title: Armswing: using arm swings for accessible and immersive navigation in AR/VR spaces

abstract: Navigating in a natural way in augmented reality (AR) and virtual reality (VR) spaces is a large challenge. To this end, we present ArmSwingVR, a locomotion solution for AR/VR spaces that preserves immersion, while being low profile compared to current solutions, particularly walking-in-place (WIP) methods. The user simply needs to swing their arms naturally to navigate in the direction where the arms are swung, without any feet or head movement. The benefits of ArmSwingVR are that arm swinging feels natural for bipedal organisms second only to leg movement, no additional peripherals or sensors are required, it is less obtrusive to swing our arms as opposed to WIP methods, and requires less energy allowing prolong uses for AR/VR. A conducted user study found that our method does not sacrifice immersion while also being more low profile and less energy consumption compared to WIP.


title: Abstract linguistic structure correlates with temporal activity during naturalistic comprehension

abstract: Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations.


title: Workspace Analysis of a Cable Driven Leg Exoskeleton

abstract: Robotic systems are being used for gait rehabilitation of patients with neurological disorders. These are externally powered devices that can apply external forces on human limbs to assist the limb motion. Human walking pattern involves repetitive and well coordinated lower limb movements. A cable driven leg exoskeleton (CDLE) uses actuated cables to apply external torques at anatomical hip and knee joints. However, a cable can apply only pulling force on a body which limits a cable driven system functionality compared to a conventional robotic manipulator. Noting that a CDLE is proposed to assist in complex lower limb motion during walking We present workspace analysis of CDLE considering planar and spatial leg model. Human walking data were used for the analysis and to study the feasibility of CDLE architecture for human gait rehabilitation.1


title: Low-loss compact ku-band waveguide low-pass filter

abstract: A lower-loss, more compact alternative to the classical E-plane corrugated waveguide low-pass filter is proposed in this paper. The novel design is capable of achieving very steep slopes in the fundamental TE 10 -mode frequency response along with a drastic reduction in terms of insertion loss and size. The design method is based on step-shaped bandstop elements separated by very short waveguide sections. Moreover, the matching of the novel filter is achieved by very short input/output networks based on stubs of optimized heights. A simple method is proposed allowing the designer to obtain a compact low-pass filter fulfilling stringent specifications.


title: The Watershed Transformation Applied To Image Segmentation

abstract: Image segmentation by mathematical morphology is a methodology based upon the notions of watershed and homotopy modification. This paper aims at introducing this methodology through various examples of segmentation in materials sciences, electron microscopy and scene analysis. First, we define our basic tool, the watershed transform. We show that this transformation can be built by implementing a flooding process on a grey-tone image. This flooding process can be performed by using elementary morphological operations such as geodesic skeleton and reconstruction. Other algorithms are also briefly presented (arrows representation). Then, the use of this transformation for image segmentation purposes is discussed. The application of the watershed transform to gradient images and the problems raised by over-segmentation are emphasized. This leads, in the third part, to the introduction of a general methodology for segmentation, based on the definition of markers and on a transformation called homotopy modification. This complex tool is defined in detail and various types of implementation are given. Many examples of segmentation are presented. These examples are taken from various fields : transmission electron microscopy, SEM, 3D holographic pictures, radiography, non destructive control and so on. The final part of this paper is devoted to the use of the watershed transformation for hierarchical segmentation. This tool is particularly efficient for defining different levels of segmentation starting from a graph representation of the images based on the mosaic image transform. This approach will be explained by means of examples in industrial vision and scene analysis.


title: PTSD symptom reports of patients evaluated for the New Mexico Medical Cannabis Program.

abstract: BACKGROUND ::: New Mexico was the first state to list post-traumatic stress disorder (PTSD) as a condition for the use of medical cannabis. There are no published studies, other than case reports, of the effects of cannabis on PTSD symptoms. The purpose of the study was to report and statistically analyze psychometric data on PTSD symptoms collected during 80 psychiatric evaluations of patients applying to the New Mexico Medical Cannabis Program from 2009 to 2011. ::: ::: ::: METHODS ::: The Clinician Administered Posttraumatic Scale for DSM-IV (CAPS) was administered retrospectively and symptom scores were then collected and compared in a retrospective chart review of the first 80 patients evaluated. ::: ::: ::: RESULTS ::: Greater than 75% reduction in CAPS symptom scores were reported when patients were using cannabis compared to when they were not. ::: ::: ::: CONCLUSIONS ::: Cannabis is associated with reductions in PTSD symptoms in some patients, and prospective, placebo-controlled study is needed to determine efficacy of cannabis and its constituents in treating PTSD.


title: Single word reading in developmental stutterers and fluent speakers

abstract: Ten fluent speakers and nine developmental stutterers read isolated nouns aloud in a delayed reading paradigm. Cortical activation sequences were mapped with a whole-head magnetoencephalography system. The stutterers were mostly fluent in this task. Although the overt performance was essentially identical in the two groups, the cortical activation patterns showed clear differences, both in the evoked responses, time-locked to word presentation and mouth movement onset, and in task-related suppression of 20-Hz oscillations. Within the first 400 ms after seeing the word, processing in fluent speakers advanced from the left inferior frontal cortex (articulatory programming) to the left lateral central sulcus and dorsal premotor cortex (motor preparation). This sequence was reversed in the stutterers, who showed an early left motor cortex activation followed by a delayed left inferior frontal signal. Stutterers thus appeared to initiate motor programmes before preparation of the articulatory code. During speech production, the right motor/premotor cortex generated consistent evoked activation in fluent speakers but was silent in stutterers. On the other hand, suppression of motor cortical 20-Hz rhythm, reflecting task-related neuronal processing, occurred bilaterally in both groups. Moreover, the suppression was right-hemisphere dominant in stutterers, as opposed to left-hemisphere dominant in fluent speakers. Accordingly, the right frontal cortex of stutterers was highly active during speech production but did not generate synchronous time-locked responses. The speech-related 20-Hz suppression concentrated in the mouth area in fluent speakers, but was evident in both the hand and mouth areas in stutterers. These findings may reflect imprecise functional connectivity within the right frontal cortex and incomplete segregation between the adjacent hand and mouth motor representations in stutterers during speech production. A network including the left inferior frontal cortex and the right motor/premotor cortex, likely to be relevant in merging linguistic and affective prosody with articulation during fluent speech, thus appears to be partly dysfunctional in developmental stutterers.


title: Research Review: attention bias modification (ABM): a novel treatment for anxiety disorders

abstract: Attention bias modification (ABM) is a newly emerging therapy for anxiety disorders that is rooted in current cognitive models of anxiety and in established experimental data on threat-related attentional biases in anxiety. This review describes the evidence indicating that ABM has the potential to become an enhancing tool for current psychological and pharmacological treatments for anxiety or even a novel standalone treatment. The review also outlines the gaps in need of bridging before ABM techniques could be routinely applied and incorporated into standard treatment protocols.


title: Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

abstract: Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes.


title: Annotating Needles in the Haystack without Looking: Product Information Extraction from Emails

abstract: Business-to-consumer (B2C) emails are usually generated by filling structured user data (e.g.purchase, event) into templates. Extracting structured data from B2C emails allows users to track important information on various devices. However, it also poses several challenges, due to the requirement of short response time for massive data volume, the diversity and complexity of templates, and the privacy and legal constraints. Most notably, email data is legally protected content, which means no one except the receiver can review the messages or derived information. In this paper we first introduce a system which can extract structured information automatically without requiring human review of any personal content. Then we focus on how to annotate product names from the extracted texts, which is one of the most difficult problems in the system. Neither general learning methods, such as binary classifiers, nor more specific structure learning methods, suchas Conditional Random Field (CRF), can solve this problem well. To accomplish this task, we propose a hybrid approach, which basically trains a CRF model using the labels predicted by binary classifiers (weak learners). However, the performance of weak learners can be low, therefore we use Expectation Maximization (EM) algorithm on CRF to remove the noise and improve the accuracy, without the need to label and inspect specific emails. In our experiments, the EM-CRF model can significantly improve the product name annotations over the weak learners and plain CRFs.


title: A Review of Scar Scales and Scar Measuring Devices

abstract: Objective: Pathologic scarring affects millions of people worldwide. Quantitative and qualitative measurement modalities are needed to effectively evaluate and monitor treatments. Methods: This article reviews the literature on available tools and existent assessment scales used to subjectively and objectively characterize scar. Results: We describe the attributes and deficiencies of each tool and scale and highlight areas where further development is critical. Conclusion: An optimal, universal scar scoring system is needed in order to better characterize, understand and treat pathologic scarring.


title: Caching in HTTP Adaptive Streaming: Friend or Foe?

abstract: Video streaming is a major source of Internet traffic today and usage continues to grow at a rapid rate. To cope with this new and massive source of traffic, ISPs use methods such as caching to reduce the amount of traffic traversing their networks and serve customers better. However, the presence of a standard cache server in the video transfer path may result in bitrate oscillations and sudden rate changes for Dynamic Adaptive Streaming over HTTP (DASH) clients. In this paper, we investigate the interactions between a client and a cache that result in these problems, and propose an approach to solve it. By adaptively controlling the rate at which the client downloads video segments from the cache, we can ensure that clients will get smooth video. We verify our results using simulation and show that compared to a standard cache our approach (1) can reduce bitrate oscillations (2) prevents sudden rate changes, and compared to a no-cache scenario (3) provides traffic savings, and (4) improves the quality of experience of clients.


title: Sparsity preserving projections with applications to face recognition

abstract: Dimensionality reduction methods (DRs) have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new unsupervised DR method called sparsity preserving projections (SPP). Unlike many existing techniques such as local preserving projection (LPP) and neighborhood preserving embedding (NPE), where local neighborhood information is preserved during the DR procedure, SPP aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a L1 regularization-related objective function. The obtained projections are invariant to rotations, rescalings and translations of the data, and more importantly, they contain natural discriminating information even if no class labels are provided. Moreover, SPP chooses its neighborhood automatically and hence can be more conveniently used in practice compared to LPP and NPE. The feasibility and effectiveness of the proposed method is verified on three popular face databases (Yale, AR and Extended Yale B) with promising results.


title: CNTR: lightweight OS containers

abstract: Container-based virtualization has become the de-facto standard for deploying applications in data centers. However, deployed containers frequently include a wide-range of tools (e.g., debuggers) that are not required for applications in the common use-case, but they are included for rare occasions such as in-production debugging. As a consequence, containers are significantly larger than necessary for the common case, thus increasing the build and deployment time. ::: ::: CNTR provides the performance benefits of lightweight containers and the functionality of large containers by splitting the traditional container image into two parts: the "fat" image--containing the tools, and the "slim" image -- containing the main application. At run-time, CNTR allows the user to efficiently deploy the "slim" image and then expand it with additional tools, when and if necessary, by dynamically attaching the "fat" image. ::: ::: To achieve this, CNTR transparently combines the two container images using a new nested namespace, without any modification to the application, the container manager, or the operating system. We have implemented CNTR in Rust, using FUSE, and incorporated a range of optimizations. CNTR supports the full Linux filesystem API, and it is compatible with all container implementations (i.e., Docker, rkt, LXC, systemd-nspawn). Through extensive evaluation, we show that CNTR incurs reasonable performance overhead while reducing, on average, by 66.6% the image size of the Top-50 images available on Docker Hub.


title: Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

abstract: In recent years, convolutional neural network (CNN) based methods have achieved great success in a large number of applications and have been among the most powerful and widely used techniques in computer vision. However, CNN-based methods are com-putational-intensive and resource-consuming, and thus are hard to be integrated into embedded systems such as smart phones, smart glasses, and robots. FPGA is one of the most promising platforms for accelerating CNN, but the limited bandwidth and on-chip memory size limit the performance of FPGA accelerator for CNN. In this paper, we go deeper with the embedded FPGA platform on accelerating CNNs and propose a CNN accelerator design on embedded FPGA for Image-Net large-scale image classification. We first present an in-depth analysis of state-of-the-art CNN models and show that Convolutional layers are computational-centric and Fully-Connected layers are memory-centric. Then the dynamic-precision data quantization method and a convolver design that is efficient for all layer types in CNN are proposed to improve the bandwidth and resource utilization. Results show that only 0.4% accuracy loss is introduced by our data quantization flow for the very deep VGG16 model when 8/4-bit quantization is used. A data arrangement method is proposed to further ensure a high utilization of the external memory bandwidth. Finally, a state-of-the-art CNN, VGG16-SVD, is implemented on an embedded FPGA platform as a case study. VGG16-SVD is the largest and most accurate network that has been implemented on FPGA end-to-end so far. The system on Xilinx Zynq ZC706 board achieves a frame rate at 4.45 fps with the top-5 accuracy of 86.66% using 16-bit quantization. The average performance of convolutional layers and the full CNN is 187.8 GOP/s and 137.0 GOP/s under 150MHz working frequency, which outperform previous approaches significantly.


title: Social Network Marketing: A Segmentation Approach to Understanding Purchase Intention

abstract: This study investigates the effect of online and offline pre-purchase influences and the role of fashion brand involvement and online brand engagement in predicting purchase intention of products marketed in social media. A four-construct structural model was developed and validated on a sample of 799 shoppers in North America. Partial least squares structural equation modeling (PLS-SEM) was used to test the model. All six hypotheses were supported and fashion brand involvement was identified as a mediator. The analysis incorporated an advanced segmentation technique, Partial Least Squares Prediction Oriented Segmentation, (PLS-POS). Two groups of similar size emerged with differences that are of theoretical and managerial interest. Expansion of the model and future testing in different contexts will help to refine and develop it, providing insights into social media marketing.


title: Gambler's Ruin Bandit Problem

abstract: In this paper, we propose a new multi-armed bandit problem called the Gambler's Ruin Bandit Problem (GRBP). In the GRBP, the learner proceeds in a sequence of rounds, where each round is a Markov Decision Process (MDP) with two actions (arms): a continuation action that moves the learner randomly over the state space around the current state; and a terminal action that moves the learner directly into one of the two terminal states (goal and dead-end state). The current round ends when a terminal state is reached, and the learner incurs a positive reward only when the goal state is reached. The objective of the learner is to maximize its long-term reward (expected number of times the goal state is reached), without having any prior knowledge on the state transition probabilities. We first prove a result on the form of the optimal policy for the GRBP. Then, we define the regret of the learner with respect to an omnipotent oracle, which acts optimally in each round, and prove that it increases logarithmically over rounds. We also identify a condition under which the learner's regret is bounded. A potential application of the GRBP is optimal medical treatment assignment, in which the continuation action corresponds to a conservative treatment and the terminal action corresponds to a risky treatment such as surgery.


Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification.


title: Identifying Vulnerabilities in SCADA Systems via Fuzz-Testing

abstract: Security vulnerabilities typically arise from bugs in input validation and in the application logic. Fuzz-testing is a popular security evaluation technique in which hostile inputs are crafted and passed to the target software in order to reveal bugs. However, in the case of SCADA systems, the use of proprietary protocols makes it difficult to apply existing fuzz-testing techniques as they work best when the protocol semantics are known, targets can be instrumented and large network traces are available. This paper describes a fuzz-testing solution involving LZFuzz, an inline tool that provides a domain expert with the ability to effectively fuzz SCADA devices.


title: A pseudo alamouti transceiver design for OFDM/OQAM modulation with cyclic prefix

abstract: Multi-Carrier Modulation (MCM) and especially Orthogonal Frequency Division Multiplexing (OFDM) are currently used in many radio transmission standards. Among these modulations, OFDM Offset QAM (OFDM/OQAM) is an interesting alternative to OFDM. However, differently to OFDM, OFDM/OQAM modulation cannot directly use Alamouti coding scheme due to the remaining interference. In this paper, we propose a scheme which permits to adapt OFDM/OQAM to Multiple-Input Multiple-Output (MIMO) Alamouti system. The simulation results illustrate its efficiency.


title: CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit

abstract: Continuous time Bayesian network classifiers are designed for temporal classification of multivariate streaming data when time duration of events matters and the class does not change over time. This paper introduces the CTBNCToolkit: an open source Java toolkit which provides a stand-alone application for temporal classification and a library for continuous time Bayesian network classifiers. CTBNCToolkit implements the inference algorithm, the parameter learning algorithm, and the structural learning algorithm for continuous time Bayesian network classifiers. The structural learning algorithm is based on scoring functions: the marginal log-likelihood score and the conditional log-likelihood score are provided. CTBNCToolkit provides also an implementation of the expectation maximization algorithm for clustering purpose. The paper introduces continuous time Bayesian network classifiers. How to use the CTBNToolkit from the command line is described in a specific section. Tutorial examples are included to facilitate users to understand how the toolkit must be used. A section dedicate to the Java library is proposed to help further code extensions.


title: Infrared and visible image fusion via gradient transfer and total variation minimization

abstract: We propose a new IR/visible fusion method based on gradient transfer and TV minimization.It can keep both the thermal radiation and the appearance information in the source images.We generalize the proposed method to fuse image pairs without pre-registration.Our fusion results look like sharpened IR images with highlighted target and abundant textures.To the best of our knowledge, the proposed fusion strategy has not yet been studied. In image fusion, the most desirable information is obtained from multiple images of the same scene and merged to generate a composite image. This resulting new image is more appropriate for human visual perception and further image-processing tasks. Existing methods typically use the same representations and extract the similar characteristics for different source images during the fusion process. However, it may not be appropriate for infrared and visible images, as the thermal radiation in infrared images and the appearance in visible images are manifestations of two different phenomena. To keep the thermal radiation and appearance information simultaneously, in this paper we propose a novel fusion algorithm, named Gradient Transfer Fusion (GTF), based on gradient transfer and total variation (TV) minimization. We formulate the fusion problem as an ?1-TV minimization problem, where the data fidelity term keeps the main intensity distribution in the infrared image, and the regularization term preserves the gradient variation in the visible image. We also generalize the formulation to fuse image pairs without pre-registration, which greatly enhances its applicability as high-precision registration is very challenging for multi-sensor data. The qualitative and quantitative comparisons with eight state-of-the-art methods on publicly available databases demonstrate the advantages of GTF, where our results look like sharpened infrared images with more appearance details.


title: Geometrical Kinematic Modeling on Human Motion using Method of Multi-Sensor Fusion

abstract: Abstract Human motion sensing based on wearable sensors could be viewed as a multi-objects tracking issue of human body joints. Sensor drift errors and distortion are the main challenges of the tracking accuracy of human motion. Traditional filtering and fusion methods, such as Kalman filter, can to some extent reduce the instantaneous error but cannot avoid sensor drift fundamentally. Physical characteristics of human body should be considered and human motion models should be exhibited to describe human motions. The existing models such as skeleton model and cylinder model are either too simple or too complicated for practical applications. In this study, we put forward a geometrical kinematic characteristics based human motion model. The whole human body is viewed as an articulated skeleton and Denavit–Hartenberg convention is adopted to describe the forward kinematics structure. Theoretical analysis is conducted with the derivation of Posterior Cramer–Rao Lower Bound (PCRLB) in human movement scenes based on proposed model. Significant superiority is shown in simulation results. An experiment on human lower limb motions is carried out to verify the validity of the proposed human motion model in practice applications, from the angles of both capturing accuracy and energy consumption. The capturing accuracy has an obvious increase in the testing results, with acceptable energy consumption. It is far more efficient than traditional methods.


title: Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm

abstract: The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data. To address this problem, we propose a learn-memorize-recall-reduce paradigm for robotic cloud computing. The learning stage converts incoming unstructured data into structured data; the memorization stage provides effective storage for the massive amount of data; the recall stage provides efficient means to retrieve the raw data; while the reduction stage provides means to make sense of this massive amount of unstructured data with limited computing resources.


title: Spectral clustering for sensing urban land use using Twitter activity

abstract: Individuals generate vast amounts of geolocated content through the use of mobile social media applications. In this context, Twitter has become an important sensor of the interactions between individuals and their environment. Building on this idea, this paper proposes the use of geolocated tweets as a complementary source of information for urban planning applications, focusing on the characterization of land use. The proposed technique uses unsupervised learning and automatically determines land uses in urban areas by clustering geographical regions with similar tweeting activity patterns. Three case studies are presented and validated for Manhattan (NYC), London (UK) and Madrid (Spain) using Twitter activity and land use information provided by the city planning departments. Results indicate that geolocated tweets can be used as a powerful data source for urban planning applications.


title: An Efficient Mutual Authentication and Access Control Scheme for Wireless Sensor Networks in Healthcare

abstract: Wireless sensor networks (WSNs) will play an active role in the 21th Century Healthcare IT to reduce the healthcare cost and improve the quality of care. The protection of data confidentiality and patient privacy are the most critical requirements for the ubiquitous use of WSNs in healthcare environments. This requires a secure and lightweight user authentication and access control. Symmetric key - based access control is not suitable for WSNs in healthcare due to dynamic network topology, mobility, and stringent resource constraints. In this paper, we propose a secure, lightweight public key - based security scheme, Mutual Authentication and Access Control based on Elliptic curve cryptography (MAACE). MAACE is a mutual authentication protocol where a healthcare professional can authenticate to an accessed node (a PDA or medical sensor) and vice versa. This is to ensure that medical data is not exposed to an unauthorized person. On the other hand, it ensures that medical data sent to healthcare professionals did not originate from a malicious node. MAACE is more scalable and requires less memory compared to symmetric key-based schemes. Furthermore, it is much more lightweight than other public key-based schemes. Security analysis and performance evaluation results are presented and compared to existing schemes to show advantages of the proposed scheme.


title: Privacy-preserving collaborative filtering

abstract: With the evolution of the Internet, collaborative filtering (CF) techniques are becoming increasingly popular. Such techniques are widely used by many E-commerce companies to suggest products to customers, based on like-minded customers' preferences; but they fail to protect users' privacy. Data from customers is gathered for recommendation purposes; however, collecting high quality data is not an easy task, due to privacy concerns. Since many users are worried about their privacy, they sometimes refuse to contribute their data; or occasionally they might decide to give false information. Producing accurate referrals based on insufficient data and/or false data is impossible. CF systems provide referrals on existing databases comprised of ratings recorded from groups of people evaluating various items; sometimes, however, the systems' ratings are split among different parties. To provide better filtering services, the parties may wish to share their data; but they may not want to disclose information of a private nature. ::: We propose to use randomization techniques to protect users' privacy while still producing accurate referrals. We investigate providing predictions on memory- and model-based CF algorithms with privacy assured by using randomized perturbation techniques (RPT) in Chapter 3 and Chapter 4, respectively; while we show how to produce recommendations without violating users' privacy using randomized response techniques (RRT) in Chapter 5. In Chapter 6, we explore inconsistently disguised data-based CF with privacy provided by the RPT. When data owners wish to provide recommendations using a joint database without disclosing sensitive data to each other, they may achieve accurate privacy-preserving referrals on integrated data with schemes we have proposed. We investigate performing filtering tasks; including prediction generations and top- N recommendations, on vertically or horizontally partitioned data, with privacy preservation, in Chapter 7 and Chapter 8. We conduct various experiments using well-known existing data sets to evaluate the overall performance of our approaches. Furthermore, we analyze the effects of different parameters on accuracy and privacy. Finally, in Chapter 9, we present our results and explain future directions.


title: Thick legs - not always lipedema

abstract: Due to its increased presence in the press and on television, the diagnosis of lipedema is on the way to becoming a trendy diagnosis for those with thick legs. Despite this, one must recognize that lipedema is a very rare disease. It is characterized by disproportional obesity of the extremities, especially in the region of the hip and the legs, hematoma development after minimal trauma, and increased pressure-induced or spontaneous pain. Aids for making the correct diagnosis are (duplex) sonography, the waist-hip index or the waist-height index and lymphoscintigraphy. Important differential diagnoses are constitutional variability of the legs, lipohypertrophy in obesity, edema in immobility, edema in chronic venous insufficiency and rheumatic diseases. The symptom-based therapy of lipedema consists of conservative (compression, manual lymphatic drainage, exercise) and surgical treatments (liposuction). Until now there is no curative therapy. Obesity is an important risk factor for the severity and prognosis of lipedema. Further studies for a better understanding of the pathogenesis of lipedema and in the end possible curative treatments are urgently needed.


title: Teaching the history of computing (painlessly)

abstract: Knowledge about the history of computing can provide students with a broad perspective and valuable insights into the "real world" and how things came to be the way that they are. However, there is precious little time in today's crowded computer science curriculum to devote to this subject matter. This paper investigated a leaning technique in which a significant "history component" was added to a computer architecture course. The students studied the history component entirely on their own and completely outside the classroom. An analysis of student opinions indicates that the students greatly appreciated the opportunity to learn about the history of computing and that the technique was very successful -- and is likely to be equally applicable in other academic areas.


title: Pose-invariant Face Recognition

abstract: Pose-variation problem, there are various approaches for a 3D face recognition system which is able to use depth information. However, this created another head-pose estimation problem in terms of normalizing the 3D face data. This paper presents a pose-invariant face recognition system using an iterative feature-based 3D head pose-estimation. The proposed method uses 2D information with the AAM(Active Appearance Model) and 3D information with a 3D facial normal vector for a 3D face data. In order to verify the performance of the proposed method, we decompose rotational properties of 3D face database using the proposed iterative 3D head pose-estimation method and design two different experiments. Experimental results show that the proposed method is robust against pose variation.


title: Smart ration card using RFID and GSM technique

abstract: In this paper, we have developed a smart ration card using Radio Frequency Identification (RFID) technique to prevent the ration forgery as there are chances that the shopkeeper may sell the material to someone else and take the profit and put some false amount in their records. In this system, a RFID tag is used that carries the family member details and the customer needs to show this tag to the RFID reader. The microcontroller connected to the reader will checks for the user authentication. If the user is found authentic then the quantity of ration to be given to the customer according to the total number of family members will be displayed on display device. This smart ration card is free from theft as the information about the delivered ration will be send directly to the government without manual feeding using Global system for Mobile Communication (GSM) technique.


title: Analytic relation for recovering the mutual intensity by means of intensity information

abstract: An analytic relation for recovering the mutual intensity by means of intensity information under the condition of the fractional Fourier transform is derived. The results may simplify the reconstruction of the mutual intensity in comparison with the Wigner tomographic method and can be regarded as an inverse transform formula that expresses output intensity in terms of input mutual intensity under partially coherent illumination.


title: Does intrinsic motivation fuel the prosocial fire? Motivational synergy in predicting persistence, performance, and productivity.

abstract: Researchers have obtained conflicting results about the role of prosocial motivation in persistence, performance, and productivity. To resolve this discrepancy, I draw on self-determination theory, proposing that prosocial motivation is most likely to predict these outcomes when it is accompanied by intrinsic motivation. Two field studies support the hypothesis that intrinsic motivation moderates the association between prosocial motivation and persistence, performance, and productivity. In Study 1, intrinsic motivation strengthened the relationship between prosocial motivation and the overtime hour persistence of 58 firefighters. In Study 2, intrinsic motivation strengthened the relationship between prosocial motivation and the performance and productivity of 140 fundraising callers. Callers who reported high levels of both prosocial and intrinsic motivations raised more money 1 month later, and this moderated association was mediated by a larger number of calls made. I discuss implications for theory and research on work motivation.


title: E-commerce: the role of familiarity and trust

abstract: Familiarity is a precondition for trust, claims Luhmann [28: Luhmann N. Trust and power. Chichester UK: Wiley, 1979. (translation from German)], and trust is a prerequisite of social behavior, especially regarding important decisions. This study examines this intriguing idea in the context of the E-commerce involved in inquiring about and purchasing books on the Internet. Survey data from 217 potential users support and extend this hypothesis. The data show that both familiarity with an Internet vendor and its processes and trust in the vendor influenced the respondents' intentions to inquire about books, and their intentions to purchase them. Additionally, the data show that while familiarity indeed builds trust, it is primarily people's disposition to trust that affected their trust in the vendor. Implications for research and practice are discussed.


title: Pathwise coordinate optimization

abstract: We consider ``one-at-a-time'' coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the $L_1$-penalized regression (lasso) in the literature, but it seems to have been largely ignored. Indeed, it seems that coordinate-wise algorithms are not often used in convex optimization. We show that this algorithm is very competitive with the well-known LARS (or homotopy) procedure in large lasso problems, and that it can be applied to related methods such as the garotte and elastic net. It turns out that coordinate-wise descent does not work in the ``fused lasso,'' however, so we derive a generalized algorithm that yields the solution in much less time that a standard convex optimizer. Finally, we generalize the procedure to the two-dimensional fused lasso, and demonstrate its performance on some image smoothing problems.


title: Protection of personal data in security alert sharing platforms

abstract: In order to ensure confidentiality, integrity and availability (so called CIA triad) of data within network infrastructure, it is necessary to be able to detect and handle cyber security incidents. For this purpose, it is vital for Computer Security Incident Response Teams (CSIRT) to have enough data on relevant security events and threats. That is why CSIRTs share security alerts and incidents data using various sharing platforms. Even though they do so primarily to protect data and privacy of users, their use also lead to additional processing of personal data, which may cause new privacy risks. European data protection law, especially with the adoption of the new General data protection regulation, sets out very strict rules on processing of personal data which on one hand leads to greater protection of individual's rights, but on the other creates great obstacles for those who need to share any personal data. This paper analyses the General Data Protection Regulation (GDPR), relevant case-law and analyses by the Article 29 Working Party to propose optimal methods and level of personal data processing necessary for effective use of security alert sharing platforms, which would be legally compliant and lead to appropriate balance between risks.


title: Improving deep learning performance using random forest HTM cortical learning algorithm

abstract: Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction. The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.


title: Self-poled transparent and flexible UV light-emitting cerium complex-PVDF composite: a high-performance nanogenerator.

abstract: Cerium(III)-N,N-dimethylformamide-bisulfate [Ce(DMF)(HSO4)3] complex is doped into poly(vinylidene fluoride) (PVDF) to induce a higher yield (99%) of the electroactive phases (β- and γ-phases) of PVDF. A remarkable enhancement of the output voltage (∼32 V) of a nanogenerator (NG) based on a nonelectrically poled cerium(III) complex containing PVDF composite film is achieved by simple repeated human finger imparting, whereas neat PVDF does not show this kind of behavior. This high electrical output resembles the generation of self-poled electroactive β-phase in PVDF due to the electrostatic interactions between the fluoride of PVDF and the surface-active positive charge cloud of the cerium complex via H-bonding and/or bipolar interaction among the opposite poles of cerium complex and PVDF, respectively. The capacitor charging capability of the flexible NG promises its applicability as piezoelectric-based energy harvester. The cerium(III) complex doped PVDF composite film exhibit an intense photoluminescence in the UV region, which might be due to a participation of electron cloud from negative pole of bipolarized PVDF. This fact may open a new area for prospective development of high-performance energy-saving flexible solid-state UV light emitters.


title: Image segmentation using clique based shape prior and the Mumford Shah Functional

abstract: A novel shape prior segmentation model is proposed that utilizes the cliques invariant signature along with a polygonal piecewise constant implementation of the Mumford-Shah Functional. The model will be shown to be useful in the context of difficult segmentation problems including and not limited to segmenting objects amidst clutter, or recognizing objects that contain components with largely varying non-uniform image intensities. In addition, the model will also be shown to be effective for image disocclusion. Lastly, the proposed model can accomplish all the aforementioned tasks both efficiently and with near automation.


title: Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation

abstract: We introduce language-driven image generation, the task of generating an image visualizing the semantic contents of a word embedding, e.g., given the word embedding of grasshopper, we generate a natural image of a grasshopper. We implement a simple method based on two mapping functions. The first takes as input a word embedding (as produced, e.g., by the word2vec toolkit) and maps it onto a high-level visual space (e.g., the space defined by one of the top layers of a Convolutional Neural Network). The second function maps this abstract visual representation to pixel space, in order to generate the target image. Several user studies suggest that the current system produces images that capture general visual properties of the concepts encoded in the word embedding, such as color or typical environment, and are sufficient to discriminate between general categories of objects.


title: An improved algorithm for video compression

abstract: The use of permutations in data compression is an aspect that is worthy of further exploration. The work that has been done in video compression based on permutations was primarily oriented toward lossless algorithms. The study of previous algorithms has led to a new algorithm that could be either lossless or lossy, for which the amount of compression and the quality of the output can be controlled. The lossless version of our algorithm performs close to lossy versions of H.264 and it improves on them for the majority of the videos that we analyzed. Our algorithm could be used in situations where there is a need for lossless compression and the video sequences are part of a single scene, e.g., medical videos, where loss of information could be risky or expensive.


Deep learning


title: Efficient spatiotemporal interpolation with spark machine learning

abstract: To better assess the relationships between environmental exposures and health outcomes, an appropriate spatiotemporal interpolation is critical. Traditional spatiotemporal interpolation methods either consider the spatial and temporal dimensions separately or incorporate both dimensions simultaneously by simply treating time as another dimension in space. Such interpolation results suffer from relatively low accuracy as the true space-time domain is skewed inappropriately and the distance calculation in such domain is not accurate. We employ the efficient k-d tree structure to store spatiotemporal data and adopt several machine learning methods to learn optimal parameters. To overcome the computational difficulty with large data sets, we implement our method on an efficient cluster computing framework – Apache Spark. Real world PM2.5 data sets are utilized to test our implementation and the experimental results demonstrate the computational power of our method, which significantly outperforms the previous work in terms of both speed and accuracy.


title: Hierarchical Quantized Representations for Script Generation

abstract: Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature of the knowledge. For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen. To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables. We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value. This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting. Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modeling-based method.


title: Adjustable gamma correction circuit for TFT LCD

abstract: Gamma correction is an essential function in every display device such as CRT, plasma TV, and TFT LCD. In this paper, a new gamma correction architecture of the source drive IC used in the TFT LCD is proposed. Instead of using non-linear DAC architecture, a linear DAC architecture is used to reduce the number of resistors required. The complexity of our design is also reduced due to less resistors. Moreover, the proposed design is capable to support multiple gamma values from 2.2 to 2.5 without further cost. In short, the proposed gamma correction design possesses several advantages which makes it a better alternative.


title: Ontology change: classification and survey

abstract: Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includes several related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it is being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as the same term is often used with different meanings in the relevant literature, creating a certain amount of confusion. The purpose of this paper is to identify the exact relationships between these research areas and to determine the boundaries of each field, by performing a broad review of the relevant literature.


title: Learning to Teach with Dynamic Loss Functions

abstract: Teaching is critical to human society: it is with teaching that prospective students are educated and human civilization can be inherited and advanced. A good teacher not only provides his/her students with qualified teaching materials (e.g., textbooks), but also sets up appropriate learning objectives (e.g., course projects and exams) considering different situations of a student. When it comes to artificial intelligence, treating machine learning models as students, the loss functions that are optimized act as perfect counterparts of the learning objective set by the teacher. In this work, we explore the possibility of imitating human teaching behaviors by dynamically and automatically outputting appropriate loss functions to train machine learning models. Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher). The ultimate goal of teacher model is cultivating the student to have better performance measured on development dataset. Towards that end, similar to human teaching, the teacher, a parametric model, dynamically outputs different loss functions that will be used and optimized by its student model at different training stages. We develop an efficient learning method for the teacher model that makes gradient based optimization possible, exempt of the ineffective solutions such as policy optimization. We name our method as"learning to teach with dynamic loss functions"(L2T-DLF for short). Extensive experiments on real world tasks including image classification and neural machine translation demonstrate that our method significantly improves the quality of various student models.


title: Don't forget, there is more than forgetting: new metrics for Continual Learning

abstract: Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills. The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and forward knowledge transfer, memory overhead as well as computational efficiency. Drawing inspiration from the standard Multi-Attribute Value Theory (MAVT) we further propose to fuse these metrics into a single score for ranking purposes and we evaluate our proposal with five continual learning strategies on the iCIFAR-100 continual learning benchmark.


title: Automatic 3D Facial Expression Recognition Based on a Bayesian Belief Net and a Statistical Facial Feature Model

abstract: Automatic facial expression recognition on 3D face data is still a challenging problem. In this paper we propose a novel approach to perform expression recognition automatically and flexibly by combining a Bayesian Belief Net (BBN) and Statistical facial feature models (SFAM). A novel BBN is designed for the specific problem with our proposed parameter computing method. By learning global variations in face landmark configuration (morphology) and local ones in terms of texture and shape around landmarks, morphable Statistic Facial feature Model (SFAM) allows not only to perform an automatic landmarking but also to compute the belief to feed the BBN. Tested on the public 3D face expression database BU-3DFE, our automatic approach allows to recognize expressions successfully, reaching an average recognition rate over 82%.


title: Comparison of denture base adaptation between CAD-CAM and conventional fabrication techniques

abstract: Abstract Statement of problem Currently no data comparing the denture base adaptation of CAD-CAM and conventional denture processing techniques have been reported. Purpose The purpose of this in vitro study was to compare the denture base adaptation of pack and press, pour, injection, and CAD-CAM techniques for fabricating dentures to determine which process produces the most accurate and reproducible adaptation. Material and methods A definitive cast was duplicated to create 40 gypsum casts that were laser scanned before any fabrication procedures were initiated. A master denture was made using the CAD-CAM process and was then used to create a putty mold for the fabrication of 30 standardized wax festooned dentures, 10 for each of the conventional processing techniques (pack and press, pour, injection). Scan files from 10 casts were sent to Global Dental Science, LLC for fabrication of the CAD-CAM test specimens. After specimens for each of the 4 techniques had been fabricated, they were hydrated for 24 hours and the intaglio surface laser scanned. The scan file of each denture was superimposed on the scan file of the corresponding preprocessing cast using surface matching software. Measurements were made at 60 locations, providing evaluation of fit discrepancies at the following areas: apex of the denture border, 6 mm from the denture border, crest of the ridge, palate, and posterior palatal seal. The use of median and interquartile range was used to assess accuracy and reproducibility. The Levine and Kruskal-Wallis analysis of variance was used to evaluate differences between processing techniques at the 5 specified locations (α=.05). Results The ranking of results based on median and interquartile range determined that the accuracy and reproducibility of the CAD-CAM technique was more consistently localized around zero at 3 of the 5 locations. Therefore, the CAD-CAM technique showed the best combination of accuracy and reproducibility among the tested fabrication techniques. The pack and press technique was more accurate at 2 of the 5 locations; however, its interquartile range (reproducibility) was the greatest of the 4 tested processing techniques. The pour technique was the most reproducible at 2 of the 5 locations; however, its accuracy was the lowest of the tested techniques. Conclusions The CAD-CAM fabrication process was the most accurate and reproducible denture fabrication technique when compared with pack and press, pour, and injection denture base processing techniques.


title: Integrating computational thinking across the K--8 curriculum

abstract: The excitement around K-12 Computing Education in the United States is rapidly increasing and K-8 holds great potential as the entry point for the integration of computing. We examine how young learners can gain early exposure and engage in rich computational experiences in K-8. These experiences can build students’ computational thinking, understanding of CS concepts, experience with collecting and analyzing data, programming skills and confidence as critical thinkers. We discuss how three types of computational activities: digital storytelling, data collection and analysis, and computational science investigations, can be used to incorporate computational thinking (CT) across the curriculum.


title: A Sarcasm Extraction Method Based on Patterns of Evaluation Expressions

abstract: Sarcasm presents a negative meaning with positive expressions and is a non-literalistic expression. Sarcasm detection is an important task because it contributes directly to the improvement of the accuracy of sentiment analysis tasks. In this study, we propose a extraction method of sarcastic sentences in product reviews. First, we analyze sarcastic sentences in product reviews and classify the sentences into 8 classes by focusing on evaluation expressions. Next, we generate classification rules for each class and use them to extract sarcastic sentences. Our method consists of three stage, judgment processes based on rules for 8 classes, boosting rules and rejection rules. In the experiment, we compare our method with a baseline based on a simple rule. The experimental result shows the effectiveness of our method.


title: Cloud computing: state-of-the-art and research challenges

abstract: Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.


title: An investigation into the applicability of building information models in geospatial environment in support of site selection and fire response management processes

abstract: Some tasks in the construction industry and urban management field such as site selection and fire response management are usually managed by using a Geographical Information System (GIS), as the tasks in these processes require a high level and amount of integrated geospatial information. Recently, a key element of this integrated geospatial information to emerge is detailed geometrical and semantic information about buildings. In parallel, Building Information Models (BIMs) of today have the capacity for storing and representing such detailed geometrical and semantic information. In this context, the research aimed to investigate the applicability of BIMs in geospatial environment by focusing specifically on these two domains; site selection and fire response management. In the first phase of the research two use case scenarios were developed in order to understand the processes in these domains in a more detailed manner and to establish the scope of a possible software development for transferring information from BIMs into the geospatial environment. In the following phase of the research two data models were developed - a Schema-Level Model View and a geospatial data model. The Schema-Level Model View was used in simplifying the information acquired from the BIM, while the geospatial data model acted as the template for creating physical files and databases in the geospatial environment. Following this, three software components to transfer building information into the geospatial environment were designed, developed, and validated. The first component served for acquiring the building information from the BIM, while the latter two served for transforming the information into the geospatial environment. The overall research demonstrated that it is possible to transfer (high level of geometric and semantic) information acquired from BIMs into the geospatial environment. The results also demonstrated that BIMs provide a sufficient level and amount of (geometric and semantic) information (about the building) for the seamless automation of data management tasks in the site selection and fire response management processes.


title: N-gram: a language independent approach to IR and NLP

abstract: With the increasingly widespread use of computers & the Internet in India, large amounts of information in Indian languages are becoming available on the web. Automatic information processing and retrieval is therefore becoming an urgent need in the Indian context. Moreover, since India is a multilingual country, any effective approach to IR in the Indian context needs to be capable of handling a multilingual collection of documents. In this paper, we discuss the N-gram approach to developing some basic tools in the area of IR and NLP. This approach is statistical and language independent in nature, and therefore eminently suited to the multilingual Indian context. We first present a brief survey of some language-processing applications in which N-grams have been successfully used. We also present the results of some preliminary experiments on using N-grams for identifying the language of an Indian language document, based on a method proposed by Cavnar et al [1].


Digital Forensic Analysis of SIM Cards


title: Clinical Impact of Amino Acid PET in Gliomas

abstract: With an incidence of 20.5 per 100,000 people each year, tumors of the central nervous system are a heterogeneous group, with meningiomas (35.5%) and gliomas (30%) being the most common (1). Although 97% of meningiomas are benign, 54% of gliomas are highly malignant glioblastomas of World Health Organization (WHO) grade IV. Survival rates are 35.7% at 1 y and 4%–7% at 5 y for glioblastoma, 60%–80% at 1 y and 26%–46% at 5 y for astrocytomas and oligodendrogliomas of WHO grade III, and 94% at 1 y and 67% at 5 y for astrocytomas and oligodendrogliomas of WHO grade II (2). Additionally, the central nervous system may be invaded by metastases from other malignancies. Brain tumors create structural lesions whose diagnosis is primarily dependent on imaging with CT and MR. However, it is possible to improve clinical management by using PET to provide physiologic and biochemical information on tumor metabolism, proliferation rate, and invasiveness, as well as to determine the relationship of the tumor to important functional tissue and to monitor the effects of treatment (3). Imaging of brain tumors with 18 F-FDG was the first oncologic application of PET. As in other malignancies, glucose consumption is increased in brain tumors, especially in malignant gliomas, but differentiating tumors from normal tissue or nontumorous lesions is often difficult because of the high metabolism in normal cortex. 18 FFDG uptake in low-grade tumors is usually similar to that in normal white matter, and uptake in high-grade tumors can be less than or similar to that in normal gray matter. The sensitivity of detection of lesions is further decreased by the high variance of 18 F-FDG uptake and its heterogeneity within a single tumor. Labeled amino acids and their analogs are particularly attractive for imaging brain tumors because of the high uptake in tumor tissue and low uptake in normal brain. This increased amino acid uptake, especially in gliomas, is not a direct measure of protein synthesis or dependent on blood–brain barrier breakdown but rather is related to increased transport mediated by type L amino acid carriers: facilitated transport is upregulated because of the increased transporter expression in tumor vasculature (4). Additionally, the countertransport system A is overexpressed in neoplastic cells and seems to correlate positively with the rate of tumor cell growth (5). Therefore, elevated transport of amino acids not only is a result of increased protein synthesis but also reflects the increased demand by different types of metabolism in the tumor cell. The most frequently used radiolabeled amino acid is methyl- 11 CL-methionine (MET) (6,7). Especially in low-grade gliomas, amino acid uptake is related to prognosis a nd survival. With regard to tumor extentandinfiltrationintosurroundingtissue,assessmentofaminoacid uptake is superior to measurement of glucose consumption (8 )a nd


title: Variance Reduction with Sparse Gradients

abstract: Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients to reduce the variance of stochastic gradients. Compared to SGD, these methods require at least double the number of operations per update to model parameters. To reduce the computational cost of these methods, we introduce a new sparsity operator: The random-top-k operator. Our operator reduces computational complexity by estimating gradient sparsity exhibited in a variety of applications by combining the top-k operator and the randomized coordinate descent operator. With this operator, large batch gradients offer an extra benefit beyond variance reduction: A reliable estimate of gradient sparsity. Theoretically, our algorithm is at least as good as the best algorithm (SpiderBoost), and further excels in performance whenever the random-top-k operator captures gradient sparsity. Empirically, our algorithm consistently outperforms SpiderBoost using various models on various tasks including image classification, natural language processing, and sparse matrix factorization. We also provide empirical evidence to support the intuition behind our algorithm via a simple gradient entropy computation, which serves to quantify gradient sparsity at every iteration.


title: Issues in Mapping LOINC Laboratory Tests to SNOMED CT

abstract: Comprehensive clinical terminologies such as SNOMED CT tend to overlap with specialized terminologies such as LOINC (e.g., for the domain of laboratory procedures). Terminological systems such as the UMLS are often used to bridge between terminologies. However, the integration of LOINC in the UMLS and with other terminologies remains suboptimal. We mapped concepts for laboratory tests from LOINC to pre-coordinated SNOMED CT concepts, based on shared relations to other concepts. As LOINC is finer-grained than SNOMED CT, several LOINC codes tend to map to the same SNOMED CT concept. However, a large proportion of LOINC codes could not be mapped to SNOMED CT through this approach, because of underspecified definitions in SNOMED CT and a lack of fine-grained, precoordinated concepts in SNOMED CT.


title: A study of the additive manufacturing technology for RF/microwave components

abstract: In this work an overview of the different additive manufacturing techniques will be presented. Each technique will be compared in terms of dimensional accuracy, surface roughness, and quality taking into account the most suitable applications for each one. Then, two applications will be studied: (a) fast prototyping of waveguide component designs using professional grade 3D printers for electromagnetic validation purposes, and (b) 3D printing of waveguide hardware products as a production technology using production grade 3D printers. The first application will be studied with a research center approach, where many different designs are manufactured in order to validate a design technique or a model. The second application will be studied with a company approach, where the best quality at a lower price is pursued.


Reference Reading #4 THE K FACTOR: A NEW MATHEMATICAL TOOL FOR STABILITY ANALYSIS AND SYNTHESIS


title: Radiation Effects in MOS Oxides

abstract: Electronic devices in space environments can contain numerous types of oxides and insulators. Ionizing radiation can induce significant charge buildup in these oxides and insulators leading to device degradation and failure. Electrons and protons in space can lead to radiation-induced total-dose effects. The two primary types of radiation-induced charge are oxide-trapped charge and interface-trap charge. These charges can cause large radiation-induced threshold voltage shifts and increases in leakage currents. Two alternate dielectrics that have been investigated for replacing silicon dioxide are hafnium oxides and reoxidized nitrided oxides (RNO). For advanced technologies, which may employ alternate dielectrics, radiation-induced voltage shifts in these insulators may be negligible. Radiation-induced charge buildup in parasitic field oxides and in SOI buried oxides can also lead to device degradation and failure. Indeed, for advanced commercial technologies, the total-dose hardness of ICs is normally dominated by radiation-induced charge buildup in either parasitic field oxides and/or SOI buried oxides. Heavy ions in space can also degrade the oxides in electronic devices through several different mechanisms including single-event gate rupture, reduction in device lifetime, and large voltage shifts in power MOSFETs.


title: A game-theoretic approach to apprenticeship learning

abstract: We study the problem of an apprentice learning to behave in an environment with an unknown reward function by observing the behavior of an expert. We follow on the work of Abbeel and Ng [1] who considered a framework in which the true reward function is assumed to be a linear combination of a set of known and observable features. We give a new algorithm that, like theirs, is guaranteed to learn a policy that is nearly as good as the expert's, given enough examples. However, unlike their algorithm, we show that ours may produce a policy that is substantially better than the expert's. Moreover, our algorithm is computationally faster, is easier to implement, and can be applied even in the absence of an expert. The method is based on a game-theoretic view of the problem, which leads naturally to a direct application of the multiplicative-weights algorithm of Freund and Schapire [2] for playing repeated matrix games. In addition to our formal presentation and analysis of the new algorithm, we sketch how the method can be applied when the transition function itself is unknown, and we provide an experimental demonstration of the algorithm on a toy video-game environment.


title: Test Methods for Silicon Die Strength

abstract: Recently, the 3D or stacked-die packages become increasingly popular for packaging ICs into a system or subsystem to satisfy the needs of low cost, small form factor, and high performance. For the applications of these packages, IC silicon wafers have to be ground to be relatively thin through the wafer-thinning processes (such as grinding, polishing, and plasma etching). The strength of dies has to be determined for the design requirement and reliability assurance of the packages. From the published data, there still exist some issues, including a large scatter existed in die strength data and difficulties in differentiating the causes of the low strength between from the wafer grinding and from wafer sawing by either the three-point bending or four-point bending test. The purposes of this study are to develop new, reliable, and simple test methods for determination of die strength, in order to improve the data scatter, and to provide a solution for differentiating the factors that affect the variability of die strength for finding out the causes of the weakness of the die strength. In this study, two new test methods, point-loaded circular plate with simple supports test (PLT-I) and point-loaded plate on elastic foundation test (PLT-II), are proposed and then evaluated by testing two groups of silicon dies with different surface conditions. The surface conditions (roughness) of the specimens are determined by atomic force microscopy and correlated to failure strength. The failure forces from both tests have to be modified by using maximum stress obtained from theories or finite element analyses to obtain the failure strength. The test results are compared to each other and further with a widely used four-point bending test. The results suggest that, unlike the four-point bending test suffering the chipping effect, both methods provide very consistent data with a small scatter for each group of specimens and can be used for identifying the effect of surface grinding (roughness) on the die strength. It is also shown that the die strength is highly dependent on the surface roughness. Accordingly, these two methods can provide not only a (biaxial) stress field similar to temperature-loaded die in the packages, but also simple, feasible, reliable, and chipping-free tests for silicon dies of dummy or real IC chips, without strict geometrical limitation, such as beam-type geometry for the three-point or four-point bending test.


title: Comparing fiducial marker systems in the presence of occlusion

abstract: A fiducial marker system is a system of unique 2D (planar) marker, which is placed in an environment and automatically will be detected with a camera with a help of a corresponding detection algorithm. Application areas of these markers include industrial systems, augmented reality, robots navigation, human-robot interaction and others. Marker system designed for such different applications must be robust to such factors as view angles, occlusions, changing distances, etc. This paper compares three existing systems of markers: ARTag, AprilTag, and CALTag. As a benchmark, we use their reliability and detection rate in presence of occlusions of various types and intensity. The paper presents experimental comparison of these markers. The marker detection was performed with a simple inexpensive Web camera.


title: Optimal pulse-width modulation for three-level inverters

abstract: The three-level neutral-point-clamped voltage source inverter is widely used in high power, medium voltage applications. This paper studies continuous and discontinuous pulse-width modulation for this inverter. Detailed analysis of discontinuous modulation shows that the average switching frequency is not directly proportional to the carrier or sampling frequency, since additional switching transitions occur between different regions of discontinuity. At typical switching frequencies for high power applications (up to 2 kHz) these additional transitions contribute significantly to the inverter's total losses, so that a proper comparison of the harmonic performance can only be carried out under constant loss conditions with varying carrier frequency. This comparison is performed for a typical industrial medium voltage inverter. The paper then considers the major issues of neutral-point voltage balancing and loss distribution within the inverter, for the identified optimal modulation schemes.


title: Social-Ecological, Motivational and Volitional Factors for Initiating and Maintaining Physical Activity in the Context of HIV

abstract: Sport and exercise can have several health benefits for people living with HIV. These benefits can be achieved through different types of physical activity, adapting to disease progression, motivation and social-ecological options. However, physical activity levels and adherence to exercise are generally low in people living with HIV. At the same time, high drop-out rates in intervention studies are prevalent; even though they often entail more favourable conditions than interventions in the natural settings. Thus, in the framework of an intervention study, the present study aims to explore social-ecological, motivational and volitional correlates of South African women living with HIV with regard to physical activity and participation in a sport and exercise health promotion programme. The qualitative data was produced in the framework of a non-randomised pre-post intervention study that evaluated structure, processes and outcomes of a 10-week sport and exercise programme. All 25 participants of the programme were included in this analysis, independent of compliance. Data was produced through questionnaires, participatory group discussions, body image pictures, research diaries and individual semi-structured interviews. All participants lived in a low socioeconomic, disadvantaged setting. Hence, the psychological correlates are contextualised and social-ecological influences on perception and behaviour are discussed. The results show the importance of considering social-cultural and environmental influences on individual motives, perceptions and expectancies, the fear of disclosure and stigmatisation, sport and exercise-specific group dynamics and self-supporting processes. Opportunities and strategies to augment physical activity and participation in sport and exercise programmes in the context of HIV are discussed.


title: Corno – Enhancing Interactivity for Self-Evaluation in XMLbased Courseware, paper presented at “WebNet 2000” conference

abstract: The paper describes an educational application, oriented to university students, to help them to learn the SQL language in the ambit of a course on database systems and applications. This tool is integrated with a XML-based framework for courseware authoring, whose main characteristic is flexibility, since contents are presented to the students in different ways, depending on their previous knowledge, on the kind of hardware used to access the content, and on some preferences of the user. The implemented application is interactive, and allows the students to practice their understanding and their skills about the SQL language, following the approach of “leaning by doing”.


title: Extracting information from neuronal populations: information theory and decoding approaches

abstract: Recording from neuronal populations is a promising and powerful neuroscience technique; however, interpreting the resulting spike trains presents several challenges. Quian Quiroga and Panzeri discuss how decoding algorithms and information theory can be used to extract information from population recordings.


title: Robust statistics for outlier detection

abstract: When analyzing data, outlying observations cause problems because they may strongly influence the result. Robust statistics aims at detecting the outliers by searching for the model fitted by the majority of the data. We present an overview of several robust methods and outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data such as estimation of location and scatter, linear regression, principal component analysis, and classification. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 73-79 DOI: 10.1002/widm.2 ::: ::: This article is categorized under: ::: ::: Algorithmic Development > Biological Data Mining ::: Algorithmic Development > Spatial and Temporal Data Mining ::: Application Areas > Health Care ::: Technologies > Structure Discovery and Clustering


title: Cross-Scene Counting Based on Domain Adaptation-Extreme Learning Machine

abstract: Cross-scene counting is difficult if only limited training samples are available in the new scene. In this paper, a cross-scene counting model is learned with information transferred from other scenes. Counting is achieved through regression, which maps the features of crowds to their counts. Hand-crafted features are extracted from segmented crowd foregrounds obtained through block robust principal component analysis. Samples of existing scenes (source domain) are adaptively transferred into the new scene (target domain) through domain adaptation. Then, a counting model based on domain adaptation-extreme learning machine (DA-ELM) is efficiently learned via iterative optimization with training samples of both domains. Quantitative analysis indicates that the DA-ELM can count the crowds of a new scene with only a half of the training samples compared with counting without domain adaptation. Contrastive evaluations based on three benchmarking data sets are implemented with several state-of-the-art domain adaptation approaches, including hand-crafted feature-based and deep neural network-based approaches. Results reveal the effectiveness of DA-ELM in transferring information through embedding domain adaptation into an ELM framework.


Wavelet Transform Based Wafer Defect Map Pattern Recognition System in Semiconductor Manufacturing


title: Concurrent replicating garbage collection

abstract: We have implemented a concurrent copying garbage collector that uses replicating garbage collection. In our design, the client can continuously access the heap during garbage collection. No low-level synchronization between the client and the garbage collector is required on individual object operations. The garbage collector replicates live heap objects and periodically synchronizes with the client to obtain the client's current root set and mutation log. An experimental implementation using the Standard ML of New Jersey system on a shared-memory multiprocessor demonstrates excellent pause time performance and moderate execution time speedups.


title: Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences

abstract: In this paper we explore the problem of fitting a 3D morphable model to single face images using only sparse geometric features (edges and landmark points). Previous approaches to this problem are based on nonlinear optimisation of an edge-derived cost that can be viewed as forming soft correspondences between model and image edges. We propose a novel approach, that explicitly computes hard correspondences. The resulting objective function is non-convex but we show that a good initialisation can be obtained efficiently using alternating linear least squares in a manner similar to the iterated closest point algorithm. We present experimental results on both synthetic and real images and show that our approach outperforms methods that use soft correspondence and other recent methods that rely solely on geometric features.


title: A Taxonomy of mHealth Apps -- Security and Privacy Concerns

abstract: With the increasing use of smartphones for healthcare purposes, more and more people now share their personal healthcare information using a variety of applications. The vast number of existing mobile health (mHealth) applications creates a serious problem for users, as often times they are unaware of how their data are managed and used. We propose a taxonomy incorporating the most significant security and privacy aspects of mHealth applications. This artifact can help outline some of the problems related to creating and downloading mHealth applications. The taxonomy was tested with 38 top-rated Android and iOS healthcare applications. Results of the evaluation suggest that having a unified mechanism to categorize mHealth applications with respect to security and privacy is important and can be beneficial. This study contributes to literature, as it builds upon prior work and adds knowledge to a still new and relatively unexplored domain such as mobile healthcare.


title: Sego: Pervasive Trusted Metadata for Efficiently Verified Untrusted System Services

abstract: Sego is a hypervisor-based system that gives strong privacy and integrity guarantees to trusted applications, even when the guest operating system is compromised or hostile. Sego verifies operating system services, like the file system, instead of replacing them. By associating trusted metadata with user data across all system devices, Sego verifies system services more efficiently than previous systems, especially services that depend on data contents. We extensively evaluate Sego's performance on real workloads and implement a kernel fault injector to validate Sego's file system-agnostic crash consistency and recovery protocol.


title: Hypothesis Spaces for Minimum Bayes Risk Training in Large Vocabulary Speech Recognition

abstract: The Minimum Bayes Risk (MBR) framework has been a successful strategy for the training of hidden Markov models for large vocabulary speech recognition. Practical implementations of MBR must select an appropriate hypothesis space and loss function. The set of word sequences and a word-based Levenshtein distance may be assumed to be the optimal choice but use of phoneme-based criteria appears to be more successful. This paper compares the use of different hypothesis spaces and loss functions defined using the system constituents of word, phone, physical triphone, physical state and physical mixture component. For practical reasons the competing hypotheses are constrained by sampling. The impact of the sampling technique on the performance of MBR training is also examined.


title: Facebook and MySpace: Complement or Substitute for Face-to-Face Interaction?

abstract: Abstract Previous studies have claimed that social-networking sites are used as a substitute for face-to-face interaction, resulting in deteriorating relationship quality and decreased intimacy among its users. The present study hypothesized that this type of communication is not a substitute for face-to-face interaction; rather, that it is an extension of communication with face-to-face partners. A survey was administered to examine the use of Facebook and MySpace in this regard among 183 college students. The study confirmed that Facebook and MySpace do act as an extension of face-to-face interaction, but that some users do tend to rely on Facebook and MySpace for interpersonal communication more than face-to-face interaction.


title: Validating Spatial Augmented Reality for interactive rapid prototyping

abstract: This paper investigates the use of Spatial Augmented Reality in the prototyping of new human-machine interfaces, such as control panels or car dashboards. The prototyping system uses projectors to present the visual appearance of controls onto a mock-up of a product. Finger tracking is employed to allow real-time interactions with the controls. This technology can be used to quickly and inexpensively create and evaluate interface prototypes for devices. In the past, evaluating a prototype involved constructing a physical model of the device with working components such as buttons. We have conducted a user study to compare these two methods of prototyping and to validate the use of spatial augmented reality for rapid iterative interface prototyping. Participants of the study were required to press pairs of buttons in sequence and interaction times were measured. The results indicate that while slower, users can interact naturally with projected control panels.


title: Aesthetic considerations for automated platformer design

abstract: We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.


title: A universal model for flexible item selection in conversational dialogs

abstract: Human-computer interaction and statistical natural language understanding has changed with the addition of a visual display screen in modern mobile devices, as visual rendering is used to communicate the dialog system's response. Onscreen item identification and resolution when interpreting the user utterances is one critical problem to achieve the natural and accurate human-machine communication. This problem, also called Flexible Item Selection (FIS), has been posed as a classification task to correctly identify intended on-screen item(s) from user utterances. This paper presents a universal FIS model that can be applied to dialog systems developed in different languages. We design a set of input features for the FIS model that makes it largely language-independent. We demonstrate that a single universal FIS model can be used in place of language specific FIS models with no loss in accuracy. We also show that such a model can generalize well to new unseen languages with minimal loss in accuracy on held out languages including English, French, Spanish, Italian, German, and Chinese. Eliminating the need for building and maintaining a separate FIS model for each new language, the universal FIS model helps scaling an existing dialogue system to new languages faster at a lower development cost.


title: Effective car parking reservation system based on internet of things technologies

abstract: Nowadays with the rapid increase in urban population, there is a major problem with the parking system in almost every major city across the globe. Many of us get highly disturbed when there is no proper space for parking space for our vehicles. In this article, we have proposed a smart parking application, where users will be able to park their automobiles by finding an empty parking lot through Android Application or can even park their automobiles directly through Embedded Hardware. An Intelligent Parking System is implemented based on Slot Allotment. There are two modes using which the Android user can book the parking slots easily like Advance & Current Booking. Using these modes the application user can also choose the easiest and nearest route and destination. The Android Application itself will serve as a payment gateway. Embedded Hardware is implemented for Direct Parking. Server will monitor the Slot Allotment dynamically.


title: Posttraumatic hepatic vein thrombosis (Budd-Chiari syndrome) in a child

abstract: The Budd-Chiari syndrome (BCS) results from hepatic venous outflow obstruction and is characterized by massive ascites and painful hepatomegaly. It is a rare condition, particularly in children, and is often fatal without surgical intervention. Although several conditions resulting in a hypercoagulable state appear to predispose individuals to developing the disorder in adulthood, the etiology in children is often obscure. We present the case of a 6-year-old girl treated nonoperatively for blunt abdominal trauma and hemoperitoneum. Two weeks following her injury, she developed an acute BCS with rapid liver failure, ascites, and abdominal pain. The diagnosis of hepatic vein thrombosis was confirmed radiographically and the patient underwent a C-type mesocaval shunt procedure. The etiology, diagnosis, and surgical management of BCS in children and a review of the literature are discussed.


title: twAwler: A lightweight twitter crawler

abstract: This paper presents twAwler, a lightweight twitter crawler that targets language-specific communities of users. twAwler takes advantage of multiple endpoints of the twitter API to explore user relations and quickly recognize users belonging to the targetted set. It performs a complete crawl for all users, discovering many standard user relations, including the retweet graph, mention graph, reply graph, quote graph, follow graph, etc. twAwler respects all twitter policies and rate limits, while able to monitor large communities of active users. twAwler was used between August 2016 and March 2018 to generate an extensive dataset of close to all Greek-speaking twitter accounts (about 330 thousand) and their tweets and relations. In total, the crawler has gathered 750 million tweets of which 424 million are in Greek; 750 million follow relations; information about 300 thousand lists, their members (119 million member relations) and subscribers (27 thousand subscription relations); 705 thousand trending topics; information on 52 million users in total of which 292 thousand have been since suspended, 141 thousand have deleted their account, and 3.5 million are protected and cannot be crawled. twAwler mines the collected tweets for the retweet, quote, reply, and mention graphs, which, in addition to the follow relation crawled, offer vast opportunities for analysis and further research.


Transparent Runtime Shadow Stack: Protection against malicious return address modifications


title: Key Compression for Isogeny-Based Cryptosystems

abstract: We present a method for key compression in quantumresistant isogeny-based cryptosystems, which allows a reduction in and transmission costs of per-party public information by a factor of two, with no e ect on security. We achieve this reduction by associating a canonical choice of elliptic curve to each j-invariant, and representing elements on the curve as linear combinations with respect to a canonical choice of basis. This method of compressing public information can be applied to numerous isogeny-based protocols, such as key exchange, zero-knowledge identi cation, and public-key encryption. We performed personal computer and ARM implementations of the key exchange with compression and decompression in C and provided timing results, showing the computational cost of key compression and decompression at various security levels. Our results show that isogeny-based cryptosystems achieve by far the smallest possible key sizes among all existing families of post-quantum cryptosystems at practical security levels; e.g. 3073-bit public keys at the quantum 128-bit security level, comparable to (non-quantum) RSA key sizes.


A News Editorial Corpus for Mining Argumentation Strategies


title: Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams

abstract: The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. An automated construction of a denoising autoeconder, namely deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. DEVDAN features an open structure both in the generative phase and in the discriminative phase where input features can be automatically added and discarded on the fly. A network significance (NS) method is formulated in this paper and is derived from the bias-variance concept. This method is capable of estimating the statistical contribution of the network structure and its hidden units which precursors an ideal state to add or prune input features. Furthermore, DEVDAN is free of the problem- specific threshold and works fully in the single-pass learning fashion. The efficacy of DEVDAN is numerically validated using nine non-stationary data stream problems simulated under the prequential test-then-train protocol where DEVDAN is capable of delivering an improvement of classification accuracy to recently published online learning works while having flexibility in the automatic extraction of robust input features and in adapting to rapidly changing environments.


title: Careful what you share in six seconds: Detecting cyberbullying instances in Vine

abstract: As online social networks have grown in popularity, teenage users have become increasingly exposed to the threats of cyberbullying. The primary goal of this research paper is to investigate cyberbullying behaviors in Vine, a mobile based video-sharing online social network, and design novel approaches to automatically detect instances of cyberbullying over Vine media sessions. We first collect a set of Vine video sessions and use CrowdFlower, a crowd-sourced website, to label the media sessions for cyberbullying and cyberaggression. We then perform a detailed analysis of cyberbullying behavior in Vine. Based on the labeled data, we design a classifier to detect instances of cyberbullying and evaluate the performance of that classifier.


title: Innovation management in context: Environment, organization and performance

abstract: Several decades of research into innovation management have failed to provide clear and consistent findings or coherent advice to managers. In this paper, I argue that this is because innovation management ‘best practice‘ is contingent on a range of factors, and that we need better characterizations of the technological and market contingencies which affect the opportunity for, and constraints on, innovation. I review research on innovation together with relevant studies from organizational behaviour and strategic management, and develop a model which may help to guide future innovation research on the relationships between environmental contingencies, organization configurations and performance. I identify uncertainty and complexity as the key environmental contingencies that influence organizational structure and management processes for innovation.