title: Local Background Enclosure for RGB-D Salient Object Detection

abstract: Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the background frequently contains regions that are highly variable in depth. Here, we propose a novel RGB-D saliency feature. Local Background Enclosure (LBE) captures the spread of angular directions which are background with respect to the candidate region and the object that it is part of. We show that our feature improves over state-of-the-art RGB-D saliency approaches as well as RGB methods on the RGBD1000 and NJUDS2000 datasets.


title: Current opinions concerning the restoration of endodontically treated teeth: basic principles

abstract: The goal of this general article is to present a survey of the current knowledge about the clinical approach of restoring endodontically treated teeth. The best way to restore teeth after root canal treatment has long been and still is a controversial subject of debate to this day. The clinical approach of restoring endodontically treated teeth needs taking into consideration several issues: aims of coronal restoration, criteria for establishing the various modalities of coronal restoration, clinical solutions of restoring teeth after endodontic treatment, guidelines regarding restorative materials and techniques, possibilities and limits of restoration using direct adhesive materials and techniques. The aims of coronal restoration of endodontically treated teeth are generally considered to be the following ones: to prevent recontamination of the root canal system and/or periapical space, to replace missing hard dental tissues and to restore coronal morphology and functions, to provide the necessary strength for the restoration/tooth complex in order to withstand functional stress and prevent crown and/or root fracture. The criteria for establishing the modalities of coronal restoration for endodontically treated teeth are: amount and quality of remaining hard dental tissues, topography and coronal morphology of the tooth, functional occlusal forces that the restoration/tooth complex has to withstand, restoring requirements in order to include the treated tooth in a comprehensive oral rehabilitation treatment plan, esthetic requirements.


title: Cascade2D: A design-aware partitioning approach to monolithic 3D IC with 2D commercial tools

abstract: Monolithic 3D IC (M3D) can continue to improve power, performance, area and cost beyond traditional Moore's law scaling limitations by leveraging the third-dimension and fine-grained monolithic inter-tier vias (MIVs). Several recent studies present methodologies to implement M3D designs, but most, if not all of these studies implement top and bottom tier separately after partitioning, which results in inaccurate buffer insertion. In this paper, we present a new methodology called 'Cascade2D' that utilizes design and micro-architecture insight to partition and implement an M3D design using 2D commercial tools. By modeling MIVs with sets of anchor cells and dummy wires, we implement and optimize both top and bottom tier simultaneously in a single 2D design. M3D designs of a commercial, in-order, 32-bit application processor at the foundry 28nm, 14/16nm and predictive 7nm technology nodes are implemented using this new methodology and we investigate the power, performance and area improvements over 2D designs. Our new methodology consistently outperforms the state-of-the-art M3D design flow with up to 4X better power savings. In the best case scenario, M3D designs from the Cascade2D flow show 25% better performance at iso-power and 20% lower power at iso-performance.


title: Interference Measurements in the European 868 MHz ISM Band with Focus on LoRa and SigFox

abstract: In this measurement study the signal activity and power levels are measured in the European Industrial, Scientific, and Medical band 863-870 MHz in the city of Aalborg, Denmark. The target is to determine if there is any interference, which may impact deployment of Internet of Things devices. The focus is on the Low Power Wide Area technologies LoRa and SigFox. The measurements show that there is a 22-33 % probability of interfering signals above -105 dBm within the mandatory LoRa and SigFox 868.0-868.6 MHz band in a shopping area and a business park in downtown Aalborg, which thus limits the potential coverage and capacity of LoRa and SigFox. However, the probability of interference is less than 3 % in the three other measurement locations in Aalborg. Finally, a hospital and an industrial area are shown to experience high activity in the RFID subband 865-868 MHz, while the wireless audio band 863-865 MHz has less activity.


title: Hyperspectral image unmixing using autoencoder cascade

abstract: Hyperspectral image unmixing is the process of estimating pure source signals (endmemebers) and their proportions (abundances) from highly mixed spectroscopic images. Due to model inaccuracies and observation noise, unmixing has been a very challenging problem. In this paper, we exploit the potential of using autoencoder to tackle the unmixing challenges. Two important facts are considered in the algorithm: first, the observation noise in the hyperspectral image generally exists and largely affects the unmixing results; second, the mixing process contains sparsity priori which should be considered to assist the endmember extraction. The proposed autoencoder cascade concatenates a marginalized denoising autoencoder and a non-negative sparse autoencoder to solve the unmixing problem which implicitly denoises the observation data and employs the self-adaptive sparsity constraint. The algorithm is tested on a set of synthetic mixtures and a real hyperspectral image. The experimental results demonstrate the proposed algorithm outperforms several advanced unmixing approaches in highly noisy environment.


Advantages and Challenges of Using Augmented Reality for Library Orientations in an Academic/Research Library Setting


title: Attention regulation and monitoring in meditation

abstract: Meditation can be conceptualized as a family of complex emotional and attentional regulatory training regimes developed for various ends, including the cultivation of well-being and emotional balance. Among these various practices, there are two styles that are commonly studied. One style, focused attention meditation, entails the voluntary focusing of attention on a chosen object. The other style, open monitoring meditation, involves nonreactive monitoring of the content of experience from moment to moment. The potential regulatory functions of these practices on attention and emotion processes could have a long-term impact on the brain and behavior.


title: Impact of facebook addiction on narcissistic behavior and self-esteem among students.

abstract: OBJECTIVE ::: To investigate the relationship between Facebook addiction, narcissism and self-esteem and to see if gender played any role in this equation. ::: ::: ::: METHODS ::: The correlational study was conducted from February to March 2013 at the Department of Psychology, University of Sargodha, Punjab, Pakistan. Using convenient sampling, two equal groups of male and female students were enrolled from different departments of the university. Bergen Facebook Addiction Scale, Hypersensitive Narcissism Scale and Rosenberg's Self-esteem Scale were used for evaluation. SPSS 17 was used for statistical analysis. ::: ::: ::: RESULTS ::: Of the 200 subjects in the study, 100(50%) each were males and females. Facebook addiction was positively correlated with narcissism(r=0.20; p<0.05) and negatively with self-esteem(r=-0.18; p<0.05). Relationship between narcissism and self-esteem was non-significant(r=0.05; p>0.05). Facebook addiction was a significant predictor of narcissistic behaviour (b=0.202; p<0.001) and low self-esteem (b=-0.18; p<0.001). There were no significant gender differences in the three variables (p>0.05 each). ::: ::: ::: CONCLUSIONS ::: Facebook addiction was a significant predictor of narcissistic behaviour and low levels of self-esteem among students.


title: Ontology Reasoning with Deep Neural Networks

abstract: The ability to conduct logical reasoning is a fundamental aspect of intelligent behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, symbolic logic-based methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than symbolic logic-based formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning. This is an important and at the same time very natural logical reasoning task, which is why the presented approach is applicable to a plethora of important real-world problems. We present the outcomes of several experiments, which show that our model learned to perform precise ontology reasoning on diverse and challenging tasks. Furthermore, it turned out that the suggested approach suffers much less from different obstacles that prohibit logic-based symbolic reasoning, and, at the same time, is surprisingly plausible from a biological point of view.


Segment-Level Neural Conditional Random Fields for Named Entity Recognition


title: AtomMap: A probabilistic amorphous 3D map representation for robotics and surface reconstruction

abstract: We present a new 3D probabilistic occupancy map representation for robotics applications by relaxing the commonly-assumed constraint that space must be perfectly tessellated. We replace the regular structure of 3D grids with an unstructured collection of non-overlapping, equally-sized spheres, which we call “atoms”. Abandoning the grid structure allows a more accurate representation of space directly tangent to surfaces, which facilitates a number of applications such as high fidelity surface reconstruction and surface-guided path planning. Maps composed of atoms can distinguish between free, occupied, and unknown space, support computationally efficient insertions and collision queries, provide free space planning guarantees, and achieve state-of-the-art memory efficiency over large volumes. This is achieved while simultaneously reducing quantization effects in the vicinity of surfaces and defining a useful implicit surface representation.


title: Bazaar-Extension: A CloudSim Extension for Simulating Negotiation Based Resource Allocations

abstract: Negotiation processes taking place between two or more parties need to agree on a viable execution mechanism. Auctioning protocols have proven useful as electronic negotiation mechanisms in the past. Auctions are a way of determining the price of a resource in a dynamic way. Additionally, auctions have well defined rules such as winner and loser determination, time restrictions or minimum price increment. These restrictions are necessary to ensure fair and transparent resource allocation. However, these rules are limiting flexibility of consumers and providers. In this paper we introduce a novel negotiation based resource allocation mechanisms using the offer-counteroffer negotiation protocol paradigm. This allocation mechanism shows similarities to the supermarket approach as consumer and provider are able to communicate directly. Further, the price is determined in a dynamic way similar to auctioning. We developed a Bazaar-Extension for CloudSim which simulates negotiation processes with different strategies. In this paper we introduce and analyze a specific Genetic Algorithm based negotiation strategy. For the comparison of the efficiency of resource allocations a novel Bazaar-Score was used.


title: Ultra-Dense Networks: Survey of State of the Art and Future Directions

abstract: Within the foreseeable future, the growing number of mobile devices, and their diversity, will challenge the current network architecture. Furthermore, users will expect greater data rates, lower latency, lower packet drop rates, etc. in future wireless networks. Ultra Dense Networks (UDN), considered to be one of the best ways to meet user expectations and support future wireless network deployment, will face multiple significant hurdles, including interference, mobility, and cost. In this paper, we review existing research efforts toward addressing those challenges and present future avenues for research. We first develop a taxonomy to review and describe existing research efforts. Next, we focus on inter-cell interference, handover performance, and energy efficiency as the key techniques to addressing the most pressing challenges. Finally, we present several future research directions, including emergent Internet-of-Things (IoT) applications, security and privacy, modeling and realistic simulations, and relevant techniques.


title: Cyanide suicide after deep web shopping: a case report

abstract: Cyanide is a product that is known for its use in industrial or laboratory processes, as well as for intentional intoxication. The toxicity of cyanide is well described in humans with rapid inhibition of cellular aerobic metabolism after ingestion or inhalation, leading to severe clinical effects that are frequently lethal. We report the case of a young white man found dead in a hotel room after self-poisoning with cyanide ordered in the deep Web. This case shows a probable complex suicide kit use including cyanide, as a lethal tool, and dextromethorphan, as a sedative and anxiolytic substance. This case is an original example of the emerging deep Web shopping in illegal drug procurement. SafetyLit note: It appears that the author has confused the term 'deep web' with the 'dark web'. This is an important distinction. The deep web is a technical term (coined by Michael Berman) that refers to that portion of the World Wide Web where the contents are not indexed by standard search engines for any reason. The deep web is opposite to the surface web. The deep web includes many very common uses of the internet such as web mail, company or government records and files, cloud storage, online banking and services accessed via a paywall such as video on demand, subscription news or database sites, etc Media outlets began to use the term 'deep web' synonymously with the 'dark web' or 'darknet' during time of the Silk Road criminal controversy; and consequently is an ongoing source of confusion. Statements that a majority of the internet or the web is hidden are true because there are many legitimate reasons (some listed above) why the information is unavailable to the general public. There is a part of the web that remains hidden and anonymous. It can be and is used for criminal purposes. It is also used be dissidents within the control of oppressive governments as a way to avoid identification when they view or transmit information. Language: en


title: An open source and open hardware embedded metric optical flow CMOS camera for indoor and outdoor applications

abstract: Robust velocity and position estimation at high update rates is crucial for mobile robot navigation. In recent years optical flow sensors based on computer mouse hardware chips have been shown to perform well on micro air vehicles. Since they require more light than present in typical indoor and outdoor low-light conditions, their practical use is limited. We present an open source and open hardware design 1 of an optical flow sensor based on a machine vision CMOS image sensor for indoor and outdoor applications with very high light sensitivity. Optical flow is estimated on an ARM Cortex M4 microcontroller in real-time at 250 Hz update rate. Angular rate compensation with a gyroscope and distance scaling using a ultrasonic sensor are performed onboard. The system is designed for further extension and adaption and shown in-flight on a micro air vehicle.


title: Kinematic model analysis and design optimization of a bionic pectoral fins

abstract: Cownose rays have excellent characteristics as the fish with oscillating pectoral fins. Therefore, in order to construct bionic fish prototype with high efficiency, low noise and high stability characteristics, cownose rays is chosen as the prototype in bionic research. In the paper, simplified flapping kinematic model is established based on the study of the characteristic of cow rays' pectoral fins. The mechanism characteristics and motion feature of the Robo-rayI-IV generation robotic fish mimicking cownose rays are summarized, and the shortcomings in the current design is analyzed. Then, optimization design ideas of heaving and pitching airfoil based on space parallel mechanism with two degree of freedom are proposed in the design of pectoral fin skeleton. The feasibility of new pectoral fin skeleton is verified through simulation. Bionic pectoral fin is constructed and processed. Finally, experiments were carried out and the rationality of the optimal design is verified. Experiments shows that the new pectoral fin skeleton has good active and passive flexible deformation capacity, and robotic fish with new pectoral fin has better propulsion performance than Robo-rayIV while maintaining the high mobility of the premise.


title: Secure and Efficient Data Transmission for Cluster-Based Wireless Sensor Networks

abstract: Secure data transmission is a critical issue for wireless sensor networks (WSNs). Clustering is an effective and practical way to enhance the system performance of WSNs. In this paper, we study a secure data transmission for cluster-based WSNs (CWSNs), where the clusters are formed dynamically and periodically. We propose two secure and efficient data transmission (SET) protocols for CWSNs, called SET-IBS and SET-IBOOS, by using the identity-based digital signature (IBS) scheme and the identity-based online/offline digital signature (IBOOS) scheme, respectively. In SET-IBS, security relies on the hardness of the Diffie-Hellman problem in the pairing domain. SET-IBOOS further reduces the computational overhead for protocol security, which is crucial for WSNs, while its security relies on the hardness of the discrete logarithm problem. We show the feasibility of the SET-IBS and SET-IBOOS protocols with respect to the security requirements and security analysis against various attacks. The calculations and simulations are provided to illustrate the efficiency of the proposed protocols. The results show that the proposed protocols have better performance than the existing secure protocols for CWSNs, in terms of security overhead and energy consumption.


title: Human tissues in a dish: The research and ethical implications of organoid technology

abstract: BACKGROUND Organoids are stem cell–derived structures generated in vitro that display the three-dimensional architecture and physiology of intact organs. They offer unique possibilities for modeling and studying normal development and disease processes and open up innovative approaches to medical research, drug discovery, and toxicology testing. Together with reprogramming technology and gene editing methods, organoids hold the promise to influence the innovation cycle in biomedical research, including fields that historically have been the subject of intense ethical debate. In this Review, we discuss the ethical implications of organoid technology and the impact on biomedical research. ADVANCES Owing to their great potential, organoids are likely to affect ongoing ethical debates over subjects ranging from the role of animal experiments and the use of human embryos and tissues in biomedical research to precision medicine, stem cell transplantation, and gene therapy. Most societies have formulated public policies to balance the advancement of biomedical science with the various concerns regarding the use of animals and human embryos and tissues for biomedical research. Organoids may necessitate a recalibration of these ethical and legal policies. However, they should not be presented as a simple solution that can abrogate controversial technologies. We suggest that the use of organoids is complementary to, rather than in competition with, these classical research methodologies. In addition, organoids should not be seen as a morally neutral alternative. They are grown from cells and tissues obtained from human individuals, and establishing the moral and legal status of human organoids requires ethical discussion and empirical research, particularly for sensitive cases such as brain organoids. The storage and use of organoids in so-called living biobanks raise ethical and governance challenges—for example, questions about the type of donor consent and ethics review needed for long-term storage and use and for feedback of clinically relevant findings to the patient. Personalized drug testing in organoids may close the gap between preclinical drug development and clinical trials. It will further blur the line between research and care and will challenge policies for drug reimbursement by insurance companies. Cautious ethical approaches are needed for the first clinical transplantations of organoids, particularly when organoid technologies are combined with gene editing. Last, the strong public interest in organoids, together with the immature nature of the field, requires particular attention regarding public (media) communication to avoid inaccurate or incomplete representations and excessive expectations. OUTLOOK Organoid research holds considerable potential for investigating human development and disease and for advancing precision and regenerative medicine. Despite these promising applications, there are several layers of complexity, not only in a technological sense, but also with regard to the ethical introduction in research, clinical care, and society. By engaging scientists, clinicians, patients, ethicists, policy-makers, and the public in constructive interdisciplinary collaborations and dialog around the challenges discussed in this Review, we strive for responsible research and innovation and long-term acceptance of this exciting technology.


Impulse noise reduction for texture images using real word spelling correction algorithm and local binary patterns.


title: A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques

abstract: Multi-criteria collaborative filtering (MC-CF) presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects of items. However, scalability and sparsity are two main problems in MC-CF which this paper attempts to solve them using dimensionality reduction and Neuro-Fuzzy techniques. Considering the user behavior about items' features which is frequently vague, imprecise and subjective, we solve the sparsity problem using Neuro-Fuzzy technique. For the scalability problem, higher order singular value decomposition along with supervised learning (classification) methods is used. Thus, the objective of this paper is to propose a new recommendation model to improve the recommendation quality and predictive accuracy of MC-CF and solve the scalability and alleviate the sparsity problems in the MC-CF. The experimental results of applying these approaches on Yahoo!Movies and TripAdvisor datasets with several comparisons are presented to show the enhancement of MC-CF recommendation quality and predictive accuracy. The experimental results demonstrate that SVM dominates the K-NN and FBNN in improving the MC-CF predictive accuracy evaluated by most broadly popular measurement metrics, F1 and mean absolute error. In addition, the experimental results also demonstrate that the combination of Neuro-Fuzzy and dimensionality reduction techniques remarkably improves the recommendation quality and predictive accuracy of MC-CF in relation to the previous recommendation techniques based on multi-criteria ratings.


title: Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations

abstract: The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.


title: Still looking: investigating seamless gaze-supported selection, positioning, and manipulation of distant targets

abstract: We investigate how to seamlessly bridge the gap between users and distant displays for basic interaction tasks, such as object selection and manipulation. For this, we take advantage of very fast and implicit, yet imprecise gaze- and head-directed input in combination with ubiquitous smartphones for additional manual touch control. We have carefully elaborated two novel and consistent sets of gaze-supported interaction techniques based on touch-enhanced gaze pointers and local magnification lenses. These conflict-free sets allow for fluently selecting and positioning distant targets. Both sets were evaluated in a user study with 16 participants. Overall, users were fastest with a touch-enhanced gaze pointer for selecting and positioning an object after some training. While the positive user feedback for both sets suggests that our proposed gaze- and head-directed interaction techniques are suitable for a convenient and fluent selection and manipulation of distant targets, further improvements are necessary for more precise cursor control.


title: Local Convolutional Features with Unsupervised Training for Image Retrieval

abstract: Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval. We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval. The proposed family of descriptors, called Patch-CKN, adapt the recently introduced Convolutional Kernel Network (CKN), an unsupervised framework to learn convolutional architectures. We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel "RomePatches" dataset. Patch-CKN descriptors yield competitive results compared to supervised CNN alternatives on patch and image retrieval.


title: Mining touch interaction data on mobile devices to predict web search result relevance

abstract: Fine-grained search interactions in the desktop setting, such as mouse cursor movements and scrolling, have been shown valuable for understanding user intent, attention, and their preferences for Web search results. As web search on smart phones and tablets becomes increasingly popular, previously validated desktop interaction models have to be adapted for the available touch interactions such as pinching and swiping, and for the different device form factors. In this paper, we present, to our knowledge, the first in-depth study of modeling interactions on touch-enabled device for improving Web search ranking. In particular, we evaluate a variety of touch interactions on a smart phone as implicit relevance feedback, and compare them with the corresponding fine-grained interactions on a desktop computer with mouse and keyboard as the primary input devices. Our experiments are based on a dataset collected from two user studies with 56 users in total, using a specially instrumented version of a popular mobile browser to capture the interaction data. We report a detailed analysis of the similarities and differences of fine-grained search interactions between the desktop and the smart phone modalities, and identify novel patterns of touch interactions indicative of result relevance. Finally, we demonstrate significant improvements to search ranking quality by mining touch interaction data.


title: Pill Recognition Using Minimal Labeled Data

abstract: Inappropriate medication use such as wrong drug or wrong dose intake can be harmful to patients. In this work we present a method to automatically identify a pill from a single image using Convolutional Neural Network (CNN). We first localize the pill in the image by detecting the region with the highest concentration of edges. To overcome the challenge of minimal labeled training data and domain shift from the training images taken under the controlled lab environment to the consumer images taken under natural living conditions, several data augmentation techniques are applied on the Region of Interest to generate synthetic pill images for training the CNN. We adopted GoogLeNet Inception Network as our main classifier. Three GoogLeNet models with different specialties on color, shape and feature are trained on the augmented dataset. We evaluate our proposed method with a publicly available dataset provided by National Institute of Health that contains 1000 different pill classes.


title: A Randomized, Double-Blind, Placebo-Controlled Trial of Intravenous Acetaminophen on Hospital Length of Stay in Obese Individuals Undergoing Sleeve Gastrectomy

abstract: BACKGROUND ::: Retrospective studies indicate that acetaminophen iv administration reduces hospital length of stay (LoS) and opiate consumption in patients undergoing bariatric surgery. ::: ::: ::: OBJECTIVE ::: This study sought to determine whether using acetaminophen iv in morbidly obese subjects undergoing sleeve gastrectomy decreased LoS and total hospital charges as compared to patients receiving saline placebo. ::: ::: ::: SETTING ::: Single-center university hospital METHODS: Using a randomized, double-blind, placebo-controlled design, subjects were assigned to receive either acetaminophen iv (group A) or saline placebo iv (group P). Data were collected between Jan 1 and Dec 31, 2016. Group A received acetaminophen every 6 h for a total of four doses. The first dose was administered following the induction of general anesthesia; group P received saline iv on the same schedule. Anesthetic management and prophylactic antiemetic regimen were standardized in all subjects. Postoperative pain management consisted of hydromorphone via patient-controlled infusion pump. Primary outcomes include hospital LoS and associated hospital costs. Secondary outcomes include patient satisfaction and postoperative nausea and pain scores. ::: ::: ::: RESULTS ::: Subject demographics (n = 127) and intraoperative management were similar in the two groups. Across all subjects, median hospital LoS in group A (n = 63) was 1.87 vs. 1.97 days in group P (n = 64) (p = 0.03, Wilcoxon rank-sum test). Postoperatively, daily quality-of-recovery (QoR-15) scores, narcotic consumption, and the use of rescue antiemetics were not significantly different between groups. Median hospital costs were as follows: group A, $12,885 vs. group P, $12,977 (n = 64). ::: ::: ::: CONCLUSIONS ::: Acetaminophen iv may reduce hospital LoS in subjects undergoing sleeve gastrectomy.


title: 79 GHz Slot Antennas Based on Substrate Integrated Waveguides (SIW) in a Flexible Printed Circuit Board

abstract: The design, fabrication and characterization of 79 GHz slot antennas based on substrate integrated waveguides (SIW) are presented in this paper. All the prototypes are fabricated in a polyimide flex foil using printed circuit board (PCB) fabrication processes. A novel concept is used to minimize the leakage losses of the SIWs at millimeter wave frequencies. Different losses in the SIWs are analyzed. SIW-based single slot antenna, longitudinal and four-by-four slot array antennas are numerically and experimentally studied. Measurements of the antennas show approximately 4.7%, 5.4% and 10.7% impedance bandwidth (S11=-10 dB) with 2.8 dBi, 6.0 dBi and 11.0 dBi maximum antenna gain around 79 GHz, respectively. The measured results are in good agreement with the numerical simulations.


title: An empirical study of practitioners' perspectives on green software engineering

abstract: The energy consumption of software is an increasing concern as the use of mobile applications, embedded systems, and data center-based services expands. While research in green software engineering is correspondingly increasing, little is known about the current practices and perspectives of software engineers in the field. This paper describes the first empirical study of how practitioners think about energy when they write requirements, design, construct, test, and maintain their software. We report findings from a quantitative, targeted survey of 464 practitioners from ABB, Google, IBM, and Microsoft, which was motivated by and supported with qualitative data from 18 in-depth interviews with Microsoft employees. The major findings and implications from the collected data contextualize existing green software engineering research and suggest directions for researchers aiming to develop strategies and tools to help practitioners improve the energy usage of their applications.


title: Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings

abstract: Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower computational complexity when compared with a super-resolution method based on convolutional neural nets (SRCNN15). Compared with the previous SI method that is limited with a scale factor of 2, GLM-SI shows superior performance with average 0.79 dB higher in PSNR, and can be used for scale factors of 3 or higher.


title: Beauty and the burst: remote identification of encrypted video streams

abstract: The MPEG-DASH streaming video standard contains an information leak: even if the stream is encrypted, the segmentation prescribed by the standard causes content-dependent packet bursts. We show that many video streams are uniquely characterized by their burst patterns, and classifiers based on convolutional neural networks can accurately identify these patterns given very coarse network measurements. We demonstrate that this attack can be performed even by a Web attacker who does not directly observe the stream, e.g., a JavaScript ad confined in a Web browser on a nearby machine.


title: A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA

abstract: In the Area of Security, Intrusion Detection System (IDS) form an individual trailing and plays an essential role in information Security. As the usability of the internet among the users in a wide area is increasing day by day so as the importance of security and to keep the system aware of the malicious activities is also increasing. It has the following limitations on low detection rate, high false alarm rate and so on which is been indicated by the traditional Intrusion Detection System. A performance of the classifier is based on the necessity of the terms of its effectiveness, and it is also concerned with the number of features to be examined by the IDS should be improved. In this, J48 is been performed on the hybrid IDS and is applied using J48 Decision Tree algorithm, J48 Decision Tree is used for the feature selection and Naive Bayes Algorithm. Basically Intrusion detection systems (IDSs) is been used on the basis of two fundamental approaches first the recognition of anomalous activities as it generally occurs on the turns from usual or unusual behavior and second misuse detection by observing unauthorized “signatures” of those recognized malicious assaults and classification vulnerabilities. Anomaly or the anonymous (behavior-based) IDSs presume the difference of normal behavior beneath attacks and achieve abnormal activities evaluated and recognized with predefined system or user behavior reference model. The main focus of this survey is on WEKA (Waikato Environment for Knowledge Analysis) Tool and its various algorithms of classification used for detecting and analyzing the various intrusions. Lastly, In this survey, we lead to elaborate the mostly used dataset in information security research KDDCUP and NSL KDD and its various components.


title: A 28 GHz 480 elements digital AAS using GaN HEMT amplifiers with 68 dBm EIRP for 5G long-range base station applications

abstract: This paper reports the design, development and performance of a 28 GHz 480 elements digital Active Antenna System (AAS) prototype for 5G long-range applications. AAS accommodates 32 channels of 0.15 um GaN HEMT power amplifier chains. The antenna array prototype consists of full digitally controlled 32 streams of 15 elements sub-arrays. They can deliver a total conductive power of 41 dBm and Average Effective Isotropically Radiated Power (EIRP) of 68 dBm for the macro-cell coverage at 28 GHz. The prototype also features newly proposed high density heat spreading structure dissipating around 450 W in an 11 liter compact enclosure. The paper proposes and demonstrates one of direction of the high power GaN HEMT application to the millimeter-wave 5G long-range base stations.


title: Overview of Stator-Permanent Magnet Brushless Machines

abstract: Permanent magnet (PM) brushless machines having magnets and windings in stator (the so-called stator-PM machines) have attracted more and more attention in the past decade due to its definite advantages of robust structure, high power density, high efficiency, etc. In this paper, an overview of the stator-PM machine is presented, with particular emphasis on concepts, operation principles, machine topologies, electromagnetic performance, and control strategies. Both brushless ac and dc operation modes are described. The key features of the machines, including the merits and drawbacks of the machines, are summarized. Moreover, the latest development of the machines is also discussed.


title: Directional blade antenna with increased sectoral gain

abstract: In this paper, method for increasing the sectoral gain of a 3-notched directional blade antenna operating in C-band is introduced. By inserting an additional 4th notch and using a cylindrical parasitic element all around the radiator, sectoral gain covering 70 degrees along the broadside radiation of the blade antenna is increased by approximately % 30. Antenna configuration is modeled and optimized using CST Microwave Studio Suite, EM simulation Software.


title: An adaptive recurrent-neural-network motion controller for X-Y table in CNC Machine

abstract: In this paper, an adaptive recurrent-neural-network (ARNN) motion control system for a biaxial motion mechanism driven by two field-oriented control permanent magnet synchronous motors (PMSMs) in the computer numerical control (CNC) machine is proposed. In the proposed ARNN control system, a RNN with accurate approximation capability is employed to approximate an unknown dynamic function, and the adaptive learning algorithms that can learn the parameters of the RNN on line are derived using Lyapunov stability theorem. Moreover, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series, external disturbances, cross-coupled interference and friction torque of the system. To relax the requirement for the value of lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is investigated. Using the proposed control, the position tracking performance is substantially improved and the robustness to uncertainties including cross-coupled interference and friction torque can be obtained as well. Finally, some experimental results of the tracking of various reference contours demonstrate the validity of the proposed design for practical applications.


title: A performance study for camera pose estimation using visual marker based tracking

abstract: Vision-based tracking systems are widely used for augmented reality (AR) applications. Their registration can be very accurate and there is no delay between real and virtual scene. However, vision-based tracking often suffers from limited range, errors, heavy processing time and present erroneous behavior due to numerical instability. To address these shortcomings, robust method are required to overcome these problems. In this paper, we survey classic vision-based pose computations and present a method that offers increased robustness and accuracy in the context of real-time AR tracking. In this work, we aim to determine the performance of four pose estimation methods in term of errors and execution time. We developed a hybrid approach that mixes an iterative method based on the extended Kalman filter (EKF) and an analytical method with direct resolution of pose parameters computation. The direct method initializes the pose parameters of the EKF algorithm which performs an optimization of these parameters thereafter. An evaluation of the pose estimation methods was obtained using a series of tests and an experimental protocol. The analysis of results shows that our hybrid algorithm improves stability, convergence and accuracy of the pose parameters.


A Review towards Evolutionary Multiobjective optimization Algorithms


title: Automatic Hair Detection in the Wild

abstract: This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour. The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images.


title: A fast algorithm for computing the Euler number of an image and its VLSI implementation

abstract: Euler number is a fundamental topological feature of an image. In this paper, a novel algorithm for computing the Euler number of a binary image based on divide-and-conquer paradigm, is proposed, which outperforms significantly the conventional techniques used in image processing tools. The algorithm can be easily parallelized for computing the Euler number of an N/spl times/N image in O(N) time, with O(N) processors. Using a simple architecture, the proposed method can be implemented as a special purpose VLSI chip.


title: The anatomy demonstrator of the future: an examination of the role of the medically-qualified anatomy demonstrator in the context of tomorrow's doctors and modernizing medical careers.

abstract: In 1993, the UK General Medical Council published Tomorrow's Doctors leading to a nationwide restructuring of undergraduate medical courses. Traditional courses with distinct pre-clinical and clinical phases gave way to a more integrated approach to undergraduate medical education, with an emphasis on the quality and variety of teaching provided. More than a decade after Tomorrow's Doctors, postgraduate medical training is being transformed. Modernising Medical Careers is leading to the introduction of a two-year Foundation Programme, with subsequent streamlined specialist training. In the context of these changes, we consider how the creation of posts for medically-qualified anatomy demonstrators would present an opportunity to fulfil needs in both undergraduate education and postgraduate training. We outline the threats facing established posts, and how these problems may be resolved. We hope that this overview of the challenges facing undergraduate and postgraduate education in the UK, with particular reference to anatomy, may offer some useful insight to teachers and learners in other countries. We conclude that the role of the medically-qualified anatomy demonstrator has proved valuable in the context of Tomorrow's Doctors, and that this role can evolve and expand as part of the 21st century "modern medical career."


title: Minimizing Nonconvex Population Risk from Rough Empirical Risk.

abstract: Population risk---the expectation of the loss over the sampling mechanism---is always of primary interest in machine learning. However, learning algorithms only have access to empirical risk, which is the average loss over training examples. Although the two risks are typically guaranteed to be pointwise close, for applications with nonconvex nonsmooth losses (such as modern deep networks), the effects of sampling can transform a well-behaved population risk into an empirical risk with a landscape that is problematic for optimization. The empirical risk can be nonsmooth, and it may have many additional local minima. ::: This paper considers a general optimization framework which aims to find approximate local minima of a smooth nonconvex function $F$ (population risk) given only access to the function value of another function $f$ (empirical risk), which is pointwise close to $F$ (i.e., $\|F-f\|_{\infty} \le \nu$). We propose a simple algorithm based on stochastic gradient descent (SGD) on a smoothed version of $f$ which is guaranteed to find an $\epsilon$-second-order stationary point if $\nu \le O(\epsilon^{1.5}/d)$, thus escaping all saddle points of $F$ and all the additional local minima introduced by $f$. We also provide an almost matching lower bound showing that our SGD-based approach achieves the optimal trade-off between $\nu$ and $\epsilon$, as well as the optimal dependence on problem dimension $d$, among all algorithms making a polynomial number of queries. As a concrete example, we show that our results can be directly used to give sample complexities for learning a ReLU unit, whose empirical risk is nonsmooth.


title: Software execution freeze-safe microcontroller using power profile tracking for IoT-driven connected services

abstract: When we consider the safety of Internet of Things (IoT), it is quite important that a fault on a single IoT device affects the whole IoT system. If this fault causes isolation at a certain point, the system will freeze. To prevent this, we attempt dynamic monitoring using data samples made from power profile tracking. This paper proposes MCU with low-power monitoring hardware and software using power profile tracking. IoT devices connected with Bluetooth are used to make up a state power map. Then, it is transmitted via Wi-Fi and becomes a system power map, which is stored in LUT, and the monitoring unit watches for whether a fault occurs or not. By the proposed method, we enable low-power, freeze-safe IoT development.


title: grecs: A group recommendation system based on user clustering

abstract: In this demonstration paper, we present gRecs, a system for group recommendations that follows a collaborative strategy. We enhance recommendations with the notion of support to model the confidence of the recommendations. Moreover, we propose partitioning users into clusters of similar ones. This way, recommendations for users are produced with respect to the preferences of their cluster members without extensively searching for similar users in the whole user base. Finally, we leverage the power of a top-k algorithm for locating the top-k group recommendations.


title: Novel FPGA based Haar classifier face detection algorithm acceleration

abstract: We present here a novel approach to use FPGA to accelerate the Haar-classifier based face detection algorithm. With highly pipelined microarchitecture and utilizing abundant parallel arithmetic units in the FPGA, wepsilave achieved real-time performance of face detection having very high detection rate and low false positives. Moreover, our approach is flexible toward the resources available on the FPGA chip. This work also provides us an understanding toward using FPGA for implementing non-systolic based vision algorithm acceleration. Our implementation is realized on a HiTech Global PCIe card that contains a Xilinx XC5VLX110T FPGA chip.


title: A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series ∗

abstract: Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. Wemore » experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).« less


title: Representation Learning for Answer Selection with LSTM-Based Importance Weighting

abstract: We present an approach to non-factoid answer selection with a separate component based on BiLSTM to determine the importance of segments in the input. In contrast to other recently proposed attention-based models within the same area, we determine the importance while assuming the independence of questions and candidate answers. Experimental results show the effectiveness of our approach, which outperforms several state-of-the-art attention-based models on the recent non-factoid answer selection datasets InsuranceQA v1 and v2. We show that it is possible to perform effective importance weighting for answer selection without relying on the relatedness of questions and answers. The source code of our experiments is publicly available.


title: Low-Input Power-Level CMOS RF Energy-Harvesting Front End

abstract: RF energy-harvesting front ends consist of $LC$ matching networks and RF rectifiers. The minimum detectable power (sensitivity) is dependent on the losses of both parts. In this paper, RF energy-harvesting sensitivity limits at steady state and design tradeoffs for matching networks and rectifiers are introduced. These limits and tradeoffs are examined for standard CMOS 0.18- $\mu$ m technology. Two designs, one with off-chip matching network and the other with on-chip matching network, are presented, compared, and measured for an output voltage of 1 V. The sensitivity of the off-chip design is ${-}{\hbox{27.3}}$ dBm while taking 180 $\,\times\,$ 90 $\mu$ m $^{2}$ die area and off-chip high quality inductor and capacitor, which takes an extra 7.28 mm $^{2}$ printed circuit board area. The fully integrated on-chip design has a sensitivity of ${-}{\hbox{21.7}}$ dBm while taking 820 $\,\times\,$ 450 $\mu$ m $^{2}$ die area. The sensitivity of the former proved superior while the latter is more attractive in terms of compactness, low cost, and easier tunability.


title: Extracting informative textual parts from web pages containing user-generated content

abstract: The vast amount of user-generated content on the Web has increased the need for handling the problem of automatically processing content in web pages. The segmentation of web pages and noise (non-informative segment) removal are important pre-processing steps in a variety of applications such as sentiment analysis, text summarization and information retrieval. Currently, these two tasks tend to be handled separately or are handled together without emphasizing the diversity of the web corpora and the web page type detection. We present a unified approach that is able to provide robust identification of informative textual parts in web pages along with accurate type detection. The proposed algorithm takes into account visual and non-visual characteristics of a web page and is able to remove noisy parts from three major categories of pages which contain user-generated content (News, Blogs, Discussions). Based on a human annotated corpus consisting of diverse topics, domains and templates, we demonstrate the learning abilities of our algorithm, we examine its effectiveness in extracting the informative textual parts and its usage as a rule-based classifier for web page type detection in a realistic web setting.


title: Two Cases of Giant Congenital Cutaneous Hamartoma: Nevus Sebaceus of Jadassohn and Congenital Melanocytic Nevus

abstract: During the normal development of skin, pluripotential cells give rise to keratinocytes, sebaceus glands, hair follicles, apocrine glands and eccrine glands. A hamartoma, or an abnormal mixture of a tissue’s normal components, results from an error in development, among which some show malignant potential. We report two Chinese cases of giant congenital cutaneous hamartoma, a nevus sebaceus of Jadassohn in a male and a congenital melanocytic nevus in a girl, which appear to be more extensive than any previously reported case.


title: An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games

abstract: Continuing interest in digital games indicated that it would be useful to update Connolly et al.'s (2012) systematic literature review of empirical evidence about the positive impacts and outcomes of games. Since a large number of papers was identified in the period from 2009 to 2014, the current review focused on 143 papers that provided higher quality evidence about the positive outcomes of games. Connolly et al.'s multidimensional analysis of games and their outcomes provided a useful framework for organising the varied research in this area. The most frequently occurring outcome reported for games for learning was knowledge acquisition, while entertainment games addressed a broader range of affective, behaviour change, perceptual and cognitive and physiological outcomes. Games for learning were found across varied topics with STEM subjects and health the most popular. Future research on digital games would benefit from a systematic programme of experimental work, examining in detail which game features are most effective in promoting engagement and supporting learning. The current systematic literature review updates Author (date).The review looks at impacts and outcomes of playing digital games from 2009 to 2014.Multi-component coding of papers, games and learning outcomes was used.Many papers were found with 143 papers providing high quality evidence.Games for entertainment and learning addressed different outcomes.


title: Fast relocalisation and loop closing in keyframe-based SLAM

abstract: In this paper we present for the first time a relocalisation method for keyframe-based SLAM that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of keyframes. As this method relies on local features, it permits the interoperability between cameras, allowing a camera to relocalise in a map built by a different camera. We also perform loop closing (detection + correction), at keyframerate, in loops containing hundreds of keyframes. For both relocalisation and loop closing, we propose a bag of words place recognizer with ORB features, which is able to recognize places spending less than 39 ms, including feature extraction, in databases containing 10K images (without geometrical verification). We evaluate the performance of this recognizer in four different datasets, achieving high recall and no false matches, and getting better results than the state-of-art in place recognition, being one order of magnitude faster.


title: LinSched: The Linux Scheduler Simulator.

abstract: The Linux kernel 2.6.23 scheduler includes substantial changes that may entice researchers with no prior interest in Linux to attempt to understand or modify its behavior. Often, this is no easy task, particularly for someone new to Linux kernel development. Virtual machines and hardware simulators can help make the task easier; however, they introduce their own problems. Motivated by these observations, we present the LinSched tool to host a Linux scheduler at a high simulation speed in an isolated environment, within which its behavior can be observed on a variety of different platforms independently of other Linux subsystems. The tool runs as a user-space process, so bugs within the scheduling code crash a single process instead of the entire system, reducing development time substantially. Experiments show that scheduling behavior within LinSched and the Linux kernel are nearly identical—this, combined with our personal experiences, suggest that the tool can be highly useful both for gaining an understanding of the scheduler and for the rapid prototyping of new Linux scheduling policies.


title: A Bipedal Walking Robot with Efficient and Human-Like Gait

abstract: Here we present the design of a passive-dynamics based, fully autonomous, 3-D, bipedal walking robot that uses simple control, consumes little energy, and has human-like morphology and gait. Design aspects covered here include the freely rotating hip joint with angle bisecting mechanism; freely rotating knee joints with latches; direct actuation of the ankles with a spring, release mechanism, and reset motor; wide feet that are shaped to aid lateral stability; and the simple control algorithm. The biomechanics context of this robot is discussed in more detail in [1], and movies of the robot walking are available at Science Online and http://www.tam.cornell.edu/~ruina/powerwalk.html. This robot adds evidence to the idea that passive-dynamic approaches might help design walking robots that are simpler, more efficient and easier to control.


title: A method for semi-automatic grading of human blastocyst microscope images.

abstract: BACKGROUND ::: The precise assessment of embryo viability is an extremely important factor for the optimization of IVF treatments. In order to assess embryo viability, several embryo scoring systems have been developed. However, they rely mostly on a subjective visual analysis of embryo morphological features and thus are subject to inter- and intra-observer variation. In this paper, we propose a method for image segmentation (the dividing of an image into its meaningful constituent regions) and classification of human blastocyst images with the aim of automating embryo grading. ::: ::: ::: METHODS ::: The delineation of the boundaries (segmentation) of the zona pellucida, trophectoderm (TE) and inner cell mass (ICM) were performed using advanced image analysis techniques (level set, phase congruency and fitting of ellipse methods). The fractal dimension and mean thickness of TE and ICM image texture descriptors (texture spectrum and grey-level run lengths) were calculated to characterize the main morphological features of the blastocyst with the aim of automatic grading using Support Vector Machine classifiers. ::: ::: ::: RESULTS ::: The fractal dimension calculated from the delineated TE boundary provided a good indication of cell number (presented a 0.81 Pearson correlation coefficient with the number of cells), a feature closely associated with blastocyst quality. The classifiers showed different accuracy levels for each grade. They presented accuracy ranges from 0.67 to 0.92 for the embryo development classification, 0.67-0.82 for the ICM classification and 0.53-0.92 for the TE classification. The value 0.92 was the highest accuracy achieved in the tests with 73 blastocysts. ::: ::: ::: CONCLUSIONS ::: Semi-automatic grading of human blastocysts by a computer is feasible and may offer a more precise comparison of embryos, reducing subjectivity and allowing embryos with apparently identical morphological scores to be distinguished.


title: Face recognition using FLDA with single training image per person

abstract: Fisher linear discriminant analysis (FLDA) has been widely used for feature extraction in face recognition. However, it cannot be used when each object has only one training sample because the intra-class variations cannot be statistically measured in this case. In this paper, a novel method is proposed to solve this problem by evaluating the within-class scatter matrix from the available single training image. By using singular value decomposition (SVD), we decompose the face image into two complementary parts: a smooth general appearance image and a difference image. The later is used to approximately evaluate the within-class scatter matrix and thus the FLDA can be applied to extract the discriminant face features. Experimental results show that the proposed method is efficient and it can achieve higher recognition accuracy than many existing schemes.


title: Where is my puppy? Retrieving lost dogs by facial features

abstract: A pet that goes missing is among many people's worst fears: a moment of distraction is enough for a dog or a cat wandering off from home. Some measures help matching lost animals to their owners; but automated visual recognition is one that - although convenient, highly available, and low-cost - is surprisingly overlooked. In this paper, we inaugurate that promising avenue by pursuing face recognition for dogs. We contrast four ready-to-use human facial recognizers (EigenFaces, FisherFaces, LBPH, and a Sparse method) to two original solutions based upon convolutional neural networks: BARK (inspired in architecture-optimized networks employed for human facial recognition) and WOOF (based upon off-the-shelf OverFeat features). Human facial recognizers perform poorly for dogs (up to 60.5% accuracy), showing that dog facial recognition is not a trivial extension of human facial recognition. The convolutional network solutions work much better, with BARK attaining up to 81.1% accuracy, and WOOF, 89.4%. The tests were conducted in two datasets: Flickr-dog, with 42 dogs of two breeds (pugs and huskies); and Snoopybook, with 18 mongrel dogs.


title: A novel blending technique for two-dimensional forward-looking sonar mosaicing

abstract: High-resolution forward-looking sonars are becoming a tool of choice for exploring underwater environments under low visibility conditions. Their imagery can be mosaiced to obtain a global overview of submerged areas of interest and the spatial arrangement of different target features. However, in order to achieve an informative and smooth image composition, the individual sonar frames must be fused. Unlike the blending in optical mosaics, this implies dealing with a high number of overlapping images as well as with sonar specific artifacts arising from its image formation geometry. This work presents a novel blending pipeline designed to cope with these artifacts involving strategies to diminish the impact of all the photometric irregularities that might be present when mosaicing forward-looking sonar imagery. Results of blended mosaics, including data gathered with different sonar models and presenting several artifacts, are presented here to show the applicability of the method.


title: Do Female and Male Role Models Who Embody STEM Stereotypes Hinder Women’s Anticipated Success in STEM?

abstract: Women who have not yet entered science, technology, engineering, and mathematics (STEM) fields underestimate how well they will perform in those fields (e.g., Correll, 2001; Meece, Parsons, Kaczala, & Goff, 1982). It is commonly assumed that female role models improve women’s beliefs that they can be successful in STEM. The current work tests this assumption. Two experiments varied role model gender and whether role models embody computer science stereotypes. Role model gender had no effect on success beliefs. However, women who interacted with nonstereotypical role models believed they would be more successful in computer science than those who interacted with stereotypical role models. Differences in women’s success beliefs were mediated by their perceived dissimilarity from stereotypical role models. When attempting to convey to women that they can be successful in STEM fields, role model gender may be less important than the extent to which role models embody current STEM stereotypes.


title: What survivors want: Understanding the needs of sexual assault survivors.

abstract: OBJECTIVE ::: Sexual assault is a pervasive crime on our college campuses and many survivors do not seek post-assault resources. This study will explore components of alternative interventions to consider in the development of campus-based interventions for sexual assault survivors. ::: ::: ::: PARTICIPANTS ::: Three stakeholder groups including survivors (n = 8), healthcare providers (n = 6), and advocates (n = 19) were recruited from May 2014 to December 2014 from two university campus communities using flyers and purposive sampling. ::: ::: ::: METHODS ::: A qualitative study design utilized semi-structured interviews and semi-structured focus groups with survivors, healthcare providers, and advocates. ::: ::: ::: RESULTS ::: Five themes emerged to consider for future campus-based sexual assault interventions: (a) culture of caring, (b) one-stop shop, (c) validation, (d) survivor control and agency, and (e) confidentiality. ::: ::: ::: CONCLUSIONS ::: Although accessible and effective options for post-assault care do currently exist, participants described alternative future interventions that were either (a) print materials or (b) technology-based.


A minutiae-based matching algorithms in fingerprint recognition systems


title: Lane Departure Identification for Advanced Driver Assistance

abstract: In this paper, a technique for the identification of the unwanted lane departure of a traveling vehicle on a road is proposed. A piecewise linear stretching function (PLSF) is used to improve the contrast level of the region of interest (ROI). Lane markings on the road are detected by dividing the ROI into two subregions and applying the Hough transform in each subregion independently. This segmentation approach improves the computational time required for lane detection. For lane departure identification, a distance-based departure measure is computed at each frame, and a necessary warning message is issued to the driver when such measure exceeds a threshold. The novelty of the proposed algorithm is the identification of the lane departure only using three lane-related parameters based on the Euclidean distance transform to estimate the departure measure. The use of the Euclidean distance transform in combination with the PLSF keeps the false alarm around 3% and the lane detection rate above 97% under various lighting conditions. Experimental results indicate that the proposed system can detect lane boundaries in the presence of several image artifacts, such as lighting changes, poor lane markings, and occlusions by a vehicle, and it issues an accurate lane departure warning in a short time interval. The proposed technique shows the efficiency with some real video sequences.


title: Implementing BitLocker Drive Encryption for forensic analysis

abstract: This paper documents the BitLocker Drive Encryption system included with some versions of Microsoft's Windows Vista. In particular it describes the key management system, the algorithms and modes used, and the metadata format. Particular attention is given to methods forensic examiners can use to access protected data. There are some unanswered questions about how the cryptosystem operates, including an undocumented key management decision. This decision could allow, in a particular usage scenario, unauthorized access to a protected volume.


title: Diorama Construction From a Single Image

abstract: Diorama artists produce a spectacular 3D effect in a confined space by generating depth illusions that are faithful to the ordering of the objects in a large real or imaginary scene. Indeed, cognitive scientists have discovered that depth perception is mostly affected by depth order and precedence among objects. Motivated by these findings, we employ ordinal cues to construct a model from a single image that similarly to Dioramas, intensifies the depth perception. We demonstrate that such models are sufficient for the creation of realistic 3D visual experiences. ::: ::: ::: ::: The initial step of our technique extracts several relative depth cues that are well known to exist in the human visual system. Next, we integrate the resulting cues to create a coherent surface. We introduce wide slits in the surface, thus generalizing the concept of cardboard cutout layers. Lastly, the surface geometry and texture are extended alongside the slits, to allow small changes in the viewpoint which enriches the depth illusion.


title: Learning and Problem Solving with Multilayer Connectionist Systems

abstract: THE DIFFICULTIES OF LEARNING IN MULTILAYERED NETWORKS OF COMPUTATIONAL UNITS HAS LIMITED THE USE OF CONNECTIONIST SYSTEMS IN COMPLEX DOMAINS. THIS DISSERTATION ELUCIDATES THE ISSUES OF LEARNING IN A NETWORK''S HIDDEN UNITS, AND REVIEWS METHODS FOR ADDRESSING THESE ISSUES THAT HAVE BEEN DEVELOPED THROUGH THE YEARS. ISSUES OF LEARNING IN HIDDEN UNITS ARE SHOWN TO BE ANALOGOUS TO LEARNING ISSUES FOR MULTILAYER SYSTEMS EMPLOYING SYMBOLIC REPRSENTATIONS. COMPARISONS OF A NUMBER OF ALGORITHMS FOR LEARNING IN HIDDEN UNITS ARE MADE BY APPLYING THEM IN A CONSISTENT MANNER TO SEVERAL TASKS. RECENTLY DEVELOPED ALGORITHMS, INCLUDING RUMELHART, ET AL''S, ERROR BACK-PROPOGATIONS ALGORITHM AND BARTO, ET AL''S, REINFORCEMENT-LEARNING ALGORITHMS, LEARN THE SOLUTIONS TO THE TASKS MUCH MORE SUCCESSFULLY THAN METHODS OF THE PAST. A NOVEL ALGORITHM IS EXAMINED THAT COMBINES ASPECTS OF REINFORCEMENT LEARNING AND A DATA-DIRECTED SEARCH FOR USEFUL WEIGHTS, AND IS SHOWN TO OUT PERFORM REINFORMCEMENT-LEARNING ALGORITHMS. A CONNECTIONIST FRAMEWORK FOR THE LEARNING OF STRATEGIES IS DESCRIBED WHICH COMBINES THE ERROR BACK-PROPOGATION ALGORITHM FOR LEARNING IN HIDDEN UNITS WITH SUTTON''S AHC ALGORITHM TO LEARN EVALUATION FUNCTIONS AND WITH A REINFORCEMENT-LEARNING ALGORITHM TO LEARN SEARCH HEURISTICS. THE GENERAL- ITY OF THIS HYBRID SYSTEM IS DEMONSTRATED THROUGH SUCCESSFUL APPLICATIONS


title: Particle PHD Filter Based Multiple Human Tracking Using Online Group-Structured Dictionary Learning

abstract: An enhanced sequential Monte Carlo probability hypothesis density (PHD) filter-based multiple human tracking system is presented. The proposed system mainly exploits two concepts: a novel adaptive gating technique and an online group-structured dictionary learning strategy. Conventional PHD filtering methods preset the target birth intensity and the gating threshold for selecting real observations for the PHD update. This often yields inefficiency in false positives and missed detections in a cluttered environment. To address this issue, a measurement-driven mechanism based on a novel adaptive gating method is proposed to adaptively update the gating sizes. This yields an accurate approach to discriminate between survival and residual measurements by reducing the clutter inferences. In addition, online group-structured dictionary learning with a maximum voting method is used to robustly estimate the target birth intensity. It enables the new-born targets to be automatically detected from noisy sensor measurements. To improve the adaptability of our group-structured dictionary to appearance and illumination changes, we employ the simultaneous code word optimization algorithm for the dictionary update stage. Experimental results demonstrate our proposed method achieves the best performance amongst state-of-the-art random finite set-based methods, and the second best online tracker ranked on the leaderboard of latest MOT17 challenge.


title: Nonlinear 3D Face Morphable Model

abstract: As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM is learned from a set of well-controlled 2D face images with associated 3D face scans, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as the linear bases, the representation power of 3DMM can be limited. To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of unconstrained face images, without collecting 3D face scans. Specifically, given a face image as input, a network encoder estimates the projection, shape and texture parameters. Two decoders serve as the nonlinear 3DMM to map from the shape and texture parameters to the 3D shape and texture, respectively. With the projection parameter, 3D shape, and texture, a novel analytically-differentiable rendering layer is designed to reconstruct the original input face. The entire network is end-to-end trainable with only weak supervision. We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart, and its contribution to face alignment and 3D reconstruction.


title: An Improved Intrusion Detection Algorithm Based on GA and SVM

abstract: In the era of big data, with the increasing number of audit data features, human-centered smart intrusion detection system performance is decreasing in training time and classification accuracy, and many support vector machine (SVM)-based intrusion detection algorithms have been widely used to identify an intrusion quickly and accurately. This paper proposes the FWP-SVM-genetic algorithm (GA) (feature selection, weight, and parameter optimization of support vector machine based on the genetic algorithm) based on the characteristics of the GA and the SVM algorithm. The algorithm first optimizes the crossover probability and mutation probability of GA according to the population evolution algebra and fitness value; then, it subsequently uses a feature selection method based on the genetic algorithm with an innovation in the fitness function that decreases the SVM error rate and increases the true positive rate. Finally, according to the optimal feature subset, the feature weights and parameters of SVM are simultaneously optimized. The simulation results show that the algorithm accelerates the algorithm convergence, increases the true positive rate, decreases the error rate, and shortens the classification time. Compared with other SVM-based intrusion detection algorithms, the detection rate is higher and the false positive and false negative rates are lower.


title: A Secure Low-Cost Edge Device Authentication Scheme for the Internet of Things

abstract: Because of the enhanced capability of adversaries, edge devices of Internet of Things (IoT) infrastructure are now increasingly vulnerable to counterfeiting and piracy. Ensuring the authenticity of such devices is of great concern since an adversary can create a backdoor either to bypass the security, and/or to leak secret information over an unsecured communication channel. The reliability of such devices could also be called into question because they might be counterfeit, defective and/or of inferior quality. It is of prime importance to design and develop solutions for authenticating such edge devices. In this paper, we present a novel low-cost solution for authenticating edge devices. We use SRAM based PUF to generate unique "digital fingerprints" for every device, which can be used to generate a unique device ID. We propose a novel ID matching scheme to verify the identity of an edge device even though the PUF is extremely unreliable. We show that the probability of impersonating an ID by an adversary is extremely low. In addition, our proposed solution is resistant to various known attacks.


Categorization and Machine Learning Methods : Current State of the Art By Durga Bhavani Dasari


title: Posture-to-posture trajectory planning for an autonomous car using triple clothoid segments

abstract: We consider a steering plan for an autonomous car as a function of path length. The initial state of a car is composed of its position, direction, and curvature corresponding to its front-wheel direction. In the case that the final state of the steering plan is taken as one point on a curved road, not only the position but also the direction of the car and curvature corresponding to the curved road are specified. Defining posture as a state that consists of the car's position, direction, and curvature, we consider this steering plan as a trajectory plan from the start posture to the end posture. In cars, the upper and lower limits of allowable curvature exist due to the minimum turning radius of the cars. Lateral acceleration and jerk, which affect ride quality, are respectively proportional to curvature and sharpness, which is the rate of change of curvature. We chose clothoid segments, for which curvature and sharpness are easily adjusted, as components of a trajectory. Single or double clothoid segments are not flexible enough to match the end posture. We propose a trajectory planning method that uses triple clothoid segments whose lengths are variable and introduce a four-parameter model for this method. We moreover show a concrete numerical solution method based on an optimization problem framework.


title: Soft-Switching DC/DC Converter With a Full ZVS Range and Reduced Output Filter for High-Voltage Applications

abstract: A new soft-switching dc/dc converter, which can solve the drawbacks of existing phase-shifted full-bridge converters such as narrow zero-voltage-switching (ZVS) range, large circulating current, large duty-cycle loss, and a large output filter in high-voltage applications, is proposed in this paper. The proposed converter is composed of two symmetric half-bridge inverters that are placed in parallel on the primary side and are driven in a phase-shifting manner to regulate the output voltage. At the rectifier stage, two full-bridge rectifiers sharing two low-current-rating diodes are employed. This structure allows the proposed converter to have the advantages of a full ZVS range, no problems related to duty-cycle loss, no circulating current, and a significantly reduced output filter. In this paper, the circuit configuration, operation principle, and relevant analysis results of the proposed converters are presented. Experimental results on a prototype converter realized with the specification of 80-in plasma display panel sustain power module (320-385 Vdc input, 205 Vdc/5 A output) validate the theoretical analysis.


title: POMCPOW: An online algorithm for POMDPs with continuous state, action, and observation spaces

abstract: Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge. This paper begins by investigating double progressive widening (DPW) as a solution to this challenge. However, we prove that this modification alone is not sufficient because the belief representations in the search tree collapse to a single particle causing the algorithm to converge to a policy that is suboptimal regardless of the computation time. The main contribution of the paper is to propose a new algorithm, POMCPOW, that incorporates DPW and weighted particle filtering to overcome this deficiency and attack continuous problems. Simulation results show that these modifications allow the algorithm to be successful where previous approaches fail.


title: Ischiofemoral impingement: evaluation with new MRI parameters and assessment of their reliability

abstract: OBJECTIVE ::: The aim of this study was to describe the magnetic resonance imaging (MRI) findings in patients with ischiofemoral impingement (IFI) and to evaluate the reliability of these MRI findings. ::: ::: ::: MATERIALS AND METHODS ::: Seventy hips of 50 patients with hip pain and quadratus femoris muscle (QFM) edema and 38 hips of 30 control cases were included in the study. The QFM edema and fatty replacement were assessed visually. Ischiofemoral space (IFS), quadratus femoris space (QFS), inclination angle (IA), hamstring tendon area (HTA), and total quadratus femoris muscle volume (TQFMV) measurements were performed independently by two musculoskeletal radiologists. The intra- and interobserver reliabilities were obtained for quantitative variables. ::: ::: ::: RESULTS ::: IFS, QFS, and TQFMV values of the patient group were significantly lower than those of controls (P < 0.001). HTA and IA measurements of the patient group were also significantly higher than in controls (P < 0.05). The QFM fatty replacement grades were significantly higher in the patient group than in the control group (P < 0.001). Inter- and intra-observer reliabilities were strong for all continuous variables. ::: ::: ::: CONCLUSION ::: Clinicians and radiologists should be aware of IFI in patients with hip or groin pain, and MRI should be obtained for the presence of the QFM edema/fatty replacement, narrowing of the IFS-QFS, and other features that may help in the clinical diagnosis of IFI for the proper diagnosis and treatment of the disease.


title: “I’ll stop procrastinating now!” Fostering specific processes of self-regulated learning to reduce academic procrastination

abstract: ABSTRACTAcademic procrastination is considered to be a result of self-regulation failure having detrimental effects on students’ well-being and academic performance. In the present study, we developed and evaluated a group training that aimed to reduce academic procrastination. We based the training on a cyclical process model of self-regulated learning, thus, focusing on improving deficient processes of self-regulated learning among academic procrastinators (e.g., time management, dealing with distractions). The training comprised five sessions and took place once a week for 90 min in groups of no more than 10 students. Overall, 106 students completed the training. We evaluated the training using a comprehensive control group design with repeated measures (three points of measurement); the control group was trained after the intervention group’s training. The results showed that our training was successful. The trained intervention group significantly reduced academic procrastination and improved specifi...


title: Simultaneous transmit and receive with digital phased arrays

abstract: A new architecture is proposed for achieving Simultaneous Transmit and Receive (STAR) with a digital phased array. We demonstrate how digital beamforming and cancellation enables adjacent transmitting and receiving sub-arrays to operate simultaneously in the same frequency band without a significant reduction in performance. Our approach uses only digital signal processing techniques and does not require custom radiators or analog cancelling circuits that can increase front-end losses and add significant size, weight and cost to the array. Simulated results are presented for a 50-element array that achieves more than 160 dB of effective isolation between transmit and receive beams over a 100 MHz instantaneous band centered at 2.45 GHz.


title: Excess current noise in amplifiers with switched input

abstract: A basic model for excess current noise in amplifiers with switched input is presented. Thermal noise in the input switches causes charge fluctuations in the amplifier input capacitance with a bandwidth much higher than the switching frequency. This wideband charge noise is undersampled by the switching process, leading to excess current noise with a spectral density proportional to temperature, capacitance and switching frequency. The model was verified using a test chopper operated at room temperature and liquid Nitrogen (77 K). Good agreement between theory and experiment was found.


title: Tobacco use and caries risk among adolescents – a longitudinal study in Sweden

abstract: BackgroundSmoking and the use of smokeless tobacco have a detrimental impact on general and oral health. The relationship to dental caries is however still unclear. As caries is a multi-factorial disease with clear life-style, socio-economic and socio-demographic gradients, the tobacco use may be a co-variable in this complex rather than a direct etiological factor. Our aim was to analyze the impact of tobacco use on caries incidence among adolescents, with consideration to socio-economic variables by residency, using epidemiological data from a longitudinal study in the region of Halland, Sweden.MethodsThe study population consisted of 10,068 adolescents between 16–19 years of age from whom yearly data on caries and tobacco use (cigarette smoking and use of smokeless tobacco) were obtained during the period 2006–2012. Reported DMFS increment between 16 and 19 years of age (∆DMFS) for an individual was considered as the primary caries outcome. The outcome data were compared for self-reported never vs. ever users of tobacco, with consideration to neighborhood-level socio-economy (4 strata), baseline (i.e., 16 years of age) DMFS and sex. The region consists of 65 parishes with various socio-economic conditions and each study individual was geo-coded with respect to his/her residence parish. Neighborhood (parish-level) socio-economy was assessed by proportion of residing families with low household purchasing power.Results∆DMFS differed evidently between ever and never users of tobacco (mean values: 1.8 vs. 1.2; proportion with ∆DMFS > 0: 54.2% vs. 40.5%; p < 0.0001). Significant differences were observed in each neighborhood-level socio-economic stratum. Even after controlling for baseline DMFS and sex, ∆DMFS differed highly significantly between the ever and never users of tobacco (overall p < 0.0001).ConclusionTobacco use was clearly associated with increased caries increment during adolescence. Hence, this factor is relevant to consider in the clinical caries risk assessment of the individual patient as well as for community health plans dealing with oral health.


title: Major craniofacial clefts: case series and treatment philosophy.

abstract: BACKGROUND ::: The treatment of major facial clefts has evolved greatly over the past 40 years. Early in the authors' experience, soft tissues were treated by Z-plasty and local flap rotation, resulting in a patchwork effect and noticeable scars. Bony deficits were treated with bone grafts that often failed to restore normal facial contour. In 1985, the authors developed a treatment philosophy to resolve these issues, and its tenets have formed the foundation of treatment since then. ::: ::: ::: METHODS ::: From January of 1965 to January of 2006, a series of 495 patients with major craniofacial clefts were treated in the authors' unit. Sixty-five percent had multiple clefts and 35 percent had a single cleft. In 33 percent of patients, the clefts were unilateral and in 67 percent the clefts were bilateral. The objectives of the authors' current treatment philosophy are restoration of the craniofacial skeleton, reconstruction with skin and soft tissue of like color and texture, generous use of tissue expanders, aesthetic unit reconstruction, scar location at limits of aesthetic subunits, and symmetric repositioning of key facial landmarks. ::: ::: ::: RESULTS ::: Patients underwent a total of 1338 operations. Follow-up ranged from 8 months to 35 years. All patients who underwent primary reconstruction after 1985 underwent successful reconstruction using the aesthetic subunit principle. Fifty-three percent of patients required formal craniofacial osteotomy and 24 percent required bone grafting to restore the craniofacial skeleton. ::: ::: ::: CONCLUSIONS ::: The authors' results have improved considerably both in primary and in secondary reconstruction by incorporating their revised treatment philosophy. The authors have reduced scar visibility and facial asymmetry, allowing these patients to live more normal lives.


title: Dual-resonator MEMS Lorentz force magnetometer based on differential frequency modulation

abstract: This paper presents a new dual-resonator MEMS magnetic sensor utilizing the Lorentz force. Sensor operation is demonstrated using quadrature frequency modulated (QFM) readout, where the magnetic field strength is measured by monitoring the change in oscillation frequency. The Lorentz force sensor, comprising of a matched pair of differentially operated closed-loop resonators on the same silicon die, produces two similar oscillation frequencies with nearly identical temperature sensitivities, providing a temperature-compensated FM output. The frequencies of the two oscillators are experimentally demonstrated to track each other over temperature, reducing the maximum drift error by a factor of 27 to ±2.2 µT, improving the sensor's bias instability from 170 nT to 63 nT, and increasing the averaging time to reach the bias instability from 0.3 s to 1.1 s. With 1 mA bias current, the device has a measured sensitivity of 2180 Hz/T and Brownian-noise-limited resolution of 60 nT/√Hz which is comparable to, or even better than, that of Hall-effect sensors available today.


title: Sparsity-based image denoising via dictionary learning and structural clustering

abstract: Where does the sparsity in image signals come from? Local and nonlocal image models have supplied complementary views toward the regularity in natural images — the former attempts to construct or learn a dictionary of basis functions that promotes the sparsity; while the latter connects the sparsity with the self-similarity of the image source by clustering. In this paper, we present a variational framework for unifying the above two views and propose a new denoising algorithm built upon clustering-based sparse representation (CSR). Inspired by the success of l 1 -optimization, we have formulated a double-header l 1 -optimization problem where the regularization involves both dictionary learning and structural structuring. A surrogate-function based iterative shrinkage solution has been developed to solve the double-header l 1 -optimization problem and a probabilistic interpretation of CSR model is also included. Our experimental results have shown convincing improvements over state-of-the-art denoising technique BM3D on the class of regular texture images. The PSNR performance of CSR denoising is at least comparable and often superior to other competing schemes including BM3D on a collection of 12 generic natural images.


title: Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles

abstract: Recently developed Serial Analysis of Gene Expression (SAGE) technology enables us to simultaneously quantify the expression levels of tens of thousands of genes in a population of cells. SAGE is better than Microarray in that SAGE can monitor both known and unknown genes while Microarray can only measure known genes. SAGE gene expression profiling based cancer classification is a better choice since cancers may be due to some unknown genes. Whereas a wide range of methods has been applied to traditional Microarray based cancer classification, relatively few studies have been done on SAGE based cancer classification. In our study we evaluate popular machine learning methods (SVM, Naive Bayes, Nearest Neighbor, C4.5 and RIPPER) for classifying cancers based on SAGE data. In order to deal with the high dimensional problem, we propose to use Chi-square for tag/gene selection. Both binary classification and multicategory classification are investigated. The experiments are based on two human SAGE datasets: brain and breast. The results show that SVM and Naive Bayes are the top-performing SAGE classifiers and that Chi-square based gene selection can improve the performance of all the five classifiers investigated.


title: Review of Mobile Learning Trends 2010-2015: A Meta-Analysis

abstract: Introduction With the advent of mobile technologies, new paradigm of teaching and learning with technology aid had been emerged, that is mobile learning (M-Learning). Mobile technologies purvey opportunities to hold new and interesting methods of teaching and learning, both beyond and inside the classroom. Apropos to the teaching-learning process, the integration of mobile devices into educational context has considerable benefits and profoundly potential which consistence with Valero et al. (2012) who claimed that the technological features of M-Learning are portability, immediacy, connectivity, ubiquity and adaptability. It enables collaboration among pupils, knowledge creation, information searching and improved interaction and communication between teacher and student. Moreover, it facilitates access to learning anytime and anywhere by enabling connectivity and the employ of multiple apps for educational purposes (Fundacion Telefonica, 2013). In short, M-Learning has been recognized as one of the most influential technologies for education (Johnson, Adams, & Cummins, 2012). Therefore, this paper intends to provide insights into the research trends and issues in the studies of M-Learning through content analysis of selected journals from January, 2010 to December, 2015, covering six major journals: (1) Computer & Education (CE Keengwe, 2014) that integrates several software and firmware technology in multimedia applications (Lavin-Mera et al., 2008) which facilitate learning through a variety of wireless mobile devices (Kukulska-Hulme, 2005; Stevens & Kitchenham, 2011) using wireless networks (WiFi) or broadband services (Caudill, 2007) without limit in terms of location or time. (Kukulska-Hulme, 2005; Hussin et al., 2012; Quinn, 2000). Furthermore, Keegan (2002) contemplates the possibility of M-Learning as a harbinger of the future of learning. M-Learning research The use of mobile devices for educational purposes, recognized as M-Learning has gained substantial attention from researchers in the technology-enhanced learning discipline. Recent research findings on using mobile devices in different learning environments have exemplified their ability to effectively enhance students' learning knowledge. …


title: Finding Bernie Madoff: Detecting Fraud by Investment Managers." http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1471631

abstract: Using a panel of mandatory SEC disclosure filings we test the predictability of investment fraud. We find that past regulatory and legal violations, conflicts of interest, and monitoring, are significantly associated with future fraud. Avoiding the 5% of firms with the highest fraud risk allows investors to avoid 29% of investment frauds, and over 40% of the total dollar losses from fraud. Even though our predictions are based on publicly available information, we do not find evidence that investors are compensated for fraud risk through superior performance or lower fees. The results suggest that the currently required disclosures contain relevant information but this information is not fully utilized by investors. We suggest changes in SEC disclosure policy that increase investors' ability to detect fraud at minimal cost.