title: OpenACC: first experiences with real-world applications

abstract: Today's trend to use accelerators like GPGPUs in heterogeneous computer systems has entailed several low-level APIs for accelerator programming. However, programming these APIs is often tedious and therefore unproductive. To tackle this problem, recent approaches employ directive-based high-level programming for accelerators. In this work, we present our first experiences with OpenACC, an API consisting of compiler directives to offload loops and regions of C/C++ and Fortran code to accelerators. We compare the performance of OpenACC to PGI Accelerator and OpenCL for two real-world applications and evaluate programmability and productivity. We find that OpenACC offers a promising ratio of development effort to performance and that a directive-based approach to program accelerators is more efficient than low-level APIs, even if suboptimal performance is achieved.


title: Can micro-volunteering help in Africa?

abstract: Micro-volunteering has been defined as convenient, bite-sized, crowd-sourced, and network-managed. Micro-volunteers donate their time and energy for organisations which they may not have previously encountered (crowd-sourced), at a time which is convenient to the micro-volunteer, and in small pieces of time (bite-sized). This paper looks at a micro-volunteering project where participants can volunteer for five to ten minutes at a time using a smart phone and assist pupils with their mathematics homework.


title: Design of low-phase-noise CMOS ring oscillators

abstract: This paper presents a framework for modeling the phase noise in complementary metal-oxide-semiconductor (CMOS) ring oscillators. The analysis considers both linear and nonlinear operations, and it includes both device noise and digital switching noise coupled through the power supply and substrate. In this paper, we show that fast rail-to-rail switching is required in order to achieve low phase noise. Further, flicker noise from the bias circuit can potentially dominate the phase noise at low offset frequencies. We define the effective Q factor for ring oscillators with large and nonlinear voltage swings and predict its increase for CMOS processes with smaller feature sizes. Our phase-noise analysis is validated via simulation and measurement results for ring oscillators fabricated in a number of CMOS processes.


title: Road sign recognition system on Raspberry Pi

abstract: Digital image processing, i.e. the use of computer systems to process pictures, has applications in many fields, including of medicine, space exploration, geology and oceanography and continues to increase in its applicability. The main objective of this paper is to demonstrate the ability of image processing algorithms on a small computing platform. Specifically we created a road sign recognition system based on an embedded system that reads and recognizes speed signs. The paper describes the characteristics of speed signs, requirements and difficulties behind implementing a real-time base system with embedded system, and how to deal with numbers using image processing techniques based on shape and dimension analysis. The paper also shows the techniques used for classification and recognition. Color analysis also plays a specifically important role in many other different applications for road sign detection, this paper points to many problems regarding stability of color detection due to daylight conditions, so absence of color model can led a better solution. In this project lightweight techniques were mainly used due to limitation of real- time based application and Raspberry Pi capabilities. Raspberry Pi is the main target for the implementation, as it provides an interface between sensors, database, and image processing results, while also performing functions to manipulate peripheral units (usb dongle, keyboard etc.).


title: Open R&D and open innovation: exploring the phenomenon

abstract: There is currently a broad awareness of open innovation and its relevance to corporate R&D. The implications and trends that underpin open innovation are actively discussed in terms of strategic, organizational, behavioral, knowledge, legal and business perspectives, and its economic implications. This special issue aims to advance the R&D, innovation, and technology management perspective by building on past and present studies in the field and providing future directions. Recent research, including the papers in this special issue, demonstrates an increasing range of situations where the concept is regarded as applicable. Most research to date has followed the outside-in process of open innovation, while the inside-out process remains less explored. A third coupled process of open innovation is also attracting significant research attention. These different processes show why it is necessary to have a full understanding of how and where open innovation can add value in knowledge-intensive processes. There may be a need for a creative interpretation and adaptation of the value propositions, or business models, in each situation. In other words, there are important implications for new and emerging methods of R&D management.


title: A Game-theoretic Approach to Data Interaction: A Progress Report

abstract: As most database users cannot precisely express their information needs in the form of database queries, it is challenging for database query interfaces to understand and satisfy their intents. Database systems usually improve their understanding of users' intents by collecting their feedback on the answers to the users' imprecise and ill-specified queries. Users may also learn to express their queries precisely during their interactions with the database system. In this paper, we report our progress on developing a formal framework for representing and understanding information needs in database querying and exploration. Our framework considers querying as a collaboration between the user and the database system to establish a mutual language for representing information needs. We formalize this collaboration as a signaling game between two potentially rational agents: the user and the database system. We believe that this framework naturally models the long-term interaction of users and database systems.


title: Elementary Gates for Quantum Computation

abstract: We show that a set of gates that consists of all one-bit quantum gates (U(2)) and the two-bit exclusive-or gate (that maps Boolean values (x,y) to (x,x ⊕y)) is universal in the sense that all unitary operations on arbitrarily many bits n (U(2 n )) can be expressed as compositions of these gates. We investigate the number of the above gates required to implement other gates, such as generalized Deutsch-Toffoli gates, that apply a specific U(2) transformation to one input bit if and only if the logical AND of all remaining input bits is satisfied. These gates play a central role in many proposed constructions of quantum computational networks. We derive upper and lower bounds on the exact number of elementary gates required to build up a variety of two- and three-bit quantum gates, the asymptotic number required for n-bit Deutsch-Toffoli gates, and make some observations about the number required for arbitrary n-bit unitary operations.


title: Evaluating the helpfulness of linked entities to readers

abstract: When we encounter an interesting entity (e.g., a person's name or a geographic location) while reading text, we typically search and retrieve relevant information about it. Entity linking (EL) is the task of linking entities in a text to the corresponding entries in a knowledge base, such as Wikipedia. Recently, EL has received considerable attention. EL can be used to enhance a user's text reading experience by streamlining the process of retrieving information on entities. Several EL methods have been proposed, though they tend to extract all of the entities in a document including unnecessary ones for users. Excessive linking of entities can be distracting and degrade the user experience. In this paper, we propose a new method for evaluating the helpfulness of linking entities to users. We address this task using supervised machine-learning with a broad set of features. Experimental results show that our method significantly outperforms baseline methods by approximately 5.7%-12% F1. In addition, we propose an application, Linkify, which enables developers to integrate EL easily into their web sites.


title: The air-borne particulate pollution in Beijing—concentration, composition, distribution and sources

abstract: Abstract Aerosol samples of PM 2.5 and PM 10 were collected in both summer and winter seasons from 2002 to 2003 synchronously at a traffic site, an industrial site, and a residential site in Beijing, which could basically be the representatives over Beijing. Twenty-three elements and 15 ions together with organic carbon and elemental carbon were analyzed systematically for characterization of Beijing aerosol. PM 2.5 was the major part of the inhalable particles (PM 10 ), as the ratios of PM 2.5 /PM 10 were 0.45–0.48 in summer and 0.52–0.73 in winter. SO 4 2− , NO 3 − , NH 4 + , organic matter, crustal matter, and element carbon were the six dominant species, which totally accounted for 85.8–97.7% of PM 2.5 . Secondary aerosol (SO 4 2− , NO 3 − , and NH 4 + ), road dust or/and long-range transported dust from outside Beijing, industry and motor vehicles emission, coal burning were the major contributors to the air-borne particulate pollution in Beijing. Overall, coal burning and the traffic exhausts, plus the dust from the long-range transport, could be the major sources of the aerosol pollution at Beijing. A relatively even spatial distribution of chemical species in PM 2.5 was found while in PM 10 a significant variation with the highest concentrations at the industrial site in summer and at the residential site in winter was observed. The concentrations of PM 10 , PM 2.5 as well as various chemical species were higher in winter than in summer. The contributions of mineral aerosol from outside Beijing were first estimated with a newly developed element tracer technique, which accounted for 79% and 37% of the total mineral in PM 10 and PM 2.5 in winter, and 19% and 20% in summer, respectively. During the dust storm period from 20 to 22 March, it reached up to 97% in TSP, 79% in PM 10 and 76% in PM 2.5 . This is the technique, firstly, developed for estimating the relative contributions of sources from inside and outside Beijing to the total mineral aerosol and it could provide the basic information in controlling the air-borne particulate pollution at Beijing.


title: Forecasting daily time series using periodic unobserved components time series models

abstract: A periodic time series analysis is explored in the context of unobserved components time series models that include stochastic time functions for trend, seasonal and irregular effects. Periodic time series models allow dynamic characteristics (autocovariances) to depend on the period of the year, month, week or day. In the standard multivariate approach one can interpret a periodic time series analysis as a simultaneous treatment of typically yearly time series where each series is related to a particular season. Here, the periodic analysis applies to a vector of monthly time series related to each day of the month. Particular focus is on the forecasting performance and therefore on the underlying periodic forecast function, defined by the in-sample observation weights for producing (multi-step) forecasts. These weight patterns facilitate the interpretation of periodic model extensions. A statistical state space approach is used to estimate the model and allows for irregularly spaced observations in daily time series. Recent algorithms are adopted for the computation of observation weights for forecasting based on state space models with regressor variables. The methodology is illustrated for daily Dutch tax revenues that appear to have periodic dynamic properties. The dimension of our periodic unobserved components model is relatively large as we allow each element (day) of the vector of monthly time series to have a changing seasonal pattern. Nevertheless, even with only five years of data we find that the increased periodic flexibility can help in out-of-sample forecasting for two extra years of data.


title: RNA/DNA hybrid binding affinity determines telomerase template-translocation efficiency

abstract: Telomerase synthesizes telomeric DNA repeats onto chromosome termini from an intrinsic RNA template. The processive synthesis of DNA repeats relies on a unique, yet poorly understood, mechanism whereby the telomerase RNA template translocates and realigns with the DNA primer after synthesizing each repeat. Here, we provide evidence that binding of the realigned RNA/DNA hybrid by the active site is an essential step for template translocation. Employing a template-free human telomerase system, we demonstrate that the telomerase active site directly binds to RNA/DNA hybrid substrates for DNA polymerization. In telomerase processivity mutants, the template-translocation efficiency correlates with the affinity for the RNA/DNA hybrid substrate. Furthermore, the active site is unoccupied during template translocation as a 5 bp extrinsic RNA/DNA hybrid effectively reduces the processivity of the template-containing telomerase. This suggests that strand separation and template realignment occur outside the active site, preceding the binding of realigned hybrid to the active site. Our results provide new insights into the ancient RNA/DNA hybrid binding ability of telomerase and its role in template translocation.


title: A real-time grading method of apples based on features extracted from defects

abstract: This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73%. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.


SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications


title: Re-embedding words

abstract: We present a fast method for re-purposing existing semantic word vectors to improve performance in a supervised task. Recently, with an increase in computing resources, it became possible to learn rich word embeddings from massive amounts of unlabeled data. However, some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train. We propose a method that takes as input an existing embedding, some labeled data, and produces an embedding in the same space, but with a better predictive performance in the supervised task. We show improvement on the task of sentiment classification with respect to several baselines, and observe that the approach is most useful when the training set is sufficiently small.


title: A Unified Test Case Prioritization Approach

abstract: Test case prioritization techniques attempt to reorder test cases in a manner that increases the rate at which faults are detected during regression testing. Coverage-based test case prioritization techniques typically use one of two overall strategies: a total strategy or an additional strategy. These strategies prioritize test cases based on the total number of code (or code-related) elements covered per test case and the number of additional (not yet covered) code (or code-related) elements covered per test case, respectively. In this article, we present a unified test case prioritization approach that encompasses both the total and additional strategies. Our unified test case prioritization approach includes two models (basic and extended) by which a spectrum of test case prioritization techniques ranging from a purely total to a purely additional technique can be defined by specifying the value of a parameter referred to as the fp value. To evaluate our approach, we performed an empirical study on 28 Java objects and 40 C objects, considering the impact of three internal factors (model type, choice offp value, and coverage type) and three external factors (coverage granularity, test case granularity, and programming/testing paradigm), all of which can be manipulated by our approach. Our results demonstrate that a wide range of techniques derived from our basic and extended models with uniform fp values can outperform purely total techniques and are competitive with purely additional techniques. Considering the influence of each internal and external factor studied, the results demonstrate that various values of each factor have nontrivial influence on test case prioritization techniques.


title: EventBuilder: Real-time Multimedia Event Summarization by Visualizing Social Media

abstract: Due to the ubiquitous availability of smartphones and digital cameras, the number of photos/videos online has increased rapidly. Therefore, it is challenging to efficiently browse multimedia content and obtain a summary of an event from a large collection of photos/videos aggregated in social media sharing platforms such as Flickr and Instagram. To this end, this paper presents the EventBuilder system that enables people to automatically generate a summary for a given event in real-time by visualizing different social media such as Wikipedia and Flickr. EventBuilder has two novel characteristics: (i) leveraging Wikipedia as event background knowledge to obtain more contextual information about an input event, and (ii) visualizing an interesting event in real-time with a diverse set of social media activities. According to our initial experiments on the YFCC100M dataset from Flickr, the proposed algorithm efficiently summarizes knowledge structures based on the metadata of photos/videos and Wikipedia articles.


title: Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case

abstract: Enhancing the effectiveness of city services and assisting on a more sustainable development of cities are two of the crucial drivers of the smart city concept. This paper portrays a field trial that leverages an internet of things (IoT) platform intended for bringing value to existing and future smart city infrastructures. The paper highlights how IoT creates the basis permitting integration of current vertical city services into an all-encompassing system, which opens new horizons for the progress of the effectiveness and sustainability of our cities. Additionally, the paper describes a field trial on provisioning of real time data about available parking places both indoor and outdoor. The trial has been carried out at Santander’s (Spain) downtown area. The trial takes advantage of both available open data sets as well as of a large-scale IoT infrastructure. The trial is a showcase on how added-value services can be created on top of the proposed architecture.


title: Some Consequences for Helpers Who Deliver “Cold Comfort”: Why it's Worse for Women than Men to be Inept When Providing Emotional Support

abstract: Two experiments were conducted to assess whether responses to helpers who used insensitive emotional support vary as a function of the interaction between sex of participant and helper. We hypothesized that women would evaluate an insensitive female helper and her behavior more negatively than they would an insensitive male helper. In Experiment 1, participants (N = 137) read conversations in which male or female helpers sought to comfort emotionally distressed friends. In Experiment 2, participants (N = 87) engaged in face-to-face interactions in which they were comforted by either a male or female helper who used insensitive comforting messages. Overall, the results imply that women with deficient emotional support skills may be at high risk of rejection by same-sex peers.


title: Behavioral Software Engineering : a Definition and Systematic Literature Review

abstract: Throughout the history of software engineering, the human aspects have repeatedly been recognized as important. Even though research that investigates them has been growing in the past decade, these aspects should be more generally considered.The main objective of this study is to clarify the research area concerned with human aspects of software engineering and to create a common platform for future research. In order to meet the objective, we propose a definition of the research area behavioral software engineering (BSE) and present results from a systematic literature review based on the definition.The result indicates that there are knowledge gaps in the research area of behavioral software engineering and that earlier research has been focused on a few concepts, which have been applied to a limited number of software engineering areas. The individual studies have typically had a narrow perspective focusing on few concepts from a single unit of analysis. Further, the research has rarely been conducted in collaboration by researchers from both software engineering and social science.Altogether, this review can help put a broader set of human aspects higher on the agenda for future software engineering research and practice. We present behavioral SE (BSE) and a systematic literature review.Result shows gaps in existing research; it has focused on only a few BSE concepts.BSE studies rarely consider multiple levels and focus on only a few concepts.Even though a majority of BSE research is empirical, few papers are experiments.This paper can help put a broader set of human aspects higher on the agenda.


title: Car-Park Management using Wireless Sensor Networks

abstract: A complete wireless sensor network solution for car-park management is presented in this paper. The system architecture and design are first detailed, followed by a description of the current working implementation, which is based on our DSYS25z sensing nodes. Results of a series of real experimental tests regarding connectivity, sensing and network performance are then discussed. The analysis of link characteristics in the car-park scenario shows unexpected reliability patterns which have a strong influence on MAC and routing protocol design. Two unexpected link reliability patterns are identified and documented. First, the presence of the objects (cars) being sensed can cause significant interference and degradation in communication performance. Second, link quality has a high temporal correlation but a low spatial correlation. From these observations we conclude that a) the construction and maintenance of a fixed topology is not useful and b) spatial rather than temporal message replicates can improve transport reliability.


title: Motion capture from body-mounted cameras

abstract: Motion capture technology generally requires that recordings be performed in a laboratory or closed stage setting with controlled lighting. This restriction precludes the capture of motions that require an outdoor setting or the traversal of large areas. In this paper, we present the theory and practice of using body-mounted cameras to reconstruct the motion of a subject. Outward-looking cameras are attached to the limbs of the subject, and the joint angles and root pose are estimated through non-linear optimization. The optimization objective function incorporates terms for image matching error and temporal continuity of motion. Structure-from-motion is used to estimate the skeleton structure and to provide initialization for the non-linear optimization procedure. Global motion is estimated and drift is controlled by matching the captured set of videos to reference imagery. We show results in settings where capture would be difficult or impossible with traditional motion capture systems, including walking outside and swinging on monkey bars. The quality of the motion reconstruction is evaluated by comparing our results against motion capture data produced by a commercially available optical system.


Demands on Virtual Representation of Physical Industrie 4.0 Components


title: PCB inspection for missing or misaligned components using background subtraction

abstract: Automated visual inspection (AVI) is becoming an integral part of modern surface mount technology (SMT) assembly process. This high technology assembly, produces printed circuit boards (PCB) with tiny and delicate electronic components. With the increase in demand for such PCBs, high-volume production has to cater for both the quantity and zero defect quality assurance. The ever changing technology in fabrication, placement and soldering of SMT electronic components have caused an increase in PCB defects both in terms of numbers and types. Consequently, a wide range of defect detecting techniques and algorithms have been reported and implemented in AVI systems in the past decade. Unfortunately, the turn-over rate for PCB inspection is very crucial in the electronic industry. Current AVI systems spend too much time inspecting PCBs on a component-by-component basis. In this paper, we focus on providing a solution that can cover a larger inspection area of a PCB at any one time. This will reduce inspection time and increase the throughput of PCB production. Our solution is targeted for missing and misalignment defects of SMT devices in a PCB. An alternative visual inspection approach using color background subtraction is presented to address the stated defect. Experimental results of various defect PCBs are also presented.


title: Knee Point Detection in BIC for Detecting the Number of Clusters

abstract: Bayesian Information Criterion (BIC) is a promising method for detecting the number of clusters. It is often used in model-based clustering in which a decisive first local maximum is detected as the number of clusters. In this paper, we re-formulate the BIC in partitioning based clustering algorithm, and propose a new knee point finding method based on it. Experimental results show that the proposed method detects the correct number of clusters more robustly and accurately than the original BIC and performs well in comparison to several other cluster validity indices.


title: Adaptive Orientation Model Fitting for Latent Overlapped Fingerprints Separation

abstract: Overlapped fingerprints are commonly encountered in latent fingerprints lifted from crime scenes. Such overlapped fingerprints can hardly be processed by state-of-the-art fingerprint matchers. Several methods have been proposed to separate the overlapped fingerprints. However, these methods neither provide robust separation results, nor could be generalized for most overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on adaptive orientation model fitting. Different from existing methods, our algorithm estimates the initial orientation fields in a more accurate way and then separates the orientation fields for component fingerprints through an iterative correction process. Global orientation field models are used to predict and correct the orientations in overlapped regions. Experimental results on the latent overlapped fingerprints database show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy.


title: Bluetooth RSSI based collision avoidance in multirobot environment

abstract: Multi-robot system is gaining its importance in robotic research. One critical issue in multi-robot system is collision among the mobile robots while sharing same workspace. This paper deals with the collision-free path planning for multiple mobile robots using Bluetooth RSSI value. In the proposed collision avoidance algorithm a decentralized approach with fixed priority level for robots is considered. A variable speed technique based on the RSSI value of robots is used and the obstacle avoidance is implemented based on State based Obstacle Avoidance Algorithm. Proposed algorithm is implemented and tested using Webots 3D simulator.


title: Pick-by-Vision: A first stress test

abstract: In this paper we report on our ongoing studies around the application of Augmented Reality methods to support the order picking process of logistics applications. Order picking is the gathering of goods out of a prepared range of items following some customer orders. We named the visual support of this order picking process using Head-mounted Displays “Pick-by-Vision”. This work presents the case study of bringing our previously developed Pickby-Vision system from the lab to an experimental factory hall to evaluate it under more realistic conditions. This includes the execution of two user studies. In the first one we compared our Pickby-Vision system with and without tracking to picking using a paper list to check picking performance and quality in general. In a second test we had subjects using the Pick-by-Vision system continuously for two hours to gain in-depth insight into the longer use of our system, checking user strain besides the general performance. Furthermore, we report on the general obstacles of trying to use HMD-based AR in an industrial setup and discuss our observations of user behaviour.


title: Comparison of HEVC coding schemes for tile-based viewport-adaptive streaming of omnidirectional video

abstract: Virtual reality applications make use of 360-degree panoramic or omnidirectional video with high resolution and high frame rate in order to create the immersive experience to the user. The user views only a portion of the captured 360-degree scene at each time instant, hence streaming the whole omnidirectional video in highest quality is not efficient. In order to alleviate the problem of bandwidth wastage, viewport-adaptive encoding and streaming schemes have been proposed. In these schemes, part of the captured scene that is within the viewer's field of view is delivered at highest quality while the rest of the scene in a lower quality. In this work, three tile-based viewport-adaptive methods using motion-constrained tile sets (MCTS), region-of-interest scalability and simulcast approach have been studied for streaming omnidirectional content. In the performed experiments with various tiling arrangements, MCTS-based scheme required highest bitrate compared to other methods. The scalable coding scheme provided the highest performance in terms of streaming bitrate saving on average up to 53% and 35% compared to streaming the whole omnidirectional video and MCTS-based method, respectively.


title: FACE RECOGNITION FOR SOCIAL M EDIA WITH M OBILE CLOUD COMPUTING

abstract: Social Networking has become today’s lifestyle and anyone can easily receive information about everyone in the world. It is very useful if a personal identity can be obtained from the mobile device and also connected to social networking.Therefore, we proposed a face recognition system on mobile devices by combining cloud computing services . Our system is designed in the form of an a pplication developed on Android mobile deviceswhich utilized the Face.com API as an image data processor for cloud computing services. We also applied the Augmented Reality as an information viewer to the users. The result of testing shows that the system is able to recognize face samples withthe average percentage of 85% with thetotal computation time for the face recognition system reached 7 .45 seconds, and the average augmented reality translation time is 1.03 seconds to get someone’s information.


title: Assessing the Relationship between Innovation and Survival in Organizations: An Empirical Review, Research Synthesis, and Analytical Case Study

abstract: IntroductionAlthough considerable effort has been made to promote innovation and identify how to manage it, much less work has gone into considering the effects of innovations. This study considers the effects of innovations on their organizations and consolidates what is known now about these effects, then considers new methodological implications of this research synthesis, including the aptness of the case study method, movement among macro-, meso-, and micro-level analyses (i.e., inter-organizational, organizational/group, and individual levels of analysis), and prospects for resource-generation, structural-change, and other established theoretical streams. First, it examines the effects of innovations on their organizations by identifying a research framework, examines the relevant literature for normal and changed organizational populations that might apply to each hypothesis, develops four possible hypotheses about the effect of innovations on the fate of their organizations, and explores how this might apply to innovating organizations. Second, the study examines a broader range of research questions about innovation that could be examined in the future. This empirical review concludes with a case study causally linking project termination and organizational crisis, and innovation and survival. It is an historic study of the U.S. Bureau of Land Management (BLM) Automated Land and Mineral Record System (ALMRS), from its genesis to its demise, and the birth of its successor National Integrated Land System (NILS).The Effects of Innovations on their OrganizationsPerhaps those who fear the effects of innovation on organizations are correct: it may threaten the organization-or it may help the organization survive. Without integrative research on the subject, no one will ever know with any certainty. Researchers studying these issues face numerous challenges-isolating the effects of innovations from those of other factors, identifying ways to identify the effects of innovations on their organizations, and ascertaining how to deal with the different levels of innovations, organizations, organizational communities and populations. These require different levels of analysis, from individual to group to organizational and inter-organizational levels, as to causal paths and impacts, with qualtitative and quantitative research.While organizations can be created or reorganized to facilitate changes in policies, processes and programs, at the same time, "Organizational change usually accompanies policy change" (Lewis, 2002: 102). Both innovations and their organizations must thus be considered. The following section of the study focuses on a research framework for studying and the effects of innovations on their organizations and leaves the separation of innovation from other factors influencing the fate of organizations for another time.Approaches to Studying the Impact of Innovation: A FrameworkIn order to study the impacts of innovations on the fate of innovating organizations, researchers must decide which issues to study and on an approach. To take these decisions, they must make judgments about what might be important to innovating organizati ons' fates.Astley and Van de Ven (1983) developed a framework that considered micro (organization)/macro (population) levels of organizational analysis and deterministic/voluntaristic considerations of human nature to create a four-type framework for analysis of organization theories. Glor (2002) examined the innovations of a population, the Government of Saskatchewan, 1971-82 in deterministic and voluntaristic terms. Van de Ven and Poole (1995) found that the variety of concepts used to study how organizations changed have led to the compartmentalization of perspectives. They developed four basic (primitive) theories to explain processes of change in organizations: life cycle, teleology, dialectics and evolution and recommended considering how they interact. …


title: Supervised Term Weighting for Automated Text Categorization

abstract: The construction of a text classifier usually involves (i) a phase of term selection, in which the most relevant terms for the classification task are identified, (ii) a phase of term weighting, in which document weights for the selected terms are computed, and (iii) a phase of classifier learning, in which a classifier is generated from the weighted representations of the training documents. This process involves an activity of supervised learning, in which information on the membership of training documents in categories is used. Traditionally, supervised learning enters only phases (i) and (iii). In this paper we propose instead that learning from training data should also affect phase (ii), i.e. that information on the membership of training documents to categories be used to determine term weights. We call this idea supervised term weighting (STW). As an example, we propose a number of "supervised variants" of t f idf weighting, obtained by replacing the idf function with the function that has been used in phase (i) for term selection. We present experimental results obtained on the standard Reuters-21578 benchmark with one classifier learning method (support vector machines), three term selection functions (information gain, chi-square, and gain ratio), and both local and global term selection and weighting.


title: Cosmophobia: Urania's Lament and Lessons Learned

abstract: NASA astrobiologist David Morrison coined the term cosmophobia – fear of the cosmos – in response to the plethora of 2012 disaster scenarios that depict Urania, the muse of astronomy, as a wicked destroyer rather than a beautiful inspiration. The purveyors of these scenarios relied on the public’s lack of science literacy and ability to tell the difference between science and pseudoscience, as well as the ubiquitous nature of the Internet and social media, in their effort to sell books, DVDs, and subscription-only web content. Also adding to the public’s inability to critically analyze the veritable flood of 2012 apocalypse suppositions is the lamentable disconnect between the average 21 st century citizen and the night sky (in part due to light pollution). The result is a general public who views the astronomical knowledge of ancient cultures as mystical and even supernatural, adding to the fear factor of December 21, 2012. While this date passed with neither the end of the world nor the rash of suicides and murders some feared, the 2012 apocalypse hoax proved to be one of the most important teachable moments in recent memory in terms of science education and outreach.


title: Dual-SLIP model based galloping gait control for quadruped robot: A Task-space Formulation

abstract: This paper presents a novel locomotion control framework that achieves stable galloping gait for a torque-controlled quadruped robot. By analytically exploiting the stance dynamics of the Spring-Loaded Inverted Pendulum (SLIP) model, a two-layered Dual-SLIP model based Task-space Formulation (DS-TSF) is developed to control the 12-DoF quadruped robot with an active spine. On the higher layer, a dead-beat controller based on the derived Approximate Apex Return Map (AARM) with guaranteed high prediction accuracy is devised to provide desired apex state. This reference SLIP-like behavior serves as the target Center of Mass (CoM) trajectories of the fore- and hind-body of the quadruped robot. On the lower layer, a prioritized multi-task controller is further developed to enforce the dual-CoMs of the fore- and hind-body to behave following the target dynamics of two uncoupled SLIP hoppers on sagittal plane. The compatible motion control of the active spinal joint is fulfilled on the null-space of the prior task without generating confliction. The simulation results have demonstrated the effectiveness of the proposed locomotion control method.


title: Behaviour based botnet detection with traffic analysis and flow interavals using PSO and SVM

abstract: Since botnet becomes one of the most significant Internet threats, numerous research efforts have been devoted to mitigate botnet threats. We reviewed various botnet detection mechanisms that can be classified according to their approaches, detection object, and source data. The bots provide a different kind of implemented features to a corresponding controlling entity. This entity is commonly a command-and-control server under the control of one or more persons, called the bot masters or botherders, who relay commands through this server. Depending on the infrastructure of the network, the bots are connected with each other to enable this loosly coupled control structure. Alternatively, they can exist completely independently, not knowing of the existence of other bots. In the early period of the age of botnet, many botnet detection approaches have been suggested and host-based detection is the one of them. Host-based detection approaches mainly aim to investigate abnormal activities in a computer by analyzing the internal components of the computer system. Main advantage in using host based detection approaches is that they allows normal users to easily and actively detect abnormal activities in their system. The recent botnets have begun using common protocols such as TCP/HTTP which makes it even harder to distinguish their communication patterns. Most of the TCP/HTTP bot transportations are founded on TCP connections. Here we are using behavior based technique for TCP/HTTP botnet detection because TCP/HTTP bots are variant in nature thus not suitable for signature based detection.


title: Spatial and Spatio-Temporal Multidimensional Data Modelling: A Survey

abstract: Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user's needs for supporting the decision making process. Since it is estimated that 80% of data used for decision making has a spatial or location component, spatial data have been widely integrated in Data Warehouses and in OLAP systems. Extending a multidimensional data model by the inclusion of spatial data provides a concise and organised spatial data warehouse representation. This paper aims to provide a comprehensive review of literature on developed and suggested spatial and spatio-temporel multidimensional models. A benchmarking study of the proposed models is presented. Several evaluation criteria's are used to identify the existence of trends as well as potential needs for further investigations. Despite the relatively short history of spatial and spatio- temporal modelling, a substantial number of models have been presented. In this section, we examine most of spatial and spatio-temporal multidimensional models proposed in the literature. Each model is discussed and evaluated relatively to the other models and a comparative study between the different models is realised.


title: Time after time: The psychological reality of the Ego- and Time-Reference-Point distinction in metaphorical construals of time.

abstract: Research in cognitive linguistics and in processing of temporal metaphors has traditionally distinguished between Moving-Ego and Moving-Time mappings: Either the Ego is construed as moving regarding fixed temporal landmarks or Time is construed as moving regarding the Ego. Both of these metaphors involve time events in reference to an Ego, which specifies the present time Now.We build on recent theoretical suggestions for a more fundamental classification of temporal metaphors: Ego- and Time-Reference-Point metaphors (Ego-RP and Time-RP). The distinction focuses on the role of reference points in ascribing orientation, rather than on the identity of a moving entity (Ego or Time). Using visual priming experiments we provide evidence of the psychological reality of the Time-RP metaphor, a temporal metaphor with no reference to an Ego.


title: Very small size UHF RFID tag for racing pigeon ring applications

abstract: A very small size RFID (Radio Frequency Identification) tag antenna specifically designed for racing pigeon ring applications is proposed. The structure of this UHF tag is a closed-loop type printed on a 30 × 8 mm2 polyimide film of thickness 0.063 mm. Thus, it can be wrapped into a double layer plastic cylindrical ring of size similar to those used for racing pigeon. By simply tuning the inner width of the loop, good conjugate matching between the tag chip input impedance and the tag antenna can be achieved. When inserted into a real preserved dried pigeon feet specimen, the measured 10-dB return loss of the tag antenna was 901-929 MHz, and it has a maximum reading range of 22 cm.


title: A Direct Load Control Model for Virtual Power Plant Management

abstract: In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability.


title: Learning sequences of actions in collectives of autonomous agents

abstract: In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. Directly applying Reinforcement Learning (RL) concepts to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in designing multi-agent systems focuses on how to set the rewards for the RL algorithm of each agent so that as the agents attempt to maximize those rewards, the system reaches a globally "desirable" solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence [15,23] to design rewards for the agents that are "aligned" with the global reward, and are "learnable" in that agents can readily see how their behavior affects their reward. We show that reinforcement learning agents using those rewards outperform both "natural" extensions of single agent algorithms and global reinforcement learning solutions based on "team games".


A Preliminary Description of the Javanese Kinship System.


title: G-Free: defeating return-oriented programming through gadget-less binaries

abstract: Despite the numerous prevention and protection mechanisms that have been introduced into modern operating systems, the exploitation of memory corruption vulnerabilities still represents a serious threat to the security of software systems and networks. A recent exploitation technique, called Return-Oriented Programming (ROP), has lately attracted a considerable attention from academia. Past research on the topic has mostly focused on refining the original attack technique, or on proposing partial solutions that target only particular variants of the attack. In this paper, we present G-Free, a compiler-based approach that represents the first practical solution against any possible form of ROP. Our solution is able to eliminate all unaligned free-branch instructions inside a binary executable, and to protect the aligned free-branch instructions to prevent them from being misused by an attacker. We developed a prototype based on our approach, and evaluated it by compiling GNU libc and a number of real-world applications. The results of the experiments show that our solution is able to prevent any form of return-oriented programming.


title: A novel LLC resonant controller with best-in-class transient performance and low standby power consumption

abstract: This paper introduces a novel LLC resonant controller with better light load efficiency and best-in-class transient performance. A new control algorithm — hybrid hysteretic control, is proposed which combines the benefit of charge control and frequency control. It maintains the good transient performance of charge control, but avoids the related stability issues by adding slope compensation. The slope compensation also helps to sense the resonant capacitor voltage by only using lossless capacitor divider. Burst mode operation is developed to improve the light load efficiency. By turning on the power devices for only a short period during light load, it helps to reduce the equivalent switching frequency and achieve high efficiency. The effectiveness of the proposed controller has been validated in a 12 V/120 W half-bridge LLC converter, as well as a commercial power supply unit.


title: Generating a Fusion Image: One's Identity and Another's Shape

abstract: Generating a novel image by manipulating two input images is an interesting research problem in the study of generative adversarial networks (GANs). We propose a new GAN-based network that generates a fusion image with the identity of input image x and the shape of input image y. Our network can simultaneously train on more than two image datasets in an unsupervised manner. We define an identity loss LI to catch the identity of image x and a shape loss LS to get the shape of y. In addition, we propose a novel training method called Min-Patch training to focus the generator on crucial parts of an image, rather than its entirety. We show qualitative results on the VGG Youtube Pose dataset, Eye dataset (MPIIGaze and UnityEyes), and the Photo-Sketch-Cartoon dataset.


title: Distance Estimation of Smart Device using Bluetooth

abstract: Distance estimation identifies the distance between two machines in wireless network. The Received Signal Strength Indication (RSSI) of Bluetooth can be used to estimate distance between smart devices. The characteristic of Bluetooth RSSI value is different as environments. So, we have tested the relation between distance and Bluetooth RSSI value in several environments, such as indoor hall, meeting room, and ElectroMagnetic Compatibility (EMC) chamber environment. This paper shows the distance characteristic of Bluetooth RSSI from these experiment results. There are a lot of measurement errors at Bluetooth RSSI raw data. The minimum RSSI value is -88 dBm and the maximum RSSI value is -66 dBm at 11m of the indoor hall environment. The difference between maximum value and minimum value is 22 dBm. So, it is hard to estimate the distance using Bluetooth RSSI raw data. Therefore, we use the Low Pass Filter (LPF) for reducing the measurement errors. The minimum RSSI value is -80.6 dBm and the maximum RSSI value is -71 dBm in the same environment. The difference between maximum value and minimum value is just 8.4 dBm. The measurement error is significantly reduced. We compare the distance estimation between the Bluetooth RSSI raw data and LPF data at the EMC environment. This paper shows that the distance estimation is possible with small error rates using Bluetooth RSSI LPF data.


title: On Shannon capacity and causal estimation

abstract: The problem of estimating causal relationships from purely observational data is studied in this paper. We observe samples from a pair of random variables (X,Y) and wish to estimate whether X causes Y or Y causes X. Any joint distribution can be factored as pX,Y = pX pY|X = pY pX|Y and therefore the “causal” direction cannot be inferred from the joint distribution without further assumptions. In this paper, we propose and study the utility of Shannon capacity as a metric for causal directionality estimation. This opens up several open questions and directions for future study.


Identifying Political Bias in News Articles


title: Deep features-based speech emotion recognition for smart affective services

abstract: Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.


title: On transfer learning using a MAC model variant

abstract: We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with a simplified set of equations that achieves comparable accuracy, while training faster. We evaluate both models on CLEVR and CoGenT, and show that, transfer learning with fine-tuning results in a 15 point increase in accuracy, matching the state of the art. Finally, in contrast, we demonstrate that improper fine-tuning can actually reduce a model's accuracy as well.


DroidCat: Unified Dynamic Detection of Android Malware


title: A Simplified Callosal Angle Measure Best Differentiates Idiopathic-Normal Pressure Hydrocephalus from Neurodegenerative Dementia

abstract: Idiopathic normal-pressure hydrocephalus (iNPH) can resemble or occur in combination with other brain disorders frequently present in the elderly such as Alzheimer's disease (AD) and dementia with Lewy bodies (DLB).To study the accuracy of a simplified callosal angle measure in differentiating iNPH from DLB and AD using conventional brain MRI.76 patients (24 iNPH, 30 DLB, 22 AD) and 40 healthy controls served as discovering cohort. The callosal angle measure was obtained on standard coronal brain MRI images crossing the corpus callosum midpoint. 41 patients (21 iNPH and 20 DLB/AD) were used as independent validation cohort. A set of other conventional MRI markers of iNPH was also evaluated.iNPH showed a significantly decreased mean callosal angle value compared to both disease groups and controls (iNPH = 109±9; DLB = 136.9±8.2; AD = 135.4±11.3; Controls = 138.5±5.2; p < 0.00001). Using a cut off angle of 123, derived by the mean -3SD of the control group, an accuracy of 96% (sensitivity 100% , specificity 95.4% ) was obtained. By ROC analysis, the area under the curve was 0.99 (95% CI: 0.97-1). The measure was consistent (intra-rater: r = 0.94) and reproducible (inter-rater: r = 0.89). In the validation cohort, this cut off angle value discriminated iNPH from DLB/AD with 97.5% accuracy. None of the conventional MRI signs reached the same accuracy.This simplified callosal angle measure represents an accurate, reproducible, and easy marker of iNPH.


title: Internet traffic classification demystified: myths, caveats, and the best practices

abstract: Recent research on Internet traffic classification algorithms has yield a flurry of proposed approaches for distinguishing types of traffic, but no systematic comparison of the various algorithms. This fragmented approach to traffic classification research leaves the operational community with no basis for consensus on what approach to use when, and how to interpret results. In this work we critically revisit traffic classification by conducting a thorough evaluation of three classification approaches, based on transport layer ports, host behavior, and flow features. A strength of our work is the broad range of data against which we test the three classification approaches: seven traces with payload collected in Japan, Korea, and the US. The diverse geographic locations, link characteristics and application traffic mix in these data allowed us to evaluate the approaches under a wide variety of conditions. We analyze the advantages and limitations of each approach, evaluate methods to overcome the limitations, and extract insights and recommendations for both the study and practical application of traffic classification. We make our software, classifiers, and data available for researchers interested in validating or extending this work.


title: Catching Worms, Trojan Horses and PUPs: Unsupervised Detection of Silent Delivery Campaigns

abstract: The growing commoditization of the underground economy has given rise to malware delivery networks, which charge fees for quickly delivering malware or unwanted software to a large number of hosts. To provide this service, a key method is the orchestration of silent delivery campaigns, which involve a group of downloaders that receive remote commands and that deliver their payloads without any user interaction. These campaigns have not been characterized systematically, unlike other aspects of malware delivery networks. Moreover, silent delivery campaigns can evade detection by relying on inconspicuous downloaders on the client side and on disposable domain names on the server side. We describe Beewolf, a system for detecting silent delivery campaigns from Internet-wide records of download events. The key observation behind our system is that the downloaders involved in these campaigns frequently retrieve payloads in lockstep. Beewolf identifies such locksteps in an unsupervised and deterministic manner. By exploiting novel techniques and empirical observations, Beewolf can operate on streaming data. We utilize Beewolf to study silent delivery campaigns at scale, on a data set of 33.3 million download events. This investigation yields novel findings, e.g. malware distributed through compromised software update channels, a substantial overlap between the delivery ecosystems for malware and unwanted software, and several types of business relationships within these ecosystems. Beewolf achieves over 92% true positives and fewer than 5% false positives. Moreover, Beewolf can detect suspicious downloaders a median of 165 days ahead of existing anti-virus products and payload-hosting domains a median of 196 days ahead of existing blacklists.


title: Information Security Policy Compliance: The Role of Information Security Awareness

abstract: Compliance and systems misuse has been the focus of researchers in the last couple of years. However, given that voids in this area is still significant and systems abuse is a pressing issue likely to persist in the future, more investigation is needed in this area. Toward this end, we conducted a research study to help understand factors motivating compliance behavior intentions. Drawing on Theory of Planned Behavior, we investigated the role of users’ self-learning and knowledge of security issues in shaping their attitudes toward compliance with information security policies (ISPs). We collected data from nine financial organizations to test the proposed research model. Results show that employees’ previous knowledge of security issues and technologies have significant positive impact on their attitudes toward compliance with ISPs. This study sheds light on the importance of users’ general awareness of security issues and technologies in shaping their attitudes to comply with ISPs.


Academic Engagement Among African American Males Who Hold Aspirations for Athletic Careers in Professional Sports


title: www.mdpi.com/journal/jsan Semantic Models for Scalable Search in the Internet of Things

abstract: The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.


title: Designing a super-peer network

abstract: A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.


title: Development of automatic voltage regulator for synchronous generator

abstract: This paper presents the development of an automatic voltage regulator (AVR) for synchronous generator in industries applications. The AVR operates as a voltage stabilizer and controller because the output voltage is controlled and regulated through a power electronic system. It is observed that the automatic voltage regulator managed to achieve up to 2% voltage regulation at full load operation of 500 W. A thyristor controller is used to control the excitation voltage. The design also includes protection such as noise suppressor, radio frequency interference (RFI), surge protection, current limiter and electronic control system.


title: Research on data mining models for the internet of things

abstract: In this paper, we propose four data mining models for the Internet of Things, which are multi-layer data mining model, distributed data mining model, Grid based data mining model and data mining model from multi-technology integration perspective. Among them, multi-layer model includes four layers: 1) data collection layer, 2) data management layer, 3) event processing layer, and 4) data mining service layer. Distributed data mining model can solve problems from depositing data at different sites. Grid based data mining model allows Grid framework to realize the functions of data mining. Data mining model from multi-technology integration perspective describes the corresponding framework for the future Internet. Several key issues in data mining of IoT are also discussed.


title: Sliding versus Deciding: Inertia and the Premarital Cohabitation Effect.

abstract: Abstract: Premarital cohabitation has consistently been found to be associated with increased risk for divorce and marital distress in the United States. Two explanations for this “cohabitation effect” are discussed: selection and experience. We present an empirically based view of how the experience of cohabitation may increase risk for relationship distress or divorce for some people beyond what is accounted for by selection. Specifically, using a commitment framework, we suggest that some couples who otherwise would not have married end up married because of the inertia of cohabitation. We discuss practice implications for relationship transitions that are characterized more by sliding than deciding, especially where a transition such as cohabitation increases inertia to remain in a relationship regardless of quality or fit.


title: Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos

abstract: Unmanned aerial vehicles (UAVs) are gaining popularity in traffic monitoring due to their low cost, high flexibility, and wide view range. Traffic flow parameters such as speed, density, and volume extracted from UAV-based traffic videos are critical for traffic state estimation and traffic control and have recently received much attention from researchers. However, different from stationary surveillance videos, the camera platforms move with UAVs, and the background motion in aerial videos makes it very challenging to process for data extraction. To address this problem, a novel framework for real-time traffic flow parameter estimation from aerial videos is proposed. The proposed system identifies the directions of traffic streams and extracts traffic flow parameters of each traffic stream separately. Our method incorporates four steps that make use of the Kanade–Lucas–Tomasi (KLT) tracker, k-means clustering, connected graphs, and traffic flow theory. The KLT tracker and k-means clustering are used for interest-point-based motion analysis; then, four constraints are proposed to further determine the connectivity of interest points belonging to one traffic stream cluster. Finally, the average speed of a traffic stream as well as density and volume can be estimated using outputs from previous steps and reference markings. Our method was tested on five videos taken in very different scenarios. The experimental results show that in our case studies, the proposed method achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively. The method also achieves a fast processing speed that enables real-time traffic information estimation.


title: MiniCount: Efficient Rewriting of COUNT-Queries Using Views

abstract: We present MiniCount, the first efficient sound and complete algorithm for finding maximally contained rewritings of conjunctive queries with count, using conjunctive views with count and conjunctive views without aggregation. An efficient and scalable solution to this problem yields significant benefits for data warehousing and decision support systems, as well as for powerful data integration systems.We first present a naive rewriting algorithm implicit in the recent theoretical results by Cohen et al. [5] and identify three independent sources of exponential complexity in the naive algorithm, including an expensive containment check. Then we present and discuss MiniCount and prove it sound and complete. We also present an experimental study that shows Mini- Count to be orders of magnitude faster than the naive algorithm, and to be able to scale to large numbers of views


title: Intuitionistic fuzzy hypergraphs with applications

abstract: Hypergraphs are considered a useful tool for modeling system architectures and data structures and to represent a partition, covering and clustering in the area of circuit design. In this paper, we apply the concept of intuitionistic fuzzy set theory to generalize results concerning hypergraphs. For each intuitionistic fuzzy structure defined, we use cut-level sets to define an associated sequence of crisp structures. We determine what properties of the sequence of crisp structures characterize a given property of the intuitionistic fuzzy structure. We also present applications of intuitionistic fuzzy hypergraphs.


title: On the performance of 169 MHz WM-Bus and 868 MHz LoRa technologies in smart metering applications

abstract: In this manuscript the use of 169 MHz Wireless Meter-Bus and 868 MHz Semtech Long Range technologies in smart metering is investigated. Our study is based on a set of experimental results, acquired in various measurement campaigns. A description of the employed measurement tools and of our measurement campaigns is provided first. Then, some preliminary indications about the packet error rate estimated within the coverage area in different scenarios are illustrated. These results show that, on the one hand, the considered technologies perform similarly at the same transmission power in urban scenarios; on the other hand, Long Range outperforms Wireless Meter-Bus in suburban scenarios.


title: Attitude stabilization control of an aerial manipulator using a quaternion-based backstepping approach

abstract: Aerial manipulation is a new research area that extends the potential of aerial vehicles, allowing them to physically interact with the environment. Modeling and control of such a system is not a trivial problem due to the coupled dynamics of the aerial vehicle and the robotic manipulator. This paper presents a robust and non linear control system for a Hexacopter equipped with a two-degrees-of-freedom robotic arm. The proposed mathematical model, which is based on a quaternion representation, takes into account the changing inertia and the mass distribution, depending on the arm configuration. We show how the attitude control, using a quaternion-based backstepping, is able to react to the disturbances caused by the arm. In the backstepping design, we introduce a command filter, which contributes to reduce the computational complexity of the algorithm. Finally, we present simulation results that demonstrate the stability of our approach and provide a comparison to a standard PD control.


title: A subset spanner for Planar graphs,: with application to subset TSP

abstract: Let e>0 be a constant. For any edge-weighted planar graph G and a subset S of nodes of G, there is a subgraph H of G of weight a constant times that of the minimum Steiner tree for S such that distances in H between nodes in S are at most 1+e times the corresponding distances in G. As a consequence, there is an O(n log n)-time approximation scheme for finding a TSP among a given subset of nodes of a planar graph. This is the first PTAS for the problem.


title: Aggregating Machine Learning and Rule Based Heuristics for Named Entity Recognition

abstract: This paper, submitted as an entry for the NERSSEAL-2008 shared task, describes a system build for Named Entity Recognition for South and South East Asian Languages. Our paper combines machine learning techniques with language specific heuristics to model the problem of NER for Indian languages. The system has been tested on five languages: Telugu, Hindi, Bengali, Urdu and Oriya. It uses CRF (Conditional Random Fields) based machine learning, followed by post processing which involves using some heuristics or rules. The system is specifically tuned for Hindi and Telugu, we also report the results for the other four languages.


title: Generating Natural Adversarial Examples

abstract: Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.


title: What can be learned from China’s success?

abstract: Since 1978, China has experienced the most rapid economic growth of any country in world history, and the most rapid growth in living standards of any major economy. Following the latest international financial crisis, China outperformed any other major economy -- from the second quarter of 2007 to the second quarter of 2014, China’s economy grew by 78% and the USA by 8%. In a single generation, China has gone from a ‘low income economy’ to the verge of achieving ‘high income’ status by World Bank criteria. Achieving this would double the population living in ‘high income’ economies globally. This extremely rapid development is sometimes explained in terms of unique ‘Chinese characteristics’, but research over the last 30 years suggests it is rooted in universal economic processes. While the combination of global forces producing economic growth is unique in China and produces unique ‘Chinese characteristics’, they can operate throughout the world economy. If other developing economies could achieve the scale of China’s economic success, global problems of poverty and its consequences would be solved. China’s policy response to the international financial crisis was far more effective than that of other major economies. This paper examines the chief strategic lessons to be drawn from China’s success.


title: Spatiotemporal Multi-Task Network for Human Activity Understanding

abstract: Recently, remarkable progress has been achieved in human action recognition and detection by using deep learning techniques. However, for action detection in real-world untrimmed videos, the accuracies of most existing approaches are still far from satisfactory, due to the difficulties in temporal action localization. On the other hand, the spatiotempoal features are not well utilized in recent work for video analysis. To tackle these problems, we propose a spatiotemporal, multi-task, 3D deep convolutional neural network to detect (including temporally localize and recognition) actions in untrimmed videos. First, we introduce a fusion framework which aims to extract video-level spatiotemporal features in the training phase. And we demonstrate the effectiveness of video-level features by evaluating our model on human action recognition task. Then, under the fusion framework, we propose a spatiotemporal multi-task network, which has two sibling output layers for action classification and temporal localization, respectively. To obtain precise temporal locations, we present a novel temporal regression method to revise the proposal window which contains an action. Meanwhile, in order to better utilize the rich motion information in videos, we introduce a novel video representation, interlaced images, as an additional network input stream. As a result, our model outperforms state-of-the-art methods for both action recognition and detection on standard benchmarks.


title: DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies

abstract: The highly anticipated transition from next generation sequencing (NGS) to third generation sequencing (3GS) has been difficult primarily due to high error rates and excessive sequencing cost. The high error rates make the assembly of long erroneous reads of large genomes challenging because existing software solutions are often overwhelmed by error correction tasks. Here we report a hybrid assembly approach that simultaneously utilizes NGS and 3GS data to address both issues. We gain advantages from three general and basic design principles: (i) Compact representation of the long reads leads to efficient alignments. (ii) Base-level errors can be skipped; structural errors need to be detected and corrected. (iii) Structurally correct 3GS reads are assembled and polished. In our implementation, preassembled NGS contigs are used to derive the compact representation of the long reads, motivating an algorithmic conversion from a de Bruijn graph to an overlap graph, the two major assembly paradigms. Moreover, since NGS and 3GS data can compensate for each other, our hybrid assembly approach reduces both of their sequencing requirements. Experiments show that our software is able to assemble mammalian-sized genomes orders of magnitude more quickly than existing methods without consuming a lot of memory, while saving about half of the sequencing cost.


title: An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge

abstract: For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method. We show a comprehensive ablation study to examine the key performance factors of the proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset, placing second in the ECCV2018 3D human pose estimation challenge. Code will be released to facilitate future work.


title: Why Not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

abstract: ABSTRACTGlobal teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless la...


title: Relaxed Wasserstein with Applications to GANs

abstract: Comparing probability distributions is an integral part of many modern data-driven applications, such as generative adversarial networks (GANs) and distributionally robust optimization (DRO). We propose a novel class of statistical divergences called \textit{Relaxed Wasserstein} (RW) divergence, which generalizes Wasserstein divergence and is parametrized by the class of strictly convex and differentiable functions. We establish for RW divergence several probabilistic properties, many of which are crucial for the success of Wasserstein divergence. In addition, we derive theoretical results showing that the underlying convex function in RW plays an important role in variance stabilization, shedding light on the choice of appropriate convex function. We develop a version of GANs based on RW divergence and demonstrate via empirical experiments that RW-based GANs (RWGANs) lead to superior performance in image generation problems compared to existing approaches. In particular, we find that in our experiments RWGANs are fastest in generating meaningful images compared to other GANs. We also illustrate the use of RW divergence in constructing ambiguity sets for DRO problems, and the robust portfolio problem under mean-variance framework.


title: Learning the Bayesian Network Structure: Dirichlet Prior versus Data

abstract: In the Bayesian approach to structure learning of graphical models, the equivalent sample size (ESS) in the Dirichlet prior over the model parameters was recently shown to have an important effect on the maximum-a-posteriori estimate of the Bayesian network structure. In our first contribution, we theoretically analyze the case of large ESS-values, which complements previous work: among other results, we find that the presence of an edge in a Bayesian network is favoured over its absence even if both the Dirichlet prior and the data imply independence, as long as the conditional empirical distribution is notably different from uniform. In our second contribution, we focus on realistic ESS-values, and provide an analytical approximation to the "optimal" ESS-value in a predictive sense (its accuracy is also validated experimentally): this approximation provides an understanding as to which properties of the data have the main effect determining the "optimal" ESS-value.


Design and Testing of a Permanent Magnet Axial Flux Wind Power Generator


The Changing Role of Phonology in Reading Development


Design and Simulation of CMOS Schmitt Trigger


abstract: As the title indicates, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence covers the design and development of multiagent and distributed AI systems. The purpose of this book is to provide a comprehensive overview of the field. It is an excellent collection of closely related papers that provides a wonderful introduction to multiagent systems and distributed AI.


title: A Two-Stage Outdoor - Indoor Scene Classification Framework: Experimental Study for the Outdoor Stage

abstract: The state-of-the-art image classification methods require an intensive learning stage and a considerable amount of training images. Recently, with the introduction of these models (and in particular convolutional neural network (CNN)), it is believed that the best solution to achieve a system with high performance on scene classification is to learn deep scene features using CNN. While this can be true for large-scale image datasets (in the scale of one-million training images), the feasibility of reaching state-of-the-art performances in other complex datasets with a handful training examples remains unclear. Here, we argue that we can reach a highly accurate performance by presenting a simple model on the scene classification problem. This paper presents a hierarchical two-stage scene classification framework based on indoor versus outdoor notion. The suggested approach is a simple, yet a very efficient global image representation model for scene recognition. Though the term of indoor versus outdoor has been in the literature for quite a while, here we go further than that and propose a scene classifier model which classifies all the outdoor scene classes versus one undifferentiated indoor class. To achieve this, we tackle the problem of scene classification by using distributions of quantized filter responses to characterize the scenes. Later the classifier outputs either one of the several outdoor scene classes or a generic indoor scene class. We validate the proposed approach by feeding the algorithm with two benchmark datasets: 15-Scene category and SUN397. Successful experiments demonstrate the validity of the proposed approach. With the presented model, we reach an average accuracy of 97.84% and 55.1% in outdoor scenes and 93.09% and 92.4% in the indoor scene class on 15-Scene and SUN397 respectively.


title: Intergenerational Differences in the Physical Activity Pattern: A Familial Study From Rural Haryana, India

abstract: This study was aimed to investigate the patterns and possible differences in the levels of Physical Activity (PA) among members of different generations in a North Indian State. A cross-sectional survey investigated intergenerational PA level up to third generations of Jat males and females (N = 329; 42 families). PA was quantified using the validated Global Physical Activity Questionnaire. Significant differences were observed with respect to the various domains of the PA among generations. Females of the second generation were more active than males, whereas females of the third generation were less active and their activities are confined to household chores only. No such sex-wise differences were observed among first generation. Only 20% of the individuals were performing recreational activities, and almost negligible in the first and second generation. Study supports the need for increasing awareness regarding health benefits of PA especially in terms of recreational activities.


title: FEKO based ISAR analysis for 3D object reconstruction

abstract: In this paper, we constructed a 3D ISAR image to better understand electromagnetic scattering by an electrically large PEC cylinder. Imaging in this context is simply the reconstruction of the spatial distribution of the conductivity of the imaged target, σ(x, y, z). According to the geometric theory of diffraction (GTD), if the wavelength at the operating frequency is small relative to the target size, then the scattered field consists of contributions from a number of electrically isolated scattering centers. ISAR imaging algorithms rely upon an assumption that the area under observation consists of a collection of infinitesimally small isotropic scatterers (i.e., the point scatterer model). This approximation is based upon the application of a scalar contrast function which ignores directional dependency, since point scatterers radiate isotropically in all directions. 3D image reconstruction is obtained through retrieval of the scalar reflectivity function from simulated data, which provides information regarding the target under investigation.


title: Experimental validation of dual PPG local pulse wave velocity probe

abstract: A novel dual photoplethysmograph (PPG) probe and measurement system for local pulse wave velocity (PWV) is proposed and demonstrated. The developed probe design employs reflectance PPG transducers for non-invasive detection of blood pulse propagation waveforms from two adjacent measurement points (28 mm apart). Transit time delay between the continuously acquired dual pulse waveform was used for beat-to-beat local PWV measurement. An in-vivo experimental validation study was conducted on 10 healthy volunteers (8 male and 2 female, 21 to 33 years of age) to validate the PPG probe design and developed local PWV measurement system. The proposed system was able to measure carotid local PWV from multiple subjects. Beat-to-beat variation of baseline carotid PWV was less than 7.5% for 7 out of 10 subjects, a maximum beat-to-beat variation of 16% was observed during the study. Variation in beat-to-beat carotid local PWV and brachial blood pressure (BP) values during post-exercise recovery period was also examined. A statistically significant correlation between intra-subject local PWV variation and brachial BP parameters was observed (r > 0.85, p < 0.001). The results demonstrated the feasibility of proposed PPG probe for continuous beat-to-beat local PWV measurement from the carotid artery. Such a non-invasive local PWV measurement unit can be potentially used for continuous ambulatory BP measurements.


title: Fluorescence-Activated Cell Sorting of Humanl-asparaginase Mutant Libraries for Detecting Enzyme Variants with Enhanced Activity

abstract: Immunogenicity is one of the most common complications occurring during therapy making use of protein drugs of nonhuman origin. A notable example of such a case is bacterial l-asparaginases (L-ASNases) used for the treatment of acute lymphoblastic leukemia (ALL). The replacement of the bacterial enzymes by human ones is thought to set the basis for a major improvement of antileukemic therapy. Recently, we solved the crystal structure of a human enzyme possessing L-ASNase activity, designated hASNase-3. This enzyme is expressed as an inactive precursor protein and post-translationally undergoes intramolecular processing leading to the generation of two subunits which remain noncovalently, yet tightly associated and constitute the catalytically active form of the enzyme. We discovered that this intramolecular processing can be drastically and selectively accelerated by the free amino acid glycine. In the present study, we report on the molecular engineering of hASNase-3 aiming at the improvement of its catalytic properties. We created a fluorescence-activated cell sorting (FACS)-based high-throughput screening system for the characterization of rationally designed mutant libraries, capitalizing on the finding that free glycine promotes autoproteolytic cleavage, which activates the mutant proteins expressed in an E. coli strain devoid of aspartate biosynthesis. Successive screening rounds led to the isolation of catalytically improved variants showing up to 6-fold better catalytic efficiency as compared to the wild-type enzyme. Our work establishes a powerful strategy for further exploitation of the human asparaginase sequence space to facilitate the identification of in vitro-evolved enzyme species that will lay the basis for improved ALL therapy.


title: Drive-By Download Attacks: A Comparative Study

abstract: Malware authors are exploiting browsers in a stealthy manner to distribute infectious code on the Internet. Drive-by download attacks are a prominent attack method in support of this cause. During such attacks, well-designed automated applications called browser exploit packs (BEPs) are deployed on infected servers. The authors' feature-oriented study analyzes the built-in features provided by BEPs used in the successful execution of drive-by downloads.


title: VOT4CS: A Virtualization Obfuscation Tool for C#

abstract: Software protection is a difficult task especially for managed code, which executes only on a runtime environment such as C# or Java. Applications developed in such languages can be accurately decompiled, as opposed to x86 machine code. This facilitates reverse engineering attacks, with the goal of extracting proprietary algorithms. Due to the ease of distributing software copies across different jurisdictions, software developers cannot only rely on legal means for protection against reverse engineering attacks. Therefore, they have to employ technical means for software protection such as obfuscation. This paper presents an open source tool for virtualization obfuscation of programs written in the C# language, called VOT4CS. Our tool offers several possibilities for randomization that aim to confuse attacks based on pattern recognition. An evaluation of VOT4CS is performed based on several case-studies, which show the performance-security trade-off offered by the tool.


title: Surface reconstruction from 3D scattered points with normals using both delaunay tetrahedralization and implicit function

abstract: A robust visualization method for reconstructing surfaces from 3D scattered points with normals is proposed. The method is based on both the Delaunay tetrahedralization and the implicit function. The method enables to reduce total computational costs of the surface visualization by using the Delaunay tetrahedralization mainly. Even if some unexpected surfaces are generated in a result of the Delaunay tetrahedralization, the method can modify the result by using an implicit function. In experiments, some results are shown as examples of applying the method.


title: AndroSimilar: robust statistical feature signature for Android malware detection

abstract: Android Smartphone popularity has increased malware threats forcing security researchers and AntiVirus (AV) industry to carve out smart methods to defend Smartphone against malicious apps. Robust signature based solutions to mitigate threats become necessary to protect the Smartphone and confidential user data. In this paper we present AndroSimilar, a robust approach which generates signature by extracting statistically improbable features, to detect malicious Android apps. Proposed method is effective against code obfuscation and repackaging, widely used techniques to evade AV signature and to propagate unseen variants of known malware. AndroSimilar is a syntactic foot-printing mechanism that finds regions of statistical similarity with known malware to detect those unknown, zero day samples. Syntactic file similarity of whole file is considered instead of just opcodes for faster detection compared to known fuzzy hashing approaches. Results demonstrate robust detection of variants of known malware families. Proposed approach can be refined to deploy as Smartphone AV.


title: Anti-Reconnaissance Tools: Detecting Targeted Socialbots

abstract: Advanced attackers use online social networks to extract useful information about the target organization, including its members and their connections, affiliations, and positions. Socialbots are artificial, machine-operated, social network profiles that connect to real members of an organization, greatly increasing the amount of information an attacker can collect. To connect socialbots, attackers can employ several strategies. The authors' approach hunts socialbots using a carefully chosen monitoring strategy by intelligently selecting organization member profiles and monitoring their activity. Their results demonstrate their method's efficacy--specifically, when attackers know the defense strategy being deployed, the attack they will most likely use is randomly sprayed friend requests, which eventually lead to a low number of connections.


title: CoreTSAR: Core Task-Size Adapting Runtime

abstract: Heterogeneity continues to increase at all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other co-processors into everything from desktops to supercomputers. As a consequence, efficiently managing such disparate resources has become increasingly complex. CoreTSAR seeks to reduce this complexity by adaptively worksharing parallel-loop regions across compute resources without requiring any transformation of the code within the loop. Our results show performance improvements of up to three-fold over a current state-of-the-art heterogeneous task scheduler as well as linear performance scaling from a single GPU to four GPUs for many codes. In addition, CoreTSAR demonstrates a robust ability to adapt to both a variety of workloads and underlying system configurations.


title: Privacy-Preserving Audit and Extraction of Digital Contents,” Cryptology ePrint Archive, Report 2008/186

abstract: A growing number of online services, such as Google, Yahoo!, and Amazon, are starting to charge users for their storage. Customers often use these services to store valuable data such as email, family photos and videos, and disk backups. Today, a customer must entirely trust such external services to maintain the integrity of hosted data and return it intact. Unfortunately, no service is infallible. To make storage services accountable for data loss, we present protocols that allow a thirdparty auditor to periodically verify the data stored by a service and assist in returning the data intact to the customer. Most importantly, our protocols are privacy-preserving, in that they never reveal the data contents to the auditor. Our solution removes the burden of verification from the customer, alleviates both the customer’s and storage service’s fear of data leakage, and provides a method for independent arbitration of data retention contracts.


title: Extensive Atypical Genital Herpes Simplex Type 2 Infection as an Initial Manifestation of Acquired Immune Deficiency Syndrome

abstract: Accepted: June 20, 2008 SUMMARY We present a case of an ulcerative lesion of the genitalia starting one year before in a 33-year-old man. Histopathologic examination revealed herpes virus infection, which suggested the existence of cell-mediated immunodeficiency. Human immunodeficiency virus (HIV) infection was confirmed by ELISA and Western blot test. The patient was treated with intravenous acyclovir, which led to complete remission. We underline the importance of early detecting and diagnosing patients with similar clinical manifestation as a sign of significant underlying immunodeficiency.


title: Flexible Sensors Based on Nanoparticles

abstract: Flexible sensors can be envisioned as promising components for smart sensing applications, including consumer electronics, robotics, prosthetics, health care, safety equipment, environmental monitoring, homeland security and space flight. The current review presents a concise, although admittedly nonexhaustive, didactic review of some of the main concepts and approaches related to the use of nanoparticles (NPs) in flexible sensors. The review attempts to pull together different views and terminologies used in the NP-based sensors, mainly those established via electrical transduction approaches, including, but, not confined to: (i) strain-gauges, (ii) flexible multiparametric sensors, and (iii) sensors that are unaffected by mechanical deformation. For each category, the review presents and discusses the common fabrication approaches and state-of-the-art results. The advantages, weak points, and possible routes for future research, highlighting the challenges for NP-based flexible sensors, are presented an...


title: Compound Cue Processing within the Fast and Frugal Heuristics Approach in Nonlinearly Separable Environments.

abstract: Abstract Three experiments investigated whether participants used Take The Best (TTB) Configural, a fast and frugal heuristic that processes configurations of cues when making inferences concerning which of two alternatives has a higher criterion value. Participants were presented with a compound cue that was nonlinearly separable from its elements. The compound was highly valid in Experiments 1 and 2, but invalid in Experiment 3. Participants’ causal mental models were manipulated via instructions: participants were either told that cues acted through the same causal mechanism (configural causal model), through different causal mechanisms (elemental causal model), or the causal mechanisms were not specified (neutral causal model). A high percentage of participants used TTB-Configural when they had a configural causal model and a highly valid compound existed, suggesting that causal knowledge can be incorporated in otherwise very basic cognitive mechanisms to allow fine-grained adaptation to complex task structures.


title: Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment

abstract: In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.