title: PKLot-A robust dataset for parking lot classification

abstract: Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due the large number of practical applications. However, a common problem that researchers in this field very often face is the lack of a representative dataset to perform their experiments. To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695,899 images captured from two parking lots with three different camera views. The acquisition protocol allows obtaining static images showing illumination variance related to sunny, overcast and rainy days. We believe that researchers will find this dataset a very useful tool since it allows future benchmarking and evaluation. The dataset is currently available for research purposes upon request. To gain a better insight into this dataset we have evaluated two textural descriptors, Local Binary Patterns and Local Phase Quantization, with a Support Vector Machine classifier to detect parking lot vacancy. In the experiments where the same view was used for both training and testing, we have reached outstanding recognition rates, greater than 99%. The main challenge, though, lies in building a general classifier that is able to detect parking spaces from the parking lots that were not used for training. In this sense, the best result achieved by the texture-based classifier was about 89%. The observed drop in terms of performance shows that additional investigation is necessary to create classification schemes less dependent on the training set. Other researchers can use these results as a baseline performance when testing their own algorithms on this dataset.


title: The contribution of attentional bias to worry: Distinguishing the roles of selective engagement and disengagement

abstract: This study investigated the effect on worry of biased attentional engagement and disengagement. Variants of a novel attention modification paradigm were developed, designed to induce a group difference either in participants’ tendency to selectively engage with, or disengage from, threatening meanings. An index of threat bias, reflecting relative speeding to process threat word compared to non-threat word content, confirmed that both procedures were effective in inducing differential attentional bias. Importantly, when the induced group difference in attentional bias followed the procedure designed to influence selective engagement with threat meanings, it also gave rise to a corresponding group difference in worry. This was not the case when it was induced by the procedure designed to influence selective disengagement from threat meanings. These findings suggest that facilitated attentional engagement with threat meanings may causally contribute to variability in worry.


title: Exploring New Backbone and Attention Module for Semantic Segmentation in Street Scenes

abstract: Semantic segmentation, as dense pixel-wise classification task, played an important tache in scene understanding. There are two main challenges in many state-of-the-art works: 1) most backbone of segmentation models that often were extracted from pretrained classification models generated poor performance in small categories because they were lacking in spatial information and 2) the gap of combination between high-level and low-level features in segmentation models has led to inaccurate predictions. To handle these challenges, in this paper, we proposed a new tailored backbone and attention select module for segmentation tasks. Specifically, our new backbone was modified from the original Resnet, which can yield better segmentation performance. Attention select module employed spatial and channel self-attention mechanism to reinforce the propagation of contextual features, which can aggregate semantic and spatial information simultaneously. In addition, based on our new backbone and attention select module, we further proposed our segmentation model for street scenes understanding. We conducted a series of ablation studies on two public benchmarks, including Cityscapes and CamVid dataset to demonstrate the effectiveness of our proposals. Our model achieved a mIoU score of 71.5% on the Cityscapes test set with only fine annotation data and 60.1% on the CamVid test set.


title: A survey on SQL injection attacks, detection and prevention techniques

abstract: SQL Injection poses a serious security issue over the Internet or over web application. In SQL injection attacks, hackers can take advantage of poorly coded Web application software to introduce malicious code into the organization's systems and network. The vulnerability exists when a Web application do not properly filter or validate the entered data by a user on a Web page. Large Web applications have hundreds of places where users can input data, each of which can provide a SQL injection opportunity. Attacker can steal confidential data of the organization with these attacks resulting loss of market value of the organization. This paper presents an effective survey of SQL Injection attack, detection and prevention techniques.


title: DeeSIL: Deep-Shallow Incremental Learning

abstract: Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces catastrophic forgetting and delays related to the retraining needed in order to incorpo- rate new classes. Here we introduce DeeSIL, an adaptation of a known transfer learning scheme that combines a fixed deep representation used as feature extractor and learning independent shallow classifiers to in- crease recognition capacity. This scheme tackles the two aforementioned challenges since it works well with a limited memory budget and each new concept can be added within a minute. Moreover, since no deep re- training is needed when the model is incremented, DeeSIL can integrate larger amounts of initial data that provide more transferable features. Performance is evaluated on ImageNet LSVRC 2012 against three state of the art algorithms. Results show that, at scale, DeeSIL performance is 23 and 33 points higher than the best baseline when using the same and more initial data respectively.


title: Effect of Electrode Impedance on the Transient Response of ECG Recording Amplifiers

abstract: The transient response of ECG amplifiers is investigated in the light of the IEC 60601 performance standard for ECG recording equipment. Simulations carried out using models of several modern electrodes reveal that the undershoot limit of 100 μV and the recovery slope limit of 300 μVs-1 in response to a narrow 3 mV–100 ms rectangular pulse are violated when the recommended input impedance of 10 MΩ and a high-pass cut-off frequency of 0.05 Hz are used in the amplifier. Signal distortions have been observed which have clinical diagnostic implications. The results suggest that an amplmer input impedance exceeding 1 GΩ and a cut-off frequency somewhat lower than 0.05 Hz are necessary in order to meet the IEC 60601 performance criteria and avoid distortion of the recorded ECG signal. The authors therefore recommend that future revisions and releases of the IEC 60601 standard should include an electrical model for the skin-electrode interface in the transient response specification.


title: A Novel Layer Jamming Mechanism With Tunable Stiffness Capability for Minimally Invasive Surgery

abstract: This paper presents a novel “layer jamming” mechanism that can achieve variable stiffness. The layer jamming mechanism exploits the friction present between layers of thin material, which can be controlled by a confining pressure. Due to the mechanism's hollow geometry, compact size, and light weight, it is well suited for various minimally invasive surgery applications, where stiffness change is required. This paper describes the concept, the mathematical model, and a tubular snake-like manipulator prototype. Various characteristics of layer jamming, such as stiffness and yield strength, are studied both theoretically and experimentally.


title: Cellular and molecular mechanisms of hepatocellular carcinoma: an update

abstract: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor that accounts for ~80 % of all liver cancer cases worldwide. It is a multifactorial disease caused by a variety of risk factors and often develops in the background of underlying cirrhosis. A number of cellular phenomena, such as tumor microenvironment, inflammation, oxidative stress, and hypoxia act in concert with various molecular events to facilitate tumor initiation, progression, and metastasis. The emergence of microRNAs and molecular-targeted therapies adds a new dimension in our efforts to combat this deadly disease. Intense research in this multitude of areas has led to significant progress in our understanding of cellular processes and molecular mechanisms that occur during multistage events that lead to hepatocarcinogenesis. In this review, we discuss the current knowledge of HCC, focusing mainly on advances that have occurred during the past 5 years and on the development of novel therapeutics for liver cancer.


title: Object Tracking via Partial Least Squares Analysis

abstract: We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.


title: Computational Capabilities of Graph Neural Networks

abstract: In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G, n) isin R m that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.


title: Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning

abstract: Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Nevertheless, when training data is scarce their performance is significantly degraded due to overfitting. In this paper, we mit- igate overfitting with a data-dependent regularization technique that relies on the principles of multi-task learning. Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task. The latter acts as an inductive bias, leading to solutions that generalize better. The proposed model outperforms the existing autoencoder-based models designed for matrix completion, achieving high reconstruction accuracy in well-known datasets.


title: The Privacy Advocate: Assertion of Privacy by Personalised Contracts

abstract: Privacy has been a hot topic in research for several years. A lot of different approaches to protect privacy have been proposed recently. Among these there are several tools for negotiation of privacy contracts. In this paper we present our privacy negotiation framework called “The Privacy Advocate”. It consists of three main parts: the policy evaluation unit, the signature unit and the preferences. In addition, our framework supports an interface for negotiation strategies, so that they are independent of the framework. The preferences can be expressed with a combination of P3P and APPEL. The tests we executed using a state of the art PDA clearly indicate that our framework can be used on mobile devices. The completion of a successful negotiation usually takes about 2 sec. including message transfer via an 802.11b wireless link. This involves multiple evaluations of proposals. Each of them is done in less than 250 msec.


title: Ontology-Based Security Problem Definition and Solution for the Common Criteria Compliant Development Process

abstract: The paper shows how to apply a new ontology-based approach to the security problem definition (SPD), which is the key stage of the IT security development process compliant with the ISO/IEC 15408 Common Criteria standard. The SPD specifies threats, security policies and assumptions concerning the developed target of evaluation (TOE). On the SPD basis the security objectives (SO) are elaborated expressing the SPD problem solution, being the basis for further implementation works. The paper presents shortly the Specification Means Ontology (SMO), the related knowledge base and their use by the IT security developers while the security problem is formulated and solved. The paper gives some examples concerning a simple firewall, summarizes the results and experiences, and defines the plans of future works.


On Road Defects Detection and Classification


title: Activity theory as a potential framework for Human-Computer Interaction research

abstract: During the recent years the mainstream framework for HCI research — the informationprocessing cognitive psychology —has gained more and more criticism because of serious problems in applying it both in research and practical design. In a debate within HCI research the capability of information processing psychology has been questioned and new theoretical frameworks searched. This paper presents an overview of the situation and discusses potentials of Activity Theory as an alternative framework for HCI research and design.


title: Role of hope in academic and sport achievement.

abstract: Hope is the sum of goal thoughts as tapped by pathways and agency. Pathways reflect the perceived capability to produce goal routes; agency reflects the perception that one can initiate action along these pathways. Using trait and state hope scales, studies explored hope in college student athletes. In Study 1, male and female athletes were higher in trait hope than nonathletes; moreover, hope significantly predicted semester grade averages beyond cumulative grade point average and overall self-worth. In Study 2, with female cross-country athletes, trait hope predicted athletic outcomes; further, weekly state hope tended to predict athletic outcomes beyond dispositional hope, training, and self-esteem, confidence, and mood. In Study 3, with female track athletes, dispositional hope significantly predicted athletic outcomes beyond variance related to athletic abilities and affectivity; moreover, athletes had higher hope than nonathletes. A typical dictionary definition of hope as "a desire and the confident expectation of its fulfillment" captures one of the fundamental reasons that humans enjoy sporting activities (Osterhoudt, 1978). From the First Olympiad in 776 B.C. to neighborhood basketball games to next year's Super Bowl, the athletes' hopes for desired sport goals are pivotal to understanding these activities. Nevertheless, the role of hope has remained unexplored among personality researchers interested in individual differences in motivation, as well as by sport psychologists, largely because a theoretical model of hope and the associated measurement instruments have not been available. Accordingly, on the basis of the recent development of a theory of hope and measurement indices related to this construct, the present series of studies was performed to provide initial information about the role of hope in the academic and sport achievements of college students. Previously, scholarly writings have defined hope as a unidimensional construct involving an overall perception that goals can be met (French, 1952; Lewin, 1935; Stotland, 1969). Expanding on this unidimensional model, Snyder and his colleagues (Snyder, 1994a, 1994b; Snyder, Harris, et al., 1991;


title: An Interleaved EPE-Immune PA-DPL Structure for Resisting Concentrated EM Side Channel Attacks on FPGA Implementation

abstract: Early propagation effect (EPE) is a critical problem in conventional dual-rail logic implementations against Side Channel Attacks (SCAs). Among previous EPE-resistant architectures, PA-DPL logic offers EPE-free capability at relatively low cost. However, its separate dual core structure is a weakness when facing concentrated EM attacks where a tiny EM probe can be precisely positioned closer to one of the two cores. In this paper, we present an PA-DPL dual-core interleaved structure to strengthen resistance against sophisticated EM attacks on Xilinx FPGA implementations. The main merit of the proposed structure is that every two routing in each signal pair are kept identical even the dual cores are interleaved together. By minimizing the distance between the complementary routings and instances of both cores, even the concentrated EM measurement cannot easily distinguish the minor EM field unbalance. In PA-DPL, EPE is avoided by compressing the evaluation phase to a small portion of the clock period, therefore, the speed is inevitably limited. Regarding this, we made an improvement to extend the duty cycle of evaluation phase to more than 40 percent, yielding a larger maximum working frequency. The detailed design flow is also presented. We validate the security improvement against EM attack by implementing a simplified AES co-processor in Virtex-5 FPGA.


title: Deep Learning for Imbalanced Multimedia Data Classification

abstract: Classification of imbalanced data is an important research problem as lots of real-world data sets have skewed class distributions in which the majority of data instances (examples) belong to one class and far fewer instances belong to others. While in many applications, the minority instances actually represent the concept of interest (e.g., fraud in banking operations, abnormal cell in medical data, etc.), a classifier induced from an imbalanced data set is more likely to be biased towards the majority class and show very poor classification accuracy on the minority class. Despite extensive research efforts, imbalanced data classification remains one of the most challenging problems in data mining and machine learning, especially for multimedia data. To tackle this challenge, in this paper, we propose an extended deep learning approach to achieve promising performance in classifying skewed multimedia data sets. Specifically, we investigate the integration of bootstrapping methods and a state-of-the-art deep learning approach, Convolutional Neural Networks (CNNs), with extensive empirical studies. Considering the fact that deep learning approaches such as CNNs are usually computationally expensive, we propose to feed low-level features to CNNs and prove its feasibility in achieving promising performance while saving a lot of training time. The experimental results show the effectiveness of our framework in classifying severely imbalanced data in the TRECVID data set.


title: Image Sensors and Signal Processing for Digital Still Cameras

abstract: Preface DIGITAL STILL CAMERAS AT A GLANCE Kenji Toyoda What Is a Digital Still Camera? History of Digital Still Cameras Variations of Digital Still Cameras Basic Structure of Digital Still Cameras Applications of Digital Still Cameras OPTICS IN DIGITAL STILL CAMERAS Takeshi Koyama Optical System Fundamentals and Standards for Evaluating Optical Performance Characteristics of DSC Imaging Optics Important Aspects of Imaging Optics Design for DSCs DSC Imaging Lens Zoom Types and Their Applications Conclusion References BASICS OF IMAGE SENSORS Junichi Nakamura Functions of an Image Sensor Photodetector in a Pixel Noise Photoconversion Characteristics Array Performance Optical Format and Pixel Size CCD Image Sensor vs. CMOS Image Sensor References CCD IMAGE SENSORS Tetsuo Yamada Basics of CCDs Structures and Characteristics of CCD Image Sensor DSC Applications Future Prospects References CMOS IMAGE SENSORS Isao Takayanagi Introduction to CMOS Image Sensors CMOS Active Pixel Technology Signal Processing and Noise Behavior CMOS Image Sensors for DSC Applications Future Prospects of CMOS Image Sensors for DSC Applications References EVALUATION OF IMAGE SENSORS Toyokazu Mizoguchi What is Evaluation of Image Sensors? Evaluation Environment Evaluation Methods COLOR THEORY AND ITS APPLICATION TO DIGITAL STILL CAMERAS Po-Chieh Hung Color Theory Camera Spectral Sensitivity Characterization of a Camera White Balance Conversion for Display (Color Management) Summary References IMAGE-PROCESSING ALGORITHMS Kazuhiro Sato Basic Image-Processing Algorithms Camera Control Algorithm Advanced Image Processing: How to Obtain Improved Image Quality References IMAGE-PROCESSING ENGINES Seiichiro Watanabe Key Characteristics of an Image-Processing Engine Imaging Engine Architecture Comparison Analog Front End (AFE) Digital Back End (DBE) Future Design Engines References EVALUATION OF IMAGE QUALITY Hideaki Yoshida What is Image Quality? General Items or Parameters Detailed Items or Factors Standards Relating to Image Quality SOME THOUGHTS ON FUTURE DIGITAL STILL CAMERAS Eric R. Fossum The Future of DSC Image Sensors Some Future Digital Cameras References


title: Refund Attacks on Bitcoin’s Payment Protocol

abstract: BIP70 is a community-accepted Payment Protocol standard that governs how merchants and customers perform payments in Bitcoin. This standard is supported by most major wallets and the two dominant Payment Processors: Coinbase and BitPay, who collectively provide the infrastructure for accepting Bitcoin as a form of payment to more than 100,000 merchants. In this paper, we present new attacks on the Payment Protocol, which affect all BIP70 merchants. The Silkroad Trader attack highlights an authentication vulnerability in the Payment Protocol while the Marketplace Trader attack exploits the refund policies of existing Payment Processors. Both attacks have been experimentally verified on real-life merchants using a modified Bitcoin wallet. The attacks have been acknowledged by both Coinbase and Bitpay with temporary mitigation measures put in place. However, to fully address the identified issues will require revising the BIP70 standard. We present a concrete proposal to revise BIP70 by providing the merchant with publicly verifiable evidence to prevent both attacks.


title: Charitable Giving Around the World: Thoughts on How to Expand the Pie

abstract: One fact that has emerged in modern societies is that people help others. Whether it is donating a few dollars to help feed the poor or volunteering time to help rebuild someone's life after a natural disaster, people around the globe commonly lend a hand. This study provides an overview of that support, summarizing gifts of both time and money around the globe. We also highlight research that indicates useful ways in which we can enhance the charitable pie. Our discussion revolves around both individual giving and corporate philanthropy, but we focus on empirical insights from recent charitable fundraising field experiments in the Western World. We present information that is useful for policymakers, fundraising practitioners, and academicians.


title: A practical guide for using statistical tests to assess randomized algorithms in software engineering

abstract: Randomized algorithms have been used to successfully address many different types of software engineering problems. This type of algorithms employ a degree of randomness as part of their logic. Randomized algorithms are useful for difficult problems where a precise solution cannot be derived in a deterministic way within reasonable time. However, randomized algorithms produce different results on every run when applied to the same problem instance. It is hence important to assess the effectiveness of randomized algorithms by collecting data from a large enough number of runs. The use of rigorous statistical tests is then essential to provide support to the conclusions derived by analyzing such data. In this paper, we provide a systematic review of the use of randomized algorithms in selected software engineering venues in 2009. Its goal is not to perform a complete survey but to get a representative snapshot of current practice in software engineering research. We show that randomized algorithms are used in a significant percentage of papers but that, in most cases, randomness is not properly accounted for. This casts doubts on the validity of most empirical results assessing randomized algorithms. There are numerous statistical tests, based on different assumptions, and it is not always clear when and how to use these tests. We hence provide practical guidelines to support empirical research on randomized algorithms in software engineering


title: Protecting IMSI and User Privacy in 5G Networks

abstract: In recent years, many cases of compromising users' privacy in telecom networks have been reported. Stories of "fake" base stations capable of tracking users and collecting their personal data without users' knowledge have emerged. The current way of protecting privacy does not provide any protection against an active attacker on the air-interface, claiming to be a legitimate network that has lost the temporary identity. Moreover, there is also no protection against passive eavesdroppers who are present when requests for International Mobile Subscriber Identity (IMSI) are made. This paper presents a new method for protecting the IMSI by means of establishing a pseudonym between the user equipment and the home network. The pseudonym is derived locally at the user equipment and the home network without affecting existing Universal Subscriber Identity Modules (USIMs). We analyse the solution from a technical perspective, as well as from a regulatory and operational perspective. The presented method protects the IMSI from passive and active IMSI-catchers as well as honest but curious serving networks. Moreover, it can recover from lock-out situations where one party has lost the pseudonym.


title: An INS Monitor to Detect GNSS Spoofers Capable of Tracking Vehicle Position

abstract: In this paper, we describe and evaluate a new monitor that uses inertial navigation system (INS) measurements to detect spoofing attacks on global navigation satellite system (GNSS) receivers. Spoofing detection is accomplished by monitoring the Kalman filter innovations in a tightly coupled INS/GNSS mechanization. Monitor performance is evaluated against worst case spoofing attacks, including spoofers capable of tracking vehicle position. There are two main contributions of this paper. The first is a mathematical framework to quantify postmonitor spoofing integrity risk. The second is an analytical expression of the worst case sequence of spoofed GNSS signals. We then apply these to an example spoofing attack on a Boeing 747 on final approach. The results show that GNSS spoofing is easily detected, with high integrity, unless the spoofer's position-tracking devices have unrealistic, near-perfect accuracy, and no delays.


THE REGULATION OF BODY TEMPERATURE


title: Steroid-responsive paraneoplastic demyelinating neuropathy and myelopathy associated with breast carcinoma.

abstract: Paraneoplastic myeloneuropathy has rarely been reported with breast cancer. We report the case of a 59-year-old woman who presented with a peripheral neuropathy and cranial involvement and later developed a myelopathy. The neuropathy was found to be electrophysiologically and histologically demyelinating in nature. Magnetic resonance imaging studies failed to identify any structural brain or spinal cord abnormalities. The patient was diagnosed with breast carcinoma 4 months after initial presentation and underwent resective surgery, radiotherapy, and hormonotherapy. Paraneoplastic antibodies (anti-Hu, anti-Yo, anti-Ri, anti-CV2, anti-Ma, and anti-amphiphysin) were all negative. Her condition did not progress further after cancer treatment. Partial neurologic improvement occurred with oral steroid therapy, with subsequent deterioration on treatment withdrawal.


title: Automatic Question Generation using Discourse Cues

abstract: In this paper, we present a system that automatically generates questions from natural language text using discourse connectives. We explore the usefulness of the discourse connectives for Question Generation (QG) that looks at the problem beyond sentence level. Our work divides the QG task into content selection and question formation. Content selection consists of finding the relevant part in text to frame question from while question formation involves sense disambiguation of the discourse connectives, identification of question type and applying syntactic transformations on the content. The system is evaluated manually for syntactic and semantic correctness.


title: A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

abstract: Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF), and multivariate Gaussian distribution (MGD) is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.


title: A meta-analysis of after-school programs that seek to promote personal and social skills in children and adolescents.

abstract: A meta-analysis of after-school programs that seek to enhance the personal and social skills of children and adolescents indicated that, compared to controls, participants demonstrated significant increases in their self-perceptions and bonding to school, positive social behaviors, school grades and levels of academic achievement, and significant reductions in problem behaviors. The presence of four recommended practices associated with previously effective skill training (SAFE: sequenced, active, focused, and explicit) moderated several program outcomes. One important implication of current findings is that ASPs should contain components to foster the personal and social skills of youth because youth can benefit in multiple ways if these components are offered. The second implication is that further research is warranted on identifying program characteristics that can help us understand why some programs are more successful than others.


title: An Improved Low-Voltage Ride-Through Control Strategy of Doubly Fed Induction Generator During Grid Faults

abstract: This paper presents a control strategy to improve the low-voltage ride-through capability of a doubly fed induction generator (DFIG); since the stator of a DFIG is directly connected to a grid, this sort of machine is very sensitive to grid disturbance. Grid voltage sag causes overcurrents and overvoltages in rotor windings, which can damage the rotor-side converter (RSC). In order to protect the RSC, a classical solution based on installation of the so-called crowbar is adopted; however, as the DFIG absorbs reactive power from the grid, this type of solution deteriorates grid voltage sags and cannot meet the requirements of a new grid code. An improved control strategy which uses virtual resistance to limit rotor side overcurrents is proposed in this paper, which can make a crowbar inactive and supply reactive power to fulfill the latest grid code requirement during voltage sags. In order to validate the proposed strategy, simulations and experiments have been carried out, and the results demonstrate the effectiveness of the proposed strategy.


title: 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

abstract: With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.


System Dynamics and Uncertainty


title: Exploring neural transducers for end-to-end speech recognition

abstract: In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue - RNNTransducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models - when all encoder layers are forward only, and when encoders downsample the input representation aggressively.


title: A method for automatic stock trading combining technical analysis and nearest neighbor classification

abstract: In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.


title: What else do college students "do" while studying? An investigation of multitasking

abstract: Abstract We investigated the frequency and duration of distractions and media multitasking among college students engaged in a 3-h solitary study/homework session. Participant distractions were assessed with three different kinds of apparatus with increasing levels of potential intrusiveness: remote surveillance cameras, a head-mounted point-of-view video camera, and a mobile eyetracker. No evidence was obtained to indicate that method of assessment impacted multitasking behaviors. On average, students spent 73 min of the session listening to music while studying. In addition, students engaged with an average of 35 distractions of 6 s or longer over the course of 3 h, with an aggregated mean duration of 25 min. Higher homework task motivation and self-efficacy to concentrate on homework were associated with less frequent and shorter duration multitasking behaviors, while greater negative affect was linked to longer duration multitasking behaviors during the session. We discuss the implications of these data for assessment and for understanding the nature of distractions and media multitasking during solitary studying.


title: Application of linear matrix inequalities for load frequency control with communication delays

abstract: Load frequency control has been used for decades in power systems. Traditionally, this has been a centralized control by area with communication over a dedicated and closed network. New regulatory guidelines allow for competitive markets to supply this load frequency control. In order to allow an effective market operation, an open communication infrastructure is needed to support an increasing complex system of controls. While such a system has great advantage in terms of cost and reliability, the possibility of communication signal delays and other problems must be carefully analyzed. This paper presents a load frequency control method based on linear matrix inequalities. The primary aim is to find a robust controller that can ensure good performance despite indeterminate delays and other problems in the communication network.


title: Building a targeted mobile advertising system for location-based services

abstract: Over the years, mobile advertising has grown to become a technology that allows an advertiser to promote products or services to targeted users efficiently and effectively. This is because the ubiquitous nature of mobile devices can provide contextual information and allow users to demonstrate preferences. This study proposes a targeted mobile advertising system (TMAS) that works as a platform to provide both merchants and consumers with context-aware advertisements. The approach integrates the advantages of both mobile and targeted advertising to allow merchants to disseminate location-based targeted advertisements while providing pull-type and personalized advertisements for consumers. To demonstrate the TMAS, we build a platform to provide highly relevant advertising to consumers and to guarantee that advertisements have an equal opportunity of being presented to consumers.


title: A Fully Convolutional Neural Network for Speech Enhancement

abstract: In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a `mapping' between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using fully Convolutional Neural Networks, which consist of lesser number of parameters than fully connected networks. The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.


title: Research Note - How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use

abstract: The five-factor model (FFM) of personality has been used to great effect in management and psychology research to predict attitudes, cognitions, and behaviors, but has largely been ignored in the IS field. We demonstrate the potential utility of incorporating this model into IS research by using the FFM personality factors in the context of technology acceptance. We propose a dispositional perspective to understanding user attitudes and beliefs, and examine the effect of user personality---captured using the FFM's big five factors---on both the perceived usefulness of and subjective norms toward the acceptance and use of technology. Using logged usage data from 180 new users of a collaborative technology, we found general support for our hypotheses that the FFM personality dimensions can be useful predictors of users' attitudes and beliefs. We also found strong support for the relationships between intention to use and system use.


title: Can artificial intelligence benefit from quantum computing?

abstract: In this article, we will try to present from an academic point of view some of the relevant characteristics of both artificial intelligence (AI) and quantum computing (QC) in order to explore the possibility of a meaningful cooperation between both areas of computer science (CS). The quantum part of this paper is based on “The Quantum Circuit Model” which, in the opinion of the author, could be easier understood by computer scientists and/or artificial intelligence researchers, rather than other approaches such as, for example, “Adiabatic Quantum Computation”. Many fundamental questions will arise along this paper for which we will need to give an answer in order to analyze the basic principles that allow researchers and engineers to put AI and QC working together. With this in mind, we will briefly describe the behavior of the biological brain focusing on the identification of the singularities that allow them to perform in such an efficient manner. Energy consumption and parallelism are in the core of the above-mentioned efficiency. Then we will present some of the well-established artificial intelligence approaches that are potentially related to the operation of biological brains. After identifying common characteristics we will introduce basic concepts and issues related to the so-called reversible computing and QC that may eventually help to increase the efficiency of our current intelligent systems. In this respect, we will pay special attention to ‘speed’ and ‘energy consumption’. Some examples, as well as an outline of algorithms from both quantum computing and artificial intelligence, will be used to illustrate the ideas presented in the paper. We conclude with a discussion about what can we expect from the cooperation between both, apparently unconnected fields of computer science, artificial intelligence and quantum computing.


title: Using Social Media to Enhance Emergency Situation Awareness

abstract: Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.


title: Context-Aware Route Planning

abstract: In context-aware route planning, there is a set of transportation agents each with a start and destination location on a shared infrastructure. Each agent wants to find a shortest-time route plan without colliding with any of the other agents, or ending up in a deadlock situation. We present a single-agent route planning algorithm that is both optimal and conflict-free. We also present a set of experiments that compare our algorithm to finding a conflict-free schedule along a fixed path. In particular, we will compare our algorithm to the approach where the shortest conflict-free schedule is chosen along one of k shortest paths. Although neither approach can guarantee optimality with regard to the total set of agent route plans -- and indeed examples can be constructed to show that either approach can outperform the other -- our experiments show that our approach consistently outperforms fixed-path scheduling.


title: Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach

abstract: Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975±0.006 and a mean absolute surface distance error of 0.84±0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.


title: Tiled-MapReduce: Efficient and Flexible MapReduce Processing on Multicore with Tiling

abstract: The prevalence of chip multiprocessors opens opportunities of running data-parallel applications originally in clusters on a single machine with many cores. MapReduce, a simple and elegant programming model to program large-scale clusters, has recently been shown a promising alternative to harness the multicore platform. ::: The differences such as memory hierarchy and communication patterns between clusters and multicore platforms raise new challenges to design and implement an efficient MapReduce system on multicore. This article argues that it is more efficient for MapReduce to iteratively process small chunks of data in turn than processing a large chunk of data at a time on shared memory multicore platforms. Based on the argument, we extend the general MapReduce programming model with a “tiling strategy”, called Tiled-MapReduce (TMR). TMR partitions a large MapReduce job into a number of small subjobs and iteratively processes one subjob at a time with efficient use of resources; TMR finally merges the results of all subjobs for output. Based on Tiled-MapReduce, we design and implement several optimizing techniques targeting multicore, including the reuse of the input buffer among subjobs, a NUCA/NUMA-aware scheduler, and pipelining a subjob’s reduce phase with the successive subjob’s map phase, to optimize the memory, cache, and CPU resources accordingly. Further, we demonstrate that Tiled-MapReduce supports fine-grained fault tolerance and enables several usage scenarios such as online and incremental computing on multicore machines. ::: Performance evaluation with our prototype system called Ostrich on a 48-core machine shows that Ostrich saves up to 87.6% memory, causes less cache misses, and makes more efficient use of CPU cores, resulting in a speedup ranging from 1.86x to 3.07x over Phoenix. Ostrich also efficiently supports fine-grained fault tolerance, online, and incremental computing with small performance penalty.


title: Improved novel view synthesis from depth image with large baseline

abstract: In this paper, a new algorithm is developed for recovering the large disocclusion regions in depth image based rendering (DIBR) systems on 3DTV. For the DIBR systems, undesirable artifacts occur in the disocclusion regions by using the conventional view synthesis techniques especially with large baseline. Three techniques are proposed to improve the view synthesis results. The first is the preprocessing of the depth image by using the bilateral filter, which helps to sharpen the discontinuous depth changes as well as to smooth the neighboring depth of similar color, thus restraining noises from appearing on the warped images. Secondly, on the warped image of a new viewpoint, we fill the disocclusion regions on the depth image with the background depth levels to preserve the depth structure. For the color image, we propose the depth-guided exemplar-based image inpainting that combines the structural strengths of the color gradient to preserve the image structure in the restored regions. Finally, a trilateral filter, which simultaneous combines the spatial location, the color intensity, and the depth information to determine the weighting, is applied to enhance the image synthesis results. Experimental results are shown to demonstrate the superior performance of the proposed novel view synthesis algorithm compared to the traditional methods.


title: Security Enhancements for Mobile Ad Hoc Networks With Trust Management Using Uncertain Reasoning

abstract: The distinctive features of mobile ad hoc net- works (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vul- nerabilities. In this paper, using recent advances in uncertain rea- soning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observa- tion and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.


ploring new graduate nurse confidence in interprofessional llaboration : A mixed methods study thryn


title: K-means clustering of proportional data using L1 distance

abstract: We present a new L1-distance-based k-means clustering algorithm to address the challenge of clustering high-dimensional proportional vectors. The new algorithm explicitly incorporates proportionality constraints in the computation of the cluster centroids, resulting in reduced L1 error rates. We compare the new method to two competing methods, an approximate L1-distance k-means algorithm, where the centroid is estimated using cluster means, and a median L1 k-means algorithm, where the centroid is estimated using cluster medians, with proportionality constraints imposed by normalization in a second step. Application to clustering of projects based on distribution of labor hours by skill illustrates the advantages of the new algorithm.


title: A 64 GHz 2 Gbps transmit/receive phased-array communication link in SiGe with 300 meter coverage

abstract: This paper presents a 64 GHz transmit/receive communication link between two 32-element SiGe-based phased arrays. The antenna element is a series-fed patch array, which provides directivity in the elevation plane. The transmit array results in an EIRP of 42 dBm, while the receive array provides an electronic gain of 33 dB and a system NF 4 Gbps data rate at 100 meters, and ∼ 500 Mbps data rate at 800 meters.


The digital transformation of research support


title: Predicting School Failure and Dropout by Using Data Mining Techniques

abstract: This paper proposes to apply data mining techniques to predict school failure and dropout. We use real data on 670 middle-school students from Zacatecas, Mexico, and employ white-box classification methods, such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification. The outcomes have been compared and the models with the best results are shown.


Safety and feasibility of platelet rich fibrin matrix injections for treatment of common urologic conditions


title: Estimating rice growth parameters using X-band scatterometer data

abstract: In this study, we constructed an X-band automatic scatterometer system and analyzed scattering characteristics of paddy rice over the whole period of rice growth from transplanting to harvesting. The backscattering coefficients were calculated from the measured data at incidence angle 45° and full polarization (HH, VV, HV, VH) by applying the radar equation and compared with rice growth data such as plant height, stem number, fresh and dry weight and Leaf Area Index (LAI) that were collected at the same time of each scatterometer measurement. Based on the analysis of the relation between backscattering coefficients at X-band and rice growth parameters, we predicted the rice growth parameters using the radar backscattering data. Relationship between measured and estimated grain dry weight using the X-band backscattering coefficients (VV-polarization) is highly correlated (R2=0.94***). Results from this study show that backscattering coefficients X-band appear effective to estimate rice growth parameters.


title: Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition

abstract: We present a multiple classifier system for model-free tracking. The tasks of detection (finding the object of interest), recognition (distinguishing similar objects in a scene), and tracking (retrieving the object to be tracked) are split into separate classifiers in the spirit of simplifying each classification task. The supervised and semi-supervised classifiers are carefully trained on-line in order to increase adaptivity while limiting accumulation of errors, i.e. drifting. In the experiments, we demonstrate real-time tracking on several challenging sequences, including multi-object tracking of faces, humans, and other objects. We outperform other on-line tracking methods especially in case of occlusions and presence of similar objects.


title: A Kalman Based Hybrid Precoding for Multi-User Millimeter Wave MIMO Systems

abstract: Millimeter-wave communication in the 60-GHz band requires large antenna arrays at both the transmit and receive terminals to achieve beamforming gains, in order to counteract the high pathloss. Fully digital techniques are infeasible with large antenna arrays due to hardware constraints at such frequencies, while purely analog solutions suffer severe performance limitations. Hybrid analog/digital beamforming is a promising solution, especially, when extended to a multi-user scenario. This paper conveys three main contributions: 1) a Kalman-based formulation for hybrid analog/digital precoding in multi-user environment is proposed; 2) an analytical expression of the error between the transmitted and estimated data is formulated, so that the Kalman algorithm at the base station does not require information on the estimated data at the mobile stations, and instead, relies only on the precoding/combining matrix; and 3) an iterative solution is designed for the hybrid precoding scheme with affordable complexity. Simulation results confirm significant improvement of the proposed approach in terms of both bit error rate and spectral efficiency–achieving almost 7 b/s/Hz, at 20 dB with 10 channel paths with respect to the analog-only beamsteering, and almost 1 b/s/Hz with respect to the hybrid minimum mean square error precoding under the same conditions.


title: Attack vulnerability of complex networks

abstract: We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.


title: KNADIA: Enterprise KNowledge Assisted DIAlogue Systems Using Deep Learning

abstract: In this paper we present the design, architecture and implementation of KNADIA, a conversational dialogue system for intra-enterprise use, providing knowledge-assisted question answering and transactional assistance to employees of a large organization. KNADIA has been deployed in production in TCS, a large organization with over 380,000 employees distributed globally; the system is currently supporting a few thousand active users making hundreds of queries per day. We identify, define and distinguish two distinct classes of use-cases: virtual assistance and knowledge synthesis, which we have found to cover a variety of enterprise needs. KNADIA supports both types of conversational agents, with multiple instances of each, while presenting a common digital persona. KNADIA disambiguates which agent should respond to each query. Further, since individual components use deep learning algorithms giving probabilistic outputs, the confidence with which different components answer is often required before answering or acting. The user's dialogue context also needs to be maintained judiciously. Due to these and many other challenges, the overall architecture of KNADIA is non-trivial. We present instances of HR-assistance and technical knowledge synthesis that are in production use in TCS along with accuracy figures and key performance metrics. Finally, we suggest that many elements of our architecture are also generally applicable to other complex deep learning systems.


title: Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

abstract: Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.


title: Polyglot: Distributed Word Representations for Multilingual NLP

abstract: Distributed word representations (word embeddings) have recently contributed to competitive performance in language modeling and several NLP tasks. In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias. We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages. We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish. Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings. We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.


title: Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer

abstract: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to the data, probabilistically erasing the occurrences of the motif thus found, and repeating the process to find successive motifs. The algorithm requires only a set of unaligned sequences and a number specifying the width of the motifs as input. It returns a model of each motif and a threshold which together can be used as a Bayes-optimal classifier for searching for occurrences of the motif in other databases. The algorithm estimates how many times each motif occurs in each sequence in the dataset and outputs an alignment of the occurrences of the motif. The algorithm is capable of discovering several different motifs with differing numbers of occurrences in a single dataset.


title: Hypothalamic‐Pituitary‐Adrenocortical Axis: Neuropsychiatric Aspects

abstract: Evidence of aberrant hypothalamic-pituitary-adrenocortical (HPA) activity in many psychiatric disorders, although not universal, has sparked long-standing interest in HPA hormones as biomarkers of disease or treatment response. HPA activity may be chronically elevated in melancholic depression, panic disorder, obsessive-compulsive disorder, and schizophrenia. The HPA axis may be more reactive to stress in social anxiety disorder and autism spectrum disorders. In contrast, HPA activity is more likely to be low in PTSD and atypical depression. Antidepressants are widely considered to inhibit HPA activity, although inhibition is not unanimously reported in the literature. There is evidence, also uneven, that the mood stabilizers lithium and carbamazepine have the potential to augment HPA measures, while benzodiazepines, atypical antipsychotics, and to some extent, typical antipsychotics have the potential to inhibit HPA activity. Currently, the most reliable use of HPA measures in most disorders is to predict the likelihood of relapse, although changes in HPA activity have also been proposed to play a role in the clinical benefits of psychiatric treatments. Greater attention to patient heterogeneity and more consistent approaches to assessing treatment effects on HPA function may solidify the value of HPA measures in predicting treatment response or developing novel strategies to manage psychiatric disease. © 2014 American Physiological Society. Compr Physiol 4:715-738, 2014.


title: Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays

abstract: We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.


The Effect of Musical Lyrics on Short Term Memory Physiology 435 – Lab 603 – Group 1


title: Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

abstract: Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices. In this paper, we propose a new categorization system for side-channel attacks, which is necessary as side-channel attacks have evolved significantly since their scientific investigations during the smart card era in the 1990s. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive survey of existing attacks and attack strategies provides valuable insights into the evolving field of side-channel attacks, especially when focusing on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.


title: LAVA: Large-Scale Automated Vulnerability Addition

abstract: Work on automating vulnerability discovery has long been hampered by a shortage of ground-truth corpora with which to evaluate tools and techniques. This lack of ground truth prevents authors and users of tools alike from being able to measure such fundamental quantities as miss and false alarm rates. In this paper, we present LAVA, a novel dynamic taint analysis-based technique for producing ground-truth corpora by quickly and automatically injecting large numbers of realistic bugs into program source code. Every LAVA bug is accompanied by an input that triggers it whereas normal inputs are extremely unlikely to do so. These vulnerabilities are synthetic but, we argue, still realistic, in the sense that they are embedded deep within programs and are triggered by real inputs. Using LAVA, we have injected thousands of bugs into eight real-world programs, including bash, tshark, and the GNU coreutils. In a preliminary evaluation, we found that a prominent fuzzer and a symbolic execution-based bug finder were able to locate some but not all LAVA-injected bugs, and that interesting patterns and pathologies were already apparent in their performance. Our work forms the basis of an approach for generating large ground-truth vulnerability corpora on demand, enabling rigorous tool evaluation and providing a high-quality target for tool developers.


title: Adaptive dropout for training deep neural networks

abstract: Recently, it was shown that deep neural networks can perform very well if the activities of hidden units are regularized during learning, e.g, by randomly dropping out 50% of their activities. We describe a method called 'standout' in which a binary belief network is overlaid on a neural network and is used to regularize of its hidden units by selectively setting activities to zero. This 'adaptive dropout network' can be trained jointly with the neural network by approximately computing local expectations of binary dropout variables, computing derivatives using back-propagation, and using stochastic gradient descent. Interestingly, experiments show that the learnt dropout network parameters recapitulate the neural network parameters, suggesting that a good dropout network regularizes activities according to magnitude. When evaluated on the MNIST and NORB datasets, we found that our method achieves lower classification error rates than other feature learning methods, including standard dropout, denoising auto-encoders, and restricted Boltzmann machines. For example, our method achieves 0.80% and 5.8% errors on the MNIST and NORB test sets, which is better than state-of-the-art results obtained using feature learning methods, including those that use convolutional architectures.


title: Being good by doing good: Daily eudaimonic activity and well-being

abstract: Abstract Eudaimonic theories of well-being assert the importance of achieving one’s full potential through engaging in inherently meaningful endeavors. In two daily diary studies, we assessed whether reports of engagement in behaviors representative of eudaimonic theories were associated with well-being. We also examined whether eudaimonic behaviors were more strongly related to well-being than behaviors directed toward obtaining pleasure or material goods. In both studies, eudaimonic behaviors had consistently stronger relations to well-being than hedonic behaviors. Data also provided support for a temporal sequence in which eudaimonic behaviors were related to greater well-being the next day. Overall, our results suggest that “doing good” may be an important avenue by which people create meaningful and satisfying lives.


title: Modeling and control of a tilt tri-rotor airplane

abstract: A helicopter offers the capability of hover, slow forward displacement, vertical take-off and landing while a conventional airplane has the performance of fast forward movement, long reach and superior endurance. The aim of this paper is to present the modelling and control of a tilt tri-rotor UAV's configuration that combines the advantages of both rotary wing and fixed wing vehicle.


title: Metagenes and molecular pattern discovery using matrix factorization

abstract: We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.


title: A survey of design techniques for system-level dynamic power management

abstract: Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited). In this paper, we survey several approaches to system-level dynamic power management. We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption. We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components.


title: Hashed and Hierarchical Timing Wheels: Efficient Data Structures for Implementing a Timer Facility

abstract: The performance of timer algorithms is crucial to many network protocol implementations that use timers for failure recovery and rate control. Conventional algorithms to implement an operating system timer module take O(n) time to start or maintain a timer, where n is the number of outstanding timers: this is expensive for large n. This paper shows that by using a circular buffer or timing wheel, it takes O(1) time to start, stop, and maintain timers within the range of the wheel. Two extensions for larger values of the interval are described. In the first, the timer interval is hashed into a slot on the timing wheel. In the second, a hierarchy of timing wheels with different granularities is used to span a greater range of intervals. The performance of these two schemes and various implementation tradeoffs are discussed. We have used one of our schemes to replace the current BSD UNIX callout and timer facilities. Our new implementation can support thousands of outstanding timers without much overhead. Our timer schemes have also been implemented in other operating systems and network protocol packages.


title: Cloud robotics: architecture, challenges and applications

abstract: We extend the computation and information sharing capabilities of networked robotics by proposing a cloud robotic architecture. The cloud robotic architecture leverages the combination of an ad-hoc cloud formed by machine-to-machine (M2M) communications among participating robots, and an infrastructure cloud enabled by machine-to-cloud (M2C) communications. Cloud robotics utilizes an elastic computing model, in which resources are dynamically allocated from a shared resource pool in the ubiquitous cloud, to support task offloading and information sharing in robotic applications. We propose and evaluate communication protocols, and several elastic computing models to handle different applications. We discuss the technical challenges in computation, communications and security, and illustrate the potential benefits of cloud robotics in different applications.


title: Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by gene regulatory networks Tailored conceptus-maternal communication at implantation

abstract: Interactions between embryo and endometrium at implantation are critical for the progression and the issue of pregnancy. These reciprocal actions involve exchange of paracrine signals that govern implantation and placentation. However, it remains unknown how these interactions between the conceptus and the endometrium are coordinated at the level of an individual pregnancy. Under the hypothesis that gene expression of endometrium is dependent on gene expression of extraembryonic tissues, we performed an integrative analysis of transcriptome profiles of paired conceptuses and endometria obtained from pregnancies initiated by artificial insemination. We quantified strong dependence (|r|>0.95, eFDR<0.01) in transcript abundance of genes expressed in the extraembryonic tissues and genes expressed in the endometrium. The profiles of connectivity revealed distinct co-expression patterns of extraembryonic tissues with caruncular and intercaruncular areas of the endometrium. Notably, a subset of highly co-expressed genes between conceptus (n=229) and caruncular areas of the endometrium (n=218, r>0.9999, eFDR<0.001) revealed a blueprint of gene expression specific to each pregnancy. Functional analyses of genes co-expressed between conceptus and endometrium revealed significantly enriched functional modules with critical contribution for implantation and placentation, including “in utero embryonic development”, “placenta development” and “regulation of transcription”. Functional modules were remarkably specific to caruncular or intercaruncular areas of the endometrium. The quantitative and functional association between genes expressed in conceptus and endometrium emphasize a coordinated communication between these two entities in mammals. To our knowledge, we provide first evidence that implantation in mammalian pregnancy relies on the ability of the conceptus and the endometrium to develop a fine-tuned adaptive response characteristic of each pregnancy.


title: Study on Government Anti-Corruption Based on Game Theory

abstract: It is a well-established fact that corruption is a widespread phenomenon and it is widely acknowledged because of negative impact on economy and society. An important aspect of corruption is that two parties act separately or jointly in order to further their own interests at the expense of society. To strengthen prevent corruption, most of countries have construct special organization. The paper presents a new measure based on introducing game theory as an analytical tool for analyzing the relation between anti-corruption and corruption. Firstly, the paper introduces the corruption situation in China, gives the definition of the game theory and studies government anti-corruption activity through constructing the game theoretic models between anti-corruption and corruption. The relation between supervisor and the anti-corruption will be explained next. A thorough analysis of the mechanism of informant system has been made accordingly in the third part. At last, some suggestions for preventing and fight corruption are put forward.


title: A robust congestion control scheme for fast and reliable dissemination of safety messages in VANETs

abstract: In this paper, we address the beacon congestion issue in Vehicular Ad Hoc Networks (VANETs) due to its devastating impact on the performance of ITS applications. The periodic beacon broadcast may consume a large part of the available bandwidth leading to an increasing number of collisions among MAC frames, especially in case of high vehicular density. This will severely affect the performance of the Intelligent Transportation Systems (ITS) safety based applications that require timely and reliable dissemination of the event-driven warning messages. To deal with this problem, we propose an original solution that consists of three phases as follows; priority assignment to the messages to be transmitted /forwarded according to two different metrics, congestion detection phase, and finally transmit power and beacon transmission rate adjustment to facilitate emergency messages spread within VANETs. Our solution outperforms the existing works since it doesn't alter the performance of the running ITS applications unless a VANET congestion state is detected. Moreover, it ensures that the most critical and nearest dangers are advertised prior to the farther and less damaging events. The simulation results show promising results and validate our solution.


title: Bimodal HCI-related affect recognition

abstract: Perhaps the most fundamental application of affective computing will be Human-Computer Interaction (HCI) in which the computer should have the ability to detect and track the user's affective states, and make corresponding feedback. The human multi-sensor affect system defines the expectation of multimodal affect analyzer. In this paper, we present our efforts toward audio-visual HCI-related affect recognition. With HCI applications in mind, we take into account some special affective states which indicate users' cognitive/motivational states. Facing the fact that a facial expression is influenced by both an affective state and speech content, we apply a smoothing method to extract the information of the affective state from facial features. In our fusion stage, a voting method is applied to combine audio and visual modalities so that the final affect recognition accuracy is greatly improved. We test our bimodal affect recognition approach on 38 subjects with 11 HCI-related affect states. The extensive experimental results show that the average person-dependent affect recognition accuracy is almost 90% for our bimodal fusion.


title: Big Data Analysis: Recommendation System with Hadoop Framework

abstract: Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework. We have implemented Mahout Interfaces for analyzing the data provided by review and rating site for movies.


title: Effects of fermentation on the hemolytic activity and degradation of Camellia oleifera saponins by Lactobacillus crustorum and Bacillus subtilis

abstract: &NA; The saponins, as components of tea seed meal, are undesirable hemolytic components and should be degraded for reducing their hemolytic activity in order to be used in animal feed. In this study, &bgr;‐glucuronidase was verified to be a potent hydrolase of tea seed saponins to reduce their hemolytic activity and a &bgr;‐glucuronidase‐producing Lactobacillus crustorum strain was screened from raw bovine milk. Next, solid‐state fermentation with the isolated L. crustorum and a Bacillus subtilis natto strain, which can produce cellulase and hence improve the fermentation performance of tea seed meal, was carried out for detoxification of tea seed meal. The 50% hemolytic dosage (HD50) value of tea seed saponins was increased from 6.69 to 27.43 &mgr;g mL−1. The results of LC‐MS analysis showed that the percentage of saponin aglycones increased from 30.95 to 84.25% after the fermentation. According to the roles of sugar moieties in hemolytic activity, and the enzymatic hydrolysis characteristics of &bgr;‐glucuronidase, the degradation of tea seed saponins from glucosides to aglycones may contribute to the reduction of hemolytic activity. Therefore, tea seed meal may be used as animal feed after fermentation with the tested saponin‐degrading microbial strains.


title: The hub-and-spoke organization design: an avenue for serving patients well

abstract: The healthcare industry is characterized by intensive, never-ending change occurring on a multitude of fronts. Success in such tumultuous environments requires healthcare providers to be proficient in myriad areas, including the manner in which they organize and deliver services. Less efficient designs drain precious resources and hamper efforts to deliver the best care possible to patients, making it imperative that optimal pathways are identified and pursued. One particular avenue that offers great potential for serving patients efficiently and effectively is known as the hub-and-spoke organization design. The hub-and-spoke organization design is a model which arranges service delivery assets into a network consisting of an anchor establishment (hub) which offers a full array of services, complemented by secondary establishments (spokes) which offer more limited service arrays, routing patients needing more intensive services to the hub for treatment. Hub-and-spoke networks afford many benefits for healthcare providers, but in order to capitalize fully, proper assembly is required. To advance awareness, knowledge, and use of the hub-and-spoke organization design, this article profiles Willis-Knighton Health System’s service delivery network which has utilized the model for over three decades. Among other things, the hub-and-spoke organization design is defined, benefits are stipulated, and applications are discussed, permitting healthcare providers essential insights for the establishment and operation of these networks. The change-rich nature of the healthcare industry places a premium on incorporating advancements that permit health and medical providers to operate as optimally as possible. The hub-and-spoke organization design represents an option that, when deployed correctly, can greatly assist healthcare establishments in their quests to serve patients well.


title: Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

abstract: Liver segmentation is still a challenging task in medical image processing area due to the complexity of the liver's anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.


title: Human-Centric Automation and Optimization for Smart Homes

abstract: A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners —Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community.


title: A Deep Reinforcement Learning Algorithm with Expert Demonstrations and Supervised Loss and its application in Autonomous Driving

abstract: In this paper, we propose a deep reinforcement learning(DRL) algorithm which combines Deep Deterministic Policy Gradient (DDPG) with expert demonstrations and supervised loss for decision making for autonomous driving. Training DRL agent with supervised learning is adopted to accelerate the exploration process and increase the stability. A supervised loss function is introduced in the algorithm to update the actor networks. In addition, reward construction is combined to make the training process more stable and efficient. The proposed algorithm is applied to a popular autonomous driving simulator called TORCS. The experimental results show that the training efficiency and stability are improved by utilizing our algorithm in autonomous driving.


title: Characterizing Humour: An Exploration of Features in Humorous Texts

abstract: This paper investigates the problem of automatic humour recognition, and provides and in-depth analysis of two of the most frequently observed features of humorous text: human-centeredness and negative polarity. Through experiments performed on two collections of humorous texts, we show that these properties of verbal humour are consistent across different data sets.


title: On Operationalizing Syntactic Complexity

abstract: In the recent functional linguistic literature, the notion of syntactic complexity and similar concepts have received considerable attention. Yet, when trying to operationalize syntactic complexity as an independent variable in statistical research designs, it soon emerges that the notion is somewhat underdefined. I will report the results from an experiment comparing three measures of syntactic complexity — node counts, word counts, and a so-called ‘Index of Syntactic Complexity’ — with regard to their accuracy and applicability. While presupposing that node counts are cognitively the most ‘real’ measure, it turns out that the other two measures are near-perfect proxies of the former. I conclude that since node counts are in most cases unreasonably resource demanding to conduct, researchers can feel safe in using the measure that is most economically to conduct, word counts.


title: A cadaveric study on mylohyoid herniation of the sublingual gland

abstract: The purpose of this study was to document the presence of a sublingual gland (SLG) herniating inferiorly through the mylohyoid muscle into the submandibular area. A total of 100 half-heads of 50 adult Korean cadavers were enrolled in this study. The floor of the mouth was dissected from the neck, and mylohyoid muscle patency and position of the sublingual gland were evaluated. Demographic factors of the donor and characteristics of the herniation were evaluated. Herniation was found in 29 (58.0 %) of the 50 cadavers or 42 of the 100 half-heads. Herniation was more frequently observed in females than in males (p = 0.009). However, no laterality was observed. Classifying the location of SLG herniation from the midpoint of the mandible to the hyoid bone into 3 regions, 32 (63 %) of herniations were found in the anterior one-third. No ranula formation was observed. The size and weight of normal glands tended to be larger than those of herniated glands, but no statistical significance was observed. An SLG hernia is a very common condition and is more frequently observed in females. As such, SLG herniation should be considered when a submental neck mass is evaluated.


title: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features

abstract: P lant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products. Automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and classification of plant leaf diseases. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, then the green pixels are masked and removed using specific threshold value followed by segmentation process, the texture statistics are computed for the useful segments, finally the extracted features are passed through the classifier. The proposed algorithm’s efficiency can successfully detect and classify the examined diseases with an accuracy of 94%. Experimental results on a database of about 500 plant leaves confirm the robustness of the proposed approach. Keywords: HSI, c olor c o-occurrence m atrix, t exture, SVM, p lant leaf diseases


title: Improving Morphology Induction by Learning Spelling Rules

abstract: Pathfinding on a map is a fundamental problem in many applications, including robotics and computer games. Typically a grid is superimposed over the map where each cell in the grid forms a unique state. A state-space-based search algorithm, such as A* or IDA*, is then used for finding the optimal (shortest) path. In this paper we analyze the search behavior of both A* and IDA* using different grid representations, providing various new insights via analytical and empirical results.


title: Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery

abstract: Abstract Detailed land use/land cover classification at ecotope level is important for environmental evaluation. In this study, we investigate the possibility of using airborne hyperspectral imagery for the classification of ecotopes. In particular, we assess two tree-based ensemble classification algorithms: Adaboost and Random Forest, based on standard classification accuracy, training time and classification stability. Our results show that Adaboost and Random Forest attain almost the same overall accuracy (close to 70%) with less than 1% difference, and both outperform a neural network classifier (63.7%). Random Forest, however, is faster in training and more stable. Both ensemble classifiers are considered effective in dealing with hyperspectral data. Furthermore, two feature selection methods, the out-of-bag strategy and a wrapper approach feature subset selection using the best-first search method are applied. A majority of bands chosen by both methods concentrate between 1.4 and 1.8 μm at the early shortwave infrared region. Our band subset analyses also include the 22 optimal bands between 0.4 and 2.5 μm suggested in Thenkabail et al. [Thenkabail, P.S., Enclona, E.A., Ashton, M.S., and Van Der Meer, B. (2004). Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, 354–376.] due to similarity of the target classes. All of the three band subsets considered in this study work well with both classifiers as in most cases the overall accuracy dropped only by less than 1%. A subset of 53 bands is created by combining all feature subsets and comparing to using the entire set the overall accuracy is the same with Adaboost, and with Random Forest, a 0.2% improvement. The strategy to use a basket of band selection methods works better. Ecotopes belonging to the tree classes are in general classified better than the grass classes. Small adaptations of the classification scheme are recommended to improve the applicability of remote sensing method for detailed ecotope mapping.


title: Sadism and psychopathy in violent and sexually violent offenders

abstract: A nonrandom sample (N = 41) of inmates from a maximum security prison were classified as either psychopathic or nonpsychopathic (using the Psychopathy Checklist-Revised (PCL-R)) and violent or sexually violent. Sadism was measured using the Millon Clinical Multiaxial Inventory-II (MCMI-II) Scale 6B, the Personality Disorder Examination (PDE) items for sadistic personality disorder, and the sexual sadism criteria of DSM-IV. Psychopaths were found to be significantly more sadistic than nonpsychopaths (MCMI-II and PDE). Overall power was relatively high. Sadism did not differentiate the violent and sexually violent groups. A diagnosis of sexual sadism was too infrequent (n = 3) for meaningful statistical analysis. The trait measures of sadism and psychopathy measures (PCL-R, Factor 1 and Factor 2) significantly and positively correlated. Results provide further empirical validity for the theoretically proposed and clinically observed relationship between sadistic traits and psychopathic personality.


title: Knowledge Discovery and Data Mining: Towards a Unifying Framework

abstract: This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe links between data mining, knowledge discovery, and other related fields. We then define the KDD process and basic data mining algorithms, discuss application issues and conclude with an analysis of challenges facing practitioners in the field.