title: The role of spreading depression, spreading depolarization and spreading ischemia in neurological disease

abstract: The term spreading depolarization describes a wave in the gray matter of the central nervous system characterized by swelling of neurons, distortion of dendritic spines, a large change of the slow electrical potential and silencing of brain electrical activity (spreading depression). In the clinic, unequivocal electrophysiological evidence now exists that spreading depolarizations occur abundantly in individuals with aneurismal subarachnoid hemorrhage, delayed ischemic stroke after subarachnoid hemorrhage, malignant hemispheric stroke, spontaneous intracerebral hemorrhage or traumatic brain injury. Spreading depolarization is induced experimentally by various noxious conditions including chemicals such as potassium, glutamate, inhibitors of the sodium pump, status epilepticus, hypoxia, hypoglycemia and ischemia, but it can can also invade healthy, naive tissue. Resistance vessels respond to it with tone alterations, causing either transient hyperperfusion (physiological hemodynamic response) in healthy tissue or severe hypoperfusion (inverse hemodynamic response, or spreading ischemia) in tissue at risk for progressive damage, which contributes to lesion progression. Therapies that target spreading depolarization or the inverse hemodynamic response may potentially treat these neurological conditions.


title: Flex-N-Feel: The Design and Evaluation of Emotive Gloves for Couples to Support Touch Over Distance

abstract: Many couples live apart due to work, educational situations, or frequent travel. While technology can help mediate these relationships, there is a lack of designs that allow couples to share a sense of touch over distance. We present a design case study of a tangible communication system called Flex- N-Feel--a pair of gloves that allows distance-separated couples to feel the flexing of their remote partners' fingers through vibrotactile sensations on their skin. We evaluated our design with nine couples where the system was augmented with either a Skype audio call or a video connection. Our study showed that participants enjoyed their conversation more with the gloves, felt more emotionally connected, and experienced intimate moments together. Couples used the glove for shared actions, playful episodes, intimate touches, and to simply feel each other's presence. Video was important to aid couples in understanding each other's actions. Our results illustrate that designs focusing on physical touch over distance should be open to appropriation such that they can augment existing communication routines and technologies.


title: A constraint programming approach to the multiple-venue , sport-scheduling problem

abstract: In this paper, we consider the problem of scheduling sports competitions over several venues which are not associated with any of the competitors. A two-phase, constraint programming approach is developed, first identifying a solution that designates the participants and schedules each of the competitions, then assigning each competitor as the "home" or the "away" team. Computational experiments are conducted and the results are compared with an integer goal programming approach. The constraint programming approach achieves optimal solutions for problems with up to sixteen teams, and near-optimal solutions for problems with up to thirty teams.


title: Classification of MRI data using deep learning and Gaussian process-based model selection

abstract: The classification of MRI images according to the anatomical field of view is a necessary task to solve when faced with the increasing quantity of medical images. In parallel, advances in deep learning makes it a suitable tool for computer vision problems. Using a common architecture (such as AlexNet) provides quite good results, but not sufficient for clinical use. Improving the model is not an easy task, due to the large number of hyper-parameters governing both the architecture and the training of the network, and to the limited understanding of their relevance. Since an exhaustive search is not tractable, we propose to optimize the network first by random search, and then by an adaptive search based on Gaussian Processes and Probability of Improvement. Applying this method on a large and varied MRI dataset, we show a substantial improvement between the baseline network and the final one (up to 20% for the most difficult classes).


title: Interpolating and approximating implicit surfaces from polygon soup

abstract: This paper describes a method for building interpolating or approximating implicit surfaces from polygonal data. The user can choose to generate a surface that exactly interpolates the polygons, or a surface that approximates the input by smoothing away features smaller than some user-specified size. The implicit functions are represented using a moving least-squares formulation with constraints integrated over the polygons. The paper also presents an improved method for enforcing normal constraints and an iterative procedure for ensuring that the implicit surface tightly encloses the input vertices.


title: Guidelines for medical care of children evaluated for suspected sexual abuse: an update for 2008

abstract: PURPOSE OF REVIEW: Child sexual abuse is a common problem in our society and medical professionals who provide evaluations of children who may have been abused need to be updated as to recent research findings and recommendations for conducting examinations and interpreting results. RECENT FINDINGS: Research studies have provided important new information regarding the qualifications of examiners, the recovery of forensic evidence in children, the frequency of abnormal findings in children and adolescents, the healing of genital injuries, and the interpretation of medical findings and sexually transmissible infections with respect to abuse. SUMMARY: The recommendations for the timing and type of examinations for prepubertal children, in contrast to adolescent sexual assault victims, may need to be changed. Studies showing that partial tears of the hymen, as well as abrasions and contusions, may heal to leave very little or no sign of previous injury emphasize the importance of urgent evaluations. There is a need for standardization of the training of medical professionals who perform child sexual abuse evaluations to ensure continuing competence. Language: en


title: Low-voltage CMOS analog bootstrapped switch for sample-and-hold circuit: design and chip characterization

abstract: This paper presents the design and characterization of a sample-and-hold circuit based on a novel implementation of the bootstrapped low-voltage analog CMOS switch. The heart of this circuit is a new low-voltage and low-stress CMOS clock voltage doubler. Through the use of a dummy switch, the charge injection induced by the bootstrapped switch is greatly reduced, resulting in improved sample-and-hold accuracy. Experimental results in a 0.18 /spl mu/m digital CMOS process show that a resolution greater than 10 bits can be obtained with a 1.0 V supply voltage. Circuit operation is also possible for supply voltages close to the transistor threshold (e.g., 0.65 V).


Controlling the Motion of an Autonomous Mobile Robot Using Various Techniques: a Review


title: Data structures and algorithms to support interactive spatial analysis using dynamic Voronoi diagrams

abstract: To support the need for interactive spatial analysis, it is often necessary to rethink the data structures and algorithms underpinning applications. This paper describes the development of an interactive environment in which a number of different Voronoi models of space can be manipulated together in real time to: (1) study their behaviour; (2) select appropriate models for specific analysis tasks; and (3) to examine how choice of one model over another will affect the interpretation of data. The paper studies six specific Voronoi diagram variants: the Ordinary Voronoi Diagram, the Farthest-point Voronoi Diagram, the Order-k Voronoi Diagram, the Ordered Order-k Voronoi Diagram, the kth Nearest-point Voronoi Diagram and the Multiplicatively Weighted Voronoi Diagram, and develops algorithms and data structures to store, rebuild and query these variants. From this, a generalised Voronoi data structure is proposed, from which specific Voronoi variants can be reconstructed dynamically as required. Algorithms for diagram reconstruction and for querying neighbourhood (topology or adjacency relations) of generator points and Voronoi regions are presented. An application program, developed on these ideas, is used to generate example results as proof of concept. It may be downloaded from a supporting website.


title: Capacitive coupling through a hydrodynamic journal bearing to power rotating electrical loads without contact

abstract: A capacitive power coupler has been devised using a hydrodynamic journal bearing assembly that facilitates sufficient coupling for kilowatt-scale non-contact power transfer to a rotating load. The capacitive coupler, combined with associated driving circuitry, is an efficient low maintenance solution for powering a variety of rotating or pivoting electrical loads in machines and automation. A hydrodynamic journal bearing capacitor assembly utilizing an ultra-thin cushion of lubricant as dielectric between concentric capacitor electrodes is presented. Hydrodynamic operation ensures high coupling capacitance, ease of manufacturability and eliminates brush and slip ring maintenance. The system's small size, weight, and simplicity are competitive with contemporary inductive wireless power transfer methods in this small gap application. Experimental results for a ~5nF prototype coupler operating at 840 kHz are presented.


title: RC Snubber Design using Root-Loci Approach for Synchronous Buck SMPS

abstract: A vacuum mold for shaping hot glass sheets having a rigid back plate, a flexible perforated lower shaping wall, adjustable spacers positioned between the back plate and shaping wall and easily removable side wall members that seal the vacuum chamber and allow easy access to the internal adjustable spacers. The peripheral edge portions of a refractory cloth which covers the mold is coated with heat resistant silicone rubber to seal the vacuum chamber of the mold.


title: Preceding Vehicle Trajectory Prediction by Multi-Cue Integration

abstract: In this paper we describe an approach to detect and predict the driving trajectory of a preceding vehicle on highway. In particular, we focus on detecting and predicting the changing lane intention and action of the preceding vehicle. Our algorithm employs SVM for driving pattern recognition by integrating two different cues: motion cue and appearance cue, which is trained on two class feature sets extracted from examples of lane changing and lane keeping video sequences. The method is evaluated on the real-world data collected in an intelligent vehicle test-bed. The method is applied to a vision-based safety driving system, which tracks the lane, the preceding vehicle, and uses the vehicle lane-change warning to serve for other intelligent vehicle controls.


title: SSH: Single Stage Headless Face Detector

abstract: We introduce the Single Stage Headless (SSH) face detector. Unlike two stage proposal-classification detectors, SSH detects faces in a single stage directly from the early convolutional layers in a classification network. SSH is headless. That is, it is able to achieve state-of-the-art results while removing the "head" of its underlying classification network -- i.e. all fully connected layers in the VGG-16 which contains a large number of parameters. Additionally, instead of relying on an image pyramid to detect faces with various scales, SSH is scale-invariant by design. We simultaneously detect faces with different scales in a single forward pass of the network, but from different layers. These properties make SSH fast and light-weight. Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset. Even though, unlike the current state-of-the-art, SSH does not use an image pyramid and is 5X faster. Moreover, if an image pyramid is deployed, our light-weight network achieves state-of-the-art on all subsets of the WIDER dataset, improving the AP by 2.5%. SSH also reaches state-of-the-art results on the FDDB and Pascal-Faces datasets while using a small input size, leading to a runtime of 50 ms/image on a GPU. The code is available at this https URL.


title: A Hybrid Variational Autoencoder for Collaborative Filtering

abstract: In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the work of [11] on using variational autoencoders (VAEs) for collaborative filtering with implicit feedback by proposing a hybrid, multi-modal approach. Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation. We empirically show how the VAE network is empowered by incorporating movie embeddings. We also visualize movie and user embeddings by clustering their latent representations obtained from a VAE.


title: The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience

abstract: The past few decades have seen a rapid increase in the use of functional near‐infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state‐of‐the‐art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience.


title: Stochastic Neural Networks for Hierarchical Reinforcement Learning

abstract: Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments show that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.


title: An ultra low power 1V, 220nW temperature sensor for passive wireless applications

abstract: This work presents a low power temperature sensor that is suitable for passive wireless systems. The test chip is fabricated with a 0.18 mum CMOS technology and the total area is 0.05 mm2. With temperature inaccuracy of -1.6degC/+3degC from 0degC to 100degC, the temperature sensor consumes only 220 nW at 1V under room temperature. The data conversion rate is 100 sample/s with an output resolution of 0.3degC, which is sufficient for most sensor applications.


title: Two-Phase Calibration for a Mirror Metaphor Augmented Reality System

abstract: According to the ways to see the real environments, mirror metaphor augmented reality systems can be classified into video see-through virtual mirror displays and reflective half-mirror displays. The two systems have distinctive characteristics and application fields with different types of complexity. In this paper, we introduce a system configuration to implement a prototype of a reflective half-mirror display-based augmented reality system. We also present a two-phase calibration method using an extra camera for the system. Finally, we describe three error sources in the proposed system and show the result of analysis of these errors with several experiments.


title: A Combined Transmembrane Topology and Signal Peptide Prediction Method

abstract: An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at


title: Diagnosing childhood tumors: A review of 147 cutting needle biopsies in 110 children.

abstract: BACKGROUND/PURPOSE ::: The aims of this study were to evaluate the diagnostic accuracy and safety of cutting needle biopsy for diagnosis in children with tumors suspected for malignancy. ::: ::: ::: METHODS ::: Medical records and biopsy slides recorded from 1988 to 1999 were reviewed. One hundred ten patients had undergone a total of 147 cutting needle biopsies. The biopsy was performed under ultrasound guidance, using a 1.2-mm (18 gauge) Biopsy-cut biopsy needle. The diagnoses were benign tumors (n = 8), malignant tumors (n = 84), and nonneoplastic diseases (n = 18), with repeat biopsy performed in 24 patients. ::: ::: ::: RESULTS ::: The diagnostic accuracy of cutting needle biopsies was 89% (131 of 147). The accuracy for pretreatment diagnosis was 88% (102 of 116), and for confirming or excluding a recurrence or metastasis 93% (26 of 28). The sensitivity of this method was 82% (86 of 105), and the positive predictive value 99% (86 of 87). Nondiagnostic cutting needle biopsy (n = 16) was not related to the age of the patient, experience of the radiologist, number of needle insertions, or site of puncture. No patient exhibited tumor growth along the needle tract. Complications occurred in 10 cases (7%) and pain in 20 (14%). ::: ::: ::: CONCLUSION ::: Cutting needle biopsy is an accurate and safe procedure with a low learning threshold that is recommended for diagnosing malignancies in childhood.


title: A Survey and Critique of American Sign Language Natural Language Generation and Machine Translation Systems

abstract: 2 The Problem of ASL Generation 3 The Linguistics of American Sign Language 3 The Concept of Animated Avatars 4 A Language without a Writing System 5 Motivations and Applications 6 Brief Introduction to the Systems Under Consideration 6 ViSiCAST Translator 7 ZARDOZ 7 ASL Workbench 8 TEAM Project 8 Thematic Critique and Comparison of the Systems 9 Underlying Machine Translation Architecture 10 Coverage, Specificity, and Development Time 14 ASL Grammar Formalism and Generation Approach 16 Expressiveness of ASL Representations 18 The Animation Output 20 Classifier Predicates and the Use of Three-Dimensional Space 22 Sign Lexicon Specification 24 Degree of User Intervention Needed for Translation 26 Greatest Strengths of the Four Systems 27 ZARDOZ and the Use of Reasoning During Translation 27 Workbench and the Model of ASL Phonology 28 ViSiCAST and the Discourse Representation Structures 29 TEAM and the EMOTE Motion Parameterization 30 Conclusion 32 New Directions 33 References 35


title: Two/Too Simple Adaptations of Word2Vec for Syntax Problems

abstract: We present two simple modifications to the models in the popular Word2Vec tool, in order to generate embeddings more suited to tasks involving syntax. The main issue with the original models is the fact that they are insensitive to word order. While order independence is useful for inducing semantic representations, this leads to suboptimal results when they are used to solve syntax-based problems. We show improvements in part-ofspeech tagging and dependency parsing using our proposed models.


title: iBump: Smartphone application to detect car accidents

abstract: Abstract Traffic accidents are a fact of life. While accidents are sometimes unavoidable, studies show that the long response time required for emergency responders to arrive is a primary reason behind increased fatalities in serious accidents. One way to reduce this response time is to reduce the amount of time it takes to report an accident. Smartphones are ubiquitous and with network connectivity are perfect devices to immediately inform relevant authorities about the occurrence of an accident. This paper presents the development of a system that uses smartphones to automatically detect and report car accidents in a timely manner. Data is continuously collected from the smartphone’s accelerometer and analyzed using Dynamic Time Warping (DTW) and the Hidden Markov Models (HMMs) to determine the severity of the accident, reduce false positives and to notify first responders of the accident location and owner’s medical information. In addition, accidents can be viewed on the smartphone over the Internet offering instant and reliable access to the information concerning the accident. By implementing this application and adding a notification system, the response time required to notify emergency responders of traffic accidents can potentially reduce the response time which may help in reducing fatalities.


title: Optical single sideband polarization modulation for radio-over-fiber system and microwave photonic signal processing

abstract: An approach to implementing optical single sideband (OSSB) polarization modulation, which is a combination of two orthogonally polarized OSSB modulations with complementary phase differences between the optical carrier and the sideband, is demonstrated based on two cascaded polarization modulators (PolMs). The two PolMs are driven by two RF signals that are 90° out of phase. By properly adjusting the polarization state between the two PolMs, OSSB polarization modulation with large operation bandwidth can be realized. An experiment is performed. OSSB polarization modulation with an operation bandwidth from 2 to 35 GHz is successfully demonstrated. The spectral profile of the OSSB polarization-modulated signal is observed through an optical spectrum analyzer, and its complementary phase properties are analyzed by sending the signal to a photodetector (PD) for square-law detection. Due to the complementary phase differences between the optical carrier and the sideband along the two polarization directions, no microwave frequency component is generated after the PD. The generated OSSB polarization-modulated signal is transmitted through 25 and 50 km single-mode fiber with 50 Mbaud 16 quadrature amplitude modulation baseband data to investigate the transmission performance of the proposed system in radio-over-fiber applications, and very small error vector magnitude degradation is observed. OSSB polarization modulation is also employed to realize a microwave photonic phase shifter. A full-range tunable phase shift is obtained for 2 and 35 GHz microwave signals.


title: Temporal Readout Noise Analysis and Reduction Techniques for Low-Light CMOS Image Sensors

abstract: In this paper, an analytical noise calculation is presented to derive the impact of process and design parameters on $1/f$ and thermal noise for a low-noise CMOS image sensor (CIS) readout chain. It is shown that dramatic noise reduction is obtained by using a thin-oxide transistor as the source follower of a typical 4T pixel. This approach is confirmed by a test chip designed in a 180-nm CIS process and embedding small arrays of the proposed new pixels together with state-of-the-art 4T pixels for comparison. The new pixels feature a pitch of 7.5 $\mu \text{m}$ and a fill factor of 66%. A 0.4e- rms input-referred noise and a 185- $\mu \text{V}$ /e- conversion gain are obtained. Compared with state-of-the-art pixels, also present onto the test chip, the rms noise is divided by more than 2 and the conversion gain is multiplied by 2.2.


title: Empirical Bayesian Graphical Entity Resolution

abstract: Databases often contain corrupted, degraded, and noisy data with duplicate entries across and within each database. Such problems arise in citations, medical databases, genetics, human rights databases, and a variety of other applied settings. The target of statistical inference can be viewed as an unsupervised problem of determining the edges of a bipartite graph that links the observed records to unobserved latent entities. Bayesian approaches provide attractive benefits, naturally providing uncertainty quantification via posterior probabilities. We propose a novel record linkage approach based on empirical Bayesian principles. Specifically, the empirical Bayesian--type step consists of taking the empirical distribution function of the data as the prior for the latent entities. This approach improves on the earlier HB approach not only by avoiding the prior specification problem but also by allowing both categorical and string-valued variables. Our extension to string-valued variables also involves the proposal of a new probabilistic mechanism by which observed record values for string fields can deviate from the values of their associated latent entities. Categorical fields that deviate from their corresponding true value are simply drawn from the empirical distribution function. We apply our proposed methodology to a simulated data set of German names and an Italian household survey, showing our method performs favorably compared to several standard methods in the literature. We also consider the robustness of our methods to changes in the hyper-parameters.


title: Towards a software-defined Network Operating System for the IoT

abstract: The heterogeneity characterizing the Internet of Things (IoT) landscape can be addressed by leveraging network operating systems (NOS)s. Current NOS solutions have been developed for traditional infrastructured networks: they do not take the specific features of fundamental IoT components such as wireless sensor and actuator networks into account. Accordingly, in this paper an innovative integrated network operating system for the IoT which is obtained as the evolution of the Open Network Operating System (ONOS) is proposed. This has been appropriately extended to enhance the recently proposed SDN-WISE platform for supporting SDN in wireless sensor networks and let it interact with standard OpenFlow (OF) switches. The proposed integrated system has been prototyped.


title: Visualization of patent analysis for emerging technology

abstract: Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future.


title: Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework

abstract: Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.


title: Smart Mining for Deep Metric Learning

abstract: To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the training convergence of this triplet model can be compromised by the fact that the vast majority of the training samples will produce gradients with magnitudes that are close to zero. This issue has motivated the development of methods that explore the global structure of the embedding and other methods that explore hard negative/positive mining. The effectiveness of such mining methods is often associated with intractable computational requirements. In this paper, we propose a novel deep metric learning method that combines the triplet model and the global structure of the embedding space. We rely on a smart mining procedure that produces effective training samples for a low computational cost. In addition, we propose an adaptive controller that automatically adjusts the smart mining hyper-parameters and speeds up the convergence of the training process. We show empirically that our proposed method allows for fast and more accurate training of triplet ConvNets than other competing mining methods. Additionally, we show that our method achieves new state-of-the-art embedding results for CUB-200-2011 and Cars196 datasets.


title: Data Breaches and Identity Theft: When is Mandatory Disclosure Optimal?

abstract: Data breaches occur when personal consumer information is lost or stolen, and can result in the loss of hundreds or millions of records (e.g., local schools or small retail stores; TJX or Heartland). They can occur from the improper disposal of documents containing personal information, from the loss of a laptop or thumb-drive, or when criminals penetrate corporate networks to steal information. The personal data compromised include individuals’ names, addresses, social security numbers, dates of birth, driver’s licenses, passport numbers, and financial data. This information can then be used to commit crimes, including fraudulent unemployment claims (Goodin 2008), fraudulent tax returns (McMillan 2008), fraudulent loans (Hogan 2008), home equity fraud (Krebs 2008), and payment card fraud. Consumers can also suffer the burden of increased loan interest rates, being denied utility services, civil suits or criminal investigation (Baum 2004). While the consumer costs incurred from credit card fraud may be negligible, out of pocket expenses can reach thousands of dollars (Federal Trade Commission 2007).As a result of these losses, in recent years U.S. policy makers have enacted laws that require organizations to notify individuals when personally identifiable information has been lost or stolen. As of late 2009, 45 states (as well as other countries around the world) have adopted data breach disclosure, or security breach notification, laws (Maurushat 2009). Aside from two studies (one showing an improvement in firm practices (Samuelson Law 2007), and another finding only a marginal reduction in consumer rates of identity theft (Romanosky et al. 2008)), however, the effects of data breach disclosure laws have yet to be rigorously studied.One of the main intents of notification laws is to empower consumers to take action and mitigate their loss (Majoras 2005). In addition, the possibility of loss from a breach and resulting costs from notification, it is argued, forces firms to internalize more of the cost of a data breach, thereby inducing them to increase their investment in security measures. This, in turn, is expected to reduce the probability, or magnitude, of future breaches. In short, data breach disclosure “drive[s] performance through transparency and oversight” (Mulligan 2007).However, critics argue that such laws inflict unnecessary costs for both firms and consumers if indeed firms already bear most of the loss (Lenard and Rubin 2005) or when lost data is recovered before it is even accessed (Majoras 2005). Moreover, when the risk of harm is low, unnecessary notification may desensitize individuals, preventing them from acting when a serious threat does exist (Majoras 2005). Further, consumers may be unable to properly respond to the breach notifications, as the notices may present a substantial cognitive and psychological barrier to tacking action, also causing them to under-react (Romanosky and Acquisti 2009). Alternatively, news media and a burgeoning market of identity theft prevention services may breed panic and confusion, causing consumers to over-react by unnecessarily purchasing such products, increasing their expected costs.But mandatory disclosure may also affect firms in conflicting ways. On the one hand, disclosure is costly. Firms will incur costs of notification, customer services operations (call centers, customer support), consumer redress (such as identity theft insurance or credit monitoring), legal fees, regulatory fines, and the potential loss of market valuation or lost business (customer churn) (GAO 2007, Ponemon 2010). On the other hand, notifications may also cause consumers to take appropriate action and reduce their harm (either by preventing or mitigating identity theft) - this would lower the firm’s own expected costs, because the amount of consumer harm that the firm internalizes is reduced.In short, it is unclear whether disclosure would result in a net increase or decrease of firm, consumer, or overall social costs. Using both analytical and numerical modeling, we show that even though firm costs will be higher under disclosure regimes, firms can be induced to increase their investment in care, which may lower social costs. Moreover, disclosure can induce consumers to increase their level of care, thus lowering their total costs. Finally, we find that the change in social costs are typically increasing in disclosure tax (costs imposed on the firm due to disclosure laws) and decreasing in consumer redress (compensation paid by the firm to the consumer). However, when the firm compensates consumers for only a small amount of loss, some disclosure tax may be necessary to optimally reduce social costs.The next section discusses the literature related to information disclosure in IT security and the economics of (accident) law, which we leverage to frame information disclosure within the context of other common means of reducing externalities. We then define the costs involved in a data breach absent any legal regime, and illustrate how these costs change under mandatory breach disclosure.Next, we use analytical methods to determine the conditions under which disclosure reduces social costs. Finally, we provide discussion and empirical validation, followed by some model extensions and our conclusion


title: The Sampling Oscilloscope as a Microwave Instrument

abstract: Most of the equipment required is readily available in most microwave labs: a vector network analyzer, a microwave signal generator, and, of course, a sampling oscilloscope. In this paper, the authors summarize many of the corrections discussed in " Terminology for high-speed sampling-oscilloscope calibration" [Williams et al., 2006] and "Magnitude and phase calibrations for RF, microwave, and high-speed digital signal measurements" [Remley and Hale, 2007] that are necessary for metrology-grade measurements and Illustrate the application of these oscilloscopes to the characterization of microwave signals.


title: Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning

abstract: Integrating diagnosis and repair is particularly crucial when gaining sufficient information to discriminate between several candidate diagnoses requires carrying out some repair actions. A typical case is supply restoration in a faulty power distribution system. This problem, which is a major concern for electricity distributors, features partial observability, and stochastic repair actions which axe more elaborate than simple replacement of components. This paper analyses the difficulties in applying existing work on integrating model-based diagnosis and repair and on planning in partially observable stochastic domains to this real-world problem, and describes the pragmatic approach we have retained so far.


title: Modeling emotional content of music using system identification

abstract: Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R/sup 2/ statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.


title: Role of tempo entrainment in psychophysiological differentiation of happy and sad music?

abstract: Abstract Respiration rate allows to differentiate between happy and sad excerpts which may be attributable to entrainment of respiration to the rhythm or the tempo rather than to emotions [Etzel, J.A., Johnsen, E.L., Dickerson, J., Tranel, D., Adolphs, R., 2006. Cardiovascular and respiratory responses during musical mood induction. Int. J. Psychophysiol. 61(1), 57–69]. In order to test for this hypothesis, this study intended to verify whether fast and slow rhythm, and/or tempo alone are sufficient to induce differential physiological effects. Psychophysiological responses (electrodermal responses, facial muscles activity, blood pressure, heart and respiration rate) were then measured in fifty young adults listening to fast/happy and slow/sad music, and to two control versions of these excerpts created by removing pitch variations (rhythmic version) and both pitch and temporal variations (beat-alone). The results indicate that happy and sad music are significantly differentiated (happy > sad) by diastolic blood pressure, electrodermal activity, and zygomatic activity, while the fast and slow rhythmic and tempo control versions did not elicit such differentiations. In contrast, respiration rate was faster with stimuli presented at fast tempi relative to slow stimuli in the beat-alone condition. It was thus demonstrated that the psychophysiological happy/sad distinction requires the tonal variations and cannot be explained solely by entrainment to tempo and rhythm. The tempo entrainment exists in the tempo alone condition but our results suggest this effect may disappear when embedded in music or with rhythm.


title: Multi-Domain Orchestration for NFV: Challenges and Research Directions

abstract: In this paper we focus on the problem of multidomain orchestration for Network Function Virtualization (NFV), over multi-technology environments. In order to facilitate service deployment in end-to-end setups, new orchestration designs are required that exploit and advance existing methodologies. We examine in detail the challenges on multi-domain NFV orchestration for the general case and we provide the current landscape and existing technologies. We also describe a reference architecture for the problem of multi-domain NFV orchestration, that also supports the concept of Network Slicing. Finally, we present a realization of the architecture proposed for the LTE network and we describe a use case with LTE-specific considerations.


Parametrized topologies for simulation programs


title: Ransomware Analysis and Defense-WannaCry and the Win32 environment

abstract: Ransomware is a specific type of malware that threatens the victim’s access to her data unless a ransom is paid. It is also known as a cryptovirus due to its method of operation. Typically, ransomware encrypts the contents of the victim’s hard drive thereby rendering it inaccessible to the victim. Upon payment of the ransom, the decryption key is released to the victim. This is therefore also called cryptoviral extortion. The ransomware itslef is delivered to the victim using several channels. The most common channel of delivery is by masquerading the malware as a trojan horse via an email attachment. In this paper, we study a high-profile example of a ransomware called the WannaCry worm . This ransomware is particularly malicious since it had the ability to traverse computing equipment on a network without any human intervention. To better understand the inner workings of this high-profile ransomware, we obtain a sample of WannaCry and dissect it completely using advanced static and dynamic malware analysis techniques. This effort, we hope, will shed light on the inner workings of the malware and will enable cyber security experts to better thwart similar attacks in the future. Our analysis is conducted in a Win32 environment and we present our detailed analysis so as to enable reproduction of our work by other malware analysts. Lastly, we present a protoype software that will enable a user to prevent this malware from unleashing its payload and protect the user on a Win32 environment.


The Impact of Motivation and Personality on Academic Performance in Online and Blended Learning Environments


Medical Image Fusion Schemes using Contourlet Transform and PCA Based


title: Improved feature extraction method based on Histogram of Oriented Gradients for pedestrian detection

abstract: In recent years, pedestrian detection for Automobile Driver Assistance System (ADAS) is a primordial task in the smart vehicle. Histogram of oriented gradients (HoG) is one of the most effective pedestrian feature extraction approaches to the study. In this paper, an optimization of pedestrian detection based on HOG method is presented and investigated to achieve an accurate human detection system. The study of different computation steps of the standard algorithm shows the possibility of improving the system performance, specifically in the build histograms step. The main idea is to customize each bin weight according to its contribution in the pedestrian extracted features. Actually, the different bins of a HoG improved vector that encodes a single cell will not have the same weight. Indeed, after the histograms computation, we will distribute an amplification factor for each bin in order to increase the weight bins that describe the relevant pedestrian features from a side. Top of that, we were interested to decrease the bins weight that affect the irrelevant features such as, other obstacle or the image background. The classification system is performed using a linear SVM classifier which is simple and easy to implement in ADAS applications. The performance studies using MATLAB simulation, proves the effectiveness of our approach.


title: An experimental unification of reservoir computing methods

abstract: Three different uses of a recurrent neural network (RNN) as a reservoir that is not trained but instead read out by a simple external classification layer have been described in the literature: Liquid State Machines (LSMs), Echo State Networks (ESNs) and the Backpropagation Decorrelation (BPDC) learning rule. Individual descriptions of these techniques exist, but a overview is still lacking. Here, we present a series of experimental results that compares all three implementations, and draw conclusions about the relation between a broad range of reservoir parameters and network dynamics, memory, node complexity and performance on a variety of benchmark tests with different characteristics. Next, we introduce a new measure for the reservoir dynamics based on Lyapunov exponents. Unlike previous measures in the literature, this measure is dependent on the dynamics of the reservoir in response to the inputs, and in the cases we tried, it indicates an optimal value for the global scaling of the weight matrix, irrespective of the standard measures. We also describe the Reservoir Computing Toolbox that was used for these experiments, which implements all the types of Reservoir Computing and allows the easy simulation of a wide range of reservoir topologies for a number of benchmarks.


title: Tri-Branch Vein Structure Assisted Finger Vein Recognition

abstract: In the template matching of finger vein recognition, the probe will be accepted if the number of its overlapped vein points with the enrolled user is larger than the predefined threshold. However, the acceptance may be false owing to ignoring the structure of the vein pattern. We find that local vein branches near the bifurcation point of vein pattern vary largely between the imposter images. So, this paper tries to explore this kind of local vein structure to improve the recognition performance of the template matching. The bifurcation point and its local vein branches, named tri-branch vein structure, are extracted from the vein pattern, and fused with the whole vein pattern by a user-specific threshold-based filter framework. The experimental results on two public databases prove the effectiveness of the proposed framework for improving the performance of vein pattern-based finger vein recognition.


Speech-driven 3 D Facial Animation with Implicit Emotional Awareness : A Deep Learning Approach


title: Secure attachment and eudaimonic well-being in late adulthood: The mediating role of self-compassion

abstract: ABSTRACT Objectives: Attachment style refers to a systematic pattern of emotions, behaviors, and expectations that people have for how others will respond in relationships. Extensive evidence has documented the importance of attachment security in infants, children, adolescents, and adults, but the effects of attachment among exclusively older adult populations have received less attention. The present study explored the relationships between attachment style in late adulthood and eudaimonic well-being, which refers to a life replete with meaning, productive activity, and striving to reach one's potential. It also explored the mediating role of self-compassion, which can be described as a kind and forgiving attitude toward the self. Method: A sample of 126 community-dwelling older adults (mean age = 70.40 years) completed measures tapping adult attachment, self-compassion, and six theoretically derived markers of eudaimonic well-being. Results: Attachment anxiety and avoidance were inversely related to self-acceptance, personal growth, interpersonal relationship quality, purpose in life, and environmental mastery. Mediation analyses showed that self-compassion mediated each of these relationships. Conclusion: Results support the importance of attachment orientation for psychological well-being in late life and indicate that secure attachment facilitates an attitude of kindness and acceptance toward the self.


title: Meta-ontology for innovative product design with semantic TRIZ

abstract: In the manufacturing industry, SCM (Supply Chain Management) is playing an important role is providing profit to the enterprise. Market globalization and rapid advancement of technologies require that companies differentiate themselves with innovative products and services to create competitive advantage. Increasingly, manufacturers face higher pressure to shorten their time for product design to enter market. TRIZ (Theory of Inventive Problem Solving) supports designers for innovative product design by searching from knowledge base. The existing TRIZ ontology supports innovative design of a product. But it considers about specific products (Flashlight and SmartFan) for TRIZ ontology. We applied the semantic TRIZ to products as an interim stage towards meta-ontology that can manage general products and other concepts. The research in this paper aims at developing meta-ontology for innovative product design that can be applied to multiple products in different domain areas. Modeling ontologies of real world (SmartPen and SmartMachine) is taken as an evaluation for the meta-ontology. This may open up new possibilities to innovative product designs.


title: Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly

abstract: Mobility is a good indicator of health status and thus objective mobility data could be used to assess the health status of elderly patients. Accelerometry has emerged as an effective means for long-term physical activity monitoring in the elderly. However, the output of an accelerometer varies at different positions on a subject’s body, even for the same activity, resulting in high within-class variance. Existing accelerometer-based activity recognition systems thus require firm attachment of the sensor to a subject’s body. This requirement makes them impractical for long-term activity monitoring during unsupervised free-living as it forces subjects into a fixed life pattern and impede their daily activities. Therefore, we introduce a novel single-triaxial-accelerometer-based activity recognition system that reduces the high within-class variance significantly and allows subjects to carry the sensor freely in any pocket without its firm attachment. We validated our system using seven activities: resting (lying/sitting/standing), walking, walking-upstairs, walking-downstairs, running, cycling, and vacuuming, recorded from five positions: chest pocket, front left trousers pocket, front right trousers pocket, rear trousers pocket, and inner jacket pocket. Its simplicity, ability to perform activities unimpeded, and an average recognition accuracy of 94% make our system a practical solution for continuous long-term activity monitoring in the elderly.


title: A compact, high repetition-rate, nanosecond pulse generator based on magnetic pulse compression system

abstract: Magnetic pulse compression (MPC) system has been widely-used over a few decades as a technique for producing short duration, high peak power pulses reliably. A compact pulse generator with a 3-stage MPC system (assembled with amorphous cores and ferrite cores) is constructed to generate repetitive pulses of maximum 7.5 MW peak power with pulse width (FWHM) of 70 ns and rise time of 30 ns at the maximum pulse repetition rate of 2 kHz into a 307 resistive load. In this paper, the detailed system design and operational characteristics of the MPC modulator are presented. The MPC has 3-stage pulse compression circuit using two saturable pulse transformers and a magnetic switch. The saturable pulse transformer has two functions, a step-up transformer and a magnetic switch. The generator consists of 2 units with distinct functions. The resonant charge unit is a diode based auto L-C resonator recharger. The 3-stage MPC unit comprises a saturable pulse transformer which compress the pulse width to 1.5 μs and step up the amplitude to 7 kV, and a 2-stage magnetic pulse compressor using another saturable pulse transformer and a magnetic switch, which compress the pulse rise time to 30 ns and step up the amplitude to 62 kV at most (1 kΩ resistive load).


Osteoarticular Tuberculosis in Iran, 2002 to 2011


title: A computationally efficient design procedure for actuator motors using magnetic reluctance-and thermal resistance network models

abstract: This paper deals with a computationally efficient design framework for permanent magnet electrical machines within a multi-motor drive environment. A magnetic reluctance based machine model, together with a thermal resistance network model are used to model the most significant machine non-linearities during both healthy and faulty conditions. The model structure and functionality is described and is applied to the optimisation of a Permanent Magnet Synchronous Machine (PMSM) in an aerospace actuator arrangement. Both magnetic reluctance and thermal resistance network models are validated against Finite Elements (FEM) calculations for the electromechanical behaviour and simulation in a special thermal motor design tool for the thermal behaviour respectively.


title: Diagnosis of mitochondrial disease: assessment of mitochondrial DNA heteroplasmy in blood.

abstract: Mitochondrial DNA (mtDNA) mutations are an important cause of neurological disease. The identification of causative mtDNA mutations may be particularly troublesome in blood where there are often low levels of mutant mtDNA. This is evident from a recent study in which heteroplasmic mtDNA mutations in cytochrome c oxidase genes were incorrectly thought to be linked to Alzheimer's disease. We wished to explore whether analysis of blood mtDNA, prepared by a number of DNA extraction procedures, influenced the diagnosis of mtDNA disease. DNA was extracted by different procedures from 4 patients with heteroplasmic mtDNA mutations, and the level of heteroplasmy investigated by radioactive PCR-RFLP analysis. Whilst there was no consistent decrease in the level of mtDNA heteroplasmy, we observed the coamplification of a novel mtDNA pseudogene from DNA samples extracted by a simple 'boiling' procedure using primers designed to screen for the tRNALeu(UUR) A3243G mutation. This pseudogene was readily amplified from DNA extracted from rho degrees (mtDNA-less) cells, confirming its nuclear location. We believe that mtDNA pseudogenes may therefore present significant difficulties in the accurate identification of pathogenic heteroplasmic mtDNA mutations in blood.


title: Heat shock factors in rice (Oryza sativa L.): genome-wide expression analysis during reproductive development and abiotic stress

abstract: Plants respond to heat stress by enhancing the expression of genes encoding heat shock protein (HSPs) genes through activation of heat shock factors (HSFs) which interact with heat shock elements present in the promoter of HSP genes. Plant HSFs have been divided into three conserved classes viz A, B and C. In the present study, a detailed analysis has been done of all rice HSFs, along with their spliced variants. Their chromosomal localization reveals that six HSFs are segmentally duplicated and four pairs of these segmentally duplicated HSF encoding genes show pseudo-functionalization. Expression profiling through microarray and quantitative real-time PCR showed that eight OsHsfs express at a higher level during seed development, while six HSFs are up-regulated in all the abiotic stresses studied. The expression of OsHsfA2a gene in particular was greatly stimulated by heat stress in both root and shoot tissues and also during panicle and seed development. OsHsfA3 was found more responsive to cold and drought stress, while OsHsfA7 and OsHsfA9 showed developing seed-specific expression. This study also revealed that spliced variants generally accumulated at a higher level in all the tissues examined. Different hormones/elicitors like ABA, brassinosteroids and salicylic acid also alter OsHsf gene expression. Calcium in combination with heat stress elevated further the level of HSF transcripts. Expression analysis by both microarray and real-time RT-PCR revealed a unique stable constitutive expression of OsHsfA1 across all the tissues and stresses. A detailed in silico analysis involving identification of unidentified domains has been done by MEME-motif tool in their full-length proteins as well as in DNA-binding domains. Analysis of 1 kb putative promoter region revealed presence of tissue-specific, abiotic stress and hormone-related cis-acting elements, correlating with expression under stress conditions.


title: A Social Recommender Based on Factorization and Distance Metric Learning

abstract: Traditional recommender systems often suffer from the problem of data sparsity, because most users rate only a few of the millions of possible items. With the development of social platforms, incorporating abundant social relationships into recommenders can help to overcome this issue, because users’ preferences can be inferred from those of their friends. Most existing social recommenders are based on matrix factorization, a collaborative filtering model that has been proven to be effective. In this paper, we introduce a novel social recommender based on the idea that distance reflects likability. Compared with matrix factorization, the proposed model enables us to obtain a spatial understanding of the latent factor space and how users and items are positioned inside the space by combining the factorization model and distance metric learning. In our method, users and items are initially mapped into a unified low-dimensional space. The positions of users and items are jointly determined by ratings and social relations, which can help to determine appropriate locations for users who have few ratings. Finally, the learned metrics and positions are used to generate understandable and reliable recommendations. Experiments conducted on real-world data sets have shown that compared with methods based on only matrix factorization, our method significantly improves the recommendation accuracy.


title: Electrically programmable fuse (eFUSE) using electromigration in silicides

abstract: For the first time we describe a positive application of electromigration, as an electrically programmable fuse device (eFUSE). Upon programming, eFUSE's show a large increase in resistance that enable easy sensing. The transient device characteristics show that the eFUSE stays in a low resistance state during programming due to the local heating of the fuse link. The programming is enhanced by a device design that uses a large cathode which increases the temperature gradient and minimizes the effect of microstructural variations.


title: Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers

abstract: Currently, the only mass-market service robots are floor cleaners and lawn mowers. Although available for more than 20 years, they mostly lack intelligent functions from modern robot research. In particular, the obstacle detection and avoidance is typically a simple physical collision detection. In this work, we discuss a prototype autonomous lawn mower with camera-based non-contact obstacle avoidance. We devised a low-cost compact module consisting of color cameras and an ARM-based processing board, which can be added to an autonomous lawn mower with minimal effort. For testing our system, we conducted a field test with 20 prototype units distributed in eight European countries with a total mowing time of 3,494 hours. The results show that our proposed system is able to work without expert interaction for a full season and strongly reduces collision events while still keeping the good mowing performance. Furthermore, a questionnaire with the testers revealed that most people would favor the camera-based mower over a non-camera-based mower.


title: ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

abstract: This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition. The goal of the competition was to automatically evaluate five “apparent” personality traits (the so-called “Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the final phase. Despite the difficulty of the task, the teams made great advances in this round of the challenge.


Non-Negative Tensor Factorization with RESCAL


title: Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity

abstract: Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better. Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus.


title: Effects of Parental Alcoholism, Sense of Belonging, and Resilience on Depressive Symptoms: A Path Model

abstract: This paper explored the relationships between parental alcoholism, sense of belonging, resilience, and depressive symptoms among Koreans in the U.S. Data from 206 Koreans (Mean age = 28.4 years; 59.8% females) living in a Midwestern state were collected in 2009, using a web-based survey, which included Children of Alcoholic Screening Test, Sense of Belonging Instrument, Connor-Davidson Resilience Scale, and Beck Depression Inventory-II. Path analysis results revealed sense of belonging as the most powerful, and resilience as the second important factor, resisting depressive symptoms associated with parental alcoholism. Implications for practice and research and study limitations are discussed. The study's limitations are noted.


title: Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand

abstract: BACKGROUND ::: Using raw acceleration data to assess the intensity of physical activity enables direct comparisons between studies using different accelerometer brands. Mean amplitude deviation (MAD in mg) calculated from resultant tri-axial raw acceleration signal was recently shown to perform best in classifying the intensity of physical activity in adults irrespective of the accelerometer brand. This study compared MAD values and cut-points between two different accelerometers in adolescents. ::: ::: ::: METHODS ::: Twenty voluntary participants (10 girls and 10 boys) of average age of 14 wore two accelerometers (Actigraph GTX3, Pensacola FL, USA and Hookie AM13, Espoo, Finland) and heart rate monitors (M61, Polar Electro Oy, Kempele, Finland) while completing ten 2-min patterns of typical activities ranging from sedentary behaviour to light, moderate and vigorous-intensity locomotion. Bland-Altman method examined the agreement of MAD values between the accelerometers. Correlation coefficient between individual heart rates and MAD values indicated the validity of pattern-based intensity classification. Generalized ordinal logistic regression determined the intensity-specific MAD cut-points for both accelerometers. ::: ::: ::: RESULTS ::: MAD values varied from 3 mg (lying supine) to 1609 mg (running). Hookie gave higher values than Actigraph in accelerations exceeding 700 mg. The correlation coefficient between MAD values and heart rates was 0.96 for Hookie and 0.97 for Actigraph. Respectively, the MAD cut-points were 29 and 27 (light), 338 and 330 (moderate), and 604 and 558 (vigorous). ::: ::: ::: CONCLUSIONS ::: MAD values and cut-points of Hookie and Actigraph showed excellent agreement. Analysing raw accelerometer data with MAD values may enable the comparison of accelerometer results between different studies also in adolescents.


title: Muscle Insertion and Strength of the Muscle Contraction as Guidelines to Enhance Duration of the Botulinum Toxin Effect in the Upper Face

abstract: In this series of patients the cutaneous insertion and strength of voluntary contraction of the muscles in the upper third of the face were used as guidelines for botulinum toxin application named “BTA Codes.” Anatomical dissection of fresh cadavers identified the shape and cutaneous insertions of the muscles in the upper third of the face. Patient voluntary contraction of the muscles in the upper third of the face created different patterns of skin lines classified by the 4-grade Facial Wrinkle Scale. For botulinum toxin application injections points followed the muscle cutaneous insertion and dose the 4-grade Facial Wrinkle Scale. Injection points ranged from 3 to 23 points per patient, average of 9.4 points. Dose per point varied from 2.5 to 7.5 U, ranging from 12.5 to 72.5 U per patient, average of 33.82 U. Skin lines resulting from the voluntary contraction of the muscle prior to toxin application were stated as baseline 1. The absence of skin lines and muscle activity on day fifteen after toxin application defined baseline 2. Skin lines resulting from the recovered voluntary contraction of the muscle after toxin application like those of baseline 1 established baseline 3. The botulinum toxin effect was the time elapsed between baselines 2 and 3, ranging from 171 to 204 days, average of 183.72 days, greater than the 3 or 4 months reported in the literature. “BTA Codes” is a set of rules to apply botulinum toxin supported by muscle anatomy and degree of voluntary contraction to enhance the duration of its effect. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: Learning to Generate and Edit Hairstyles

abstract: Modeling hairstyles for classification, synthesis and image editing has many practical applications. However, existing hairstyle datasets, such as the Beauty e-Expert dataset, are too small for developing and evaluating computer vision models, especially the recent deep generative models such as generative adversarial network (GAN). In this paper, we contribute a new large-scale hairstyle dataset called Hairstyle30k, which is composed of 30k images containing 64 different types of hairstyles. To enable automated generating and modifying hairstyles in images, we also propose a novel GAN model termed Hairstyle GAN (H-GAN) which can be learned efficiently. Extensive experiments on the new dataset as well as existing benchmark datasets demonstrate the effectiveness of proposed H-GAN model


title: Sentiment and affect analysis of Dark Web forums: Measuring radicalization on the internet

abstract: Dark Web forums are heavily used by extremist and terrorist groups for communication, recruiting, ideology sharing, and radicalization. These forums often have relevance to the Iraqi insurgency or Al-Qaeda and are of interest to security and intelligence organizations. This paper presents an automated approach to sentiment and affect analysis of selected radical international Ahadist Dark Web forums. The approach incorporates a rich textual feature representation and machine learning techniques to identify and measure the sentiment polarities and affect intensities expressed in forum communications. The results of sentiment and affect analysis performed on two large-scale Dark Web forums are presented, offering insight into the communities and participants.


title: Ensemble diversity measures and their application to thinning

abstract: Abstract The diversity of an ensemble of classifiers can be calculated in a variety of ways. Here a diversity metric and a means for altering the diversity of an ensemble, called “thinning”, are introduced. We evaluate thinning algorithms created by several techniques on 22 publicly available datasets. When compared to other methods, our percentage correct diversity measure shows a greatest correlation between the increase in voted ensemble accuracy and the diversity value. Also, the analysis of different ensemble creation methods indicates that they generate different levels of diversity. Finally, the methods proposed for thinning show that ensembles can be made smaller without loss in accuracy.


title: Do display ads influence search? Attribution and dynamics in online advertising

abstract: As firms increasingly rely on online media to acquire consumers, marketing managers rely on online metrics such as click-through rate (CTR) and cost per acquisition (CPA). However, these standard online advertising metrics are plagued with attribution problems and do not account for synergy or dynamics. These issues can easily lead firms to overspend on some actions and thus waste money and/or underspend in others, leaving money on the table.


title: A 79-GHz radar transceiver with switchable TX and LO feedthrough in a Silicon-Germanium technology

abstract: This contribution shows the successful implementation of a fully-differential single-chip radar transceiver with switchable transmit path. The chip facilitates cascading of multiple modules via daisy-chaining using the integrated LO power splitter and LO output on the chip edge opposite to the LO input (LO feedthrough). An on-chip differential rat-race coupler provides for separation of transmit (TX) and receive (RX) paths, therefore only a single antenna port is required. The circuit performance is demonstrated in on-board measurements.


title: Real-time hyperlapse creation via optimal frame selection

abstract: Long videos can be played much faster than real-time by recording only one frame per second or by dropping all but one frame each second, i.e., by creating a timelapse. Unstable hand-held moving videos can be stabilized with a number of recently described methods. Unfortunately, creating a stabilized timelapse, or hyperlapse, cannot be achieved through a simple combination of these two methods. Two hyperlapse methods have been previously demonstrated: one with high computational complexity and one requiring special sensors. We present an algorithm for creating hyperlapse videos that can handle significant high-frequency camera motion and runs in real-time on HD video. Our approach does not require sensor data, thus can be run on videos captured on any camera. We optimally select frames from the input video that best match a desired target speed-up while also resulting in the smoothest possible camera motion. We evaluate our approach using several input videos from a range of cameras and compare these results to existing methods.


title: Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

abstract: Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.


title: Pose Machines: Articulated Pose Estimation via Inference Machines

abstract: State-of-the-art approaches for articulated human pose estimation are rooted in parts-based graphical models. These models are often restricted to tree-structured representations and simple parametric potentials in order to enable tractable inference. However, these simple dependencies fail to capture all the interactions between body parts. While models with more complex interactions can be defined, learning the parameters of these models remains challenging with intractable or approximate inference. In this paper, instead of performing inference on a learned graphical model, we build upon the inference machine framework and present a method for articulated human pose estimation. Our approach incorporates rich spatial interactions among multiple parts and information across parts of different scales. Additionally, the modular framework of our approach enables both ease of implementation without specialized optimization solvers, and efficient inference. We analyze our approach on two challenging datasets with large pose variation and outperform the state-of-the-art on these benchmarks.


title: On the relation between accuracy and fairness in binary classification

abstract: Our study revisits the problem of accuracy-fairness tradeoff in binary classification. We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions. We provide methodological recommendations for sound comparison of non-discriminatory classifiers, and present a brief theoretical and empirical analysis of tradeoffs between accuracy and non-discrimination.


title: Theoretical Frameworks and Mechanistic Aspects of Alcohol Addiction: Alcohol Addiction as a Reward Deficit Disorder

abstract: Alcoholism can be defined by a compulsion to seek and take drug, loss of control in limiting intake, and the emergence of a negative emotional state when access to the drug is prevented. Alcoholism impacts multiple motivational mechanisms and can be conceptualized as a disorder that includes a progression from impulsivity (positive reinforcement) to compulsivity (negative reinforcement). The compulsive drug seeking associated with alcoholism can be derived from multiple neuroadaptations, but the thesis argued here is that a key component involves the construct of negative reinforcement. Negative reinforcement is defined as drug taking that alleviates a negative emotional state. The negative emotional state that drives such negative reinforcement is hypothesized to derive from dysregulation of specific neurochemical elements involved in reward and stress within the basal forebrain structures involving the ventral striatum and extended amygdala, respectively. Specific neurochemical elements in these structures include not only decreases in reward neurotransmission, such as decreased dopamine and γ-aminobutyric acid function in the ventral striatum, but also recruitment of brain stress systems, such as corticotropin-releasing factor (CRF), in the extended amygdala. Acute withdrawal from chronic alcohol, sufficient to produce dependence, increases reward thresholds, increases anxiety-like responses, decreases dopamine system function, and increases extracellular levels of CRF in the central nucleus of the amygdala. CRF receptor antagonists also block excessive drug intake produced by dependence. A brain stress response system is hypothesized to be activated by acute excessive drug intake, to be sensitized during repeated withdrawal, to persist into protracted abstinence, and to contribute to the compulsivity of alcoholism. Other components of brain stress systems in the extended amygdala that interact with CRF and that may contribute to the negative motivational state of withdrawal include norepinephrine, dynorphin, and neuropeptide Y. The combination of loss of reward function and recruitment of brain stress systems provides a powerful neurochemical basis for a negative emotional state that is responsible for the negative reinforcement driving, at least partially, the compulsivity of alcoholism.


title: Objective self-awareness theory : Recent progress and enduring problems

abstract: Objective self-awareness theory has undergone fundamental changes in the 3 decades since Duval and Wicklund's (1972) original formulation. We review new evidence that bears on the basic tenets of the theory. Many of the assumptions of self-awareness theory require revision, particularly how expectancies influence approach and avoidance of self-standard discrepancies; the nature of standards, especially when they are changed; and the role of causal attribution in directing discrepancy reduction. However, several unresolved conceptual issues remain; future theoretical and empirical directions are discussed.


title: Discriminative Reranking For Machine Translation

abstract: This paper describes the application of discriminative reranking techniques to the problem of machine translation. For each sentence in the source language, we obtain from a baseline statistical machine translation system, a ranked best list of candidate translations in the target language. We introduce two novel perceptroninspired reranking algorithms that improve on the quality of machine translation over the baseline system based on evaluation using the BLEU metric. We provide experimental results on the NIST 2003 Chinese-English large data track evaluation. We also provide theoretical analysis of our algorithms and experiments that verify that our algorithms provide state-of-theart performance in machine translation.


title: Content Analysis for Audio Classification and Segmentation

abstract: We present our study of audio content analysis for classification and segmentation, in which an audio stream is segmented according to audio type or speaker identity. We propose a robust approach that is capable of classifying and segmenting an audio stream into speech, music, environment sound, and silence. Audio classification is processed in two steps, which makes it suitable for different applications. The first step of the classification is speech and nonspeech discrimination. In this step, a novel algorithm based on K-nearest-neighbor (KNN) and linear spectral pairs-vector quantization (LSP-VQ) is developed. The second step further divides nonspeech class into music, environment sounds, and silence with a rule-based classification scheme. A set of new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. We also develop an unsupervised speaker segmentation algorithm using a novel scheme based on quasi-GMM and LSP correlation analysis. Without a priori knowledge, this algorithm can support the open-set speaker, online speaker modeling and real time segmentation. Experimental results indicate that the proposed algorithms can produce very satisfactory results.


title: To go where no man has gone before: Virtual reality in architecture, landscape architecture and environmental planning

abstract: Abstract The use of virtual reality has its roots in visual communication science but disparate mechanisms and applications set it apart from the many tools of visualization. This paper reviews the use of virtual reality (VR) environments for research and teaching in the context of three disciplines: architecture, landscape architecture and environmental planning. As opposed to other uses of virtual environments, for example, in the health sciences or engineering, simulations using virtual reality theatres or labs in the three fields we explore are used to display inaccessible realities. VR environments are typically used in these fields for planned and designed realities, not yet existent or with nonexistent components. Each field has different reasons for spatial or temporal inaccessibility to reality, prompting the need and eventually the capability to achieve various levels of accuracy in the virtual setting. We describe current VR research opportunities and challenges in each discipline and emphasize what they can gain from sharing virtual reality systems for research and education.


title: Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting

abstract: We propose a novel algorithm to detect visual saliency from video signals by combining both spatial and temporal information and statistical uncertainty measures. The main novelty of the proposed method is twofold. First, separate spatial and temporal saliency maps are generated, where the computation of temporal saliency incorporates a recent psychological study of human visual speed perception. Second, the spatial and temporal saliency maps are merged into one using a spatiotemporally adaptive entropy-based uncertainty weighting approach. The spatial uncertainty weighing incorporates the characteristics of proximity and continuity of spatial saliency, while the temporal uncertainty weighting takes into account the variations of background motion and local contrast. Experimental results show that the proposed spatiotemporal uncertainty weighting algorithm significantly outperforms state-of-the-art video saliency detection models.


title: Photometric Stereo by UV-Induced Fluorescence to Detect Protrusions on Georgia O'Keeffe's Paintings

abstract: A significant number of oil paintings produced by Georgia O’Keeffe (1887–1986) show surface protrusions of varying width, up to several hundreds of microns. These protrusions are similar to those described in the art conservation literature as metallic soaps. Since the presence of these protrusions raises questions about the state of conservation and long-term prospects for deterioration of these artworks, a 3D-imaging technique, photometric stereo using ultraviolet illumination, was developed for the long-term monitoring of the surface shape of the protrusions and the surrounding paint. Because the UV fluorescence response of painting materials is isotropic, errors typically caused by non-Lambertian (anisotropic) specularities when using visible reflected light can be avoided providing a more accurate estimation of shape. As an added benefit, fluorescence provides additional contrast information contributing to material characterization. The developed methodology aims to detect, characterize, and quantify the distribution of micro-protrusions and their development over the surface of entire artworks. Combined with a set of analytical in situ techniques, and computational tools, this approach constitutes a novel methodology to investigate the selective distribution of protrusions in correlation with the composition of painting materials at the macroscale. While focused on O’Keeffe’s paintings as a case study, we expect the proposed approach to have broader significance by providing a non-invasive protocol to the conservation community to probe topological changes for any relatively flat painted surface of an artwork, and more specifically to monitor the dynamic formation of protrusions, in relation to paint composition and modifications of environmental conditions, loans, exhibitions, and storage over the long term.


title: A 3T Gain Cell Embedded DRAM Utilizing Preferential Boosting for High Density and Low Power On-Die Caches

abstract: Circuit techniques for enabling a sub-0.9 V logic-compatible embedded DRAM (eDRAM) are presented. A boosted 3T gain cell utilizes Read Word-line (RWL) preferential boosting to increase read margin and improve data retention time. Read speed is enhanced with a hybrid current/voltage sense amplifier that allows the Read Bit-line (RBL) to remain close to VDD. A regulated bit-line write scheme for driving the Write Bit-line (WBL) is equipped with a steady-state storage node voltage monitor to overcome the data `1' write disturbance problem of the PMOS gain cell without introducing another boosted supply for the Write Word-line (WWL) over-drive. An adaptive and die-to-die adjustable read reference bias generator is proposed to cope with PVT variations. Monte Carlo simulations compare the 6-sigma read and write performance of proposed eDRAM against conventional designs. Measurement results from a 64 kb eDRAM test chip implemented in a 65 nm low-leakage CMOS process show a 1.25 ms data retention time with a 2 ns random cycle time at 0.9 V, 85°C, and a 91.3 μW per Mb static power dissipation at 1.0 V, 85°C.


title: Intelligent driver monitoring systems based on physiological sensor signals: A review

abstract: Drowsiness, stress and lack of concentration caused by a variety of different factors is a serious problem in traffic. Many traffic accidents are due to these risky behaviors of the drivers. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different physiological sensor signals such as Electrocardiogram (ECG), Elektro-okulogram (EOG), Electroencephalogram (EEG) and Pulse Oximeter (Oxygen saturation measurements) enable clinician to determine psychological and behavioral state with high accuracy. There are researchers working on developing intelligent systems help to monitor potential risky behaviors of drivers using sensor signals. Thus, this paper provides an overview and analysis of driver monitoring/alerting systems developments and implementations which are based on physiological sensor signals. Summarizing published research works and systems this review provides a resource for researchers, scholars and developers working in the area.


title: Engineering of CRISPR/Cas9‐mediated potyvirus resistance in transgene‐free Arabidopsis plants

abstract: Summary ::: Members of the eukaryotic translation initiation factor (eIF) gene family, including eIF4E and its paralogue eIF(iso)4E, have previously been identified as recessive resistance alleles against various potyviruses in a range of different hosts. However, the identification and introgression of these alleles into important crop species is often limited. In this study, we utilise CRISPR/Cas9 technology to introduce sequence-specific deleterious point mutations at the eIF(iso)4E locus in Arabidopsis thaliana to successfully engineer complete resistance to Turnip mosaic virus (TuMV), a major pathogen in field-grown vegetable crops. By segregating the induced mutation from the CRISPR/Cas9 transgene, we outline a framework for the production of heritable, homozygous mutations in the transgene-free T2 generation in self-pollinating species. Analysis of dry weights and flowering times for four independent T3 lines revealed no differences from wild-type plants under standard growth conditions, suggesting that homozygous mutations in eIF(iso)4E do not affect plant vigour. Thus, the established CRISPR/Cas9 technology provides a new approach for the generation of Potyvirus resistance alleles in important crops without the use of persistent transgenes.


title: Interactive whiteboards: Real beauty or just lipstick?

abstract: There has been extensive investment by governments and individual schools in interactive whiteboard technology in developed countries premised on the assumption that their use in education will impact positively on learners' achievements. Developing countries, such as South Africa, keen to raise attainment among their learners are following suit. While at least one of the nine provinces in South Africa had undertaken pilot roll-outs of interactive whiteboards (IWBs) in schools, the Eastern Cape Department of Education commissioned a feasibility study to determine teachers and learners perceptions of the potential benefits and drawbacks of using interactive pen technology, specifically the eBeam, in their teaching and learning environments, before embarking upon a large scale roll-out. This paper reports on a case study of three government schools and highlights the learners and teachers' enthusiasm about the ''big screen'' and the multimedia options, but also raises concerns about the lack of ICT literacy displayed by teachers and learners and the cost of technology. As most of the benefits mentioned by the teachers and learners seemed to accrue to the use of the laptop and data projector combination and most of the drawbacks emanated from the use of the interactive pen technology itself, we suggest that it may not be expeditious to attempt to ''leap-frog'' the use of interactive technologies. Instead we suggest that an evolution of ICT related pedagogy is necessary to make optimal use of interactive pen technologies such as the eBeam and that teachers should be offered technologies, not have them imposed upon them.


Analysis and Prediction of Diabetes Mellitus using Machine Learning Algorithm


title: Virtual Reality Exposure Therapy for Post-Traumatic Stress Disorder and Other Anxiety Disorders

abstract: Anxiety disorders, including phobias and post-traumatic stress disorder, are common and disabling disorders that often involve avoidance behavior. Cognitive-behavioral treatments, specifically imaginal and in vivo forms of exposure therapy, have been accepted and successful forms of treatment for these disorders. Virtual reality exposure therapy, an alternative to more traditional exposure-based therapies, involves immersion in a computer-generated virtual environment that minimizes avoidance and facilitates emotional processing. In this article, we review evidence on the application of virtual reality exposure therapy to the treatment of specific phobias and post-traumatic stress disorder and discuss its advantages and cautions.


title: A 15-Gb/s 0.5-mW/Gbps Two-Tap DFE Receiver With Far-End Crosstalk Cancellation

abstract: This paper presents a low-power receiver with two-tap decision feedback equalization (DFE) and novel far-end crosstalk (FEXT) cancellation capability, implemented in a 45-nm SOI CMOS process. The receiver employs a half-rate speculative DFE architecture to allow for the use of low-power front-end circuitry and CMOS clock buffers. In the proposed architecture, a switched-capacitor sample-hold at the front-end is employed to perform DFE tap summation. This technique is generalized to implement n taps of equalization. The receiver compensates the effect of crosstalk without making a decision on the received aggressor signal. Due to the low-power nature of the switched-capacitor front-end, the crosstalk cancellation is possible with only 33 μW/Gbps/lane power overhead. The receiver was tested over channels with different levels of loss and coupling. The signaling rate with BER <; 10-12 was significantly increased with the use of DFE and crosstalk cancellation scheme for highly coupled and lossy PCB traces. The DFE receiver equalizes 15-Gb/s data over a channel with more than 14-dB loss while consuming about 7.5 mW from a 1.2-V supply. At lower data rates it equalizes channels with over 21-dB loss.


title: Learning-based optimization of cache content in a small cell base station

abstract: Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.


title: Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

abstract: is important problem in data mining. Given a data set, classifier generates meaningful description for each class. Decision trees are most effective and widely used classification methods. There are several algorithms for induction of decision trees. These trees are first induced and then prune subtrees with subsequent pruning phase to improve accuracy and prevent overfitting. In this paper, various pruning methods are discussed with their features and also effectiveness of pruning is evaluated. Accuracy is measured for diabetes and glass dataset with various pruning factors. The experiments are shown for this two datasets for measuring accuracy and size of the tree.


title: Managing stress: the influence of gender, age and emotion regulation on coping among university students in Botswana

abstract: This study focused on the influence of gender, age and emotion regulation on coping strategies among university students in Botswana. Sixty-four males and 64 females, ranging in age from 18 to 29 years completed the Difficulty in Emotion Regulation Scale and the Coping Strategy Inventory. Female students used wishful thinking and problem-focused disengagement more than male students; however, there were no other significant gender differences in coping strategies. Older students were more likely to use problem-solving, cognitive restructuring and express emotion coping strategies. In addition, problems in emotion regulation significantly predicted problem-and emotion-focused engagement, problem- and emotion-focused disengagement and coping strategies. There was a unique finding that non-acceptance of emotional responses, a type of emotion suppression, was positively correlated with problem solving, cognitive restructuring, expressing emotion, social support, problem avoidance and wishful thinking coping strategies. Cultural context and implications for student well-being and university support are discussed.