title: A Reduced Reference Image Quality assessment for Multiply Distorted Images

text: In this paper, we propose a new Reduced Reference Image Quality Metric for multiply degraded images based on a features extraction step and its combination. The selected features are extracted from the original image and its degraded version. Some of them aim to quantify the level of the considered degradation types, while the others quantify its sharpness. These features are then combined to obtain a single value, which corresponds to the predicted subjective score. Our method has been evaluated and compared in terms of correlation with subjective judgments to some recent methods by using the LIVE Multiply Distorted Image Quality Database.


title: From Social Media to GeoSocial Intelligence: Crowdsourcing Civic Co-management for Flood Response in Jakarta, Indonesia

text: Here we present a review of PetaJakarta.org, a system designed to harness social media use in Jakarta for the purpose of relaying information about flood locations from citizen to citizen and from citizens and the city’s emergency management agency. The project aimed to produce an open, real-time situational overview of flood conditions and provide decision support for the management agency, as well as offering the government a data source for post-event analysis. As such, the platform was designed as a socio-technological system and developed as a civic co-management tool to enable climate adaptation and community resilience in Jakarta, a delta megacity suffering enormous infrastructural instability due to a troubled confluence of environmental factors—the city’s rapid urbanization, its unique geographic limitations, and increasing sea-levels and monsoon rainfalls resulting from climate change. The chapter concludes with a discussion of future research in open source platform and their role in infrastructure and disaster management. From now on there is an interconnection, an intertwining, even a symbiosis of technologies, exchanges, movements, which makes it so that a flood—for instance—wherever it may occur, must necessarily involve relationships with any number of technical, social, economic, political intricacies that keep us from regarding it simply as a misadventure or a misfortune whose consequences can be more or less easily circumscribed. — Jean-Luc Nancy (2015, 3–4) * E. Turpin & T. Holderness SMART Infrastructure Facility, University of Wollongong, Australia e-mail: eturpin@uow.edu.au | tomas@uow.edu.au Chapter 6 in Social Media for Government Services, Springer 2016 (preprint version)


title: Physically Based Real-Time Translucency for Leaves

text: This paper presents a new shading model for real-time rendering of plant leaves that reproduces all important attributes of a leaf and allows for a large number of leaves to be shaded. In particular, we use a physically based model for accurate subsurface scattering on the translucent side of directly lit leaves. For real-time rendering of this model, we formulate it as an image convolution process and express the result in an efficient directional basis that is fast to evaluate. We also propose a data acquisition method for leaves that uses off-the-shelf devices.


title: Beagle: Automated Extraction and Interpretation of Visualizations from the Web

text: "How common is interactive visualization on the web?" "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 85% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare for the visualization tools that were studied. Our findings also suggest that the total visualization types supported by a given tool could factor into its ease of use. However this effect appears to be mitigated by providing a variety of diverse expert visualization examples to users.


title: A review on plant disease detection using image processing

text: India is the agriculture based country, since it contributes 7.68 percent of total global agricultural output. In India, agricultural sector contributes about seventeen percentage of total Indian gross domestic product (GDP). Effective growth and improved yield of plants are necessary for increment of farmer's profit and economy of India. For this purpose farmers need domain experts for manual monitoring of plants. But manual monitoring will not give satisfactory result all the time. Moreover, domain experts are not available at all regions and are expensive as farmers have to pay fees including travelling charges. Hence, it requires developing an efficient smart farming technique which will help for better yield and growth with less human efforts. In this paper, we provide a review on methods developed by various researchers for detection of diseases in plants, in the field of image processing. It includes research in disease detection of plants such as apple, grapes, pepper, pomegranate, tomato etc.


title: Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks

text: A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of ) among sentences. To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. Using this approach, we could construct a how-to knowledge base from the largest procedure sharing-community, wikihow.com. The evaluation of our approach shows that it outperforms the existing entity relationship extraction algorithms.


title: A distributed camera system for multi-resolution surveillance

text: We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database.


title: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters.

text: Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.


title: Environmental Sensor Networks : A revolution in the earth system science ?

text: Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them. © 2006 Elsevier B.V. All rights reserved.


title: Sensor networks: evolution, opportunities, and challenges

text: Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.


title: Structured low-rank approximation and its applications

text: Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data and the rank constraint is related to a bound on the model complexity. In the special case of fitting by a static model, the data matrix and its low-rank approximation are unstructured. We outline applications in system theory (approximate realization, model reduction, output error, and errors-in-variables identification), signal processing (harmonic retrieval, sum-of-damped exponentials, and finite impulse response modeling), and computer algebra (approximate common divisor). Algorithms based on heuristics and local optimization methods are presented. Generalizations of the low-rank approximation problem result from different approximation criteria (e.g., weighted norm) and constraints on the data matrix (e.g., nonnegativity). Related problems are rank minimization and structured pseudospectra. 2007 Elsevier Ltd. All rights reserved.


title: A novel ripple-based constant on-time control with virtual inductance and offset cancellation for DC power converters

text: In recent years, there has been a growing trend of mandating high power conversion efficiency, for both the heavy-load and the light-load conditions. To achieve this purpose, a ripple-based constant on-time (RBCOT) control for DC-DC converters has received wide attentions because of its natural characteristic of switching frequency reduction under light-load condition. However, a RBCOT converter suffers from output-voltage offset problem and sub-harmonic instability problem. In this paper, a modified RBCOT buck converter circuit will be proposed to solve both problems using the concept of virtual inductor current to stabilize the feedback and an offset cancellation circuit to cancel out the output DC offset. A control model based on describing function is also developed for the modified converter. From the model, it's found out that it's much easier to accomplish adaptive-voltage-positioning (AVP) using the proposed modified RBCOT scheme compared to a conventional constant-frequency controller. Simulation and experimental results are also given to verify the proposed scheme.


title: Unbiased scalable softmax optimization

text: Recent neural network and language models rely on softmax distributions with an extremely large number of categories. Since calculating the softmax normalizing constant in this context is prohibitively expensive, there is a growing literature of efficiently computable but biased estimates of the softmax. In this paper we propose the first unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints (and no extra work is required at the end of each epoch). We show that our proposed unbiased methods comprehensively outperform the state-of-the-art on seven real world datasets.


title: Position sensing in brake-by-wire callipers using resolvers

text: Recent designs for brake-by-wire systems use "resolvers" to provide accurate and continuous measurements for the absolute position and speed of the rotor of the electric actuators in brake callipers (permanent magnet DC motors). Resolvers are absolute-angle transducers that are integrated with estimator modules called "angle tracking observer" and together they provide position and speed measurements. Current designs for angle-tracking observers are unstable in applications with high acceleration and/or speed. In this paper, we introduce a new angle-tracking observer in which a closed-loop linear time-invariant (LTI) observer is integrated with a quadrature encoder. Finite-gain stability of the proposed design and its robustness to three different kinds of parameter variations are proven based on theorems of input-output stability in nonlinear control theory. In our experiments, we examined the performance of our observer and two other methods (a well-known LTI observer and an extended Kalman filter) to estimate the position and speed of a brake-by-wire actuator. The results show that because of the very high speed and acceleration of the actuator in this application, the LTI observer and Kalman filter cannot track the rotor position and diverge. In contrast, with a properly designed open-loop transfer function and selecting a suitable switching threshold, our proposed angle-tracking observer is stable and highly accurate in a brake-by-wire application


title: Identifying the Information Structure of Scientific Abstracts: An Investigation of Three Different Schemes

text: Many practical tasks require accessing specific types of information in scientific literature; e.g. information about the objective, methods, results or conclusions of the study in question. Several schemes have been developed to characterize such information in full journal papers. Yet many tasks focus on abstracts instead. We take three schemes of different type and granularity (those based on section names, argumentative zones and conceptual structure of documents) and investigate their applicability to biomedical abstracts. We show that even for the finest-grained of these schemes, the majority of categories appear in abstracts and can be identified relatively reliably using machine learning. We discuss the impact of our results and the need for subsequent task-based evaluation of the schemes.


title: Derive: a tool that automatically reverse-engineers instruction encodings

text:
Many binary tools, such as disassemblers, dynamic code generation systems, and executable code rewriters, need to understand how machine instructions are encoded. Unfortunately, specifying such encodings is tedious and error-prone. Users must typically specify thousands of details of instruction layout, such as opcode and field locations values, legal operands, and jump offset encodings. We have built a tool called DERIVE that extracts these details from existing software: the system assembler. Users need only provide the assembly syntax for the instructions for which they want encodings. DERIVE automatically reverse-engineers instruction encoding knowledge from the assembler by feeding it permutations of instructions and doing equation solving on the output.
DERIVE is robust and general. It derives instruction encodings for SPARC, MIPS, Alpha, PowerPC, ARM, and x86. In the last case, it handles variable-sized instructions, large instructions, instruction encodings determined by operand size, and other CISC features. DERIVE is also remarkably simple: it is a factor of 6 smaller than equivalent, more traditional systems. Finally, its declarative specifications eliminate the mis-specification errors that plague previous approaches, such as illegal registers used as operands or incorrect field offsets and sizes. This paper discusses our current DERIVE prototype, explains how it computes instruction encodings, and also discusses the more general implications of the ability to extract functionality from installed software.


title: Multi-user lax communications: A multi-armed bandit approach

text: Inspired by cognitive radio networks, we consider a setting where multiple users share several channels modeled as a multi-user multi-armed bandit (MAB) problem. The characteristics of each channel are unknown and are different for each user. Each user can choose between the channels, but her success depends on the particular channel chosen as well as on the selections of other users: if two users select the same channel their messages collide and none of them manages to send any data. Our setting is fully distributed, so there is no central control. As in many communication systems, the users cannot set up a direct communication protocol, so information exchange must be limited to a minimum. We develop an algorithm for learning a stable configuration for the multi-user MAB problem. We further offer both convergence guarantees and experiments inspired by real communication networks, including comparison to state-of-the-art algorithms.


title: A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer

text: In this paper, we propose an acceleration-based human activity recognition method using popular deep architecture, Convolution Neural Network (CNN). In particular, we construct a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals. Also, for comparison, we use some widely used methods to accomplish the recognition task on the same dataset. The large dataset we constructed consists of 31688 samples from eight typical activities. The experiment results show that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.


title: SketchInsight: Natural data exploration on interactive whiteboards leveraging pen and touch interaction

text: In this work, we advance research efforts in combining the casual sketching approach of whiteboards with the machine's computing power. We present SketchInsight, a system that applies the familiar and collaborative features of a whiteboard interface to the accurate data exploration capabilities of interactive visualizations. SketchInsight enables data analysis with more fluid interaction, allowing people to visually explore their data by drawing simple charts and directly manipulating them. In addition, we report results from a qualitative study conducted to evaluate user experience in exploring data with SketchInsight, expanding our understanding on how people use a pen- and touch-enabled digital whiteboard for data exploration. We also discuss the challenges in building a working system that supports data analytic capabilities with pen and touch interaction and freeform annotation.


title: Spare parts inventory control : a literature review

text: Spare parts inventory are needed for maintenance and repair of final products, vehicles, industrial machines and equipments, frequently requiring high investments and significantly affecting customer satisfaction. Inventory management is complex due to the large number of different items and low demands. This article presents a literature review on single location spare parts inventory control, embracing both demand forecasting techniques and inventory control decisions on the different life cycle stages. Overall, the literature review identified the following research opportunities on inventory management: criteria to decide to stock or not an item, how much to order in the first and the last batch, demand forecasting and inventory control models integration and case studies on real applications.


title: Towards Deviceless Edge Computing: Challenges, Design Aspects, and Models for Serverless Paradigm at the Edge

text: Recently, Cloud Computing, Edge Computing, and the Internet of Things (IoT) have been converging ever stronger, sparking creation of very large-scale, geographically distributed systems [1, 2]. Such systems intensively exploit Cloud Computing models and technologies, predominantly by utilizing large and remote data centers, but also nearby Cloudlets [3, 4] to enhance resource-constrained Edge devices (e.g., in terms of computation offloading [5–7] and data staging [8]) or to provide an execution environment for cloud-centric IoT/Edge applications [9, 10]. Serverless computing is an emerging paradigm, typically referring to a software architecture where application is decomposed into “triggers” and “actions” (or functions), and there is a platform that provides seamless hosting and execution of developer-defined functions (FaaS), making it easy to develop, manage, scale, and operate them. This complexity mitigation is mainly achieved by incorporating sophisticated runtime mechanisms into serverless or FaaS platforms. Hence, such platforms are usually characterized by fully automating many of the management and operations processes. Therefore, serverless computing can be considered as the next step in the evolution of Cloud platforms, such as PaaS, or more generally of the utility computing. While originally designed for centralized cloud deployments, the benefits of serverless paradigm become especially evident in the context of Edge Computing [11]. This is mainly because in such systems, traditional infrastructure and application management solutions are tedious, ineffective, error-prone, and ultimately very costly. Luckily, some of the existing serverless techniques, such


title: Tensor-based approach to accelerate deformable part models

text: This article provides next step towards solving speed bottleneck of any system that intensively uses convolutions operations (e.g. CNN). Method described in the article is applied on deformable part models (DPM) algorithm. Method described here is based on multidimensional tensors and provides efficient tradeoff between DPM performance and accuracy. Experiments on various databases, including Pascal VOC, show that the proposed method allows decreasing a number of convolutions up to 4.5 times compared with DPM v.5, while maintaining similar accuracy. If insignificant accuracy degradation is allowable, higher computational gain can be achieved. The method consists of filters tensor decomposition and convolutions shortening using the decomposed filter. Mathematical overview of the proposed method as well as simulation results are provided.


title: User activities outlier detection system using principal component analysis and fuzzy rule-based system

text: In this paper, a user activities outlier detection system is introduced. The proposed system is implemented in a smart home environment equipped with appropriate sensory devices. An activity outlier detection system consist of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based System (FRBS). In the first stage, the Hamming distance is used to measure the distances between the activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error (SPE). In the second stage of the process, the calculated indices are provided as inputs to FRBSs to model them heuristically. They are used to identify the outliers and classify them. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers and helps in distinguishing between the normal and abnormal behaviour patterns of the Activities of Daily Living (ADL).


title: World ocean simulation system (WOSS): a simulation tool for underwater networks with realistic propagation modeling

text:
Network simulators are a fundamental tool for the performance evaluation of protocols and applications in complex scenarios, which would be too expensive or infeasible to realize in practice.
 With the aim to provide a shared environment for the simulation of underwater networks we have adapted the ns2 network simulator to provide a detailed reproduction of the propagation of sound in water (i.e., by means of ray tracing instead of empirical relations). This has been tied to formerly available simulation frameworks (such as the MIRACLE extensions to ns2) to provide a completely customizable tool, including acoustic propagation, physical layer modeling, and cross-layer specification of networking protocols. In this paper, we describe our tool, and use it for a case study involving the comparison of three MAC protocols for underwater networks over different kinds of physical layers. Our results compare the transmission coordination approach chosen by each protocol, and show when it is better to rely on random access, as opposed to loose or tight coordination.


title: Dialectic Perspective on Innovation 1 A Dialectic Perspective on Innovation : Conflicting Demands , Multiple Pathways , and Ambidexterity

text: Innovation, the development and intentional introduction of new and useful ideas by individuals, teams, and organizations, lies at the heart of human adaptation. Decades of research in different disciplines and at different organizational levels have produced a wealth of knowledge about how innovation emerges and the factors that facilitate and inhibit innovation. We propose that this knowledge needs integration. In an initial step toward this goal, we apply a dialectic perspective on innovation to overcome limitations of dichotomous reasoning and to gain a more valid account of innovation. We point out that individuals, teams, and organizations need to self-regulate and manage conflicting demands of innovation and that multiple pathways can lead to idea generation and innovation. By scrutinizing the current use of the concept of organizational ambidexterity and extending it to individuals and teams, we develop a framework to help guide and facilitate future research and practice. Readers expecting specific and universal prescriptions of how to innovate will be disappointed as current research does not allow such inferences. Rather, we think innovation research should focus on developing and testing principles of innovation management in addition to developing decision aids for organizational practice. To this end, we put forward key propositions and action principles of innovation management. Dialectic Perspective on Innovation 3 A Dialectic Perspective on Innovation: Conflicting Demands, Multiple Pathways, and Ambidexterity Throughout this paper we use the term creativity for the generation of new and useful ideas and that of innovation to include both creative ideas and their implementation. Innovation can take different forms, including technological innovation, product and service innovation, and process innovation. The importance of innovation is widely acknowledged by organizational scholars, practitioners, and the wider society in an economic environment characterized by fierce competition, rapid change, and the global challenges of climate change and economic booms and busts. Decades of research have produced a wealth of knowledge about the characteristics of individuals, teams, and organizations that are related to outcomes of innovation (e.g., Anderson, De Dreu, & Nijstad, 2004; Damanpour, 1991). Some of these findings converge around factors that have been reliably found to influence innovation, such as a shared vision, innovative organizational culture (Miron, Erez, & Naveh, 2004; Naveh & Erez, 2004), emphasis on exploration rather than exploitation, and investment in R&D (Zahra & George, 2002; Cohen & Levinthal, 1990). Findings with respect to other factors, such as team diversity (Gibson & Gibbs, 2006), task related conflict, monetary rewards (Amabile, 2000; Eisenberger & Rhoades, 2001) remain contradictory. While we think that the scientific understanding of innovation is an important endeavor in its own right, we also suggest that the impact of scientific knowledge about innovation can be improved in organizational practice. While many reasons may account for a lack of transfer of scientific knowledge to management practices, one reason may be that the scientific findings do not readily or easily produce actionable knowledge. On the other hand, simplistically inferring practical implications or trying to be overly prescriptive can do harm, particularly if the context of an individual, team, or organization is not Dialectic Perspective on Innovation 4 taken into account. We, therefore, chose to integrate empirical findings of the extant literature with an attempt to develop a set of broad principles of innovation management that can guide decision making and action in organizations. We base this integration on a perspective on innovation which we term “dialectic”; we present and develop this perspective below. Toward this goal we offer three contributions to the literature: • By developing and applying a dialectic perspective on innovation, we aim to gain a more valid account of innovation in organizations that can enrich research and practice. • By reviewing core findings about innovation we illustrate how multiple pathways can lead to idea generation and innovation. • By redefining and extending the concept of ambidexterity, we propose a cross-level framework for the successful management of inherently conflicting demands of innovation. Tensions of Innovation: Theoretical Perspectives A pervasive theme in research on organizational innovation is that innovation is characterized by tensions (Lewis, Welsh, Dehler, & Green, 2002), paradoxes (Miron et al., 2004), contradictions (King, Anderson, & West, 1991), dilemmas (Benner & Tushman, 2003), and the so-called “dark side” of innovation processes (Anderson & Gasteiger, 2007). Table 1 presents a number of examples of tensions related to innovation that have been noted in the published literature. We organize Table 1 by the referent level of the tension (individual, team, or organizational) and by whether the tension is primarily focused on antecedents of innovation or on innovative processes and outcomes. That tensions have been described frequently at all levels of analysis and with regard to antecedents, processes, and consequences of innovation provides Dialectic Perspective on Innovation 5 what we think is compelling evidence for their pervasiveness within organizations attempting to innovate. -----------------------------Insert Table 1 about here -----------------------------We propose with others that understanding and managing these tensions is central to successful innovation and use the terms conflicting demands and conflicting activities to refer to the origins of tensions, paradoxes, contradictions and dilemmas. In the following we contrast two theoretical perspectives and the strategies they imply for dealing with these tensions. One strategy deals with tensions by emphasizing the separation of conflicting activities to different sub-organizations or even different organizations altogether (O'Reilly & Tushman, 2004). The top management of the organization is responsible for the necessary integration of activities that produce the tensions. In a way, this strategy deals with tensions by reducing them as much as possible. This strategy derives from a dichotomous theory perspective (March, Sproull, & Tamuz, 1991). We contrast the dichotomous approach with a second theoretical perspective which argues that a strict separation of conflicting activities to sub-organizations leads to disadvantages. Given that a system has sufficient levels of internal complexity (Brown & Eisenhardt, 1997), the tensions should be kept within the system to be managed rather than organized “out” of the system. Both the dichotomous (“keep separate”) and dialectic (“integrate and manage”) perspectives concur that the innovation process poses potentially conflicting task demands on individuals, teams, and organizations. By task demands we refer to the patterns of requisite Dialectic Perspective on Innovation 6 activity an individual, team, or organization must engage in to achieve an outcome. Innovation confronts individuals, teams, and organizations with fundamentally different demands in several important and unavoidable ways: First, the demands of innovation differ from the demands of routine performance. Whereas routine performance is based on the exploitation of knowledge, skills, and abilities that emphasize quality and efficiency criteria, innovation requires exploratory action and creative thinking. People and teams who need to be creative and innovative must crave newness and be curious, while people and teams who are supposed to produce efficiently must be able to close their minds to new ideas that just interrupt the clear pattern of existing routines and hinder the further development of those routines into ever more efficient production. Second, the innovation process itself encompasses different sets of activities, such as those related to idea generation and innovation implementation: These sets of activities are linked to different or conflicting antecedents. For instance, granting autonomy is linked to the generation of new ideas (Shalley, Zhou, & Oldham, 2004), while initiating structure is related to the success of implementing incremental innovation (Keller, 2006). Maximizing the conditions fostering creativity is unlikely to translate directly into innovation because innovation encompasses much more than idea generation. Indeed, the maximization of factors that facilitate the development of new ideas is likely to simultaneously cause conditions that may inhibit idea implementation, and thus innovation overall. For example, Xerox Parc developed many innovations in software design, PC hardware and PC connectivity. The creativity of this group was enormous. And these innovations were often implemented in products. However, Xerox made little economic use of these enormously creative new products that essentially were fed into innovations marketed by Apple and Microsoft (Bergin, 2006; Miller & Steinberg, 2006). Dialectic Perspective on Innovation 7 Third, in addition, there are different types of innovation: An innovation is incremental when it builds on and exploits existing products and processes; an innovation is radical or disruptive if it departs strongly from the status quo (Benner & Tushman, 2003). Incremental innovation leads to expected increases in mean levels of performance, while radical innovation creates variability in performance (potential for high losses or high returns). The expected level of returns is lower for radical than for incremental innovation (Taylor & Greve, 2006). We assume with others that for radical innovation the problem of conflicting task demands of innovation is more pronounced than for incremental innovation and the management of tensions is particularly challenging (Christensen, 1997). But even incremental inn


title: Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia

text: Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety. Problem Statement The costs of fatalities and injuries due to road traffic accidents (RTAs) have a tremendous impact on societal well-being and socioeconomic development. RTAs are among the leading causes of death and injury worldwide, causing an estimated 1.2 million deaths and 50 million injuries each year (World Health Organization, 2004). Ethiopia has the highest rate of RTAs, owing to the fact that road transport is the major transportation system in the country. The Ethiopian traffic control system archives data on various aspects of the traffic system, such as traffic volume, concentration, and vehicle accidents. With more vehicles and traffic, the capital city of Addis Ababa takes the lion’s share of the risk, with an average of 20 accidents being recorded every day and even more going unreported. The basic hypothesis of this research is that accidents are not randomly scattered along the road network, and that drivers are not involved in accidents at random. There are complex circumstantial relationships between several characteristics (driver, road, car, etc.) and the accident occurrence. As such, one cannot improve safety without successfully relating accident frequency and severity to the causative variables (Kononov and Janson 2002). We will attempt to extend the authors’ previous work in this area by generating additional attributes and focusing on the contribution of road-related factors to accident severity in Ethiopia. This will help to identify the parts of a road that are risky, thus supporting traffic accident data analysis in decision-making processes. The general objective of the research is to investigate the role of road-related factors in accident severity, using RTA data from Ethiopia and predictive models. Our three specific objectives include: 1) exploring the underlying variables (especially road-related ones) that impact car accident severity, 2) predicting accident severity using different data mining techniques, and 3) comparing standard classification models for this task. Literature Review Various studies have addressed the different aspects of RTAs, with most focusing on predicting or establishing the critical factors influencing injury severity (Chong, A. et al. 2005). Numerous data mining-related studies have been undertaken to analyze RTA data locally and globally, with results frequently varying depending on the socio-economic conditions and infrastructure of a given location. Ossenbruggen, Pendharkar et al. (2001) used a logistic regression model to identify the prediction factors of crashes and crash-related injuries, using models to perform a risk assessment of a given region. These models included attributes describing a site by its land use activity, roadside design, use of traffic control devices, and traffic exposure. Their study illustrated that village sites were less hazardous than residential or shopping sites. Abdalla et al. (1997) also studied the relationship between casualty frequency and the distance of an accident from residential zones. Not surprisingly, casualty frequencies were higher in accidents that occurred nearer to residential zones, possibly due to higher exposure. The


title: A knowledge-based approach for duplicate elimination in data cleaning

text: Existing duplicate elimination methods for data cleaning work on the basis of computing the degree of similarity between nearby records in a sorted database. High recall can be achieved by accepting records with low degrees of similarity as duplicates, at the cost of lower precision. High precision can be achieved analogously at the cost of lower recall. This is the recall–precision dilemma. We develop a generic knowledge-based framework for effective data cleaning that can implement any existing data cleaning strategies and more. We propose a new method for computing transitive closure under uncertainty for dealing with the merging of groups of inexact duplicate records and explain why small changes to window sizes has little effect on the results of the sorted neighborhood method. Experimental study with two real-world datasets show that this approach can accurately identify duplicates and anomalies with high recall and precision, thus effectively resolving the recall–precision dilemma. r 2001 Published by Elsevier Science Ltd.


title: Identifying Exoplanets with Deep Learning : A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90

text: NASA’s Kepler Space Telescope was designed to determine the frequency of Earth-sized planets orbiting Sun-like stars, but these planets are on the very edge of the mission’s detection sensitivity. Accurately determining the occurrence rate of these planets will require automatically and accurately assessing the likelihood that individual candidates are indeed planets, even at low signal-to-noise ratios. We present a method for classifying potential planet signals using deep learning, a class of machine learning algorithms that have recently become state-of-theart in a wide variety of tasks. We train a deep convolutional neural network to predict whether a given signal is a transiting exoplanet or a false positive caused by astrophysical or instrumental phenomena. Our model is highly effective at ranking individual candidates by the likelihood that they are indeed planets: 98.8% of the time it ranks plausible planet signals higher than false-positive signals in our test set. We apply our model to a new set of candidate signals that we identified in a search of known Kepler multi-planet systems. We statistically validate two new planets that are identified with high confidence by our model. One of these planets is part of a five-planet resonant chain around Kepler-80, with an orbital period closely matching the prediction by three-body Laplace relations. The other planet orbits Kepler-90, a star that was previously known to host seven transiting planets. Our discovery of an eighth planet brings Kepler-90 into a tie with our Sun as the star known to host the most planets.


title: An Introduction to Restricted Boltzmann Machines

text: Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. The increase in computational power and the development of faster learning algorithms have made them applicable to relevant machine learning problems. They attracted much attention recently after being proposed as building blocks of multi-layer learning systems called deep belief networks. This tutorial introduces RBMs as undirected graphical models. The basic concepts of graphical models are introduced first, however, basic knowledge in statistics is presumed. Different learning algorithms for RBMs are discussed. As most of them are based on Markov chain Monte Carlo (MCMC) methods, an introduction to Markov chains and the required MCMC techniques is provided.


title: Automatic generation of buffer overflow attack signatures: an approach based on program behavior models

text: Buffer overflows have become the most common target for network-based attacks. They are also the primary mechanism used by worms and other forms of automated attacks. Although many techniques have been developed to prevent server compromises due to buffer overflows, these defenses still lead to server crashes. When attacks occur repeatedly, as is common with automated attacks, these protection mechanisms lead to repeated restarts of the victim application, rendering its service unavailable. To overcome this problem, we develop a new approach that can learn the characteristics of a particular attack, and filter out future instances of the same attack or its variants. By doing so, our approach significantly increases the availability of servers subjected to repeated attacks. The approach is fully automatic, does not require source code, and has low runtime overheads. In our experiments, it was effective against most attacks, and did not produce any false positives


title: Human detection in surveillance videos and its applications - a review

text: Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.


title: Survey on Independent Component Analysis

text: A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA.


title: Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

text: Large multilayer neural networks trained with backpropagation have recently achieved state-ofthe-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights.


title: Query Segmentation and Resource Disambiguation Leveraging Background Knowledge

text: Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for computing the parameter estimations. In order to maximize accuracy and usability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is obtained leveraging the underlying knowledge base; then the final correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identification in keyword queries reveal very promising results.


title: Humanoid locomotion on uneven terrain using the time-varying divergent component of motion

text: This paper presents a framework for dynamic humanoid locomotion on uneven terrain using a novel time-varying extension to the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic CoM height trajectories during stepping. The proposed planning algorithm computes admissible DCM reference trajectories given desired ZMP plans for single and double support. This is accomplished using reverse-time integration of the discretized DCM dynamics over a finite time horizon. To account for discontinuities during replanning, linear Model Predictive Control (MPC) is implemented over a short preview window. DCM tracking control is achieved using a time-varying proportional-integral controller based on the Virtual Repellent Point (VRP). The effectiveness of the combined approach is verified in simulation using a 30-DoF model of THOR, a compliant torque-controlled humanoid.


title: From Hunt the Wumpus to EverQuest: Introduction to Quest Theory

text: The paper will explore how the landscape types and the quest types are used in various games, how they structure the gameplay, how they act as bones for the game-content (graphics, dialogue, sound) and how they sometimes form the base on which a story is imposed and related to the player. The question then becomes, how does the quest structure influence the story structure? How do the limitations of the quest combinations limit the kinds of story that are possible? How rich can the imposed story be, without breaking the gameplay? Are landscape and quest-structure the dominant factors in quest game design, to which the story-ambitions must defer? The main thesis of the paper is that if we understand the powerful but simple structure the grammar of quests (how they work, how they are used) we can understand both the limits and the potential of these kinds of games.


title: Two practical man-in-the-middle attacks on Bluetooth secure simple pairing and countermeasures

text: We propose two new Man-In-The-Middle (MITM) attacks on Bluetooth Secure Simple Pairing (SSP). The attacks are based on the falsification of information sent during the input/output capabilities exchange and also the fact that the security of the protocol is likely to be limited by the capabilities of the least powerful or the least secure device type. In addition, we devise countermeasures that render the attacks impractical, as well as improvements to the existing Bluetooth SSP in order to make it more secure. Moreover, we provide a comparative analysis of the existing MITM attacks on Bluetooth.


title: Optimizing orthodontic treatment in patients taking bisphosphonates for osteoporosis.

text: Bisphosphonates have unique pharmacological characteristics unlike those of any other drug group. Millions of adults take oral bisphosphonates for long-term treatment of osteoporosis and osteopenia; some of these people will most likely also seek orthodontic treatment. Adverse dental effects from bisphosphonates have been reported, including decreased tooth movement, impaired bone healing, and osteonecrosis in the mandible and the maxilla. Osteonecrosis has been rarely observed after bisphosphonate use for osteoporosis. However, adverse drug effects might occur more frequently in orthodontic patients, and they would probably be noted before the end-stage pathology of osteonecrosis. Adverse effects during orthodontic treatment, including decreased tooth movement, could last for years after the drug therapy is stopped. Successful orthodontic treatment requires optimal bone healing to prevent excessive tooth mobility. Bisphosphonates appear to have 2 bone elimination rates--a fast elimination of weeks from the bone surface and a slow elimination of years after incorporation into the bone structure. This article presents methods to clinically and radiographically monitor orthodontic patients who are taking oral bisphosphonates. Efforts to minimize adverse effects and optimize orthodontic procedures with physician-approved drug holidays are discussed. The orthodontic treatment results of 3 patients who received bisphosphonate therapy are reported.


title: Use of e-books in an academic and research environment: A case study from the Indian Institute of Science

text: Purpose: E-books are not as popular as other types of e-publications, such as e-journals and e-newspapers. The possible reasons for this could be because the technology for creating/accessing e-books (both hardware and software) is not yet matured, users’ perspectives about e-books need to be changed and the awareness of e-books needs to be raised. The purpose of this study is to investigate the use and usability of e-books from users’ perspectives in an academic and research environment.


title: Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS

text: Tracking by detection based object tracking methods encounter numerous complications including object appearance changes, size and shape deformations, partial and full occlusions, which make online adaptation of classifiers and object models a substantial challenge. In this paper, we employ an object proposal network that generates a small yet refined set of bounding box candidates to mitigate the this object model refitting problem by concentrating on hard negatives when we update the classifier. This helps improving the discriminative power as hard negatives are likely to be due to background and other distractions. Another intuition is that, in each frame, applying the classifier only on the refined set of object-like candidates would be sufficient to eliminate most of the false positives. Incorporating an object proposal makes the tracker robust against shape deformations since they are handled naturally by the proposal stage. We demonstrate evaluations on the PETS 2016 dataset and compare with the state-of-theart trackers. Our method provides the superior results.


title: Design and Sensitivity Analysis of Highly Compact Comparator for Ku-Band Monopulse Radar

text: This paper gives the design of a highly compact comparator at a Ku-band frequency and presents analysis results of the comparator for the fabrication inaccuracies. First an unconventional magic-t using a nonstandard waveguide is designed at 15.50 GHz. To reduce the volume occupied by the magic-t, its E-arm (or difference port) is kept parallel to the plane of two inputs of the magic-t instead of perpendicular to them as is done in a convention magic-t. The sum and the difference ports of the above folded magic-t are then matched using inductive windows at 15.50 GHz. Keeping the required location of the outputs of the comparator in mind, four of these matched folded magic-ts are suitably interconnected to design a highly compact comparator. The effects of the fabrication errors in the waveguide and matching elements dimensions on the centre frequency, magnitude and phase response of the comparator are also analyzed and presented.


title: 5G 3GPP-Like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

text: For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.


title: Breathing at a rate of 5.5 breaths per minute with equal inhalation-to-exhalation ratio increases heart rate variability.

text:
OBJECTIVES
Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation-to-exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns.

METHODS
Forty-seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6). The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns.

RESULTS
The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline.

CONCLUSION
The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.


title: Data transmission via GSM voice channel for end to end security

text: Global System for Mobile Communications (GSM) technology still plays a key role because of its availability, reliability and robustness. Recently, additional consumer applications are proposed in which GSM is used as a backup or data transmission service. Unfortunately sending data via GSM channel is a challenging task since it is speech sensitive and suppresses other forms of signals. In this paper, a systematic method is proposed to develop a modem that transmits data over GSM voice channel (DoGSMV) using speech like (SL) symbols. Unlike the previous approaches an artificial search space is produced to find best SL symbols and analyses by synthesis (AbyS) method is introduced for parameter decoding. As a result 1.6 kbps simulation data rate is achieved when wireless communication errors are ignored.


title: Frequency-Reconfigurable Low-Profile Circular Monopolar Patch Antenna

text: A novel frequency-reconfigurable antenna is presented based on a circular monopolar patch antenna. The antenna comprises of a center-fed circular patch surrounded by four sector-shaped patches. Eight varactor diodes are introduced to bridge the gaps between the circular patch and the sector-shaped patches. By changing the reverse bias voltage of the varactor diodes, the antenna can be switched in the operating frequency. A fully functional prototype is developed and tested, which exhibits a continuously tunable frequency band from 1.64 GHz to 2.12 GHz. The measured efficiency rises from about 45% to about 85% as the operating frequency increases from 1.64 GHz to 2.12 GHz. Stable monopole like radiation patterns are achieved at all operating frequencies. In addition, the antenna owns a low-profile structure (0.04 free-space wavelengths at 1.9 GHz). The frequency selective feature and stable radiation patterns make the antenna potentially suitable for future wireless communication systems, such as cognitive radio.


title: Three-dimensional soft-tissue and hard-tissue changes in the treatment of bimaxillary protrusion.

text:
INTRODUCTION
Facial convexity related to bimaxillary protrusion is prevalent in many populations. Underlying skeletal protrusion combined with increased dentoalveolar protrusion contributes to facial muscle imbalance and lip incompetence, which is undesirable for many patients. In this study, we evaluated the relationship between soft-tissue and hard-tissue changes in an orthodontically treated Asian population.

METHODS
Twenty-four consecutive adult Asian patients (mean age, 24 years), diagnosed with severe bimaxillary dentoalveolar protrusion, were evaluated using pretreatment and posttreatment cone-beam computed tomography. The patients were treated with 4 first premolar extractions followed by anterior retraction with either skeletal or intraoral anchorage. Serial cone-beam computed tomography radiographs were registered on the entire cranial base and fossa. Soft-tissue and hard-tissue changes were determined through landmark displacement and color mapping.

RESULTS
Upper lip retraction was concentrated between the nasolabial folds and commissures. Lower lip retraction was accompanied by significant redistribution of soft tissues at pogonion. Soft-tissue changes correlated well with regional facial muscle activity. Significant retractions (2-4 mm) of the soft tissues occurred beyond the midsagittal region. Use of skeletal anchorage resulted in 1.5 mm greater lower lip retraction than intraoral anchorage, with greater retraction of the maxillary and mandibular incisor root apices.

CONCLUSIONS
Profound soft-tissue changes accompanied retraction of the anterior dentition with both treatment modalities.


title: Management of a Large Frontal Encephalocoele With Supraorbital Bar Remodeling and Advancement.

text: Of all the craniofacial abnormalities, facial clefts are the most disfiguring. Facial clefts are classified according to the affected anatomical area as described by Tessier. Through this classification, the location and extent of the cleft can be designated numerically.A 2-month-old male infant was referred to authors' craniofacial unit, from a hospital in a rural province of South Africa, with a problem of a supranasal encephalocoele. Bilateral raised eyebrows were noted as was a right-sided upper lid central third coloboma. Computed tomography and magnetic resonance imaging scans confirmed the presence of a supranasal encephalocoele with a large frontal bone defect and splayed nasal bones. Bilateral enlarged orbits were noted with tented orbital roofs that we classified as Tessier number 10 facial clefts. The child was booked for an encephalocoele excision and calvarial reconstruction at 4 months of age.As a result of the encephalocoele, the supraorbital bar with its adjacent nasal bones was cleaved in 2, resulting in a significant frontal bone defect. Osteotomies were performed to remove the supraorbital bar and nasal bones from the calvarium. The supraorbital bar segment was remodeled and plated with absorbable poly-L-lactic acid plates. Osteotomies of the nasal bones allowed them to be united centrally, also with absorbable plates. This entire construct was transferred and secured to the calvarium, but in a more caudal position thereby obliterating the frontal bone and Tessier number 10 facial cleft defects with a naturally contoured construct.


title: Parallel Cat Swarm Optimization

text: We investigate a parallel structure of cat swarm optimization (CSO) in this paper, and we call it parallel cat swarm optimization (PCSO). In the experiments, we compare particle swarm optimization (PSO) with CSO and PCSO. The experimental results indicate that both CSO and PCSO perform well. Moreover, PCSO is an effective scheme to improve the convergent speed of cat swarm optimization in case the population size is small and the whole iteration is less.


title: WiFi-Nano: reclaiming WiFi efficiency through 800 ns slots

text:
The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.
 In this paper, we propose WiFi-Nano, a system that uses 800 ns slots} to significantly improve WiFi efficiency. Reducing slot time from 9 us to 800 ns makes backoffs efficient, but clear channel assessment can no longer be completed in one slot since preamble detection can now take multiple slots. Instead of waiting for multiple slots for detecting preambles, nodes speculatively transmit preambles as their backoff counters expire, while continuing to detect premables using self-interference cancellation. Upon detection of preambles from other transmitters, nodes simply abort their own preamble transmissions, thereby allowing the earliest transmitter to succeed. Further, receivers speculatively transmit their ack preambles at the end of packet reception, thereby reducing ack overhead. We validate the effectiveness of WiFi-Nano through implementation on an FPGA-based software defined radio platform, and through extensive simulations, demonstrate efficiency gains of up to 100%.


title: Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data

text: Given the abundance of online information available to mobile users, particularly tourists and weekend travelers, recommender systems that effectively filter this information and suggest interesting participatory opportunities will become increasingly important. Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times and days. Thus, systems that provide collaborative recommendations involving multiple dimensions such as location, activities and time would enhance the overall experience of users.The relationship among these dimensions can be modeled by higher-order matrices called tensors which are then solved by tensor factorization. However, these tensors can be extremely sparse. In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data. We formulate an objective function which simultaneously factorizes coupled tensors and matrices constructed from heterogeneous data sources. We evaluate our system and approach on large-scale real world data sets consisting of 588,000 Flickr photos collected from three major metro regions in USA. We compare our approach with several state-of-the-art baselines and demonstrate that it outperforms all of them.


title: A System to Verify Network Behavior of Known Cryptographic Clients

text: Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. In this paper, we develop a system for verifying in near real-time that a cryptographic client’s message sequence is consistent with its known implementation. Moreover, we accomplish this without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client’s messages explores multiple candidate execution paths in the client concurrently, an innovation useful for aspects of certain cryptographic protocols such as message padding (which will be permitted in TLS 1.3). In addition, our toolchain includes a novel approach to symbolically executing the client software in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL and BoringSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet. On legitimate traffic our verification keeps pace with Gmail-shaped workloads, with a median lag of 0.85s.


title: Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

text: This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.


title: Managing Employee Compliance with Information Security Policies: The Critical Role of Top Management and Organizational Culture

text: We develop an individual behavioral model that integrates the role of top management and organizational culture into the theory of planned behavior in an attempt to better understand how top management can influence security compliance behavior of employees. Using survey data and structural equation modeling, we test hypotheses on the relationships among top management participation, organizational culture, and key determinants of employee compliance with information security policies. We find that top management participation in information security initiatives has significant direct and indirect influences on employees’ attitudes towards, subjective norm of, and perceived behavioral control over compliance with information security policies. We also find that the top management participation strongly influences organizational culture which in turn impacts employees’ attitudes towards and perceived behavioral control over compliance with information security policies. Furthermore, we find that the effects of top management participation and organizational culture on employee behavioral intentions are fully mediated by employee cognitive beliefs about compliance with information security policies. Our findings extend information security research literature by showing how top management can play a proactive role in shaping employee compliance behavior in addition to the deterrence oriented remedies advocated in the extant literature. Our findings also refine the theories about the role of organizational culture in shaping employee compliance behavior. Significant theoretical and practical implications of ∗This project was partially funded by a grant to the authors from the Defense Information Systems Agency (DISA) of the Department of Defense (DoD). The authors express their thanks to the editor, senior editor, associate editor, and two anonymous reviewers for their detailed and constructive comments and suggestions throughout the review process. †Corresponding author.


title: A Social Network-Based Recommender System (SNRS)

text: Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, an item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most


title: CRCTOL: A semantic-based domain ontology learning system

text: Keywords: ontologies < controlled vocabularies < index languages < language types < (language), knowledge acquisition < knowledge engineering < artificial intelligence < computer applications < computer operations < (activities and operations)


title: A study of the uniqueness of source code

text: This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6,000 software projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of `uniqueness' that we call syntactic redundancy. We parameterized our study over a variety of variables, the most important of which being the level of granularity at which we view source code. Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible applications to genetic programming and developer productivity tools.


title: Parameterized Pattern Matching: Algorithms and Applications

text: The problem of finding sections of code that either are identical or are related by the systematic renaming of variables or constants can be modeled in terms of parameterized strings ( p-strings) and parameterized matches ( p-matches). P-strings are strings over two alphabets, one of which represents parameters. Two p-strings are a parameterized match ( p-match) if one p-string is obtained by renaming the parameters of the other by a one-to-one function. In this paper, we investigate parameterized pattern matching via parameterized suffix trees (p-suffix trees). We give two algorithms for constructing p-suffix trees: one (eager) that runs in linear time for fixed alphabets, and another that uses auxiliary data structures and runs in O(n log(n)) time for variable alphabets, where n is input length. We show that using a p-suffix tree for a pattern p-string P, it is possible to search for all p-matches of P within a text p-string T in space linear in |P| and time linear in |T | for fixed alphabets, or O( |T | log(min( |P|, _)) time and O( |P|) space for variable alphabets, where _ is the sum of the alphabet sizes. The simpler p-suffix tree construction algorithm eager has been implemented, and experiments show it to be practical. Since it runs faster than predicted by the above worst-case bound, we reanalyze the algorithm and show that eager runs in time O(min(t |S |+m(t, S) | t>0) log _)), where for an input p-string S, m(t, S) is the number of maximal p-matches of length at least t that occur within S, and _ is the sum of the alphabet sizes. Experiments with the author's program dup (B. Baker, in ``Comput. Sci. Statist.,'' Vol. 24, 1992) for finding all maximal p-matches within a p-string have found m(t, S) to be less than |S | in practice unless t is small. ] 1996 Academic Press, Inc.


title: Speech and Language Processing

text: CHAPTER 18 Lexicons for Sentiment and Affect Extraction " [W]e write, not with the fingers, but with the whole person. The nerve which controls the pen winds itself about every fibre of our being, threads the heart, pierces the liver. " Virginia Woolf, Orlando " She runs the gamut of emotions from A to B. " Dorothy Parker, reviewing Hepburn's performance in Little Women In this chapter we turn to tools for interpreting affective meaning, extending our affective study of sentiment analysis in Chapter 7. We use the word 'affective', following the tradition in affective computing (Picard, 1995) to mean emotion, sentiment, personality , mood, and attitudes. Affective meaning is closely related to subjectivity, subjectivity the study of a speaker or writer's evaluations, opinions, emotions, and speculations (Wiebe et al., 1999). How should affective meaning be defined? One influential typology of affec-tive states comes from Scherer (2000), who defines each class of affective states by factors like its cognition realization and time course: Emotion: Relatively brief episode of response to the evaluation of an external or internal event as being of major significance. Mood: Diffuse affect state, most pronounced as change in subjective feeling, of low intensity but relatively long duration, often without apparent cause. Interpersonal stance: Affective stance taken toward another person in a specific interaction, colouring the interpersonal exchange in that situation. Figure 18.1 The Scherer typology of affective states, with descriptions from Scherer (2000).


title: Programming with a Differentiable Forth Interpreter

text: Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.


title: Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

text: Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.


title: TOLDI: An effective and robust approach for 3D local shape description

text: Feature description for the 3D local shape in the presence of noise, varying mesh resolutions, clutter and occlusion is a quite challenging task in 3D computer vision. This paper tackles the problem by proposing a new local reference frame (LRF) together with a novel triple orthogonal local depth images (TOLDI) representation, forming the TOLDI method for local shape description. Compared with previous methods, TOLDI manages to perform efficient, distinctive and robust description for the 3D local surface simultaneously under various feature matching contexts. The proposed LRF differs from many prior ones in its calculation of the z-axis and x-axis, the z-axis is calculated using the normal of the keypoint and the x-axis is computed by aggregating the weighted projection vectors of the radius neighbors. TOLDI feature descriptors are then obtained by concatenating three local depth images (LDI) captured from three orthogonal view planes in the LRF into feature vectors. The performance of our TOLDI approach is rigorously evaluated on several public datasets, which contain three major surface matching scenarios, namely shape retrieval, object recognition and 3D registration. Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of our method. Our method is also applied to aligning 3D object ∗Corresponding author Email addresses: jqyang@hust.edu.cn (Jiaqi Yang), hangfanzq@163.com (Qian Zhang), Yang Xiao@hust.edu.cn (Yang Xiao), zgcao@hust.edu.cn (Zhiguo Cao) Preprint submitted to Journal of LTEX Templates November 23, 2016 and indoor scene point clouds obtained by different devices (i.e., LiDAR and Kinect), the accurate outcomes further confirm the effectiveness of our method.


title: Synaptic patterning and the timescales of cortical dynamics

text: Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical organization of this selectivity mirrors anatomical and physiological relations throughout the cortex and is likely determined by the regional electrochemical composition. Being consistently patterned and actively regulated, the expression of molecules involved in synaptic transmission constitutes the most significant source of laminar and regional variability. Due to their complex kinetics and adaptability, synapses form a natural primary candidate underlying this regional temporal selectivity. The ability of cortical networks to reflect the temporal structure of the sensory environment can thus be regulated by evolutionary and experience-dependent processes.


title: Consistent Generative Query Networks

text: Stochastic video prediction is usually framed as an extrapolation problem where the goal is to sample a sequence of consecutive future image frames conditioned on a sequence of observed past frames. For the most part, algorithms for this task generate future video frames sequentially in an autoregressive fashion, which is slow and requires the input and output to be consecutive. We introduce a model that overcomes these drawbacks – it learns to generate a global latent representation from an arbitrary set of frames within a video. This representation can then be used to simultaneously and efficiently sample any number of temporally consistent frames at arbitrary time-points in the video. We apply our model to synthetic video prediction tasks and achieve results that are comparable to state-of-the-art video prediction models. In addition, we demonstrate the flexibility of our model by applying it to 3D scene reconstruction where we condition on location instead of time. To the best of our knowledge, our model is the first to provide flexible and coherent prediction on stochastic video datasets, as well as consistent 3D scene samples. Please check the project website https://bit.ly/2jX7Vyu to view scene reconstructions and videos produced by our model.


title: Single-label and multi-label conceptor classifiers in pre-trained neural networks

text: Training large neural network models from scratch is not feasible due to over-fitting on small datasets and too much time consumed on large datasets. To address this, transfer learning, namely utilizing the feature extracting capacity learned by large models, becomes a hot spot in neural network community. At the classifying stage of pre-trained neural network model, either a linear SVM classifier or a Softmax classifier is employed and that is the only trained part of the whole model. In this paper, inspired by transfer learning, we propose a classifier based on conceptors called Multi-label Conceptor Classifier (MCC) to deal with multi-label classification in pre-trained neural networks. When no multi-label sample exists, MCC equates to Fast Conceptor Classifier, a fast single-label classifier proposed in our previous work, thus being applicable to single-label classification. Moreover, by introducing a random search algorithm, we further improve the performance of MCC on single-label datasets Caltech-101 and Caltech-256, where it achieves state-of-the-art results. Also, its evaluations with pre-trained rather than fine-tuning neural networks are investigated on multi-label dataset PASCAL VOC-2007, where it achieves comparable results.


title: A fuzzy logic-based text classification method for social media

text: A FUZZY LOGIC-BASED TEXT CLASSIFICATION METHOD FOR SOCIAL MEDIA


title: Exploiting the web of data in model-based recommender systems

text: The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -- both content-based and collaborative filtering ones.


title: Figurative language processing after traumatic brain injury in adults: A preliminary study

text: Figurative speech (e.g., proverb, irony, metaphor, and idiom) has been reported to be particularly sensitive to measurement of abstract thinking in patients who suffer from impaired abstraction and language abilities. Metaphor processing was investigated with fMRI in adults with moderate to severe post-acute traumatic brain injury (TBI) and healthy age-matched controls using a valence-judgment task. We hypothesized that TBI patients would display decreased activation of the left inferior frontal gyrus (LIFG), which is considered central to semantic memory retrieval and abstract thought, in comparison with healthy controls. We also predicted that decreased activation in TBI individuals would correlate with their behavioral response times. A whole-brain analysis across the two participant groups revealed that patients did not strongly engage frontal and temporal regions related to semantic processing for novel metaphor comprehension, whereas control participants exhibited more intensive and concentrated activation within frontal and temporal areas. A region of interest (ROI) analysis verified that the LIFG was underactivated in TBI patients compared to controls across all conditions. TBI patients' impaired abstraction of novel stimuli may stem from reduced prefrontal control of semantic memory as well as disrupted interconnectivity of prefrontal cortex with other regions.


title: High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis

text: High-throughput genotyping platforms play important roles in plant genomic studies. Cotton (Gossypium spp.) is the world’s important natural textile fiber and oil crop. Upland cotton accounts for more than 90% of the world’s cotton production, however, modern upland cotton cultivars have narrow genetic diversity. The amounts of genomic sequencing and re-sequencing data released make it possible to develop a high-quality single nucleotide polymorphism (SNP) array for intraspecific genotyping detection in cotton. Here we report a high-throughput CottonSNP80K array and its utilization in genotyping detection in different cotton accessions. 82,259 SNP markers were selected from the re-sequencing data of 100 cotton cultivars and used to produce the array on the Illumina Infinium platform. 77,774 SNP loci (94.55%) were successfully synthesized on the array. Of them, 77,252 (99.33%) had call rates of >95% in 352 cotton accessions and 59,502 (76.51%) were polymorphic loci. Application tests using 22 cotton accessions with parent/F1 combinations or with similar genetic backgrounds showed that CottonSNP80K array had high genotyping accuracy, good repeatability, and wide applicability. Phylogenetic analysis of 312 cotton cultivars and landraces with wide geographical distribution showed that they could be classified into ten groups, irrelevant of their origins. We found that the different landraces were clustered in different subgroups, indicating that these landraces were major contributors to the development of different breeding populations of modern G. hirsutum cultivars in China. We integrated a total of 54,588 SNPs (MAFs >0.05) associated with 10 salt stress traits into 288 G. hirsutum accessions for genome-wide association studies (GWAS), and eight significant SNPs associated with three salt stress traits were detected. We developed CottonSNP80K array with high polymorphism to distinguish upland cotton accessions. Diverse application tests indicated that the CottonSNP80K play important roles in germplasm genotyping, variety verification, functional genomics studies, and molecular breeding in cotton.


title: Comparison of different designs of cylindrical printed quadrifilar Helix antennas

text: INMARSAT allocated frequencies are 1.525 GHz to 1.559 GHz as downlink and 1.625 GHz to 1.6605 GHz as uplink GPS allocated frequencies 1.57542 GHz in L1 band. In this paper we design two types of Helix antenna as PQHA to operate in allocated frequencies for INMARSAT and GPS application. We used for their optimum design Electromagnetic simulations and were verified by fabrication and VSWR measurement.


title: Normalization at the field level: Fractional counting of citations

text: Van Raan et al. (2010) accepted our critique for the case of journal normalization (previously CPP/JCSm); CWTS has in the meantime adapted its procedures. However, a new indicator was proposed for field normalization (previously CPP/FCSm), called the “mean normalized citation score” (MNCS; cf. Lundberg, 2007). In our opinion, this latter change does not sufficiently resolve the problems. Since the new indicator is considered another “crown indicator,” it seems urgent to warn against and elaborate on these remaining problems. In addition to damaging evaluation processes at the level of individuals and institutions, the “crown indicator” is also used by CWTS for the “Leiden Rankings,” and flaws in it can therefore misguide policies at national levels. also


title: Caesar: A content router for high-speed forwarding on content names

text: Today, high-end routers forward hundreds of millions of packets per second by means of longest prefix match on forwarding tables with less than a million IP prefixes. Information-Centric Networking, a novel form of networking where content is requested by its name, poses a new challenge in the design of high-end routers: process at least the same amount of packets, assuming a forwarding table that contains hundreds of millions of content prefixes. In this work we design and preliminarily evaluate Caesar, the first content router that supports name-based forwarding at high speed. Caesar efficiently uses available processing and memory units in a high-end router to support forwarding tables containing a billion content prefixes with unlimited characters.


title: Ultra wideband antennas: past and present

text: Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5GHz (from 3.1GHz to 10.6GHz) for ultra wideband (UWB) wireless communications, UWB is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. However, there are more challenges in designing a UWB antenna than a narrow band one. A suitable UWB antenna should be capable of operating over an ultra wide bandwidth as allocated by the FCC. At the same time, satisfactory radiation properties over the entire frequency range are also necessary. This paper focuses on UWB planar printed circuit board (PCB) antenna design and analysis. Studies have been undertaken covering the areas of UWB fundamentals and antenna theory. Extensive investigations were also carried out on the development of UWB antennas from the past to present. First, the planar PCB antenna designs for UWB system is introduced and described. Next, the special design considerations for UWB antennas are summarized. State-of-the-art UWB antennas are also reviewed. Finally, a new concept for the design of a UWB antenna with a bandwidth ranging from 2GHz-11.3GHz is introduced, which satisfies the system requirements for S-DMB, WiBro, WLAN, CMMB and the entire UWB . KeywordsUltra wideband, planar PCB Antenna and bandwidth.


title: SfePy - Write Your Own FE Application

text: SfePy (Simple Finite Elements in Python) is a framework for solving various kinds of problems (mechanics, physics, biology, ...) described by partial differential equations in two or three space dimensions by the finite element method. The paper illustrates its use in an interactive environment or as a framework for building custom finite-element based solvers.