title: On the future of project management innovation: A call for discussion towards project management 2030

abstract: Rethinking Project Management (PM)∗ has been a recurring theme for some time [1]–[9]. In context of the current era of Digital Transformation, which is also referred to as the time of VUCA (Volatile, Uncertain, Complex and Ambiguous) it seems fair to assess if the current PM curricula match the needs of the market, both public and private. Professional bodies like IPMA† and PMI‡ similarly re-assess such which then leads to new editions of their standards to emerge. The current paper provides a short overview of the current developments of the profession. As well, it identifies related recent, current and ongoing research addressing similar questions regarding the success chances and rates of projects in light of the use and application of various PM methods. It aims to propose a new approach to learn using “Research through Project Management” (RtPM). Understanding the impact PM can have on the manner in which products, services, processes and systems are being developed, and the manner in which PM is performed. The authors propose the profession of PM will need to develop itself different from the way it has been conducted to-date and refer to it as “Towards Project Management 2030”, having the profession develop itself in a much more continuous manner. The paper ends with suggesting how to organize such ongoing research on, learning of and adoption by PM in practice embracing lifelong learning.


title: Airway Management and Smoke Inhalation Injury in the Burn Patient

abstract: Smoke inhalation injury, a unique form of acute lung injury, greatly increases the occurrence of postburn morbidity and mortality. In addition to early intubation for upper-airway protection, subsequent critical care of patients who have this injury should be directed at maintaining distal airway patency. High-frequency ventilation, inhaled heparin, and aggressive pulmonary toilet are among the therapies available. Even so, immunosuppression, intubation, and airway damage predispose these patients to pneumonia and other complications.


title: A compliance/stiffness matrix formulation of general Green's function and effective permittivity for piezoelectric multilayers

abstract: This paper presents an efficient and stable recursive compliance matrix method for analyzing wave propagation in multilayered piezoelectric media. The effective permittivity and generalized Green's functions for a layered system, a layered system on a substrate, and a layered system between two substrates have been obtained from the elements of the total or surface compliance/stiffness matrices, which are calculated recursively, layer by layer, from the layer compliance/stiffness matrices. The method is very closely related to the transfer matrix method and retains its simplicity and efficiency, but it is numerically stable for high thickness-to-wavelength ratio. Numerical examples for wave propagation in zinc oxide (ZnO)/Diamond/silicon(Si) structures are presented using the compliance matrix formulation for effective permittivity and Green's function.


title: Interactive surface design with interlocking elements

abstract: We present an interactive tool for designing physical surfaces made from flexible interlocking quadrilateral elements of a single size and shape. With the element shape fixed, the design task becomes one of finding a discrete structure---i.e., element connectivity and binary orientations---that leads to a desired geometry. In order to address this challenging problem of combinatorial geometry, we propose a forward modeling tool that allows the user to interactively explore the space of feasible designs. Paralleling principles from conventional modeling software, our approach leverages a library of base shapes that can be instantiated, combined, and extended using two fundamental operations: merging and extrusion. In order to assist the user in building the designs, we furthermore propose a method to automatically generate assembly instructions. We demonstrate the versatility of our method by creating a diverse set of digital and physical examples that can serve as personalized lamps or decorative items.


title: Solid-State Light Sources Getting Smart

abstract: More than a century after the introduction of incandescent lighting and half a century after the introduction of fluorescent lighting, solid-state light sources are revolutionizing an increasing number of applications. Whereas the efficiency of conventional incandescent and fluorescent lights is limited by fundamental factors that cannot be overcome, the efficiency of solid-state sources is limited only by human creativity and imagination. The high efficiency of solid-state sources already provides energy savings and environmental benefits in a number of applications. However, solid-state sources also offer controllability of their spectral power distribution, spatial distribution, color temperature, temporal modulation, and polarization properties. Such "smart" light sources can adjust to specific environments and requirements, a property that could result in tremendous benefits in lighting, automobiles, transportation, communication, imaging, agriculture, and medicine.


title: An Architecture-Based Approach to Self-Adaptive Software

abstract: Self-adaptive software requires high dependability robustness, adaptability, and availability. The article describes an infrastructure supporting two simultaneous processes in self-adaptive software: system evolution, the consistent application of change over time, and system adaptation, the cycle of detecting changing circumstances and planning and deploying responsive modifications.


title: Semantic Framework of Internet of Things for Smart Cities: Case Studies

abstract: In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.


title: Design Guidelines for Domain Specific Languages

abstract: Designing a new domain specific language is as any other complex task sometimes error-prone and usually time consuming, especially if the language shall be of high-quality and comfortably usable. Existing tool support focuses on the simplification of technical aspects but lacks support for an enforcement of principles for a good language design. In this paper we investigate guidelines that are useful for designing domain specific languages, largely based on our experience in developing languages as well as relying on existing guidelines on general purpose (GPLs) and modeling languages. We defined guidelines to support a DSL developer to achieve better quality of the language design and a better acceptance among its users.


title: A SURE Approach for Digital Signal/Image Deconvolution Problems

abstract: In this paper, we are interested in the classical problem of restoring data degraded by a convolution and the addition of a white Gaussian noise. The originality of the proposed approach is twofold. First, we formulate the restoration problem as a nonlinear estimation problem leading to the minimization of a criterion derived from Stein's unbiased quadratic risk estimate. Secondly, the deconvolution procedure is performed using any analysis and synthesis frames that can be overcomplete or not. New theoretical results concerning the calculation of the variance of the Stein's risk estimate are also provided in this work. Simulations carried out on natural images show the good performance of our method with respect to conventional wavelet-based restoration methods.


title: An analysis of ML typability

abstract: We carry out an analysis of typability of terms in ML. Our main result is that this problem is DEXPTIME-hard, where by DEXPTIME we mean DTIME(2n0(1)). This, together with the known exponential-time algorithm that solves the problem, yields the DEXPTIME-completeness result. This settles an open problem of P. Kanellakis and J. C. Mitchell. ::: Part of our analysis is an algebraic characterization of ML typability in terms of a restricted form of semi-unification, which we identify as acyclic semi-unification. We prove that ML typability and acyclic semi-unification can be reduced to each other in polynomial time. We believe this result is of independent interest.


Cannibalization Effects of Products in Zara ' s Stores and Demand Forecasting


title: Psychopathy and the DSM-IV criteria for antisocial personality disorder.

abstract: The Axis II Work Group of the Task Force on DSM—IV has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. We describe an alternative to the approach taken in the rev. 3rd ed. of the Diagnostic and Statistical Manual of Mental Disorders ( DSM—III—R ; American Psychiatric Association, 1987 ), namely, the revised Psychopathy Checklist. We also discuss the multisite APD field trials designed to evaluate and compare four criteria sets: the DSM—III—R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th ed. of the International Classification of Diseases ( World Health Organization, 1990 ), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist.


title: On the multivariate Laplace distribution

abstract: In this letter, we discuss the multivariate Laplace probability model in the context of a normal variance mixture model. We briefly review the derivation of the probability density function (pdf) and discuss a few important properties. We then present two methods for estimating its parameters from data and include an example of usage, where we apply the model to represent the statistics of the discrete Fourier transform coefficients of a speech signal. Since the pdf is given in closed form, and the model parameters can be easily obtained, this distribution may be useful for representing multivariate, sparsely distributed data, with mutually dependent components.


title: The effect of LUT and cluster size on deep-submicron FPGA performance and density

abstract: We use a fully timing-driven experimental flow [4] [15] in which a set of benchmark circuits are synthesized into different cluster-based [2] [3] [15] logic block architectures, which contain groups of LUTs and flip-flops. We look across all architectures with LUT sizes in the range of 2 inputs to 7 inputs, and cluster size from 1 to 10 LUTs. In order to judge the quality of the architecture we do both detailed circuit level design and measure the demand of routing resources for every circuit in each architecture. These experiments have resulted in several key contributions. First, we have experimentally determined the relationship between the number of inputs required for a cluster as a function of the LUT size (K) and cluster size (N). Second, contrary to previous results, we have shown that when the cluster size is greater than four, that smaller LUTs (size 2 and 3) are almost as area efficient as 4-input LUTs, as suggested in [11]. However, our results also show that the performance of FPGAs with these small LUT sizes is significantly worse (by almost a factor of 2) than larger LUTs. Hence, as measured by area-delay product, or by performance, these would be a bad choice. Also, we have discovered that LUT sizes of 5 and 6 produce much better area results than were previously believed. Finally, our results show that a LUT size of 4 to 6 and cluster size of between 4 and 10 provides the best area-delay product for an FPGA.


title: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality.

abstract: Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they worked on those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question, "Who is the best connected scientist?"


title: Object scene flow for autonomous vehicles

abstract: This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which cannot be handled by existing methods.


title: Estimation of Quantile Mixtures via

abstract: Moments or cumulants have been traditionally used to characterize a probability distribution or an observed data set. Recently, L-moments and trimmed L-moments have been noticed as appealing alternatives to the conventional moments. This paper promotes the use of L-moments proposing new parametric families of distributions that can be estimated by the method of L-moments. The theoretical L-moments are defined by the quantile function i.e. the inverse of cumulative distribution function. An approach for constructing parametric families from quantile functions is presented. Because of the analogy to mixtures of densities, this class of parametric families is called quantile mixtures. The method of L-moments is a natural way to estimate the parameters of quantile mixtures. As an example, two parametric families are introduced: the normal-polynomial quantile mixture and the Cauchy-polynomial quantile mixture. The proposed quantile mixtures are applied to model monthly, weekly and daily returns of some major stock indexes.


abstract: Almost every university have their own management system to manage the students' records. Currently, even though there is a student management system that manages the students' records in Universiti Malaysia Sarawak (UNIMAS), no permission is provided for lecturers to access the system. This is because the access permission is only to top management such as Deans and Deputy Deans of Undergraduate and Student Development due to its privacy setting. Thus, this project proposes a system named Student performance analysis system (SPAS) to keep track of students' result in the Faculty of Computer Science and Information Technology (FCSIT). The proposed system offer a predictive system that is able to predict the students' performance in course “TMC1013 System Analysis and Design”, which in turns assists the lecturers from Information System department to identify students that are predicted to have bad performance in course “TMC1013 System Analysis and Design”. The proposed system offers student performance prediction through the rules generated via data mining technique. The data mining technique used in this project is classification, which classifies the students based on students' grade.


How to Teach the Usage of Project Management Tools in Computer Courses: A Systematic Literature Review


title: A Scalable Framework for Wireless Distributed Computing

abstract: We consider a wireless distributed computing system, in which multiple mobile users, connected wirelessly through an access point, collaborate to perform a computation task. In particular, users communicate with each other via the access point to exchange their locally computed intermediate computation results, which is known as data shuffling. We propose a scalable framework for this system, in which the required communication bandwidth for data shuffling does not increase with the number of users in the network. The key idea is to utilize a particular repetitive pattern of placing the dataset (thus a particular repetitive pattern of intermediate computations), in order to provide coding opportunities at both the users and the access point, which reduce the required uplink communication bandwidth from users to access point and the downlink communication bandwidth from access point to users by factors that grow linearly with the number of users. We also demonstrate that the proposed dataset placement and coded shuffling schemes are optimal (i.e., achieve the minimum required shuffling load) for both a centralized setting and a decentralized setting, by developing tight information-theoretic lower bounds.


title: Generic methods for optimization-based modeling

abstract: “Energy” models for continuous domains can be applied to many problems, but often suffer from high computational expense in training, due to the need to repeatedly minimize the energy function to high accuracy. This paper considers a modified setting, where the model is trained in terms of results after optimization is truncated to a fixed number of iterations. We derive “backpropagating” versions of gradient descent, heavy-ball and LBFGS. These are simple to use, as they require as input only routines to compute the gradient of the energy with respect to the domain and parameters. Experimental results on denoising and image labeling problems show that learning with truncated optimization greatly reduces computational expense compared to “full” fitting.


title: Consecutive Repeating State Cycles Determine Periodic Points in a Turing Machine

abstract: The Turing machine is studied with new methods motivated by the notion of recurrence in classical dynamical systems theory. The state cycle of a Turing machine is introduced. It is proven that each consecutive repeating state cycle in a Turing machine determines a unique periodic configuration (point) and vice versa. This characterization is a periodic point theorem for Turing machines. A Turing machine is defined to be periodic if it has at least one periodic configuration or it only has halting configurations. Using the notion of a prime directed edge and a mathematical operation called edge pattern substitution, a search procedure finds consecutive repeating state cycles. If the Turing machine is periodic, then this procedure eventually finds each periodic point or this procedure determines that the machine has only halting configurations. New mathematical techniques are demonstrated such as edge pattern substitution and prime directed edge sequences that could be quite useful in the further study of the aperiodic Turing machines. The aperiodicity appears to play an integral role in the undecidability of the Halting problem.


title: Graphical Templates for Model Registration

abstract: A new method of model registration is proposed using graphical templates. A graph of landmarks is chosen in the template image. All possible candidates for these landmarks are found in the data image using local operators. A dynamic programming algorithm on decomposable subgraphs of the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination of local operators to describe points of interest/landmarks and a graph to describe their geometric orientation in the plane, yields fast and precise matches of the model to the data, with no initialization required.


title: Contiki - a lightweight and flexible operating system for tiny networked sensors

abstract: Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks, it is important to be able to download code into the network dynamically. We present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.


title: Move Generation with Perfect Hash Functions

abstract: We present two new perfect hashing schemes that can be used for efficient bitboard move generation for sliding pieces in chess-like board games without the need to use rotated bitboards. Moreover, we show that simple variations of these schemes give minimal perfect hashing schemes. The new method is applicable provided N , the number of k-bit spaced positions that may be set to 1, is not more than k + 1. In chess, for a Rook’s movement along a file N = k = 8; for a Bishop’s movement N ≤ 8, and k = 9 for a north-east diagonal and k = 7 for a north-west diagonal. The results of computational experiments comparing the efficiency of move generation with the standard method show that using the hashing scheme gives an average improvement of approximately 40%. The schemes we suggest are simple, efficient, and easy to understand and implement.


title: HUNT: Scavenger Hunt with Augmented Reality

abstract: Introduction A scavenger hunt is a popular game in which the organizers define a list of objects or tasks for players to find or perform, often within a set time limit. When the individual or team participants find objects and perform tasks, they collect the found objects and/or take photos/videos to prove they have satisfied the tasks on the list. While scavenger hunts are fun to play, game organizers often design games to encourage players to explore specific geographic areas or to familiarize them with certain topics of interest. A university setting can be an ideal place to hold scavenger hunt games. Every year new students arrive, and before starting their academic life they are often required to attend various orientations to familiarize them with university life and policies. Gamification, the application of game elements to non-game problems, can be used to enhance student experiences in orientations and can provide memorable learning experiences in various programs and services. Similarly, libraries and museums could also benefit from this popular game to showcase their facilities. Material published as part of this publication, either on-line or in print, is copyrighted by the Informing Science Institute. Permission to make digital or paper copy of part or all of these works for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage AND that copies 1) bear this notice in full and 2) give the full citation on the first page. It is permissible to abstract these works so long as credit is given. To copy in all other cases or to republish or to post on a server or to redistribute to lists requires specific permission and payment of a fee. Contact Publisher@InformingScience.org to request redistribution permission. This project explores an innovative approach to the scavenger hunt game by developing an iPhone application, HUNT, with Augmented Reality (AR) capability for the users to play the game. There is also an administrative website that can be used by game organizers to create games for users. The authors were asked to develop an app for their university library, whose staff recognized a need to find a better approach to introducing library facilities and services to their visitors in a fun and attractive way, while also enhancing the effectiveness of their visits. With rapidly developing new technologies, scavenger hunt games have seen a resurgence. One well known example is reported by Talton, Peterson, Kamin, Israel, & Al-Muhtadi (2006) in which the University of Illinois at Urbana-Champaign used handheld computers to run scavenger hunt games with incoming freshmen in the Department of Computer Science for the purposes of orientation and retention. They found a significant correlation between student participation in the scavenger hunt and retention rates in the department. They also found that playing the game improved the attitude of new students toward computer science. Augmented reality is a technology that refers to the use of computational displays to add virtual information to a user's sensory perceptions (Feiner, 2002). It allows users to see more, hear more, or even touch more. People can perceive entirely computational components and objects within the real world experience (Van Krevelen & Poelman, 2010). For example, people could read the reviews of the restaurants that they pass by as they walk down a street, visitors could see additional information in multimedia form of special exhibits in a museum, machinists repairing a broken piece of equipment could receive instructions highlighting in real time what to do next, firefighters could see the structure of a burning building to avoid invisible hazards on the scene, and soldiers could see the position of enemy snipers who had been spotted by unmanned reconnaissance aircraft (Feiner, 2002). According to a survey of augmented reality conducted by Azuma and others (Azuma, 1997; Azuma et al. …


title: Incremental Dialog Processing in a Task-Oriented Dialog

abstract: Incremental Dialog Processing (IDP) enables Spoken Dialog Systems to gradually process minimal units of user speech in order to give the user an early system response. In this paper, we present an application of IDP that shows its effectiveness in a task-oriented dialog system. We have implemented an IDP strategy and deployed it for one month on a real-user system. We compared the resulting dialogs with dialogs produced over the previous month without IDP. Results show that the incremental strategy significantly improved system performance by eliminating long and often off-task utterances that generally produce poor speech recognition results. User behavior is also affected; the user tends to shorten utterances after being interrupted by the system.


title: Capacitor-Current-Feedback Active Damping With Reduced Computation Delay for Improving Robustness of LCL-Type Grid-Connected Inverter

abstract: This paper investigates the capacitor-current-feedback active damping for the digitally controlled LCL-type grid-connected inverter. It turns out that proportional feedback of the capacitor current is equivalent to virtual impedance connected in parallel with the filter capacitor due to the computation and pulse width modulation (PWM) delays. The LCL-filter resonance frequency is changed by this virtual impedance. If the actual resonance frequency is higher than one-sixth of the sampling frequency (fs/6), where the virtual impedance contains a negative resistor component, a pair of open-loop unstable poles will be generated. As a result, the LCL-type grid-connected inverter becomes much easier to be unstable if the resonance frequency is moved closer to fs/6 due to the variation of grid impedance. To address this issue, this paper proposes a capacitor-current-feedback active damping with reduced computation delay, which is achieved by shifting the capacitor current sampling instant towards the PWM reference update instant. With this method, the virtual impedance exhibits more like a resistor in a wider frequency range, and the open-loop unstable poles are removed; thus, high robustness against the grid-impedance variation is acquired. Experimental results from a 6-kW prototype confirm the theoretical expectations.


title: Distributed Network Forensics Framework: A Systematic Review

abstract: Network forensics is a branch of digital forensics, which applies to network security. It is used to relate monitoring and analysis of the computer network traffic, that helps us in collecting information and digital evidence, for the protection of network that can use as firewall and IDS. Firewalls and IDS can’t always prevent and find out the unauthorized access within a network. This paper presents an extensive survey of several forensic frameworks. There is a demand of a system which not only detects the complex attack, but also it should be able to understand what had happened. Here it talks about the concept of the distributed network forensics. The concept of the Distributed network forensics is based on the distributed techniques, which are useful for providing an integrated platform for the automatic forensic evidence gathering and important data storage, valuable support and an attack attribution graph generation mechanism to depict hacking events.


title: An Environmental Air Pollution Monitoring System Based on the IEEE 1451 Standard for Low Cost Requirements

abstract: An Environmental Air Pollution Monitoring System (EAPMS) for monitoring the concentrations of major air pollutant gases has been developed, complying with the IEEE 1451.2 standard. This system measures concentrations of gases such as CO, NO2, SO2, and O3 using semiconductor sensors. The smart transducer interface module (STIM) was implemented using the analog devices' ADuC812 microconverter. Network Capable Application Processor (NCAP) was developed using a personal computer and connected to the STIM via the transducer independent interface. Three gas sensors were calibrated using the standard calibration methods. Gas concentration levels and information regarding the STIM can be seen on the graphical user interface of the NCAP. Further, the EAPMS is capable of warning when the pollutant levels exceed predetermined maxima and the system can be developed into a low cost version for developing countries.


title: Comparison and Reduction of Conducted EMI in SiC JFET and Si IGBT-Based Motor Drives

abstract: This paper compares the conducted electromagnetic interference (EMI) in Si insulated gate bipolar transistor (IGBT) and silicon carbide (SiC) junction field-effect transistor (JFET) based motor drives. Two inverters, with the SiC and Si transistors, respectively, are built using the same circuit layout and investigated accordingly. Their conducted EMI levels are compared under the conditions of without filter and with traditional common mode (CM) filters. Reasons of the exhibited different noise emissions are analyzed. To verify the discussions, two inverters are tested in the CM. This allows for the identification and analysis of their maximized CM and differential mode (DM) wave shapes. It is shown that the excited parasitic oscillations during the switching transients are magnified more in the SiC JFET inverter, which is the main cause of the noise difference. Lastly, improved filtering solutions are proposed, which effectively suppressed the increased high-frequency noise due to the faster SiC switching speed.


title: From human factors to human actors: the role of psychology and human-computer interaction studies in system design

abstract: Publisher Summary This chapter examines the role of psychology and human–computer interaction (HIC) studies in system design. Human factors (HF), or ergonomics considerations, are often incorporated into the design process simply as a set of specifications to which the delivered system must adhere. The actual work of the human factors personnel is seen as operator task analyses to be fed into these specifications, and perhaps some interface retouching near the end of the development cycle, when the system design has already been fixed. In general, the role of these people has been seen as ancillary to the main task of building the system. The role of HF or HCI in system design today should be more fluid and pragmatic. Input is vital in discussing the initial capabilities of the system and its required functionality, persisting in the development and evaluation of prototypes, and in final screen layout considerations. This chapter presents an approach which, although acknowledging the contribution that different disciplines can make to the design process, ultimately depends upon the users themselves to articulate their requirements, along with the system design team composed of a variety of specialists acting in the capacity of consultants to the project.


title: On Detecting Spatial Outliers

abstract: The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers, may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast, and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes, and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to validate the effectiveness of the proposed algorithms.


title: Crowd Counting with Density Adaption Networks

abstract: Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different lightning, camera perspective and occlusion states. Previous approaches estimate head counts despite that they can vary dramatically in different density settings; the crowd is often unevenly distributed and the results are therefore unsatisfactory. In this paper, we propose a lightweight deep learning framework that can automatically estimate the crowd density level and adaptively choose between different counter networks that are explicitly trained for different density domains. Experiments on two recent crowd counting datasets, UCF_CC_50 and ShanghaiTech, show that the proposed mechanism achieves promising improvements over state-of-the-art methods. Moreover, runtime speed is 20 FPS on a single GPU.


title: Pharmacogenomics knowledge for personalized medicine.

abstract: The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene-drug associations and genotype-phenotype relationships. Curators assign levels of evidence to variant-drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine-implementation projects.


title: Health Disparities Research Among Small Tribal Populations: Describing Appropriate Criteria for Aggregating Tribal Health Data

abstract: In response to community concerns, we used the Tribal Participatory Research framework in collaboration with 5 American-Indian communities in Washington, Idaho, and Montana to identify the appropriate criteria for aggregating health data on small tribes. Across tribal sites, 10 key informant interviews and 10 focus groups (n = 39) were conducted between July 2012 and April 2013. Using thematic analysis of focus group content, we identified 5 guiding criteria for aggregating tribal health data: geographic proximity, community type, environmental exposures, access to resources and services, and economic development. Preliminary findings were presented to focus group participants for validation at each site, and a culminating workshop with representatives from all 5 tribes verified our final results. Using this approach requires critical assessment of research questions and study designs by investigators and tribal leaders to determine when aggregation or stratification is appropriate and how to group data to yield robust results relevant to local concerns. At project inception, tribal leaders should be consulted regarding the validity of proposed groupings. After regular project updates, they should be consulted again to confirm that findings are appropriately contextualized for dissemination.


title: A Challenge for Machine Ethics

abstract: That the successful development of fully autonomous artificial moral agents (AMAs) is imminent is becoming the received view within artificial intelligence research and robotics. The discipline of Machines Ethics, whose mandate is to create such ethical robots, is consequently gaining momentum. Although it is often asked whether a given moral framework can be implemented into machines, it is never asked whether it should be. This paper articulates a pressing challenge for Machine Ethics: To identify an ethical framework that is both implementable into machines and whose tenets permit the creation of such AMAs in the first place. Without consistency between ethics and engineering, the resulting AMAs would not be genuine ethical robots, and hence the discipline of Machine Ethics would be a failure in this regard. Here this challenge is articulated through a critical analysis of the development of Kantian AMAs, as one of the leading contenders for being the ethic that can be implemented into machines. In the end, however, the development of Kantian artificial moral machines is found to be anti-Kantian. The upshot of all this is that machine ethicists need to look elsewhere for an ethic to implement into their machines.


CJM-ex: Goal-oriented Exploration of Customer Journey Maps using Event Logs and Data Analytics


title: Food recognition using statistics of pairwise local features

abstract: Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft pixellevel segmentation of the image into eight ingredient types. We accumulate these statistics in a multi-dimensional histogram, which is then used as a feature vector for a discriminative classifier. Our experiments show that the proposed representation is significantly more accurate at identifying food than existing methods.


title: A mathematical theory of communication: Meaning, information, and topology

abstract: This article proposes a new mathematical theory of communication. The basic concepts of meaning and information are defined in terms of complex systems theory. Meaning of a message is defined as the attractor it generates in the receiving system; information is defined as the difference between a vector of expectation and one of perception. It can be sown that both concepts are determined by the topology of the receiving system. © 2010 Wiley Periodicals, Inc. Complexity 16: 10–26, 2011 ::: ::: (I wish to thank Dwight Read for several very useful suggestions.)


CTC in the Context of Generalized Full-Sum HMM Training


title: Modeling Video Dynamics with Deep Dynencoder

abstract: Videos always exhibit various pattern motions, which can be modeled according to dynamics between adjacent frames. Previous methods based on linear dynamic system can model dynamic textures but have limited capacity of representing sophisticated nonlinear dynamics. Inspired by the nonlinear expression power of deep autoencoders, we propose a novel model named dynencoder which has an autoencoder at the bottom and a variant of it at the top (named as dynpredictor). It generates hidden states from raw pixel inputs via the autoencoder and then encodes the dynamic of state transition over time via the dynpredictor. Deep dynencoder can be constructed by proper stacking strategy and trained by layer-wise pre-training and joint fine-tuning. Experiments verify that our model can describe sophisticated video dynamics and synthesize endless video texture sequences with high visual quality. We also design classification and clustering methods based on our model and demonstrate the efficacy of them on traffic scene classification and motion segmentation.


title: Multimedia Retrieval via Deep Learning to Rank

abstract: Many existing learning-to-rank approaches are incapable of effectively modeling the intrinsic interaction relationships between the feature-level and ranking-level components of a ranking model. To address this problem, we propose a novel joint learning-to-rank approach called Deep Latent Structural SVM (DL-SSVM), which jointly learns deep neural networks and latent structural SVM (connected by a set of latent feature grouping variables) to effectively model the interaction relationships at two levels (i.e., feature-level and ranking-level). To make the joint learning problem easier to optimize, we present an effective auxiliary variable-based alternating optimization approach with respect to deep neural network learning and structural latent SVM learning. Experimental results on several challenging datasets have demonstrated the effectiveness of the proposed learning to rank approach in real-world information retrieval.


title: DEEP LEARNING BASED SPEED ESTIMATION FOR CONSTRAINING STRAPDOWN INERTIAL NAVIGATION ON SMARTPHONES

abstract: Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. We show the feasibility of the model using a wide range of data from an iPhone, and present proof-of-concept results for how the model can be combined with an inertial navigation system for three-dimensional inertial navigation.


Sexual Functioning and Behavior of Men with Body Dysmorphic Disorder Concerning Penis Size Compared with Men Anxious about Penis Size and with Controls: A Cohort Study


title: Advancing the State-of-the-Art in Hardware Trojans Detection

abstract: Over the past decade, Hardware Trojans (HTs) research community has made significant progress towards developing effective countermeasures for various types of HTs, yet these countermeasures are shown to be circumvented by sophisticated HTs designed subsequently. Therefore, instead of guaranteeing a certain (low) false negative rate for a small constant set of publicly known HTs, a rigorous security framework of HTs should provide an effective algorithm to detect any HT from an exponentially large class (exponential in number of wires in IP core) of HTs with negligible false negative rate. In this work, we present HaTCh, the first rigorous algorithm of HT detection within the paradigm of pre-silicon logic testing based tools. HaTCh detects any HT from $H_D$ , a huge class of deterministic HTs which is orders of magnitude larger than the small subclass (e.g., TrustHub) considered in the current literature. We prove that HaTCh offers negligible false negative rate and controllable false positive rate for the class $H_D$ . Given certain global characteristics regarding the stealthiness of the HT within $H_D$ , the computational complexity of HaTCh for practical HTs scales polynomially with the number of wires in the IP core. We implement and test HaTCh on TrustHub and other sophisticated HTs.


A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews


title: Real-Time System for Monitoring Driver Vigilance

abstract: This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented


title: Research on public opinion based on Big Data

abstract: Public opinion is the people's response for social phenomena, issues, hot topics, attitudes, emotions, and so on. It reflects the focus problems of the current time of the society. By analyzing the public opinion, we can infer what will happen in the next time, and give better decision support for governments and businesses. Big Data technology is becoming a powerful data analyzing tools for massive data in recent years. Hadoop is an open source massive data processing platform based on Big Data. Mahout is a data mining algorithms' set based on Hadoop, which is designed for processing large-scale and complex data. In most instances, the public opinion information contains many text messages. For many traditional text mining algorithms, it is almost impossible to handle high dimensional data concerns large-volume and complex data sets. Hence, this paper uses Mahout text mining algorithms to process public opinion information.


title: Large-scale distributed L-BFGS

abstract: With the increasing demand for examining and extracting patterns from massive amounts of data, it is critical to be able to train large models to fulfill the needs that recent advances in the machine learning area create. L-BFGS (Limited-memory Broyden Fletcher Goldfarb Shanno) is a numeric optimization method that has been effectively used for parameter estimation to train various machine learning models. As the number of parameters increase, implementing this algorithm on one single machine can be insufficient, due to the limited number of computational resources available. In this paper, we present a parallelized implementation of the L-BFGS algorithm on a distributed system which includes a cluster of commodity computing machines. We use open source HPCC Systems (High-Performance Computing Cluster) platform as the underlying distributed system to implement the L-BFGS algorithm. We initially provide an overview of the HPCC Systems framework and how it allows for the parallel and distributed computations important for Big Data analytics and, subsequently, we explain our implementation of the L-BFGS algorithm on this platform. Our experimental results show that our large-scale implementation of the L-BFGS algorithm can easily scale from training models with millions of parameters to models with billions of parameters by simply increasing the number of commodity computational nodes.


title: How voice call technology poses security threats in 4G LTE networks

abstract: To support voice calls vital to mobile users and carriers, 4G LTE cellular networks adopt two solutions: VoLTE (Voice Over LTE) and CSFB (Circuit-Switched FallBack). In this paper, we disclose that both schemes are harmful to mobile users from a security perspective. The adoption of the latest VoLTE allows an attacker to manipulate the radio resource states of the victim's device in a silent call attack, thereby draining the victim's battery 5–8 times faster. CSFB exhibits two vulnerabilities of exposing 4G↔3G network switch to adversaries. This can be further exploited to launch ping-pong attacks where mobile users may suffer from up to 91.5% performance downgrade, or 4G denial-of-service (DoS) attacks where mobile users are deprived of 4G LTE connectivity without their consent. We devise two proof-of-concept attacks as showcases, and demonstrate their viability over operational LTE networks. We analyze their root causes and uncover that the problems lie in seemingly sound design decisions for functional correctness but such choices bear unexpected and intriguing implications for security design. We finally propose remedies to mitigate the attack damage.


title: New MOS-C biquad filter using the current feedback operational amplifier

abstract: A new MOS-C bandpass-low-pass filter using the current feedback operational amplifier (CFOA) is presented. The filter employs two CFOAs, eight MOS transistors operating in the nonsaturation region, and two grounded capacitors. The proposed MOS-C filter has the advantage of independent control of Q and /spl omega//sub 0/. PSpice simulation results for the proposed filter are given.


title: Large Scale Terrain Generation from Tectonic Uplift and Fluvial Erosion

abstract: At large scale, landscapes result from the combination of two major processes: tectonics which generate the main relief through crust uplift, and weather which accounts for erosion. This paper presents the first method in computer graphics that combines uplift and hydraulic erosion to generate visually plausible terrains. Given a user-painted uplift map, we generate a stream graph over the entire domain embedding elevation information and stream flow. Our approach relies on the stream power equation introduced in geology for hydraulic erosion. By combining crust uplift and stream power erosion we generate large realistic terrains at a low computational cost. Finally, we convert this graph into a digital elevation model by blending landform feature kernels whose parameters are derived from the information in the graph. Our method gives high-level control over the large scale dendritic structures of the resulting river networks, watersheds, and mountains ridges.


title: A comparison of message passing and shared memory architectures for data parallel programs

abstract: Shared memory and message passing are two opposing communication models for parallel multicomputer architectures. Comparing such architectures has been difficult, because applications must be hand-crafted for each architecture, often resulting in radically different sources for comparison. While it is clear that shared memory machines are currently easier to program, in the future, programs will be written in high-level languages and compiled to the specific parallel target, thus eliminating this difference.In this paper, we evaluate several parallel architecture alternatives --- message passing, NUMA, and cachecoherent shared memory --- for a collection of scientific benchmarks written in C*, a data-parallel language. Using a single suite of C* source programs, we compile each benchmark and simulate the interconnect for the alternative models. Our objective is to examine underlying, technology-independent costs inherent in each alternative. Our results show the relative work required to execute these data parallel programs on the different architectures, and point out where some models have inherent advantages for particular data-parallel program styles.


title: Involution of a Large Parotid Hemangioma with Oral Propranolol: An Illustrative Report and Review of the Literature

abstract: Propranolol has emerged as a new treatment option for infantile hemangiomas. We describe a 20-month-old boy with a large right parotid hemangioma diagnosed at the age of 37 days. Starting at the age of 2.5 months, he received oral propranolol for 6.5 months. Although the mass regressed, it recurred when propranolol was discontinued. He was successfully retreated at the age of 11 months with propranolol for another 5.5 months without further recurrences. Treatment was tolerated well. Our paper and a review of the literature demonstrate that propranolol appears to be safe and effective for symptomatic infantile parotid gland hemangiomas.


title: One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case Study on Time Series Classification

abstract: Bio-inspired optimization algorithms have been successfully applied to solve many problems in engineering, science, and economics. In computer science bio-inspired optimization has different applications in different domains such as software engineering, networks, data mining, and many others. However, some applications may not be appropriate or even correct. In this paper we study this phenomenon through a particular method which applies the genetic algorithms on a time series classification task to set the weights of the similarity measures used in a combination that is used to classify the time series. The weights are supposed to be obtained by applying an optimization process that gives optimal classification accuracy. We show in this work, through examples, discussions, remarks, explanations, and experiments, that the aforementioned method of optimization is not correct and that completely randomly-chosen weights for the similarity measures can give the same classification accuracy.


title: Linking ecosystem processes and ecosystem services

abstract: The metaphor of ecosystem service may blind us to the complexity of the natural systems which underpin and produce services. We reviewed key references and propose a framework to illustrate the social system relying on the ecological system and the relationships between ecosystem composition, ecosystem structure, ecosystem processes and ecosystem services, in order to reduce this complexity. We argue that plans to manage ecosystem services will not be successful without a deep understanding of their link with the ecosystem processes that support them. By linking ecosystem processes and ecosystem services, we explore the possible determinants of the biodiversity components on the quantity, quality and reliability of ecosystem services at all levels, and its usefulness in making targeted decisions. Disentangling the complex interrelationships among multiple ecosystem services from the driven processes is helpful in lowering the risk of unwanted trade-offs, and taking advantage of synergies. In landscape management, it is advisable to design suitable ecosystem structures for maximizing ecosystem services based on knowledge of the natural ecosystem processes.


title: Structures of the first and second double-stranded RNA-binding domains of human TAR RNA-binding protein.

abstract: The TAR RNA-binding Protein (TRBP) is a double-stranded RNA (dsRNA)-binding protein, which binds to Dicer and is required for the RNA interference pathway. TRBP consists of three dsRNA-binding domains (dsRBDs). The first and second dsRBDs (dsRBD1 and dsRBD2, respectively) have affinities for dsRNA, whereas the third dsRBD (dsRBD3) binds to Dicer. In this study, we prepared the single domain fragments of human TRBP corresponding to dsRBD1 and dsRBD2 and solved the crystal structure of dsRBD1 and the solution structure of dsRBD2. The two structures contain an α-β-β-β-α fold, which is common to the dsRBDs. The overall structures of dsRBD1 and dsRBD2 are similar to each other, except for a slight shift of the first α helix. The residues involved in dsRNA binding are conserved. We examined the small interfering RNA (siRNA)-binding properties of these dsRBDs by isothermal titration colorimetry measurements. The dsRBD1 and dsRBD2 fragments both bound to siRNA, with dissociation constants of 220 and 113 nM, respectively. In contrast, the full-length TRBP and its fragment with dsRBD1 and dsRBD2 exhibited much smaller dissociation constants (0.24 and 0.25 nM, respectively), indicating that the tandem dsRBDs bind simultaneously to one siRNA molecule. On the other hand, the loop between the first α helix and the first β strand of dsRBD2, but not dsRBD1, has a Trp residue, which forms hydrophobic and cation-π interactions with the surrounding residues. A circular dichroism analysis revealed that the thermal stability of dsRBD2 is higher than that of dsRBD1 and depends on the Trp residue.


Neighboring gray level dependence matrix for texture classification


title: Cable-suspended load lifting by a quadrotor UAV: hybrid model, trajectory generation, and control

abstract: In the last years, autonomous aerial vehicles have become promising mobile robotic platforms capable of manipulating external objects. In particular, quadrotors, rotorcrafts with four propellers, have been used for aerial transportation of cable-suspended loads. A critical step before transporting a payload is the lift maneuver. However, the analysis and planning of this maneuver have received a little attention in the literature so far. In this work, we decompose the cable-suspended load lifting into three simpler discrete states or modes: Setup, Pull, and Raise. Each of these states represents the dynamics of the quadrotor-load system at particular regimes during the maneuver. Furthermore, we define a hybrid system based on these states and show that it is a differentially-flat hybrid system. Exploiting this property, we generate a trajectory by using a series of waypoints associated with each mode. We design a nonlinear hybrid controller to track this trajectory and therefore execute the lift maneuver. We verify the proposed approach by carrying out experiments on an actual quadrotor with a cable-suspended load.


title: Normalization as a canonical neural computation

abstract: There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.


title: Leading Johnny to Water: Designing for Usability and Trust

abstract: Although the means and the motivation for securing private messages and emails with strong end-to-end encryption exist, we have yet to see the widespread adoption of existing implementations. Previous studies have suggested that this is due to the lack of usability and understanding of existing systems such as PGP. A recent study by Ruoti et al. suggested that transparent, standalone encryption software that shows ciphertext and allows users to manually participate in the encryption process is more trustworthy than integrated, opaque software and just as usable. In this work, we critically examine this suggestion by revisiting their study, deliberately investigating the effect of integration and transparency on users’ trust. We also implement systems that adhere to the OpenPGP standard and use end-to-end encryption without reliance on third-party key escrow servers. We find that while approximately a third of users do in fact trust standalone encryption applications more than browser extensions that integrate into their webmail client, it is not due to being able to see and interact with ciphertext. Rather, we find that users hold a belief that desktop applications are less likely to transmit their personal messages back to the developer of the software. We also find that despite this trust difference, users still overwhelmingly prefer integrated encryption software, due to the enhanced user experience it provides. Finally, we provide a set of design principles to guide the development of future consumerfriendly end-to-end encryption tools.


title: The diffusion of misinformation on social media: Temporal pattern, message, and source

abstract: This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temporal pattern, content mutation, and sources of misinformation. We traced the lifecycle of 17 popular political rumors that circulated on Twitter over 13 months during the 2012 U.S. presidential election. Using text analysis based on time series, we found that while false rumors (misinformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension around the target dissolves. We observed that rumors resurface by partisan news websites that repackage the old rumor into news and, gain visibility by influential Twitter users who introduce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mechanism by which these messages evolve over time. False political rumors tend to resurface multiple times after the initial publication.False political rumors often turn into a more intense and extreme version over time.Resurged old political rumors tend to be presented as news.None-traditional partisan media are often behind the constant generation of false news.


title: Enhancing Cognition with Video Games: A Multiple Game Training Study

abstract: Background ::: Previous evidence points to a causal link between playing action video games and enhanced cognition and perception. However, benefits of playing other video games are under-investigated. We examined whether playing non-action games also improves cognition. Hence, we compared transfer effects of an action and other non-action types that required different cognitive demands. ::: ::: ::: Methodology/Principal Findings ::: We instructed 5 groups of non-gamer participants to play one game each on a mobile device (iPhone/iPod Touch) for one hour a day/five days a week over four weeks (20 hours). Games included action, spatial memory, match-3, hidden- object, and an agent-based life simulation. Participants performed four behavioral tasks before and after video game training to assess for transfer effects. Tasks included an attentional blink task, a spatial memory and visual search dual task, a visual filter memory task to assess for multiple object tracking and cognitive control, as well as a complex verbal span task. Action game playing eliminated attentional blink and improved cognitive control and multiple-object tracking. Match-3, spatial memory and hidden object games improved visual search performance while the latter two also improved spatial working memory. Complex verbal span improved after match-3 and action game training. ::: ::: ::: Conclusion/Significance ::: Cognitive improvements were not limited to action game training alone and different games enhanced different aspects of cognition. We conclude that training specific cognitive abilities frequently in a video game improves performance in tasks that share common underlying demands. Overall, these results suggest that many video game-related cognitive improvements may not be due to training of general broad cognitive systems such as executive attentional control, but instead due to frequent utilization of specific cognitive processes during game play. Thus, many video game training related improvements to cognition may be attributed to near-transfer effects.


title: Unsupervised Representation Learning by Predicting Image Rotations

abstract: Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: https://github.com/gidariss/FeatureLearningRotNet .


title: A Band-Notched UWB Monopole Antenna With High Notch-Band-Edge Selectivity

abstract: In this paper, a novel band-notched ultrawideband (UWB) monopole antenna with high notch-band-edge selectivity is proposed and demonstrated. The proposed antenna consists of a radiation patch and an embedded second-order bandstop filter. Using the same substrate area as a fundamental UWB antenna, a bandstop filter composed of a nonuniform short-circuited stub and coupled open-/short-circuited stub resonators, is designed into the fundamental antenna. A detail design procedure for the proposed antenna with a second-order maximally flat bandstop filter at 5.5 GHz is presented. As compared to the fundamental antenna, the proposed UWB antenna provides good notch-band suppression from 5.15 to 5.95 GHz, in which the normalized total radiated powers in the notch band are lower than -12 dB. Also, the proposed structure provides high band-edge selectivity and flat return loss in the notch band. The measured results, including the return loss and total radiated power, agree with the designed ones.


title: An attention-based effective neural model for drug-drug interactions extraction

abstract: Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory. In this study, we propose an effective model that classifies DDIs from the literature by combining an attention mechanism and a recurrent neural network with long short-term memory (LSTM) units. In our approach, first, a candidate-drug-oriented input attention acting on word-embedding vectors automatically learns which words are more influential for a given drug pair. Next, the inputs merging the position- and POS-embedding vectors are passed to a bidirectional LSTM layer whose outputs at the last time step represent the high-level semantic information of the whole sentence. Finally, a softmax layer performs DDI classification. Experimental results from the DDIExtraction 2013 corpus show that our system performs the best with respect to detection and classification (84.0% and 77.3%, respectively) compared with other state-of-the-art methods. In particular, for the Medline-2013 dataset with long and complex sentences, our F-score far exceeds those of top-ranking systems by 12.6%. Our approach effectively improves the performance of DDI classification tasks. Experimental analysis demonstrates that our model performs better with respect to recognizing not only close-range but also long-range patterns among words, especially for long, complex and compound sentences.


title: Log Analysis and Event Correlation Using Variable Temporal Event Correlator (VTEC)

abstract: System administrators have utilized log analysis for decades to monitor and automate their environments. As compute environments grow, and the scope and volume of the logs increase, it becomes more difficult to get timely, useful data and appropriate triggers for enabling automation using traditional tools like Swatch. Cloud computing is intensifying this problem as the number of systems in datacenters increases dramatically. To address these problems at AMD, we developed a tool we call the Variable Temporal Event Correlator, or VTEC. ::: ::: VTEC has unique design features, such as inherent multi-threaded/multi-process design, a flexible and extensible programming interface, built-in job queuing, and a novel method for storing and describing temporal information about events, that well suit it for quickly and efficiently handling a broad range of event correlation tasks in real-time. These features also enable VTEC to scale to tens of gigabytes of log data processed per day. This paper describes the architecture, use, and efficacy of this tool, which has been in production at AMD for more than four years.


title: Breast milk, microbiota, and intestinal immune homeostasis

abstract: Newborns adjust to the extrauterine environment by developing intestinal immune homeostasis. Appropriate initial bacterial colonization is necessary for adequate intestinal immune development. An environmental determinant of adequate colonization is breast milk. Although the full-term infant is developmentally capable of mounting an immune response, the effector immune component requires bacterial stimulation. Breast milk stimulates the proliferation of a well-balanced and diverse microbiota, which initially influences a switch from an intrauterine TH2 predominant to a TH1/TH2 balanced response and with activation of T-regulatory cells by breast milk-stimulated specific organisms (Bifidobacteria, Lactobacillus, and Bacteroides). As an example of its effect, oligosaccharides in breast milk are fermented by colonic bacteria producing an acid milieu for bacterial proliferation. In addition, short-chain fatty acids in breast milk activate receptors on T-reg cells and bacterial genes, which preferentially mediate intestinal tight junction expression and anti-inflammation. Other components of breast milk (defensins, lactoferrin, etc.) inhibit pathogens and further contribute to microbiota composition. The breast milk influence on initial intestinal microbiota also prevents expression of immune-mediated diseases (asthma, inflammatory bowel disease, type 1 diabetes) later in life through a balanced initial immune response, underscoring the necessity of breastfeeding as the first source of nutrition.


title: Attention to Scale: Scale-Aware Semantic Image Segmentation

abstract: Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. One common way to extract multi-scale features is to feed multiple resized input images to a shared deep network and then merge the resulting features for pixelwise classification. In this work, we propose an attention mechanism that learns to softly weight the multi-scale features at each pixel location. We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model. The proposed attention model not only outperforms average- and max-pooling, but allows us to diagnostically visualize the importance of features at different positions and scales. Moreover, we show that adding extra supervision to the output at each scale is essential to achieving excellent performance when merging multi-scale features. We demonstrate the effectiveness of our model with extensive experiments on three challenging datasets, including PASCAL-Person-Part, PASCAL VOC 2012 and a subset of MS-COCO 2014.


title: A-Lamp: Adaptive Layout-Aware Multi-patch Deep Convolutional Neural Network for Photo Aesthetic Assessment

abstract: Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the original images is impaired because of potential loss of fine grained details and holistic image layout. However, such fine grained details and holistic image layout is critical for evaluating an images aesthetics. In this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This novel scheme is able to accept arbitrary sized images, and learn from both fined grained details and holistic image layout simultaneously. To enable training on these hybrid inputs, we extend the method by developing a dedicated double-subnet neural network structure, i.e. a Multi-Patch subnet and a Layout-Aware subnet. We further construct an aggregation layer to effectively combine the hybrid features from these two subnets. Extensive experiments on the large-scale aesthetics assessment benchmark (AVA) demonstrate significant performance improvement over the state-of-the-art in photo aesthetic assessment.


title: A survey of spectrum sensing algorithms for cognitive radio applications

abstract: The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.


title: Tuning of PID Controllers Based on Bode?s Ideal Transfer Function

abstract: This paper presents a new strategy for tuning PID controllers based on a fractional reference model. The model is represented as an ideal closed-loop system whose open-loop is given by the Bode’s ideal transfer function. The PID controller parameters are determined by the minimization of the integral square error (ISE) between the time responses of the desired fractional reference model and of the system with the PID controller. The resulting closed-loop system (with the PID controller) has the desirable feature of being robust to gain variations with step responses exhibiting an iso-damping property. Several examples are presented that demonstrate the effectiveness and validity of the proposed methodology.


Growing homophilic networks are natural optimal navigable small worlds


title: DataMPI: Extending MPI to Hadoop-Like Big Data Computing

abstract: MPI has been widely used in High Performance Computing. In contrast, such efficient communication support is lacking in the field of Big Data Computing, where communication is realized by time consuming techniques such as HTTP/RPC. This paper takes a step in bridging these two fields by extending MPI to support Hadoop-like Big Data Computing jobs, where processing and communication of a large number of key-value pair instances are needed through distributed computation models such as MapReduce, Iteration, and Streaming. We abstract the characteristics of key-value communication patterns into a bipartite communication model, which reveals four distinctions from MPI: Dichotomic, Dynamic, Data-centric, and Diversified features. Utilizing this model, we propose the specification of a minimalistic extension to MPI. An open source communication library, DataMPI, is developed to implement this specification. Performance experiments show that DataMPI has significant advantages in performance and flexibility, while maintaining high productivity, scalability, and fault tolerance of Hadoop.


title: The Cloud Adoption Toolkit: Supporting Cloud Adoption Decisions in the Enterprise

abstract: Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the adoption of cloud computing in their organisations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the cloud. The case study shows that running systems on the cloud using a traditional "always on" approach can be less cost effective, and the elastic nature of the cloud has to be used to reduce costs. Therefore, decision makers have to be able to model the variations in resource usage and their systems deployment options to obtain accurate cost estimates.


title: Performances of Texas Instruments DSP and Xilinx FPGAs for Cooley-Tukey and Grigoryan FFT Algorithms

abstract: Frequency analysis plays a vital role in the applications like cryptanalysis, steganalysis, system identification, controller tuning, speech recognition, noise filters, etc. Discrete Fourier transform (DFT) is a principal mathematical method for the frequency analysis. The way of splitting the DFT gives out various fast algorithms. In this paper, we present the implementation of two fast algorithms for the DFT for evaluating their performance. One of them is the popular radix-2 Cooley-Tukey fast Fourier transform (FFT) algorithm and the other one is the Grigoryan FFT based on the splitting by the paired transform. We evaluate the performance of these algorithms by implementing them on the TMS320C5416 DSP and also on the Virtex-II FPGAs. Finally, we show that the paired-transform-based algorithm of the FFT is faster than the radix-2 FFT; consequently, it is useful for higher sampling rates. We also discuss the performances of TMS DSP and Xilinx FPGAs and tradeoffs.


title: Perineurium: evidence for contractile elements.

abstract: Electron-microscopic study of mouse sciatic nerve reveals that perineurial cells contain filaments and associated opaque regions similar to those observed in smooth muscle. This finding is consistent with obsevations Which suggest that nerve might have a contractile property. In addition to their function in maintaining the connective tissue stroma of perineurium, as well as being a selective diffusion barrier, perineurial cells may serve the nerve in a contractile capacity.


title: Discovery of Association Rules in Temporal Databases

abstract: Temporal databases naturally contain a wealth of information that can be unearthed by knowledge discovery and data mining techniques. Discovering association rules in market basket data have been widely studied and many algorithms have been developed. In this study, we examine discovery of association rules in temporal databases. We use the enumeration operation of the temporal relational algebra to prepare the data for discovery of association rules. To observe the changes in association rules and their statistics over the time, we can apply an incremental association rule mining technique to a series of datasets obtained over consecutive time intervals


title: Agile Methods and User-Centered Design: How These Two Methodologies are Being Successfully Integrated in Industry

abstract: A core principle of agile development is to satisfy the customer by providing valuable software on an early and continuous basis. For a software application to be valuable it should have a user interface that is usable. Recently there has been some evidence that suggests using agile methods alone does not ensure that an applications UI is usable. As a result, there is currently interest in combining Agile methods with user-centered design (UCD) practices. To support existing empirical evidence that these methodologies co-exist effectively we have conducted a study with participants that have previously combined these two methodologies. Our findings, combined with existing work show that the existing model used for agile UCD integration can be broadened into a more common model. In this paper we describe three different approaches taken by our participants to achieve this integration. We term these approaches the generalist, specialist, and the hybrid approach.


title: ISO-Standardized Smart City Platform Architecture and Dashboard

abstract: A concept guided by the ISO 37120 standard for city services and quality of life is suggested as unified framework for smart city dashboards. The slow (annual, quarterly, or monthly) ISO 37120 indicators are enhanced and complemented with more detailed and person-centric indicators that can further accelerate the transition toward smart cities. The architecture supports three tasks: acquire and manage data from heterogeneous sensors; process data originated from heterogeneous sources (sensors, OpenData, social data, blogs, news, and so on); and implement such collection and processing on the cloud. A prototype application based on the proposed architecture concept is developed for the city of Skopje, Macedonia. This article is part of a special issue on smart cities.


title: Physiology of soccer : An update

abstract: Soccer is the most popular sport in the world and is performed by men and women, children and adults with different levels of expertise. Soccer performance depends upon a myriad of factors such as technical/biomechanical, tactical, mental and physiological areas. One of the reasons that soccer is so popular worldwide is that players may not need to have an extraordinary capacity within any of these performance areas, but possess a reasonable level within all areas. However, there are trends towards more systematic training and selection influencing the anthropometric profiles of players who compete at the highest level. As with other activities, soccer is not a science, but science may help improve performance. Efforts to improve soccer performance often focus on technique and tactics at the expense of physical fitness.


title: BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

abstract: Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing. The massive and economic computing power brought forth by the emerging GPU architectures drives interest in implementation of compute-intensive level 3 BLAS on multi-GPU systems. In this paper, we investigate existing multi-GPU level 3 BLAS and present that 1) issues, such as the improper load balancing, inefficient communication, insufficient GPU stream level concurrency and data caching, impede current implementations from fully harnessing heterogeneous computing resources; 2) and the inter-GPU Peer-to-Peer(P2P) communication remains unexplored. We then present BLASX: a highly optimized multi-GPU level-3 BLAS. We adopt the concepts of algorithms-by-tiles treating a matrix tile as the basic data unit and operations on tiles as the basic task. Tasks are guided with a dynamic asynchronous runtime, which is cache and locality aware. The communication cost under BLASX becomes trivial as it perfectly overlaps communication and computation across multiple streams during asynchronous task progression. It also takes the current tile cache scheme one step further by proposing an innovative 2-level hierarchical tile cache, taking advantage of inter-GPU P2P communication. As a result, linear speedup is observable with BLASX under multi-GPU configurations; and the extensive benchmarks demonstrate that BLASX consistently outperforms the related leading industrial and academic implementations such as cuBLAS-XT, SuperMatrix, MAGMA.


title: Energy Harvesting system for smart shoes

abstract: The aim of Energy Harvesting is to capture free energy, available without costs, from the environment. The development of advanced techniques allowed to capture, to store and to manage amounts of natural energy, transforming them into electrical energy. Moreover, advancements in microprocessor technology have increased power efficiency, effectively reducing power consumption requirements. From the point of view of wearable electronics devices, the most efficient Energy Harvesting system for energy capturing is that to use devices inserted into the shoes. These devices are situated into the soles where, during the movement, a force is exerted. Using piezoelectric elements and electromagnetic induction systems, this force allows recovering a high quantity of electrical energy useful for sensor supply and complex monitoring systems. In this paper, four different solutions of smart shoes that use Energy Harvesting systems are presented, with the aim to recover energy to supply a GPS device. Preliminary comparative results of 4 different solutions are compared on the bases of costs, production feasibility and energy harvesting capabilities.


title: An investigation on touch biometrics: Behavioral factors on screen size, physical context and application context

abstract: With increasing privacy concerns and security demands present within mobile devices, behavioral biometric solutions, such as touch based user recognition, have been researched as of recent. However, several vital contextual behavior factors (i.e., screen size, physical and application context) and how those effect user identification performance, remains unaddressed in previous studies. In this paper we first introduce a context-aware mobile user recognition method. Then a comparative experiment to evaluate the impacts of these factors in relation to user identification performance is presented. Experimental results have demonstrated that a user's touch screen usage behavior may be affected given different contextual behavior information. Furthermore, several interesting occurrences have been found in the results: 1) screen size of a smartphone device changes the way a user touches and holds the device. A larger screen size will provide more potential methods of interacting with the device and in effect, a higher user recognition accuracy as well; and 2) application context and physical activity context can aid in achieving higher accuracy for user recognition.


An Algorithm for Predictive Data Mining Approach in Medical Diagnosis


Low Cost Self-assistive Voice Controlled Technology for Disabled People


title: Hardware Counted Profile-Guided Optimization

abstract: Profile-Guided Optimization (PGO) is an excellent means to improve the performance of a compiled program. Indeed, the execution path data it provides helps the compiler to generate better code and better cacheline packing. ::: At the time of this writing, compilers only support instrumentation-based PGO. This proved effective for optimizing programs. However, few projects use it, due to its complicated dual-compilation model and its high overhead. Our solution of sampling Hardware Performance Counters overcome these drawbacks. In this paper, we propose a PGO solution for GCC by sampling Last Branch Record (LBR) events and using debug symbols to recreate source locations of binary instructions. ::: By using LBR-Sampling, the generated profiles are very accurate. This solution achieved an average of 83% of the gains obtained with instrumentation-based PGO and 93% on C++ benchmarks only. The profiling overhead is only 1.06% on average whereas instrumentation incurs a 16% overhead on average.


title: The effects of pay level on organization-based self-esteem and performance : A field study

abstract: Most compensation managers implicitly assume (or perhaps hope) that high pay levels will maintain and enhance future performance. To date, this assumption has been largely untested. Given the importance of pay level and the large expense that pay represents to most organizations, understanding how and why pay level influences the behaviour of employees in organizations is an important question. The purpose of this study is to examine the motivational effects of pay level on employee performance. To examine these issues, we collected field study data from a variety of sources, at three different times, and assessed the effects of employee pay level on subsequent self-esteem and performance. Specifically, we hypothesized that the effects of pay level on performance would be mediated by pay level effects on organization-based self-esteem. We base this hypothesis on the premise that level of pay within an organization communicates a sense of how much the organization values an employee and thus affects employee organization-based self-esteem which, in turn, enhances job performance. After controlling for organization tenure, and previous pay change, results supported a mediated model that suggests that pay level affects employee self-esteem, which in turn, affects employee performance.


title: INSIGNIA: An IP-Based Quality of Service Framework for Mobile ad Hoc Networks

abstract: We present the design, implementation, and evaluation of INSIGNIA, an IP-based quality of service framework that supports adaptive services in mobile ad hoc networks. The framework is based on an in-band signaling and soft-state resource management approach that is well suited to supporting mobility and end-to-end quality of service in highly dynamic environments where the network topology, node connectivity, and end-to-end quality of service are time varying. Architecturally INSIGNIA is designed to support fast reservation, restoration, and end-to-end adaptation based on the inherent flexibility and robustness and scalability found in IP networks. We evaluate the framework, paying particular attention to the performance of the in-band signaling system, which helps counter time-varying network dynamics in support of the delivery of adaptive services. Our results show the benefit of our framework under diverse mobility, traffic, and channel conditions.


title: Vehicle localization using road markings

abstract: Reliable lane-level localization is a requirement for many driver-assistance methods as well as for autonomous driving. Localization using cameras is desirable due to ubiquity and cheapness of sensors but is hard to achieve reliably. We propose a method towards reliable visual localization using traffic signs painted on the road such as arrows, pedestrian crossings, and speed limits. These road markings are relatively easily detected since they are designed to be highly conspicuous. Our method automatically recognizes road markings and uses features detected within them to compute the location of the vehicle. This provides an absolute global localization if the road markings have been surveyed before hand, and relative positioning information otherwise. We demonstrate using experiments and with groundtruth data that our method provides accurate lane-level visual localization under various lighting conditions and using various types of road markings.


title: Addressing the Class Imbalance Problem in Medical Datasets

abstract: A well balanced dataset is very important for creating a good prediction model. Medical datasets are often not balanced in their class labels. Most existing classification methods tend to perform poorly on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper we examine the performance of over-sampling and under-sampling techniques to balance cardiovascular data. Well known over-sampling technique SMOTE is used and some under-sampling techniques are also explored. An improved under sampling technique is proposed. Experimental results show that the proposed method displays significant better performance than the existing methods.


title: Feed-forward frequency compensator for APWM HB flyback converter

abstract: A feed-forward frequency compensator for the asymmetrical pulse-width modulated (APWM) half-bridge (HB) flyback converter is proposed to increase power conversion efficiency. Under variable input voltage, the APWM HB flyback converter cannot be satisfied with the soft commutation of the output rectifier because the variation of the PWM duty cycle induces an improper turn-off duration. It means that the resonance of the primary current can be out of the ZCS condition with respect to the input voltage variation. The proposed feed-forward frequency compensator can properly change the operating frequency of the converter to preserve the soft commutation of the output rectifier with respect to the input voltage variation. Experimental results using a 240 W prototype converter were obtained to verify that the proposed compensation algorithm can improve the power conversion efficiency under the input voltage and load variations.


title: 2T 2:1 MUX based 1 bit full adder design

abstract: This paper puts forward a methodology for designing 1 bit full adder using a 2T mux. The 2T mux is combined in a specific manner to get a full adder with sum and carry output. The resulting 1 bit full adder is made up of 16 transistors. The simulation is done using Cadence Virtuoso Simulator using 180nm technology and 1.8V power supply. The results show the efficiency of the design.


title: Pathology imaging informatics for quantitative analysis of whole-slide images

abstract: Objectives With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. ::: ::: Target audience This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. ::: ::: Scope First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.