title: Reputation‐based credibility analysis of Twitter social network users

abstract: Summary ::: This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph-based, considering the relationships among users to predict the spread information; human-based, using human perspectives to determine reliable sources; or machine learning-based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright © 2016 John Wiley & Sons, Ltd.


title: A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor, and Intelligent Medicine Box

abstract: In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.


title: Real-Time Mobile Food Recognition System

abstract: We propose a mobile food recognition system the poses of which are estimating calorie and nutritious of foods and recording a user's eating habits. Since all the processes on image recognition performed on a smart-phone, the system does not need to send images to a server and runs on an ordinary smartphone in a real-time way. To recognize food items, a user draws bounding boxes by touching the screen first, and then the system starts food item recognition within the indicated bounding boxes. To recognize them more accurately, we segment each food item region by GrubCut, extract a color histogram and SURF-based bag-of-features, and finally classify it into one of the fifty food categories with linear SVM and fast 2 kernel. In addition, the system estimates the direction of food regions where the higher SVM output score is expected to be obtained, show it as an arrow on the screen in order to ask a user to move a smartphone camera. This recognition process is performed repeatedly about once a second. We implemented this system as an Android smartphone application so as to use multiple CPU cores effectively for real-time recognition. In the experiments, we have achieved the 81.55% classification rate for the top 5 category candidates when the ground-truth bounding boxes are given. In addition, we obtained positive evaluation by user study compared to the food recording system without object recognition.


title: The Coevolution of Cultural Groups and Ingroup Favoritism

abstract: Cultural boundaries have often been the basis for discrimination, nationalism, religious wars, and genocide. Little is known, however, about how cultural groups form or the evolutionary forces behind group affiliation and ingroup favoritism. Hence, we examine these forces experimentally and show that arbitrary symbolic markers, though initially meaningless, evolve to play a key role in cultural group formation and ingroup favoritism because they enable a population of heterogeneous individuals to solve important coordination problems. This process requires that individuals differ in some critical but unobservable way and that their markers be freely and flexibly chosen. If these conditions are met, markers become accurate predictors of behavior. The resulting social environment includes strong incentives to bias interactions toward others with the same marker, and subjects accordingly show strong ingroup favoritism. When markers do not acquire meaning as accurate predictors of behavior, players show a markedly reduced taste for ingroup favoritism. Our results support the prominent evolutionary hypothesis that cultural processes can reshape the selective pressures facing individuals and so favor the evolution of behavioral traits not previously advantaged.


title: New media and the changing face of information technology use: The importance of task pursuit, social influence, and experience

abstract: The technology adoption and use question has been extensively researched; however, gaining synthesis in the literature has been challenging owing to the myriad of theoretical frameworks and study contexts. A consolidation was surmised by the Unified Theory of Acceptance and Use of Technology (UTAUT), although recent studies have yielded new questions as technologies and societies change. We sought to determine whether factors grounded by the UTAUT would be predictive of the use of ''new'' media. To do this, we conducted a field study of non-work related and discretionary use of ''social media'' and ''smart device'' applications. Using linear regression with interactions, we learned that technology use may evolve on a continuum, and that use may depend on the technology itself. Moreover, our research indicated that perhaps age and gender may not play as significant a role in new technology use and adoption as previously reported in the literature. We concluded that each medium is reflected in differential use characteristics and may not be accurately predicted by a unified use concept. Our findings have both research and practical implications.


title: Learning to Learn: Model Regression Networks for Easy Small Sample Learning

abstract: We develop a conceptually simple but powerful approach that can learn novel categories from few annotated examples. In this approach, the experience with already learned categories is used to facilitate the learning of novel classes. Our insight is two-fold: (1) there exists a generic, category agnostic transformation from models learned from few samples to models learned from large enough sample sets, and (2) such a transformation could be effectively learned by high-capacity regressors. In particular, we automatically learn the transformation with a deep model regression network on a large collection of model pairs. Experiments demonstrate that encoding this transformation as prior knowledge greatly facilitates the recognition in the small sample size regime on a broad range of tasks, including domain adaptation, fine-grained recognition, action recognition, and scene classification.


title: DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

abstract: The past few years have witnessed the fast development of different regularization methods for deep learning models such as fully-connected deep neural networks (DNNs) and Convolutional Neural Networks (CNNs). Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks. DropConnect select to drop connections between fully-connected layers. By randomly discard some features or connections, the above mentioned methods control the overfitting problem and improve the performance of neural networks. In this paper, we proposed two novel regularization methods, namely DropFilter and DropFilter-PLUS, for the learning of CNNs. Different from the previous methods, DropFilter and DropFilter-PLUS selects to modify the convolution filters. For DropFilter-PLUS, we find a suitable way to accelerate the learning process based on theoretical analysis. Experimental results on MNIST show that using DropFilter and DropFilter-PLUS may improve performance on image classification tasks.


Impact of school quality on house prices and estimation of parental demand for good schools in Korea


title: Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information

abstract: Accurate detection of fingertips in depth image is critical for human-computer interaction. In this paper, we present a novel two-stream convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge image is extracted from raw depth image using random forest. Then the edge information is combined with depth information in our CNN structure. We study several fusion approaches and suggest a slow fusion strategy as a promising way of fingertip detection. As shown in our experiments, our real-time algorithm outperforms state-of-the-art fingertip detection methods on the public dataset HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of fingertip estimation on NYU hand dataset.


title: Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing

abstract: The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.


title: Histograms of optical flow orientation for abnormal events detection

abstract: In this paper, we propose an algorithm to detect abnormal events based on video streams. The algorithm is based on histograms of the orientation of optical flow descriptor and one-class SVM classifier. We introduce grids of Histograms of the Orientation of Optical Flow (HOF) as the descriptors for motion information of the monolithic video frame. The one-class SVM, after a learning period characterizing normal behaviors, detects the abnormality which is considered as the event needed to be recognized in the current frame. Extensive testing on dataset corroborates the effectiveness of the proposed detection method.


title: Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning

abstract: In the Fundamentals of Laparoscopic Surgery (FLS) standard medical training regimen, the Pattern Cutting task requires residents to demonstrate proficiency by maneuvering two tools, surgical scissors and tissue gripper, to accurately cut a circular pattern on surgical gauze suspended at the corners. Accuracy of cutting depends on tensioning, wherein the gripper pinches a point on the gauze in R3 and pulls to induce and maintain tension in the material as cutting proceeds. An automated tensioning policy maps the current state of the gauze to output a direction of pulling as an action. The optimal tensioning policy depends on both the choice of pinch point and cutting trajectory. We explore the problem of learning a tensioning policy conditioned on specific cutting trajectories. Every timestep, we allow the gripper to react to the deformation of the gauze and progress of the cutting trajectory with a translation unit vector along an allowable set of directions. As deformation is difficult to analytically model and explicitly observe, we leverage deep reinforcement learning with direct policy search methods to learn tensioning policies using a finite-element simulator and then transfer them to a physical system. We compare the Deep RL tensioning policies with fixed and analytic (opposing the error vector with a fixed pinch point) policies on a set of 17 open and closed curved contours in simulation and 4 patterns in physical experiments with the da Vinci Research Kit (dVRK). Our simulation results suggest that learning to tension with Deep RL can significantly improve performance and robustness to noise and external forces.


title: Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines

abstract: The profit resulting from customer relationship is essential to ensure companies viability, so an improvement in customer retention is crucial for competitiveness. As such, companies have recognized the importance of customer centered strategies and consequently customer relationship management (CRM) is often at the core of their strategic plans. In this context, a priori knowledge about the risk of a given customer to mitigate or even end the relationship with the provider is valuable information that allows companies to take preventive measures to avoid defection. This paper proposes a model to predict partial defection, using two classification techniques: Logistic regression and Multivariate Adaptive Regression Splines (MARS). The main objective is to compare the performance of MARS with Logistic regression in modeling customer attrition. This paper considers the general form of Logistic regression and Logistic regression combined with a wrapper feature selection approach, such as stepwise approach. The empirical results showed that MARS performs better than Logistic regression when variable selection procedures are not used. However, MARS loses its superiority when Logistic regression is conducted with stepwise feature selection.


Topology based approach for Efficient Load flow Solution of Radial Distribution Networks


title: Knowledge transfer: what, how, and why

abstract: People learn from prior experiences. We first learn how to use a spoon and then know how to use a different size of spoon. We first learn how to sew and then learn how to embroider. Transferring knowledge from one situation to another related situation often increases the speed of learning. This observation is relevant to human learning, as well as machine learning. ::: This thesis focuses on the problem of knowledge transfer — an area of study in machine learning. The goal of knowledge transfer is to train a system to recognize and apply knowledge acquired from previous tasks to new tasks or new domains. An effective knowledge transfer system facilitates learning processes for novel tasks, where little information is available. For example, the ability to transfer knowledge from a model that identifies writers born in the U.S. to identify writers born in Kiribati, a much lesser known country, would increase the speed of learning to identify writers born in Kiribati from scratch. ::: In this thesis, we investigate three dimensions of knowledge transfer: what, how, and why. We present and elaborate on these questions: What type of knowledge to transfer? How to transfer knowledge across entities? Why a certain pattern of knowledge transfer is observed? We first propose Segmented Transfer — a novel knowledge transfer model — to identify and learn from the most informative partitions from prior tasks. The proposed model is applied to Wikipedia vandalism detection problem and to entity search and retrieval problem and improves the predictions. ::: Based on the foundation of knowledge transfer and network theory, we propose Knowledge Transfer Network (KTN), a novel type of network describing transfer learning relationships among problems. KTN is not only a knowledge representation, but also a framework to select an effective and efficient ensemble of learners to improve a predictive model. This novel type of network provides insights on identifying ontological connections that were initially obscured. For example, we may observe knowledge transfer occurs among dissimilar tasks, such as transferring from using a knife and fork to using chopsticks.


title: The Nature–Nurture Debates: 25 Years of Challenges in Understanding the Psychology of Gender

abstract: Nature–nurture debates continue to be highly contentious in the psychology of gender despite the common recognition that both types of causal explanations are important. In this article, we provide...


title: Recurrent neural networks and robust time series prediction

abstract: We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series. >


title: Decentralized Control for Parallel Operation of Distributed Generation Inverters in Microgrids Using Resistive Output Impedance

abstract: In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an ac-distributed system is proposed. The paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed taking into account the special nature of a low voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop control method, the proposed controller uses resistive output impedance, and as a result a different control law is obtained. The controller is implemented by using a DSP board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6 kVA inverters connected in parallel, showing the features of the proposed wireless control.


title: Optimum color spaces for skin detection

abstract: The objective of this paper is to show that for every color space there exists an optimum skin detector scheme such that the performance of all these skin detectors schemes is the same. To that end, a theoretical proof is provided and experiments are presented which show that the separability of the skin and no skin classes is independent of the color space chosen.


title: GraSS: Graph structure summarization

abstract: Large graph databases are commonly collected and analyzed in numerous domains. For reasons related to either space efficiency or for privacy protection (e.g., in the case of social network graphs), it sometimes makes sense to replace the original graph with a summary, which removes certain details about the original graph topology. However, this summarization process leaves the database owner with the challenge of processing queries that are expressed in terms of the original graph, but are answered using the summary. In this paper, we propose a formal semantics for answering queries on summaries of graph structures. At its core, our formulation is based on a random worlds model. We show that important graph-structure queries (e.g., adjacency, degree, and eigenvector centrality) can be answered efficiently and in closed form using these semantics. Further, based on this approach to query answering, we formulate three novel graph partitioning/compression problems. We develop algorithms for finding a graph summary that least affects the accuracy of query results, and we evaluate our proposed algorithms using both real and synthetic data.


title: Revisiting the role of language in spatial cognition: Categorical perception of spatial relations in English and Korean speakers

abstract: The spatial relation of support has been regarded as universally privileged in nonlinguistic cognition and immune to the influence of language. English, but not Korean, obligatorily distinguishes support from nonsupport via basic spatial terms. Despite this linguistic difference, previous research suggests that English and Korean speakers show comparable nonlinguistic sensitivity to the support/nonsupport distinction. Here, using a paradigm previously found to elicit cross-language differences in color discrimination, we provide evidence for a difference in sensitivity to support/nonsupport between native English speakers and native Korean speakers who were late English learners and tested in a context that privileged Korean. Whereas the former group showed categorical perception (CP) when discriminating spatial scenes capturing the support/nonsupport distinction, the latter did not. An additional group of native Korean speakers—relatively early English learners tested in an English-salient context—patterned with the native English speakers in showing CP for support/nonsupport. These findings suggest that obligatory marking of support/nonsupport in one’s native language can affect nonlinguistic sensitivity to this distinction, contra earlier findings, but that such sensitivity may also depend on aspects of language background and the immediate linguistic context.


title: Music therapy in the assessment and treatment of autistic spectrum disorder: clinical application and research evidence

abstract: Background Children and adolescents with autistic spectrum disorder (ASD) presenting with significant limitations in conventional forms of verbal and non-verbal communication are found to respond positively to music therapy intervention involving both active, improvizational methods and receptive music therapy approaches. Improvizational musical activity with therapeutic objectives and outcomes has been found to facilitate motivation, communication skills and social interaction, as well as sustaining and developing attention. The structure and predictability found in music assist in reciprocal interaction, from which tolerance, flexibility and social engagement to build relationships emerge, relying on a systematic approach to promote appropriate and meaningful interpersonal responses. Results Published reports of the value and effectiveness of music therapy as an intervention for children with ASD range from controlled studies to clinical case reports. Further documentation has emphasized the role music therapy plays in diagnostic and clinical assessment. Music therapy assessment can identify limitations and weaknesses in children, as well as strengths and potentials. Research evidence from a systematic review found two randomized controlled trials that examined short-term effects of structured music therapy intervention. Significant effects were found in these studies even with extremely small samples, and the findings are important because they demonstrate the potential of the medium of music for autistic children. Case series studies were identified that examined the effects of improvizational music therapy where communicative behaviour, language development, emotional responsiveness, attention span and behavioural control improved over the course of an intervention of improvizational music therapy.


title: CLUSTERRANK: A GRAPH BASED METHOD FOR MEETING SUMMARIZATION

abstract: This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, informed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AM I meeting corpus and show a significant improvement over TextRank and other baseline methods.


title: Aurasium: Practical policy enforcement for android applications

abstract: The increasing popularity of Google's mobile platform Android makes it the prime target of the latest surge in mobile malware. Most research on enhancing the platform's security and privacy controls requires extensive modification to the operating system, which has significant usability issues and hinders efforts for widespread adoption. We develop a novel solution called Aurasium that bypasses the need to modify the Android OS while providing much of the security and privacy that users desire. We automatically repackage arbitrary applications to attach user-level sandboxing and policy enforcement code, which closely watches the application's behavior for security and privacy violations such as attempts to retrieve a user's sensitive information, send SMS covertly to premium numbers, or access malicious IP addresses. Aurasium can also detect and prevent cases of privilege escalation attacks. Experiments show that we can apply this solution to a large sample of benign and malicious applications with a near 100 percent success rate, without significant performance and space overhead. Aurasium has been tested on three versions of the Android OS, and is freely available.


A Survey on Privacy in Mobile Crowd Sensing Task Management


title: Toward a Learning Science for Complex Crowdsourcing Tasks

abstract: We explore how crowdworkers can be trained to tackle complex crowdsourcing tasks. We are particularly interested in training novice workers to perform well on solving tasks in situations where the space of strategies is large and workers need to discover and try different strategies to be successful. In a first experiment, we perform a comparison of five different training strategies. For complex web search challenges, we show that providing expert examples is an effective form of training, surpassing other forms of training in nearly all measures of interest. However, such training relies on access to domain expertise, which may be expensive or lacking. Therefore, in a second experiment we study the feasibility of training workers in the absence of domain expertise. We show that having workers validate the work of their peer workers can be even more effective than having them review expert examples if we only present solutions filtered by a threshold length. The results suggest that crowdsourced solutions of peer workers may be harnessed in an automated training pipeline.


title: Big Data Processing: Scalability with Extreme Single-Node Performance

abstract: Contemporary frameworks for data analytics, such as Hadoop, Spark, and Flink seek to allow applications to scale performance flexibly by adding hardware nodes. However, we find that when the computation on each individual node is optimized, peripheral activities such as creating data partitions, messaging and synchronizing between nodes diminish the speedup obtainable from adding more hardware. We analyze workloads which distribute operations on correlated data—such as joins and aggregation found in SQL, text similarity searches, and image disparity computations. After optimizing computation on efficient, custom processors, we discover challenges in scaling the applications to hundreds of nodes on a high-bandwidth network. We then describe techniques to overcome these challenges towards prototyping a 512-node system which is able to execute SQL queries offloaded from a commercial database, and outperform SQL-on-hadoop and traditional parallel RDBMS executions by 173x and 7x respectively.


title: Surgical Anatomy of the Face Implications for Modern Face-lift Techniques

abstract: OBJECTIVE ::: To delineate the anatomic architecture of the melolabial fold with surrounding structures and to elucidate potential implications for face-lift techniques. ::: ::: ::: METHODS ::: A total of 100 facial halves (from 50 cadaveric heads) were studied, including gross and microscopic dissection and histologic findings. Laboratory findings were correlated with intraoperative findings in more than 150 deep-plane face-lift dissections (300 facial halves) performed during the study period. ::: ::: ::: RESULTS ::: In contrast to previous reports, the superficial musculoaponeurotic system (SMAS) was not found to form an investing layer in the midface. The SMAS, zygomatici muscles, and levator labii superioris alaeque nasi were found to be located in corresponding anatomic layers and to form a functional unit. Additional findings of the present study include the description of 3 structurally different portions of the melolabial fold, of an anatomic space below the levator labii superioris alaeque nasi (sublevator space), and of extensions of the buccal fat pad into the sublevator space and the middle third of the melolabial fold. ::: ::: ::: CONCLUSIONS ::: The findings of the present study may contribute to augment our understanding of the complex anatomy of the midface and melolabial fold. Potential implications for modern face-lift techniques are discussed.


title: The Pharmacologic and Clinical Effects of Medical Cannabis

abstract: Cannabis, or marijuana, has been used for medicinal purposes for many years. Several types of cannabinoid medicines are available in the United States and Canada. Dronabinol (schedule III), nabilone (schedule II), and nabiximols (not U.S. Food and Drug Administration approved) are cannabis-derived pharmaceuticals. Medical cannabis or medical marijuana, a leafy plant cultivated for the production of its leaves and flowering tops, is a schedule I drug, but patients obtain it through cannabis dispensaries and statewide programs. The effect that cannabinoid compounds have on the cannabinoid receptors (CB(1) and CB(2) ) found in the brain can create varying pharmacologic responses based on formulation and patient characteristics. The cannabinoid Δ(9) -tetrahydrocannabinol has been determined to have the primary psychoactive effects; the effects of several other key cannabinoid compounds have yet to be fully elucidated. Dronabinol and nabilone are indicated for the treatment of nausea and vomiting associated with cancer chemotherapy and of anorexia associated with weight loss in patients with acquired immune deficiency syndrome. However, pain and muscle spasms are the most common reasons that medical cannabis is being recommended. Studies of medical cannabis show significant improvement in various types of pain and muscle spasticity. Reported adverse effects are typically not serious, with the most common being dizziness. Safety concerns regarding cannabis include the increased risk of developing schizophrenia with adolescent use, impairments in memory and cognition, accidental pediatric ingestions, and lack of safety packaging for medical cannabis formulations. This article will describe the pharmacology of cannabis, effects of various dosage formulations, therapeutics benefits and risks of cannabis for pain and muscle spasm, and safety concerns of medical cannabis use.


title: A Factored Approximate Inverse Preconditioner with Pivoting

abstract: In this paper we develop new techniques for stabilizing factored approximate inverse preconditioners (AINV) using pivoting. This method yields stable preconditioners in many cases and can provide successful preconditioners in many situations when the underlying system is highly indefinite. Numerical examples illustrate the effectiveness of this approach.


title: A 15 GHz, 1.8V, variable-gain, modified Cherry-Hooper amplifier

abstract: This paper describes the design of an inductor-less variable-gain amplifier cell with a bandwidth over 15 GHz. A high bandwidth is maintained even when the amplifier cells are cascaded, for example in implementing a limiting amplifier. The amplifier cell is implemented using a modified Cherry-Hooper architecture that utilizes active negative feedback to obtain a variable gain from -4 to +17dB for 16 cascaded cells, and an adjustable bandwidth from 9GHz to 15GHz per cell with a nominal bandwidth of 12.4GHz. By avoiding the use of broadbanding techniques, such as peaking inductors, area is greatly reduced. The optimized amplifier cell occupies an area of 93/spl mu/m by 42/spl mu/m, and is critically damped to limit peaking to 0dB.


title: Personality and Health Care Decision-Making Style

abstract: Using the Wisconsin Longitudinal Study Graduate Survey (N = 5,830), a population-based cohort of older adults (most aged 63–66 years), we explored relationships between five factors of personality and four preference types that account for multiple components of the health care decision-making process (information exchange, deliberation, and selection of treatment choice). After adjustment for personal, health, social, and economic factors, we found that increased conscientiousness and openness to experience and decreased agreeableness and neuroticism corresponded to preferring the most active decision-making style compared with the least active. A better understanding of how personality traits relate to patient decision-making styles may help clinicians tailor treatment discussions to the needs and preferences of individual patients.


title: An Exploratory Study of User Perceptions of Payment Methods in UK and the US

abstract: This paper presents the design and the results of a ::: cross-cultural study of user perceptions and attitudes toward electronic payment methods. We conduct a series of semi-structured interviews involving forty participants (20 in London, UK, and 20 in Manhattan, KS, USA) to explore how individuals use the mechanisms available to them within their routine payment and banking activities. We also study their comprehension of payment processes, the perceived effort and impact of using different methods, as well as direct or indirect recollections of (suspected or actual) fraud and related interactions with banks and retailers. By comparing UK and US participants, we also elicit commonalities and differences that may help better understand, if not predict, attitudes of US customers once technologies like Chip-and-PIN are rolled out – for instance, several US participants were confused by how to use it, while UK participants found it convenient. Our results show that purchasing habits as well as the availability of rewards schemes are primary criteria influencing choices relating to payment technologies, and that inconsistencies, glitches, and other difficulties with newer technologies generate frustration sometimes leading to complete avoidance of new payment methods.


title: Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study

abstract: In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-a-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of clustering quantified through a convincing comparative analysis. Our focal objective is to understand the performance gains and the importance of parameter selection for kernelized fuzzy clustering. Generic Fuzzy C-Means (FCM) and Gustafson-Kessel (GK) FCM are compared with two typical generalizations of kernel-based fuzzy clustering: one with prototypes located in the feature space (KFCM-F) and the other where the prototypes are distributed in the kernel space (KFCM-K). Both generalizations are studied when dealing with the Gaussian kernel while KFCM-K is also studied with the polynomial kernel. Two criteria are used in evaluating the performance of the clustering method and the resulting clusters, namely classification rate and reconstruction error. Through carefully selected experiments involving synthetic and Machine Learning repository (http://archive.ics.uci.edu/beta/) data sets, we demonstrate that the kernel-based FCM algorithms produce a marginal improvement over standard FCM and GK for most of the analyzed data sets. It has been observed that the kernel-based FCM algorithms are in a number of cases highly sensitive to the selection of specific values of the kernel parameters.


title: X509Cloud — Framework for a ubiquitous PKI

abstract: The SSL protocol has been widely used for verifying digital identities and to secure Internet traffic since the early days of the web. Although X.509 certificates have been in existence for more than two decades, individual user uptake has been low due to the high cost of issuance and maintenance of such certs. This has led to a situation whereby users are able to verify the identity of an organization or e-commerce retailer via their digital certificate, but organizations have to rely on weak username and password combinations to verify the identity of customers registered with their service. We propose the X509Cloud framework which enables organizations to issue certificates to their users at zero cost, and allows them to securely store and disseminate client certificates using the Bitcoin inspired blockchain protocol. This in turn will enable organizations and individuals to authenticate and to securely communicate with other users on the Internet.


title: Semantic Stixels: Depth is not enough

abstract: In this paper we present Semantic Stixels, a novel vision-based scene model geared towards automated driving. Our model jointly infers the geometric and semantic layout of a scene and provides a compact yet rich abstraction of both cues using Stixels as primitive elements. Geometric information is incorporated into our model in terms of pixel-level disparity maps derived from stereo vision. For semantics, we leverage a modern deep learning-based scene labeling approach that provides an object class label for each pixel. Our experiments involve an in-depth analysis and a comprehensive assessment of the constituent parts of our approach using three public benchmark datasets. We evaluate the geometric and semantic accuracy of our model and analyze the underlying run-times and the complexity of the obtained representation. Our results indicate that the joint treatment of both cues on the Semantic Stixel level yields a highly compact environment representation while maintaining an accuracy comparable to the two individual pixel-level input data sources. Moreover, our framework compares favorably to related approaches in terms of computational costs and operates in real-time.


title: Protocols for self-organization of a wireless sensor network

abstract: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes.


title: Neuronal signalling of fear memory

abstract: The learning and remembering of fearful events depends on the integrity of the amygdala, but how are fear memories represented in the activity of amygdala neurons? Here, we review recent electrophysiological studies indicating that neurons in the lateral amygdala encode aversive memories during the acquisition and extinction of Pavlovian fear conditioning. Studies that combine unit recording with brain lesions and pharmacological inactivation provide evidence that the lateral amygdala is a crucial locus of fear memory. Extinction of fear memory reduces associative plasticity in the lateral amygdala and involves the hippocampus and prefrontal cortex. Understanding the signalling of aversive memory by amygdala neurons opens new avenues for research into the neural systems that support fear behaviour.


Automatic Extraction of the Central Symmetry (Mid-Sagittal) Plane from Neuroradiology Images


title: Adjustable Robust Optimization via Fourier-Motzkin Elimination

abstract: We demonstrate how adjustable robust optimization (ARO) problems with fixed recourse can be cast as static robust optimization problems via Fourier–Motzkin elimination (FME). Through the lens of FM...


ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA


title: Mobile Phishing Attacks and Mitigation Techniques

abstract: Mobile devices have taken an essential role in the portable computer world. Portability, small screen size, and lower cost of production make these devices popular replacements for desktop and laptop computers for many daily tasks, such as surfing on the Internet, playing games, and shopping online. The popularity of mobile devices such as tablets and smart phones has made them a frequent target of traditional web-based attacks, especially phishing. Mobile device-based phishing takes its share of the pie to trick users into entering their credentials in fake websites or fake mobile applications. This paper discusses various phishing attacks using mobile devices followed by some discussion on countermeasures. The discussion is intended to bring more awareness to emerging mobile device-based phishing attacks.


title: Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium

abstract: Regularized inversion methods for image reconstruction are used widely due to their tractability and their ability to combine complex physical sensor models with useful regularity criteria. Such methods were used in the recently developed Plug-and-Play prior method, which provides a framework to use advanced denoising algorithms as regularizers in inversion. However, the need to formulate regularized inversion as the solution to an optimization problem severely limits both the expressiveness of possible regularity conditions and the variety of provably convergent Plug-and-Play denoising operators. ::: In this paper, we introduce the concept of consensus equilibrium (CE), which generalizes regularized inversion to include a much wider variety of regularity operators without the need for an optimization formulation. Consensus equilibrium is based on the solution of a set of equilibrium equations that balance data fit and regularity. In this framework, the problem of MAP estimation in regularized inversion is replaced by the problem of solving these equilibrium equations, which can be approached in multiple ways, including as a fixed point problem that generalizes the ADMM approach used in the Plug-and-Play method. ::: We present the Douglas-Rachford (DR) algorithm for computing the CE solution as a fixed point and prove the convergence of this algorithm under conditions that include denoising operators that do not arise from optimization problems and that may not be nonexpansive. We give several examples to illustrate the idea of consensus equilibrium and the convergence properties of the DR algorithm and demonstrate this method on a sparse interpolation problem using electron microscopy data.


title: Characters in Computer Games: Toward Understanding Interpretation and Design

abstract: Interpretation of characters is a fundamental feature of human behavior. Even with limited information available, people will assign personality – even to inanimate objects. Characters in computer games will be attributed personality based on their appearance and behavior. The interpretation of these characters affects the whole game experience. Designing the protagonist character in computer games is different from the design of static characters (e.g. film or literature), because the player’s actions will affect the nature of the character. There are, however, many ways to control and guide the actions of the protagonist and thus the character’s nature. By setting goals, scripting predefined actions and choosing what kind of actions to implement, the game designer can restrict the player’s freedom. This, together with the characterization of the character, will affect the interpretation of the character.


title: The Impact of Corporate Sustainability on Organizational Processes and Performance

abstract: We investigate the effect of corporate sustainability on organizational processes and performance. Using a matched sample of 180 US companies, we find that corporations that voluntarily adopted sustainability policies by 1993 - termed as High Sustainability companies - exhibit by 2009 distinct organizational processes compared to a matched sample of companies that adopted almost none of these policies - termed as Low Sustainability companies. The boards of directors of High Sustainability companies are more likely to be formally responsible for sustainability and top executive compensation incentives are more likely to be a function of sustainability metrics. High Sustainability companies are more likely to have established processes for stakeholder engagement, to be more long-term oriented, and to exhibit higher measurement and disclosure of nonfinancial information. Finally, High Sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market and accounting performance.


title: From computational narrative analysis to generation: a preliminary review

abstract: In this paper we present a survey of two of the main areas of research within the field of computational narrative, namely narrative analysis and generation. We argue that there is a gap between these two lines of work and propose a taxonomy of the computational models of narrative used within each. We outline potential mappings between computational narrative models with the goal of bridging this gap and alleviating the authorial bottleneck problem occurring when authoring content for computational narrative generation systems. Finally we discuss related work in this direction and report on our work-in-progress towards an end-to-end computational narrative system to bridge the gap.


title: Intensive or extensive use of soil moisture: Plant strategies to cope with stochastic water availability

abstract: Some plants rely on a dependable winter recharge, as opposed to others that quickly respond to the intermittent and uncertain rainfall during the growing season. Using a stochastic model for the soil moisture process and a quantitative measure of plant water stress, we find climate, soil, and vegetation characteristics leading to the dominance or possible coexistence of these two strategies of water use.


title: Textual Requirement Analysis for UML Diagram Extraction by using NLP

abstract: Requirement analysis is the preliminary step in software development process. The requirements stated by the clients are analyzed and an abstraction of it, is created which is termed as requirement model. The automatic generation of UML diagram from natural language requirements is highly challenging and demanding very efficient methodology. Unified Modeling Language (UML) models are helpful for understanding the problems, communicating with application experts and preparing documentation. The static design view of the system can be modeled using a UML class diagram. System requirements stated by the user are usually in natural language form. This is an imprecise and inconsistent form which is difficult to be used by the developer for design UML model. We present a new methodology for generating UML diagrams or models from natural language problem statement or requirement specification. We have named our methodology as Requirement analysis and UML diagram extraction (RAUE). General Terms


title: Turbidity affects social dynamics in Trinidadian guppies

abstract: Turbidity, caused by suspended particles in the water column, induces light scattering and shifts in the wavelengths of light. These changes may impair the ability of fish to use physical cues and hence may modify social interactions. We experimentally investigated the social interactions of guppies, Poecilia reticulata, in clear and turbid water. Fish were significantly less active, formed smaller shoals and were found to be more often alone in turbid than in clear water. A Markov chain analysis revealed significant differences in the social dynamics when comparing clear and turbid water conditions. The probability of leaving a particular nearest neighbour and the probability of choosing some neighbour after swimming around alone differed between the treatments. Our results indicate that turbidity has a number of different effects on the social interactions of the guppy, and we discuss their potential costs and benefits and wider implications.


title: Fuzzy logic based traffic light controller

abstract: Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In a conventional traffic light controller, the traffic lights change at constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers implemented in current practice, are based on the 'time-of-the-day' scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. A traffic light controller based on fuzzy logic can be used for optimum control of fluctuating traffic volumes such as over saturated or unusual load conditions. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. The length of the current green phase is extended or terminated depending upon the 'arrival' i.e. the number of vehicles approaching the green phase and the 'queue' that corresponds to the number of queuing vehicles in red phases. A prototype system for controlling traffic at an intersection is designed using VB6 and Matlab tool. The traffic intersection is simulated in VB6 and the data regarding the traffic parameters is collected in VB6 environment. The decision on the duration of the extension is taken using the Matlab tool. This decision is based on the Arrival and Queue of vehicles, which is imported in Matlab from VB6 environment. The time delay experienced by the vehicles using the fixed as well as fuzzy traffic controller is then compared to observe the effectiveness of the fuzzy traffic controller.


title: Bacterial resistance to antimicrobial agents and its impact on veterinary and human medicine

abstract: BACKGROUND ::: Antimicrobial resistance has become a major challenge in veterinary medicine, particularly in the context of bacterial pathogens that play a role in both humans and animals. ::: ::: ::: OBJECTIVES ::: This review serves as an update on acquired resistance mechanisms in bacterial pathogens of human and animal origin, including examples of transfer of resistant pathogens between hosts and of resistance genes between bacteria. ::: ::: ::: RESULTS ::: Acquired resistance is based on resistance-mediating mutations or on mobile resistance genes. Although mutations are transferred vertically, mobile resistance genes are also transferred horizontally (by transformation, transduction or conjugation/mobilization), contributing to the dissemination of resistance. Mobile genes specifying any of the three major resistance mechanisms - enzymatic inactivation, reduced intracellular accumulation or modification of the cellular target sites - have been found in a variety of bacteria that may be isolated from animals. Such resistance genes are associated with plasmids, transposons, gene cassettes, integrative and conjugative elements or other mobile elements. Bacteria, including zoonotic pathogens, can be exchanged between animals and humans mainly via direct contact, but also via dust, aerosols or foods. Proof of the direction of transfer of resistant bacteria can be difficult and depends on the location of resistance genes or mutations in the chromosomal DNA or on a mobile element. ::: ::: ::: CONCLUSION ::: The wide variety in resistance and resistance transfer mechanisms will continue to ensure the success of bacterial pathogens in the future. Our strategies to counteract resistance and preserve the efficacy of antimicrobial agents need to be equally diverse and resourceful.


title: A high-accuracy CMOS on-chip RC oscillator

abstract: In this paper, a high-accuracy CMOS on-chip RC oscillator (OSC) for MCU application is presented. The oscillator can provide a 16MHz output frequency with the total accuracy in ±1% in all process corner at 1.8v and 55°C condition, ±2% in all process, supply voltage and temperature condition after 7bit digital trimming. This design features low sensitivity to power supply from 1.8v to 6.0v and temperature range from −45°C to 85°C. The on-chip oscillator was fabricated in a 130nm standard digital CMOS process. The final die size is 78um×132um.Experimental result shows that all performance satisfies the specification.


title: A dual two-level inverter scheme with common mode voltage elimination for an induction motor drive

abstract: Pulse-width modulated (PWM) inverters are known to generate common mode voltages which cause motor bearing currents in the induction motor drives. They also result in leakage currents which act as sources of conducted electromagnetic interference in the drive system. The common mode voltage generated by a conventional three-level inverter can be eliminated by switching only the voltage space vectors which do not produce the common mode voltage. This paper presents a PWM switching strategy to eliminate common mode voltage using the open-end winding configuration for the induction motor. The switching strategy presented in this paper, does not generate any alternating common mode voltages in the drive system and hence the electrostatic coupling of the common mode voltage, which results in the bearing currents and the leakage currents, is avoided. The proposed scheme is devoid of neutral point voltage fluctuations and does not require neutral point clamping diodes, when compared to the common mode elimination scheme based on the conventional three-level inverter topology. Also, the present scheme uses a single dc-link with half the voltage compared to the conventional three-level inverter based scheme.


title: A module-integrated isolated solar micro-inverter without electrolytic capacitors

abstract: This paper presents a module-integrated isolated solar micro-inverter. The studied grid-tied micro-inverters can individually extract the maximum solar power from each photovoltaic PV panel and transfer to the AC utility system. A harmonic suppression technique is used to reduce the DC-bus capacitance. Electrolytic capacitors are not needed in the studied solar micro-inverter. High conversion efficiency, high maximum power point tracking accuracy and long lifespan can be achieved. The operation principles and design considerations of the studied PV inverter are analyzed and discussed. A laboratory prototype is implemented and tested to verify its feasibility. Copyright © 2012 John Wiley & Sons, Ltd.


title: A 5.3GHz 8T-SRAM with Operation Down to 0.41V in 65nm CMOS

abstract: A 32 kb subarray demonstrates practical implementation of a 65 nm node 8T-SRAM cell for variability tolerance in highspeed caches. Ideal cell stability allows single-supply operation down to 0.41 V at 295 MHz without dynamic voltage techniques. Despite a larger cell, array area is competitive with 6T-SRAM due to higher array efficiency. With an LSDL decoder, a gated diode sense amplifier, and design tradeoffs enabled by the 8 T cell, 5.3 GHz operation at 1.2 V is achieved.


title: Multiresolution convolutional neural network for robust speech recognition

abstract: Convolutional neural networks (CNNs) have been recently used for acoustic modeling and feature extraction in speech recognition systems, where their inputs have been speech spectrogram or even raw speech signal. In this paper, we propose to use CNN for learning a filter bank and robust feature extraction from the noisy speech spectrum. In the proposed manner, CNN inputs are noisy speech spectrum and its outputs are denoised logarithm of Mel filter bank energies (LMFBs) and convolution filter size is fixed. Furthermore, we propose to use multiple CNNs with different convolution filter sizes to provide different frequency resolutions for feature extraction from the speech spectrum. We named this method as Multiresolution CNN (MRCNN). We behave in two manners with multiple CNNs outputs. In the first manner, we concatenate all outputs to construct the feature vector. In the second manner, we choose some outputs from each CNN based on the convolution filter size and concatenate them to construct feature vector. Recognition accuracy on Aurora 2 database, show that MRCNN with two CNNs and corresponding 1×6 and 1×20 convolution filter sizes outperforms CNNs and other MRCNNs setting in extracting robust features.


title: Volume minimization of the main DM/CM EMI filter stage of a bidirectional three-phase three-level PWM rectifier system

abstract: Three-phase voltage source AC-DC converters disclose a differential mode (DM) and a considerable common mode (CM) bridge-leg output voltage at the switching frequency, which need to be filtered by an EMI input filter to comply with conducted EMI standards (e.g. CISPR 11) at the mains terminal. Additionally, the EMI filter (which in this paper includes the boost inductor) should usually limit the maximum peak-to-peak bridge-leg output current ripple, to which a DM and CM component contribute, to 20%-40% of the nominal peak current. Typically, the DM current ripple is defined by the boost inductance Ldm, 1 and the CM current ripple by the CM inductance Lcm, 1. Therefore, a degree of freedom in the filter design exists: different combinations of Ldm, 1 and Lcm, 1 lead to the same peak-to-peak bridge-leg output current ripple. Accordingly, this paper investigates the optimal ratio kL, opt = Lcm, 1/Ldm, 1, which minimizes the volume of the main LC stage of the DM/CM EMI input filter. To achieve a compact filter, it is demonstrated for a 10 kW PWM rectifier system that for kL ≥ 3 the boxed filter volume is almost constant. Additionally, kL, opt = 14 minimizes the stray field of the CM choke Lcm, 1 and leads to a minimal boxed filter volume of 0.43 ltr. for a filter cooled by natural convection.


On The Atomic Weight of Radium


title: If walls could talk: Patterns and anomalies in Facebook wallposts

abstract: How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7K users over three years during four month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, etc., can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step towards a systematic and quantitative wall-centric profiling of Facebook user activity.


title: Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness

abstract: Since Internet has become an excellent source of consumer reviews, the area of sentiment analysis (also called sentiment extraction, opinion mining, opinion extraction, and sentiment mining) has seen a large increase in academic interest over the last few years. Sentiment analysis mines opinions at word, sentence, and document levels, and gives sentiment polarities and strengths of articles. As known, the opinions of consumers are expressed in sentiment Chinese phrases. But due to the fuzziness of Chinese characters, traditional machine learning techniques can not represent the opinion of articles very well. In this paper, we propose a multi-strategy sentiment analysis method with semantic fuzziness to solve the problem. The results show that this hybrid sentiment analysis method can achieve a good level of effectiveness.


title: ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

abstract: BackgroundElucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.ResultsWe prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.ConclusionARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.


Hierarchical Bayesian Neural Networks for Personalized Classification


title: Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

abstract: Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.


title: Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback

abstract: Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy.


title: Design of back-drivable joint mechanism for in-pipe robot

abstract: In this paper, a back-drivable joint mechanism for an in-pipe robot is presented. The joint mechanism consists of 2-pitch and 1-roll joint. The back-drivability of the joint mechanism is realized by using a ball-screw and a steel wire rope. When the in-pipe robot with the back-drivable joint mechanism passes through pipeline elements except the miter and the branch, the control of the joint mechanism is not required. In addition, a rescue mechanism is not needed in case of emergency such as the loss of power, communication and control. Besides, the 2-pitch joint of this joint module assists the in-pipe robot to pass through the miter and the branch safely and quickly. Finally, the performance of back-drivable joint module is experimentally validated.


title: Effects of Internet Display Advertising in the Purchase Funnel: Model-Based Insights from a Randomized Field Experiment

abstract: This study examines the effects of Internet display advertising using cookie-level data from a field experiment at a financial tools provider. The experiment randomized assignment of cookies to treatment (firm ads) and control conditions (charity ads), enabling the authors to handle different sources of selection bias, including targeting algorithms and browsing behavior. They analyze display ad effects for users at different stages of the company's purchase funnel (i.e., nonvisitor, visitor, authenticated user, and converted customer) and find that display advertising positively affects visitation to the firm's website for users in most stages of the purchase funnel, but not for those who previously visited the site without creating an account. Using a binary logit model, the authors calculate marginal effects and elasticities by funnel stage and analyze the potential value of reallocating display ad impressions across users at different stages. Expected visits increase almost 10% when display ad impress...


title: Design of a large-scale storage-class RRAM system

abstract: Resistive Random Access Memory (RRAM) is a promising next generation non-volatile memory (NVM) technology, thanks to its performance potential, endurance and ease-of-integration with standard silicon CMOS processes. While prior work has evaluated RRAM as a replacement for DRAM or even cache memory, to our knowledge there is no prior study that has investigated whether RRAM could be a viable NAND flash replacement in building large-scale storage-class memory systems. Motivated by this observation, our paper first discusses and quantifies the main problems associated with RRAM that prevent it from replacing NAND flash. The main solution we propose, "slab-based memory access with local/global bitlines," enables dense RRAM islands but can also cause performance related problems. To compensate for the latter, we also propose exploiting internal resource parallelism in RRAM and employing optimized data movement interfaces. Our extensive experimental evaluation using a cycle-level NVM simulator and real workloads under diverse computing domains indicate that the proposed architecture can provide 2.95 ~ 8.28 times better bandwidth and 66% ~ 88% shorter latency as compared to the conventional NAND flash, and improve the system-level performance of our workloads by 5x, with a storage capacity similar to that of the state-of-the-art NAND flash.


title: Low and moderate plyometric training frequency produces greater jumping and sprinting gains compared with high frequency.

abstract: The purpose of this study was to examine the effect of 3 different plyometric training frequencies (e.g., 1 day per week, 2 days per week, 4 days per week) associated with 3 different plyometric training volumes on maximal strength, vertical jump performance, and sprinting ability. Forty-two students were randomly assigned to 1 of 4 groups: control (n = 10, 7 sessions of drop jump (DJ) training, 1 day per week, 420 DJs), 14 sessions of DJ training (n = 12, 2 days per week, 840 DJs), and 28 sessions of DJ training (n = 9, 4 days per week, 1680 DJs). The training protocols included DJ from 3 different heights 20, 40, and 60 cm. Maximal strength (1 repetition maximum [1RM] and maximal isometric strength), vertical height in countermovement jumps and DJs, and 20-m sprint time tests were carried out before and after 7 weeks of plyometric training. No significant differences were observed among the groups in pre-training in any of the variables tested. No significant changes were observed in the control group in any of the variables tested at any point. Short-term plyometric training using moderate training frequency and volume of jumps (2 days per week, 840 jumps) produces similar enhancements in jumping performance, but greater training efficiency (~12% and 0.014% per jump) compared with high jumping (4 days per week, 1680 jumps) training frequency (~18% and 0.011% per jump). In addition, similar enhancements in 20-m-sprint time, jumping contact times and maximal strength were observed in both a moderate and low number of training sessions per week compared with high training frequencies, despite the fact that the average number of jumps accomplished in 7S (420 jumps) and 14S (840 jumps) was 25 and 50% of that performed in 28S (1680 jumps). These observations may have considerable practical relevance for the optimal design of plyometric training programs for athletes, given that a moderate volume is more efficient than a higher plyometric training volume.


cube 2 net : E icient ality Network Construction with Data Cube Organization


title: Deriving dexterous, in-hand manipulation primitives for adaptive robot hands

abstract: Adaptive robot hands have changed the way we approach and think of robot grasping and manipulation. Traditionally, pinch, fingertip grasping and dexterous, in-hand manipulation tasks were executed with fully actuated, rigid robot hands and relied on analytic methods, computation of the hand object Jacobians and extensive numerical simulations for deriving optimal and minimal effort grasps. However, even insignificant uncertainties in the modeling space could render the extraction of candidate grasps or manipulation paths infeasible. Adaptive hands use underactuated mechanisms and structural compliance, facilitating by design the successful extraction of stable grasps and the robust execution of manipulation tasks, even under significant object pose or other environmental uncertainties. In this paper, we propose a methodology for the automated extraction of dexterous, in-hand manipulation strategies / primitives for adaptive hands. To do so, we use a constrained optimization scheme that describes the kinematics of adaptive hands during the grasping and manipulation processes, an automated experimental setup for data collection, a clustering technique that groups together similar manipulation strategies, and a dimensionality reduction technique that projects the robot kinematics to lower dimensional manifolds. In these manifolds, control is simplified and hand operation becomes more intuitive. In this work, we also assess the effect of the extracted manipulation primitives on the object pose perturbations. The efficiency of the proposed methods is experimentally verified for various adaptive robot hands. The extracted primitives can simplify the operation and control of the open-source robot hand designs of the Yale Open Hand project in dexterous manipulation tasks.


title: Architecture Design and Implementation of an Autonomous Vehicle

abstract: Architecture design is one of the most important problems for an intelligent system. In this paper, a practical framework of hardware and software is proposed to reveal the external configuration and internal mechanism of an autonomous vehicle—a typical intelligent system. The main contributions of this paper are as follows. First, we compare the advantages and disadvantages of three typical sensor plans and introduce a general autopilot for a vehicle. Second, we introduce a software architecture for an autonomous vehicle. The perception and planning performances are improved with the help of two inner loops of simultaneous localization and mapping. An algorithm to enlarge the detection range of the sensors is proposed by adding an inner loop to the perception system. A practical feedback to restrain mutations of two adjacent planning periods is also realized by the other inner loop. Third, a cross-platform virtual server (named project cocktail) for data transmission and exchange is presented in detail. Through comparisons with the robot operating system, the performance of project cocktail is proven to be considerably better in terms of transmission delay and throughput. Finally, a report on an autonomous driving test implemented using the proposed architecture is presented, which shows the effectiveness, flexibility, stability, and low-cost of the overall autonomous driving system.


title: STRs vs. SNPs: thoughts on the future of forensic DNA testing.

abstract: Largely due to technological progress coming from the Human Genome and International HapMap Projects, the issue has been raised in recent years within the forensic DNA typing community of the potential for single nucleotide polymorphism (SNP) markers as possible replacements of the currently used short tandem repeat (STR) loci. Our human identity testing project team at the U.S. National Institute of Standards and Technology (NIST) has explored numerous SNP and STR loci and assays as well as developing miniSTRs for degraded DNA samples. Based on their power of discrimination, use in deciphering mixture components, and ability to be combined in multiplex assays in order to recover information from low amounts of biological material, we believe that STRs rather than SNPs will fulfill the dominant role in human identity testing for the foreseeable future. However, SNPs may play a useful role in specialized applications such as mitochondrial DNA (mtDNA) testing, Y-SNPs as lineage markers, ancestry informative markers (AIMs), the prediction of phenotypic traits, and other potential niche forensic casework applications.


title: Fuzzy Logic for Enhancing the Sensitivity of COCOMO Cost Model

abstract: The accuracy of algorithmic models for software cost prediction is limited due to their inability to handle imprecision and uncertainties associated with the software project attributes like size, programmer experience, etc. This paper enhances the accuracy and sensitivity of one of a widely used models COCOMO81 intermediate by incorporating a fuzzy component into the model. The fuzzy component deals with the vagueness and imprecision of the model’s cost drivers. MATLAB fuzzy toolbox was used in implementing the proposed fuzzy model. Artificial datasets was derived from COCOMONASA2 dataset to evaluate the proposed fuzzy model. The results showed that the sensitivity of the proposed fuzzy model is superior to COCOMO81 intermediate. It’s worth mentioning that the idea of the paper isn’t restricted to COCOMO81 intermediate; it could be applied to other algorithmic models.


title: Anxiety Disorders in Primary Care: Prevalence, Impairment, Comorbidity, and Detection

abstract: BACKGROUND ::: Anxiety, although as common as depression, has received less attention and is often undetected and undertreated. ::: ::: ::: OBJECTIVE ::: To determine the current prevalence, impairment, and comorbidity of anxiety disorders in primary care and to evaluate a brief measure for detecting these disorders. ::: ::: ::: DESIGN ::: Criterion-standard study performed between November 2004 and June 2005. ::: ::: ::: SETTING ::: 15 U.S. primary care clinics. ::: ::: ::: PARTICIPANTS ::: 965 randomly sampled patients from consecutive clinic patients who completed a self-report questionnaire and agreed to a follow-up telephone interview. ::: ::: ::: MEASUREMENTS ::: 7-item anxiety measure (Generalized Anxiety Disorder [GAD]-7 scale) in the clinic, followed by a telephone-administered, structured psychiatric interview by a mental health professional who was blinded to the GAD-7 results. Functional status (Medical Outcomes Study Short Form-20), depressive and somatic symptoms, and self-reported disability days and physician visits were also assessed. ::: ::: ::: RESULTS ::: Of the 965 patients, 19.5% (95% CI, 17.0% to 22.1%) had at least 1 anxiety disorder, 8.6% (CI, 6.9% to 10.6%) had posttraumatic stress disorder, 7.6% (CI, 5.9% to 9.4%) had a generalized anxiety disorder, 6.8% (CI, 5.3% to 8.6%) had a panic disorder, and 6.2% (CI, 4.7% to 7.9%) had a social anxiety disorder. Each disorder was associated with substantial impairment that increased significantly (P < 0.001) as the number of anxiety disorders increased. Many patients (41%) with an anxiety disorder reported no current treatment. Receiver-operating characteristic curve analysis showed that both the GAD-7 scale and its 2 core items (GAD-2) performed well (area under the curve, 0.80 to 0.91) as screening tools for all 4 anxiety disorders. ::: ::: ::: LIMITATION ::: The study included a nonrandom sample of selected primary care practices. ::: ::: ::: CONCLUSIONS ::: Anxiety disorders are prevalent, disabling, and often untreated in primary care. A 2-item screening test may enhance detection.


title: Recognition of printed arabic text based on global features and decision tree learning techniques

abstract: Abstract Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic in both on-line and off-line, has been achieved towards the automatic recognition of Arabic characters. This is a result of the lack of adequate support in terms of funding, and other utilities such as Arabic text databases, dictionaries, etc., and of course because of the cursive nature of its writing rules, and this problem is still an open research field. This paper presents a new technique for the recognition of Arabic text using the C4.5 machine learning system. The advantage of machine learning are twofold: it can generalize over the large degree of variations between different fonts and writing style and recognition rules can be constructed by examples. The technique can be divided into three major steps. The first step is digitization and pre-processing to create connected component, detect the skew of a document image and correct it. Second, feature extraction, where global features of the input Arabic word is used to extract features such as number of subwords, number of peaks within the subword, number and position of the complementary character, etc., to avoid the difficulty of segmentation stage. Finally, machine learning C4.5 is used to generate a decision tree for classifying each word. The system was tested with 1000 Arabic words with different fonts (each word has 15 samples) and the correct average recognition rate obtained using cross-validation was 92%.


title: Neural network-based clustering using pairwise constraints

abstract: This paper presents a neural network-based end-to-end clustering framework. We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering. The network is trained with weak labels, specifically partial pairwise relationships between data instances. The cluster assignments and their probabilities are then obtained at the output layer by feed-forwarding the data. The framework has the interesting characteristic that no cluster centers need to be explicitly specified, thus the resulting cluster distribution is purely data-driven and no distance metrics need to be predefined. The experiments show that the proposed approach beats the conventional two-stage method (feature embedding with k-means) by a significant margin. It also compares favorably to the performance of the standard cross entropy loss for classification. Robustness analysis also shows that the method is largely insensitive to the number of clusters. Specifically, we show that the number of dominant clusters is close to the true number of clusters even when a large k is used for clustering.


title: Utilizing dependency relationships between math expressions in math IR

abstract: Current mathematical search systems allow math expressions within a document to be queried using math expressions and keywords. To accept such queries, math search systems must index both math expressions and textual information in documents. Each indexed math expression is usually associated with all the words in its surrounding context within a given window size. However, we found that this context is often ineffective for explaining math expressions in scientific papers. The meaning of an expression is usually defined in the early part of a document, and the meaning of each symbol contained in the expression can be useful for explaining the entire expression. This explanation may not be captured within the context of a math expression, unless we set the context to have a very wide window size. However, widening the window size also increases the proportion of words that are unrelated to the expression. This paper proposes the use of dependency relationships between math expressions to enrich the textual information of each expression. We examine the influence of this enrichment in a math search system. The experimental results show that significantly better precision can be obtained using the enriched textual information rather than the math expressions' own textual information. This indicates that the enrichment of textual information for each math expression using dependency relationships enhances the math search system.


title: An Efficient SQL-based RDF Querying Scheme

abstract: Devising a scheme for efficient and scalable querying of Resource Description Framework (RDF) data has been an active area of current research. However, most approaches define new languages for querying RDF data, which has the following shortcomings: 1) They are difficult to integrate with SQL queries used in database applications, and 2) They incur inefficiency as data has to be transformed from SQL to the corresponding language data format. This paper proposes a SQL based scheme that avoids these problems. Specifically, it introduces a SQL table function RDF_MATCH to query RDF data. The results of RDF_MATCH table function can be further processed by SQL's rich querying capabilities and seamlessly combined with queries on traditional relational data. Furthermore, the RDF_MATCH table function invocation is rewritten as a SQL query, thereby avoiding run-time table function procedural overheads. It also enables optimization of rewritten query in conjunction with the rest of the query. The resulting query is executed efficiently by making use of B-tree indexes as well as specialized subject-property materialized views. This paper describes the functionality of the RDF_MATCH table function for querying RDF data, which can optionally include user-defined rulebases, and discusses its implementation in Oracle RDBMS. It also presents an experimental study characterizing the overhead eliminated by avoiding procedural code at runtime, characterizing performance under various input conditions, and demonstrating scalability using 80 million RDF triples from UniProt protein and annotation data.


title: Torch7: A Matlab-like Environment for Machine Learning

abstract: Torch7 is a versatile numeric computing framework and machine learning library that extends Lua. Its goal is to provide a flexible environment to design and train learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High performance is obtained via efficient OpenMP/SSE and CUDA implementations of low-level numeric routines. Torch7 can easily be interfaced to third-party software thanks to Lua’s light interface.


title: Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach

abstract: This paper presents a study of using deep bidirectional long short-term memory (DBLSTM) recurrent neural network as acoustic model for DBLSTM-HMM based large vocabulary continuous speech recognition (LVCSR), where a context-sensitive-chunk (CSC) back-propagation through time (BPTT) approach is used to train DBLSTM by splitting each training sequence into chunks with appended contextual observations, and a CSC-based decoding method with possibly overlapped CSCs is used for recognition. Our approach makes mini-batch based training on GPU more efficient and reduces the latency of DBLSTM-based LVCSR from a whole utterance to a short chunk. Evaluations have been made on Switchboard-I benchmark task. In comparison with epoch-wise BPTT training, our method can achieve more than three times speedup on a single GPU card without degrading recognition accuracy. In comparison with a highly optimized DNN-HMM system trained by a frame-level cross entropy (CE) criterion, our CE-trained DBLSTM-HMM system achieves relative word error rate reductions of 9% and 5% on Eval2000 and RT03S testing sets, respectively. Furthermore, by running model averaging based parallel training of DBLSTM on a cluster of GPUs, CSC-BPTT incurs less accuracy degradation than epoch-wise BPTT while achieves a linear speedup.


title: Trustability-Based Dynamic Active Learning for Crowdsourced Labelling of Emotional Audio Data

abstract: The process of collecting annotated data is expensive and time-consuming. Making use of crowdsourcing instead of experts in a laboratory setting is a viable alternative to reduce these costs. However, without adequate quality control the obtained labels may be less reliable. Whereas crowdsourcing reduces only the costs per annotation, another technique, active learning, aims at reducing the overall annotation costs by selecting the most important instances of the dataset and only asking for manual annotations for these selected samples. Herein, we investigate the advantages of combining crowdsourcing and different iterative active learning paradigms for audio data annotation. Further, we incorporate an annotator trustability score to further reduce the labelling effort needed and, at the same time, to achieve better classification results. In this context, we introduce a novel active learning algorithm, called Trustability-based dynamic active learning, which accumulates manual annotations in each step until a trustability-weighted agreement level of annotators is reached. Furthermore, we bring this approach into the real world and integrate it in our gamified intelligent crowdsourcing platform iHEARu-PLAY. Key experimental results on an emotion recognition task indicate that a considerable relative annotation cost reduction of up to 90.57 % can be achieved when compared with a non-intelligent annotation approach. Moreover, our proposed method reaches an unweighted average recall value of 73.71 %, while a conventional passive learning algorithm peaks at 60.03 %. Therefore, our novel approach not only efficiently reduces the manual annotation work load but also improves the classification performance.


title: Knowledge Guided Hierarchical Multi-Label Classification Over Ticket Data

abstract: Maximal automation of routine IT maintenance procedures is an ultimate goal of IT service management. System monitoring, an effective and reliable means for IT problem detection, generates monitoring ticket. In light of the ticket description, the underlying categories of the IT problem are determined, and the ticket is assigned to the corresponding processing teams for problem resolving. Automatic IT problem category determination acts as a critical part during the routine IT maintenance procedures. In practice, IT problem categories are naturally organized in a hierarchy by specialization. Utilizing the category hierarchy, this paper comes up with a hierarchical multi-label classification method to classify the monitoring tickets. In order to find the most effective classification, a novel contextual hierarchy (CH) loss is introduced in accordance with the problem hierarchy. Consequently, an arising optimization problem is solved by a new greedy algorithm named GLabel . Furthermore, as well as the ticket instance itself, the knowledge from the domain experts, which partially indicates some categories the given ticket may or may not belong to, can also be leveraged to guide the hierarchical multi-label classification. Accordingly, a multi-label inference with the domain expert knowledge is conducted on the basis of the given label hierarchy. The experiment demonstrates the great performance improvement by incorporating the domain knowledge during the hierarchical multi-label classification over the ticket data.


Selecta: Heterogeneous Cloud Storage Configuration for Data Analytics.


title: A Review on the Use of Blockchain for the Internet of Things

abstract: The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.


title: Ontology learning: state of the art and open issues

abstract: Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.


Comment Abuse Classification with Deep Learning


title: An overview of localization techniques for Wireless Sensor Networks

abstract: Localization of sensor nodes is an important aspect in Wireless Sensor Networks (WSNs). This paper presents an overview of the major localization techniques for WSNs. These techniques are classified into centralized and distributed depending on where the computational effort is carried out. The paper concentrates on the factors that need to be considered when selecting a localization technique. The advantages and limitation of various techniques are also discussed. Finally, future research directions and challenges are highlighted.


title: Diffusion tensor imaging of the brain

abstract: Diffusion tensor imaging (DTI) is a promising method for characterizing microstructural changes or differences with neuropathology and treatment. The diffusion tensor may be used to characterize the magnitude, the degree of anisotropy, and the orientation of directional diffusion. This review addresses the biological mechanisms, acquisition, and analysis of DTI measurements. The relationships between DTI measures and white matter pathologic features (e.g., ischemia, myelination, axonal damage, inflammation, and edema) are summarized. Applications of DTI to tissue characterization in neurotherapeutic applications are reviewed. The interpretations of common DTI measures (mean diffusivity, MD; fractional anisotropy, FA; radial diffusivity, D(r); and axial diffusivity, D(a)) are discussed. In particular, FA is highly sensitive to microstructural changes, but not very specific to the type of changes (e.g., radial or axial). To maximize the specificity and better characterize the tissue microstructure, future studies should use multiple diffusion tensor measures (e.g., MD and FA, or D(a) and D(r)).


title: Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

abstract: Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents’ suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Three parental variables showed a relevant association with suicide attempts in adolescents – (all protective): mother’s warmth and father’s warmth in childhood and mother’s control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk – as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD.