title: Sparse Coral Classification Using Deep Convolutional Neural Networks

abstract: Autonomous repair of deep-sea coral reefs is a recent proposed idea to support the oceans ecosystem in which is vital for commercial fishing, tourism and other species. This idea can be operated through using many small autonomous underwater vehicles (AUVs) and swarm intelligence techniques to locate and replace chunks of coral which have been broken off, thus enabling re-growth and maintaining the habitat. The aim of this project is developing machine vision algorithms to enable an underwater robot to locate a coral reef and a chunk of coral on the seabed and prompt the robot to pick it up. Although there is no literature on this particular problem, related work on fish counting may give some insight into the problem. The technical challenges are principally due to the potential lack of clarity of the water and platform stabilization as well as spurious artifacts (rocks, fish, and crabs). We present an efficient sparse classification for coral species using supervised deep learning method called Convolutional Neural Networks (CNNs). We compute Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component Analysis (ZCA) Whitening to extract shape and texture feature descriptors, which are employed to be supplementary channels (feature-based maps) besides basic spatial color channels (spatial-based maps) of coral input image, we also experiment state-of-art preprocessing underwater algorithms for image enhancement and color normalization and color conversion adjustment. Our proposed coral classification method is developed under MATLAB platform, and evaluated by two different coral datasets (University of California San Diego's Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea).


title: Self-interruption on the computer: a typology of discretionary task interleaving

abstract: The typical information worker is interrupted every 12 minutes, and half of the time they are interrupting themselves. However, most of the research on interruption in the area of human-computer interaction has focused on understanding and managing interruptions from external sources. Internal interruptions -- user-initiated switches away from a task prior to its completion -- are not well understood. In this paper we describe a qualitative study of self-interruption on the computer. Using a grounded theory approach, we identify seven categories of self-interruptions in computer-related activities. These categories are derived from direct observations of users, and describe the motivation, potential consequences, and benefits associated with each type of self-interruption observed. Our research extends the understanding of the self-interruption phenomenon, and informs the design of systems to support discretionary task interleaving on the computer.


title: Enhancing Vocabulary Retention by Embedding L2 Target Words in L1 Stories: An Experiment with Chinese Adult e-Learners

abstract: This study aims to investigate the effectiveness of a storytelling approach in Chinese adult e-learners’ vocabulary learning. Two classes of 60 students participated in the experiment, with 30 in the experimental group and 30 in the control group. The storytelling approach and the rote memorization approach were employed by the two groups respectively in one teaching session of 30 minutes. Two post-tests were administered to the two groups, with one completed immediately after the teaching and the other assigned three weeks later. A questionnaire survey was also assigned to the experimental group to obtain the learners’ perceptions of the storytelling approach. The results of the data analysis showed that the storytelling method was more effective than rote memorization in both short-term retention and long-term retention, though its effects tended to diminish a little bit with time. Possible suggestions to perfect the method were given in the paper.


title: Analysis and equalization of data-dependent jitter

abstract: Data-dependent jitter limits the bit-error rate (BER) performance of broadband communication systems and aggravates synchronization in phase- and delay-locked loops used for data recovery. A method for calculating the data-dependent jitter in broadband systems from the pulse response is discussed. The impact of jitter on conventional clock and data recovery circuits is studied in the time and frequency domain. The deterministic nature of data-dependent jitter suggests equalization techniques suitable for high-speed circuits. Two equalizer circuit implementations are presented. The first is a SiGe clock and data recovery circuit modified to incorporate a deterministic jitter equalizer. This circuit demonstrates the reduction of jitter in the recovered clock. The second circuit is a MOS implementation of a jitter equalizer with independent control of the rising and falling edge timing. This equalizer demonstrates improvement of the timing margins that achieve 10/sup -12/ BER from 30 to 52 ps at 10 Gb/s.


title: High performance true random number generator based on FPGA block RAMs

abstract: This paper presents a new method for creating TRNGs in Xilinx FPGAs. Due to its simplicity and ease of implementation, the design constitutes a valuable alternative to existing methods for creating single-chip TRNGs. Its main advantages are the high throughput, the portability and the low amount of resources it occupies inside the chip. Therefore, it could further extend the use of FPGA chips in cryptography. Our primary source of entropy is a True Dual-Port Block-RAM operating at high frequency, which is used in a special architecture that creates a concurrent write conflict. The paper also describes the practical issues which make it possible to convert that conflict into a strong entropy source. Depending on the users' requirements, it is possible to connect many units of this generator in parallel on a single FPGA device, thus increasing the bit generation throughput up to the Gbps level. The generator has successfully passed the major statistical test batteries.


FOR PREDICTING STUDENT PERFORMANCE


title: Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

abstract: Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.


title: CRATS: An LDA-Based Model for Jointly Mining Latent Communities, Regions, Activities, Topics, and Sentiments from Geosocial Network Data

abstract: Geosocial networks like Yelp and Foursquare have been rapidly growing and accumulating plenty of data such as social links between users , user check-ins to venues , venue geographical locations , venue categories , and user textual comments on venues . These data contain rich knowledge on the user's social interactions in communities, geographical mobility patterns between regions, categorical preferences on activities, aspect interests in topics, and opinion expressions for sentiments. Such knowledge is essential for two key applications, namely, text sentiment classification and venue recommendations, which will be developed in this paper. To extract the knowledge from the data, the key task is to discover the latent communities , regions , activities , topics , and sentiments of users. However, these latent variables are interdependent, e.g., users in the same community usually travel on nearby regions and share common activities and topics, which renders a big challenge for modeling these latent variables. To tackle this challenge, in this study, we propose an LDA-based model called CRATS that jointly mines the latent Communities, Regions, Activities, Topics, and Sentiments based on the important dependencies among these latent variables. To the best of our knowledge, this is the first study to jointly model these five latent variables. Finally, we conduct a comprehensive performance evaluation for CRATS in different applications, including text sentiment classification and venue recommendations, using three large-scale real-world geosocial network data sets collected from Yelp and Foursquare. Experimental results show that CRATS achieves significantly superior performance against other state-of-the-art techniques.


title: Corpus-driven Metaphor Harvesting

abstract: The paper presents a corpus-based method for finding metaphorically used lexemes and prevailing semantico-conceptual source domains, given a target domain corpus. It is exemplified by a case study on the target domain of European politics, based on a French 800,000 token corpus.


title: A subjunctive exploratory search interface to support media studies researchers

abstract: Media studies concerns the study of production, content, and/or reception of various types of media. Today's continuous production and storage of media is changing the way media studies researchers work and requires the development of new search models and tools. We investigate the research cycle of media studies researchers and find that it is an iterative process consisting of several search processes in which data is gathered and the research question is refined. Changes in the research question, however, trigger new data gathering processes. Based on these outcomes we propose a subjunctive exploratory search interface to support media studies researchers in refining their research question in an earlier stage of their research. To assess the subjunctive interface we conduct a user study and compare to a traditional exploratory search interface. We find that with the subjunctive interface users explore more diverse topics than with the standard interface and that users formulate more specific research questions. Although the subjunctive interface is more complex, this does not decrease its usability. These findings suggest that the subjunctive interface supports media studies researchers. The advantage of a subjunctive interface for exploration suggests a new direction for the development of exploratory search systems.


title: Grounded theory in nursing research: Part 2--Critique.

abstract: In the first article in this series (McCann and Clark 2003), the methodological characteristics of grounded theory were examined. In this, the second article on grounded theory, Terence McCann and Eileen Clark critique the methodology. They then summarise the main differences between Glaser's approach and Strauss and Corbin's approach to grounded theory.


title: Post-editing of Machine Translation: Processes and Applications

abstract: Post-editing is possibly the oldest form of human-machine cooperation for translation. It has been a common practice for just about as long as operational machine translation systems have existed. Recently, however, there has been a surge of interest in post-editing among the wider user community, partly due to the increasing quality of machine translation output, but also to the availability of free, reliable software for both machine translation and post-editing. As a result, the practices and processes of the translation industry are changing in fundamental ways. This volume is a compilation of work by researchers, developers and practitioners of post-editing, presented at two recent events on post-editing: The first Workshop on Post-editing Technology and Practice, held in conjunction with the 10th Conference of the Association for Machine Translation in the Americas, held in San Diego, in 2012; and the International Workshop on Expertise in Translation and Post-editing Research and Application, held at the Copenhagen Business School, in 2012.


title: Marginalized Denoising Dictionary Learning With Locality Constraint

abstract: Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative sub-dictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low rankness, and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation. To this end, we develop a unified feature learning framework by incorporating the marginalized denoising auto-encoder into a locality-constrained dictionary learning scheme, named marginalized denoising dictionary learning. Overall, we deploy low-rank constraint on each sub-dictionary and locality constraint instead of sparsity on coefficients, in order to learn a more concise and pure feature spaces meanwhile inheriting the discrimination from sub-dictionary learning. Finally, we evaluate our algorithm on several face and object data sets. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.


title: DOM based content extraction via text density

abstract: In addition to the main content, most web pages also contain navigation panels, advertisements and copyright and disclaimer notices. This additional content, which is also known as noise, is typically not related to the main subject and may hamper the performance of web data mining, and hence needs to be removed properly. In this paper, we present Content Extraction via Text Density (CETD) a fast, accurate and general method for extracting content from diverse web pages, and using DOM (Document Object Model) node text density to preserve the original structure. For this purpose, we introduce two concepts to measure the importance of nodes: Text Density and Composite Text Density. In order to extract content intact, we propose a technique called DensitySum to replace Data Smoothing. The approach was evaluated with the CleanEval benchmark and with randomly selected pages from well-known websites, where various web domains and styles are tested. The average F1-scores with our method were 8.79% higher than the best scores among several alternative methods.


title: submitted). Mixed-effects modeling with crossed random effects for subjects and items

abstract: This paper provides an introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects. A worked-out example of how to use recent software for mixed-effects modeling is provided. Simulation studies illustrate the advantages offered by mixed-effects analyses compared to traditional analyses based on quasi-F tests, by-subjects analyses, combined by-subjects and by-items analyses, and random regression. Applications and possibilities across a range of domains of inquiry are discussed.


title: Forest Floor Visualizations of Random Forests

abstract: We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model is difficult, as the explicit ensemble model of hundreds of deep trees is complex. Nonetheless, it is possible to visualize a RF model fit by its mapping from feature space to prediction space. Hereby the user is first presented with the overall geometrical shape of the model structure, and when needed one can zoom in on local details. Dimensional reduction by projection is used to visualize high dimensional shapes. The traditional method to visualize RF model structure, partial dependence plots, achieve this by averaging multiple parallel projections. We suggest to first use feature contributions, a method to decompose trees by splitting features, and then subsequently perform projections. The advantages of forest floor over partial dependence plots is that interactions are not masked by averaging. As a consequence, it is possible to locate interactions, which are not visualized in a given projection. Furthermore, we introduce: a goodness-of-visualization measure, use of colour gradients to identify interactions and an out-of-bag cross validated variant of feature contributions.


title: Report on the SIGIR 2014 Workshop on Medical Information Retrieval (MedIR)

abstract: The workshop on Medical Information Retrieval took place at SIGIR 2014 in Gold Coast, Australia on July 11. The workshop included eight oral presentations of referred papers and an invited keynote presentation. This allowed time for lively discussions among the participants. These showed the significant interest in the medical information retrieval domain and the many research challenges arising in this space which need to be addressed to give added value to the wide variety of users that can profit from medical information search, such as patients, general health professionals and specialist groups such as radiologists who mainly search for images and image related information.


title: Public perceptions of arguments supporting and opposing recreational marijuana legalization.

abstract: In debates about recreational marijuana legalization, pro-legalization arguments highlighting economic and other potential policy benefits compete with anti-legalization arguments emphasizing public health risks. In 2016, we conducted a national survey using an online panel (N=979) designed to answer two main research questions: (1) How do Americans perceive the relative strength of competing arguments about recreational marijuana legalization? (2) How are perceptions of argument strength associated with public support for recreational marijuana legalization? We examined differences in attitudes among individuals living in states that have/have not legalized recreational marijuana and among Democrats/Independents/Republicans. Ordered logit regression assessed the relationship between perceived argument strength and public support for recreational marijuana legalization. Respondents rated pro-legalization arguments highlighting beneficial economic and criminal justice consequences as more persuasive than anti-legalization arguments emphasizing adverse public health effects. Respondents were more likely to agree with arguments highlighting legalization's potential to increase tax revenue (63.9%) and reduce prison overcrowding (62.8%) than arguments emphasizing negative consequences on motor vehicle crashes (51.8%) and youth health (49.6%). The highest rated anti-legalization arguments highlighted the conflict between state and federal marijuana laws (63.0%) and asserted that legalization will fail to eliminate the black market (57.2%). Respondents who endorsed pro-legalization economic and criminal justice arguments were more likely than other respondents to support legalization. Our findings indicate that, on both side of the recreational marijuana legalization debate, there are arguments that resonate with the American public. However, public health risk messages were viewed as less compelling than pro-legalization economic and criminal justice-oriented arguments.


title: Analyzing of two types water cooling electric motors using computational fluid dynamics

abstract: The focus of this work consists in analyzing water flow inside a water cooling electric motor frame. Aiming of work is in the comparison of load losses and the avoidance of hot spots in two types water cooled frames. Total losses of electrical machine were considered as thermal load. Electrical motor is new designed electrically excited synchronous machine for automotive industry. For the development of this work computational fluid dynamics (CFD) was used.


title: Deformation Behavior of a Copper Single Crystal upon Loading by Spherically Converging Shock Waves

abstract: X-ray diffraction analysis, optical metallography examination, and microhardness measurements were used to investigate a copper single crystal sphere 34 mm in diameter after loading by spherically converging shock waves. It has been established that the deformation structure formed after shock loading depends on both the direction of shock wave propagation in the initial single crystal and the depth of the layer location in the sphere. This study found that high-rate plastic deformation of the copper single crystal under the studied loading conditions occurs mainly by slipping. In addition, “noncrystallographic” slip bands and localized deformation bands were observed in middle and deep layers. Shear bands were shown to nucleate both on the loading surface and inside the single crystal during shock wave propagation. No adiabatic shear bands were observed.


title: Understanding Customer Behavior in Shopping Mall from Indoor Tracking Data

abstract: The prosperity of various indoor positioning technologies makes possible the large collection of tracking data in indoor spaces. Much of the focus has been on several fundamental problems such as indoor localization, indoor space modeling, indoor data cleansing, indexing and querying. However, this paper attempts to analyze customer behavior from a unique indoor tracking data, which will promote the convergence between various applications and the underlying data. In particular, we introduce a real-life indoor tracking data collected at an electrical mall in China. Then, we cluster users into several groups and summarize the most characteristic behaviors of each cluster. Last but not least, we analyze customer's individual behaviors through two aspects: 1) the regression model is used to reveal hot regions where customers are likely to stay in long time; and 2) a transition matrix combined with the connectivity is presented to demonstrate hot paths.


title: Physical Unclonable Function Based Authentication Scheme for Smart Devices in Internet of Things

abstract: Internet of Things (IoT) comprises of huge number of connected smart devices to exchange sensed data across different networks. With the rapid growth in security applications development, Physical Unclonable Functions (PUF) has gain interest of researchers. PUF exploits the random factors of IC manufacturing process to generate a variety of mismatches. It helps to generate different unique challenge-response pairs to store in repository and then use for secure associations between the smart devices in IoT. Memory based and Analog electronic based PUFs are mostly used for providing dependable security where MOS transistors based PUF achieve a large variety of mismatches with smaller sizes as compared to others. This paper presents a PUF based Authentication Scheme (PAS) along session keying in order to ensure the secure interaction among smart devices in IoT. We have also proposed the registration and authentication mechanism along with session keying based on challenge-response pair. Next, we present the secure command execution protocol among requesting devices, gateway and smart devices. To validate our work, we have setup a testbed and compared the existing schemes with our proposed PAS in terms of communication overhead during authentication and resilience against device capturing. Results ensure the supremacy of proposed approach over the counterparts.


title: A Deep Dictionary Model for Image Super-Resolution

abstract: Inspired by the recent success of deep neural network architectures and the recent effort to develop multi-layer sparse models, we propose a novel deep dictionary learning architecture which is optimized to address a specific regression task known as single image super-resolution. Contrary to other multi-layer dictionaries, our architecture contains $L-1$ analysis dictionaries to extract high-level features and one synthesis dictionary which is designed to optimize the regression task. We propose a variation of an existing method to learn the analysis dictionaries and we update them without the need to use a back-propagation approach. Results on image super-resolution are satisfactory.


title: Regional cerebral blood flow changes associated with clitorally induced orgasm in healthy women

abstract: There is a severe lack of knowledge regarding the brain regions involved in human sexual performance in general, and female orgasm in particular. We used [(15)O]-H(2)O positron emission tomography to measure regional cerebral blood flow (rCBF) in 12 healthy women during a nonsexual resting state, clitorally induced orgasm, sexual clitoral stimulation (sexual arousal control) and imitation of orgasm (motor output control). Extracerebral markers of sexual performance and orgasm were rectal pressure variability (RPstd) and perceived level of sexual arousal (PSA). Sexual stimulation of the clitoris (compared to rest) significantly increased rCBF in the left secondary and right dorsal primary somatosensory cortex, providing the first account of neocortical processing of sexual clitoral information. In contrast, orgasm was mainly associated with profound rCBF decreases in the neocortex when compared with the control conditions (clitoral stimulation and imitation of orgasm), particularly in the left lateral orbitofrontal cortex, inferior temporal gyrus and anterior temporal pole. Significant positive correlations were found between RPstd and rCBF in the left deep cerebellar nuclei, and between PSA and rCBF in the ventral midbrain and right caudate nucleus. We propose that decreased blood flow in the left lateral orbitofrontal cortex signifies behavioural disinhibition during orgasm in women, and that deactivation of the temporal lobe is directly related to high sexual arousal. In addition, the deep cerebellar nuclei may be involved in orgasm-specific muscle contractions while the involvement of the ventral midbrain and right caudate nucleus suggests a role for dopamine in female sexual arousal and orgasm.


title: Intelligent control of four DOF robotic arm

abstract: In this paper; Fuzzy control and Adaptive Network based Fuzzy Interference System (ANFIS) techniques have been investigated for decentralised joint position control of an four DOF robotic arm for desired motion. Four different types of membership functions like Triangular; Pi; Gaussian; Dsigmf are implemented in the fuzzy PD and ANFIS controller. The performance of controller is evaluated and compared in terms of maximum overshoot; settling time and steady state error with conventional PID controller. Simulation results and comparative analysis validates the effectiveness of Fuzzy and ANFIS based controller with the proper selection of membership function for improving the system performance and reducing position tracking error.


title: Text Messaging as a Tool for Behavior Change in Disease Prevention and Management

abstract: Mobile phone text messaging is a potentially powerful tool for behavior change because it is widely available, inexpensive, and instant. This systematic review provides an overview of behavior change interventions for disease management and prevention delivered through text messaging. Evidence on behavior change and clinical outcomes was compiled from randomized or quasi-experimental controlled trials of text message interventions published in peer-reviewed journals by June 2009. Only those interventions using text message as the primary mode of communication were included. Study quality was assessed by using a standardized measure. Seventeen articles representing 12 studies (5 disease prevention and 7 disease management) were included. Intervention length ranged from 3 months to 12 months, none had long-term follow-up, and message frequency varied. Of 9 sufficiently powered studies, 8 found evidence to support text messaging as a tool for behavior change. Effects exist across age, minority status, and nationality. Nine countries are represented in this review, but it is problematic that only one is a developing country, given potential benefits of such a widely accessible, relatively inexpensive tool for health behavior change. Methodological issues and gaps in the literature are highlighted, and recommendations for future studies are provided.


title: Complete Mesocolic Excision With Central Vascular Ligation Produces an Oncologically Superior Specimen Compared With Standard Surgery for Carcinoma of the Colon

abstract: Purpose The plane of surgery in colonic cancer has been linked to patient outcome although the optimal extent of mesenteric resection is still unclear. Surgeons in Erlangen, Germany, routinely perform complete mesocolic excision (CME) with central vascular ligation (CVL) and report 5-year survivals of higher than 89%. We aimed to further investigate the importance of CME and CVL surgery for colonic cancer by comparison with a series of standard specimens. Methods The fresh photographs of 49 CME and CVL specimens from Erlangen and 40 standard specimens from Leeds, United Kingdom, for primary colonic adenocarcinoma were collected. Precise tissue morphometry and grading of the plane of surgery were performed before comparison to histopathologic variables. Results CME and CVL surgery removed more tissue compared with standard surgery in terms of the distance between the tumor and the high vascular tie (median, 131 v 90 mm; P .0001), the length of large bowel (median, 314 v 206 mm; P .0001), and ileum removed (median, 83 v 63 mm; P .003), and the area of mesentery (19,657 v 11,829 mm 2 ; P .0001). In addition, CME and CVL surgery was associated with more mesocolic plane resections (92% v 40%; P .0001) and a greater lymph node yield (median, 30 v 18; P .0001). Conclusion Surgeons in Erlangen routinely practicing CME and CVL surgery remove more mesocolon and are more likely to resect in the mesocolic plane when compared with standard excisions. This, along with the associated greater lymph node yield, may partially explain the high 5-year survival rates reported in Erlangen. J Clin Oncol 28:272-278. © 2009 by American Society of Clinical Oncology


title: Narcissistic personality disorder and the DSM-V.

abstract: We address 3 issues relevant to narcissistic personality disorder (NPD) and the DSM-V. First, we argue that excluding NPD while retaining other traditional personality disorder constructs (e.g., avoidant) makes little sense given the research literature on NPD and trait narcissism and their association with clinically relevant consequences such as aggression, self-enhancement, distorted self-presentation, failed relationships, cognitive biases, and internalizing and externalizing dysregulation. Second, we argue that the DSM-V must include content (in diagnostic form or within a dimensional trait model) that allows for the assessment of both grandiose and vulnerable variants of narcissism. Finally, we suggest that any dimensional classification of personality disorder should recover all of the important component traits of narcissism and be provided with official recognition in the coding system. Language: en


title: SUXES - user experience evaluation method for spoken and multimodal interaction.

abstract: Much work remains to be done with subjective evaluations of speech-based and multimodal systems. In particular, user experience is still hard to evaluate. SUXES is an evaluation method for collecting subjective metrics with user experiments. It captures both user expectations and user experiences, making it possible to analyze the state of the application and its interaction methods, and compare results. We present the SUXES method with examples of user experiments with different applications and modalities. Index Terms: evaluation, subjective metrics, user experience


Mohit Iyyer - Research Statement- Statement of Purpose


A Joint Model of Rhetorical Discourse Structure and Summarization


title: A 0.8–1.2 V 10–50 MS/s 13-bit Subranging Pipelined-SAR ADC Using a Temperature-Insensitive Time-Based Amplifier

abstract: This paper presents an energy-efficient 13-bit 10–50 MS/s subranging pipelined-successive approximation register (SAR) analog-to-digital converter (ADC) with power supply scaling. In the presented ADC, an SAR-assisted subranging floating capacitive DAC switching algorithm reduces switching energy along with enhanced linearity and speed in the first-stage SAR ADC. A following temperature-insensitive time-based residue amplifier realizes open-loop residual amplification without background calibration, while maintaining the benefits of dynamic operation and noise filtering. Furthermore, asynchronous SAR control logic employs a pre-window technique to accelerate SAR logic operations. The prototype ADC was fabricated in a 130-nm CMOS process with an active area of 0.22 mm2. With a 1.2-V power supply and a Nyquist frequency input, the ADC consumes 1.32 mW at 50 MS/s and achieves signal-to-noise and distortion ratio and spurious-free dynamic range of 69.1 and 80.7 dB, respectively. The operating speed is scalable from 10 to 50 MS/s with a scalable power supply range of 0.8–1.2 V. Walden FoMs of 4–11.3 fJ/conversion-step are achieved.


title: Robust Constrained Model Predictive Control using Linear Matrix Inequalities

abstract: The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis that allows explicit incorporation of the description of plant uncertainty in the problem formulation. The uncertainty is expressed in both the time and frequency domains. The goal is to design, at each time step, a state-feedback control law that minimizes a ‘worst-case’ infinite horizon objective function, subject to constraints on the control input and plant output. Using standard techniques, the problem of minimizing an upper bound on the ‘worst-case’ objective function, subject to input and output constraints, is reduced to a convex optimization involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants. Several extensions, such as application to systems with time delays, problems involving constant set-point tracking, trajectory tracking and disturbance rejection, which follow naturally from our formulation, are discussed. The controller design is illustrated with two examples.


title: A framework for personal data protection in the IoT

abstract: IoT personal devices are having an undoubtedly explosive growth as many different products become available on the market such as smart watches, contact lenses, fitness bands, microchips under skin, among others. These devices and their related applications process the collected sensor data and use them to provide services to their users; in addition most of them require data from other applications in order to enhance their service. However, data sharing increases the risk for privacy protection since the aggregation of multiple data may favor the prediction of unrevealed private information. This paper presents a general framework for managing the issue of privacy protection from unwanted disclosure of personal data. The framework integrates two approaches in privacy protection: the use of personal data managers to control and manage user data sharing and the use of techniques for inference prevention. The contribution of the framework is to exploit the advantages of the two mentioned lines of research in a user-centric approach that empowers users with higher control of their data and makes them aware about data-sharing decisions.


title: Novel Concept of the Three-Dimensional Vertical FG nand Flash Memory Using the Separated-Sidewall Control Gate

abstract: Recently, we proposed a novel 3-D vertical floating gate (FG)-type nand Flash memory cell array using the separated-sidewall control gate (CG) (S-SCG). This novel cell consists of one cylindrical FG with line-type CG and S-SCG structures. For simplifying the process flow, we realized the common S-SCG lines by using the prestacked polysilicon layer, through which variable medium voltages are applied not only to control the electrically inverted S/D region but also to assist the program and erase operations. In this paper, we successfully demonstrate the normal Flash cell operation and show its superior performances in comparison with the recent 3-D FG nand cells by using the cylindrical device simulation. It is shown that the proposed cell can realize the highest CG coupling ratio, low-voltage cell operations of program with 15 V at Vth = 4V and erase with 14 V at Vth = -3V , good retention-mode electric field, and sufficient read-mode on-current margin. Moreover, the proposed S-SCG cell array can fully suppress both the interference effects and the disturbance problems at the same time by removing the direct coupling effects in the same cell string, which are the most critical problems of the recent 3-D vertical stacked cell structures. Above all, the proposed cell array has good potential for terabit 3-D vertical nand Flash cell array with highly reliable multilevel cell operation.


title: Backprojection regularization with weighted ramp filter for tomographic reconstruction

abstract: Although filtered backprojection (FBP) is popular, backprojection then filtering (BPF) still receives a few attentions. Usually, BPF is inferior to FBP in terms of reconstruction quality. There are two main causes. First, BPF has to use a 2-dimensional discrete ramp filter formed by sampling the continuous ramp filter, resulting in DC shift and aliasing artefacts. Second, the common ramp filter amplifies high frequency noise much. To address such two issues, a weighted ramp filter is investigated to reduce the amplification of high frequency noise, and then a total-variation based backprojection regularization (BPR) method is developed to mitigate the DC shift and improve the robustness to noise. The experimental results show that BPR outperforms FBP for low-dose CT imaging reconstruction.


title: The Bcl-2 apoptotic switch in cancer development and therapy

abstract: Impaired apoptosis is both critical in cancer development and a major barrier to effective treatment. In response to diverse intracellular damage signals, including those evoked by cancer therapy, the cell’s decision to undergo apoptosis is determined by interactions between three factions of the Bcl-2 protein family. The damage signals are transduced by the diverse ‘BH3-only’ proteins, distinguished by the BH3 domain used to engage their pro-survival relatives: Bcl-2, Bcl-xL, Bcl-w, Mcl-1 and A1. This interaction ablates pro-survival function and allows activation of Bax and Bak, which commit the cell to apoptosis by permeabilizing the outer membrane of the mitochondrion. Certain BH3-only proteins (e.g. Bim, Puma) can engage all the pro-survival proteins, but others (e.g. Bad, Noxa) engage only subsets. Activation of Bax and Bak appears to require that the BH3-only proteins engage the multiple pro-survival proteins guarding Bax and Bak, rather than binding to the latter. The balance between the pro-survival proteins and their BH3 ligands regulates tissue homeostasis, and either overexpression of a pro-survival family member or loss of a proapoptotic relative can be oncogenic. Better understanding of the Bcl-2 family is clarifying its role in cancer development, revealing how conventional therapy works and stimulating the search for ‘BH3 mimetics’ as a novel class of anticancer drugs.


Extending Differential Privacy for Treating Dependent Records via Information Theory.


title: Color and Personality: Strong's Interest Inventory and Cattell's 16PF

abstract: Three studies addressed the relation between color preference and personality, based on subsets of respondents from a pool of volunteers who participated in a series of Career Transition Clinics provided by an Atlanta, GA church. It was hypothesized that the mostly inconclusive findings of earlier research are primarily due to the piecemeal nature of the analysis afforded by standard statistical method. Hence, the central data analyses were performed using a neural net approach. Study I (n = 885) showed that the Strong's Basic Interest Scales (BIS) could be predicted reliably (Median r = 0.68) from the Dewey Color System Test, while Study II (n = 1010) showed slightly weaker correlations when predicting respondents' scores on the Cattell's 16PF (Median r = 0.51). Consistent with earlier research, study III (n = 1245) indicated that yellow was preferred more by women than by men. Also, as was hypothesized, our results suggest that previous research was inconclusive due to many-to-one nature of the relation between color choices and personality. Unfortunately, our neural nets could not fully exploit sex related patterns of color preference differences so as to identify meaningful differences between men and women. ********** Color is an important aspect of our efforts to create personal spaces to our own liking. Yet, little is known about why people like or dislike the colors they do. This paper asks whether people's color preferences reflect meaningful information about their personalities, interpersonal styles, and behaviors. Surprisingly, relatively little research has been done to investigate the links between such variables and individuals' color preferences. The research summarized here represents our efforts to identify links between people's color preferences and their personal characteristics as derived from two well-established psychological inventories. From a practical perspective, the use of color preference tests promises to assess personality and occupational qualifications such that the stimuli have no perceived face validity to the respondents. In other words, the purpose of administering the test will not be apparent to respondents as they do not know how their color preferences relate to their personality characteristics or occupational proclivities. Color preference tests thus have similar potential advantages to Cattell's classical culture fair tests (CFIT, Cattell, 1949), i.e., they promise to eliminate any social or cultural advantages, or disadvantages, that a person may have due to their upbringing or cultural background. Also, the test can be administered regardless of the test taker's native language, thus obviating the need for translation, recalibrating, renorming, etc. Likewise, issues of socially desirable responding should be less of a concern than in standard paper-and-pencil tests, thereby decreasing response bias. At the most basic level, color has been shown to affect our mood, thereby affecting the way we interact with our environment. A growing body of research in environmental psychology has shown that the color of a room or work setting can have profound effects on individual enjoyment and performance on a variety of tasks. For instance, Stone (2003) showed that task performance varied as a function of the color of the room in which the task was performed. In another study by Stone (2001), positive mood tended to be higher when individuals worked in a blue carrel compared to a red carrel. Performance is also affected because individuals read slower and comprehended less when performing a reading task in a red environment. This study thus provides direct evidence that color has an effect on cognitive performance, suggesting that the cognitive impairments produced by color could be driven by physiological arousal. Indeed, Stone's (2003) findings indicate that the color red increased individuals' levels of arousal, which when paired with a stimulating task, caused deficits in cognitive performance. …


title: Open Packet Processor: a programmable architecture for wire speed platform-independent stateful in-network processing

abstract: This paper aims at contributing to the ongoing debate on how to bring programmability of stateful packet processing tasks inside the network switches, while retaining platform independency. Our proposed approach, named "Open Packet Processor" (OPP), shows the viability (via an hardware prototype relying on commodity HW technologies and operating in a strictly bounded number of clock cycles) of eXtended Finite State Machines (XFSM) as low-level data plane programming abstraction. With the help of examples, including a token bucket and a C4.5 traffic classifier based on a binary tree, we show the ability of OPP to support stateful operation and flow-level feature tracking. Platform independence is accomplished by decoupling the implementation of hardware primitives (registries, conditions, update instructions, forwarding actions, matching facilities) from their usage by an application formally described via an abstract XFSM. We finally discuss limitations and extensions.


title: Optimal Bundling of Technological Products with Network Externality

abstract: For many high-tech and Internet-related products, utility to consumers depends in part on the size of the user base, a phenomenon called network externality. A firm with a portfolio of these and other products---that are often asymmetric in their degree of network externality or marginal cost---may have to look beyond the traditional strategies of pure components, pure bundling, and mixed bundling. One such strategic alternative in a two-product case would be a so-called mixed bundling-1 under which the bundle and product 1 are offered, but the other product can only be purchased in a bundled form. The purpose of this study is to compare and contrast the impact of such asymmetry or symmetry in (direct) network externality and cost on the choice of bundling strategies. We model a monopolist firm that has a product in each of two categories and faces heterogeneous consumers. Results suggest that pure bundling is more profitable when both products have low marginal costs or high network externality whereas pure components or mixed bundling-1 is more profitable when the products diverge in their costs and network externality (e.g., only one product has network externality). Traditional mixed bundling is optimal in other instances.


title: Are expressive suppression and cognitive reappraisal associated with stress-related symptoms?

abstract: Emotion dysregulation is thought to be critical to the development of negative psychological outcomes. Gross (1998b) conceptualized the timing of regulation strategies as key to this relationship, with response-focused strategies, such as expressive suppression, as less effective and more detrimental compared to antecedent-focused ones, such as cognitive reappraisal. In the current study, we examined the relationship between reappraisal and expressive suppression and measures of psychopathology, particularly for stress-related reactions, in both undergraduate and trauma-exposed community samples of women. Generally, expressive suppression was associated with higher, and reappraisal with lower, self-reported stress-related symptoms. In particular, expressive suppression was associated with PTSD, anxiety, and depression symptoms in the trauma-exposed community sample, with rumination partially mediating this association. Finally, based on factor analysis, expressive suppression and cognitive reappraisal appear to be independent constructs. Overall, expressive suppression, much more so than cognitive reappraisal, may play an important role in the experience of stress-related symptoms. Further, given their independence, there are potentially relevant clinical implications, as interventions that shift one of these emotion regulation strategies may not lead to changes in the other.


title: Extraction of Multi-word Expressions from Small Parallel Corpora

abstract: We present a general methodology for extracting multi-word expressions (of various types), along with their translations, from small parallel corpora. We automatically align the parallel corpus and focus on misalignments; these typically indicate expressions in the source language that are translated to the target in a non-compositional way. We then use a large monolingual corpus to rank and filter the results. Evaluation of the quality of the extraction algorithm reveals significant improvements over naive alignment-based methods. External evaluation shows an improvement in the performance of machine translation that uses the extracted dictionary.


title: State and Trait Cheerfulness and the Induction of Exhilaration

abstract: The present paper examines whether state and trait cheerfulness represent actual and habitual dispositions for the emotion of exhilaration. In Experiment I, 60 research participants were involved in a 10-minute interaction with a clowning vs. neutral experimenter. Individuals high in trait cheerfulness, as assessed by the State-Trait-Cheerfulness Inventory (STCI), displayed facial signs of exhilaration with higher frequency, intensity, and duration than did low cheerful individuals. In Experiment II (N = 60), the experimenter was instructed to laugh or not laugh at certain preselected scenes while watching a movie. The experimenter's laughter facilitated enjoyment displays among individuals high in state cheerfulness, but not among those low in state cheerfulness. In both experiments, smiling and laughter predicted the subsequent mood level.


title: A 50-mA 1-nF Low-Voltage Low-Dropout Voltage Regulator for SoC Applications

abstract: In this paper, we present a low-voltage low-dropout voltage regulator (LDO) for a system-on-chip (SoC) application which, exploiting the multiplication of the Miller effect through the use of a current amplifier, is frequency compensated up to 1-nF capacitive load. The topology and the strategy adopted to design the LDO and the related compensation frequency network are described in detail. The LDO works with a supply voltage as low as 1.2 V and provides a maximum load current of 50 mA with a drop-out voltage of 200 mV: the total integrated compensation capacitance is about 40 pF. Measurement results as well as comparison with other SoC LDOs demonstrate the advantage of the proposed topology.


title: Environmental Correlates of Physical Activity in a Sample of Belgian Adults

abstract: PURPOSE ::: This study investigated the variance in sitting, walking, and moderate and vigorous physical activity explained by neighborhood design and recreational environmental variables above and beyond the variance accounted for by individual demographic variables. ::: ::: ::: DESIGN ::: Cross-sectional analyses of self-reported survey data. ::: ::: ::: SETTING ::: A random sample of inhabitants of Ghent, Belgium, aged 18-65 years, was drawn. ::: ::: ::: SUBJECTS ::: Five hundred twenty-one adults completed questionnaires (52.1% response rate). The average age of the sample was 41 years, and 48.2% were female. ::: ::: ::: MEASURES ::: A questionnaire developed to assess neighborhood design and recreational environmental variables with a total of 81 items was administered. The environmental questionnaire showed acceptable to good reliability and acceptable validity. The previously validated International Physical Activity Questionnaire (IPAQ) was used to quantify physical activity in the past 7 days. Additional demographic information was also obtained. ::: ::: ::: RESULTS ::: Regression analyses showed that environmental variables were related to all types of physical activity in both sexes. However, the range of variance explained by the models including demographic and environmental variables was low, only 5% to 13%. Minutes of walking and of moderate-intensity activity were related to quality of sidewalks and accessibility of shopping and public transportation. Vigorous physical activity was related to presence of activity supplies in the home and number of convenient activity facilities outside the home. ::: ::: ::: CONCLUSIONS ::: Both neighborhood design and recreational environment variables had small but significant associations with multiple types of physical activity in a sample of Belgian adults.


The Impact of Modulated Color Light on the Autonomic Nervous System


title: A machine learning approach to anomaly detection

abstract: Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.


title: Mesh repair with user-friendly topology control

abstract: Limitations of current 3D acquisition technology often lead to polygonal meshes exhibiting a number of geometrical and topological defects which prevent them from widespread use. In this paper we present a new method for model repair which takes as input an arbitrary polygonal mesh and outputs a valid 2-manifold triangle mesh. Unlike previous work, our method allows users to quickly identify areas with potential topological errors and to choose how to fix them in a user-friendly manner. Key steps of our algorithm include the conversion of the input model into a set of voxels, the use of morphological operators to allow the user to modify the topology of the discrete model, and the conversion of the corrected voxel set back into a 2-manifold triangle mesh. Our experiments demonstrate that the proposed algorithm is suitable for repairing meshes of a large class of shapes.


title: Approximation by Superpositions of a Sigmoidal Function

abstract: The present invention relates to a vehicle rearview mirror remote-control device, mounted in a casing containing a control mechanism including a support member integral with the body of a vehicle and which extends inside the casing through an opening. The support member has a first axis around which is pivotally mounted a cross-piece, on which cross-piece is pivotally mounted the casing carrying the mirror, on a second axis perpendicular to the first, first movement-controlling means being provided between the support member and the cross-piece and second-movement controlling means being provided, in an offset manner with respect to the second axis, between the cross-piece and the casing.


Overview of NTCIR-13 Actionable Knowledge Graph ( AKG ) Task


title: Burnout and health behaviors in health professionals from seven European countries

abstract: Objectives ::: Within an underlying health-impairing process, work stressors exhaust employees’ mental and physical resources and lead to exhaustion/burnout and to health problems, with health-impairing behaviors being one of the potential mechanisms, linking burnout to ill health. The study aims to explore the associations between burnout and fast food consumption, exercise, alcohol consumption and painkiller use in a multinational sample of 2623 doctors, nurses and residents from Greece, Portugal, Bulgaria, Romania, Turkey, Croatia and Macedonia, adopting a cross-national approach.


title: Novel Additive Manufacturing Pneumatic Actuators and Mechanisms for Food Handling Grippers

abstract: Conventional pneumatic grippers are widely used in industrial pick and place robot processes for rigid objects. They are simple, robust and fast, but their design, motion and features are limited, and they do not fulfil the final purpose. Food products have a wide variety of shapes and textures and are susceptible to damaged. Robot grippers for food handling should adapt to this wide range of dimensions and must be fast, cheap, reasonably reliable, and with cheap and reasonable maintenance costs. They should not damage the product and must meet hygienic conditions. The additive manufacturing (AM) process is able to manufacture parts without significant restrictions, and is Polyamide approved as food contact material by FDA. This paper presents that, taking the best of plastic flexibility, AM allows the implementation of novel actuators, original compliant mechanisms and practical grippers that are cheap, light, fast, small and easily adaptable to specific food products. However, if they are not carefully designed, the results can present problems, such as permanent deformations, low deformation limits, and low operation speed. We present possible solutions for the use of AM to design proper robot grippers for food handling. Some successful results, such as AM actuators based on deformable air chambers, AM compliant mechanisms, and grippers developed in a single part will be introduced and discussed.


title: Design of low-pass filters using defected ground structure

abstract: A method to design low-pass filters (LPF) having a defected ground structure (DGS) and broadened transmission-line elements is proposed. The previously presented technique for obtaining a three-stage LPF using DGS by Lim et al. is generalized to propose a method that can be applied in design N-pole LPFs for N/spl les/5. As an example, a five-pole LPF having a DGS is designed and measured. Accurate curve-fitting results and the successive design process to determine the required size of the DGS corresponding to the LPF prototype elements are described. The proposed LPF having a DGS, called a DGS-LPF, includes transmission-line elements with very low impedance instead of open stubs in realizing the required shunt capacitance. Therefore, open stubs, teeor cross-junction elements, and high-impedance line sections are not required for the proposed LPF, while they all have been essential in conventional LPFs. Due to the widely broadened transmission-line elements, the size of the DGS-LPF is compact.


title: Three Sorting Algorithms Using Priority Queues

abstract: We establish a lower bound of B(n) = n⌈logn ⌉ − 2⌈logn⌉ + 1 on the number of comparisons performed by any algorithm that uses priority queues to sort n elements. Three sorting algorithms using priority queues are introduced. The first algorithm performs the same comparisons as the classical Mergesort algorithm, but in a different order. The second algorithm performs at most 2nlogn + O(n) comparisons, with the advantage of being adaptive; meaning that it runs faster when the input sequence has some presortedness. In particular, we show that this algorithm sorts an already sorted sequence in linear time; a fact that is not obvious since there is no special checks to guarantee this behavior. The third algorithm is almost implicit; it can be implemented using the input array and less than n extra bits. The number of comparisons performed by this algorithm is at most B(n)+2.5n. The three algorithms have the advantage of producing every element of the sorted output, after the first, in O(log n), and can be implemented to be practically efficient.


title: Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints

abstract: This paper addresses the problem of human re-identification in videos of dense crowds. Re-identification in crowded scenes is a challenging problem due to large number of people and frequent occlusions, coupled with changes in their appearance due to different properties and exposure of cameras. To solve this problem, we model multiple Personal, Social and Environmental (PSE) constraints on human motion across cameras in crowded scenes. The personal constraints include appearance and preferred speed of each individual, while the social influences are modeled by grouping and collision avoidance. Finally, the environmental constraints model the transition probabilities between gates (entrances/exits). We incorporate these constraints into an energy minimization for solving human re-identification. Assigning 1–1 correspondence while modeling PSE constraints is NP-hard. We optimize using a greedy local neighborhood search algorithm to restrict the search space of hypotheses. We evaluated the proposed approach on several thousand frames of PRID and Grand Central datasets, and obtained significantly better results compared to existing methods.


title: Lumped-Element Fully Tunable Bandstop Filters for Cognitive Radio Applications

abstract: This paper presents tunable lumped-element bandstop filters for the UHF-band cognitive radio systems. The two-pole filters are implemented using lumped elements with both single- and back-to-back silicon varactor diodes. The single diode filter tunes from 470 to 730 MHz with a 16-dB rejection bandwidth of 5 MHz and a filter quality factor of 52-65. The back-to-back diode filter tunes from 511 to 745 MHz, also with a 16-dB rejection bandwidth of 5 MHz and a quality factor of 68-75. Both filters show a low insertion loss of 0.3-0.4 dB. Nonlinear measurements at the filter null with Δf = 2 MHz show that the back-to-back diode filter results in 12-dBm higher third-order intermodulation intercept point than the single diode filter. A scaling series capacitor is used in the resonator arm of the back-to-back diode filter and allows a power handling of 25 dBm at the 16-dB rejection null. The cascaded response of two tunable filters is also presented for multiband rejection applications, or for a deeper rejection null (>;36 dB with 0.6-dB loss at 600 MHz). The topology can be easily extended to higher order filters and design equations are presented.


title: The Determinants of Consumers ’ Adoption of Internet Banking

abstract: Bills have traditionally been paid by writing checks and sending them with statements by U.S. mail. Increasingly, bills are being paid by automated clearinghouse debits. More recently, the Internet has created new bill payment methods such as online banking and online credit card payments. These new options offer consumers convenience, speed, and the ability to better control payment timing. How do consumers decide whether or not to adopt different forms of bill payment? Are the same consumers using different methods depending on the type of bill or the amount? Will check ever disappear as a bill payment method? Tansel Yilmazer will begin this session with a summary of “The Determinants of Consumers’ Adoption of Internet Banking."


title: An efficient algorithm for decomposing a polygon into star-shaped pieces

abstract: Abstract In this paper we show how a theorem in plane geometry can be converted into a O(n log n) algorithm for decomposing a polygon into star-shaped subsets. The computational efficiency of this new decomposition contrasts with the heavy computational burden of existing methods.


title: Growing Story Forest Online from Massive Breaking News

abstract: We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion. Our real-world system has distinct requirements in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we 1) need to accurately and quickly extract distinguishable events from massive streams of long text documents that cover diverse topics and contain highly redundant information, and 2) must develop the structures of event stories in an online manner, without repeatedly restructuring previously formed stories, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. We conducted extensive evaluation based on 60 GB of real-world Chinese news data, although our ideas are not language-dependent and can easily be extended to other languages, through detailed pilot user experience studies. The results demonstrate the superior capability of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers, compared to multiple existing algorithm frameworks.


title: Creativity support for novice digital filmmaking

abstract: Machinima is a new form of creative digital filmmaking that leverages the real time graphics rendering of computer game engines. Because of the low barrier to entry, machinima has become a popular creative medium for hobbyists and novices while still retaining borrowed conventions from professional filmmaking. Can novice machinima creators benefit from creativity support tools? A preliminary study shows novices generally have difficulty adhering to cinematographic conventions. We identify and document four cinematic conventions novices typically violate. We report on a Wizard-of-Oz study showing a rule-based intelligent system that can reduce the frequency of errors that novices make by providing information about rule violations without prescribing solutions. We discuss the role of error reduction in creativity support tools.


title: Chronic Inflammation and Cytokines in the Tumor Microenvironment

abstract: Acute inflammation is a response to an alteration induced by a pathogen or a physical or chemical insult, which functions to eliminate the source of the damage and restore homeostasis to the affected tissue. However, chronic inflammation triggers cellular events that can promote malignant transformation of cells and carcinogenesis. Several inflammatory mediators, such as TNF-α, IL-6, TGF-β, and IL-10, have been shown to participate in both the initiation and progression of cancer. In this review, we explore the role of these cytokines in important events of carcinogenesis, such as their capacity to generate reactive oxygen and nitrogen species, their potential mutagenic effect, and their involvement in mechanisms for epithelial mesenchymal transition, angiogenesis, and metastasis. Finally, we will provide an in-depth analysis of the participation of these cytokines in two types of cancer attributable to chronic inflammatory disease: colitis-associated colorectal cancer and cholangiocarcinoma.


title: Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

abstract: Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.


title: SpaFIND: An Effective and Low-Cost Feature Descriptor for Pedestrian Protection Systems in Economy Cars

abstract: Pedestrian protection systems (PPSs) are crucial for reducing road traffic fatalities. However, the relatively high cost of today's PPSs prevents the majority of economy cars from receiving their benefits. In this paper, we propose an effective and low-cost sparse feature interaction descriptor (SpaFIND) that is designed for real-time pedestrian detection with limited computational power in economy cars. SpaFIND extends the histogram of oriented gradients (HOG) feature and selectively computes the pairwise relationships between neighboring components of the HOG. Therefore, SpaFIND can capture the second-order properties of object appearance while maintaining a low computational load, which is the main contribution of this paper. During the experiments, we performed comparative evaluations of the proposed system against several baseline methods on the Caltech Pedestrian Detection Benchmark in terms of detection accuracy and computational load. The results demonstrated the effectiveness of the proposed low-cost SpaFIND feature, which can meet the requirements for implementing a PPS with limited computational power, thereby contributing to the massive deployment of PPSs in economy cars in the immediate future.


title: The role of digital and social media marketing in consumer behavior

abstract: This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.


title: A Case Study of Cooperative Learning and Communication Pedagogy: Does Working in Teams Make a Difference?.

abstract: Cooperative learning has increasingly become a popular form of active pedagogy employed in academic institutions. This case study explores the relationship between cooperative learning and academic performance in higher education, specifically in the field of communication. Findings from a questionnaire administered to undergraduate students in a communication research course indicate that involvement in cooperative learning is a strong predictor of a student's academic performance. A significant positive relationship was found between the degree to which grades are important to a student and his or her active participation in cooperative learning. Further, the importance of grades and sense of achievement are strong predictors of performance on readiness assessment tests.


title: Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets

abstract: Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.


title: Multi-modal decision fusion for continuous authentication

abstract: Display Omitted Behavioral biometrics: keystroke dynamics, mouse movement, stylometry.A parallel binary decision fusion architecture with 11 sensors.A dataset collected from 67 users each working in an office environment for a week.Achieve below 1% error rates (FAR, FRR) after only 30s of activity.Characterize robustness of system to adversarial attacks. Active authentication is the process of continuously verifying a user based on their on-going interaction with a computer. In this study, we consider a representative collection of behavioral biometrics: two low-level modalities of keystroke dynamics and mouse movement, and a high-level modality of stylometry. We develop a sensor for each modality and organize the sensors as a parallel binary decision fusion architecture. We consider several applications for this authentication system, with a particular focus on secure distributed communication. We test our approach on a dataset collected from 67 users, each working individually in an office environment for a period of approximately one week. We are able to characterize the performance of the system with respect to intruder detection time and robustness to adversarial attacks, and to quantify the contribution of each modality to the overall performance.


title: Effective Implementation of Automated Fertilization Unit Using Analog pH Sensor and Arduino

abstract: Unplanned use of fertilizers leads to inferior quality of crops. Excess of one nutrient can make it difficult for the plant to absorb the other nutrients. To deal with this problem, the quality of soil is tested using a PH sensor that indicates the percentage of macronutrients present in the soil. Conventional methods used to test soil quality, involve the use of Ion Selective Field Effect Transistors (ISFET), Ion Selective Electrode (ISE) and Optical Sensors as the sensing units which were found to be very expensive. The prototype design will allow sprinkling of fertilizers to take place in zones which are deficient in these macronutrients (Nitrogen, Phosphorous and Potassium), proving it to be a cost efficient and farmer-friendly automated fertilization unit. Cost of the proposed unit is found to be one-seventh of that of the present methods, making it affordable for farmers and also saves the manual labor. Initial analysis and intensive case studies conducted in farmland situated near Ambedkar Nagar, Sarjapur also revealed the use of above mechanism to be more prominent and verified through practical implementation and experimentation as it takes lesser time to analyze the nutrient content than the other methods which require soil testing. Sprinklers cover discrete zones in the field that automate fertilization and reduce the effort of farmers in the rural areas. This novel technique also has a fast response time as it enables real time, in-situ soil nutrient analysis, thereby maintaining proper soil pH level required for a particular crop, reducing potentially negative environmental impacts.


title: Generalize Symbolic Knowledge With Neural Rule Engine

abstract: Neural-symbolic learning aims to take the advantages of both neural networks and symbolic knowledge to build better intelligent systems. As neural networks have dominated the state-of-the-art results in a wide range of NLP tasks, it attracts considerable attention to improve the performance of neural models by integrating symbolic knowledge. Different from existing works, this paper investigates the combination of these two powerful paradigms from the knowledge-driven side. We propose Neural Rule Engine (NRE), which can learn knowledge explicitly from logic rules and then generalize them implicitly with neural networks. NRE is implemented with neural module networks in which each module represents an action of the logic rule. The experiments show that NRE could greatly improve the generalization abilities of logic rules with a significant increase on recall. Meanwhile, the precision is still maintained at a high level.


Student satisfaction with EFL speaking classes : Relating speaking self-efficacy and skills achievement


FOOD SECURITY IN DEVELOPING COUNTRIES : A CASE STUDY FOR INDIA


title: Cognitive Behavioral Therapy for Panic Disorder

abstract: OBJECTIVE ::: Panic disorder is a chronic and recurrent condition that impairs an individual's psychosocial functioning and quality of life. Despite the efficacy of psychopharmacological treatment in reducing panic attacks, many patients fail to respond adequately to these interventions. Cognitive behavioral therapy provides an alternative and efficacious method for treating panic disorder and agoraphobic avoidance. The objective of the study is to describe the use of cognitive behavioral therapy for panic disorder. ::: ::: ::: METHOD ::: Narrative review of data collected from Medline, SciELO and PsycInfo and specialized textbooks. ::: ::: ::: RESULTS ::: We describe the cognitive-behavioral model for the treatment of panic disorder, and review both short and long-term efficacy findings. We also discuss the role of combined treatment (cognitive behavioral therapy and psychopharmacology). ::: ::: ::: CONCLUSIONS ::: Cognitive behavioral therapy, either individual or in group, can be used as first-line therapy for panic disorder. This treatment modality can also be indicated as a next step for patients failing to respond to other treatments.


title: Global research trends of World Health Organization’s top eight emerging pathogens

abstract: On December 8th, 2015, World Health Organization published a priority list of eight pathogens expected to cause severe outbreaks in the near future. To better understand global research trends and characteristics of publications on these emerging pathogens, we carried out this bibliometric study hoping to contribute to global awareness and preparedness toward this topic. Scopus database was searched for the following pathogens/infectious diseases: Ebola, Marburg, Lassa, Rift valley, Crimean-Congo, Nipah, Middle Eastern Respiratory Syndrome (MERS), and Severe Respiratory Acute Syndrome (SARS). Retrieved articles were analyzed to obtain standard bibliometric indicators. A total of 8619 journal articles were retrieved. Authors from 154 different countries contributed to publishing these articles. Two peaks of publications, an early one for SARS and a late one for Ebola, were observed. Retrieved articles received a total of 221,606 citations with a mean ± standard deviation of 25.7 ± 65.4 citations per article and an h-index of 173. International collaboration was as high as 86.9%. The Centers for Disease Control and Prevention had the highest share (344; 5.0%) followed by the University of Hong Kong with 305 (4.5%). The top leading journal was Journal of Virology with 572 (6.6%) articles while Feldmann, Heinz R. was the most productive researcher with 197 (2.3%) articles. China ranked first on SARS, Turkey ranked first on Crimean-Congo fever, while the United States of America ranked first on the remaining six diseases. Of retrieved articles, 472 (5.5%) were on vaccine – related research with Ebola vaccine being most studied. Number of publications on studied pathogens showed sudden dramatic rise in the past two decades representing severe global outbreaks. Contribution of a large number of different countries and the relatively high h-index are indicative of how international collaboration can create common health agenda among distant different countries.


title: Investigation of a Wideband 90 $^{\circ}$ Hybrid Coupler With an Arbitrary Coupling Level

abstract: This paper presents a new class of wideband 90° hybrid coupler with an arbitrary coupling level. The physical size of the proposed coupler is close to that of a conventional two-section branch-line coupler, but it has an additional phase inverter. The impedance bandwidth of the proposed coupler is close to that of a four-section branch-line coupler. The proposed coupler is a backward-wave coupler with a port assignment different from that of a conventional branch-line coupler. The design formulas of the proposed coupler are proved based on its even- and odd-mode half structures. We demonstrated three couplers at the center frequency of 2 GHz with different design parameters.


title: The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution

abstract: In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup of facial landmarks). Furthermore, we increase considerably the number of annotated images so that deep learning algorithms can be robustly applied to the problem. The results of the Menpo challenge demonstrate that recent deep learning architectures when trained with the abundance of data lead to excellent results. Finally, we discuss directions for future benchmarks in the topic.


title: The Design and Implementation of the FreeBSD Operating System

abstract: As in earlier Addison-Wesley books on the UNIX-based BSD operating system, Kirk McKusick and George Neville-Neil deliver here the most comprehensive, up-to-date, and authoritative technical information on the internal structure of open source FreeBSD. Readers involved in technical and sales support can learn the capabilities and limitations of the system; applications developers can learn effectively and efficiently how to interface to the system; system administrators can learn how to maintain, tune, and configure the system; and systems programmers can learn how to extend, enhance, and interface to the system.The authors provide a concise overview of FreeBSD's design and implementation. Then, while explaining key design decisions, they detail the concepts, data structures, and algorithms used in implementing the systems facilities. As a result, readers can use this book as both a practical reference and an in-depth study of a contemporary, portable, open source operating system.This book: Details the many performance improvements in the virtual memory system Describes the new symmetric multiprocessor support Includes new sections on threads and their scheduling Introduces the new jail facility to ease the hosting of multiple domains Updates information on networking and interprocess communicationAlready widely used for Internet services and firewalls, high-availability servers, and general timesharing systems, the lean quality of FreeBSD also suits the growing area of embedded systems. Unlike Linux, FreeBSD does not require users to publicize any changes they make to the source code.


title: A Novel Variable Reluctance Resolver with Nonoverlapping Tooth–Coil Windings

abstract: A novel variable reluctance (VR) resolver with nonoverlapping tooth–coil windings is proposed in this paper. It significantly simplifies the manufacturing process of multilayer windings in conventional products. Finite element (FE) analysis is used to illustrate the basic operating principle, followed by analytical derivation of main parameters and optimization of major dimensions, including air-gap length and slot opening width. Based on winding distributions and FE results, it is shown that identical stator and winding can be employed for a resolver with three different numbers of rotor poles. Further, other stator slot/rotor pole combinations based on the nonoverlapping tooth–coil windings are generalized. In addition, the influence of eccentricity and end-winding leakage on the proposed topology is investigated. Finally, a prototype is fabricated and tested to verify the analysis, including main parameters and electrical angle error.


Physical seed quality of soybean


title: Considerations for using high-impedance or low-impedance relays for bus differential protection

abstract: Two drastically different types of differential relays, one with a single set of very high-impedance inputs and another with multiple sets of low-impedance inputs, are available for bus differential protection. Protection engineers often question which type to choose for their bus protection applications. This paper discusses for each type of relay the concepts used, the pros and cons, and the importance that current transformer selection plays in applications.


Sexual orientation and gender identity: review of concepts, controversies and their relation to psychopathology classification systems


title: Exploring author name disambiguation on PubMed-scale

abstract: Author name disambiguation (AND) creates a daunting challenge in that disambiguation techniques often draw false conclusions when applied to incomplete or incorrect publication data. It becomes a more critical issue in the biomedical domain where PubMed articles are written by a wide range of researchers internationally. To tackle this issue, we create a carefully hand-crafted training set drawn from the entire PubMed collection by going through multiple iterations. We assess the quality of our training set by comparing it with SCOPUS-based training set. In addition, for the performance enhancement of the AND techniques, we propose a new set of publication features extracted by text mining techniques. The results of the experiments show that all four supervised learning techniques (Random Forest, C4.5, KNN, and SVM) with the new publication features (called NER model) achieve improved performance over the baseline and hybrid edit distance model.


title: Small-Signal Stability Analysis of a Grid Connected Doubly-Fed Induction Generator Under Decoupled P-Q Control

abstract: Small signal stability analysis is conducted considering grid connected doubly-fed induction generator (DFIG) type. The modeling of a grid connected DFIG system is first set up and the whole model is formulated by a set of differential algebraic equations (DAE). Then, the mathematical model of rotor-side converter is built with decoupled P-Q control techniques to implement stator active and reactive powers control. Based on the abovementioned researches, the small signal stability analysis is carried out to explore and compared the differences between the whole system with the decoupled P-Q controller or not by eigenvalues and participation factors. Finally, numerical results demonstrate the system are stable, especially some conclusions and comments of interest are made.


Artificial Bee Colony ( ABC ) , Harmony Search and Bees Algorithms on Numerical Optimization


title: Cost sensitive modeling of credit card fraud using neural network strategy

abstract: Due to the rapid growth in e-business and electronic payment systems, Fraud is rising in banking transactions associated with credit cards. This paper intends to develop a credit card fraud detection (CCFD) model based on Artificial Neural Networks (ANN) and Meta Cost procedure to reduce risk reputation and risk of loss. ANN strategy have been used for credit card fraud prevention and detection. Because of the unbalanced nature of the data (Fraud and Non-Fraud cases), the detection of fraudulent transactions is difficult to achieve. To deal with the problem of imbalanced data, Meta Cost procedure is added. The proposed model, which is called Cost Sensitive Neural Network (CSNN), is based on misuse detection approach. Compared to the model based on Artificial Immune System (AIS), this model showed cost saving and increased detection rate. Data of this study is taken from real transactional data provided by a big Brazilian credit card issuer.


title: Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

abstract: Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at this https URL


title: Quorum responses and consensus decision making

abstract: Animal groups are said to make consensus decisions when group members come to agree on the same option. Consensus decisions are taxonomically widespread and potentially offer three key benefits: maintenance of group cohesion, enhancement of decision accuracy compared with lone individuals and improvement in decision speed. In the absence of centralized control, arriving at a consensus depends on local interactions in which each individual's likelihood of choosing an option increases with the number of others already committed to that option. The resulting positive feedback can effectively direct most or all group members to the best available choice. In this paper, we examine the functional form of the individual response to others' behaviour that lies at the heart of this process. We review recent theoretical and empirical work on consensus decisions, and we develop a simple mathematical model to show the central importance to speedy and accurate decisions of quorum responses, in which an animal's probability of exhibiting a behaviour is a sharply nonlinear function of the number of other individuals already performing this behaviour. We argue that systems relying on such quorum rules can achieve cohesive choice of the best option while also permitting adaptive tuning of the trade-off between decision speed and accuracy.


title: The base-rate fallacy and the difficulty of intrusion detection

abstract: Many different demands can be made of intrusion detection systems. An important requirement is that an intrusion detection system be effective ; that is, it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This article demonstrates that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate P(Intrusion***Alarm) , we have to achieve a (perhaps in some cases unattainably) low false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.


title: Application of GRNN neural network in non-texture image inpainting and restoration

abstract: We model an inpainting method based on GRNN neural network.The missing regions are separated and sorted according to their size.The missing regions are determined by performing regression analysis.We use the magnitude of the gradient of the image to determine the spread parameter.For color images, the YCbCr color space is employed. Inspired by the connectivity principle of human visual perception, a new inpainting approach based on GRNN neural network is proposed in this paper. The missing regions in this new technique are determined by performing regression analysis on the image data. The missing regions are first separated and sorted according to their size. Then the algorithm proceeds with applying a GRNN network to each one in order to repair their damaged pixels. Simplicity and efficiency are the main advantages of the proposed approach. The performance of the proposed approach is evaluated in three application contexts: text removal, scratch removal, and noise removal. Where possible, we used objective measures (e.g., PSNR) to evaluate the visual quality of the inpainted images. The results demonstrate the effectiveness of the proposed approach. Display Omitted


title: Active Sampler: Light-weight Accelerator for Complex Data Analytics at Scale

abstract: Recent years have witnessed amazing outcomes from "Big Models" trained by "Big Data". Most popular algorithms for model training are iterative. Due to the surging volumes of data, we can usually afford to process only a fraction of the training data in each iteration. Typically, the data are either uniformly sampled or sequentially accessed. ::: In this paper, we study how the data access pattern can affect model training. We propose an Active Sampler algorithm, where training data with more "learning value" to the model are sampled more frequently. The goal is to focus training effort on valuable instances near the classification boundaries, rather than evident cases, noisy data or outliers. We show the correctness and optimality of Active Sampler in theory, and then develop a light-weight vectorized implementation. Active Sampler is orthogonal to most approaches optimizing the efficiency of large-scale data analytics, and can be applied to most analytics models trained by stochastic gradient descent (SGD) algorithm. Extensive experimental evaluations demonstrate that Active Sampler can speed up the training procedure of SVM, feature selection and deep learning, for comparable training quality by 1.6-2.2x.


title: Half-CNN: A General Framework for Whole-Image Regression

abstract: The Convolutional Neural Network (CNN) has achieved great success in image classification. The classification model can also be utilized at image or patch level for many other applications, such as object detection and segmentation. In this paper, we propose a whole-image CNN regression model, by removing the full connection layer and training the network with continuous feature maps. This is a generic regression framework that fits many applications. We demonstrate this method through two tasks: simultaneous face detection & segmentation, and scene saliency prediction. The result is comparable with other models in the respective fields, using only a small scale network. Since the regression model is trained on corresponding image / feature map pairs, there are no requirements on uniform input size as opposed to the classification model. Our framework avoids classifier design, a process that may introduce too much manual intervention in model development. Yet, it is highly correlated to the classification network and offers some in-deep review of CNN structures.


title: A 0.4-V Supply Curvature-Corrected Reference Generator With 84.5-ppm/°C Average Temperature Coefficient Within −40 °C to 130 °C

abstract: This brief describes a low-power current-mode voltage reference based on subthreshold transistors. A novel circuit configuration together with a high-order temperature compensation scheme allow this voltage reference to operate with a supply voltage down to 0.4 V over a large temperature range (from −40 °C to 130 °C). The circuit, which is fabricated with a standard 0.18- $\mu\text{m}$ CMOS technology, provides an output voltage of 212 mV, while consuming 192 nW. The measured average temperature coefficient is 84.5 ppm/°C.


title: Perspectives on Infrastructure for Crowdsourcing

abstract: Human computation has gained a lot of interest lately as a paradigm for solving problems that computers can‟t do yet. Crowdsourcing marketplaces are showing that is possible to get tasks completed in time with a modest budget in a wide range of applications. However, the infrastructure that is currently available for supporting crowdsourcing tasks is very limited. There is non-trivial extra work that the experimenter has to do to get successful results. Consequently, the developer tends to spend more time dealing with the complexity and operational aspects of the task than on its design. Given the mix of different subareas of computer science that are potentially involved, defining and designing such platform presents a number of interesting problems. In this paper, I outline the rise of human computation, summarize the limitations of a popular platform, and present a perspective on the topic that can be viewed as a starting point to debate requirements for this new research area.


Knowledge Diffusion for Neural Dialogue Generation