title: Wideband microwave crossover using slotted microstrip patch

abstract: The design of a novel wideband microwave crossover is presented. The proposed structure is fully compatible with microstrip circuits as it is printed on one side of a single layer substrate. The structure is made up of a curved-edge microstrip patch with four fan-shaped slots symmetrically allocated inside the patch. The simulations show a crossover with more than 14 dB of isolation, less than 0.5 dB insertion loss across 49% fractional bandwidth centered at 7 GHz.


title: Exploring the Link Between Behavioural Information Security Governance and Employee Information Security Awareness

abstract: This paper explores the relation between a set of behavioural information security governance factors and employees’ information security awareness. To enable statistical analysis between proposed relations, data was collected from two different samples in 24 organisations: 24 information security executives and 240 employees. The results reveal that having a formal unit with explicit responsibility for information security, utilizing coordinating committees, and sharing security knowledge through an intranet site significantly correlates with dimensions of employees’ information security awareness. However, regular identification of vulnerabilities in information systems and related processes is significantly negatively correlated with employees’ information security awareness, in particular managing passwords. The effect of behavioural information security governance on employee information security awareness is an understudied topic. Therefore, this study is explorative in nature and the results are preliminary. Nevertheless, the paper provides implications for both research and practice.


title: The State of Lung Cancer Research: A Global Analysis

abstract: INTRODUCTION ::: Lung cancer is the leading cause of years of life lost because of cancer and is associated with the highest economic burden relative to other tumor types. Research remains at the cornerstone of achieving improved outcomes of lung cancer. We present the results of a comprehensive analysis of global lung cancer research between 2004 and 2013 (10 years). ::: ::: ::: METHODS ::: The study used bibliometrics to undertake a quantitative analysis of research output in the 24 leading countries in cancer research internationally on the basis of articles and reviews in the Web of Science (WoS) database. ::: ::: ::: RESULTS ::: A total of 32,161 lung cancer research articles from 2085 different journals were analyzed. Lung cancer research represented only 5.6% of overall cancer research in 2013, a 1.2% increase since 2004. The commitment to lung cancer research has fallen in most countries apart from China and shows no correlation with lung cancer burden. A review of key research types demonstrated that diagnostics, screening, and quality of life research represent 4.3%, 1.8%, and 0.3% of total lung cancer research, respectively. The leading research types were genetics (20%), systemic therapies (17%), and prognostic biomarkers (16%). Research output is increasingly basic science, with a decrease in clinical translational research output during this period. ::: ::: ::: CONCLUSIONS ::: Our findings have established that relative to the huge health, social, and economic burden associated with lung cancer, the level of world research output lags significantly behind that of research on other malignancies. Commitment to diagnostics, screening, and quality of life research is much lower than to basic science and medical research. The study findings are expected to provide the requisite knowledge to guide future cancer research programs in lung cancer.


title: Vector Perturbation Precoding Revisited

abstract: We consider the downlink of a multiuser system with multiple antennas at the base station. Vector perturbation (VP) precoding is a promising variant of transmit-side channel inversion allowing the users to detect their data in a simple, noncooperative manner. VP precoding has so far been developed and analyzed under the assumptions that the transmitter has perfect channel state information (CSI) and that the receivers know perfectly a channel-dependent transmit power normalization factor and have infinite dynamic range. We demonstrate that the violation of any of these idealizing assumptions degrades the performance of VP significantly and almost always results in an error floor. Motivated by this observation, we propose a novel scheme which we term transmit outage precoding (TOP). With TOP, the transmitter uses a prearranged power scaling known by the receivers and refrains from transmitting when channel conditions are poor. We further show how to augment TOP and conventional VP to deal with a finite dynamic range at the receiver. The performance of the proposed schemes under various levels of transmit CSI is studied in terms of a theoretical diversity analysis and illustrated by numerical results.


title: A Breadth-First Survey of Eye Tracking Applications

abstract: Eye-tracking applications are surveyed in a breadth-first manner, reporting on work from the following domains: neuroscience, psychology, industrial engineering and human factors, marketing/advertising, and computer science. Following a review of traditionally diagnostic uses, emphasis is placed on interactive applications, differentiating between selective and gaze-contingent approaches.


title: A Recurrent Encoder-Decoder Network for Sequential Face Alignment

abstract: We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model. At the temporal level, we first decouple the features in the bottleneck of the network into temporal-variant factors, such as pose and expression, and temporal-invariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporal-variant features, yielding better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state-of-the-art in standard datasets.


title: Hybrid cryptography mechanism for securing self-organized wireless networks

abstract: Nowadays use of wireless technologies tremendously increased due to the continuous development of these technologies in various applications. One of the development in wireless technologies leads to deployment of the networks in which all mobile nodes will communicate via wireless links directly without use of any base station or predefined infrastructural support. These networks are self-organized networks called mobile ad hoc networks (MANETs) and architecture used for these networks will be either stand alone with one or few nodes communicating with base station or distributed. As communication is done through wireless links, these types of networks are more prone to several kinds of attacks. Authentication and encryption acts as first line of defense while an intrusion detection system (IDS) acts as a second line of defense. One of the intrusion detection system used in MANET is Enhanced Adaptive Acknowledgement (EAACK) which is based on acknowledgement packets for detection of malicious activities. This system increases the overhead of network significantly relative to other system such as watchdog. So, in this paper we adopt the hybrid cryptography to reduce the network overhead and enhance the security of networks significantly.


title: SUPPRESSION OF GATE INDUCED DRAIN LEAKAGE CURRENT (GIDL) BY GATE WORKFUNCTION ENGINEERING: ANALYSIS AND MODEL

abstract: Leakage current reduction is of primary importance as the technology scaling trends continue towards deep sub-micrometer regime. One of the leakage mechanisms which contribute significantly to power dissipation is the Gate Induced Drain Leakage (GIDL). GIDL sets an upper limit on the VLSI MOSFET scaling and may even lead to device breakdown. Thus, in order to improve performance, static power consumption becomes a major concern in such miniaturized devices. With the CMOS technology in the nanometer regime, metal gates emerge as a powerful booster over the poly-Si gates, keeping the leakage mechanisms in control. This work presents a systematic study of the Gate Induced Drain Leakage current reduction by changing the gate workfunction. In this work, an attempt has been made to model the metal gates in the field equations in the gate-drain overlap region. The modeling has been accomplished by taking the physical property of the metals into account i.e. the workfunction. The analytical model has been used to study the impact of gate workfunction engineering on GIDL. The model includes the calculation of band bending, the resultant electric field in the gate-drain overlap region and the GIDL current. It has been observed that the electric field and the GIDL current decreases as the gate workfunction is increased continuously from 4.0~5.2 eV. Further, the results are more interesting at low applied voltages where the decrease in the current is of about 6 orders of magnitude as compared to that at higher voltages.


Active Learning for Dependency Parsing with Partial Annotation


title: Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

abstract: In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.


title: Riemannian Motion Policies

abstract: We introduce the Riemannian Motion Policy (RMP), a new mathematical object for modular motion generation. An RMP is a second-order dynamical system (acceleration field or motion policy) coupled with a corresponding Riemannian metric. The motion policy maps positions and velocities to accelerations, while the metric captures the directions in the space important to the policy. We show that RMPs provide a straightforward and convenient method for combining multiple motion policies and transforming such policies from one space (such as the task space) to another (such as the configuration space) in geometrically consistent ways. The operators we derive for these combinations and transformations are provably optimal, have linearity properties making them agnostic to the order of application, and are strongly analogous to the covariant transformations of natural gradients popular in the machine learning literature. The RMP framework enables the fusion of motion policies from different motion generation paradigms, such as dynamical systems, dynamic movement primitives (DMPs), optimal control, operational space control, nonlinear reactive controllers, motion optimization, and model predictive control (MPC), thus unifying these disparate techniques from the literature. RMPs are easy to implement and manipulate, facilitate controller design, simplify handling of joint limits, and clarify a number of open questions regarding the proper fusion of motion generation methods (such as incorporating local reactive policies into long-horizon optimizers). We demonstrate the effectiveness of RMPs on both simulation and real robots, including their ability to naturally and efficiently solve complicated collision avoidance problems previously handled by more complex planners.


title: Silhouette: Identifying YouTube Video Flows from Encrypted Traffic

abstract: Video streaming traffic often dominates mobile wireless networks, forcing Internet Service Providers (ISPs) to deploy video shaping to identify and then manage traffic during congested periods Unfortunately, the increasing use of end-to-end encryption (e.g., TSL/SSL) makes it difficult to identify video flows even with deep packet inspection. As an alternative, this paper presents Silhouette -- a real-time, lightweight video classification method suitable for ISP middle-boxes. Silhouette uses only flow statistics (i.e., "shape") for video identification making it payload-agnostic, effective for identifying video flow even when encrypted. Preliminary results with pre-classified YouTube traffic shows the promise of the Silhouette approach, yielding high identification accuracy over a range of video content and encoding qualities.


title: A Color Gamut Description Algorithm for Liquid Crystal Displays in CIELAB Space

abstract: Because the accuracy of gamut boundary description is significant for gamut mapping process, a gamut boundary calculating method for LCD monitors is proposed in this paper. Within most of the previous gamut boundary calculation algorithms, the gamut boundary is calculated in CIELAB space directly, and part of inside-gamut points are mistaken for the boundary points. While, in the new proposed algorithm, the points on the surface of RGB cube are selected as the boundary points, and then converted and described in CIELAB color space. Thus, in our algorithm, the true gamut boundary points are found and a more accurate gamut boundary is described. In experiment, a Toshiba LCD monitor's 3D CIELAB gamut for evaluation is firstly described which has regular-shaped outer surface, and then two 2D gamut boundaries ( CIE-a*b* boundary and CIE-C*L* boundary) are calculated which are often used in gamut mapping process. When our algorithm is compared with several famous gamut calculating algorithms, the gamut volumes are very close, which indicates that our algorithm's accuracy is precise and acceptable.


title: Improved Quantum Algorithm for Triangle Finding via Combinatorial Arguments

abstract: In this paper we present a quantum algorithm solving the triangle finding problem in unweighted graphs with query complexity $\tilde O(n^{5/4})$, where $n$ denotes the number of vertices in the graph. This improves the previous upper bound $O(n^{9/7})=O(n^{1.285...})$ recently obtained by Lee, Magniez and Santha. Our result shows, for the first time, that in the quantum query complexity setting unweighted triangle finding is easier than its edge-weighted version, since for finding an edge-weighted triangle Belovs and Rosmanis proved that any quantum algorithm requires $\Omega(n^{9/7}/\sqrt{\log n})$ queries. Our result also illustrates some limitations of the non-adaptive learning graph approach used to obtain the previous $O(n^{9/7})$ upper bound since, even over unweighted graphs, any quantum algorithm for triangle finding obtained using this approach requires $\Omega(n^{9/7}/\sqrt{\log n})$ queries as well. To bypass the obstacles characterized by these lower bounds, our quantum algorithm uses combinatorial ideas exploiting the graph-theoretic properties of triangle finding, which cannot be used when considering edge-weighted graphs or the non-adaptive learning graph approach.


title: MMP1, MMP9, and COX2 Expressions in Promonocytes Are Induced by Breast Cancer Cells and Correlate with Collagen Degradation, Transformation-Like Morphological Changes in MCF-10A Acini, and Tumor Aggressiveness

abstract: Tumor-associated immune cells often lack immune effector activities, and instead they present protumoral functions. To understand how tumors promote this immunological switch, invasive and noninvasive breast cancer cell (BRC) lines were cocultured with a promonocytic cell line in a Matrigel-based 3D system. We hypothesized that if communication exists between tumor and immune cells, coculturing would result in augmented expression of genes associated with tumor malignancy. Upregulation of proteases MMP1 and MMP9 and inflammatory COX2 genes was found likely in response to soluble factors. Interestingly, changes were more apparent in promonocytes and correlated with the aggressiveness of the BRC line. Increased gene expression was confirmed by collagen degradation assays and immunocytochemistry of prostaglandin 2, a product of COX2 activity. Untransformed MCF-10A cells were then used as a sensor of soluble factors with transformation-like capabilities, finding that acini formed in the presence of supernatants of the highly aggressive BRC/promonocyte cocultures often exhibited total loss of the normal architecture. These data support that tumor cells can modify immune cell gene expression and tumor aggressiveness may importantly reside in this capacity. Modeling interactions in the tumor stroma will allow the identification of genes useful as cancer prognostic markers and therapy targets.


title: Bandwidth provisioning and pricing for networks with multiple classes of service

abstract: Network service providers purchase large point-to-point connections from network owners, then offer individual users network access at a price. Appropriately provisioning (purchasing) and allocating (pricing) connections remains a difficult problem due to increasing demands and network dynamics. However, connection management is more complex with the deployment of Quality of Service (QoS). This paper describes a scalable connection management strategy for QoS-enabled networks. The management technique maximizes profit, while reducing blocking experienced by users. Important issues regarding demand estimation, connection duration, and pricing intervals, are addressed and analyzed. Simulation results are also provided to demonstrate the viability of the proposed system.


title: Factors Affecting Online Search Intention and Online Purchase Intention

abstract: This research focuses on various factors affecting online search intention which has been found to be a key predictor of online purchase intention. Data were collected from a sample consisting of mostly young adults with familiarity of computer use and online shopping experience. A structural equation model was employed to test hypotheses. According to the findings, utilitarian value of Internet information search, hedonic value of Internet information search, perceived benefits of Internet shopping, perceived risk of Internet shopping, and Internet purchase experience predicted online search intention well. The findings also showed that online search intention positively affects * Professor of Marketing, College of Business Administration, Seoul National University(jaekim@snu.ac.kr) ** Marketing, Telephone Service Team, Hanaro Telecom Co., Inc. (heechun@hanaro.com) *** M.B.A. candidate, College of Business Administration, Seoul National University(haejoo80@yahoo.com) This research was supported by the Institute of Management Research, Seoul National University. Seoul Journal of Business Volume 10, Number 2 (December 2004)


title: Design and implementation of a bi-directional power converter for electric bike with charging feature

abstract: This paper presents a bi-directional converter applied in electric bike. The main structure is a cascade buck-boost converter, which transfers the energy stored in battery for driving motor, and can recycle the energy resulted from the back electromotive force (BEMF) to charge battery by changing the operation mode. Moreover, the proposed converter can also serve as a charger by connecting with AC line directly. Besides, the single-chip DSP TMS320F2812 is adopted as a control core to manage the switching behaviors of each mode and to detect the battery capacity. In this paper, the equivalent models of each mode and complete design considerations are all detailed. All the experimental results are used to demonstrate the feasibility.


Goertzel Algorithm Based Shunt Active Power Filter Using Sliding Mode Controller


The FrameNet Constructicon


The OPUS Corpus - Parallel and Free: http: //logos.uio.no/opus


title: Mongodb, couchbase: Performance comparison for image dataset

abstract: In today's world data is growing very rapidly, which we call as big data. To deal with these large data sets, currently we are using NoSQL databases, as relational database is not capable for handling such data. These schema less NoSQL database allow us to handle unstructured data. Through this paper we are comparing two NoSQL databases MongoDB and CouchBase server, in terms of image storage and retrieval. Aim behind selecting these two databases as both comes under Document store category. Major applications like social media, traffic analysis, criminal database etc. require image database. The motivation behind this paper is to compare database performance in terms of time required to store and retrieve images from database. In this paper, firstly we are going describe advantages of NoSQL databases over SQL, then brief idea about MongoDB and CouchBase and finally comparison of time required to insert various size images in databases and to retrieve various size images using front end tool Java.


title: ELM-ART: An intelligent tutoring system on World Wide web

abstract: Making ITS available on the World Wide Web (WWW) is a way to integrate the flexibility and intelligence of ITS with world-wide availability of WWW applications. This paper discusses the problems of developing WWW-available ITS and, in particular, the problem of porting existing ITS to a WWW platform. We present the system ELM-ART which is a WWW-based ITS to support learning programming in Lisp. ELM-ART demonstrates how several known ITS technologies can be implemented in WWW context.


MusicMiner: Visualizing timbre distances of music as topograpical maps


title: eSport and the Human Body: foundations for a popular aesthetics

abstract: Notable philosophers of sport have rejected connections between videogames and sports throughout the past decade. This paper argues against an arbitrary identification of sporting activity with exhaustion and resistance. It reviews work in cognitive science and sociology that locates sporting potential in distributed cognition and team-based cooperation. Then it connects that work to an expanded discussion of the aesthetic issues raised by Henry Lowood's talk “Players Are Artists, too” at the Art History of Games Symposium. This introduction to the application of sports aesthetics to eSports revisits Lowood's major theoretical sources to expand upon the problems they raise for videogame performance. It argues that there are obstacles to the popular appreciation of eSporting aesthetics separate from the particular interests of archivists and expert spectators. And it suggests that different eSports present different sets of problems, producing multiple avenues for the advancement of aesthetic refinement in eSports broadcasts.


title: Towards Universal Dialogue State Tracking

abstract: Dialogue state tracking is the core part of a spoken dialogue system. It estimates the beliefs of possible user's goals at every dialogue turn. However, for most current approaches, it's difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don't work in the situation where slot values in ontology changes dynamically; (b) The number of model parameters is proportional to the number of slots; (c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches.


title: Life-long Cross-media Correlation Learning

abstract: With the numerous and dynamically increasing of multimedia data, such as image and text, lying in different domains, there arise two major challenges for cross-media retrieval. First, measuring the similarities for cross-media correlation between different media types is quite difficult, due to their inconsistent distributions and representations. Second, storing and retraining on such data becomes infeasible, because data of new domain arrives in sequence while the existing ones are not always available. Thus, it requires to only utilize the data of new domain for training while preserving the original correlation capabilities simultaneously. To address the above issues, in this paper we propose Cross-media Life-long Learning (CmLL) approach, which can leverage the knowledge learned from the existing data, to obtain better correlation performance in new domain. The main contributions are summarized as follows: (1) Cross-media adapting network. We construct hierarchical network to not only share the knowledge from different media types in high level, but also realize life-long learning on new cross-media domain by expanding network capacity adaptively, which can support the adaptivity and extensibility for cross-media correlation learning. (2) Cross-media life-long learning. We propose both intra-domain distribution alignment as well as inter-domain knowledge distillation, which can not only effectively preserve the correlation ability in old cross-media domains, but also improve the performance in new domain by transferring knowledge among different domains. We conduct extensive experiments to verify the effectiveness of our proposed CmLL approach, which are performed on multiple cross-media datasets for different domains under lifelong learning scenarios.


title: Biomarker-Adaptive Threshold Design: A Procedure for Evaluating Treatment With Possible Biomarker-Defined Subset Effect

abstract: Human cancers are heterogeneous with regard to their molecular and genomic properties. Recent advances in biotechnology have resulted in a shift toward molecularly targeted anticancer agents. These new therapeutics are likely to benefi t only a subset of the patients with a given cancer. Defi nitive testing of such targeted agents requires the identifi cation of the appropriate “sensitive” population. When biomarkers to identify the patients who are likely to benefi t from the new therapy are available, targeted clinical trials that restrict eligibility to sensitive patients should be used ( 1 ). However, reliable assays to identify sensitive patients are often unavailable. In the absence of a reliable biomarker, broad-eligibility clinical trials are used routinely. Most of these trials use a conventional design, in which the primary analysis is based on comparison of all randomly assigned patients. This often leads to the failure to recognize effective agents due to dilution of the treatment effect by the presence of the patients who do not benefi t from the agent. Retrospective analysis of trials with a conventional design can be used as an initial step in identifying biomarkers for the sensitive subpopulation. However, retrospectively identifi ed biomarkers


title: Reinforcement learning of motor skills with policy gradients

abstract: Autonomous learning is one of the hallmarks of human and animal behavior, and understanding the principles of learning will be crucial in order to achieve true autonomy in advanced machines like humanoid robots. In this paper, we examine learning of complex motor skills with human-like limbs. While supervised learning can offer useful tools for bootstrapping behavior, e.g., by learning from demonstration, it is only reinforcement learning that offers a general approach to the final trial-and-error improvement that is needed by each individual acquiring a skill. Neither neurobiological nor machine learning studies have, so far, offered compelling results on how reinforcement learning can be scaled to the high-dimensional continuous state and action spaces of humans or humanoids. Here, we combine two recent research developments on learning motor control in order to achieve this scaling. First, we interpret the idea of modular motor control by means of motor primitives as a suitable way to generate parameterized control policies for reinforcement learning. Second, we combine motor primitives with the theory of stochastic policy gradient learning, which currently seems to be the only feasible framework for reinforcement learning for humanoids. We evaluate different policy gradient methods with a focus on their applicability to parameterized motor primitives. We compare these algorithms in the context of motor primitive learning, and show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.


title: Sequential Decision Problems and Neural Networks

abstract: Decision making tasks that involve delayed consequences are very common yet difficult to address with supervised learning methods. If there is an accurate model of the underlying dynamical system, then these tasks can be formulated as sequential decision problems and solved by Dynamic Programming. This paper discusses reinforcement learning in terms of the sequential decision framework and shows how a learning algorithm similar to the one implemented by the Adaptive Critic Element used in the pole-balancer of Barto, Sutton, and Anderson (1983), and further developed by Sutton (1984), fits into this framework. Adaptive neural networks can play significant roles as modules for approximating the functions required for solving sequential decision problems.


title: EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks

abstract: Abstract In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH.


title: Examining students' online interaction in a live video streaming environment using data mining and text mining

abstract: This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students' patterns and themes of participation between online questions (student-instructor interaction) and online chat messages (student-students interaction or peer interaction). The results also identify disciplinary differences in students' online participation. A correlation is found between the number of online questions students asked and students' final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students' learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper.


title: Highly Enhanced Photocatalytic Water-splitting Activity of Gallium Zinc Oxynitride Derived from Flux-assisted Zn/Ga Layered Double Hydroxides

abstract: The Ga/Zn oxynitride solid solution [(GaN)1–x(ZnO)x] is one of the promising visible light harvesting photocatalysts for overall water splitting. A series of (GaN)1–x(ZnO)x (0.11 ≤ x ≤ 0.33) compounds are synthesized by calcining the carbonate-type Zn/Ga-LDH precursor with and without sodium carbonate flux at 850 °C for 8–14 h under a NH3 gas flow. The solid solutions without flux are determined to be low in crystallinity, but platelike morphology with preferred orientation could be observed. On the other hand, those with flux turn out to be better in crystallinity, and eventually exhibit significantly higher photocatalytic activity for overall water splitting under visible light irradiation than those without flux. In addition, the band gap energies can also be engineered from 2.57 to 2.72 eV by changing a synthetic parameter such as nitridation time. It is, therefore, suggested that the present new approach can offer new opportunities for designing the next generation of photocatalytic systems.


title: In Vivo Two-Photon Ca2+ Imaging Reveals Selective Reward Effects on Stimulus-Specific Assemblies in Mouse Visual Cortex

abstract: Experiences can alter functional properties of neurons in primary sensory neocortex but it is poorly understood how stimulus-reward associations contribute to these changes. Using in vivo two-photon calcium imaging in mouse primary visual cortex (V1), we show that association of a directional visual stimulus with reward results in broadened orientation tuning and sharpened direction tuning in a stimulus-selective subpopulation of V1 neurons. Neurons with preferred orientations similar, but not identical to, the CS+ selectively increased their tuning curve bandwidth and thereby exhibited an increased response amplitude at the CS+ orientation. The increase in response amplitude was observed for a small range of orientations around the CS+ orientation. A nonuniform spatial distribution of reward effects across the cortical surface was observed, as the spatial distance between pairs of CS+ tuned neurons was reduced compared with pairs of CS- tuned neurons and pairs of control directions or orientations. These data show that, in primary visual cortex, formation of a stimulus-reward association results in selective alterations in stimulus-specific assemblies rather than population-wide effects.


title: Routing a fleet of vehicles for dynamic combined pick-up and deliveries services. Universitá di Roma “Tor Vergata

abstract: This work partially reports the results of a study aiming at the design and analysis of the performance of a multi-cab metropolitan transportation system. In our model we investigate a particular multi-vehicle many-to-many dynamic request dial-a-ride problem. We present a heuristic algorithm for this problem and some preliminary results. The algorithm is based on iteratively solving a single-vehicle subproblem at optimality: a pretty efficient dynamic programming routine has been devised for this purpose.


title: Study of the Spiritual Intelligence Role in Predicting University Students’ Quality of Life

abstract: The aim of the study is to investigate the spiritual intelligence role in predicting Quchan University students’ quality of life. In order to collect data, a sample of 143 students of Quechan University was selected randomly enrolled for 89–90 academic year. The instruments of the data collecting are World Health Organization Quality of Life (WHOQOL) and Spiritual Intelligence Questionnaire. For analyzing the data, the standard deviation, and Pearson’s correlation coefficient in descriptive level, and in inferential level, the regression test was used. The results of the study show that the spiritual intelligence has effective role on predicting quality of life.


title: Attendance system based on face recognition using eigen face and PCA algorithms

abstract: The attendance maintaining system is difficult process if it is done manually. The smart and automated attendance system for managing the attendance can be implemented using the various ways of biometrics. Face recognition is one of them. By using this system, the issue of fake attendance and proxies can be solved. In the previous face recognition based attendance system, there were some disadvantages like intensity of light problem and head pose problem. Therefore to overcome these issues, various techniques like illumination invariant, Viola and Jones algorithm, Principle component analysis are used. The major steps in this system are detecting the faces and recognizing them. After these, the comparison of detected faces can be done by crosschecking with the database of student's faces. This smart system will be an effective way to maintain the attendance and records of students.


title: A preferred music listening intervention to reduce anxiety in older adults with dementia in nursing homes

abstract: Aim: This article reports the results of a study evaluating a preferred music listening intervention for reducing anxiety in older adults with dementia in nursing homes. ::: Background. Anxiety can have a significant negative impact on older adults’ functional status, quality of life and health care resources. However, anxiety is often under-diagnosed and inappropriately treated in those with dementia. Little is known about the use of a preferred music listening intervention for managing anxiety in those with dementia.---------- ::: Design: A quasi-experimental pretest and posttest design was used. ---------- ::: Methods: This study aimed to evaluate the effectiveness of a preferred music listening intervention on anxiety in older adults with dementia in nursing home. Twenty-nine participants in the experimental group received a 30-minute music listening intervention based on personal preferences delivered by trained nursing staff in mid-afternoon, twice a week for six weeks. Meanwhile, 23 participants in the control group only received usual standard care with no music. Anxiety was measured by Rating Anxiety in Dementia at baseline and week six. Analysis of covariance (ANCOVA) was used to determine the effectiveness of a preferred music listening intervention on anxiety at six weeks while controlling for pretest anxiety, age and marital status. ::: Results. ANCOVA results indicated that older adults who received the preferred music listening had a significantly lower anxiety score at six weeks compared with those who received the usual standard care with no music (F = 12AE15, p = 0AE001).---------- ::: Conclusions: Preferred music listening had a positive impact by reducing the level of anxiety in older adults with dementia. ::: Relevance to clinical practice. Nursing staff can learn how to implement preferred music intervention to provide appropriate care tailored to the individual needs of older adults with dementia. Preferred music listening is an inexpensive and viable intervention to promote mental health of those with dementia.


title: Making Problems in Design Research: The Case of Teen Shoplifters on Tumblr

abstract: HCI draws on a variety of traditions but recently there have been calls to consolidate contributions around the problems researchers set out to solve. However, with this comes the assumption that problems are tractable and certain, rather than constructed and framed by researchers. We take as a case study a Tumblr community of teen shoplifters who post on how to steal from stores, discuss shoplifting as political resistance, and share jokes and stories about the practice. We construct three different "problems" and imagine studies that might result from applying different design approaches: Design Against Crime; Critical Design and Value Sensitive Design. Through these studies we highlight how interpretations of the same data can lead to radically different design responses. We conclude by discussing problem making as a historically and politically contingent process that allow researchers to connect data and design according to certain moral and ethical principles.


title: A Comparison of Contingency Management and Cognitive-Behavioral Approaches During Methadone Maintenance Treatment for Cocaine Dependence

abstract: Background This study compared 2 psychosocial approaches for the treatment of cocaine dependence: contingency management (CM) and cognitive-behavioral therapy (CBT). Methods Patients with cocaine dependence who were receiving methadone maintenance treatment (n = 120) were randomly assigned to 1 of 4 conditions: CM, CBT, combined CM and CBT (CBT + CM), or treatment as usual (ie, methadone maintenance treatment program only [MMTP only]) (n = 30 per cell). The CM procedures and CBT materials were comparable to those used in previously published research. The active study period was 16 weeks, requiring 3 clinic visits per week. Participants were evaluated during treatment and at 17, 26, and 52 weeks after admission. Results Urinalysis results during the 16-week treatment period show that participants assigned to the 2 groups featuring CM had significantly superior in-treatment urinalysis results, whereas urinalysis results from participants in the CBT group were not significantly different than those from the MMTP-only group. At week 17, self-reported days of cocaine use were significantly reduced from baseline levels for all 3 treatment groups but not for the MMTP-only group. At the 26-week and 52-week follow-up points, CBT participants showed substantial improvement, resulting in equivalent performance with the CM groups as indicated by both urinalysis and self-reported cocaine use data. Conclusions Study findings provide solid evidence of efficacy for CM and CBT. Although the effect of CM is significantly greater during treatment, CBT appears to produce comparable long-term outcomes. There was no evidence of an additive effect for the 2 treatments in the CM + CBT group.


title: Comment on"The spin symmetry for deformed generalized P\"oschl-Teller potential"

abstract: In this comment, we show that the solutions of the-wave Dirac equation for deformed generalized P\"{o}schl-Teller potential obtained by Wei et al are valid only for $q\geq1$ and $\frac{1}{2\alpha}\ln{q}<r<\infty$. When $0<q<1$, we prove that the energy eigenvalues for the bound states are given by the solution of a transcendental equation involving the hypergeometric function. To test our results, the Morse potential is considered as limiting case.


title: Design and Analysis of Wide Frequency-Tuning-Range CMOS 60 GHz VCO by Switching Inductor Loaded Transformer

abstract: To provide wide frequency tuning range (FTR) with compact implementation area, a new inductive tuning method is introduced in this paper for CMOS 60 GHz voltage controlled oscillator (VCO). The inductive tuning is based on a switching inductor-loaded transformer by configuring different current return-paths in the secondary coil of the transformer. Different from previous inductive tuning methods, the proposed VCO topology can achieve wide FTR for multiple sub-bands at 60 GHz within compact area by only one transformer. Two 60 GHz VCOs are demonstrated in 65 nm CMOS with design targets for the maximum FTR and the balanced phase noise in each sub-band, respectively. As measured by experiments, the first VCO (asymmetric) achieves a wide FTR of 25.8% from 51.9 to 67.3 GHz with phase noise variation of ±8.2 dB ( -90.2 to -106.7 dBc/Hz at 10 MHz offset) in all sub-bands; and the second VCO (symmetric) realizes a low phase noise variation of ±2.5 dB ( -105.9 to -110.8 dBc/Hz at 10 MHz offset) in all sub-bands with a FTR of 14.2% from 57.0 GHz to 65.5 GHz.


title: GeoPose3K: Mountain landscape dataset for camera pose estimation in outdoor environments

abstract: Abstract We introduce a new dataset called GeoPose3K 1 which contains over three thousand precise camera poses of mountain landscape images. In addition to camera location and orientation, we provide data for the training and evaluation of computer vision methods and applications in the context of outdoor scenes; synthetic depth maps, normal maps, illumination simulation and semantic labels. In order to illustrate properties of the dataset, we compare results achieved by state-of-the-art visual geo-localization method on GeoPose3K with results achieved on an existing dataset for visual geo-localization. So as to foster research of computer vision algorithms for outdoor environments, several novel future use-cases of our new GeoPose3K dataset are proposed.


title: The interplay between Facebook use, social comparison, envy, and depression

abstract: In their Facebook profiles, users communicate abundant social comparison information conveying mainly positive self-portrayals. Thereby, social networking sites like Facebook provide a fertile ground for envy. This has been proposed as a mechanism for the potential negative effects of Facebook use on well-being and depression. This article reviews research on this process. Studies show that (especially passive) Facebook use indeed predicts different measures of social comparison as well as envy. In several studies social comparison or envy mediate a positive association between Facebook use and undesirable affective outcomes such as depression. However, causal relationships have not yet been sufficiently established. Methodological and conceptual variety across studies limits their comparability, but reveals viable ideas for future research.


title: A Survey of Communication Protocols for Automatic Meter Reading Applications

abstract: Utility companies (electricity, gas, and water suppliers), governments, and researchers have been urging to deploy communication-based systems to read meters, known as automatic meter reading (AMR). An AMR system is envisaged to bring on benefits to customers, utilities, and governments. The advantages include reducing peak demand for energy, supporting the time-of-use concept for billing, enabling customers to make informed decisions, and reducing the cost of meter reading, to name a few. A key element in an AMR system is communications between meters and utility servers. Though several communication technologies have been proposed and implemented at a small scale, with the wide proliferation of wireless communication, it is the right time to critique the old proposals and explore new possibilities for the next generation AMR. We provide a comprehensive review of the AMR technologies proposed so far. Next, we present how future AMRs will benefit from third generation (3G) communication systems, the DLMS/COSEM (Data Language Messaging Specification/Companion Specification for Energy Metering) standard and Internet Protocol-based SIP (Session Initiation Protocol) signaling at the application level. The DLMS/COSEM standard provides a framework for meters to report application data (i.e. meter readings) to a utility server in a reliable manner. The SIP protocol is envisaged to be used as the signaling protocol between application entities running on meters and servers. The DLMS/COSEM standard and the SIP protocol are expected to provide an application level communication abstraction to achieve reliability and scalability. Finally, we identify the challenges at the application level that need to be tackled. The challenges include handling failure, gathering meter data under different time constraints (ranging from real-time to delay-tolerance), disseminating (i.e., unicasting, multicasting, broadcasting, and geocasting) control data to the meters, and achieving secure communication.


title: Personality Recognition on Social Media With Label Distribution Learning

abstract: Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual’s personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users’ profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.


title: Fast Distributed Transactions and Strongly Consistent Replication for OLTP Database Systems

abstract: As more data management software is designed for deployment in public and private clouds, or on a cluster of commodity servers, new distributed storage systems increasingly achieve high data access throughput via partitioning and replication. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. This article describes Calvin, a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. This allows near-linear scalability on a cluster of commodity machines, without eliminating traditional transactional guarantees, introducing a single point of failure, or requiring application developers to reason about data partitioning. By replicating transaction inputs instead of transactional actions, Calvin is able to support multiple consistency levels—including Paxos-based strong consistency across geographically distant replicas—at no cost to transactional throughput. Furthermore, Calvin introduces a set of tools that will allow application developers to gain the full performance benefit of Calvin's server-side transaction scheduling mechanisms without introducing the additional code complexity and inconvenience normally associated with using DBMS stored procedures in place of ad hoc client-side transactions.


title: Architecture decisions: demystifying architecture

abstract: We believe that a key to demystifying architecture products lies in the architecture decisions concept. We can make the architecture more transparent and clarify its rationale for all stakeholders by explicitly documenting major architecture decisions.


title: Denial-of-service attacks in OpenFlow SDN networks

abstract: Software-Defined Networking (SDN) has recently gained significant momentum. However, before any large scale deployments, it is important to understand security issues arising from this new technology. This paper discusses two types of Denial-of-Service (DoS) attacks specific to OpenFlow SDN networks. We emulate them on Mininet and provide an analysis on the effect of these attacks. We find that the timeout value of a flow rule, and the control plane bandwidth have a significant impact on the switch's capability. If not configured appropriately, they may allow successful DoS attacks. Finally, we highlight possible mitigation strategies to address such attacks.


title: A speaker recognizability test

abstract: Speech intelligibility and quality are the two most often tested features of speech coding systems. However, another feature of interest in store-and-forward applications is the preservation of a speaker's identity. Here, a Speaker Recognizability Test (SRT) is presented, which tries to establish how well a given communications system preserves a speaker's identity. Contrary to previous efforts, no attempt is made to identify the cues used by listeners for speaker recognition. Instead, listeners are asked directly to identify a speaker who says an utterance by comparing the uttered sentence with reference sentences, one from each speaker. Among the issues considered in the design of the test is the choice of speakers, the use of reference sentences from the same or different sessions of data collection, and the use of processed or unprocessed speech for reference.


title: Towards efficient content-aware search over encrypted outsourced data in cloud

abstract: With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.


title: On the Use of PMUs in Power System State Estimation

abstract: Synchronized phasor measurement units (PMUs) are becoming a reality in more and more power sys- tems, mainly at the transmission level. This paper presents, in a tutorial manner, the benefits that existing and future State Estimators (SE) can achieve by incorporating these de- vices in the monitoring process. After a review of the rele- vant PMU technological aspects and the associated deploy- ment issues (observability, optimal location, etc.), the alter- native SE formulations in the presence of PMUs are revis- ited. Then, several application environments are separately addressed, regarding the enhancements potentially brought about by the use of PMUs.


title: Gender Differences in Human Brain: A Review

abstract: Why do men and women think differently? Why do they behave differently in stressed situations? Why do women act more emotionally as compared to men? Why do men and women excel at different types of tasks? Why do boys like to play with cars and trucks and superman? These are the common questions which arise commonly in minds.The human brain is a highly complex organ. Studies of perception, cognition, memory and neural functions have found apparent gender differences. These differences may be attributed to various genetic, hormonal, and environmental factors and do not reflect any overall superiority advantage to either sex. Both sexes are equal in intelligence, but tend to operate differently. Men and women appear to use different parts of the brain to encode memories, sense emotions, recognize faces, solve certain problems and make decisions. Indeed, when men and women of similar intelligence and aptitude perform equally well, their brains appear to go about it differently, as if nature had separate blueprints. Sex differences in the brain may play a role in learning processes, language development, and progression of neurologically- based diseases. Sex differences need to be considered in studying brain structure and function and may raise the possibility of sex-specific treatments for neurological diseases. In this article it is reviewed that how does the brain of a male look and function differently from a female's brain, and what accounts for these differences?


Scalable Realistic Rendering with Many-Light Methods


title: Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

abstract: Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.


title: Ramulator: A Fast and Extensible DRAM Simulator

abstract: Recently, both industry and academia have proposed many different roadmaps for the future of DRAM. Consequently, there is a growing need for an extensible DRAM simulator, which can be easily modified to judge the merits of today's DRAM standards as well as those of tomorrow. In this paper, we present Ramulator , a fast and cycle-accurate DRAM simulator that is built from the ground up for extensibility. Unlike existing simulators, Ramulator is based on a generalized template for modeling a DRAM system, which is only later infused with the specific details of a DRAM standard. Thanks to such a decoupled and modular design, Ramulator is able to provide out-of-the-box support for a wide array of DRAM standards: DDR3/4, LPDDR3/4, GDDR5, WIO1/2, HBM, as well as some academic proposals (SALP, AL-DRAM, TL-DRAM, RowClone, and SARP). Importantly, Ramulator does not sacrifice simulation speed to gain extensibility: according to our evaluations, Ramulator is 2.5 $\times$ faster than the next fastest simulator. Ramulator is released under the permissive BSD license.


title: Respiratory muscle stretch gymnastics in patients with post coronary artery bypass grafting pain: impact on respiratory muscle function, activity, mood and exercise capacity.

abstract: A new rehabilitation (New-RH) program including respiratory muscle stretch gymnastics (RMSG) was developed to alleviate post-coronary artery bypass grafting pain (PCP). Effects on respiratory muscle function, pain, activities of daily living (ADL), mood and exercise capacity were investigated. Subjects were 16 consecutive patients undergoing median full sternotomy coronary artery bypass grafting (CABG), and were randomly divided into equal New-RH (S-group) and conventional therapy (C-group) groups. Rib cage dominant breathing was observed postoperatively in both groups. With preoperative tan deltaVrc/deltaVab, increases at 1-week postoperatively and decreases at discharge for S-group tended to exceed those of C-group (p > .05). Decreased maximum inspiratory and expiratory pressure status for functional residual capacity and percent forced expiratory volume in one second at discharge again only tended to be smaller for S-group (p > .05). S-group displayed significantly reduced pain around both scapulas at discharge (p = .049), and increased mean overall ADL and profile of mood states (POMS)/Vigor scores (p = .031 and p = .018, respectively). POMS/Tension-Anxiety scores at discharge for S-group were significantly smaller than those preoperatively (p = .025), and S-group displayed significantly increased distance walked over 6-minutes at discharge than C-group (p = .029). New-RH improves patient participation in exercise therapy and increases exercise capacity by reducing PCP, relieving anxiety and tension, and improving ADL.


title: Meta-Analytic Review of Leader-Member Exchange Theory: Correlates and Construct Issues

abstract: The leader-member exchange (LMX) literature is reviewed using meta-analysis. Relationships between LMX and its correlates are examined, as are issues related to the LMX construct, including measurement and leader-member agreement. Results suggest significant relationships between LMX and job performance, satisfaction with supervision, overall satisfaction, commitment, role conflict, role clarity, member competence, and turnover intentions. The relationship between LMX and actual turnover was not significant. Leader and member LMX perceptions were only moderately related. Partial support was found for measurement instrument and perspective (i.e., leader vs. member) as moderators of the relationships between LMX and its correlates. Meta-analysis showed that the LMX7 (7-item LMX) measure has the soundest psychometric properties of all instruments and that LMX is congruent with numerous empirical relationships associated with transformational leadership. Within the broad area of organizationa l leadership, leader-member exchange (LMX) theory has evolved into one of the more interesting and useful approaches for studying hypothesized linkages between leadership processes and outcomes. First proposed by Graeri and colleagues (Dansereau, Cashman, & Graen, 1973; Dansereau, Graen, & Haga, 1975; Graen, 1976; Graen & Cashman, 1975), LMX is distinguished from other leadership theories by its focus on the dyadic relationship between a leader and a member. Unlike traditional theories that seek to explain leadership as a function of personal characteristics of the leader, features of the situation, or an interaction between the two, LMX is unique in its adoption of the dyadic relationship as the level of analysis. Although the theory has been modified and expanded


title: Sharp-Rejection Ultra-Wide Bandstop Filters

abstract: In this letter, a modified optimum bandstop filter (BSF) design is presented. Broad stopband and sharp rejection characteristics are achieved by realizing three transmission zeros near the passband edges. Stopband rejection depth and bandwidth can be controlled by the impedances of the configuration. A lossless transmission line model analysis is used to derive design equations. Further, a compact geometry is chosen by replacing the low impedance section with its equivalent dual-high impedance lines, which facilitates convenient folding. To validate the theoretical prediction, a single unit sharp-rejecting prototype BSF having a 20 dB rejection bandwidth of 126% at 1.5 GHz has been fabricated.


Language Independent Metric Support towards Refactoring Inference


Fast Tracking of Hand and Finger Articulations Using a Single Depth Camera


title: Advancements in Technology and Design of Biomimetic Flow-Sensor Arrays

abstract: This paper reports on recent developments to increase the performance of biomimetic flow-sensor arrays by means of several technological advancements in the fabrication procedures and corresponding sensor design optimizations. Advancements include fabrication procedures with higher process latitude and geometrical modifications of several parts of the flow sensor. The conclusive measurements in this paper support our sensor-model predictions for a 100-fold increase in acoustic sensitivity (down to oscillating flow amplitudes in the order of 1 mm·s-1) translating to substantially higher capacitive outputs in comparison to our first-generation biomimetic flow-sensor arrays.


title: Toward Engineering a Secure Android Ecosystem: A Survey of Existing Techniques

abstract: The openness and extensibility of Android have made it a popular platform for mobile devices and a strong candidate to drive the Internet-of-Things. Unfortunately, these properties also leave Android vulnerable, attracting attacks for profit or fun. To mitigate these threats, numerous issue-specific solutions have been proposed. With the increasing number and complexity of security problems and solutions, we believe this is the right moment to step back and systematically re-evaluate the Android security architecture and security practices in the ecosystem. We organize the most recent security research on the Android platform into two categories: the software stack and the ecosystem. For each category, we provide a comprehensive narrative of the problem space, highlight the limitations of the proposed solutions, and identify open problems for future research. Based on our collection of knowledge, we envision a blueprint for engineering a secure, next-generation Android ecosystem.


title: Local-Global Landmark Confidences for Face Recognition

abstract: A key to successful face recognition is accurate and reliable face alignment using automatically-detected facial landmarks. Given this strong dependency between face recognition and facial landmark detection, robust face recognition requires knowledge of when the facial landmark detection algorithm succeeds and when it fails. Facial landmark confidence represents this measure of success. In this paper, we propose two methods to measure landmark detection confidence: local confidence based on local predictors of each facial landmark, and global confidence based on a 3D rendered face model. A score fusion approach is also introduced to integrate these two confidences effectively. We evaluate both confidence metrics on two datasets for face recognition: JANUS CS2 and IJB-A datasets. Our experiments show up to 9% improvements when face recognition algorithm integrates the local-global confidence metrics.


title: Player Typology in Theory and Practice

abstract: Player satisfaction modeling depends in part upon quantitative or qualitative typologies of playing preferences, although such approaches require scrutiny. Examination of psychometric typologies reveal that type theories have—except in rare cases—proven inadequate and have made way for alternative trait theories. This suggests any future player typology that will be sufficiently robust will need foundations in the form of a trait theory of playing preferences. This paper tracks the development of a sequence of player typologies developing from psychometric type theory roots towards an independently validated trait theory of play, albeit one yet to be fully developed. Statistical analysis of the results of one survey in this lineage is presented, along with a discussion of theoretical and practical ways in which the surveys and their implied typological instruments have evolved.


title: CavePainting: a fully immersive 3D artistic medium and interactive experience

abstract: CavePainting is an artistic medium that uses a 3D analog of 2D brush strokes to create 3D works of art in a fully immersive Cave environment. Physical props and gestures are used to provide an intuitive interface for artists who may not be familiar with virtual reality. The system is designed to take advantage of the 8 ft. x 8 ft. x 8 ft. space in which the artist works. CavePainting enables the artist to create a new type of art and provides a novel approach to viewing this art after it has been created. In this paper, we describe CavePainting’s 3D brush strokes, color pickers, artwork viewing mode, and interface. We also present several works of art created using the system along with feedback from artists. Artists are excited about this form of art and the gestural, full-body experience of creating it. CR Categories and Subject Descriptors: I.3.6 [Computer Graphics]: Methodology and Techniques - Interaction Techniques; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Virtual Reality; J.5 [Arts and Humanities]: Fine Arts Additional Key Words: 3D painting, 3D modeling, gestures, tangible user interface, Cave


title: Nutrigenomics: Definitions and Advances of This New Science

abstract: The search for knowledge regarding healthy/adequate food has increased in the last decades among the world population, researchers, nutritionists, and health professionals. Since ancient times, humans have known that environment and food can interfere with an individual's health condition, and have used food and plants as medicines. With the advance of science, especially after the conclusion of the Human Genome Project (HGP), scientists started questioning if the interaction between genes and food bioactive compounds could positively or negatively influence an individual's health. In order to assess this interaction between genes and nutrients, the term "Nutrigenomics" was created. Hence, Nutrigenomics corresponds to the use of biochemistry, physiology, nutrition, genomics, proteomics, metabolomics, transcriptomics, and epigenomics to seek and explain the existing reciprocal interactions between genes and nutrients at a molecular level. The discovery of these interactions (gene-nutrient) will aid the prescription of customized diets according to each individual's genotype. Thus, it will be possible to mitigate the symptoms of existing diseases or to prevent future illnesses, especially in the area of Nontransmissible Chronic Diseases (NTCDs), which are currently considered an important world public health problem.


title: Sharp estimates for the Schr\"odinger equation associated to the twisted Laplacian

abstract: In this note we obtain some Strichartz estimates for the Schr\"odinger equation associated to the twisted Laplacian on $\mathbb{C}^{n}\cong \mathbb{R}^{2n}$. The initial data will be considered in suitable Sobolev spaces associated to the twisted Laplacian.


title: Object Classification With Joint Projection and Low-Rank Dictionary Learning

abstract: For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance. Although using the pre-trained network on a generic large-scale data set and fine-tune it to the small-sized target data set is a widely used technique, this would not help when the content of base and target data sets are very different. To address these issues simultaneously, we propose a joint projection and low-rank dictionary learning method using dual graph constraints. Specifically, a structured class-specific dictionary is learned in the low-dimensional space, and the discrimination is further improved by imposing a graph constraint on the coding coefficients, that maximizes the intra-class compactness and inter-class separability. We enforce structural incoherence and low-rank constraints on sub-dictionaries to reduce the redundancy among them, and also make them robust to variations and outliers. To preserve the intrinsic structure of data, we introduce a supervised neighborhood graph into the framework to make the proposed method robust to small-sized and high-dimensional data sets. Experimental results on several benchmark data sets verify the superior performance of our method for object classification of small-sized data sets, which include a considerable amount of different kinds of variation, and may have high-dimensional feature vectors.


title: Anatomical, Physiological, Molecular and Circuit Properties of Nest Basket Cells in the Developing Somatosensory Cortex

abstract: Anatomical, electrophysiological and molecular diversity of basket cell-like interneurons in layers II-IV of rat somatosensory cortex were studied using patch-clamp electrodes filled with biocytin. This multiparametric study shows that neocortical basket cells (BCs) are composed of three distinct subclasses: classical large (LBC) and small (SBC) basket cells and a third subclass, the nest basket cell (NBC). Anatomically, NBCs were distinct from LBCs and SBCs in that they formed simpler dendritic arbors and an axonal plexus of inter-mediate density, composed of a few long, smooth axonal branches. Electrophysiologically, NBCs exhibited diverse discharge responses to depolarizing current injections including accommodation, non-accommodation and stuttering. Single-cell multiplex RT-PCR revealed distinct mRNA expression patterns for the calcium binding proteins parvalbumin (PV), calbindin (CB) and calretinin (CR), and the neuropeptides somatostatin (SOM), vasoactive intestinal peptide (VIP), cholecystokinin (CCK) and neuropeptide Y (NPY) for each BC-subclass. SBCs lacked NPY expression but invariably expressed VIP, whereas neither VIP, CR nor SOM expression was detected in LBCs, and VIP and CR expression was absent in NBCs. Electro-physiologically distinct types of NBCs formed GABAergic synapses with specific dynamics onto pyramidal cells (PCs) and received either strongly facilitating or depressing synaptic inputs from PCs. Finally, NBCs were found to be the most common basket cell in layers II/III, while LBCs were the most common in layer IV. These data provide multiparametric distinguishing features of three major subclasses of basket cells and indicate that NBCs are powerful interneurons that provide most of the (peri-)somatic inhibition in the supragranular layers.


title: A Survey of Entity Resolution and Record Linkage Methodologies

abstract: A great deal of research is focused on formation of a data warehouse. This is an important area of research as it could save many computation cycles and thus allow accurate information provided to the right people at the right time. Two considerations when forming a data warehouse are data cleansing (including entity resolution) and with schema integration (including record linkage). Uncleansed and fragmented data requires time to decipher and may lead to increased costs for an organization, so data cleansing and schema integration can save a great many (human) computation cycles and can lead to higher organizational efficiency. In this study we survey the literature for the methodologies proposed or developed for entity resolution and record linkage. This survey provides a foundation for solving many problems in data warehousing. For instance, little or no research has been directed at the problem of maintenance of cleansed and linked relations.


Preparation, characterization and pharmacokinetics of cyadox nanosuspension


title: Web of Topics: An IoT-aware model-driven designing approach

abstract: In the Internet of Things, the extreme heterogeneity of sensors, actuators and user devices calls for new tools and design models able to translate the user's needs in machine-understandable scenarios. The scientific community has proposed different solution for such issue, e.g., the MQTT (MQ Telemetry Transport) protocol introduced the topic concept as “the key that identifies the information channel to which payload data is published”. This study extends the topic approach by proposing the Web of Topics (WoX), a conceptual model for the IoT. A WoX Topic is identified by two coordinates: (i) a discrete semantic feature of interest (e.g. temperature, humidity), and (ii) a URI-based location. An IoT entity defines its role within a Topic by specifying its technological and collaborative dimensions. By this approach, it is easier to define an IoT entity as a set of couples Topic-Role. In order to prove the effectiveness of the WoX approach, we developed the WoX APIs on top of an EPCglobal implementation. Then, 10 developers were asked to build a WoX-based application supporting a physics lab scenario at school. They also filled out an ex-ante and an ex-post questionnaire. A set of qualitative and quantitative metrics allowed measuring the model's outcome.


title: KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing

abstract: Classical approaches to clean data have relied on using integrity constraints, statistics, or machine learning. These approaches are known to be limited in the cleaning accuracy, which can usually be improved by consulting master data and involving experts to resolve ambiguity. The advent of knowledge bases KBs both general-purpose and within enterprises, and crowdsourcing marketplaces are providing yet more opportunities to achieve higher accuracy at a larger scale. We propose KATARA, a knowledge base and crowd powered data cleaning system that, given a table, a KB, and a crowd, interprets table semantics to align it with the KB, identifies correct and incorrect data, and generates top-k possible repairs for incorrect data. Experiments show that KATARA can be applied to various datasets and KBs, and can efficiently annotate data and suggest possible repairs.


title: Predicting Causes of Traffic Road Accidents Using Multi-class Support Vector Machines

abstract: Road traffic accidents have caused a myriad of problems for many countries, ranging from untimely loss of loved ones to disability and disruption of work. In many cases, when a road traffic accident occurs that results in the death of both drivers of the vehicles involved in the accident, there are some difficulties in identifying the cause of the accident and the driver who committed the accident. There is a need for methods to identify the cause of road traffic accidents in the absence of eyewitnesses or when there is a dispute between those who are involved in the accident. This paper attempts to predict the causes of road accidents based on real data collected from the police department in Dubai, United Arab Emirates. Data mining techniques were used to predict the causes of road accidents. Results obtained have shown that the model can predict the cause of road accidents with accuracy greater than 75%. Keywords—component; road traffic accident, data mining, multi-class SVMs


title: Electrostatic chuck with a thin ceramic insulation layer for wafer holding

abstract: An electrostatic chuck is one of the useful device holding a thin object flat on a bed by electrostatic force. The authors have investigated the fundamental characteristics of an electrostatic chuck consisted of a pair of comb type electrodes and a thin insulation layer between the electrodes and an object. When a thin polymer film is used as an insulation, the holding force for a wafer was large enough in practical use, while the large residual force remains after removing the DC applied voltage. Thus, it was concluded that AC applied voltage will be more preferable than DC, though the electrostatic force for DC applied voltage is somewhat greater than that for AC voltage. Since the electrostatic chuck is generally used in high temperature atmosphere, for example plasma etching, in the semiconductor industry, the insulating layer must be heat resistant. By using a thin ceramic plate, which was made specially for this purpose, the fundamental characteristics of the electrostatic chuck has been investigated. The greater holding force was obtained with a ceramic plate than that with a polymer film. Furthermore, almost no residual force was observed even for the DC applied voltage. The experimental results are reported both in air and in vacuum condition.


title: Genetic Algorithm in Search and Optimization: The Technique and Applications

abstract: A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a Htness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modiiied to a new population by applying three operators similar to natural genetic operatorsfreproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisiied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch of why the GA is a useful search algorithm. Thereafter, we apply a GA to solve a complex engineering design problem. Finally, we discuss how GAs can enhance the performance of other soft computing techniques-fuzzy logic and neural network techniques.


title: Security of Cached Content in NDN

abstract: In Named-Data Networking (NDN), content is cached in network nodes and served for future requests. This property of NDN allows attackers to inject poisoned content into the network and isolate users from valid content sources. Since a digital signature is embedded in every piece of content in NDN architecture, poisoned content is discarded if routers perform signature verification; however, if every content is verified by every router, it would be overly expensive to do. In our preliminary work, we have suggested a content verification scheme that minimizes unnecessary verification and favors already verified content in the content store, which reduces the verification overhead by as much as 90% without failing to detect every piece of poisoned content. Under this scheme, however, routers are vulnerable to verification attack , in which a large amount of unverified content is accessed to exhaust system resources. In this paper, we carefully look at the possible concerns of our preliminary work, including verification attack , and present a simple but effective solution. The proposed solution mitigates the weakness of our preliminary work and allows this paper to be deployed for real-world applications.


title: Examining the factors that influence early adopters' smartphone adoption: The case of college students

abstract: The influence of early adopters on potential adopters' decisions of whether or not to adopt a product is known to be critical. In this paper, we examine the factors that influence the adoption behavior of smartphone early adopters by looking at smartphone adoption behavior of college students, because a large portion of the early adopters of smartphones are college students. Our focus is on the effect of normative peer influence on a college student's smartphone adoption. We also examine the influence of other factors such as self-innovativeness, self-efficacy, the decision maker's attitudes towards a product, financial burden of using the product, familial influence, and other demographic factors (e.g., age and gender). College students' adoption behavior is studied using logit and probit choice models developed based on random utility theory. The discrete choice models are empirically estimated using survey data. We find important influence of friends, financial burden, and other family members on the smartphone adoption of college students who adopted smartphones earlier than other students.


title: Cashing in on the Cache in the Cloud

abstract: Over the past decades, caching has become the key technology used for bridging the performance gap across memory hierarchies via temporal or spatial localities; in particular, the effect is prominent in disk storage systems. Applications that involve heavy I/O activities, which are common in the cloud, probably benefit the most from caching. The use of local volatile memory as cache might be a natural alternative, but many well-known restrictions, such as capacity and the utilization of host machines, hinder its effective use. In addition to technical challenges, providing cache services in clouds encounters a major practical issue (quality of service or service level agreement issue) of pricing. Currently, (public) cloud users are limited to a small set of uniform and coarse-grained service offerings, such as High-Memory and High-CPU in Amazon EC2. In this paper, we present the cache as a service (CaaS) model as an optional service to typical infrastructure service offerings. Specifically, the cloud provider sets aside a large pool of memory that can be dynamically partitioned and allocated to standard infrastructure services as disk cache. We first investigate the feasibility of providing CaaS with the proof-of-concept elastic cache system (using dedicated remote memory servers) built and validated on the actual system, and practical benefits of CaaS for both users and providers (i.e., performance and profit, respectively) are thoroughly studied with a novel pricing scheme. Our CaaS model helps to leverage the cloud economy greatly in that 1) the extra user cost for I/O performance gain is minimal if ever exists, and 2) the provider's profit increases due to improvements in server consolidation resulting from that performance gain. Through extensive experiments with eight resource allocation strategies, we demonstrate that our CaaS model can be a promising cost-efficient solution for both users and providers.


Private Ordering and the Securities Laws: The Case of General Partnerships


title: LED light for in vitro and ex vitro efficient growth of economically important highbush blueberry (Vaccinium corymbosum L.)

abstract: Light-emitting diodes (LEDs) are useful for the growth of many plants, but not known for blueberry species. This study examined the effects of fluorescent lamps and 100 % red, 80 % red plus 20 % blue, 50 % red plus 50 % blue, and 100 % blue LEDs on the growth and development of highbush blueberry shoots under aseptic and non-aseptic conditions. Results revealed that monochromatic blue LEDs accumulated the highest contents of leaf chlorophylls. In contrast, monochromatic red LEDs inhibited chlorophyll accumulation, but produced the longest shoots and roots and provided high percentages of side shoot formation from ex vitro plants. Mixed LEDs, particularly 50 % red plus 50 % blue light, improved plant growth with respect to notably increased shoot and root biomass. Direct rooting of in vitro shoots under non-aseptic conditions was readily achieved using a commercial mixture of perlite and peat moss with high humidity controls. These findings obviously suggest the efficient use of LEDs to replace traditional fluorescent lamps in large-scale propagation of the highbush blueberry, and also pave the way for future studies on LEDs for standardizing micropropagation protocols to shrub crops and woody plants.


title: Neural representation and the cortical code.

abstract: The principle function of the central nervous system is to represent and transform information and thereby mediate appropriate decisions and behaviors. The cerebral cortex is one of the primary seats of the internal representations main- tained and used in perception, memory, decision making, motor control, and subjec- tive experience, but the basic coding scheme by which this information is carried and transformed by neurons is not yet fully understood. This article defines and reviews how information is represented in the firing rates and temporal patterns of populations of cortical neurons, with a particular emphasis on how this information mediates behavior and experience.


Plead or Pitch ? The Role of Language in Kickstarter Project Success


title: Function-Based Question Classification for General QA

abstract: In contrast with the booming increase of internet data, state-of-art QA (question answering) systems, otherwise, concerned data from specific domains or resources such as search engine snippets, online forums and Wikipedia in a somewhat isolated way. Users may welcome a more general QA system for its capability to answer questions of various sources, integrated from existed specialized sub-QA engines. In this framework, question classification is the primary task. ::: ::: However, the current paradigms of question classification were focused on some specified type of questions, i.e. factoid questions, which are inappropriate for the general QA. In this paper, we propose a new question classification paradigm, which includes a question taxonomy suitable to the general QA and a question classifier based on MLN (Markov logic network), where rule-based methods and statistical methods are unified into a single framework in a fuzzy discriminative learning approach. Experiments show that our method outperforms traditional question classification approaches.


Predicting User Preference for Movies using NetFlix


title: Neural network based speaker classification and verification systems with enhanced features

abstract: This work presents a novel framework based on feed-forward neural network for text-independent speaker classification and verification, two related systems of speaker recognition. With optimized features and model training, it achieves 100% classification rate in classification and less than 6% Equal Error Rate (ERR), using merely about 1 second and 5 seconds of data respectively. Features with stricter Voice Active Detection (VAD) than the regular one for speech recognition ensure extracting stronger voiced portion for speaker recognition, speaker-level mean and variance normalization helps to eliminate the discrepancy between samples from the same speaker. Both are proven to improve the system performance. In building the neural network speaker classifier, the network structure parameters are optimized with grid search and dynamically reduced regularization parameters are used to avoid training terminated in local minimum. It enables the training goes further with lower cost. In speaker verification, performance is improved with prediction score normalization, which rewards the speaker identity indices with distinct peaks and penalizes the weak ones with high scores but more competitors, and speaker-specific thresholding, which significantly reduces ERR in the ROC curve. TIMIT corpus with 8K sampling rate is used here. First 200 male speakerwje used to train and test the classification performance. The testing files of them are used as in-domain registered speakers, while data from the remaining 126 male speakers are used as out-of-domain speakers, i.e. imposters in speaker verification.


title: Physics-based trajectory optimization for grasping in cluttered environments

abstract: Grasping an object in a cluttered, unorganized environment is challenging because of unavoidable contacts and interactions between the robot and multiple immovable (static) and movable (dynamic) obstacles in the environment. Planning an approach trajectory for grasping in such situations can benefit from physics-based simulations that describe the dynamics of the interaction between the robot manipulator and the environment. In this work, we present a physics-based trajectory optimization approach for planning grasp approach trajectories. We present novel cost objectives and identify failure modes relevant to grasping in cluttered environments. Our approach uses rollouts of physics-based simulations to compute the gradient of the objective and of the dynamics. Our approach naturally generates behaviors such as choosing to push objects that are less likely to topple over, recognizing and avoiding situations which might cause a cascade of objects to fall over, and adjusting the manipulator trajectory to push objects aside in a direction orthogonal to the grasping direction. We present results in simulation for grasping in a variety of cluttered environments with varying levels of density of obstacles in the environment. Our experiments in simulation indicate that our approach outperforms a baseline approach that considers multiple straight-line trajectories modified to account for static obstacles by an aggregate success rate of 14% with varying degrees of object clutter.


title: Passing, Traveling and Reality: Social Constructionism and the Metaphysics of Race

abstract: Among race theorists, the view that race is a social construction is widespread. While the term 'social construction' is sometimes intended to mean merely that race does not (as once believed) constitute a robust, biological natural kind, it often labels the stronger position that race is real, but not a biological kind. For example, Charles Mills (1998) writes that, "the task of those working on race is to put race in quotes, 'race', while still insisting that nevertheless, it exists (and moves people)"(xiv, italics his). It is to "make a plausible social ontology neither essentialist, innate, nor transhistorical, but real enough for all that" (xiv). Racial constructionism, thus conceived, is a metaphysical position that contrasts both with the view that race is an important biological kind (racial naturalism) and with the more recent claim that race does not exist (racial skepticism). The desire for a constructionist metaphysics of race emerges against the background of a cluster of normative disputes, including:


title: NNCubes: Learned Structures for Visual Data Exploration.

abstract: Visual exploration of large multidimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. However, a limitation with current techniques is their lack of guidance. Exploration in existing methods is typically driven by data aggregation queries, but these are unable to suggest interesting aggregations and are limited in helping the user understand the types of queries that lead to certain aggregations. To tackle this problem, it is necessary to understand how the space of queries relates to their aggregation results. We present NNCubes: neural networks that are surrogate models for data cube techniques. NNCubes learns a function that takes as input a given query, for instance a geographic region and temporal interval, and outputs an aggregation of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Moreover, using neural networks as querying engines opens up new ways to guide user interactions that would be challenging, to do with existing techniques. First, we show how to use the network for discovering queries that lead to user-specified aggregation results, thus providing a form of direct manipulation. Second, our networks are designed in such a way that we learn meaningful 2D projections of the individual inputs, namely that they are predictive of the aggregation operation. We use these learned projections to allow the user to explore the space of aggregation queries, to help discover trends and patterns in the data. We demonstrate both of these forms of guidance using NNCubes on a variety of datasets.


title: A Randomized Clinical Trial of Eye Movement Desensitization and Reprocessing (EMDR), Fluoxetine, and Pill Placebo in the Treatment of Posttraumatic Stress Disorder: Treatment Effects and Long-Term Maintenance

abstract: Objective: The relative short-term efficacy and long-term benefits of pharmacologic versus psychotherapeutic interventions have not been studied for posttraumatic stress disorder (PTSD). This study compared the efficacy of a selective serotonin reuptake inhibitor (SSRI), fluoxetine, with a psychotherapeutic treatment, eye movement desensitization and reprocessing (EMDR), and pill placebo and measured maintenance of treatment gains at 6-month follow-up. Method: Eighty-eight PTSD subjects diagnosed according to DSM-IV criteria were randomly assigned to EMDR, fluoxetine, or pill placebo. They received 8 weeks of treatment and were assessed by blind raters posttreatment and at 6-month follow-up. The primary outcome measure was the Clinician-Administered PTSD Scale, DSM-IV version, and the secondary outcome measure was the Beck Depression Inventory-II. The study ran from July 2000 through July 2003. Results: The psychotherapy intervention was more successful than pharmacotherapy in achieving sustained reductions in PTSD and depression symptoms, but this benefit accrued primarily for adult-onset trauma survivors. At 6-month follow-up, 75.0% of adult-onset versus 33.3% of child-onset trauma subjects receiving EMDR achieved asymptomatic end-state functioning compared with none in the fluoxetine group. For most childhood-onset trauma patients, neither treatment produced complete symptom remission. Conclusions: This study supports the efficacy of brief EMDR treatment to produce substantial and sustained reduction of PTSD and depression in most victims of adult-onset trauma. It suggests a role for SSRIs as a reliable first-line intervention to achieve moderate symptom relief for adult victims of childhood-onset trauma. Future research should assess the impact of lengthier intervention, combination treatments, and treatment sequencing on the resolution of PTSD in adults with childhood-onset trauma.


title: Mathematical modelling, simulation and experimental verification of a scara robot

abstract: Abstract A complete mathematical model of SCARA robot (Serpent 1) is developed including servo actuator dynamics and presented together with dynamic simulation in this paper. The equations of motion are derived by using Lagrangian mechanics. Dc servo motors driving each robot joint is studied with PD controller action. Serpent 1 robot is instructed to achieve pick and place operations of three different size cylindirical objects through assigned holes. The performance of robot-actuator-control system is examined with numerical simulation and experimentally verified. The results of experimentation are given with comments. An agreement between the model and the experiments is certainly obtained herein.