title: The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

abstract: The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)—an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.


title: Design and deploy a wireless sensor network for precision agriculture

abstract: This paper describes the design, implementation, and deployment of wireless sensor network for precision agriculture. A wireless sensor network was built to use for precision agriculture in which the farmers can monitor and control the agricultural and environmental parameters such as air temperature, air humidity, light, soil moisture, pH, etc. The collected data is stored and transmitted wirelessly to the farmers, which they can use to control and decide appropriate actions for their farm to manage the production and quality. At the hardware side, the system consists of three components: wireless sensor nodes LAU-WSN, wireless sensor management node LAU-WMN, and a server. Software was built at each node to carry out their tasks. At the software side, we proposed a WSN Management Framework for Precision Agriculture, called MFPA, which consists of 3 modules: data collection module, controller module, and data prediction module. The interaction among these modules ensures that user is provided real time environmental and agricultural information which are used to manage the agriculture field. The deployed system has been working properly and promising to bring significant benefits in the agricultural field. Our experimental results show that the data prediction module with dynamic Bayesian network on average can achieve 77.5% and 67.6% accuracy for the temperature prediction, and for the humidity prediction, respectively.


title: Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network

abstract: In this paper, we propose an efficient transfer leaning methods for training a personalized language model using a recurrent neural network with long short-term memory architecture. With our proposed fast transfer learning schemes, a general language model is updated to a personalized language model with a small amount of user data and a limited computing resource. These methods are especially useful for a mobile device environment while the data is prevented from transferring out of the device for privacy purposes. Through experiments on dialogue data in a drama, it is verified that our transfer learning methods have successfully generated the personalized language model, whose output is more similar to the personal language style in both qualitative and quantitative aspects.


title: Automatic Detection of Microcalcification in Mammograms – A Review

abstract: In this review paper, it is intended to summarize and compare the methods of automatic detection of microcalcifications in digitized mammograms used in various stages of the Computer Aided Detection systems (CAD). In particular, the pre processing and enhancement, bilateral subtraction techniques, segmentation algorithms, feature extraction, selection and classification, classifiers, Receiver Operating Characteristic (ROC); Free-response Receiver Operating Characteristic (FROC) analysis and their performances are studied and compared.


title: Accelerating large scale centroid-based clustering with locality sensitive hashing

abstract: Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.


title: Real-time Dense Disparity Estimation based on Multi-Path Viterbi for Intelligent Vehicle Applications.

abstract: This paper proposes a new real-time stereo matching algorithm paired with an online auto-rectification framework. The algorithm treats disparities of stereo images as hidden states and conducts Viterbi process at 4 bi-directional paths to estimate them. Structural similarity, total variation constraint, and a specific hierarchical merging strategy are combined with the Viterbi process to improve the robustness and accuracy. Based on the results of Viterbi, a convex optimization equation is derived to estimate epipolar line distortion. The estimated distortion information is used for the online compensation of Viterbi process at an auto-rectification framework. Extensive experiments were conducted to compare proposed algorithm with other practical state-of-the-art methods for intelligent vehicle applications.


title: What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle

abstract: The emergent machine learning technique—extreme learning machines (ELMs)—has become a hot area of research over the past years, which is attributed to the growing research activities and significant contributions made by numerous researchers around the world. Recently, it has come to our attention that a number of misplaced notions and misunderstandings are being dissipated on the relationships between ELM and some earlier works. This paper wishes to clarify that (1) ELM theories manage to address the open problem which has puzzled the neural networks, machine learning and neuroscience communities for 60 years: whether hidden nodes/neurons need to be tuned in learning, and proved that in contrast to the common knowledge and conventional neural network learning tenets, hidden nodes/neurons do not need to be iteratively tuned in wide types of neural networks and learning models (Fourier series, biological learning, etc.). Unlike ELM theories, none of those earlier works provides theoretical foundations on feedforward neural networks with random hidden nodes; (2) ELM is proposed for both generalized single-hidden-layer feedforward network and multi-hidden-layer feedforward networks (including biological neural networks); (3) homogeneous architecture-based ELM is proposed for feature learning, clustering, regression and (binary/multi-class) classification. (4) Compared to ELM, SVM and LS-SVM tend to provide suboptimal solutions, and SVM and LS-SVM do not consider feature representations in hidden layers of multi-hidden-layer feedforward networks either.


title: Sparse Classifier Fusion for Speaker Verification

abstract: State-of-the-art speaker verification systems take advantage of a number of complementary base classifiers by fusing them to arrive at reliable verification decisions. In speaker verification, fusion is typically implemented as a weighted linear combination of the base classifier scores, where the combination weights are estimated using a logistic regression model. An alternative way for fusion is to use classifier ensemble selection, which can be seen as sparse regularization applied to logistic regression. Even though score fusion has been extensively studied in speaker verification, classifier ensemble selection is much less studied. In this study, we extensively study a sparse classifier fusion on a collection of twelve I4U spectral subsystems on the NIST 2008 and 2010 speaker recognition evaluation (SRE) corpora.


title: StreamCloud: A Large Scale Data Streaming System

abstract: Data streaming has become an important paradigm for the real-time processing of continuous data flows in domains such as finance, telecommunications, networking, Some applications in these domains require to process massive data flows that current technology is unable to manage, that is, streams that, even for a single query operator, require the capacity of potentially many machines. Research efforts on data streaming have mainly focused on scaling in the number of queries or query operators, but overlooked the scalability issue with respect to the stream volume. In this paper, we present StreamCloud a large scale data streaming system for processing large data stream volumes. We focus on how to parallelize continuous queries to obtain a highly scalable data streaming infrastructure. StreamCloud goes beyond the state of the art by using a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. StreamCloud is implemented as a middleware and is highly independent of the underlying data streaming engine. We explore and evaluate different strategies to parallelize data streaming and tackle with the main bottlenecks and overheads to achieve scalability. The paper presents the system design, implementation and a thorough evaluation of the scalability of the fully implemented system.


title: Model robust confidence intervals

abstract: Abstract Confidence intervals are constructed for real-valued parameter estimation in a general regression model with normal errors. When the error variance is known these intervals are optimal (in the sense of minimizing length subject to guaranteed probability of coverage) among all intervals estimates which are centered at a linear estimate of the parameter. When the error variance is unknown and the regression model is an approximately linear model (a class of models which permits bounded systematic departures from an underlying ideal model) then an independent estimate of variance is found and the intervals can then be appropriately scaled.


title: Toward an Affect-Sensitive Multimodal Human-Computer Interaction

abstract: The ability to recognize affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for successful interpersonal social interaction. This paper argues that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence - the ability to recognize a user's affective states-in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI-an automatic personalized analyzer of a user's nonverbal affective feedback.


title: A wireless, inexpensive optical tracker for the CAVE/sup TM/

abstract: CAVE/sup TM/ displays offer many advantages over other virtual reality (VR) displays, including a large, unencumbered viewing space. Unfortunately, the typical tracking subsystems used with CAVE/sup TM/ displays tether the user and lessen this advantage. We have designed a simple, low-cost foot tracker that is wireless, leaving the user free to move. The tracker can be assembled for less than $200 US, and achieves an accuracy of /spl plusmn/10 cm at a 20-Hz sampling rate. We have tested the prototype with two applications: a visualization supporting close visual inspection, and a walkthrough of the campus. Although the tracking was convincing, it was clear that the tracker's limitations make it less than ideal for applications requiring precise visual inspection. However the freedom of motion allowed by the tracker was a compelling supplement to our campus walkthrough, allowing users to stroll and look around corners.


title: Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach

abstract: Video super-resolution (SR) aims at estimating a high-resolution video sequence from a low-resolution (LR) one. Given that the deep learning has been successfully applied to the task of single image SR, which demonstrates the strong capability of neural networks for modeling spatial relation within one single image, the key challenge to conduct video SR is how to efficiently and effectively exploit the temporal dependence among consecutive LR frames other than the spatial relation. However, this remains challenging because the complex motion is difficult to model and can bring detrimental effects if not handled properly. We tackle the problem of learning temporal dynamics from two aspects. First, we propose a temporal adaptive neural network that can adaptively determine the optimal scale of temporal dependence. Inspired by the inception module in GoogLeNet [1] , filters of various temporal scales are applied to the input LR sequence before their responses are adaptively aggregated, in order to fully exploit the temporal relation among the consecutive LR frames. Second, we decrease the complexity of motion among neighboring frames using a spatial alignment network that can be end-to-end trained with the temporal adaptive network and has the merit of increasing the robustness to complex motion and the efficiency compared with the competing image alignment methods. We provide a comprehensive evaluation of the temporal adaptation and the spatial alignment modules. We show that the temporal adaptive design considerably improves the SR quality over its plain counterparts, and the spatial alignment network is able to attain comparable SR performance with the sophisticated optical flow-based approach, but requires a much less running time. Overall, our proposed model with learned temporal dynamics is shown to achieve the state-of-the-art SR results in terms of not only spatial consistency but also the temporal coherence on public video data sets. More information can be found in http://www.ifp.illinois.edu/~dingliu2/videoSR/ .


title: Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor

abstract: The use of the commercial video games as rehabilitation tools, such as the Nintendo WiiFit, has recently gained much interest in the physical therapy arena. Motion tracking controllers such as the Nintendo Wiimote are not sensitive enough to accurately measure performance in all components of balance. Additionally, users can figure out how to "cheat" inaccurate trackers by performing minimal movement (e.g. wrist twisting a Wiimote instead of a full arm swing). Physical rehabilitation requires accurate and appropriate tracking and feedback of performance. To this end, we are developing applications that leverage recent advances in commercial video game technology to provide full-body control of animated virtual characters. A key component of our approach is the use of newly available low cost depth sensing camera technology that provides markerless full-body tracking on a conventional PC. The aim of this research was to develop and assess an interactive game-based rehabilitation tool for balance training of adults with neurological injury.


title: Planetary-Scale Terrain Composition

abstract: Many interrelated planetary height map and surface image map data sets exist, and more data are collected each day. Broad communities of scientists require tools to compose these data interactively and explore them via real-time visualization. While related, these data sets are often unregistered with one another, having different projection, resolution, format, and type. We present a GPU-centric approach to the real-time composition and display of unregistered-but-related planetary-scale data. This approach employs a GPGPU process to tessellate spherical height fields. It uses a render-to-vertex-buffer technique to operate upon polygonal surface meshes in image space, allowing geometry processes to be expressed in terms of image processing. With height and surface map data processing unified in this fashion, a number of powerful composition operations may be uniformly applied to both. Examples include adaptation to nonuniform sampling due to projection, seamless blending of data of disparate resolution or transformation regardless of boundary, and the smooth interpolation of levels of detail in both geometry and imagery. Issues of scalability and precision are addressed, giving out-of-core access to giga-pixel data sources, and correct rendering at scales approaching one meter.


Management of systemic sclerosis.


title: Service Usage Analysis in Mobile Messaging Apps: A Multi-label Multi-view Perspective

abstract: The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced frame-work for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage sub-sequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach.


title: How Does Religion Matter and Why? Religion and the Organizational Sciences

abstract: Religion is becoming increasingly salient in and around, but not confined to, the American workplace. The rise of openly faith-based organizations and discourse surrounding the role and importance of spirituality are just a couple of the indicators that religion, in its various guises, is playing a role in organizational life. With few exceptions, however, scholarly research has sidestepped the issue of religion, and, perhaps unwittingly, discourse surrounding spirituality seems to imply that religion is a benign and positive force. Rather than implicitly or explicitly assuming that religion is a benign, positive force in organizations, in this paper, we suggest that organizational scholars need to rigorously address the potential consequences of religion at work in a dispassionate manner that acknowledges both the benefits/adaptive outcomes and the challenges/maladaptive outcomes. Specifically, adopting primarily a psychological approach, we theorize about two fundamental tensions produced by contemplations about religion and the concept of God at work and the conditions under which benefits versus challenges may prevail. These exemplary tensions, virtuousness versus “more-virtuous-than-thou” and prosociality and ethicality versus egocentrism, highlight the fact that religion has the potential to result in both adaptive and maladaptive outcomes for organizations and their members. Importantly, for each tension, we theorize about the initial conditions under which beneficial/adaptive or challenging/maladaptive outcomes will prevail. We also explore the critical role that the wider context plays in understanding these tensions and how religion affects organizational life.


title: Antecedents and Consequences of Customer – Company Identification: Expanding the Role of Relationship Marketing

abstract: This article presents an empirical test of organizational identification in the context of customer– company (C-C) relationships. It investigates whether customers identify with companies and what the antecedents and consequences of such identification are. The model posits that perceived company characteristics, construed external image, and the perception of the company’s boundary-spanning agent lead to C-C identification. In turn, such identification is expected to impact both in-role behavior (i.e., product utilization) as well as extra-role behavior (i.e., citizenship). The model was tested in a consultative selling context of pharmaceutical sales reps calling on physicians. Results from the empirical test indicated that customers do indeed identify with organizations and that C-C identification positively impacts both product utilization behavior and extra-role behavior even when the effect of brand perception is accounted for. Second, the study found that the organization’s characteristics as well as the salesperson’s characteristics contributed to the development of C-C identification.


title: Lumina: a soft kinetic material for morphing architectural skins and organic user interfaces

abstract: The pervasive computing era has seen sensor and actuator technologies integrated into the design of kinetic building skins. This paper presents an investigation of a new soft kinetic material that has potential applications for morphing architectural building skins and organic user interfaces. The material capacities of Lumina to sense the ambient environment, morph and change forms, and emit light are demonstrated in the two prototypes presented in the paper. The first prototype is Blind, a form-changing organic user interface with multiple eye-like apertures that can be programmed to accept data input for visual communication. The second prototype is Blanket, a responsive morphing architectural skin with minimal mechanical and discrete components that sense real-time space occupancy data, manipulate light effects, perform active illumination, and act as an ambient display.


A TRISOMIC MALE CASTOR BEAN PLANT


Classification of Fingerprint Images


title: Squamous cell carcinoma of the cervix metastatic to the heart

abstract: Abstract An unusual case of squamous cell carcinoma of the cervix in a 33 year old with metastases to the right ventricle is presented. The infrequency of this finding is supported by autopsy data from the literature. The postulated mechanisms of spread, clinical presentations, and diagnostic methods of genital malignancies to the heart are discussed.


title: The simulation of double inverted pendulum control based on particle swarm optimization LQR algorithm

abstract: The application of LQR controller in inverted pendulum system is common, but the choice of weight matrix Q and R of LQR controller has always been a difficult. This paper presents a new method to achieve the double inverted pendulum system LQR controller parameters optimization through the particle swarm optimization algorithm. The simulation result shows that the method used in the choice of matrix Q and R achieves very good results.


title: Lack of Association between Human Plasma Oxytocin and Interpersonal Trust in a Prisoner’s Dilemma Paradigm

abstract: Expanding interest in oxytocin, particularly the role of endogenous oxytocin in human social behavior, has created a pressing need for replication of results and verification of assay methods. In this study, we sought to replicate and extend previous results correlating plasma oxytocin with trust and trustworthy behavior. As a necessary first step, the two most commonly used commercial assays were compared in human plasma via the addition of a known quantity of exogenous oxytocin, with and without sample extraction. Plasma sample extraction was found to be critical in obtaining repeatable concentrations of oxytocin. In the subsequent trust experiment, twelve samples in duplicate, from each of 82 participants, were collected over approximately six hours during the performance of a Prisoner's Dilemma task paradigm that stressed human interpersonal trust. We found no significant relationship between plasma oxytocin concentrations and trusting or trustworthy behavior. In light of these findings, previous published work that used oxytocin immunoassays without sample extraction should be reexamined and future research exploring links between endogenous human oxytocin and trust or social behavior should proceed with careful consideration of methods and appropriate biofluids for analysis.


title: Implementation of an IOT framework for smart healthcare

abstract: It is indeed a fast pace world today. People and technology are both growing at a speed that you can never think of. With work soon becoming priority to many individuals; we tend to neglect the most important factor that helps us survive and conquer the world, our own health. It has always said that Health is Wealth. However, in the recent years people have been spending most of their hard-earned money in Hospitals and medicines. Thus, it proves our negligence towards our health. Not only that, but also many people are moving abroad leaving their old parents in their hometowns and not being able to keep a track of the health of each other. With this thought in our minds, we decided to do something that will make it easier for people to keep a track of their loved one's health as well as their own. With the evolution of internet everything is possible on it, from finding information to tracking locations nearby. Internet has become a savior for the world today. Internet of Things is taking a step ahead in helping the world be even more connected. Therefore, we decided on making an IoT based Health Monitoring System. The whole concept of it is to help people keep a track of their health and allow doctors to reduce the use of paper and maintain a database of their patients on the internet.


title: Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

abstract: Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.


title: Securing digital signatures for non-repudiation

abstract: Dispute of transactions is a common problem that could jeopardise business. Hence non-repudiation services are essential in business transactions which provide evidence to enable dispute resolution. To be eligible as non-repudiation evidence, the digital signature on an electronic document should remain valid until its expiry date which is specified by some non-repudiation policy. The conventional approaches are either inefficient or insecure to achieve non-repudiation in electronic commerce. This article presents a practical scheme to secure digital signatures as non-repudiation evidence with an adjustable degree of risk.


title: Oxidants, oxidative stress and the biology of ageing

abstract: Living in an oxygenated environment has required the evolution of effective cellular strategies to detect and detoxify metabolites of molecular oxygen known as reactive oxygen species. Here we review evidence that the appropriate and inappropriate production of oxidants, together with the ability of organisms to respond to oxidative stress, is intricately connected to ageing and life span.


title: When Perception Is More Than Reality: The Effects of Perceived Versus Actual Resource Depletion on Self-Regulatory Behavior

abstract: Considerable research demonstrates that the depletion of self-regulatory resources impairs performance on subsequent tasks that demand these resources. The current research sought to assess the impact of perceived resource depletion on subsequent task performance at both high and low levels of actual depletion. The authors manipulated perceived resource depletion by having participants 1st complete a depleting or nondepleting task before being presented with feedback that did or did not provide a situational attribution for their internal state. Participants then persisted at a problem-solving task (Experiments 1–2), completed an attention-regulation task (Experiment 3), or responded to a persuasive message (Experiment 4). The findings consistently demonstrated that individuals who perceived themselves as less (vs. more) depleted, whether high or low in actual depletion, were more successful at subsequent self-regulation. Thus, perceived regulatory depletion can impact subsequent task performance—and this impact can be independent of one’s actual state of depletion.


title: Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

abstract: We describe a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is â??rich enoughâ??, a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30 km. We also describe an integrated approach to â??loop-closureâ??, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into â??locationsâ?? defined by visibility constraints, represented in a topological graph, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a previous technical report.


title: Contribution of elastic tissues to the mechanics and energetics of muscle function during movement

abstract: Muscle force production occurs within an environment of tissues that exhibit spring-like behavior, and this elasticity is a critical determinant of muscle performance during locomotion. Muscle force and power output both depend on the speed of contraction, as described by the isotonic force-velocity curve. By influencing the speed of contractile elements, elastic structures can have a profound effect on muscle force, power and work. In very rapid movements, elastic mechanisms can amplify muscle power by storing the work of muscle contraction slowly and releasing it rapidly. When energy must be dissipated rapidly, such as in landing from a jump, energy stored rapidly in elastic elements can be released more slowly to stretch muscle contractile elements, reducing the power input to muscle and possibly protecting it from damage. Elastic mechanisms identified so far rely primarily on in-series tendons, but many structures within muscles exhibit spring-like properties. Actomyosin cross-bridges, actin and myosin filaments, titin, and the connective tissue scaffolding of the extracellular matrix all have the potential to store and recover elastic energy during muscle contraction. The potential contribution of these elements can be assessed from their stiffness and estimates of the strain they undergo during muscle function. Such calculations provide boundaries for the possible roles these springs might play in locomotion, and may help to direct future studies of the uses of elastic elements in muscle.


title: A report: the definition and classification of cerebral palsy April 2006

abstract: For a variety of reasons, the definition and the classification of cerebral palsy (CP) need to be reconsidered. Modern brain imaging techniques have shed new light on the nature of the underlying brain injury and studies on the neurobiology of and pathology associated with brain development have further explored etiologic mechanisms. It is now recognized that assessing the extent of activity restriction is part of CP evaluation and that people without activity restriction should not be included in the CP rubric. Also, previous definitions have not given sufficient prominence to the non-motor neurodevelopmental disabilities of performance and behaviour that commonly accompany CP, nor to the progression of musculoskeletal difficulties that often occurs with advancing age. In order to explore this information, pertinent material was reviewed on July 11-13, 2004 at an international workshop in Bethesda, MD (USA) organized by an Executive Committee and participated in by selected leaders in the preclinical and clinical sciences. At the workshop, it was agreed that the concept 'cerebral palsy' should be retained. Suggestions were made about the content of a revised definition and classification of CP that would meet the needs of clinicians, investigators, health officials, families and the public and would provide a common language for improved communication. Panels organized by the Executive Committee used this information and additional comments from the international community to generate a report on the Definition and Classification of Cerebral Palsy, April 2006. The Executive Committee presents this report with the intent of providing a common conceptualization of CP for use by a broad international audience.


title: OPO: Online public opinion analysis system over text streams

abstract: With the development of internet, users can express their attitudes towards public events on the social media. Monitoring and analyzing the public opinion can provide effective support for government's policy making. In this paper, a novel online public opinion (OPO) analysis platform over multi-source text streams is proposed. This OPO platform contains three layers: data collection layer, data process and analysis layer, application layer. Here, the network public opinion data is acquired by the popular Selenium component, which can dynamically generate content and parse web pages. Specifically, the platform automatically conducts data processing, including filtering texts to remove the spam information and duplicated content based on shingle algorithm, ranking the content with BM25 algorithm, and recommending high quality relevant data for analysis. The platform adopts hot topic detection and extraction technology, called “cluto”, to capture public opinion. Through these processes, OPO can explicitly analyze public opinion and reveal the focus of public on the hot topics. Experiments on social media stream show that OPO outperforms in public opinion analysis and hot topics extraction.


A vision of industry 4 . 0 from an artificial intelligence point of view


title: A fully actuated quadrotor UAV with a propeller tilting mechanism: Modeling and control

abstract: Equipped with four actuators, quadrotor Unmanned Aerial Vehicles belong to the family of underactuated systems. The lateral motion of such platforms is strongly coupled with their orientation and consequently it is not possible to track an arbitrary 6D trajectory in space. In this paper, we propose a novel quadrotor design in which the tilt angles of the propellers with respect to the quadrotor body are being simultaneously controlled with two additional actuators by employing the parallelogram principle. Since the velocity of the controlled tilt angles of the propellers does not appear directly in the derived dynamic model, the system cannot be static feedback linearized. Nevertheless, the system is linearizable at a higher differential order, leading to a dynamic feedback linearization controller. Simulations confirm the theoretical findings, highlighting the improved motion capabilities with respect to standard quadrotors.


Barkley 1 Concentration Deficit Disorder ( Sluggish Cognitive Tempo )


title: Inventory Models for Substitutable Products: Optimal Policies and Heuristics

abstract: In this paper, we examine the nature of optimal inventory policies in a system where a retailer manages substitutable products. We first consider a system with two products 1 and 2 whose total demand is D and individual demands are negatively correlated. A fixed proportion of the unsatisfied customers for an item will purchase the other item if it is available in inventory. For the single-period case, we show that the optimal inventory levels of the two items can be computed easily and follow what we refer to as “partially decoupled” policies, i.e., base stock policies that are not state dependent, in certain critical regions of interest both when D is known and random. Furthermore, we show that such a partially decoupled base-stock policy is optimal even in a multiperiod version of the problem for known D for a wide range of parameter values and in an N-product single-period model under some restrictive conditions. Using a numerical study, we show that heuristics based on the decoupled inventory policy perform well in conditions more general than the ones assumed to obtain the analytical results. The analytical and numerical results suggest that the approach presented here is most valuable in retail settings for product categories where the level of substitution between items in a category is not high, demand variation at the aggregate level is not high, and service levels or newsvendor ratios are high.


title: Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method

abstract: Generating plausible and fluent sentence with desired properties has long been a challenge. Most of the recent works use recurrent neural networks (RNNs) and their variants to predict following words given previous sequence and target label. In this paper, we propose a novel framework to generate constrained sentences via Gibbs Sampling. The candidate sentences are revised and updated iteratively, with sampled new words replacing old ones. Our experiments show the effectiveness of the proposed method to generate plausible and diverse sentences.


title: Virage image search engine: an open framework for image management

abstract: Until recently, the management of large image databases has relied exclusively on manually entered alphanumeric annotations. Systems are beginning to emerge in both the research and commercial sectors based on 'content-based' image retrieval, a technique which explicitly manages image assets by directly representing their visual attributes. The Virage image search engine provides an open framework for building such systems. The Virage engine expresses visual features as image 'primitives.' Primitives can be very general (such as color, shape, or texture) or quite domain specific (face recognition, cancer cell detection, etc.). The basic philosophy underlying this architecture is a transformation from the data-rich representation of explicit image pixels to a compact, semantic-rich representation of visually salient characteristics. In practice, the design of such primitives is non-trivial, and is driven by a number of conflicting real-world constraints (e.g. computation time vs. accuracy). The virage engine provides an open framework for developers to 'plug-in' primitives to solve specific image management problems. The architecture has been designed to support both static images and video in a unified paradigm. The infrastructure provided by the Virage engine can be utilized to address high-level problems as well, such as automatic, unsupervised keyword assignment, or image classification.


title: 3G Mobile multimedia services (MMS) utilization in Indonesia: An exploratory research

abstract: The number of subscribers to 3G mobile networks in Indonesia is around 8.2% of total mobile phone subscribers, which shows a very low number compared with other countries. There exists a huge potential market which is yet to be exploited. To capitalize on this market, however, it is important to know the potential use of the 3G mobile networks in Indonesia as a developing country. One of the potential uses of the 3G mobile networks is 3G mobile multimedia services. To understand the current status and potential of 3G mobile multimedia services use among Indonesians, it is important to explore the reason why some Indonesians have, and others haven't yet, embraced 3G mobile multimedia services, and perceiving these services. In this article, we present key findings of our research that addresses these objectives. Our study reveals that although many respondents aware of the 3G technology, only 47% of respondents use 3G mobile multimedia services. Even those who use 3G mobile multimedia services have perceived only a limited use of 3G mobile multimedia services. The service that they mostly use is the internet access followed by video call and mobile TV. The services such as video email, mobile video, movie preview, and video surveillance are not much used. 53% of respondents do not use 3G mobile multimedia services though some of them have 3G services support mobile phone. The main reasons they cited for non using 3G services are high cost of the services, no compatible hand devices, family and friends do not use 3G mobile multimedia services, and 3G applications and services are not easy to use.


title: A combined Convolutional Neural Network and Dynamic Programming approach for text line normalization

abstract: This work proposes a new normalization algorithm for handwritten text lines based on the use of Convolutional Neural Networks trained to classify pixels of the scanned text line as belonging to the main body area. The reference lines of the text line are obtained from these local estimates by means of Dynamic Programming. The obtained reference lines are used to normalize the text line images. Experimental results on the IAM offline database demonstrates the feasibility of this approach.


title: Restricted Boltzmann Machines and Deep Belief Networks on multi-core processors

abstract: Deep learning architecture models by contrast with shallow models draw on the insights of biological inspiration which has been a challenge since the inception of the idea of simulating the brain. In particular their (many) hierarchical levels of composition track the development of parallel implementation in an attempt to become accessibly fast. When it comes to performance enhancement Graphics Processing Units (GPU) have carved their own strength in machine learning. In this paper, we present an approach that relies mainly on three kernels for implementing both the Restricted Boltzmann Machines (RBM) and Deep Belief Networks (DBN) algorithms. Instead of considering the neuron as the smallest unit of computation each thread represents the connection between two (one visible and one hidden) neurons. Although conceptually it may seem weird, the rationale behind is to think of a connection as performing a simple function that multiplies the clamped input by its weight. Thus, we maximize the GPU workload avoiding idle cores. Moreover, we placed great emphasis on the kernels to avoid uncoalesced memory accesses as well as to take advantage of the shared memory to reduce global memory accesses. Additionally, our approach uses a step adaptive learning rate procedure which accelerates convergence. The approach yields very good speedups (up to 46×) as compared with a straightforward implementation when both GPU and CPU implementations are tested on the MINST database.


title: Screw retained vs. cement retained implant‐supported fixed dental prosthesis

abstract: A fixed dental prosthesis can be secured to an endosseous implant via cementation (using a provisional or definitive cement) on an implant abutment that is screw retained to the implant or directly in the implant via screw retention. The clinical decision as to which retention system best suits the individual patient depends on several factors. The aim of this review is to present a detailed overview of the factors potentially influencing whether to choose screw retention or cement retention. These factors include the individual indication, advantages and disadvantages of the different retention mechanisms, the retention provided, retrievability, provisionalization, esthetics and clinical performance, including failures and complications. The results of recently published systematic reviews on this topic are discussed and an overview is provided. A decision tree is presented to facilitate the clinical selection of the retention type. This overview concludes that the choice of retention type (screw retained or cement retained) might not influence the overall survival of the implant-supported fixed dental prosthesis, but may be responsible for the development of certain complications. The decision may depend on technical feasibility and on weighing the pros and cons.


title: A Fully Automatic Crossword Generator

abstract: This paper presents a software system that is able to generate crosswords with no human intervention including definition generation and crossword compilation. In particular, the proposed system crawls relevant sources of the Web, extracts definitions from the downloaded pages using state-of-the-art natural language processing (NLP) techniques and, finally, attempts at compiling a crossword schema with the extracted definitions using a constrain satisfaction programming (CSP) solver. The crossword generator has relevant applications in entertainment, educational and rehabilitation contexts.


title: On Evaluation of 6D Object Pose Estimation

abstract: A pose of a rigid object has 6 degrees of freedom and its full knowledge is required in many robotic and scene understanding applications. Evaluation of 6D object pose estimates is not straightforward. Object pose may be ambiguous due to object symmetries and occlusions, i.e. there can be multiple object poses that are indistinguishable in the given image and should be therefore treated as equivalent. The paper defines 6D object pose estimation problems, proposes an evaluation methodology and introduces three new pose error functions that deal with pose ambiguity. The new error functions are compared with functions commonly used in the literature and shown to remove certain types of non-intuitive outcomes. Evaluation tools are provided at: https://github.com/thodan/obj_pose_eval.


title: Steep Subthreshold Slope n- and p-Type Tunnel-FET Devices for Low-Power and Energy-Efficient Digital Circuits

abstract: In this paper, novel n- and p-type tunnel field-effect transistors (T-FETs) based on heterostructure Si/intrinsic-SiGe channel layer are proposed, which exhibit very small subthreshold swings, as well as low threshold voltages. The design parameters for improvement of the characteristics of the devices are studied and optimized based on the theoretical principles and simulation results. The proposed devices are designed to have extremely low off currents on the order of 1 fA/mum and engineered to exhibit substantially higher on currents compared with previously reported T-FET devices. Subthreshold swings as low as 15 mV/dec and threshold voltages as low as 0.13 V are achieved in these devices. Moreover, the T-FETs are designed to exhibit input and output characteristics compatible with CMOS-type digital-circuit applications. Using the proposed n- and p-type devices, the implementation of an inverter circuit based on T-FETs is reported. The performance of the T-FET-based inverter is compared with the 65-nm low-power CMOS-based inverter, and a gain of ~104 is achieved in static power consumption for the T-FET-based inverter with smaller gate delay.


title: Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts

abstract: Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive. In the last couple of years, we have observed images of digits, indoor scenes, birds, chairs, etc. being automatically generated. The expressive power of image generators have also been enhanced by introducing several forms of conditioning variables such as object names, sentences, bounding box and key-point locations. In this work, we propose a novel deep conditional generative adversarial network architecture that takes its strength from the semantic layout and scene attributes integrated as conditioning variables. We show that our architecture is able to generate realistic outdoor scene images under different conditions, e.g. day-night, sunny-foggy, with clear object boundaries.


title: Testing Agreement for Multi-Item Scales With the Indices

abstract: The most popular index of agreement has been rWG(J); more recently, the ADM(J) index also has been used. This study addresses two problems: first, how to test the statistical significance of rWG(J) and ADM(J) and, second, how to infer from the indices that were evaluated for each group about the agreement of the ensemble of groups. The authors extend the inference based on either rWG(J) or ADM(J) by focusing on multiple-item scales and on the whole ensemble of groups. Their method is based on simulations, as was done by Dunlap, Burke, and Smith-Crowe (2003) and by Cohen, Doveh, and Eick (2001). The tests are illustrated on the data of Bliese, Halverson, and Schriesheim (2002) pertaining to a sample of 2,042 U.S Army soldiers in 49 U.S. Army companies. Software for our procedures is available both as a SAS code and in the Multilevel Modeling in R package (Bliese, 2006).


Recommendation Systems in Software Engineering


title: Automatic Variational Inference in Stan

abstract: Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult for non-experts to use. We propose an automatic variational inference algorithm, automatic differentiation variational inference (ADVI); we implement it in Stan (code available), a probabilistic programming system. In ADVI the user provides a Bayesian model and a dataset, nothing else. We make no conjugacy assumptions and support a broad class of models. The algorithm automatically determines an appropriate variational family and optimizes the variational objective. We compare ADVI to MCMC sampling across hierarchical generalized linear models, nonconjugate matrix factorization, and a mixture model. We train the mixture model on a quarter million images. With ADVI we can use variational inference on any model we write in Stan.


title: Synaptic Dynamics in Analog VLSI

abstract: Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns. Within the context of hardware implementations of pulse-based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real postsynaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.


title: A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

abstract: We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.


title: Chronic Kidney Disease analysis using data mining classification techniques

abstract: Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN). The experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.


title: Motor Stator With Thick Rectangular Wire Lap Winding for HEVs

abstract: A stator structure with a rectangular wire distributed winding that exhibited high levels of productivity and extremely flexible design was designed for hybrid electric vehicle motors. We devised a method of forming thick rectangular wire diamond coils and determined the best conditions for forming high-density coils. A structure for terminal connections was also devised to achieve a short coil end. The slot-fill rate of a prototype stator was 80.5%, and its total coil end was 42 mm, which is roughly the same motor performance of a stator with segmented coil wave winding. The key advantage here is that the proposed stator had fewer coils and welding points than a stator with segmented coil wave winding and that the possible number of conductors per slot was higher than four, which is the limit for the stator with segmented coil wave winding.


title: QUBIC: a qualitative biclustering algorithm for analyses of gene expression data

abstract: Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ approximately maqin/bicluster. A server version of QUBIC is also available upon request.


title: 298 Decoding the Patterns of Self and Nonself by the Innate Immune System

abstract: The innate immune system evolved several strategies of self/nonself discrimination that are based on the recognition of molecular patterns demarcating infectious nonself, as well as normal and abnormal self. These patterns are deciphered by receptors that either induce or inhibit an immune response, depending on the meaning of these signals.


title: How to Make Lisp Go Faster than C

abstract: Contrary to popular belief, Lisp code can be very efcient today: it can run as fast as equivalent C code or even faster in some cases. In this paper, we explain how to tune Lisp code for performance by introducing the proper type declarations, using the appropriate data structures and compiler information. We also explain how e ciency is achieved by the compilers. These techniques are applied to simple image processing algorithms in order to demonstrate the announced performance on pixel access and arithmetic operations in both languages.


title: Power Transformer Economic Evaluation in Decentralized Electricity Markets

abstract: Owing to deregulation, privatization, and competition, estimating financial benefits of electrical power system projects is becoming increasingly important. In other words, it is necessary to assess the project profitability under the light of new developments in the electricity market. In this paper, a detailed methodology for the least cost choice of a distribution transformer is proposed, showing how the higher price of a facility can be traded against its operational cost over its life span. The proposed method involves the incorporation of the discounted cost of transformer losses to their economic evaluation, providing the ability to take into account variable energy cost during the transformer operating lifetime. In addition, the influence of the variability in the energy loss cost is investigated, taking into account a potential policy intended to be adopted by distribution network operators. The method is combined with statistical and probabilistic assessment of electricity price volatility in order to derive its impact on the transformer purchasing policy.


The Opensesame NIST 2016 Speaker Recognition Evaluation System.


title: Frankensteinian Methods for Evolutionary Music Composition

abstract: Victor Frankenstein sought to create an intelligent being imbued with the rules of civilized human conduct, who could further learn how to behave and possibly even evolve through successive generations into a more perfect form. Modern human composers similarly strive to create intelligent algorithmic music composition systems that can follow prespecified rules, learn appropriate patterns from a collection of melodies, or evolve to produce output more perfectly matched to some aesthetic criteria. Here we review recent efforts aimed at each of these three types of algorithmic composition. We focus particularly on evolutionary methods, and indicate how monstrous many of the results have been. We present a new method that uses coevolution to create linked artificial music critics and music composers, and describe how this method can attach the separate parts of rules, learning, and evolution together into one coherent body. “Invention, it must be humbly admitted, does not consist in creating out of void, but out of chaos; the materials must, in the first place, be afforded...” --Mary Shelley, Frankenstein (1831/1993, p. 299)


title: Discriminative multinomial Naïve Bayes for network intrusion detection

abstract: This paper applies discriminative multinomial Naive Bayes with various filtering analysis in order to build a network intrusion detection system. For our experimental analysis, we used the new NSL-KDD dataset, which is considered as a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. We perform 2 class classifications with 10-fold cross validation for building our proposed model. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network intrusion detection system.


title: Evaluating visual ADAS components on the COnGRATS dataset

abstract: We present a framework that supports the development and evaluation of vision algorithms in the context of driver assistance applications and traffic surveillance. This framework allows the creation of highly realistic image sequences featuring traffic scenarios. The sequences are created with a realistic state of the art vehicle physics model; different kinds of environments are featured, thus providing a wide range of testing scenarios. Due to the physically-based rendering technique and variable camera models employed for the image rendering process, we can simulate different sensor setups and provide appropriate and fully accurate ground truth data.


title: HD Qatari ANPR system

abstract: Recently, Automatic Number Plate Recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to provide better recognition rate, High Definition (HD) cameras have started to be used. However, most known techniques for standard definition are not suitable for real-time HD image processing due to the computationally intensive cost of localizing the number plate. In this paper, algorithms to implement the three main stages of a high definition ANPR system for Qatari number plates are presented. The algorithms have been tested using MATLAB and two databases as a proof of concept. Implementation results have shown that the system is able to process one HD image in 61 ms, with an accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR.


title: Lysates of two established human tumor lines contain heparin-binding growth factors related to bovine acidic brain fibroblast growth factor

abstract: Cell lysates of two established human tumor lines, a medulloblastoma (TE671), and a rhabdomyosarcoma (RD), contain mitogenic activity which elutes from heparin-Sepharose under conditions typical of class 1 heparin-binding growth factors, such as acidic brain fibroblast growth factor. The presence of this class of mitogen in both cell lines was confirmed by their chromatographic behavior on reversed-phase C3 columns, and by the ability of heparin to enhance their mitogenic activity. Using a specific synthetic DNA probe, RNA's were isolated from both cell lines by hybridization-selection, translated in vitro , and translated proteins affinity fractionated on heparin-Sepharose. The results demonstrate that TE671 and RD cell lysates contain mRNA's for mitogens related to acidic brain fibroblast growth factor, and also suggest that high molecular weight proteins exist that are closely related to, or are precursor forms of, the class 1 mitogens.


title: A Short Survey of Recent Advances in Graph Matching

abstract: Graph matching, which refers to a class of computational problems of finding an optimal correspondence between the vertices of graphs to minimize (maximize) their node and edge disagreements (affinities), is a fundamental problem in computer science and relates to many areas such as combinatorics, pattern recognition, multimedia and computer vision. Compared with the exact graph (sub)isomorphism often considered in a theoretical setting, inexact weighted graph matching receives more attentions due to its flexibility and practical utility. A short review of the recent research activity concerning (inexact) weighted graph matching is presented, detailing the methodologies, formulations, and algorithms. It highlights the methods under several key bullets, e.g. how many graphs are involved, how the affinity is modeled, how the problem order is explored, and how the matching procedure is conducted etc. Moreover, the research activity at the forefront of graph matching applications especially in computer vision, multimedia and machine learning is reported. The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.


title: Classification of SSH Attacks Using Machine Learning Algorithms

abstract: SSH Attacks are of various types: SSH port scanning, SSH Brute-force attacks, Attacks using compromised SSH server. Attacks using a compromised server could be DoS attacks, Phishing attacks, E- mail spamming and so on. This paper questions whether the attacks from a compromised SSH server be segregated from other attacks using the network flows. In this work, we categorize SSH attacks into two types. The first category consists of all attack activities after a successful compromise of an SSH server. We name it as "severe" attacks. The second type includes all attacks leading to a successful compromise. It consists of SSH port scanning, SSH Brute-force attack, and compromised SSH server with no activities. The second category is named as "not-so-severe" attacks. We employ Machine Learning algorithms, namely, Naive Bayes learner, Logistic Regression, J48 decision tree, and Support Vector Machine to classify these attacks. Suitable features were selected based on domain knowledge, literature survey, and feature selection technique to evaluate the performance of machine learning algorithms using the metrics accuracy, sensitivity, precision, and F-score.


title: THE EVOLUTION OF PINK: MORPHOLOGICAL AND GENETIC VARIATION AMONG THREE LITHOPHRAGMA (SAXIFRAGACEAE) SPECIES

abstract: ABSTRACT Prior molecular work using ITS and chloroplast sequence data revealed that the endemic Lithophragma trifoliatum and the broad-ranging L. parviflorum form a tight clade with a third species, L. affine. We used AFLPs to assess the fine scale relationship of these three species from populations where their distributions overlap in northern California. Our results revealed two groups of L. trifoliatum, one nested within a group of L. affine and L. parviflorum and the other grouped with other populations of L. parviflorum, contrary to predictions based on plant morphology alone. The morphological pattern was not supported by the molecular data, suggesting that pink flowers evolved more than once, concurrently with other floral traits such as size and nectary length. It is also possible that this pattern is due to recent evolution or gene flow between the two color morphs. The possible ecological importance of these differences in floral traits (e.g., for pollination) warrants further study, as well as...


title: Triple-Wideband Open-Slot Antenna for the LTE Metal-Framed Tablet device

abstract: An open-slot antenna with a low profile of 7 mm to provide a triple-wideband LTE operation in the metal-framed tablet device is presented. With the low profile, the antenna can fit in the narrow region between the metal frame and the display panel of the metal-framed tablet device. The triple-wideband operation covers 698–960 MHz (low band), 1710–2690 MHz (middle band), and 3400–3800 MHz (high band). The antenna mainly has a rectangular open slot and an L-shaped metal strip embedded therein to provide two open slots of different resonant paths. Using a microstrip feedline having a step-shaped section across the two open slots, good excitation of multiple slot resonant modes is obtained. Details of the proposed antenna are presented.


title: Implemental Control Strategy for Grid Stabilization of Grid-Connected PV System Based on German Grid Code in Symmetrical Low-to-Medium Voltage Network

abstract: In the last couple of years, the increasing penetration of renewable energy resulted in the development of grid-connected large-scale power plants. However, a high penetration harbors the risk of grid instability if the generating power plants are not able to support the grid. Therefore, grid stabilization, which depends on the system-type or grid of each country, plays an important role and has been strengthened by different grid codes. With this background, VDE-AR-N 4105 for photovoltaic (PV) systems connected to the low-voltage grid and the German Association of Energy and Water Industries (BDEW) introduced the medium-voltage grid code for connecting power plants to the grid and they are the most stringent certifications. In this paper, the control strategy of generating system is enhanced with VDE-AR-N 4105 and BDEW grid code, where both active/reactive powers are controlled. Simulation and experimental results of 100-kW PV inverter are shown to verify the effectiveness of the proposed implemental control strategy.


title: The Scyther Tool: Verification, Falsification, and Analysis of Security Protocols

abstract: With the rise of the Internet and other open networks, a large number of security protocols have been developed and deployed in order to provide secure communication. The analysis of such security protocols has turned out to be extremely difficult for humans, as witnessed by the fact that many protocols were found to be flawed after deployment. This has driven the research in formal analysis of security protocols. Unfortunately, there are no effective approaches yet for constructing correct and efficient protocols, and work on concise formal logics that might allow one to easily prove that a protocol is correct in a formal model, is still ongoing. The most effective approach so far has been automated falsification or verification of such protocols with state-of-the-art tools such as ProVerif [1] or the Avispa tools [2]. These tools have shown to be effective at finding attacks on protocols (Avispa) or establishing correctness of protocols (ProVerif).


title: Families of distributions arising from the quantile of generalized lambda distribution

abstract: In this paper, the class of T-R {generalized lambda} families of distributions based on the quantile of generalized lambda distribution has been proposed using the T-R{Y} framework. In the development of the T-R{Y} framework, the support of Y and T must be the same. It is typical that the random variable Y has one type of support and T is restricted to the same support. Taking Y to be a generalized lambda random variable leads to three different types of supports, thus, making the choice of the generator T to be much more broad and flexible. This is interesting and unique. By allowing T with different supports makes the T-R{generalized lambda} a desirable method for generating new versatile and broad families of generalized distributions for any given random variable R. Some general properties of these families of distributions are studied. Four members of the T-R{generalized lambda} families of distributions are derived. The shapes of these distributions can be symmetric, skewed to the left, skewed to the right, or bimodal. Two real life data sets are applied to illustrate the flexibility of the distributions.


title: A 0.6V to 1.6V, 46μW voltage and temperature independent 48 MHz pulsed LC oscillator for RFID tags

abstract: A low-power oscillator based on a pulsed LC tank is presented in this article. The frequency is determined by a bondwire inductor and a MiM capacitor to obtain a high-Q LC tank. Due to a novel way of driving the LC tank, the power consumption is diminished. Since the driving technique only interacts during a very short period with the LC tank, the frequency mainly depends on the stable LC tank and the temperature and supply voltage dependency is lowered drastically. The samples were measured over a 0.6 to 1.6V supply range and a −40 to 100°C temperature range. A temperature dependency of 92 ppm/°C and a voltage dependency of 73 ppm/V is obtained. The frequency standard deviation over 12 automatically bonded samples is 0.76% (359 KHz).


title: A Compact Omnidirectional Self-Packaged Patch Antenna With Complementary Split-Ring Resonator Loading for Wireless Endoscope Applications

abstract: A patch loaded with a complementary split-ring resonator (CSRR) is fabricated on a flexible substrate and folded in a cylindrical shape, forming a self-packaged folded patch antenna with a quasi-omnidirectional radiation pattern. The space inside the cylindrical cavity is electromagnetically shielded by the ground plane of the patch, and therefore electronic circuits can be accommodated in it with little electromagnetic interference (EMI) from the antenna or other external electronics. The CSRR contributes to size reduction. As a test vehicle, a 2.4-GHz ISM-band folded patch antenna is designed, fabricated, and characterized for a wireless capsule endoscope application, where the implemented antenna has a patch length of 10.5 mm (0.11λ ) and a folded cylinder diameter of 10 mm. A 74% size reduction is achieved after CSRR loading. The antenna located at the outermost surface not only functions as an electromagnetic radiator and an EMI shield, but also serve as a mechanical packaging structure.


title: Hybrid WSN and RFID indoor positioning and tracking system

abstract: Wireless sensor networks (WSNs), consisting of a large number of nodes to detect ambient environment, are widely deployed in a predefined area to provide more sophisticated sensing, communication, and processing capabilities, especially concerning the maintenance when hundreds or thousands of nodes are required to be deployed over wide areas at the same time. Radio frequency identification (RFID) technology, by reading the low-cost passive tags installed on objects or people, has been widely adopted in the tracing and tracking industry and can support an accurate positioning within a limited distance. Joint utilization of WSN and RFID technologies is attracting increasing attention within the Internet of Things (IoT) community, due to the potential of providing pervasive context-aware applications with advantages from both fields. WSN-RFID convergence is considered especially promising in context-aware systems with indoor positioning capabilities, where data from deployed WSN and RFID systems can be opportunistically exploited to refine and enhance the collected data with position information. In this papera, we design and evaluate a hybrid system which combines WSN and RFID technologies to provide an indoor positioning service with the capability of feeding position information into a general-purpose IoT environment. Performance of the proposed system is evaluated by means of simulations and a small-scale experimental set-up. The performed analysis demonstrates that the joint use of heterogeneous technologies can increase the robustness and the accuracy of the indoor positioning systems.


title: Design and control of a steerable screw in-pipe inspection robot

abstract: An in-pipe inspection robot is designed in this paper for which its pitch rate is controllable and an optimal control is implemented for it subject to input minimization. In-pipe inspection robots are requisite mobile robots to investigate the pipelines. Most of the in-pipe inspection robots are supposed to move with constant pitch of rate. An in-pipe inspection robot is proposed in this paper based on screw locomotion which is steerable in order to handle the pitch rate of the movement and bypass the probable obstacles. Considering the fact that for this robot the number of actuators of the system is more than the Degrees of Freedom (DOFs) of the system, optimization of its control inputs is performed using optimal control approach. In this paper the dynamic model of the mentioned steerable screw in-pipe inspection robot is extracted and it is controlled within a predefined trajectory in an optimal way. The proper mechanism is designed and its related kinematics and kinetics are derived. Then the objective function is defined to optimize the controlling input error simultaneously. The nonlinear state space is linearized around its operating point and optimization is implemented using Linear Quadratic Regulator (LQR). Validity and efficiency of the designed robot and controller are verified using MATLAB simulations.


title: The Linux scheduler: a decade of wasted cores

abstract: As a central part of resource management, the OS thread scheduler must maintain the following, simple, invariant: make sure that ready threads are scheduled on available cores. As simple as it may seem, we found that this invariant is often broken in Linux. Cores may stay idle for seconds while ready threads are waiting in runqueues. In our experiments, these performance bugs caused many-fold performance degradation for synchronization-heavy scientific applications, 13% higher latency for kernel make, and a 14-23% decrease in TPC-H throughput for a widely used commercial database. The main contribution of this work is the discovery and analysis of these bugs and providing the fixes. Conventional testing techniques and debugging tools are ineffective at confirming or understanding this kind of bugs, because their symptoms are often evasive. To drive our investigation, we built new tools that check for violation of the invariant online and visualize scheduling activity. They are simple, easily portable across kernel versions, and run with a negligible overhead. We believe that making these tools part of the kernel developers' tool belt can help keep this type of bug at bay.


title: UNDERSTANDING POST-ADOPTION OF ONLINE SHOPPING CONTINUANCE USAGE THROUGH THE SOCIAL EXCHANGE THEORY

abstract: Electronic commerce researchers have retention of Consumers post-adoption continuance behavior attendees induce deep trust and service quality of online shopping behavior, thereby contributing to higher revenue and marketing strategy based on social exchange theory. This study provides additional information about the path from the service quality and trust to continuous usage. Also, this study investigates the different tendencies to continuance behavior by habit as a moderating effect within the conceptual model. According to the structural invariance test across moderating effect, it showed that: First, most of main effect paths showed significant positive signs only habit as moderator on trust in service provider is negative support. Second, the habit was supported as a moderator except for the trust in shopping-site to online shopping continuance interaction path. Unexpectedly, the positive moderating effect of habit tendency towards the path of trust in shopping-site to online shopping continuance in not significant. The theoretical and practical implications are discussed.


title: LinkBench: a database benchmark based on the Facebook social graph

abstract: Database benchmarks are an important tool for database researchers and practitioners that ease the process of making informed comparisons between different database hardware, software and configurations. Large scale web services such as social networks are a major and growing database application area, but currently there are few benchmarks that accurately model web service workloads. In this paper we present a new synthetic benchmark called LinkBench. LinkBench is based on traces from production databases that store "social graph" data at Facebook, a major social network. We characterize the data and query workload in many dimensions, and use the insights gained to construct a realistic synthetic benchmark. LinkBench provides a realistic and challenging test for persistent storage of social and web service data, filling a gap in the available tools for researchers, developers and administrators.


title: Adversarially Learned Inference

abstract: We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.


title: Variational Dropout Sparsifies Deep Neural Networks

abstract: We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy.


title: Broadband Circuit Techniques for Multi-Terahertz Gain-Bandwidth-Product Low-Power Applications

abstract: In this paper, two proposed bandwidth (BW) extension techniques are discussed and analytically analyzed. The capabilities of the proposed feedback techniques are demonstrated with two design examples of different circuitry, supported by simulation and measurement results. The first design example is a broadband low-power active balun, employing one of the presented feedback techniques. It is capable of operating with a gain of 2.2 dB and BW of more than 70 GHz. The amplitude imbalance is less than 2.4 dB, while the phase imbalance is below 7 $^\circ$ up to 60 GHz and 14 $^\circ$ up to 70 GHz, consuming a low-power of 29.7 mW. The second design example is a differential three-stage amplifier, incorporating the two techniques, where their combined performance achieves a gain of 54 dB and a 3-dB BW of 25 GHz, resulting in a gain-BW product (GBP) of 12.5 THz, with a power consumption of 180 mW from a 3.3-V supply. The chips are fabricated in a 0.35- $\mu$ m SiGe:C bipolar technology with a ${ f}_t/{ f}_{\max}$ of 200/250 GHz.


title: Echinacea purpureafor Prevention of Upper Respiratory Tract Infections in Children

abstract: Objective: The aim of this study was to determine whether Echinacea purpurea given to children for the treatment of acute upper respiratory tract infection (URI) was effective in reducing the risk of subsequent URI. Design: This was a secondary analysis of data from a randomized, double-blind, placebo-controlled trial of Echinacea for the treatment of URI in children. Setting: The study was conducted as a joint project between the Puget Sound Pediatric Research Network (Seattle, WA) and Bastyr University (Kenmore, WA). Participants: A total of 524 children ages 2 to 11 years were enrolled in the study. Intervention: Children were monitored for URIs over a 4-month observation period during the fall/winters of 2000–2001 and 2001–2002. At entry the children were randomized to receive Echinacea or placebo to treat acute URIs during the observation period. Main outcome measures: The occurrence of a second URI and the number of days between the end of the first URI and the start of the second URI was ascertaine...


title: Behavior-based malware detection system for the Android platform

abstract: Malware in smartphones is growing at a significant rate. There are currently more than 250 million smartphone users in the world and this number is expected to grow in coming years. ::: In the past fe ...


title: Miniature Continuous Coverage Antenna Array for GNSS Receivers

abstract: This letter presents a miniature conformal array that provides continuous coverage and good axial ratio from 1100-1600 MHz. Concurrently, it maintains greater than 1.5 dBic RHCP gain and return loss less than -10 dB. The four element array is comprised of two-arm slot spirals with lightweight polymer substrate dielectric loading and a novel termination resistor topology. Multiple elements provide the capability to suppress interfering signals common in GNSS applications. The array, including feeding network, is 3.5" times 3.5" times 0.8" in size and fits into the FRPA-3 footprint and radome.


title: Gene Expression Profiling in Behcet's Disease Indicates an Autoimmune Component in the Pathogenesis of the Disease and Opens New Avenues for Targeted Therapy

abstract: Behcet disease (BD) is a chronic inflammatory multisystem disease characterized by oral and genital ulcers, uveitis, and skin lesions. Disease etiopathogenesis is still unclear. We aim to elucidate some aspects of BD pathogenesis and to identify specific gene signatures in peripheral blood cells (PBCs) of patients with active disease using novel gene expression and network analysis. 179 genes were modulated in 10 PBCs of BD patients when compared to 10 healthy donors. Among differentially expressed genes the top enriched gene function was immune response, characterized by upregulation of Th17-related genes and type I interferon- (IFN-) inducible genes. Th17 polarization was confirmed by FACS analysis. The transcriptome identified gene classes (vascular damage, blood coagulation, and inflammation) involved in the pathogenesis of the typical features of BD. Following network analysis, the resulting interactome showed 5 highly connected regions (clusters) enriched in T and B cell activation pathways and 2 clusters enriched in type I IFN, JAK/STAT, and TLR signaling pathways, all implicated in autoimmune diseases. We report here the first combined analysis of the transcriptome and interactome in PBCs of BD patients in the active stage of disease. This approach generates useful insights in disease pathogenesis and suggests an autoimmune component in the origin of BD.


title: Crombecq: A surrogate modeling and adaptive sampling toolbox for computer based design

abstract: An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand.


title: Fingerprint matching by thin-plate spline modelling of elastic deformations

abstract: This paper presents a novel minutiae matching method that describes elastic distortions in fingerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the fingerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed fingerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine. Furthermore, it is shown that the observed deformations are different from those described by theoretical models proposed in the literature.


title: CACTUS—clustering categorical data using summaries

abstract: Clustering is an important data mining problem. Most of the earlier work on clustering focussed on numeric attributes which have a natural ordering on their attribute values. Recently, clustering data with categorical attributes, whose attribute values do not have a natural ordering, has received some attention. However, previous algorithms do not give a formal description of the clusters they discover and some of them assume that the user post-processes the output of the algorithm to identify the final clusters. In this paper, we introduce a novel formalization of a cluster for categorical attributes by generalizing a definition of a cluster for numerical attributes. We then describe a very fast summarizationbased algorithm called CACTUS that discovers exactly such clusters in the data. CACTUS has two important characteristics. First, the algorithm requires only two scans of the dataset, and hence is very fast and scalable. Our experiments on a variety of datasets show that CACTUS outperforms previous work by a factor of 3 to 10. Second, CACTUS can find clusters in subsets of all attributes and can thus perform a subspace clustering of the data. This feature is important if clusters do not span all attributes, a likely scenario if the number of attributes is very large. In a thorough experimental evaluation, we study the performance of CACTUS on real and synthetic datasets.


title: Metabolic engineering for probiotics and their genome-wide expression profiling

abstract: Probiotic supplements in food industry have attracted a lot of attention and shown a remarkable growth in this field. Metabolic engineering (ME) approaches enable understanding their mechanism of action and increases possibility of designing probiotic strains with desired functions. Probiotic microorganisms generally referred as industrially important lactic acid bacteria (LAB) which are involved in fermenting dairy products, food, beverages and produces lactic acid as final product. A number of illustrations of metabolic engineering approaches in industrial probiotic bacteria have been described in this review including transcriptomic studies of Lactobacillus reuteri and improvement in exopolysaccharide (EPS) biosynthesis yield in Lactobacillus casei LC2W. This review summaries various metabolic engineering approaches for exploring metabolic pathways. These approaches enable evaluation of cellular metabolic state and effective editing of microbial genome or introduction of novel enzymes to redirect the carbon fluxes. In addition, various system biology tools such as in silico design commonly used for improving strain performance is also discussed. Finally, we discuss the integration of metabolic engineering and genome profiling which offers a new way to explore metabolic interactions, fluxomics and probiogenomics using probiotic bacteria like Bifidobacterium spp and Lactobacillus spp.


title: A Survey of Preference-Based Online Learning with Bandit Algorithms

abstract: In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available—instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state-of-the-art in this field, that we refer to as preference-based multi-armed bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our systematization is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.


title: A practical functional program for the CRAY X-MP *

abstract: One can have all the advantages of functional programming – correctness, clarity, simplicity, and flexibility – without any sacrifice in performance, even for a scientifically significant computation on a supercomputer. Therefore, why use Fortran? We demonstrate parity – equality of speed and storage use – between a program generated automatically from a functional specification and a program written by hand in the procedural style. To our knowledge, this demonstration of parity is the first for a program that solves a scientifically significant problem – quasi-linear hyperbolic partial differential equations – on a scientifically interesting supercomputer – the CRAY X-MP. We use pure Lisp, including higher-order functions, to express the functional specification for the PDE solver. We designed this specification for maximal clarity and flexibility, rather than for efficiency. Nevertheless, we obtain a highly efficient program to solve the PDEs: automated program transformations put back the missing efficiency as they produce an executable Fortran program from the specification. The generated Fortran program vectorizes on the CRAY X-MP and runs about 4% faster than a handwritten Fortran program for the same problem. We describe the problem and the specification, and some of the problem-domain-specific and hardware-specific transformations that we use to obtain the high-efficiency program.


A New Tool for Measuring Student Behavioral Engagement in Large University Classes


title: Sequences assessed by declarative and procedural tests of memory in amnesic patients with hippocampal damage

abstract: Previous research indicates that amnesic subjects tested on sequential learning or serial reaction time tasks can learn a repeated procedural sequence but are unable to explicitly recall the correct sequence when asked to generate the sequence. Rats with hippocampal lesions are also able to learn and remember procedural or implicit sequences but were impaired for declarative sequences. We used analogous procedures used in rats to assess the role of the hippocampus in the acquisition of declarative and procedural sequences in amnesic and control participants. Amnesic participants with damage restricted to the hippocampus and control participants were administered analogous tasks of declarative and procedural sequential learning using a computer version of the radial arm maze. The amnesic participants had slower response times during the acquisition of procedural sequences, but were not impaired compared to controls when switched to a random sequence, suggesting that both groups learned the sequence. Alternatively, the amnesic but not control participants were significantly impaired in the declarative sequence task. Our findings provide support for evolutionary continuity in cognitive function of the hippocampus in rats and humans and the dissociation between the declarative and procedural sequential learning. The performance differences on the two sequence learning tasks are likely due to the use of different strategies associated with learning sequences based on procedural versus declarative knowledge.