title: Surgical Management of Hidradenitis Suppurativa: Outcomes of 590 Consecutive Patients.

text:
BACKGROUND
Hidradenitis suppurativa is a progressive, recurrent inflammatory disease. Surgical management is potentially curative with limited efficacy data.

OBJECTIVE
To evaluate hidradenitis surgical patients.

METHODS
Retrospective review of outcomes of 590 consecutive surgically treated patients.

RESULTS
Most patients were white (91.0% [435/478]), men (337 [57.1%]), smokers (57.7% [297/515]) with Hurley Stage III disease (476 [80.7%]). Procedure types were excision (405 [68.6%]), unroofing (168 [28.5%]), and drainage (17 [2.9%]) treating disease of perianal/perineum (294 [49.8%]), axilla (124 [21.0%]), gluteal cleft (76 [12.9%]), inframammary (12 [2.0%]), and multiple surgical sites (84 [14.2%]). Postoperative complications occurred in 15 patients (2.5%) and one-fourth (144 [24.4%]) suffered postoperative recurrence, which necessitated reoperation in one-tenth (69 [11.7%]) of patients. Recurrence risk was increased by younger age (hazard ratio [HR], 0.8; 95% confidence interval [CI], 0.7-0.9), multiple surgical sites (HR, 1.6; 95% CI, 1.1-2.5), and drainage-type procedures (HR, 3.5; 95% CI, 1.2-10.7). Operative location, disease severity, gender, and operative extent did not influence recurrence rate.

CONCLUSION
Excision and unroofing procedures were effective treatments with infrequent complications and low recurrence rates. Well-planned surgical treatment aiming to remove or unroof the area of intractable hidradenitis suppurativa was highly effective in the management of this challenging disease.


title: Analysis of Massive MIMO-Enabled Downlink Wireless Backhauling for Full-Duplex Small Cells

text: Recent advancements in self-interference (SI) cancellation capability of low-power wireless devices motivate in-band full-duplex (FD) wireless backhauling in small cell networks (SCNs). In-band FD wireless backhauling concurrently allows the use of the same frequency spectrum for the backhaul as well as access links of the small cells. In this paper, using tools from stochastic geometry, we develop a framework to model the downlink rate coverage probability of a user in a given SCN with massive multiple-input-multiple-output (MIMO)-enabled wireless backhauls. The considered SCN is composed of a mixture of small cells that are configured in either in-band or out-of-band backhaul modes with a certain probability. The performance of the user in the considered hierarchical network is limited by several sources of interference, such as the backhaul interference, small cell base station (SBS)-to-SBS interference, and the SI. Moreover, due to the channel hardening effect in massive MIMO, the backhaul links only experience long term channel effects, whereas the access links experience both the long term and the short term channel effects. Consequently, the developed framework is flexible to characterize different sources of interference while capturing the heterogeneity of the access and backhaul channels. In specific scenarios, the framework enables deriving closed-form coverage probability expressions. Under perfect backhaul coverage, the simplified expressions are utilized to optimize the proportion of in-band and out-of-band small cells in the SCN in the closed form. Finally, a few remedial solutions are proposed that can potentially mitigate the backhaul interference and in turn improve the performance of in-band FD wireless backhauling. Numerical results investigate the scenarios in which in-band wireless backhauling is useful and demonstrate that maintaining a correct proportion of in-band and out-of-band FD small cells is crucial in wireless backhauled SCNs.


title: Applications of Trajectory Data in Transportation: Literature Review and Maryland Case Study

text: This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.


title: Smart Health Care: An Edge-Side Computing Perspective

text: Increasing health awareness and rapid technological advancements have resulted in significant growth in an emerging sector: smart health care. By the end of 2020, the total number of smart health-care devices is expected to reach 808.9 million (646 million devices without wearables and the remaining with wearables) [1], [2]. This proliferation of devices is expected to revolutionize health care by speeding up treatment and diagnostic processes, decreasing physician visit costs, and enhancing patient care quality.


title: Hand segmentation for hand-object interaction from depth map

text: Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.


title: Finding News Citations for Wikipedia

text:
An important editing policy in Wikipedia is to provide citations for added statements in Wikipedia pages, where statements can be arbitrary pieces of text, ranging from a sentence to a paragraph. In many cases citations are either outdated or missing altogether.
 In this work we address the problem of finding and updating news citations for statements in entity pages. We propose a two-stage supervised approach for this problem. In the first step, we construct a classifier to find out whether statements need a news citation or other kinds of citations (web, book, journal, etc.). In the second step, we develop a news citation algorithm for Wikipedia statements, which recommends appropriate citations from a given news collection. Apart from IR techniques that use the statement to query the news collection, we also formalize three properties of an appropriate citation, namely: (i) the citation should entail the Wikipedia statement, (ii) the statement should be central to the citation, and (iii) the citation should be from an authoritative source.
 We perform an extensive evaluation of both steps, using 20 million articles from a real-world news collection. Our results are quite promising, and show that we can perform this task with high precision and at scale.


title: A Modular Framework for Versatile Conversational Agent Building

text: This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.


ERP integration in a healthcare environment: a case study


title: Evaluating the influence of YouTube advertising for attraction of young customers

text: Nowadays, we have been faced with an increasing number of people who are spending tremendous amounts of time all around the world on YouTube. To date, the factors that persuade customers to accept YouTube advertising as an advertising medium are not yet fully understood. The present paper identified four dimensions towards YouTube advertising (i.e., entertainment, Informativeness, Customization and irritation) which may be affected on advertising value as well as brand awareness, and accordingly on purchase intention of consumers. The conceptual model hypothesizes that ad value strategies are positively associated with brand awareness, which in turn influence perceived usefulness of You Tube and continued purchase behavior. For this study, data were collected from students studying at the Sapienza University of Rome. In total, 315 usable questionnaires were chosen in order to analysis of data for the variables. The results show that entertainment, informativeness and customization are the strongest positive drivers, while irritation is negatively related to YouTube advertising. On the other hand, advertising value through YouTube affects both brand awareness and purchase intention of con-


title: Embedding new data points for manifold learning via coordinate propagation

text: In recent years, a series of manifold learning algorithms have been proposed for nonlinear dimensionality reduction. Most of them can run in a batch mode for a set of given data points, but lack a mechanism to deal with new data points. Here we propose an extension approach, i.e., mapping new data points into the previously learned manifold. The core idea of our approach is to propagate the known coordinates to each of the new data points. We first formulate this task as a quadratic programming, and then develop an iterative algorithm for coordinate propagation. Tangent space projection and smooth splines are used to yield an initial coordinate for each new data point, according to their local geometrical relations. Experimental results and applications to camera direction estimation and face pose estimation illustrate the validity of our approach.


title: Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

text: We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image segmentation on a number of 2D-slices from different image orientations, and (2) using “convolution” and “deconvolution” networks to achieve the conventional “coarse recognition” and “fine extraction” functions which were integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and usefulness of proposed improvement for CT image segmentations were confirmed.


title: Preserving the Archival Bond in Distributed Ledgers: A Data Model and Syntax

text: Distributed cryptographic ledgers, such as the blockchain, are now being used in recordkeeping. However, they lack a key feature of more traditional recordkeeping systems needed to establish the authenticity of records and enable reliance on them for trustworthy recordkeeping. The missing feature is known in archival science as the archival bond – the mutual relationship that exists among documents by virtue of the actions in which they participate. In this paper, we propose a novel data model and syntax using core web principles that can be used to address this shortcoming in distributed ledgers as recordkeeping systems.


title: Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations

text: Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating System (ROS).


title: Optimized Spatial Hashing for Collision Detection of Deformable Objects

text: We propose a new approach to collision and self– collision detection of dynamically deforming objects that consist of tetrahedrons. Tetrahedral meshes are commonly used to represent volumetric deformable models and the presented algorithm is integrated in a physically–based environment, which can be used in game engines and surgical simulators. The proposed algorithm employs a hash function for compressing a potentially infinite regular spatial grid. Although the hash function does not always provide a unique mapping of grid cells, it can be generated very efficiently and does not require complex data structures, such as octrees or BSPs. We have investigated and optimized the parameters of the collision detection algorithm, such as hash function, hash table size and spatial cell size. The algorithm can detect collisions and self– collisions in environments of up to 20k tetrahedrons in real–time. Although the algorithm works with tetrahedral meshes, it can be easily adapted to other object primitives, such as triangles. Figure 1: Environment with dynamically deforming objects, that consist of tetrahedrons.


title: Effects of cognitive-behavioral therapy on brain activation in specific phobia

text: Little is known about the effects of successful psychotherapy on brain function in subjects with anxiety disorders. The present study aimed to identify changes in brain activation following cognitive-behavioral therapy (CBT) in subjects suffering from specific phobia. Using functional magnetic resonance imaging (fMRI), brain activation to spider videos was measured in 28 spider phobic and 14 healthy control subjects. Phobics were randomly assigned to a therapy-group (TG) and a waiting-list control group (WG). Both groups of phobics were scanned twice. Between scanning sessions, CBT was given to the TG. Before therapy, brain activation did not differ between both groups of phobics. As compared to control subjects, phobics showed greater responses to spider vs. control videos in the insula and anterior cingulate cortex (ACC). CBT strongly reduced phobic symptoms in the TG while the WG remained behaviorally unchanged. In the second scanning session, a significant reduction of hyperactivity in the insula and ACC was found in the TG compared to the WG. These results propose that increased activation in the insula and ACC is associated with specific phobia, whereas an attenuation of these brain responses correlates with successful therapeutic intervention.


title: Identifying Relations for Open Information Extraction

text: Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-ofthe-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we introduce two simple syntactic and lexical constraints on binary relations expressed by verbs. We implemented the constraints in the REVERB Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TEXTRUNNER and WOE. More than 30% of REVERB’s extractions are at precision 0.8 or higher— compared to virtually none for earlier systems. The paper concludes with a detailed analysis of REVERB’s errors, suggesting directions for future work.1


title: Multilingual Relation Extraction using Compositional Universal Schema

text: When building a knowledge base (KB) of entities and relations from multiple structured KBs and text, universal schema represents the union of all input schema, by jointly embedding all relation types from input KBs as well as textual patterns expressing relations. In previous work, textual patterns are parametrized as a single embedding, preventing generalization to unseen textual patterns. In this paper we employ an LSTM to compositionally capture the semantics of relational text. We dramatically demonstrate the flexibility of our approach by evaluating in a multilingual setting, in which the English training data entities overlap with the seed KB, but the Spanish text does not. Additional improvements are obtained by tying word embeddings across languages. In extensive experiments on the English and Spanish TAC KBP benchmark, our techniques provide substantial accuracy improvements. Furthermore we find that training with the additional non-overlapping Spanish also improves English relation extraction accuracy. Our approach is thus suited to broad-coverage automated knowledge base construction in low-resource languages and domains.


title: Spark: Cluster Computing with Working Sets

text: MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.


title: GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles

text: Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals.


title: A Convolutional Neural Network for Modelling Sentences

text: The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.


title: DeepTLR: A single deep convolutional network for detection and classification of traffic lights

text: Reliable real-time detection of traffic lights is a major concern for the task of autonomous driving. As deep convolutional networks have proven to be a powerful tool in visual object detection, we propose DeepTLR, a camera-based system for real-time detection and classification of traffic lights. Detection and state classification are realized using a single deep convolutional network. DeepTLR does not use any prior knowledge about traffic light locations. Also the detection is executed frame by frame without using temporal information. It is able to detect traffic lights on the whole camera image without any presegmentation. This is achieved by classifying each fine-grained pixel region of the input image and performing a bounding box regression on regions of each class. We show that our algorithm is able to run on frame-rates required for real-time applications while reaching notable results.


title: Integration of Radio Frequency Identification and Wireless Sensor Networks

text: .................................................................................................................... iii ÖZ ...................................................................................................................................... v DEDICATION ................................................................................................................ vii ACKNOWLEDGMENTS ............................................................................................. viii LIST OF TABLES .......................................................................................................... xii LIST OF FIGURES ....................................................................................................... xiii LIST OF SYMBOLS/ABBREVIATIONS ...................................................................... xv


title: Low-k interconnect stack with a novel self-aligned via patterning process for 32nm high volume manufacturing

text: Interconnect process features are described for a 32nm high performance logic technology. Lower-k, yet highly manufacturable, Carbon-Doped Oxide (CDO) dielectric layers are introduced on this technology at three layers to address the demand for ever lower metal line capacitance. The pitches have been aggressively scaled to meet the expectation for density, and the metal resistance and electromigration performance have been carefully balanced to meet the high reliability requirements while maintaining the lowest possible resistance. A new patterning scheme has been used to limit any patterning damage to the lower-k ILD and address the increasingly difficult problem of via-to-metal shorting at these very tight pitches. The interconnect stack has a thick Metal-9 layer to provide a low resistance path for the power and I/O routing that has been carefully scaled to maintain a low resistance. The combined interconnect stack provides high density, performance, and reliability, and supports a Pb-free 32nm process.


title: Timing Attack against Protected RSA-CRT Implementation Used in PolarSSL

text: In this paper, we present a timing attack against the RSACRT algorithm used in the current version 1.1.4 of PolarSSL, an opensource cryptographic library for embedded systems. This implementation uses a classical countermeasure to avoid two previous attacks of Schindler and another one due to Boneh and Brumley. However, a careful analysis reveals a bias in the implementation of Montgomery multiplication. We theoretically analyse the distribution of output values for Montgomery multiplication when the output is greater than the Montgomery constant, R. In this case, we show that an extra bit is set in the top most significant word of the output and a time variance can be observed. Then we present some proofs with reasonable assumptions to explain this bias due to an extra bit. Moreover, we show it can be used to mount an attack that reveals the factorisation. We also study another countermeasure and show its resistance against attacked library.


title: Haptic Feedback for Virtual Reality 1

text: Haptic feedback is a crucial sensorial modality in virtual reality interactions. Haptics means both force feedback (simulating object hardness, weight, and inertia) and tactile feedback (simulating surface contact geometry, smoothness, slippage, and temperature). Providing such sensorial data requires desk-top or portable special-purpose hardware called haptic interfaces. Modeling physical interactions involves precise collision detection, real-time force computation, and high control-loop bandwidth. This results in a large computation load which requires multi-processor parallel processing on networked computers. Applications for haptics-intensive VR simulations include CAD model design and assembly. Improved technology (wearable computers, novel actuators, haptic toolkits) will increase the use of force/tactile feedback in future VR simulations.


title: DFT-based Transformation Invariant Pooling Layer for Visual Classification

text: We propose a novel discrete Fourier transform-based pooling layer for convolutional neural networks. The DFT magnitude pooling replaces the traditional max/average pooling layer between the convolution and fully-connected layers to retain translation invariance and shape preserving (aware of shape difference) properties based on the shift theorem of the Fourier transform. Thanks to the ability to handle image misalignment while keeping important structural information in the pooling stage, the DFT magnitude pooling improves the classification accuracy significantly. In addition, we propose the DFT method for ensemble networks using the middle convolution layer outputs. The proposed methods are extensively evaluated on various classification tasks using the ImageNet, CUB 2010-2011, MIT Indoors, Caltech 101, FMD and DTD datasets. The AlexNet, VGG-VD 16, Inception-v3, and ResNet are used as the base networks, upon which DFT and DFT methods are implemented. Experimental results show that the proposed methods improve the classification performance in all networks and datasets.


title: A systematic literature review of green software metrics

text: Green IT is getting increasing attention in software engineering research. Nevertheless energy efficiency studies have mostly focused on the hardware side of IT, the software role still requires deepening in terms of methods and techniques. Furthermore, it is necessary to understand how to assess the software“greenness” for stimulating the energy efficiency awareness, since early phases of the software lifecycle. The main goal of this study is to describe and to classify metrics related to software “greenness” present in the software engineering literature. Furthermore, this study analyzes the evolution of those metrics, in terms of type, context, and evaluation methods. To achieve this goal, a systematic literature review has been performed surveying the metrics claimed in the last decade. After examined 960 publications, we selected 23 of them as primary studies, from which we isolated extracting 96 different green metrics. Therefore, we analyzed search results in order to show what is the trend of research about green software metrics, how metrics perform measurement on resources, and what type of metrics are more appealing for defined contexts.


title: Artificial-Life Ecosystems - What are they and what could they become?

text: This paper summarises the history of the terms ecology and ecosystem, before examining their application in the early and recent literature of A-Life agent-based software simulation. It investigates trends in A-Life that have led to a predominance of simulations incorporating artificial evolution acting on generic agents, but lacking a level of detail that would allow the emergence of phenomena relating to the transfer and transformation of energy and matter between the virtual abiotic environment and biota. Implications of these characteristics for the relevance of A-Life’s virtual ecosystem models to Ecology are discussed. We argue a position that the inclusion of low-level representations of energetics, matter and evolution, in concert with pattern-oriented modelling techniques from Ecology for model validation, will improve the relevance of A-Life models to Ecology. We also suggest two methods that may allows us to meet this goal: artificial evolution can be employed as a mechanism for automating pattern-oriented ecological modelling from the level of individual species up to that of the ecosystem, or it may be employed to explore general principles of ecosystem behaviour over evolutionary time periods.


title: Pulse-Modulated Intermittent Control in Consensus of Multiagent Systems

text: This paper proposes a control framework, called pulse-modulated intermittent control, which unifies impulsive control and sampled control. Specifically, the concept of pulse function is introduced to characterize the control/rest intervals and the amplitude of the control. By choosing some specified functions as the pulse function, the proposed control scheme can be reduced to sampled control or impulsive control. The proposed control framework is applied to consensus problems of multiagent systems. Using discretization approaches and stability theory, several necessary and sufficient conditions are established to ensure the consensus of the controlled system. The results show that consensus depends not only on the network topology, the sampling period and the control gains, but also the pulse function. Moreover, a lower bound of the asymptotic convergence factor is derived as well. For a given pulse function and an undirected graph, an optimal control gain is designed to achieve the fastest convergence. In addition, impulsive control and sampled control are revisited in the proposed control framework. Finally, some numerical examples are given to verify the effectiveness of theoretical results.


title: Weighted Low-Rank Approximations

text: Weighted-norms can arise in several situations. A zero/one weighted-norm, for example, arises when some of the entries in the matrix are not observed. External estimates of the noise variance associated with each measurement may be available (e.g. gene expression analysis) and using weights inversely proportional to the noise variance can lead to better reconstruction of the underlying structure. In other applications, entries in the target matrix represent aggregates of many samples. When using unweighted low-rank approximations (e.g. for separating style and content [4]), we assume a uniform number of samples for each entry. By incorporating weights, we can account for varying numbers of samples in such situations. Low-rank approximations are also used in the design of two-dimensional digital filters, in which case weights might arise from constraints of varying importance [3].


title: The validity of the Hospital Anxiety and Depression Scale. An updated literature review.

text:
OBJECTIVE
To review the literature of the validity of the Hospital Anxiety and Depression Scale (HADS).

METHOD
A review of the 747 identified papers that used HADS was performed to address the following questions: (I) How are the factor structure, discriminant validity and the internal consistency of HADS? (II) How does HADS perform as a case finder for anxiety disorders and depression? (III) How does HADS agree with other self-rating instruments used to rate anxiety and depression?

RESULTS
Most factor analyses demonstrated a two-factor solution in good accordance with the HADS subscales for Anxiety (HADS-A) and Depression (HADS-D), respectively. The correlations between the two subscales varied from.40 to.74 (mean.56). Cronbach's alpha for HADS-A varied from.68 to.93 (mean.83) and for HADS-D from.67 to.90 (mean.82). In most studies an optimal balance between sensitivity and specificity was achieved when caseness was defined by a score of 8 or above on both HADS-A and HADS-D. The sensitivity and specificity for both HADS-A and HADS-D of approximately 0.80 were very similar to the sensitivity and specificity achieved by the General Health Questionnaire (GHQ). Correlations between HADS and other commonly used questionnaires were in the range.49 to.83.

CONCLUSIONS
HADS was found to perform well in assessing the symptom severity and caseness of anxiety disorders and depression in both somatic, psychiatric and primary care patients and in the general population.


title: An automated system to detect and recognize vehicle license plates of Bangladesh

text: Bangladesh is a country in South Asia that uses Retro Reflective license plates. The plate has two lines with words, letters, and digits. An automated system to detect and recognize these plates is presented in this paper. The system is divided into four parts: plate detection, extraction, character segmentation and recognition. At first, the input image is enhanced using CLAHE and a matched filter specially designed for license plates with two lines is applied. Then tilt correction using Radon transformation, binarization and cleaning are performed. For character segmentation, mean intensity based horizontal and vertical projection is used. In recognition, we have used two different Convolutional Neural Network (CNN) to classify digits and letters. Tesseract OCR is used for district names. We have developed a dataset of over 400 images of different vehicles (e.g., private car, bus, truck etc.) taken at different times of day (including nights). The plates in this dataset are in different angles, including blurry, worn-out and muddy ones. On this dataset, the proposed system achieved a success rate of 96.8% in detection, 89.5% in extraction, 98.6% in segmentation and 98.0% in character recognition.


title: Text Mining for Documents Annotation and Ontology Support

text: This paper presents a survey of basic concepts in the area of text data mining and some of the methods used in order to elicit useful knowledge from collections of textual data. Three different text data mining techniques (clustering/visualisation, association rules and classification models) are analysed and its exploitation possibilities within the Webocracy project are showed. Clustering and association rules discovery are well suited as supporting tools for ontology management. Classification models are used for automatic documents annotation.


title: Encouraging user behaviour with achievements: An empirical study

text: Stack Overflow, a question and answer Web site, uses a reward system called badges to publicly reward users for their contributions to the community. Badges are used alongside a reputation score to reward positive behaviour by relating a user's site identity with their perceived expertise and respect in the community. A greater number of badges associated with a user profile in some way indicates a higher level of authority, leading to a natural incentive for users to attempt to achieve as many badges as possible. In this study, we examine the publicly available logs for Stack Overflow to examine three of these badges in detail. We look at the effect of one badge in context on an individual user level and at the global scope of three related badges across all users by mining user behaviour around the time that the badge is awarded. This analysis supports the claim that badges can be used to influence user behaviour by demonstrating one instance of an increase in user activity related to a badge immediately before it is awarded when compared to the period afterwards.


title: Implicit theories about willpower predict self-regulation and grades in everyday life.

text: Laboratory research shows that when people believe that willpower is an abundant (rather than highly limited) resource they exhibit better self-control after demanding tasks. However, some have questioned whether this "nonlimited" theory leads to squandering of resources and worse outcomes in everyday life when demands on self-regulation are high. To examine this, we conducted a longitudinal study, assessing students' theories about willpower and tracking their self-regulation and academic performance. As hypothesized, a nonlimited theory predicted better self-regulation (better time management and less procrastination, unhealthy eating, and impulsive spending) for students who faced high self-regulatory demands. Moreover, among students taking a heavy course load, those with a nonlimited theory earned higher grades, which was mediated by less procrastination. These findings contradict the idea that a limited theory helps people allocate their resources more effectively; instead, it is people with the nonlimited theory who self-regulate well in the face of high demands.


title: Enabling network programmability in LTE/EPC architecture using OpenFlow

text: Nowadays, mobile operators face the challenge to sustain the future data tsunami. In fact, today's increasing data and control traffic generated by new kinds of network usage puts strain on mobile operators', without creating any corresponding equivalent revenue. In our previous work, we analyzed the 3GPP LTE/EPC architecture and showed that a redesign of this architecture is needed to suit future network usages and to provide new revenue generating services. Moreover, we proposed a new control plane based on the OpenFlow (OF) protocol for the LTE/EPC architecture that enables flexibility and programmability aspects. In this paper, we are interested in the programmability aspect. We show how the data plane can be easily configured thanks to OF. In addition, we evaluate the signaling load of our proposed architecture and compare it to that of 3GPP LTE/EPC architecture. The preliminary findings suggest that managing the data plane with OF has little impact on the signaling load while the network programmability is improved.


title: The Behavioural Paths to Wellbeing : An Exploratory Study to Distinguish Between Hedonic and Eudaimonic Wellbeing From an Activity Perspective

text: Hedonic wellbeing and eudaimonic wellbeing are two prevailing approaches to wellbeing. However, remarkably little research has distinguished them from an activity perspective; the knowledge of behavioural paths for achieving these two wellbeings is poor. This study first clarified the behavioural contents of the two approaches through a bottom-up method and then analysed the representativeness of activities to indicate to what extent activities contributed to wellness. We found that the paths to hedonic wellbeing and eudaimonic wellbeing overlapped and differed from each other. Furthermore, this study explained why hedonic activity differed from eudaimonic activity by analysing activity characteristics. We found that people reported higher frequency, sensory experience, and affective experience in hedonic activity, whereas they reported higher intellectual experience, behavioural experience, and spiritual experience in eudaimonic activity. Finally, we explored the behavioural pattern of wellbeing pursuit in both an unthreatening situation and a threatening situation. We found that the overlap between the two approaches increased in the threatening situation. Moreover, people in the threatening situation tended to score lower on all characteristics except frequency relative to those in the unthreatening situation. It seemed that the behavioural pattern in the threatening situation was less effective than its equivalent in the unthreatening situation.


title: Unsupervised outlier detection in streaming data using weighted clustering

text: Outlier detection is a very important task in many fields like network intrusion detection, credit card fraud detection, stock market analysis, detecting outlying cases in medical data etc. Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving in coming data over time. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data. In proposed scheme both density based and partitioning clustering method are combined to take advantage of both density based and distance based outlier detection. Proposed scheme also assigns weights to attributes depending upon their respective relevance in mining task and weights are adaptive in nature. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.


title: Motif-Based Classification of Time Series with Bayesian Networks and SVMs

text: Classification of time series is an important task with many challenging applications like brain wave (EEG) analysis, signature verification or speech recognition. In this paper we show how characteristic local patterns (motifs) can improve the classification accuracy. We introduce a new motif class, generalized semi-continuous motifs. To allow flexibility and noise robustness, these motifs may include gaps of various lengths, generic and more specific wildcards. We propose an efficient algorithm for mining generalized sequential motifs. In experiments on real medical data, we show how generalized semi-continuous motifs improve the accuracy of SVMs and Bayesian Networks for time series classificiation.


title: A vector quantization approach to speaker recognition

text: CH2118-8/85/0000-0387 $1.00 © 1985 IEEE 387 ABSTRACT. In this study a vector quantIzation (VQ) codebook was system. In the other, Shore and Burton 112] used word-based VQ used as an efficient means of characterizing the short-time spectral codebooks and reported good performance in speaker-trained isolatedfeatures of a speaker. A set of such codebooks were then used to word recognition experiments. Here, instead of using word-based VQ recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) codebooks to characterize the phonetic contents of isolated words, we propose to use speaker-based VQ codebooks to characterize the classification rule. A series of speaker recognition experiments was variability of short-time acoustic features of speakers. performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification H. Speaker-based VQ Codebook Approach to Speaker accuracy was achieved. The effects, on performance, of different Characterization and Recognition system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions Were also studied in detail. A set of short-time raw feature vectors of a speaker can be used directly to represent the essential acoustical, phonological or physiological characteristics of that speaker if the training set includes sufficient variations. However such a direct representation is not practical when the number of training vectors is large. The memory requirements for storage and computational complexity in the recognition phase eventually become prohibitively high. Therefore an Automatic speaker recognition has long been an interesting and challenging problem to speech researchers [1-101. The problem, efficient way of compressing the training data had to be found. In order to compress the original data to a small set of representative depending upon the nature of the final task, can be classified into two points, we used a VQ codebook with a small number of codebook different categories: speaker verification and speaker identification. In entries. a speaker verification task, the recognizer is asked to verify an identity claim made by an unknown speaker and a decision to reject or accept the identity claim is made. In a speaker identification task the recognizer is asked to decide which out of a population of N speakers is best classified as the unknown speaker. The decision may include a choice of "none of the above" (i.e., a choice that the specific speaker is not in a given closed set of speakers). The input speech material used for speaker recognition can be either text-dependent (text-constrained) or text-independent (text-free). In the text-dependent mode the speaker is asked to utter a prescribed text. The utterance is then The speaker-based VQ codebook generation can be summarized as follows: Given a set of I training feature vectors, {a1,a2 a) characterizing the variability of a speaker, we want to find a partitioning of the feature vector space, {S1,S2 SM}, for that particular speaker where, 5, the whole feature space is represented as S S1 US2 U . . . US. Each partition, S, forms a convex, nonoverlapping region and every vector inside S is represented by the respo5'tis'1g ceatcoid. vector, b1, cf 5,. The p titioning is done in such a way that the average distortion


title: A Family of Neutral Point Clamped Full-Bridge Topologies for Transformerless Photovoltaic Grid-Tied Inverters

text: Transformerless inverter topologies have attracted more attentions in photovoltaic (PV) generation system since they feature high efficiency and low cost. In order to meet the safety requirement for transformerless grid-tied PV inverters, the leakage current has to be tackled carefully. Neutral point clamped (NPC) topology is an effective way to eliminate the leakage current. In this paper, two types of basic switching cells, the positive neutral point clamped cell and the negative neutral point clamped cell, are proposed to build NPC topologies, with a systematic method of topology generation given. A family of single-phase transformerless full-bridge topologies with low-leakage current for PV grid-tied NPC inverters is derived including the existing oH5 and some new topologies. A novel positive-negative NPC (PN-NPC) topology is analyzed in detail with operational modes and modulation strategy given. The power losses are compared among the oH5, the full-bridge inverter with dc bypass (FB-DCBP) topology, and the proposed PN-NPC topologies. A universal prototype for these three NPC-type topologies mentioned is built to evaluate the topologies at conversion efficiency and the leakage current characteristic. The PN-NPC topology proposed exhibits similar leakage current with the FB-DCBP, which is lower than that of the oH5 topology, and features higher efficiency than both the oH5 and the FB-DCBP topologies.


title: Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks

text: Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.


title: Designing game-based learning environments for elementary science education: A narrative-centered learning perspective

text: Game-based learning environments hold significant promise for STEM education, yet they are enormously complex. CRYSTAL ISLAND: UNCHARTED DISCOVERY, is a gamebased learning environment designed for upper elementary science education that has been under development in our laboratory for the past four years. This article discusses curricular and narrative interaction design requirements, presents the design of the CRYSTAL ISLAND learning environment, and describes its evolution through a series of pilots and field tests. Additionally, a classroom integration study was conducted to initiate a shift towards ecological validity. Results indicated that CRYSTAL ISLAND produced significant learning gains on both science content and problem-solving measures. Importantly, gains were consistent for gender across studies. This finding is key in light of past studies that revealed disproportionate participation by boys within game-based learning environments.


title: Volumetric Object Recognition Using 3-D CNNs on Depth Data

text: Recognizing 3-D objects has a wide range of application areas from autonomous robots to self-driving vehicles. The popularity of low-cost RGB-D sensors has enabled a rapid progress in 3-D object recognition in the recent years. Most of the existing studies use depth data as an additional channel to the RGB channels. Instead of this approach, we propose two volumetric representations to reveal rich 3-D structural information hidden in depth images. We present a 3-D convolutional neural network (CNN)-based object recognition approach, which utilizes these volumetric representations and single and multi-rotational depth images. The 3-D CNN architecture trained to recognize single depth images produces competitive results with the state-of-the-art methods on two publicly available datasets. However, recognition accuracy increases further when the multiple rotations of objects are brought together. Our multirotational 3-D CNN combines information from multiple views of objects to provide rotational invariance and improves the accuracy significantly comparing with the single-rotational approach. The results show that utilizing multiple views of objects can be highly informative for the 3-D CNN-based object recognition.


title: Radar-based Fall Detection Based on Doppler Time-Frequency Signatures for Assisted Living

text: Falls are a major public health concern and main causes of accidental death in the senior U.S. population. Timely and accurate detection permits immediate assistance after a fall and, thereby, reduces complications of fall risk. Radar technology provides an effective means for this purpose because it is non-invasive, insensitive to lighting conditions as well as obstructions, and has less privacy concerns. In this paper, we develop an effective fall detection scheme for the application in continuous-wave radar systems. The proposed scheme exploits time-frequency characteristics of the radar Doppler signatures, and the motion events are classified using the joint statistics of three different features, including the extreme frequency, extreme frequency ratio, and the length of event period. Sparse Bayesian classifier based on the relevance vector machine is used to perform the classification. Laboratory experiments are performed to collect radar data corresponding to different motion patterns to verify the effectiveness of the proposed algorithm.


title: Novel Multilevel Inverter Carrier-Based PWM Method

text: The advent of the transformerless multilevel inverter topology has brought forth various pulsewidth modulation (PWM) schemes as a means to control the switching of the active devices in each of the multiple voltage levels in the inverter. An analysis of how existing multilevel carrier-based PWM affects switch utilization for the different levels of a diode-clamped inverter is conducted. Two novel carrier-based multilevel PWM schemes are presented which help to optimize or balance the switch utilization in multilevel inverters. A 10-kW prototype sixlevel diode-clamped inverter has been built and controlled with the novel PWM strategies proposed in this paper to act as a voltage-source inverter for a motor drive.


title: A New Neutral-Point-Clamped PWM Inverter

text: A new neutral-point-clamped pulsewidth modulation (PWM) inverter composed of main switching devices which operate as switches for PWM and auxiliary switching devices to clamp the output terminal potential to the neutral point potential has been developed. This inverter output contains less harmonic content as compared with that of a conventional type. Two inverters are compared analytically and experimentally. In addition, a new PWM technique suitable for an ac drive system is applied to this inverter. The neutral-point-clamped PWM inverter adopting the new PWM technique shows an excellent drive system efficiency, including motor efficiency, and is appropriate for a wide-range variable-speed drive system.


title: Multilevel inverters: a survey of topologies, controls, and applications

text: Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. This paper presents the most important topologies like diode-clamped inverter (neutral-point clamped), capacitor-clamped (flying capacitor), and cascaded multicell with separate dc sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. This paper also presents the most relevant control and modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation. Special attention is dedicated to the latest and more relevant applications of these converters such as laminators, conveyor belts, and unified power-flow controllers. The need of an active front end at the input side for those inverters supplying regenerative loads is also discussed, and the circuit topology options are also presented. Finally, the peripherally developing areas such as high-voltage high-power devices and optical sensors and other opportunities for future development are addressed.


title: Multilevel PWM Methods at Low Modulation Indices

text: When utilized at low amplitude modulation indices, existing multilevel carrier-based PWM strategies have no special provisions for this operating region, and several levels of the inverter go unused. This paper proposes some novel multilevel PWM strategies to take advantage of the multiple levels in both a diodeclamped inverter and a cascaded H-bridges inverter by utilizing all of the levels in the inverter even at low modulation indices. Simulation results show what effects the different strategies have on the active device utilization. A prototype 6-level diode-clamped inverter and an 11-level cascaded H-bridges inverter have been built and controlled with the novel PWM strategies proposed in this paper.


title: Multilevel Converters for Large Electric Drives

text: Traditional two-level high-frequency pulse width modulation (PWM) inverters for motor drives have several problems associated with their high frequency switching which produces common-mode voltage and high voltage change (dV/dt) rates to the motor windings. Multilevel inverters solve these problems because their devices can switch at a much lower frequency. Two different multilevel topologies are identified for use as a converter for electric drives, a cascade inverter with separate dc sources and a back-to-back diode clamped converter. The cascade inverter is a natural fit for large automotive allelectric drives because of the high VA ratings possible and because it uses several levels of dc voltage sources which would be available from batteries or fuel cells. The back-to-back diode clamped converter is ideal where a source of ac voltage is available such as a hybrid electric vehicle. Simulation and experimental results show the superiority of these two converters over PWM based drives.


title: Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition

text: Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet. PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called VSAD, by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).


title: Autophagy as a regulated pathway of cellular degradation.

text: Macroautophagy is a dynamic process involving the rearrangement of subcellular membranes to sequester cytoplasm and organelles for delivery to the lysosome or vacuole where the sequestered cargo is degraded and recycled. This process takes place in all eukaryotic cells. It is highly regulated through the action of various kinases, phosphatases, and guanosine triphosphatases (GTPases). The core protein machinery that is necessary to drive formation and consumption of intermediates in the macroautophagy pathway includes a ubiquitin-like protein conjugation system and a protein complex that directs membrane docking and fusion at the lysosome or vacuole. Macroautophagy plays an important role in developmental processes, human disease, and cellular response to nutrient deprivation.


title: Machine learning aided cognitive RAT selection for 5G heterogeneous networks

text: The starring role of the Heterogeneous Networks (HetNet) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current user association (cell selection) mechanisms used in cellular networks. The max-SINR algorithm, although historically effective for performing this function, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs that optimize the association and routing decisions in the context of single-RAT and multi-RAT connections, respectively. This paper proposes a framework under these guidelines that relies on Machine Learning techniques at the terminal device level for Cognitive RAT Selection and presents simulation results to suppport it.


title: An Anonymous Electronic Voting Protocol for Voting Over The Internet

text: In this work we propose a secure electronic voting protocol that is suitable for large scale voting over the Internet. The protocol allows a voter to cast his or her ballot anonymously, by exchanging untraceable yet authentic messages. The protocol ensures that (i) only eligible voters are able to cast votes, (ii) a voter is able to cast only one vote, (iii) a voter is able to verify that his or her vote is counted in the final tally, (iv) nobody, other than the voter, is able to link a cast vote with a voter, and (v) if a voter decides not to cast a vote, nobody is able to cast a fraudulent vote in place of the voter. The protocol does not require the cooperation of all registered voters. Neither does it require the use of complex cryptographic techniques like threshold cryptosystems or anonymous channels for casting votes. This is in contrast to other voting protocols that have been proposed in the literature. The protocol uses three agents, other than the voters, for successful operation. However, we do not require any of these agents to be trusted. That is, the agents may be physically co-located or may collude with one another to try to commit a fraud. If a fraud is committed, it can be easily detected and proven, so that the vote can be declared null and void. Although we propose the protocol with electronic voting in mind, the protocol can be used in other applications that involve exchanging an untraceable yet authentic message. Examples of such applications are answering confidential questionnaire anonymously or anonymous financial transactions.


title: The Love/Hate Relationship with the C Preprocessor: An Interview Study (Artifact)

text: The C preprocessor has received strong criticism in academia, among others regarding separation of concerns, error proneness, and code obfuscation, but is widely used in practice. Many (mostly academic) alternatives to the preprocessor exist, but have not been adopted in practice. Since developers continue to use the preprocessor despite all criticism and research, we ask how practitioners perceive the C preprocessor. We performed interviews with 40 developers, used grounded theory to analyze the data, and cross-validated the results with data from a survey among 202 developers, repository mining, and results from previous studies. In particular, we investigated four research questions related to why the preprocessor is still widely used in practice, common problems, alternatives, and the impact of undisciplined annotations. Our study shows that developers are aware of the criticism the C preprocessor receives, but use it nonetheless, mainly for portability and variability. Many developers indicate that they regularly face preprocessorrelated problems and preprocessor-related bugs. The majority of our interviewees do not see any current C-native technologies that can entirely replace the C preprocessor. However, developers tend to mitigate problems with guidelines, even though those guidelines are not enforced consistently. We report the key insights gained from our study and discuss implications for practitioners and researchers on how to better use the C preprocessor to minimize its negative impact. 1998 ACM Subject Classification D.3.4 Processors


title: Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments

text: Active and passive sensing technologies are providing powerful mechanisms to track, model, and understand a range of health behaviors and well-being states. Despite yielding rich, dense and high fidelity data, current sensing technologies often require highly engineered study designs and persistent participant compliance, making them difficult to scale to large populations and to data acquisition tasks spanning extended time periods. This paper situates social media as a new passive, unobtrusive sensing technology. We propose a semi-supervised machine learning framework to combine small samples of data gathered through active sensing, with large-scale social media data to infer mood instability (MI) in individuals. Starting from a theoretically-grounded measure of MI obtained from mobile ecological momentary assessments (EMAs), we show that our model is able to infer MI in a large population of Twitter users with 96% accuracy and F-1 score. Additionally, we show that, our model predicts self-identifying Twitter users with bipolar and borderline personality disorder to exhibit twice the likelihood of high MI, compared to that in a suitable control. We discuss the implications and the potential for integrating complementary sensing capabilities to address complex research challenges in precision medicine.


title: The Therapeutic Potentials of Ayahuasca: Possible Effects against Various Diseases of Civilization

text: Ayahuasca is an Amazonian psychoactive brew of two main components. Its active agents are β-carboline and tryptamine derivatives. As a sacrament, ayahuasca is still a central element of many healing ceremonies in the Amazon Basin and its ritual consumption has become common among the mestizo populations of South America. Ayahuasca use amongst the indigenous people of the Amazon is a form of traditional medicine and cultural psychiatry. During the last two decades, the substance has become increasingly known among both scientists and laymen, and currently its use is spreading all over in the Western world. In the present paper we describe the chief characteristics of ayahuasca, discuss important questions raised about its use, and provide an overview of the scientific research supporting its potential therapeutic benefits. A growing number of studies indicate that the psychotherapeutic potential of ayahuasca is based mostly on the strong serotonergic effects, whereas the sigma-1 receptor (Sig-1R) agonist effect of its active ingredient dimethyltryptamine raises the possibility that the ethnomedical observations on the diversity of treated conditions can be scientifically verified. Moreover, in the right therapeutic or ritual setting with proper preparation and mindset of the user, followed by subsequent integration of the experience, ayahuasca has proven effective in the treatment of substance dependence. This article has two important take-home messages: (1) the therapeutic effects of ayahuasca are best understood from a bio-psycho-socio-spiritual model, and (2) on the biological level ayahuasca may act against chronic low grade inflammation and oxidative stress via the Sig-1R which can explain its widespread therapeutic indications.


title: Epigenetics and the embodiment of race: developmental origins of US racial disparities in cardiovascular health.

text: The relative contribution of genetic and environmental influences to the US black-white disparity in cardiovascular disease (CVD) is hotly debated within the public health, anthropology, and medical communities. In this article, we review evidence for developmental and epigenetic pathways linking early life environments with CVD, and critically evaluate their possible role in the origins of these racial health disparities. African Americans not only suffer from a disproportionate burden of CVD relative to whites, but also have higher rates of the perinatal health disparities now known to be the antecedents of these conditions. There is extensive evidence for a social origin to prematurity and low birth weight in African Americans, reflecting pathways such as the effects of discrimination on maternal stress physiology. In light of the inverse relationship between birth weight and adult CVD, there is now a strong rationale to consider developmental and epigenetic mechanisms as links between early life environmental factors like maternal stress during pregnancy and adult race-based health disparities in diseases like hypertension, diabetes, stroke, and coronary heart disease. The model outlined here builds upon social constructivist perspectives to highlight an important set of mechanisms by which social influences can become embodied, having durable and even transgenerational influences on the most pressing US health disparities. We conclude that environmentally responsive phenotypic plasticity, in combination with the better-studied acute and chronic effects of social-environmental exposures, provides a more parsimonious explanation than genetics for the persistence of CVD disparities between members of socially imposed racial categories.


title: Face spoofing detection from single images using texture and local shape analysis

text: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.


title: Subjective Well ‐ Being and Income : Is There Any Evidence of Satiation ? *

text: Subjective Well‐Being and Income: Is There Any Evidence of Satiation? Many scholars have argued that once “basic needs” have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of “basic needs” and multiple questions about well-being, we find no support for this claim. The relationship between wellbeing and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it. JEL Classification: D6, I3, N3, O1, O4


title: Dynamic mixed membership blockmodel for evolving networks

text: In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their degrees of affiliation to these roles can also exhibit rich temporal phenomena. We propose a state space mixed membership stochastic blockmodel which can track across time the evolving roles of the actors. We also derive an efficient variational inference procedure for our model, and apply it to the Enron email networks, and rewiring gene regulatory networks of yeast. In both cases, our model reveals interesting dynamical roles of the actors.


title: Regression approaches to voice quality controll based on one-to-many eigenvoice conversion

text: This paper proposes techniques for flexibly controlling voice quality of converted speech from a particular source speaker based on one-to-many eigenvoice conversion (EVC). EVC realizes a voice quality control based on the manipulation of a small number of parameters, i.e., weights for eigenvectors, of an eigenvoice Gaussian mixture model (EV-GMM), which is trained with multiple parallel data sets consisting of a single source speaker and many pre-stored target speakers. However, it is difficult to control intuitively the desired voice quality with those parametersbecause each eigenvector doesn’t usually represent a specific physical meaning. In order to cope with this problem, we propose regression approaches to the EVC-based voice quality controller. The tractable voice quality control of the converted speech is achieved with a low-dimensionalvoice quality control vector capturing specific voice characteristics. We conducted experimental verifications of each of the proposed approaches.


title: A brief survey of blackhole detection and avoidance for ZRP protocol in MANETs

text: Within Mobile Ad-Hoc Network(MANETs) unusual categories of routing protocols have been determined. It is a group of wireless system. MANET is other susceptible to a variety of attack than wired system. Black hole attack is further meticulous intimidation to MANETs. The routing protocols of MANET is less protected and therefore consequenced the system with malicious node. There are various routing protocols being used for MANETs. All routing protocols have been briefly discussed, however, Zone Routing Protocol (ZRP) is discussed in detail. Black hole is a major security threat for MANETs. Hence, in this paper, the various techniques used for detection and avoidance of Black hole attack in MANETs using ZRP routing protocol have been discussed.


title: A Practical Attack to De-anonymize Social Network Users

text: Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates and have millions of registered users. In this paper, we introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, we show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is sufficient to uniquely identify this person, or, at least, to significantly reduce the set of possible candidates. That is, rather than tracking a user's browser as with cookies, it is possible to track a person. To determine the group membership of a user, we leverage well-known web browser history stealing attacks. Thus, whenever a social network user visits a malicious website, this website can launch our de-anonymization attack and learn the identity of its visitors. The implications of our attack are manifold, since it requires a low effort and has the potential to affect millions of social networking users. We perform both a theoretical analysis and empirical measurements to demonstrate the feasibility of our attack against Xing, a medium-sized social network with more than eight million members that is mainly used for business relationships. Furthermore, we explored other, larger social networks and performed experiments that suggest that users of Facebook and LinkedIn are equally vulnerable.


title: Cryptography on FPGAs: State of the Art Implementations and Attacks

text: In the last decade, it has become aparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as FPGAs become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.


title: Compliance with a structured bedside handover protocol : An observational , multicentred study ☆

text: Background: Bedside handover is the delivery of the nurse-to-nurse shift handover at the patient’s bedside. The method is increasingly used in nursing, but the evidence concerning the implementation process and compliance to the method is limited. Objectives: To determine the compliance with a structured bedside handover protocol following ISBARR and if there were differences in compliance between wards. Design: A multicentred observational study with unannounced and non-participatory observations (n=638) one month after the implementation of a structured bedside handover protocol. Settings and participants: Observations of individual patient handovers between nurses from the morning shift and the afternoon shift in 12 nursing wards in seven hospitals in Flanders, Belgium. Methods: A tailored and structured bedside handover protocol following ISBARR was developed, and nurses were trained accordingly. One month after implementation, a minimum of 50 observations were performed with a checklist, in each participating ward. To enhance reliability, 20% of the observations were conducted by two researchers, and inter-rater agreement was calculated. Data were analysed using descriptive statistics, one-way ANOVAs and multilevel analysis. Results: Average compliance rates to the structured content protocol during bedside handovers were high (83.63%; SD 11.44%), and length of stay, the type of ward and the nursing care model were influencing contextual factors. Items that were most often omitted included identification of the patient (46.27%), the introduction of nurses (36.51%), hand hygiene (35.89%), actively involving the patient (34.44%), and using the call light (21.37%). Items concerning the exchange of clinical information (e.g., test results, reason for admittance, diagnoses) were omitted less (8.09%–1.45%). Absence of the patients (27.29%) and staffing issues (26.70%) accounted for more than half of the non-executed bedside handovers. On average, a bedside handover took 146 s per patient. Conclusions: When the bedside handover was delivered, compliance to the structured content was high, indicating that the execution of a bedside handover is a feasible step for nurses. The compliance rate was influenced by the patient’s length of stay, the nursing care model and the type of ward, but their influence was limited. Future implementation projects on bedside handover should focus sufficiently on standard hospital procedures and patient involvement. According to the nurses, there was however a high number of situations where bedside handovers could not be delivered, perhaps indicating a reluctance in practice to use bedside


title: A Novel Feature Selection Approach Based on FODPSO and SVM

text: A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.


title: CMiner: Opinion Extraction and Summarization for Chinese Microblogs

text: Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users' opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms.


title: Applying an optimized switching strategy to a high gain boost converter for input current ripple cancellation

text: This paper discusses applying various switching strategies such as conventional complementary and proportional methods on a specific converter. This converter was recently presented and it can provide high voltage gain and has current ripple cancelation ability at a preselected duty-cycle. The input current ripple is zero when the converter works at a special duty-cycle. But load disturbance leads to output voltage changes that maybe cause the duty-cycle to deviate from its preselected value. In this situation, the input current ripple cannot be completely canceled. The proposed proportional strategy is an optimized method for minimizing input current ripple at other operating duty-cycles and also it provides a voltage gain lower than conventional complementary strategy. Here, the converter's performance is analyzed under two switching strategies and the effect of various strategies has been investigated on converter parameters including output voltage and input current ripple. These considerations are verified by implementing a 100-W prototype of the proposed converter in laboratory.


title: The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data Ronen Feldman and James Sanger (Bar-Ilan University and ABS Ventures) Cambridge, England: Cambridge University Press, 2007, xii+410 pp; hardbound, ISBN 0-521-83657-3, $70.00

text: Text mining is a new and exciting area of computer science that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. The Text Mining Handbook presents a comprehensive discussion of the latest techniques in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world applications.


title: A fractional open circuit voltage based maximum power point tracker for photovoltaic arrays

text: In this paper a fractional open circuit voltage based maximum power point tracker (MPPT) for photovoltaic (PV) arrays is proposed. The fractional open circuit voltage based MPPT utilizes the fact that the PV array voltage corresponding to the maximum power exhibits a linear dependence with respect to array open circuit voltage for different irradiation and temperature levels. This method is the simplest of all the MPPT methods described in the literature. The main disadvantage of this method is that the PV array is disconnected from the load after regular intervals for the sampling of the array voltage. This results in power loss. Another disadvantage is that if the duration between two successive samplings of the array voltage, called the sampling period, is too long, there is a considerable loss. This is because the output voltage of the PV array follows the unchanged reference during one sampling period. Once a maximum power point (MPP) is tracked and a change in irradiation occurs between two successive samplings, then the new MPP is not tracked until the next sampling of the PV array voltage. This paper proposes an MPPT circuit in which the sampling interval of the PV array voltage, and the sampling period have been shortened. The sample and hold circuit, which samples and holds the MPP voltage, has also been simplified. The proposed circuit does not utilize expensive microcontroller or a digital signal processor and is thus suitable for low cost photovoltaic applications.


title: HCI Education: Innovation, Creativity and Design Thinking

text: Human-Computer Interaction (HCI) education needs re-thinking. In this paper, we explore how and what creativity and design thinking could contribute with, if included as a part of the HCI curriculum. The findings from courses where design thinking was included, indicate that design thinking contributed to increased focus on innovation and creativity, as well as prevented too early fixation on a single solution in the initial phases of HCI design processes, fostering increased flexibility and adaptability in learning processes. The creativity and adaptability may be the best long-term foci that HCI education can add to its curriculums and offer to students when preparing them for future work


title: Compressed matching for feature vectors

text: The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method, as well as on Dense SIFT and PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT. Given two feature vectors, we suggest achieving our goal by compressing them using a lossless encoding by means of a Fibonacci code, for which the pairwise matching can be done directly on the compressed files. In our experiments, this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase.


title: Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

text: How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the PHOENIX-R model which captures the popularity dynamics of individual objects. PHOENIX-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.


title: The TaSSt: Tactile sleeve for social touch

text: In this paper we outline the design process of the TaSST (Tactile Sleeve for Social Touch), a touch-sensitive vibrotactile arm sleeve. The TaSST was designed to enable two people to communicate different types of touch over a distance. The touch-sensitive surface of the sleeve consists of a grid of 4×3 sensor compartments filled with conductive wool. Each compartment controls the vibration intensity of a vibration motor, located in a grid of 4×3 motors beneath the touch-sensitive layer. An initial evaluation of the TaSST revealed that it was mainly suitable for communicating protracted (e.g. pressing), and simple (e.g. poking) touches.


title: Kinesthetic interaction: revealing the bodily potential in interaction design

text: Within the Human-Computer Interaction community there is a growing interest in designing for the whole body in interaction design. The attempts aimed at addressing the body have very different outcomes spanning from theoretical arguments for understanding the body in the design process, to more practical examples of designing for bodily potential. This paper presents Kinesthetic Interaction as a unifying concept for describing the body in motion as a foundation for designing interactive systems. Based on the theoretical foundation for Kinesthetic Interaction, a conceptual framework is introduced to reveal bodily potential in relation to three design themes --- kinesthetic development, kinesthetic means and kinesthetic disorder; and seven design parameters --- engagement, sociality, movability, explicit motivation, implicit motivation, expressive meaning and kinesthetic empathy. The framework is a tool to be utilized when analyzing existing designs, as well as developing designs exploring new ways of designing kinesthetic interactions.


title: Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms

text: This paper presents our vision of Human Computer Interaction (HCI): "Tangible Bits." Tangible Bits allows users to "grasp & manipulate" bits in the center of users’ attention by coupling the bits with everyday physical objects and architectural surfaces. Tangible Bits also enables users to be aware of background bits at the periphery of human perception using ambient display media such as light, sound, airflow, and water movement in an augmented space. The goal of Tangible Bits is to bridge the gaps between both cyberspace and the physical environment, as well as the foreground and background of human activities. This paper describes three key concepts of Tangible Bits: interactive surfaces; the coupling of bits with graspable physical objects; and ambient media for background awareness. We illustrate these concepts with three prototype systems – the metaDESK, transBOARD and ambientROOM – to identify underlying research issues.


title: inTouch: A Medium for Haptic Interpersonal Communication

text: In this paper, we introduce a new approach for applying haptic feedback technology to interpersonal communication. We present the design of our prototype inTouch system which provides a physical link between users separated by distance.


title: Augmented reality: linking real and virtual worlds: a new paradigm for interacting with computers

text: A revolution in computer interface design is changing the way we think about computers. Rather than typing on a keyboard and watching a television monitor, Augmented Reality lets people use familiar, everyday objects in ordinary ways. The difference is that these objects also provide a link into a computer network. Doctors can examine patients while viewing superimposed medical images; children can program their own LEGO constructions; construction engineers can use ordinary paper engineering drawings to communicate with distant colleagues. Rather than immersing people in an artificially-created virtual world, the goal is to augment objects in the physical world by enhancing them with a wealth of digital information and communication capabilities.


title: Conformal printing of electrically small antennas on three-dimensional surfaces.

text: J. J. Adams ,[+] Prof. J. T. Bernhard Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801, USA E-mail: jbernhar@illinois.edu Dr. E. B. Duoss ,[+,++] T. F. Malkowski ,[+] Dr. B. Y. Ahn , Prof. J. A. Lewis Department of Materials Science and Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 USA E-mail: jalewis@illinois.edu Dr. M. J. Motala , Prof. R. G. Nuzzo Department of Chemistry University of Illinois at Urbana-Champaign Urbana, Illinois 61801, USA [+] These authors contributed equally to this work. [++] Presently at Lawrence Livermore National Laboratory, Center for Microand NanoTechnology, Livermore, CA 94550 USA


title: Oxytocin is associated with human trustworthiness

text: Human beings exhibit substantial interpersonal trust-even with strangers. The neuroactive hormone oxytocin facilitates social recognition in animals, and we examine if oxytocin is related to trustworthiness between humans. This paper reports the results of an experiment to test this hypothesis, where trust and trustworthiness are measured using the sequential anonymous "trust game" with monetary payoffs. We find that oxytocin levels are higher in subjects who receive a monetary transfer that reflects an intention of trust relative to an unintentional monetary transfer of the same amount. In addition, higher oxytocin levels are associated with trustworthy behavior (the reciprocation of trust). Absent intentionality, both the oxytocin and behavioral responses are extinguished. We conclude that perceptions of intentions of trust affect levels of circulating oxytocin.


title: Learning Partially Contracting Dynamical Systems from Demonstrations

text: An algorithm for learning the dynamics of point-to-point motions from demonstrations using an autonomous nonlinear dynamical system, named contracting dynamical system primitives (CDSP), is presented. The motion dynamics are approximated using a Gaussian mixture model (GMM) and its parameters are learned subject to constraints derived from partial contraction analysis. Systems learned using the proposed method generate trajectories that accurately reproduce the demonstrations and are guaranteed to converge to a desired goal location. Additionally, the learned models are capable of quickly and appropriately adapting to unexpected spatial perturbations and changes in goal location during reproductions. The CDSP algorithm is evaluated on shapes from a publicly available human handwriting dataset and also compared with two state-of-the-art motion generation algorithms. Furthermore, the CDSP algorithm is also shown to be capable of learning and reproducing point-to-point motions directly from real-world demonstrations using a Baxter robot.


title: A Solid-State Marx Generator With a Novel Configuration

text: A new pulsed-power generator based on a Marx generator (MG) is proposed in this paper with a reduced number of semiconductor components and with a more efficient load-supplying process. The main idea is to charge two groups of capacitors in parallel through an inductor and take advantage of the resonant phenomenon in charging each capacitor up to twice as the input-voltage level. In each resonant half-cycle, one of those capacitor groups is charged; eventually, the charged capacitors will be connected in series, and the summation of the capacitor voltages are appeared at the output of a pulsed-power converter. This topology can be considered as a modified MG, which works based on the resonant concept. The simulated models of this converter have been investigated in a MATLAB/Simulink platform, and a laboratory prototype has been implemented in a laboratory. The simulation and test results verify the operation of the proposed topology in different switching modes.


title: Neural personalized response generation as domain adaptation

text: One of the most crucial problem on training personalized response generation models for conversational robots is the lack of large scale personal conversation data. To address the problem, we propose a two-phase approach, namely initialization then adaptation, to first pre-train an optimized RNN encoder-decoder model (LTS model) in a large scale conversational data for general response generation and then fine-tune the model in a small scale personal conversation data to generate personalized responses. For evaluation, we propose a novel human aided method, which can be seen as a quasi-Turing test, to evaluate the performance of the personalized response generation models. Experimental results show that the proposed personalized response generation model outperforms the state-of-the-art approaches to language model personalization and persona-based neural conversation generation on the automatic evaluation, offline human judgment and the quasi-Turing test.