title: Exploratory investigation of theoretical predictors of nomophobia using the Mobile Phone Involvement Questionnaire (MPIQ)

abstract: Abstract Despite the advantages that the development of new technologies has brought to our lives, it is also true that the problematic use of technology can have negative effects on some people, as the prevalence of nomophobia, defined as the fear and anxiety of being unreachable via one's mobile phone. While some authors are beginning to study the relationship of nomophobia with variables such as age, no investigation has yet been carried out into psychological variables. Our study contributes to the literature by looking at personality, self-esteem, gender and age as predictors of nomophobia, evaluated by the Mobile Phone Involvement Questionnaire (MPIQ). The study comprised 242 Spanish students (46.7% men and 53.3% women; 49.2% studying for a high school certificate and 50.8% for a university degree). Multiple regressions were conducted, finding that self-esteem, extraversion, conscientiousness and emotional stability predict nomophobia. Finally, we discuss these results and provide suggestions for future research.


title: On a constructive proof of Kolmogorov ’ s superposition theorem

abstract: Kolmogorov (Dokl. Akad. Nauk USSR, 14(5):953–956, 1957) showed that any multivariate continuous function can be represented as a superposition of one-dimensional functions, i.e., ::: ::: $$f(x_{1},\ldots,x_{n})=\sum_{q=0}^{2n}\varPhi _{q}\Biggl(\sum_{p=1}^{n}\psi_{q,p}(x_{p})\Biggr).$$ ::: ::: The proof of this fact, however, was not constructive, and it was not clear how to choose the outer and inner functions Φ q and ψ q,p , respectively. Sprecher (Neural Netw. 9(5):765–772, 1996; Neural Netw. 10(3):447–457, 1997) gave a constructive proof of Kolmogorov’s superposition theorem in the form of a convergent algorithm which defines the inner functions explicitly via one inner function ψ by ψ p,q :=λ p ψ(x p +qa) with appropriate values λ p ,a∈ℝ. Basic features of this function such as monotonicity and continuity were supposed to be true but were not explicitly proved and turned out to be not valid. Koppen (ICANN 2002, Lecture Notes in Computer Science, vol. 2415, pp. 474–479, 2002) suggested a corrected definition of the inner function ψ and claimed, without proof, its continuity and monotonicity. In this paper we now show that these properties indeed hold for Koppen’s ψ, and we present a correct constructive proof of Kolmogorov’s superposition theorem for continuous inner functions ψ similar to Sprecher’s approach.


title: A Performance Evaluation of Gradient Field HOG Descriptor for Sketch Based Image Retrieval

abstract: We present an image retrieval system for the interactive search of photo collections using free-hand sketches depicting shape. We describe Gradient Field HOG (GF-HOG); an adapted form of the HOG descriptor suitable for Sketch Based Image Retrieval (SBIR). We incorporate GF-HOG into a Bag of Visual Words (BoVW) retrieval framework, and demonstrate how this combination may be harnessed both for robust SBIR, and for localizing sketched objects within an image. We evaluate over a large Flickr sourced dataset comprising 33 shape categories, using queries from 10 non-expert sketchers. We compare GF-HOG against state-of-the-art descriptors with common distance measures and language models for image retrieval, and explore how affine deformation of the sketch impacts search performance. GF-HOG is shown to consistently outperform retrieval versus SIFT, multi-resolution HOG, Self Similarity, Shape Context and Structure Tensor. Further, we incorporate semantic keywords into our GF-HOG system to enable the use of annotated sketches for image search. A novel graph-based measure of semantic similarity is proposed and two applications explored: semantic sketch based image retrieval and a semantic photo montage.


title: Face Reconstruction on Mobile Devices Using a Height Map Shape Model and Fast Regularization

abstract: We present a system which is able to reconstruct human faces on mobile devices with only on-device processing using the sensors which are typically built into a current commodity smart phone. Such technology can for example be used for facial authentication purposes or as a fast preview for further post-processing. Our method uses recently proposed techniques which compute depth maps by passive multi-view stereo directly on the device. We propose an efficient method which recovers the geometry of the face from the typically noisy point cloud. First, we show that we can safely restrict the reconstruction to a 2.5D height map representation. Therefore we then propose a novel low dimensional height map shape model for faces which can be fitted to the input data efficiently even on a mobile phone. In order to be able to represent instance specific shape details, such as moles, we augment the reconstruction from the shape model with a distance map which can be regularized efficiently. We thoroughly evaluate our approach on synthetic and real data, thereby we use both high resolution depth data acquired using high quality multi-view stereo and depth data directly computed on mobile phones.


title: RUNNING HEAD: SOCIAL SUPPORT AND WORK-FAMILY CONFLICT Social Support and Work-Family Conflict: A Test of an Indirect Effects Model 1

abstract: Abstract Most work–family research on social support is based on matching domain relationships, that is, social support in the work domain is related to work interfering with family conflict (WIF) and social support in the family domain is related to family interfering with work conflict (FIW). In this research, based on the conservation of resources (COR) model, we examined the cross domain indirect relationship between social support in the work domain and FIW and the cross domain indirect relationship between social support in the family domain and WIF. We tested this model across three samples in two studies. In Study 1, we examined the cross domain influence of supervisory and spouse support on work–family conflict using two large random samples (N = 1130; N = 2769). In Study 2, we tested an expanded model of social support to include both generic and work–family specific support, and examined the cross domain indirect relationships between these two types of support on work–family conflict using a sample of 435 employees. Specifically, we proposed that social support systems in the work domain such as family friendly organizational policies, family supportive organizational climate, perceived organizational support, and perceived supervisory support will be indirectly and negatively related to FIW via WIF and that spouse/partner support will be indirectly and negatively related to WIF via FIW. Results for Study 1 provided support for the cross domain indirect effects model and results for Study 2 provided support for the cross domain indirect effects for work–family specific social support systems. We discuss implications of these results for the theory and practice of managing work–family conflict.


title: Robust PCA via Nonconvex Rank Approximation

abstract: Numerous applications in data mining and machinelearning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a generalframework for handling this kind of problems. Nuclear normbased convex surrogate of the rank function in RPCA iswidely investigated. Under certain assumptions, it can recoverthe underlying true low rank matrix with high probability. However, those assumptions may not hold in real-world applications. Since the nuclear norm approximates the rank byadding all singular values together, which is essentially a '1-norm of the singular values, the resulting approximation erroris not trivial and thus the resulting matrix estimator canbe significantly biased. To seek a closer approximation andto alleviate the above-mentioned limitations of the nuclearnorm, we propose a nonconvex rank approximation. Thisapproximation to the matrix rank is tighter than the nuclearnorm. To solve the associated nonconvex minimization problem, we develop an efficient augmented Lagrange multiplier basedoptimization algorithm. Experimental results demonstrate thatour method outperforms current state-of-the-art algorithms inboth accuracy and efficiency.


Challenges for Molecular Magnetic Resonance Imaging


title: CRAXweb: Automatic Web Application Testing and Attack Generation

abstract: This paper proposes to test web applications and generate the feasible exploits automatically, including cross-site scripting and SQL injection attacks. We test the web applications with initial random inputs by detecting symbolic queries to SQL servers or symbolic responses to HTTP servers. After symbolic outputs detected, we are able to generate attack strings and reproduce the results, emulating the manual attack behavior. In contrast with other traditional detection and prevention methods, we can determine the presence of vulnerabilities and prove the feasibility of attacks. This automatic generation process is based on a dynamic software testing method-symbolic execution by S2E. We have applied this automatic process to several known vulnerabilities on large-scale open source web applications, and generated the attack strings successfully. Our method is web platform independent, covering PHP, JSP, Rails, and Django due to the supports of the whole system environment of S2E.


title: A constraint-based approach to constructing continuous cartograms

abstract: We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.


title: Practical Applications of Homomorphic Encryption

abstract: Homomorphic cryptography has been one of the most interesting topics of mathematics and computer security since Gentry presented the first construction of a fully homomorphic encryption (FHE) scheme in 2009. Since then, a number of different schemes have been found, that follow the approach of bootstrapping a fully homomorphic scheme from a somewhat homomorphic foundation. All existing implementations of these systems clearly proved, that fully homomorphic encryption is not yet practical, due to significant performance limitations. However, there are many applications in the area of secure methods for cloud computing, distributed computing and delegation of computation in general, that can be implemented with homomorphic encryption schemes of limited depth. We discuss a simple algebraically homomorphic scheme over the integers that is based on the factorization of an approximate semiprime integer. We analyze the properties of the scheme and provide a couple of known protocols that can be implemented with it. We also provide a detailed discussion on searching with encrypted search terms and present implementations and performance figures for the solutions discussed in this paper.


title: Sleep and Cognitive Functions in Normal Older Adults

abstract: Data on cognitive and sleep variables were collected from a large, normal aging sample. The sleep and cognitive variables selected for the study had all been demonstrated previously to be sensitive to aging processes. Only two sleep variables, number of awakenings and time in Stage 1 sleep, correlated with cognitive scores. The limited number of relationships between sleep and cognitive variables was interpreted as consistent with the discontinuity hypothesis. This formulation suggests that relationships between biological and psychological measures are only present after a threshold of deterioration in the biological substrates is passed. Thus the paucity of relationships between the two variable classes is predicted in this normal aging sample.


title: Reward Estimation for Variance Reduction in Deep Reinforcement Learning

abstract: Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the reward with external factors that are not known to the agent. Corruption or stochasticity of the reward signal can be especially problematic in robotics, where goal specification can be particularly difficult for complex tasks. While many variance reduction techniques have been studied to improve the robustness of the RL process, handling such stochastic or corrupted reward structures remains difficult. As an alternative for handling this scenario in model-free RL methods, we suggest using an estimator for both rewards and value functions. We demonstrate that this improves performance under corrupted stochastic rewards in both the tabular and non-linear function approximation settings for a variety of noise types and environments. The use of reward estimation is a robust and easy-to-implement improvement for handling corrupted reward signals in model-free RL.


title: Frontalis Muscle Asymmetry and Lateral Landmarks.

abstract: PURPOSE ::: To investigate frontalis muscle asymmetry and characterize its lateral interdigitation with the orbicularis oculi muscle. ::: ::: ::: METHODS ::: After making a mid-coronal incision and bluntly dissecting to the orbital rim, the frontalis muscle was exposed, marked, and photographed. The right and left muscle bellies were analyzed and compared in both pixels and cm ratios generated with NIH ImageJ software. A ratio of ≥1.5 was considered significantly asymmetric. The lateral interdigitation of the frontalis and orbicularis oculi muscles was measured from the supraorbital notch with a metric ruler. Data were analyzed using 2-sample t tests, paired t tests, log scales, and nonparametric tests were performed for sensitivity analyses. A p value of ≤0.05 was considered statistically significant. ::: ::: ::: RESULTS ::: Fifty-eight hemifaces of 29 Caucasian cadavers were studied for muscle belly asymmetry. Thirty-six hemifaces of 18 Caucasian cadavers (9 males) were dissected for lateral landmarks and average age of these specimens was 73 years (range: 35-91 years). Significant asymmetry in muscle belly area was found in 6/29 (20%) specimens, with the right muscle belly larger in all 6 specimens. On average, the right muscle belly area was 1.23 times that of the left (p = <0.001). The average frontalis-orbicularis interdigitation occurred 3.4 cm lateral to the supraorbital notch. ::: ::: ::: CONCLUSIONS ::: Significant frontalis muscle belly asymmetry exists in 20% of Caucasians cadavers. The right muscle belly was larger on average and in all cases of significant asymmetry. The frontalis muscle interdigitates with the orbicularis oculi on average 3.4 cm lateral to the supraorbital notch.


title: Image Classification and Retrieval are ONE

abstract: In this paper, we demonstrate that the essentials of image classification and retrieval are the same, since both tasks could be tackled by measuring the similarity between images. To this end, we propose ONE (Online Nearest-neighbor Estimation), a unified algorithm for both image classification and retrieval. ONE is surprisingly simple, which only involves manual object definition, regional description and nearest-neighbor search. We take advantage of PCA and PQ approximation and GPU parallelization to scale our algorithm up to large-scale image search. Experimental results verify that ONE achieves state-of-the-art accuracy in a wide range of image classification and retrieval benchmarks.


title: Mining Semantic Trajectory Patterns from Geo-Tagged Data

abstract: User-generated social media data tagged with geographic information present messages of dynamic spatio-temporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.


title: Tribology and hot forming performance of self-lubricious NC/NiBN and NC/WC:C hybrid composite coatings for hot forming die

abstract: Abstract Adhesion between blank and die is the main reason for seizure and short tooling life during metal forming processes at elevated temperatures. This study applied novel complex-structured composite coatings, NC/NiBN and NC/WC:C, to the hot forming die flange and radius to reduce the use of lubricant. The high-temperature adhesion resistance and hot forming properties of the hybrid coated dies were tested and correlated with the microstructure and mechanical properties of the contacted blank/tool surfaces. The results show that the coated dies experienced little adhesion at elevated temperatures, consequently, ultra-low coefficients of friction of 0.11 against steel and 0.10 against Al at 350 °C were obtained. The minimum weight of lubricant per unit area required for a successful deep drawing of AA6082 was quantified by hot forming tests at various temperatures, and it was reduced by 83% at 400 °C. Complete lubricant-free deep drawing was achieved with limited blankholding force and forming temperature. Based on this experimental data and theoretical analysis of two disparate stress states, a model comparing the frictional state of material surfaces using the geometric features on formed parts is proposed.


title: Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine

abstract: Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30 min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30 s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p < 0.001). SVM was used and obtained high and consistent recognition rate: average classification accuracy reached 95.1 % corresponding to the sensitivity 93.2 % and specificity 98.6 %. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea.


title: The Role of Color in Healthcare Environments, Emergent Bodies of Evidence-based Design Approach

abstract: The objective of this study is to overview the literature relative to color, as an environmental constituent, and its impacts in healthcare environments. Borrowing from the environmental behavioral paradigm, this study attempts to decipher myths and misconceptions as well as highlight well-evidenced research findings. Broad Literature review journal articles and reports of empirical studies in multiple disciplines were studied to identify theories, which could have design implications for color in healthcare design. Despite the incongruity and fragmentation of previous studies, it emerges from this overview that color impacts healthcare outcomes by reducing medical errors, promoting the sense of well-being, reducing stress, improving patients sleep, reducing length of stay, reducing spatial disorientation, increasing patient satisfaction, and increasing staff morale and productivity. While the review of literature indicates major findings relative to the impact of color on healthcare environments, yet ambiguities remained to be addressed. Previous studies on the use of color in healthcare environment have illustrated that there are some obvious, replicable, behavioral and perceptual effects from color that addressed their use in certain ways for design. However, color must be observed and analyzed in the contextual application to avoid generalizations about color perception and mood affects.


title: Neurofeedback training in children with ADHD: 6-month follow-up of a randomised controlled trial

abstract: Neurofeedback (NF) could help to improve attentional and self-management capabilities in children with attention-deficit/hyperactivity disorder (ADHD). In a randomised controlled trial, NF training was found to be superior to a computerised attention skills training (AST) (Gevensleben et al. in J Child Psychol Psychiatry 50(7):780–789, 2009). In the present paper, treatment effects at 6-month follow-up were studied. 94 children with ADHD, aged 8–12 years, completed either 36 sessions of NF training (n = 59) or a computerised AST (n = 35). Pre-training, post-training and follow-up assessment encompassed several behaviour rating scales (e.g., the German ADHD rating scale, FBB-HKS) completed by parents. Follow-up information was analysed in 61 children (ca. 65%) on a per-protocol basis. 17 children (of 33 dropouts) had started a medication after the end of the training or early in the follow-up period. Improvements in the NF group (n = 38) at follow-up were superior to those of the control group (n = 23) and comparable to the effects at the end of the training. For the FBB-HKS total score (primary outcome measure), a medium effect size of 0.71 was obtained at follow-up. A reduction of at least 25% in the primary outcome measure (responder criterion) was observed in 50% of the children in the NF group. In conclusion, behavioural improvements induced by NF training in children with ADHD were maintained at a 6-month follow-up. Though treatment effects appear to be limited, the results confirm the notion that NF is a clinically efficacious module in the treatment of children with ADHD.


title: Accelerating a computer vision algorithm on a mobile SoC using CPU-GPU co-processing: a case study on face detection

abstract: Recently, mobile devices have become equipped with sophisticated hardware components such as a heterogeneous multi-core SoC that consists of a CPU, GPU, and DSP. This provides opportunities to realize computationally-intensive computer vision applications using General Purpose GPU (GPGPU) programming tools such as Open Graphics Library for Embedded System (OpenGL ES) and Open Computing Language (OpenCL). As a case study, the aim of this research was to accelerate the Viola-Jones face detection algorithm which is computationally expensive and limited in use on mobile devices due to irregular memory access and imbalanced workloads resulting in low performance regarding the processing time. To solve the above challenges, the proposed method of this study adapted CPU–GPU task parallelism, sliding window parallelism, scale image parallelism, dynamic allocation of threads, and local memory optimization to improve the computational time. The experimental results show that the proposed method achieved a 3.3~6.29 times increased computational time compared to the well-optimized OpenCV implementation on a CPU. The proposed method can be adapted to other applications using mobile GPUs and CPUs.


title: The NPD Benchmark: Reality Check for OBDA Systems

abstract: In the last decades we moved from a world in which an enterprise had one central database—rather small for todays’ standards—to a world in which many different—and big—databases must interact and operate, providing the user an integrated and understandable view of the data. Ontology-Based Data Access (OBDA) is becoming a popular approach to cope with this new scenario. OBDA separates the user from the data sources by means of a conceptual view of the data (ontology) that provides clients with a convenient query vocabulary. The ontology is connected to the data sources through a declarative specification given in terms of mappings. Although prototype OBDA systems providing the ability to answer SPARQL queries over the ontology are available, a significant challenge remains when it comes to use these systems in industrial environments: performance. To properly evaluate OBDA systems, benchmarks tailored towards the requirements in this setting are needed. In this work, we propose a novel benchmark for OBDA systems based on real data coming from the oil industry: the Norwegian Petroleum Directorate (NPD) FactPages. Our benchmark comes with novel techniques to generate, from the NPD data, datasets of increasing size, taking into account the requirements dictated by the OBDA setting. We validate our benchmark on significant OBDA systems, showing that it is more adequate than previous benchmarks not tailored for OBDA.


title: Discriminative Word Alignment With Conditional Random Fields

abstract: In this paper we present a novel approach for inducing word alignments from sentence aligned data. We use a Conditional Random Field (CRF), a discriminative model, which is estimated on a small supervised training set. The CRF is conditioned on both the source and target texts, and thus allows for the use of arbitrary and overlapping features over these data. Moreover, the CRF has efficient training and decoding processes which both find globally optimal solutions.We apply this alignment model to both French-English and Romanian-English language pairs. We show how a large number of highly predictive features can be easily incorporated into the CRF, and demonstrate that even with only a few hundred word-aligned training sentences, our model improves over the current state-of-the-art with alignment error rates of 5.29 and 25.8 for the two tasks respectively.


title: How social media engagement leads to sports channel loyalty: Mediating roles of social presence and channel commitment

abstract: We examined the effect of backchannel communication on sports channel loyalty.Three dimensions of social media engagement-functional, emotional, communal-were confirmed.The communal engagement has a direct effect on channel loyalty.The emotional engagement has an indirect effect on the loyalty via channel commitment.The results imply that the functional engagement can lead to loyalty mediated by social presence. Social TV is defined as real-time backchannel communication on social networking sites (SNSs) during a live television broadcast. Despite some pessimistic forecasts concerning the impact of social TV on viewing behaviors, broadcasters have found that the social TV phenomenon plays a positive role in boosting television ratings. We examine whether television viewers' backchannel communication during a mega-sporting event has a positive effect on their sports channel loyalty. A survey of a randomly drawn sample (N=500) from national panel data in South Korea was conducted right after the 2014 Sochi Olympic Games. On the basis of theories in web interactivity, emotional attachment, and company-consumer identification, we proposed three levels of social TV engagement. Results of a confirmatory factor analysis yielded the tripartite dimensions of social media engagement (i.e., functional, emotional, and communal) among viewers of social TV. A test of the structural equation model (SEM) revealed that functional engagement had a direct impact on social presence and that the communal engagement had a direct effect on channel loyalty. The effect of emotional engagement on channel loyalty was mediated by channel commitment. The social presence led to channel loyalty mediated by channel commitment.


title: Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms

abstract: Psilocybin with psychological support is showing promise as a treatment model in psychiatry but its therapeutic mechanisms are poorly understood. Here, cerebral blood flow (CBF) and blood oxygen-level dependent (BOLD) resting-state functional connectivity (RSFC) were measured with functional magnetic resonance imaging (fMRI) before and after treatment with psilocybin (serotonin agonist) for treatment-resistant depression (TRD). Quality pre and post treatment fMRI data were collected from 16 of 19 patients. Decreased depressive symptoms were observed in all 19 patients at 1-week post-treatment and 47% met criteria for response at 5 weeks. Whole-brain analyses revealed post-treatment decreases in CBF in the temporal cortex, including the amygdala. Decreased amygdala CBF correlated with reduced depressive symptoms. Focusing on a priori selected circuitry for RSFC analyses, increased RSFC was observed within the default-mode network (DMN) post-treatment. Increased ventromedial prefrontal cortex-bilateral inferior lateral parietal cortex RSFC was predictive of treatment response at 5-weeks, as was decreased parahippocampal-prefrontal cortex RSFC. These data fill an important knowledge gap regarding the post-treatment brain effects of psilocybin, and are the first in depressed patients. The post-treatment brain changes are different to previously observed acute effects of psilocybin and other ‘psychedelics’ yet were related to clinical outcomes. A ‘reset’ therapeutic mechanism is proposed.


title: MODELING AND CONTROL OF BALL AND BEAM SYSTEM USING MODEL BASED AND NON-MODEL BASED CONTROL APPROACHES

abstract: The ball and beam system is a laboratory equipment with high nonlinearity in its dynamics. The aims of this research are to model the ball and beam system considering nonlinear factors and coupling effect and to design controllers to control the ball position. The LQR is designed considering two Degrees-of-Freedom and coupling dynamics. The parameters of the LQR are tuned using Genetic Algorithm (GA). Jacobian linearization method is used to linearize the system around operating-point. Due to the noise of the sensor in the experimental setup, a state observer is designed to observe the velocity of the ball. In order to compare the performance of the LQR and study the effect of simplifying assumptions, two control strategies are designed and implemented: Proportional Derivative Integral (PID) as non-model based control strategy, hybrid PID and Linear Quadratic Regulator (LQR) as combination of model based and non-model based control strategies. The experimental results of this research prove the model based control strategies outperforms the non-model based or hybrid controllers in a nonlinear and noisy ball and beam system. In addition, it is shown that the coupling dynamics cannot be eliminated as a simplifying assumption in designing the controller.


title: A novel Trellis Coded Modulation scheme for robust transmission of Prioritized H.264/AVC Video employing Alamouti's code for wireless MIMO systems

abstract: Recently, there has been an increasing demand for high data rate communications for wireless technologies. With the integration of these technologies and multimedia services, transmitting high quality image and video has become one of the main objectives for the present and next generations of multimedia wireless network systems. In this research work, it is proposed to apply prioritized Trellis Coded Modulation (TCM) scheme employing Alamouti codes to H.264/AVC video bitstreams for increased robust transmissions over MIMO channels. The channel data rate is kept constant at 1Mbps whilst the source rate is varied according to the TCM Alamouti code rates. In this work the inherent UEP capability of the H.264/AVC video bitstreams at the application layer is applied in order to have more robust transmission. All implementations are carried out using MATLAB R2013 R Simulink R and Cygwin. It is shown that in MIMO channels the


title: Morbidity and Mortality Associated with the Utilization of Restraints

abstract: Use of physical restraints remains a highly controversial topic. Even with proven efficacy in restraint usage across multiple settings, for years, investigators have debated whether or not the risks outweigh the benefits. There is a growing concern regarding restraints-related negative consequences. Although over the past two decades, with new regulations, education and training, there has been a reduction in the prevalence of restraint episodes, morbidity and mortality are still disconcerting. Given this subject remains an issue today, a more up-to-date review of available literature is warranted. This article reviews the current literature surrounding the utilization of restraints that has been published over last 10 years with particular emphasis on restraints-related adverse outcomes. The vast amount of literature during the past decade demonstrates an increased awareness in potential dangers, as well as highlights new areas of research in restraint utilization. Despite the proliferation of studies, there continues to remain a lack of evidence from prospective studies that would elucidate the dangers from theory to practice.


title: Survey on deep learning for radiotherapy

abstract: Abstract More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.


title: A procedural generation framework for a robot construction game

abstract: This paper explores a framework to permit the creation of modules as part of a robot creation and combat game. We explore preliminary work that offers a design solution to generate and test robots comprised of modular components. This current implementation, which is reliant on a constraint-driven process is then assessed to indicate the expressive range of content it can create and the total number of unique combinations it can establish.


title: A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining

abstract: To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.


An Empirical Study into the Security Exposure to Hosts of Hostile Virtualized Environments


An Analysis of Topic Modelling for Legislative Texts


title: Automatic Ear Landmark Localization, Segmentation, and Pose Classification in Range Images

abstract: Multibiometric systems using face and ear features are increasingly adopted for forensic and civilian applications to address the challenges of facial expressions and occlusions. Although numerous ear and face recognition techniques have been proposed, not much work has been conducted in the field of 3-D fiducial points localization and 3-D ear detection. This paper presents an effective and efficient system of ear landmark localization, ear detection, and pose classification based on 3-D ears captured under large yaw variations. By utilizing the symmetrical property of human heads and classifying the ear with respect to its pose, all three tasks can be fulfilled given either left or right ears, without any prior pose information. A novel ear tree-structured graph (ETG) is proposed to represent the 3-D ear, after which a 3-D flexible mixture model is trained to locate the landmarks automatically. Then, the ear region is segmented based on them and the pose of the ear, i.e., whether it is a left or right ear, is classified based on the detected ETG. To the best of our knowledge, this paper is the first to present automatic landmark localization of 3-D ears extracted from facial scans with significant pose variations. Experiments were conducted at the University of Notre Dame collection F, G and J2, which contain large occlusion and pose variations, validating the effectiveness of the proposed methods.


title: Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms

abstract: With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term “prototypes” refers to the reference instances used in a nearest neighbor computation — the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes.


title: Filmmaking: A Video-Based Intervention for Developing Social Skills in Children With Autism Spectrum Disorder

abstract: Video production can be easily used as a way to develop social skills in older children with higher-level autism and Asperger syndrome. The program described in this article is an inclusive program that employs a reverse inclusion strategy to teach social skills to children and adolescents with autism utilizing filmmaking. We discuss some of the social challenges faced by these children, the importance of addressing social skills, the program that we implement for developing social skills in children on the spectrum, and our experiences and observations with this program. We provide strategies that should be considered during filmmaking to help children on the spectrum develop social skills in integrated settings, share our perspectives of filmmaking, and our observations of the impact of filmmaking groups on learning.


title: Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China

abstract: With the rapid development of mobile technology and large usage rates of mobile phones, mobile instant message (MIM) services have been widely adopted in China. Although previous studies on the adoption of mobile services are quite extensive, few focus on customer satisfaction and loyalty to MIM in China. In this study, we examine the determinants of customer satisfaction and loyalty. The findings confirm that trust, perceived service quality, perceived customer value, including functional value and emotional value, contribute to generating customer satisfaction with MIM. The results also show that trust, customer satisfaction and switching cost directly enhance customer loyalty. Additionally, this study finds that age, gender, and usage time have moderating effects. Finally, implications for the marketing of MIM are discussed.


title: Examining individuals' adoption of healthcare wearable devices: An empirical study from privacy calculus perspective

abstract: BACKGROUND ::: Wearable technology has shown the potential of improving healthcare efficiency and reducing healthcare cost. Different from pioneering studies on healthcare wearable devices from technical perspective, this paper explores the predictors of individuals' adoption of healthcare wearable devices. Considering the importance of individuals' privacy perceptions in healthcare wearable devices adoption, this study proposes a model based on the privacy calculus theory to investigate how individuals adopt healthcare wearable devices. ::: ::: ::: METHOD ::: The proposed conceptual model was empirically tested by using data collected from a survey. The sample covers 333 actual users of healthcare wearable devices. Structural equation modeling (SEM) method was employed to estimate the significance of the path coefficients. ::: ::: ::: RESULTS ::: This study reveals several main findings: (1) individuals' decisions to adopt healthcare wearable devices are determined by their risk-benefit analyses (refer to privacy calculus). In short, if an individual's perceived benefit is higher than perceived privacy risk, s/he is more likely to adopt the device. Otherwise, the device would not be adopted; (2) individuals' perceived privacy risk is formed by health information sensitivity, personal innovativeness, legislative protection, and perceived prestige; and (3) individuals' perceived benefit is determined by perceived informativeness and functional congruence. The theoretical and practical implications, limitations, and future research directions are then discussed.


A Cross-Cultural Analysis of Communication Apprehension A Comparison of Three European Nations


title: Automated Analysis of Exam Questions According to Bloom's Taxonomy

abstract: Bloom's Taxonomy is a classification of learning objectives within education that educators set for students. The cognitive domain within this taxonomy is designed to verify a student's cognitive level during a written examination. Educators may sometimes face the challenge in analysing whether their examination questions comply within the requirements of the Bloom’s taxonomy at different cognitive levels. This paper proposes an automated analysis of the exam questions to determine the appropriate category based on this taxonomy. This rule-based approach applies Natural Language Processing (NLP) techniques to identify important keywords and verbs, which may assist in the identification of the category of a question. This work focuses on the computer programming subject domain. At present, a set of 100 questions (70 training set and 30 test set) is used in the research. Preliminary results indicate that the rules may successfully assist in the identification of the Bloom’s taxonomy category correctly in the exam questions. © 2011 Published by Elsevier Ltd. Selection and/or peer reviewed under responsibility of the UKM Teaching and Learning Congress 2011.


title: Face sketch recognition

abstract: Automatic retrieval of face images from police mug-shot databases is critically important for law enforcement agencies. It can effectively help investigators to locate or narrow down potential suspects. However, in many cases, a photo image of a suspect is not available and the best substitute is often a sketch drawing based on the recollection of an eyewitness. We present a novel photo retrieval system using face sketches. By transforming a photo image into a sketch, we reduce the difference between photo and sketch significantly, thus allowing effective matching between the two. Experiments over a data set containing 188 people clearly demonstrate the efficacy of the algorithm.


title: Towards night fog detection through use of in-vehicle multipurpose cameras

abstract: Compared to daytime, a larger proportion of accident happens during nighttime. The altered visibility of the road scene for the drivers may partially explain this situation. The latter becomes worse in fog presence. In this paper, two camera-based methods are proposed to detect the presence of night fog in images grabbed by in-vehicle multipurpose cameras. They rely on the visual effects of night fog. A first approach can assess the presence of fog around the vehicle thanks to the detection of the backscattered veil induced by the vehicle ego lights. It aims at detecting fog when the vehicle is alone in absence of exterior public lighting. A second approach can assess the presence of fog thanks to the detection of halos around light sources in the vehicle environment. It aims at detecting fog in presence of road traffic or public lighting. Both methods are presented and illustrated with actual images of fog. Their complementarity makes it possible to envision a complete night fog detection system. There are numerous applications for such a system: automation or adaptation of vehicle lights, contextual speed computation and reliability improvement for camera-based systems.


title: Predicting piezometric water level in dams via artificial neural networks

abstract: The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams.


title: EQ-nomics: Understanding the relationship between individual differences in Trait Emotional Intelligence and entrepreneurship

abstract: Past studies highlight the importance of Trait Emotional Intelligence (EI) in the prediction of career success. Given the evidence that trait EI predicts job performance and job satisfaction, it is reasonable to expect this construct to also predict other forms of career success. In this study, we examine whether EI predicts entrepreneurship; that is, whether higher trait EI is linked to entrepreneurial behaviours and entrepreneurial success, and whether any effects of trait EI on entrepreneurship are independent of the personality trait of Core Self-Evaluations, demographic variables, and individual differences in entrepreneurial personality. Results show that trait EI predicts only some entrepreneurial outcomes beyond other variables examined, and with small effect sizes. This suggests that individual differences in entrepreneurship result only in part from inter-personal differences in trait EI. Implications for research and practice are discussed.


title: Automatic Uncovering of Tap Points from Kernel Executions

abstract: Automatic uncovering of tap points (i.e., places to deploy active monitoring) in an OS kernel is useful in many security applications such as virtual machine introspection, kernel malware detection, and kernel rootkit profiling. However, current practice to extract a tap point for an OS kernel is through either analyzing kernel source code or manually reverse engineering of kernel binary. This paper presents AutoTap, the first system that can automatically uncover the tap points directly from kernel binaries. Specifically, starting from the execution of system calls (i.e., the user level programing interface) and exported kernel APIs (i.e., the kernel module/driver development interface), AutoTap automatically tracks kernel objects, resolves their kernel execution context, and associates the accessed context with the objects, from which to derive the tap points based on how an object is accessed (e.g., whether the object is created, accessed, updated, traversed, or destroyed). The experimental results with a number of Linux kernels show that AutoTap is able to automatically uncover the tap points for many kernel objects, which would be very challenging to achieve with manual analysis. A case study of using the uncovered tap points shows that we can use them to build a robust hidden process detection tool at the hypervisor layer with very low overhead.


title: Role-Based Access Controls

abstract: While Mandatory Access Controls (MAC) are appropriate for multilevel secure military applications, Discretionary Access Controls (DAC) are often perceived as meeting the security processing needs of industry and civilian government. This paper argues that reliance on DAC as the principal method of access c ontrol is unfounded and inappropriate for many commercial and civilian government organizations. The paper describes a type of non-discretionary access contro l - role-based access control (RBAC) that is more central to the secure processing needs of non-military systems than DAC.


title: Understanding the scope of web usage mining & applications of web data usage patterns

abstract: Due to increased usage of web as a medium of publication by entrepreneurs and federal agencies, a massive amount of data is stored in structured and unstructured forms over the web. This leads to page-cluttering, which makes it difficult to distinguish the content of interest from superfluous data such as billboards, and patent and other announcements. Web Usage Mining (WUM) is a type of data mining technique that identifies usage patterns of web data, so as to perceive and better serve the requirements of web applications. The working of WUM involves three steps - pre-processing, pattern discovery and analysis. This research paper studies the identification of web usage patterns based on the user's interest/choice, thereby creating an intelligent semantics-based web usage mining technique.


title: Swarm intelligence applied in green logistics: A literature review

abstract: Abstract Green logistics (GL) is gaining increasing attention among academic researchers and industrial practitioners, due to the escalating deterioration of the environment. Various green activities and operations aiming at improving the performance of GL have been applied synthetically, and most of the activities can be modeled as combinatorial optimization (CO) problems. Exact approaches tend to be incapable of solving the CO problems, especially with the increasing complexity. Thus, meta-heuristic approaches are widely adopted, which can generate a satisfactory solution within an acceptable time. Swarm intelligence (SI) is an innovative branch of meta-heuristics derived from imitating the behavioral pattern of natural insects. The distributed control mechanism and simple interactive rules can manage the swarm of insects effectively and efficiently. There are some pilot studies in applying SI into GL, which indicates that the integration of GL and SI could be a promising choice and of great potential. This research reviews the application of SI in GL through a comprehensive and extensive investigation and analysis of extant literature, which includes 115 publications in the last twenty years. The integration of GL and SI is analyzed from the perspective of both the problem context and the methodology. The categories of GL and SI are classified systematically. The CO problems of GL are further studied with SI algorithms, and innovative and universal guidance for algorithm customization in resolving CO problems emerges as well. Further potential research issues and opportunities of GL and SI are also identified in this research.


title: Why distance matters: effects on cooperation, persuasion and deception

abstract: In this study, we examine how geographic distance affects collaboration using computer-mediated communication technology. We investigated experimentally the effects of cooperating partners being in the same or distant city on three behaviors: cooperation, persuasion, and deception using video conferencing and instant messaging (IM). Our results indicate that subjects are more likely to deceive, be less persuaded by, and initially cooperate less, with someone they believe is in a distant city, as opposed to in the same city as them. Although people initially cooperate less with someone they believe is far away, their willingness to cooperate increases quickly with interaction. Since the same media were used in both the far and near city conditions, these effects cannot be attributed to the media, but rather to social differences. This study confirms how CSCW needs to be concerned with developing technologies for bridging social distance, as well as geographic distance.


title: A Closed-form Solution to Photorealistic Image Stylization

abstract: Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic. While several photorealistic image stylization methods exist, they tend to generate spatially inconsistent stylizations with noticeable artifacts. In this paper, we propose a method to address these issues. The proposed method consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step ensures spatially consistent stylizations. Each of the steps has a closed-form solution and can be computed efficiently. We conduct extensive experimental validations. The results show that the proposed method generates photorealistic stylization outputs that are more preferred by human subjects as compared to those by the competing methods while running much faster. Source code and additional results are available at https://github.com/NVIDIA/FastPhotoStyle.


title: 3D M-Blocks: Self-reconfiguring robots capable of locomotion via pivoting in three dimensions

abstract: This paper presents the mechanical design of a modular robot called the 3D M-Block, a 50mm cubic module capable of both independent and lattice-based locomotion. The first M-Blocks described in [1] could pivot about one axis of rotation only. In contrast, the 3D M-blocks can exert on demand both forward and backward torques about three orthogonal axes, for a total of six directions. The 3D M-Blocks transform these torques into pivoting motions which allow the new 3D M-Blocks to move more freely than their predecessors. Individual modules can employ pivoting motions to independently roll across a wide variety of surfaces as well as to join and move relative to other M-Blocks as part of a larger collective structure. The 3D M-Block maintains the same form factor and magnetic bonding system as the one-dimensional M-Blocks [1], but a new fabrication process supports more efficient and precise production. The 3D M-blocks provide a robust and capable modular self-reconfigurable robotic platform able to support swarm robot applications through individual module capabilities and self-reconfiguring robot applications using connected lattices of modules.


title: Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC

abstract: The printing press long ago and the computer today have made widespread access to information possible. Learning theorists have suggested, however, that mere information is a poor way to learn. Instead, more effective learning comes through doing. While the most popularized element of today's MOOCs are the video lectures, many MOOCs also include interactive activities that can afford learning by doing. This paper explores the learning benefits of the use of informational assets (e.g., videos and text) in MOOCs, versus the learning by doing opportunities that interactive activities provide. We find that students doing more activities learn more than students watching more videos or reading more pages. We estimate the learning benefit from extra doing (1 SD increase) to be more than six times that of extra watching or reading. Our data, from a psychology MOOC, is correlational in character, however we employ causal inference mechanisms to lend support for the claim that the associations we find are causal.


title: Composing Algorithmic Skeletons to Express High-Performance Scientific Applications

abstract: Algorithmic skeletons are high-level representations for parallel programs that hide the underlying parallelism details from program specification. These skeletons are defined in terms of higher-order functions that can be composed to build larger programs. Many skeleton frameworks support efficient implementations for stand-alone skeletons such as map, reduce, and zip for both shared-memory systems and small clusters. However, in these frameworks, expressing complex skeletons that are constructed through composition of fundamental skeletons either requires complete reimplementation or suffers from limited scalability due to required global synchronization. In the STAPL Skeleton Framework, we represent skeletons as parametric data flow graphs and describe composition of skeletons by point-to-point dependencies of their data flow graph representations. As a result, we eliminate the need for reimplementation and global synchronizations in composed skeletons. In this work, we describe the process of translating skeleton-based programs to data flow graphs and define rules for skeleton composition. To show the expressivity and ease of use of our framework, we show skeleton-based representations of the NAS EP, IS, and FT benchmarks. To show reusability and applicability of our framework on real-world applications we show an N-Body application using the FMM (Fast Multipole Method) hierarchical algorithm. Our results show that expressivity can be achieved without loss of performance even in complex real-world applications.


title: Low-Loss Integrated-Waveguide Passive Circuits Using Liquid-Crystal Polymer System-on-Package (SOP) Technology for Millimeter-Wave Applications

abstract: In this paper, we show a low-loss integrated waveguide (IWG), microstrip line-to-IWG transition, IWG bandpass filter (BPF), and system-on-package (SOP) using a liquid-crystal polymer (LCP) substrate, which can be used toward SOP technology for millimeter-wave applications. The proposed IWG can be used as a low-loss millimeter-wave transmission line on this substrate. The measured insertion loss of the IWG is -0.12 dB/mm and the measured insertion loss of two microstrip line-to-IWG transitions is -0.14 dB at 60 GHz. The evaluated IWG filter is demonstrated as the pre-select filter for RF front-end modules at the millimeter-wave band. The fabricated three-pole BPF at a center frequency of 61.1 GHz has specifications: a 3-dB bandwidth of approximately 13.4% (~8.4 GHz), an insertion loss of -1.8 dB at the center frequency of 61.1 GHz, and a rejection of >15 dB over the passband. The proposed IWG can also be used as a low-loss millimeter-wave feed-through transition and interconnection between the monolithic microwave integrated circuit and the module instead of the vertical via structure. In terms of a SOP on LCP for millimeter-wave applications, the top face of the IWG does not have any electromagnetic effects, and a package lid can be attached to provide a hermetic sealing. These low-loss IWG circuits on LCP can easily be used in many millimeter-wave packaging applications


title: Malicious behavior in online social network

abstract: Nowadays, Online Social Networks (OSNs) are very popular and have become an integral part of our life. People are dependent on Online Social Networks for various purposes. The activities of most of the users are normal, but a few of the users exhibit unusual and suspicious behavior. We term this suspicious and unusual behavior as malicious behavior. Malicious behavior in Online Social Networks includes a wide range of unethical activities and actions performed by individuals or communities to manipulate thought process of OSN users to fulfill their vested interest. Such malicious behavior needs to be checked and its effects should be minimized. To minimize effects of such malicious activities, we require proper detection and containment strategy. Such strategy will protect millions of users across the OSNs from misinformation and security threats. In this paper, we discuss the different studies performed in the area of malicious behavior analysis and propose a framework for detection of malicious behavior in OSNs.


Title Energy-Efficient Deep In-memory Architecture for NAND Flash Memories


title: Fuzzy Bag-of-Words Model for Document Representation

abstract: One key issue in text mining and natural language processing is how to effectively represent documents using numerical vectors. One classical model is the Bag-of-Words (BoW). In a BoW-based vector representation of a document, each element denotes the normalized number of occurrence of a basis term in the document. To count the number of occurrence of a basis term, BoW conducts exact word matching, which can be regarded as a hard mapping from words to the basis term. BoW representation suffers from its intrinsic extreme sparsity, high dimensionality, and inability to capture high-level semantic meanings behind text data. To address the aforementioned issues, we propose a new document representation method named fuzzy Bag-of-Words (FBoW) in this paper. FBoW adopts a fuzzy mapping based on semantic correlation among words quantified by cosine similarity measures between word embeddings. Since word semantic matching instead of exact word string matching is used, the FBoW could encode more semantics into the numerical representation. In addition, we propose to use word clusters instead of individual words as basis terms and develop fuzzy Bag-of-WordClusters (FBoWC) models. Three variants under the framework of FBoWC are proposed based on three different similarity measures between word clusters and words, which are named as $\text{FBoWC}_{\rm mean}$ , $\text{FBoWC}_{\rm max}$ , and $\text{FBoWC}_{\rm min}$ , respectively. Document representations learned by the proposed FBoW and FBoWC are dense and able to encode high-level semantics. The task of document categorization is used to evaluate the performance of learned representation by the proposed FBoW and FBoWC methods. The results on seven real-word document classification datasets in comparison with six document representation learning methods have shown that our methods FBoW and FBoWC achieve the highest classification accuracies.


title: A neural network approach for students' performance prediction

abstract: In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.


title: Game Theory for Cyber Security and Privacy

abstract: In this survey, we review the existing game-theoretic approaches for cyber security and privacy issues, categorizing their application into two classes, security and privacy. To show how game theory is utilized in cyberspace security and privacy, we select research regarding three main applications: cyber-physical security, communication security, and privacy. We present game models, features, and solutions of the selected works and describe their advantages and limitations from design to implementation of the defense mechanisms. We also identify some emerging trends and topics for future research. This survey not only demonstrates how to employ game-theoretic approaches to security and privacy but also encourages researchers to employ game theory to establish a comprehensive understanding of emerging security and privacy problems in cyberspace and potential solutions.


title: Rapid Adaptation with Conditionally Shifted Neurons

abstract: We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons. We apply this mechanism in the framework of metalearning, where the aim is to replicate some of the flexibility of human learning in machines. Conditionally shifted neurons modify their activation values with task-specific shifts retrieved from a memory module, which is populated rapidly based on limited task experience. On metalearning benchmarks from the vision and language domains, models augmented with conditionally shifted neurons achieve state-of-the-art results.


title: Neural Decomposition of Time-Series Data for Effective Generalization

abstract: We present a neural network technique for the analysis and extrapolation of time-series data called neural decomposition (ND) . Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components. We show how careful weight initialization can be combined with regularization to form a simple model that generalizes well. Our method generalizes effectively on the Mackey–Glass series, a data set of unemployment rates as reported by the U.S. Department of Labor Statistics, a time-series of monthly international airline passengers, the monthly ozone concentration in downtown Los Angeles, and an unevenly sampled time series of oxygen isotope measurements from a cave in north India. We find that ND outperforms popular time-series forecasting techniques, including long short-term memory network, echo-state networks, autoregressive integrated moving average (ARIMA), seasonal ARIMA, support vector regression with a radial basis function, and Gashler and Ashmore’s model.


Base-station Antenna Pattern Design for Maximizing Average Channel Capacity in Indoor MIMO System


title: TEXT ANALYTICS IN SOCIAL MEDIA

abstract: The rapid growth of online social media in the form of collaborativelycreated content presents new opportunities and challenges to both producers and consumers of information. With the large amount of data produced by various social media services, text analytics provides an effective way to meet usres’ diverse information needs. In this chapter, we first introduce the background of traditional text analytics and the distinct aspects of textual data in social media. We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world examples.


title: Clustering noisy data by a principal feature extraction unsupervised neural network

abstract: Principal feature classification is based on a sequential procedure for finding the principal features from an assigned data set. This paper presents an unsupervised neural network which is able to find principal features, based on neural units sensitive to density of the data space. These units adopt a modified competitive learning law, which utilizes only local information to specialize toward a single cluster. It is shown that the network presented is able to automatically select the number of units as in the rival penalized competitive network, and also to correctly detect features when the number of clusters exceed the number of units. Simulations on IRIS data set are provided and it is shown that the proposed network presents property of robust noise rejection and is suitable for features extraction in noise data sets.


title: Hybrid Concolic Testing

abstract: We present hybrid concolic testing, an algorithm that interleaves random testing with concolic execution to obtain both a deep and a wide exploration of program state space. Our algorithm generates test inputs automatically by interleaving random testing until saturation with bounded exhaustive symbolic exploration of program points. It thus combines the ability of random search to reach deep program states quickly together with the ability of concolic testing to explore states in a neighborhood exhaustively. We have implemented our algorithm on top of CUTE and applied it to obtain better branch coverage for an editor implementation (VIM 5.7, 150 K lines of code) as well as a data structure implementation in C. Our experiments suggest that hybrid concolic testing can handle large programs and provide, for the same testing budget, almost 4times the branch coverage than random testing and almost 2times that of concolic testing.


title: VIEWS OF NATURE AND SELF-DISCIPLINE: EVIDENCE FROM INNER CITY CHILDREN

abstract: Children growing up in the inner city are at risk of academic underachievement, juvenile delinquency, teenage pregnancy, and other important negative outcomes. Avoiding these outcomes requires self-discipline. Self-discipline, in turn, may draw on directed attention, a limited resource that can be renewed through contact with nature. This study examined the relationship between near-home nature and three forms of self-discipline in 169 inner city girls and boys randomly assigned to 12 architecturally identical high-rise buildings with varying levels of nearby nature. Parent ratings of the naturalness of the view from home were used to predict children’s performance on tests of concentration, impulse inhibition, and delay of grati¢cation. Regressions indicated that, on average, the more natural a girl’s view from home, the better her performance at each of these forms of self-discipline. For girls, view accounted for 20% of the variance in scores on the combined selfdiscipline index. For boys, who typically spend less time playing in and around their homes, view from home showed no relationship to performance on any measure. These ¢ndings suggest that, for girls, green space immediately outside the home can help them lead more eiective, self-disciplined lives. For boys, perhaps more distant green spaces are equally important. # 2002 Academic Press


title: The Effects of Mobile-Computer-Supported Collaborative Learning: Meta-Analysis and Critical Synthesis

abstract: One of the trends in collaborative learning is using mobile devices for supporting the process and products of collaboration, which has been forming the field of mobile-computer-supported collaborative learning (mCSCL). Although mobile devices have become valuable collaborative learning tools, evaluative evidence for their substantial contributions to collaborative learning is still scarce. The present meta-analysis, which included 48 peer-reviewed journal articles and doctoral dissertations written over a 16-year period (2000–2015) involving 5,294 participants, revealed that mCSCL has produced meaningful improvements for collaborative learning, with an overall mean effect size of 0.516. Moderator variables, such as domain subject, group size, teaching method, intervention duration, and reward method were related to different effect sizes. The results provided implications for future research and practice, such as suggestions on how to appropriately use the functionalities of mobile devices, how to best l...


title: Imitation, mirror neurons and autism

abstract: Various deficits in the cognitive functioning of people with autism have been documented in recent years but these provide only partial explanations for the condition. We focus instead on an imitative disturbance involving difficulties both in copying actions and in inhibiting more stereotyped mimicking, such as echolalia. A candidate for the neural basis of this disturbance may be found in a recently discovered class of neurons in frontal cortex, 'mirror neurons' (MNs). These neurons show activity in relation both to specific actions performed by self and matching actions performed by others, providing a potential bridge between minds. MN systems exist in primates without imitative and 'theory of mind' abilities and we suggest that in order for them to have become utilized to perform social cognitive functions, sophisticated cortical neuronal systems have evolved in which MNs function as key elements. Early developmental failures of MN systems are likely to result in a consequent cascade of developmental impairments characterised by the clinical syndrome of autism. Crown Copyright (C) 2001 Published by Elsevier Science Ltd. All rights reserved.


title: Optimizing Quality for Probabilistic Skyline Computation and Probabilistic Similarity Search

abstract: Probabilistic queries have been extensively explored to provide answers with confidence, in order to support the real-life applications struggling with uncertain data, such as sensor networks and data integration. However, the uncertainty of data may propagate, and thus, the results returned by probabilistic queries contain much noise, which <italic>degrades</italic> query quality significantly. In this paper, we propose an efficient optimization framework, termed as <inline-formula><tex-math notation="LaTeX">$\mathsf {QueryClean}$</tex-math><alternatives> <inline-graphic xlink:href="gao-ieq1-2805824.gif"/></alternatives></inline-formula>, for both probabilistic skyline computation and probabilistic similarity search. The goal of <inline-formula><tex-math notation="LaTeX">$\mathsf {QueryClean}$</tex-math><alternatives><inline-graphic xlink:href="gao-ieq2-2805824.gif"/></alternatives> </inline-formula> is to optimize query quality via selecting a group of uncertain objects to clean under limited resource available, where a joint-entropy based quality function is leveraged. We develop an efficient structure called <inline-formula><tex-math notation="LaTeX">$\mathsf {ASI}$</tex-math><alternatives> <inline-graphic xlink:href="gao-ieq3-2805824.gif"/></alternatives></inline-formula> to index the possible result sets of probabilistic queries, which helps to avoid many types of probabilistic query evaluations over a large number of the possible worlds for quality computation. Moreover, we present <italic>exact</italic> and <italic>approximate </italic> algorithms for the optimization problem, using two newly presented heuristics. Considerable experimental results on both real and synthetic data sets demonstrate the efficiency and scalability of our proposed framework <inline-formula><tex-math notation="LaTeX">$\mathsf {QueryClean}$</tex-math><alternatives> <inline-graphic xlink:href="gao-ieq4-2805824.gif"/></alternatives></inline-formula>.


title: Program Slicing: Methods and Applications

abstract: Program slicing is a viable method to restrict the focus of a task to specific sub-components of a program. Examples of applications include debugging, testing, program comprehension, restructuring, downsizing, and parallelization. The paper discusses different statement deletion based slicing methods, together with algorithms and applications to software engineering.


Evaluation of immunoturbidimetric assays for the determination of C3, C4, haptoglobin, IgA, IgG and IgM on clinical-chemistry analyzer Aeroset ® and comparison with other assays


title: Survey of scaling platforms for Deep Neural Networks

abstract: Deep Neural Networks have become a state of the art approach in perception processing like speech recognition, image processing and natural language processing. Many state of the art benchmarks for these algorithms are using deep learning techniques. The deep neural networks in today's applications need to process very large amount of data. Different approaches have been proposed to solve scaling these algorithms. Few approach look for providing a solution over existing big data processing platform which usually runs over a large scale commodity cpu cluster. As training deep learning workload require many small computations to be done and large communication to pass the data between layers, General Purpose GPUs seems to the best platforms to train these networks. Different approaches have been proposed to scale processing on cluster of GPU servers. We have summarized various approaches used in this regard.


title: Intelligent Vehicle-Trust Point: Reward based Intelligent Vehicle Communication using Blockchain

abstract: The Intelligent vehicle (IV) is experiencing revolutionary growth in research and industry, but it still suffers from many security vulnerabilities. Traditional security methods are incapable to provide secure IV communication. The major issues in IV communication, are trust, data accuracy and reliability of communication data in the communication channel. Blockchain technology works for the crypto currency, Bit-coin, which is recently used to build trust and reliability in peer-to-peer networks having similar topologies as IV Communication. In this paper, we are proposing, Intelligent Vehicle-Trust Point (IV-TP) mechanism for IV communication among IVs using Blockchain technology. The IVs communicated data provides security and reliability using our proposed IV-TP. Our IV-TP mechanism provides trustworthiness for vehicles behavior, and vehicles legal and illegal action. Our proposal presents a reward based system, an exchange of some IV-TP among IVs, during successful communication. For the data management of the IV-TP, we are using blockchain technology in the intelligent transportation system (ITS), which stores all IV-TP details of every vehicle and is accessed ubiquitously by IVs. In this paper, we evaluate our proposal with the help of intersection use case scenario for intelligent vehicles communication.


title: A 2.6 mW/Gbps 12.5 Gbps RX With 8-Tap Switched-Capacitor DFE in 32 nm CMOS

abstract: A low-power receiver circuit in 32 nm SOI CMOS is presented, which is intended to be used in a source-synchronous link configuration. The design of the receiver was optimized for power owing to the assumption that a link protocol enables a periodic calibration during which the circuit does not have to deliver valid data. In addition, it is shown that the transceiver power and the effect of high-frequency transmit jitter can be reduced by implementing a linear equalizer only on the receive side and avoiding a transmit feed-forward equalizer (TX-FFE). On the circuit level, the receiver uses a switched-capacitor (SC) approach for the implementation of an 8-tap decision-feedback equalizer (DFE). The SC-DFE improves the timing margin relative to previous DFE implementations with current feedback, and leads to a digital-style circuit implementation with compact layout. The receiver was measured at data rates up to 13.5 Gb/s, where error free operation was verified with a PRBS-31 sequence and a channel with 32 dB attenuation at Nyquist. With the clock generation circuits amortized over eight lanes, the receiver circuit consumes 2.6 mW/Gbps from a 1.1 V supply while running at 12.5 Gb/s.


title: Voxel-Based Analysis of Amyloid-Burden Measured with [11C]PiB PET in a Double Transgenic Mouse Model of Alzheimer’s Disease

abstract: PURPOSE ::: The purpose of this study is to validate the feasibility of a voxel-based analysis of in vivo amyloid-β positron emission tomography (PET) imaging studies in transgenic mouse models of Alzheimer's disease. ::: ::: ::: PROCEDURES ::: We performed [(11)C]PiB PET imaging in 20 APP/PS1 mice and 16 age-matched controls, and histologically determined the individual amyloid-β plaque load. Using SPM software, we performed a voxel-based group comparison plus a regression analysis between PiB retention and actual plaque load, both thresholded at p FWE < 0.05. In addition, we carried out an individual ROI analysis in every animal. ::: ::: ::: RESULTS ::: The automated voxel-based group comparison allowed us to identify voxels with significantly increased PiB retention in the cortical and hippocampal regions in transgenic animals compared to controls. The voxel-based regression analysis revealed a significant association between this signal increase and the actual cerebral plaque load. The validity of these results was corroborated by the individual ROI-based analysis. ::: ::: ::: CONCLUSIONS ::: Voxel-based analysis of in vivo amyloid-β PET imaging studies in mouse models of Alzheimer's disease is feasible and allows studying the PiB retention patterns in whole brain maps. Furthermore, the selected approach in our study also allowed us to establish a quantitative relation between tracer retention and actual plaque pathology in the brain in a voxel-wise manner.


title: Clustering articles based on semantic similarity

abstract: Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build representations of these documents which keep their semantics as much as possible and are also suitable for efficient similarity calculation. As we describe in Koopman et al. (Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey, 29 June to 3 July, 2015. Bogazici University Printhouse. http://www.issi2015.org/files/downloads/all-papers/1042.pdf, 2015), the metadata of articles in the Astro dataset contribute to a semantic matrix, which uses a vector space to capture the semantics of entities derived from these articles and consequently supports the contextual exploration of these entities in LittleAriadne. However, this semantic matrix does not allow to calculate similarities between articles directly. In this paper, we will describe in detail how we build a semantic representation for an article from the entities that are associated with it. Base on such semantic representations of articles, we apply two standard clustering methods, K-Means and the Louvain community detection algorithm, which leads to our two clustering solutions labelled as OCLC-31 (standing for K-Means) and OCLC-Louvain (standing for Louvain). In this paper, we will give the implementation details and a basic comparison with other clustering solutions that are reported in this special issue.


title: Sustainability guidelines for long-living software systems

abstract: Economically sustainable software systems must be able to cost-effectively evolve in response to changes in their environment, their usage profile, and business demands. However, in many software development projects, sustainability is treated as an afterthought, as developers are driven by time-to-market pressure and are often not educated to apply sustainability-improving techniques. While software engineering research and practice has suggested a large amount of such techniques, a holistic overview is missing and the effectiveness of individual techniques is often not sufficiently validated. On this behalf we created a catalog of “software sustainability guidelines” to support project managers, software architects, and developers during system design, development, operation, and maintenance. This paper describes how we derived these guidelines and how we applied selected techniques from them in two industrial case studies. We report several lessons learned about sustainable software development.


title: On the Surjectivity of the Mapping Between Utilities and Choice Probabilities. Quantitative Economics 4.1

abstract: This note considers a standard multinomial choice model. It is shown that if the distribution of additive utility shocks has a density, then the mapping from de- terministic components of utilities to choice probabilities is surjective. In other words, any vector of choice probabilities can be obtained by selecting suitable utilities for alternatives. This result has implications for at least three areas of interest to econometricians: the Hotz and Miller (1993) estimator for structural dynamic discrete choice models, nonparametric identification of multinomial choice models, and consistency of conditional density estimators based on co- variate dependent mixtures. Keywords. Multinomial choice models, identification, Hotz and Miller estimator, covariate dependent mixtures. JEL classification. C35, C61.


title: AFFDEX SDK: A Cross-Platform Real-Time Multi-Face Expression Recognition Toolkit

abstract: We present a real-time facial expression recognition toolkit that can automatically code the expressions of multiple people simultaneously. The toolkit is available across major mobile and desktop platforms (Android, iOS, Windows). The system is trained on the world's largest dataset of facial expressions and has been optimized to operate on mobile devices and with very few false detections. The toolkit offers the potential for the design of novel interfaces that respond to users' emotional states based on their facial expressions. We present a demonstration application that provides real-time visualization of the expressions captured by the camera.


title: Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

abstract: Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pairs-shortest-paths passing through it. However, complete and exact BC computation for a large-scale graph is an extraordinary challenge that requires high performance computing techniques to provide results in a reasonable amount of time. Our approach combines bi-dimensional (2-D) decomposition of the graph and multi-level parallelism together with a suitable data-thread mapping that overcomes most of the difficulties caused by the irregularity of the computation on GPUs. Furthermore, we propose novel heuristics which exploit the topology information of the graph in order to reduce time and space requirements of BC computation. Experimental results on synthetic and real-world graphs show that the proposed techniques allow the BC computation of graphs which are too large to fit in the memory of a single computational node along with a significant reduction of the computing time.


title: Health Information Management System for Elderly Health Sector: A Qualitative Study in Iran

abstract: Background: There are increasing change and development of information in healthcare systems. Given the increase in aging population, managers are in need of true and timely information when making decision. Objectives: The aim of this study was to investigate the current status of the health information management system for the elderly health sector in Iran. Materials and Methods: This qualitative study was conducted in two steps. In the first step, required documents for administrative managers were collected using the data gathering form and observed and reviewed by the researcher. In the second step, using an interview guide, the required information was gathered through interviewing experts and faculty members. The convenience, purposeful and snowball sampling methods were applied to select interviewees and the sampling continued until reaching the data saturation point. Finally, notes and interviews were transcribed and content analysis was used to analyze them. Results: The results of the study showed that there was a health information management system for the elderly health sector in Iran. However, in all primary health care centers the documentation of data was done manually; the data flow was not automated; and the analysis and reporting of data are also manually. Eventually, decision makers are provided with delayed information. Conclusions: It is suggested that the steward of health in Iran, the ministry of health, develops an appropriate infrastructure and finally puts a high priority on the implementation of the health information management system for elderly health sector in Iran.


title: Two-factor authentication: is the world ready?: quantifying 2FA adoption

abstract: As text-based passwords continue to be the dominant form for user identification today, services try to protect their costumers by offering enhanced, and more secure, technologies for authentication. One of the most promising is two-factor authentication (2FA). 2FA raises the bar for the attacker significantly, however, it is still questionable if the technology can be realistically adopted by the majority of Internet users. In this paper, we attempt a first study for quantifying the adoption of 2FA in probably the largest existing provider, namely Google. For achieving this, we leverage the password-reminder process in a novel way for discovering if 2FA is enabled for a particular account, without annoying or affecting the account's owner. Our technique has many challenges to overcome, since it requires issuing massively thousands of password reminders. In order to remain below the radar, and therefore avoid solving CAPTCHAs or having our hosts blocked, we leverage distributed systems, such as TOR and PlanetLab. After examining over 100,000 Google accounts, we conclude that 2FA has not yet been adopted by more than 6.4% of the users. Last but not least, as a side-effect of our technique, we are also able to exfiltrate private information, which can be potentially used for malicious purposes. Thus, in this paper we additionally present important findings for raising concerns about privacy risks in designing password reminders.


title: An improved DBSCAN, a density based clustering algorithm with parameter selection for high dimensional data sets

abstract: Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Cluster analysis is one of the major data analysis methods. It is the art of detecting group of similar objects in large data sets without having specified groups by means of explicit features. The problem of detecting clusters is challenging when the clusters are of different size, density and shape. This paper gives a new approach towards density based clustering approach. DBSCAN which is considered a pioneer of density based clustering technique, this paper gives a new move towards detecting clusters that exists within a cluster. Based on various parameters needed for a good clustering the algorithm is evaluated such as number of clusters formed, noise ratio on distance change, time elapsed to form cluster, unclustered instances as well as incorrectly clustered instances.


title: Robust camera pose estimation using 2d fiducials tracking for real-time augmented reality systems

abstract: Augmented reality (AR) deals with the problem of dynamically and accurately align virtual objects with the real world. Among the used methods, vision-based techniques have advantages for AR applications, their registration can be very accurate, and there is no delay between the motion of real and virtual scenes. However, the downfall of these approaches is their high computational cost and lack of robustness. To address these shortcomings we propose a robust camera pose estimation method based on tracking calibrated fiducials in a known 3D environment, the camera location is dynamically computed by the Orthogonal Iteration Algorithm. Experimental results show the robustness and the effectiveness of our approach in the context of real-time AR tracking.


title: Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity

abstract: We study the problem of minimizing the sum of a smooth convex function and a convex block-separable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a very flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordinate descent and distributed coordinate descent—both in nonaccelerated and accelerated variants. The variants with arbitrary or importance sampling are new. We provide a complexity analysis of ALPHA, from which we deduce as a direct corollary complexity bounds for its many variants, all matching or improving best known bounds.


title: LOS: A High Performance and Compatible User-level Network Operating System

abstract: With the ever growing speed of Ethernet NIC and more and more CPU cores on commodity X86 servers, the processing capability of the network stack in Linux kernel has become the bottleneck. Recently there is a trend on moving the network stack up to user level and bypassing the kernel. However, most of these stacks require changing the APIs or modifying the source code of applications, and hence are difficult to support legacy applications. In this work, we design and develop LOS, a user-level network operating system that not only gains high throughput and low latency by kernel-bypass technologies but also achieves compatibility with legacy applications. We successfully run Nginx and NetPIPE on top of LOS without touching the source code, and the experimental results show that LOS achieves significant throughput and latency gains compared with Linux kernel.


Machine Learning Using Cellular Automata Based Feature Expansion and Reservoir Computing.


title: Text recognition from images

abstract: Text recognition in images is a research area which attempts to develop a computer system with the ability to automatically read the text from images. These days there is a huge demand in storing the information available in paper documents format in to a computer storage disk and then later reusing this information by searching process. One simple way to store information from these paper documents in to computer system is to first scan the documents and then store them as images. But to reuse this information it is very difficult to read the individual contents and searching the contents form these documents line-by-line and word-by-word. The challenges involved in this the font characteristics of the characters in paper documents and quality of images. Due to these challenges, computer is unable to recognize the characters while reading them. Thus there is a need of character recognition mechanisms to perform Document Image Analysis (DIA) which transforms documents in paper format to electronic format. In this paper we have discuss method for text recognition from images. The objective of this paper is to recognition of text from image for better understanding of the reader by using particular sequence of different processing module.


title: Passive Self-Interference Suppression for Full-Duplex Infrastructure Nodes

abstract: Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the overall self-interference suppression is often due to passive suppression, i.e., isolation of the transmit and receive antennas. We present a measurement-based study of the capabilities and limitations of three key mechanisms for passive self-interference suppression: directional isolation, absorptive shielding, and cross-polarization. The study demonstrates that more than 70 dB of passive suppression can be achieved in certain environments, but also establishes two results on the limitations of passive suppression: (1) environmental reflections limit the amount of passive suppression that can be achieved, and (2) passive suppression, in general, increases the frequency selectivity of the residual self-interference signal. These results suggest two design implications: (1) deployments of full-duplex infrastructure nodes should minimize near-antenna reflectors, and (2) active cancellation in concatenation with passive suppression should employ higher-order filters or per-subcarrier cancellation.


title: Subgroup Discovery with CN2-SD

abstract: This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Experimental evaluation of CN2-SD on 23 UCI data sets shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve, when compared with the CN2 algorithm. Application of CN2-SD to a large traffic accident data set confirms these findings.


title: Successful Treatment of Congenital Lymphangioma Circumscriptum of the Vulva with CO2 and Long-Pulsed Nd:YAG Lasers

abstract: A 16-year-old girl presented with a 9-year history of vesicles on the vulva. She had initially taken a wait-and-see approach, but required treatment because of bleeding. Histological examination of a biopsied vesicle revealed dilated lymph channels in the upper dermis, suggesting lymphangioma circumscriptum (LC). The challenge for this pathology has been to find a conservative treatment with low morbidity and better results than those reported for surgical excision, which has been the mainstay of therapy. In this case, LC of the vulva was successfully treated using a 10,600-nm CO2 laser and long-pulsed Nd:YAG laser. Use of the 10,600-nm CO2 laser and long-pulsed Nd:YAG laser appeared effective for treating LC.