in addressing these scientific challenges is likely to have broad impact beyond synthetic reality. Particularly relevant to the ASPLOS community, for example, is how to build and program a robust distributed system containing millions of computers that must cooperate extensively in a harsh environment where their configuration and goals are constantly changing? It is already possible to reproduce static 3D objects [5, 2]. To create a dynamic 3D object, however, we will build upon ideas from such diverse areas as modular and reconfigurable robotics [6, 3] and amorphous computing [1]. A Claytronics system forms a shape through the interaction of the individual catoms. For example, suppose we wish to synthesize a physical “copy” of a person. The catoms would first determine their relative location and orientation. Using that information they would then form a network in a distributed fashion and organize themselves into a hierarchical structure, both to improve locality and to facilitate the planning and coordination tasks. The goal (mimicking a human form) would then be specified abstractly, perhaps as a series of “snapshots” or as a collection of virtual deforming “forces”, and then broadcast to the catoms. Compilation of the specification would then provide each catom with a local plan for achieving the desired global shape. At this point, the catoms would start to move around each other using forces generated on-board, either magnetically or electrostatically, and adhere to each other using, for example, a nanofiber-adhesive mechanism [4]. Finally, the catoms on the surface would display an image; rendering the color and texture characteristics of the source object. If the source object begins to move, a concise description of the movements would be broadcast allowing the catoms to update their positions by moving around each other. The end result is that the system appears to be a single coordinated system. One key motivation for our work is that technology has reached a point where we can realistically build a programmable matter system which is guided by design principles which will allow it to ultimately scale to millions of sub-millimeter catoms. In fact, we expect our prototype for 2D Claytronics to be operational before ASPLOS’04. Our goal is that the system be usable now and scalable for the future. Thus, the guiding design principle, behind both the hardware and the software, is scalability. Hardware mechanisms need to scale towards micronsized catoms and million-catom ensembles. For example, the catom hardware minimizes static power consumption (e.g., no static power is used for adhesion) and avoids moving parts (e.g., the locomotion mechanism currently uses magnetic forces). Software mechanisms need to be scale invariant. For example, our localization and orientation algorithms are completely distributed, parallel, and, are indifferent to catom size. Claytronics will be a test-bed for solving some of the most challenging problems we face today: how to build complex, massively distributed dynamic systems. It is also a step towards truly integrating computers into our lives—by having them integrated into the very artifacts around us and allowing them to interact with the world.


Implementing a vulnerability management process GIAC ( GSEC ) Gold Certification


title: Measures of retaining digital evidence to prosecute computer-based cyber-crimes

abstract: With the rapid growth of computer and network systems in recent years, there has also been a corresponding increase in cyber-crime. Cyber-crime takes many forms and has garnered much attention in the media, making information security a more urgent and important priority. In order to fight cyber-crime, criminal evidence must be gathered from these computer-based systems. This is quite different from the collection of conventional criminal evidence and can confuse investigators attempting to deal with the forensics of cyber-crime, highlighting the importance of computer forensics. In this paper, we offer solutions to guard against cyber-crime through the implementation of software toolkits for computer-based systems. In this way, those who engage in criminal acts in cyber-space can be more easily apprehended.


title: Surface charges on membranes

abstract: The equations of membrane potential developed by Kobatake and coworkers have been applied to the literature data on the resting membrane potential of the crayfish and Myxicola axons to derive values for the surface charge density present on the axon membranes. Some shortcomings of the method are briefly discussed. The value for the surface charge density derived for the squid axon membrane agreed with a similar value derived from measurements of shifts in Na and/or potassium conductance-voltage relations following changes in the concentration of calcium in the solutions bathing the axons.


title: The effects of technostress within the context of employee use of ICT

abstract: Technostress creators and inhibitors influence employee use of ICT at work.Creators reduce user levels of satisfaction with the use of ICT at work.Inhibitors increase user levels of satisfaction with the use of ICT at work.Creators and inhibitors influence the intention to extend ICT use through user satisfaction.Creators and inhibitors should be modelled as formative multidimensional constructs. The main purpose of the present study is to help managers cope with the negative effects of technostress on employee use of ICT. Drawing on transaction theory of stress (Cooper, Dewe, & O'Driscoll, 2001) and information systems (IS) continuance theory (Bhattacherjee, 2001) we investigate the effects of technostress on employee intentions to extend the use of ICT at work. Our results show that factors that create and inhibit technostress affect both employee satisfaction with the use of ICT and employee intentions to extend the use of ICT. Our findings have important implications for the management of technostress with regard to both individual stress levels and organizational performance. A key implication of our research is that managers should implement strategies for coping with technostress through the theoretical concept of technostress inhibitors.


title: Ensemble CCA for Continuous Emotion Prediction

abstract: This paper presents our work on ACM MM Audio Visual Emotion Corpus 2014 (AVEC 2014) using the baseline features in accordance with the challenge protocol. For prediction, we use Canonical Correlation Analysis (CCA) in affect sub-challenge (ASC) and Moore-Penrose generalized inverse (MPGI) in depression sub-challenge (DSC). The video baseline provides histograms of Local Gabor Binary Patterns from Three Orthogonal Planes (LGBP-TOP) features. Based on our preliminary experiments on AVEC 2013 challenge data, we focus on the inner facial regions that correspond to eyes and mouth area. We obtain an ensemble of regional linear regressors via CCA and MPGI. We also enrich the 2014 baseline set with Local Phase Quantization (LPQ) features extracted using Intraface toolkit detected/tracked faces. Combining both representations in a CCA ensemble approach, on the challenge test set we reach an average Pearson's Correlation Coefficient (PCC) of 0.3932, outperforming the ASC test set baseline PCC of 0.1966. On the DSC, combining modality specific MPGI based ensemble systems, we reach 9.61 Root Mean Square Error (RMSE).


title: Abusing CDNs for Fun and Profit: Security Issues in CDNs' Origin Validation

abstract: Content Delivery Networks (CDNs) are critical Internet infrastructure. Besides high availability and high performance, CDNs also provide security services such as anti-DoS and Web Application Firewalls to CDN-powered websites. However, the massive resources of CDNs may also be leveraged by attackers exploiting their architectural, implementation, or operational weaknesses. In this paper, we show that today's CDN operation is overly loose in customer-controlled forwarding policy and the lack of origin validation leads to a wide range of abuse cases such as DoS attack and stealthy port scan. We systematically study these abuse cases and demonstrate their feasibility in popular CDNs. Further, we evaluate the impact of these abuses by discovering that there are millions of CDN edge servers, and a substantial fraction of them can be abused. Lastly, we propose mitigation solutions against such abuses and discuss their feasibility.


title: Perfectionism, procrastination, and psychological distress.

abstract: Using a cross-panel design and data from 2 successive cohorts of college students (N 357), we examined the stability of maladaptive perfectionism, procrastination, and psychological distress across 3 time points within a college semester. Each construct was substantially stable over time, with procrastination being especially stable. We also tested, but failed to support, a mediational model with Time 2 (mid-semester) procrastination as a hypothesized mechanism through which Time 1 (early-semester) perfectionism would affect Time 3 (end-semester) psychological distress. An alternative model with Time 2 perfectionism as a mediator of the procrastination– distress association also was not supported. Within-time analyses revealed generally consistent strength of effects in the correlations between the 3 constructs over the course of the semester. A significant interaction effect also emerged. Time 1 procrastination had no effect on otherwise high levels of psychological distress at the end of the semester for highly perfectionistic students, but at low levels of Time 1 perfectionism, the most distressed students by the end of the term were those who were more likely to have procrastinated earlier in the semester. Implications of the stability of the constructs and their association over time, as well as the moderating effects of procrastination, are discussed in the context of maladaptive perfectionism and problematic procrastination.


title: Using the RPL protocol for supporting passive monitoring in the Internet of Things

abstract: Most devices deployed in the Internet of Things (IoT) are expected to suffer from resource constraints. Using specialized tools on such devices for monitoring IoT networks would take away precious resources that could otherwise be dedicated towards their primary task. In many IoT applications such as Advanced Metering Infrastructure (AMI) networks, higher order devices are expected to form the backbone infrastructure, to which the constrained nodes would connect. It would, as such, make sense to exploit the capabilities of these higher order devices to perform network monitoring tasks. We propose in this paper a distributed monitoring architecture that takes benefits from specificities of the IoT routing protocol RPL to passively monitor events and network flows without having impact upon the resource constrained nodes. We describe the underlying mechanisms of this architecture, quantify its performances through a set of experiments using the Cooja environment. We also evaluate its benefits and limits through a use case scenario dedicated to anomaly detection.


title: The pathological role of NLRs and AIM2 inflammasome-mediated pyroptosis in damaged blood-brain barrier after traumatic brain injury

abstract: Abstract Pyroptosis is a highly specific type of inflammatory programmed cell death that different from necrosis or apoptosis. It is initiated by cellular detection of acute damage via recognizing pathogen-associated molecular patterns (PAMPs) by NOD-like receptors (NLRs) or AIM2-like receptor (AIM2). NLRs and AIM2 could trigger the formation of a multi-protein complex, known as inflammasome. It also contains apoptotic speck-containing protein (ASC) and pro-Caspase-1, and could process the signals to induce a cascade of inflammatory response. Recently, growing evidence showed that inflammasome-mediated pyroptosis is involved in the pathogenesis of traumatic brain injury (TBI). However, less attention has been paid to their particular roles in regulating blood-brain barrier (BBB) damage, the central pathological change in secondary brain damage of TBI. Thus, we designed this research to explore the impact and mechanism of NLRs and AIM2 inflammasome-mediated pyroptosis in BBB after TBI. We employed the controlled cortical impact (CCI) mice model and manipulated the severity of pyroptosis in BBB using Caspase-1 inhibitor, Ac-YVAD-cmk. We found that TBI led to NLRs and AIM2 inflammasome-mediated pyroptosis in brain microvascular endothelial cells (BMVECs) from injured cerebral cortex. Ac-YVAD-cmk treatment inhibited pyroptosis in injured BMVECs by suppressing the expression of essential inflammasome subunit – Caspase-1 and pivotal downstream pro-inflammatory cytokines (IL-1β and IL-18), as well as hindering GSDMD cleavage and ASC oligomerization. In addition, inhibiting pyroptosis could alleviate TBI-induced BBB leakage, brain edema, loss of tight junction proteins, and the inflammatory response in injured BMVECs. These effects contributed to improving the neurological outcome of CCI mice. In conclusion, NLRs and AIM2 inflammasome-mediated pyroptosis could aggravate BBB damage after TBI. Targeting and controlling pyroptosis in injured BBB would be a promising therapeutic strategy for TBI in the future.


title: Neural Proximal Gradient Descent for Compressive Imaging

abstract: Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information. Learning a good inverse mapping from training data faces severe challenges, including: (i) scarcity of training data; (ii) need for plausible reconstructions that are physically feasible; (iii) need for fast reconstruction, especially in real-time applications. We develop a successful system solving all these challenges, using as basic architecture the recurrent application of proximal gradient algorithm. We learn a proximal map that works well with real images based on residual networks. Contraction of the resulting map is analyzed, and incoherence conditions are investigated that drive the convergence of the iterates. Extensive experiments are carried out under different settings: (a) reconstructing abdominal MRI of pediatric patients from highly undersampled Fourier-space data and (b) superresolving natural face images. Our key findings include: 1. a recurrent ResNet with a single residual block unrolled from an iterative algorithm yields an effective proximal which accurately reveals MR image details. 2. Our architecture significantly outperforms conventional non-recurrent deep ResNets by 2dB SNR; it is also trained much more rapidly. 3. It outperforms state-of-the-art compressed-sensing Wavelet-based methods by 4dB SNR, with 100x speedups in reconstruction time.


title: Multimodal Network Embedding via Attention based Multi-view Variational Autoencoder

abstract: Learning the embedding for social media data has attracted extensive research interests as well as boomed a lot of applications, such as classification and link prediction. In this paper, we examine the scenario of a multimodal network with nodes containing multimodal contents and connected by heterogeneous relationships, such as social images containing multimodal contents (e.g., visual content and text description), and linked with various forms (e.g., in the same album or with the same tag). However, given the multimodal network, simply learning the embedding from the network structure or a subset of content results in sub-optimal representation. In this paper, we propose a novel deep embedding method, i.e., Attention-based Multi-view Variational Auto-Encoder (AMVAE), to incorporate both the link information and the multimodal contents for more effective and efficient embedding. Specifically, we adopt LSTM with attention model to learn the correlation between different data modalities, such as the correlation between visual regions and the specific words, to obtain the semantic embedding of the multimodal contents. Then, the link information and the semantic embedding are considered as two correlated views. A multi-view correlation learning based Variational Auto-Encoder (VAE) is proposed to learn the representation of each node, in which the embedding of link information and multimodal contents are integrated and mutually reinforced. Experiments on three real-world datasets demonstrate the superiority of the proposed model in two applications, i.e., multi-label classification and link prediction.


title: A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

abstract: Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.


Doing It Now or Later


title: A survey of Web-based business models for e-government in the Netherlands

abstract: Abstract Governments worldwide are increasingly using Web-based business models to enhance their service delivery. Yet the concept of the business model is unexplored within the context of e-government. Drawing upon the literature on e-commerce, we develop a taxonomy for analyzing Web-based business models for e-government. Based on a systematic survey of 59 e-government Web sites in the Netherlands, our findings indicate that most of the Web sites use the content provider or direct-to-customer business models, while only a few are using novel business models. Overall, the concept of business model is appealing and useful in the public sector. Specifically it compliments research on Web site quality by analyzing and describing Web sites using atomic e-government business models and suggesting improvements by using combinations of business models.


title: ROP is still dangerous: breaking modern defenses

abstract: Return Oriented Programming (ROP) has become the exploitation technique of choice for modern memory-safety vulnerability attacks. Recently, there have been multiple attempts at defenses to prevent ROP attacks. In this paper, we introduce three new attack methods that break many existing ROP defenses. Then we show how to break kBouncer and ROPecker, two recent low-overhead defenses that can be applied to legacy software on existing hardware. We examine several recent ROP attacks seen in the wild and demonstrate that our techniques successfully cloak them so they are not detected by these defenses. Our attacks apply to many CFI-based defenses which we argue are weaker than previously thought. Future defenses will need to take our attacks into account.


title: A modular hierarchical approach to 3D electron microscopy image segmentation.

abstract: The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy.


title: Crime Pattern Detection Using Data Mining

abstract: Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff?s office and validated our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security.


title: Safeguarding a mobile manipulator using dynamic safety fields

abstract: This paper presents a novel approach for safeguarding a mobile manipulator. To overcome the shortcomings of state-of-the-art safeguarding techniques, we present a dynamic approach capable of covering different situations and adapting itself to the situation at hand. The proposed approach is based on the continuous dynamic update of a safety field using both exteroceptive and proprioceptive data. The proprioceptive data are the platform velocity vector and the manipulator position; the exteroceptive data refers to the environment in which the mobile manipulator is moving, namely distances to obstacles acquired by laser scanners. The effectiveness of the proposed approach has been validated in an industrial test case scenario in which the robot had to execute a fetch and carry task while safely coexisting with human workers. Experimental results proved that the use of dynamic protection fields allowed the robot to carry out its tasks with a significant reduction (about 50%) of the execution time.


3 Dana : Path Planning on 3 D surfaces


title: Exception handling: a field study in Java and .NET

abstract: Most modern programming languages rely on exceptions for dealing with abnormal situations. Although exception handling was a significant improvement over other mechanisms like checking return codes, it is far from perfect. In fact, it can be argued that this mechanism is seriously limited, if not, flawed. This paper aims to contribute to the discussion by providing quantitative measures on how programmers are currently using exception handling. We examined 32 different applications, both for Java and .NET. The major conclusion for this work is that exceptions are not being correctly used as an error recovery mechanism. Exception handlers are not specialized enough for allowing recovery and, typically, programmers just do one of the following actions: logging, user notification and application termination. To our knowledge, this is the most comprehensive study done on exception handling to date, providing a quantitative measure useful for guiding the development of new error handling mechanisms.


title: Organically Grown Architectures: Creating Decentralized, Autonomous Systems by Embryomorphic Engineering

abstract: Exploding growth growth in computational systems forces us to gradually replace rigid design and control with decentralization and autonomy. Information technologies will progress, instead, by“meta-designing” mechanisms of system self-assembly, self-regulation and evolution. Nature offers a great variety of efficient complex systems, in which numerous small elements form large-scale, adaptive patterns. The new engineering challenge is to recreate this self-organization and let it freely generate innovative designs under guidance. This article presents an original model of artificial system growth inspired by embryogenesis. A virtual organism is a lattice of cells that proliferate, migrate and self-pattern into differentiated domains. Each cell’s fate is controlled by an internal gene regulatory network network. Embryomorphic engineering emphasizes hyperdistributed architectures, and their development as a prerequisite of evolutionary design.


title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

abstract: Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.


title: A pack-to-cell-to-pack battery equalizer with soft-switching based on buck-boost and bidirectional LC resonant converters

abstract: A pack-to-cell-to-pack battery equalizer with soft-switching based on buck-boost converter and bidirectional LC resonant converter is proposed to achieve a fast and high-efficiency battery equalization. The buck-boost converter is employed to transmit energy from a high tension supply to a low one in the buck mode or from a low one to a high one in the boost mode with zero-voltage switching (ZVS). The bidirectional LC resonant converter is employed to automatically transfer energy with zero-current switching (ZCS) with electrical isolation. A prototype with four lithium-ion battery cells is implemented. Experimental results show the proposed scheme exhibits outstanding balancing performance, and the measured peak conversion efficiency is about 93% in the cell-to-pack mode and 72.5% in the pack-to-cell mode.