title: Improved Intrusion Detection in DDoS Applying Feature Selection Using Rank & Score of Attributes in KDD-99 Data Set

abstract: In today's networked environment, massive volume of data being generated, gathered and stored in databases across the world. This trend is growing very fast, year after year. Today it is normal to find databases with terabytes of data, in which vital information and knowledge is hidden. The unseen information in such databases is not feasible to mine without efficient mining techniques for extracting information. In past years many algorithms are created to extract knowledge from large sets of data. There are many different methodologies to approach data mining: classification, clustering, association rule, etc. Classification is the most conventional technique to analyse the large data sets. Classification can help identify intrusions, as well as for discovering new and unknown types of intrusions. For classification, feature selection provides an efficient mechanism to analyse the dataset. We are trying to analyse the NSL-KDD cup 99, dataset using various classification algorithms. Primary experiments are performed in WEKA environment. The accuracy of the various algorithms is also calculated. A feature selection method has been implemented to provide improved accuracy. The main objective of this analysis is to deliver the broad analysis feature selection methods for NSL-KDD intrusion detection dataset.


title: Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS (altmetrics) and F1000Prime (paper tags)

abstract: Purpose – The purpose of this case study is to investigate the usefulness of altmetrics for measuring the broader impact of research. Design/methodology/approach – This case study is based on a sample of 1,082 the Public Library of Science (PLOS) journal articles recommended in F1000. The data set includes altmetrics which were provided by PLOS. The F1000 data set contains tags on papers which were assigned by experts to characterise them. Findings – The most relevant tag for altmetric research is “good for teaching”, as it is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialised area. One could expect papers with this tag to be mentioned more often on Facebook and Twitter than those without this tag. The results from regression models were able to confirm these expectations: papers with this tag show significantly higher Facebook and Twitter counts than papers without this tag. This clear association could not be seen with Mendeley or Figshare counts (...


title: Projection Pursuit Regression

abstract: Abstract A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, does not require the definition of a metric in the predictor space, and lends itself to graphical interpretation.


Security Using LSB & DCT Steganography In Images


title: Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm

abstract: Lane detection can provide important information for safety driving. In this paper, a real time vision-based lane detection method is presented to find the position and type of lanes in each video frame. In the proposed lane detection method, lane hypothesis is generated and verified based on an effective combination of lane-mark edge-link features. First, lane-mark candidates are searched inside region of interest (ROI). During this searching process, an extended edge-linking algorithm with directional edge-gap closing is used to produce more complete edge-links, and features like lane-mark edge orientation and lane-mark width are used to select candidate lane-mark edge-link pairs. For the verification of lane-mark candidates, color is checked inside the region enclosed by candidate edge-link pairs in YUV color space. Additionally, the continuity of the lane is estimated employing a Bayesian probability model based on lane-mark color and edge-link length ratio. Finally, a simple lane departure model is built to detect lane departures based on lane locations in the image Experiment results show that the proposed lane detection method can work robustly in real-time, and can achieve an average speed of 30~50ms per frame for 180x120 image size, with a correct detection rate over 92%.


title: Optical recognition of music symbols

abstract: Many musical works produced in the past are still currently available only as original manuscripts or as photocopies. The preservation of these works requires their digitalization and transformation into a machine-readable format. However, and despite the many research activities on optical music recognition (OMR), the results for handwritten musical scores are far from ideal. Each of the proposed methods lays the emphasis on different properties and therefore makes it difficult to evaluate the efficiency of a proposed method. We present in this article a comparative study of several recognition algorithms of music symbols. After a review of the most common procedures used in this context, their respective performances are compared using both real and synthetic scores. The database of scores was augmented with replicas of the existing patterns, transformed according to an elastic deformation technique. Such transformations aim to introduce invariances in the prediction with respect to the known variability in the symbols, particularly relevant on handwritten works. The following study and the adopted databases can constitute a reference scheme for any researcher who wants to confront a new OMR algorithm face to well-known ones.


title: Social Behavior Prediction from First Person Videos

abstract: This paper presents a method to predict the future movements (location and gaze direction) of basketball players as a whole from their first person videos. The predicted behaviors reflect an individual physical space that affords to take the next actions while conforming to social behaviors by engaging to joint attention. Our key innovation is to use the 3D reconstruction of multiple first person cameras to automatically annotate each other's the visual semantics of social configurations. ::: We leverage two learning signals uniquely embedded in first person videos. Individually, a first person video records the visual semantics of a spatial and social layout around a person that allows associating with past similar situations. Collectively, first person videos follow joint attention that can link the individuals to a group. We learn the egocentric visual semantics of group movements using a Siamese neural network to retrieve future trajectories. We consolidate the retrieved trajectories from all players by maximizing a measure of social compatibility---the gaze alignment towards joint attention predicted by their social formation, where the dynamics of joint attention is learned by a long-term recurrent convolutional network. This allows us to characterize which social configuration is more plausible and predict future group trajectories.


title: Intelligent control over the crossing traffic lights by graph theory

abstract: In order to further solve the collocation and time control of the city's multi-crossing traffic lights, to meet many bypath, this paper will flow passage efficiency trigeminal junction with intersection traffic light control into Graph Theory of complex problems, and the traffic flow of five bypath makes a concrete analysis. Among them, using graph coloring in the theory and Powell, and combined with the operational research knowledge of mathematical software MATLAB, LINGO make visual window (GUI) and to solve issues and make planning.


title: Assessing coping strategies: A theoretically based approach

abstract: We developed a muRidimensional coping inventory to assess the different ways in which people respond to stress. Five scales (of four items each) measure conceptually distinct aspects of problemfocused coping (active coping, planning, suppression of competing activities, restraint coping, seeking of instrumental social support); five scales measure aspects of what might be viewed as emotionfocused coping (seeking of emotional social support, positive reinterpretation, acceptance, denial, turning to religion); and three scales measure coping responses that arguably are less useful (focus on and venting of emotions, behavioral disengagement, mental disengagement). Study 1 reports the development of scale items. Study 2 reports correlations between the various coping scales and several theoretically relevant personality measures in an effort to provide preliminary information about the inventory's convergent and discriminant validity, Study 3 uses the inventory to assess coping responses among a group of undergraduates who were attempting to cope with a specific stressful episode. This study also allowed an initial examination of associations between dispositional and situational coping tendencies.


title: Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements

abstract: Machine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper.


title: Abstract Core Security Requirements Artefacts

abstract: Although security requirements engineering has recently attracted increasing attention, it has lacked a context in which to operate. A number of papers have described how security requirements may be violated, but apart from a few hints in the general literature, none have described satisfactorily what security requirements are. This paper proposes a framework of core security requirements artefacts, which unifies the concepts of the two disciplines of requirements engineering and security engineering. From requirements engineering it takes the concept of functional goals, which are operationalised into functional requirements, with appropriate constraints. From security engineering it takes the concept of assets, together with threats of harm to those assets. Security goals aim to protect from those threats, and are operationalised into security requirements, which take the form of constraints on the functional requirements. In addition we explore the consequences of the fact that security is concerned with the protection of assets, while computers only provide interfaces. We show how to specify the relationship between security requirements and the specification of software behaviour, using Jackson's Problem Frames approach.


title: Challenges in teaching test driven development

abstract: We identify two main challenges in the teaching of Test Driven Development (TDD) over the last two years. The first challenge is to get students to rethink learning and design, and to really engage with this new approach. The second challenge is to explicitly develop their skills in testing, design and refactoring, given that they have little experience in these areas. This requires that fast and effective feedback be provided.


title: Disorders of compulsivity: a common bias towards learning habits

abstract: Why do we repeat choices that we know are bad for us? Decision making is characterized by the parallel engagement of two distinct systems, goal-directed and habitual, thought to arise from two computational learning mechanisms, model-based and model-free. The habitual system is a candidate source of pathological fixedness. Using a decision task that measures the contribution to learning of either mechanism, we show a bias towards model-free (habit) acquisition in disorders involving both natural (binge eating) and artificial (methamphetamine) rewards, and obsessive-compulsive disorder. This favoring of model-free learning may underlie the repetitive behaviors that ultimately dominate in these disorders. Further, we show that the habit formation bias is associated with lower gray matter volumes in caudate and medial orbitofrontal cortex. Our findings suggest that the dysfunction in a common neurocomputational mechanism may underlie diverse disorders involving compulsion.


title: Cyber Warfare: A Review of Theories, Law, Policies, Actual Incidents – and the Dilemma of Anonymity

abstract: In this paper, the authors undertake a study of cyber warfare reviewing theories, law, policies, actual incidents - and the dilemma of anonymity. Starting with the United Kingdom perspective on cyber warfare, the authors then consider United States' views including the perspective of its military on the law of war and its general inapplicability to cyber conflict. Consideration is then given to the work of the United Nations' group of cyber security specialists and diplomats who as of July 2010 have agreed upon a set of recommendations to the United Nations Secretary General for negotiations on an international computer security treaty. An examination of the use of a nation's cybercrime law to prosecute violations that occur over the Internet indicates the inherent limits caused by the jurisdictional limits of domestic law to address cross-border cybercrime scenarios. Actual incidents from Estonia (2007), Georgia (2008), Republic of Korea (2009), Japan (2010), ongoing attacks on the United States as well as other incidents and reports on ongoing attacks are considered as well. Despite the increasing sophistication of such cyber attacks, it is evident that these attacks were met with a limited use of law and policy to combat them that can be only be characterised as a response posture defined by restraint. Recommendations are then examined for overcoming the attribution problem. The paper then considers when do cyber attacks rise to the level of an act of war by reference to the work of scholars such as Schmitt and Wingfield. Further evaluation of the special impact that non-state actors may have and some theories on how to deal with the problem of asymmetric players are considered. Discussion and possible solutions are offered. A conclusion is offered drawing some guidance from the writings of the Chinese philosopher Sun Tzu. Finally, an appendix providing a technical overview of the problem of attribution and the dilemma of anonymity in cyberspace is provided.


title: Digital implementation of a galvanometric optical scanner based on DSP and FPGA

abstract: A galvanometric optical scanner in a laser marking system is controlled to achieve the demand of high speed and high accuracy. This paper presents a design of digital galvanometric optical scanner adopting a high-precision PID control algorithm with compensation based on repetitive control which is DSP+FPGA-based architecture. This thesis consists of three parts including theory of laser marking system and galvanometer scanner construction, hardware design of DSP+FPGA-based architecture, design and Simulation of scanning controller.


title: Linear SVM classifier based HOG car detection

abstract: Improving safety and reducing accidents are the most goals of Advanced Driver Assistance Systems (ADAS). For their low cost and capability of providing information about driving environments, Vision-based driver assistance systems are the most important systems in recent years. In these systems, robust and precise vehicle detection is a critical step, and the detected cars can be used for various applications. This paper presents an algorithm for vehicle detection in an urban environment which is very important for driver assistance systems and autonomous driving. To succeed the detection of a vehicle, we propose the histogram of oriented gradients features descriptor (HOG) and linear support vector machine (SVM) for the classification. Our experimental results illustrate the robustness and precision of our algorithm for different scenes.


Metadata visualization for digital libraries: interactive timeline editing and review


title: Reinforcement effect of poly (methyl methacrylate)-g-cellulose nanofibers on LDPE/thermoplastic starch composites: preparation and characterization

abstract: The effect of cellulose nanofibers (CNFs) and poly [methyl methacrylate (MMA)]-grafted cellulose nanofibers (CNF-g-PMMA) on mechanical properties and degradability of a 75/25 low density polyethylene/thermoplastic starch (LDPE/TPS) blend was investigated. Graft copolymerization on CNFs was performed in an aqueous suspension by free radical polymerization using MMA as an acrylic monomer. In addition, a LDPE/TPS blend was reinforced by different amounts of CNFs (1–5 wt%) and CNF-g-PMMA (1–7 wt%) using a twin-screw extruder. A 61% grafting of PMMA on the surface of CNFs was demonstrated by gravimetric analysis. Moreover, after modification the X-ray photoelectron spectroscopy analysis showed a 20% increase of carbon atoms on the surface of CNFs and a 22.6% decrease in the oxygen content of its surface. The mechanical properties of the CNFs-modified composites were significantly improved compared to the unmodified nanocomposites. The highest tensile strength and Young’s modulus were obtained for the composites reinforced by 3 and 7 wt% CNF-g-PMMA, respectively. The degradability of cellulose nanocomposites was studied by water absorption and soil burial tests. Surface modification of CNFs lowered water absorption, and soil burial test of the LDPE/TPS blend showed improvement in biodegradability by addition of CNF-g-PMMA.


title: Investigating Content Selection for Language Generation using Machine Learning

abstract: The content selection component of a natural language generation system decides which information should be communicated in its output. We use information from reports on the game of cricket. We first describe a simple factoid-to-text alignment algorithm then treat content selection as a collective classification problem and demonstrate that simple 'grouping' of statistics at various levels of granularity yields substantially improved results over a probabilistic baseline. We additionally show that holding back of specific types of input data, and linking database structures with commonality further increase performance.


Progress in the Synthetic and Functional Aspects of Chiral Metal‐Organic Frameworks


title: Deep Radio-Visual Localization

abstract: For many automated navigation applications, the underlying localization algorithm must be able to continuously produce both accurate and stable results by using a spectrum of redundant sensing technologies. To this end, various sensors have been used for localization, such as Wi-Fi, Bluetooth, GPS, LiDAR and cameras. In particular, a class of vision-based localization techniques using Structure from Motion (SfM) has been shown to produce very accurate position estimates in the real-world with moderate assumptions about the motion of the camera and the amount of visual texture in the environment. However, when these assumptions are violated, SfM techniques can fail catastrophically (i.e., cannot generate any estimate). Recently, a deep convolutional neural network (CNN) has been applied to images to robustly regress 6-DOF camera poses at the cost of lower accuracy than SfM. In this work, we propose improving image-based localization accuracy of deep CNN by combining Bluetooth radio-wave signal readings. In our experiments, we show that our proposed dual-stream CNN can robustly regress 6-DOF poses from images and radiowave signals better than one sensing modality alone. More importantly, we show that when both modes are used, the localization accuracy of the proposed deep CNN is comparable to that of SfM and significantly more robust than SfM.


Automatic thresholding of gray-level pictures using two-dimensional entropy


title: Outdoor flocking and formation flight with autonomous aerial robots

abstract: We present the first decentralized multi-copter flock that performs stable autonomous outdoor flight with up to 10 flying agents. By decentralized and autonomous we mean that all members navigate themselves based on the dynamic information received from other robots in the vicinity. We do not use central data processing or control; instead, all the necessary computations are carried out by miniature on-board computers. The only global information the system exploits is from GPS receivers, while the units use wireless modules to share this positional information with other flock members locally. Collective behavior is based on a decentralized control framework with bio-inspiration from statistical physical modelling of animal swarms. In addition, the model is optimized for stable group flight even in a noisy, windy, delayed and error-prone environment. Using this framework we successfully implemented several fundamental collective flight tasks with up to 10 units: i) we achieved self-propelled flocking in a bounded area with self-organized object avoidance capabilities and ii) performed collective target tracking with stable formation flights (grid, rotating ring, straight line). With realistic numerical simulations we demonstrated that the local broadcast-type communication and the decentralized autonomous control method allows for the scalability of the model for much larger flocks.


title: A Transformation Framework for Optimizing Task-Parallel Programs

abstract: Task parallelism has increasingly become a trend with programming models such as OpenMP 3.0, Cilk, Java Concurrency, X10, Chapel and Habanero-Java (HJ) to address the requirements of multicore programmers. While task parallelism increases productivity by allowing the programmer to express multiple levels of parallelism, it can also lead to performance degradation due to increased overheads. In this article, we introduce a transformation framework for optimizing task-parallel programs with a focus on task creation and task termination operations. These operations can appear explicitly in constructs such as async, finish in X10 and HJ, task, taskwait in OpenMP 3.0, and spawn, sync in Cilk, or implicitly in composite code statements such as foreach and ateach loops in X10, forall and foreach loops in HJ, and parallel loop in OpenMP. ::: Our framework includes a definition of data dependence in task-parallel programs, a happens-before analysis algorithm, and a range of program transformations for optimizing task parallelism. Broadly, our transformations cover three different but interrelated optimizations: (1) finish-elimination, (2) forall-coarsening, and (3) loop-chunking. Finish-elimination removes redundant task termination operations, forall-coarsening replaces expensive task creation and termination operations with more efficient synchronization operations, and loop-chunking extracts useful parallelism from ideal parallelism. All three optimizations are specified in an iterative transformation framework that applies a sequence of relevant transformations until a fixed point is reached. Further, we discuss the impact of exception semantics on the specified transformations, and extend them to handle task-parallel programs with precise exception semantics. Experimental results were obtained for a collection of task-parallel benchmarks on three multicore platforms: a dual-socket 128-thread (16-core) Niagara T2 system, a quad-socket 16-core Intel Xeon SMP, and a quad-socket 32-core Power7 SMP. We have observed that the proposed optimizations interact with each other in a synergistic way, and result in an overall geometric average performance improvement between 6.28× and 10.30×, measured across all three platforms for the benchmarks studied.


title: A comparison of SOAP and REST implementations of a service based interaction independence middleware framework

abstract: This paper describes the conceptual design of an interaction independence middleware framework and describes the role that web services plays within it. We investigate two pervasive service-oriented architecture paradigms, SOAP and REST, in order to gauge their potential effectiveness in meeting underlying back-end data transmission requirements; provide implementations for the service-oriented architecture and data model; and, finally, critically evaluate both implementations with an emphasis on their performance with regard to both efficiency and scalability.


title: Accessibility levels of Portuguese enterprise websites: equal opportunities for all?

abstract: Web accessibility is growing in importance. We may also find an increasing need for access to web resources by those with some sort of disability. The Web is very important for spreading information and for promoting interaction between the various elements in society. Given this, it is essential that the Web presents itself as a totally accessible resource, so that it can help citizens with disabilities and their integration in society. This obligation should be even greater for enterprises as primarily the Web is used as a marketing and business platform. With this document, we present indicators regarding the [lack of] accessibility levels of Portuguese enterprise websites. This article contains theoretical and background considerations as well as the results of two different studies that the research team undertook. First of all, the research team made a comparison between the 1000 largest Portuguese enterprises annual sales volume and the 1000 best Portuguese small and medium enterprises sales growth and profit using a specialised software tool and according to Web Content Accessibility Guidelines WCAG 1.0; the research team then also considered WCAG 2.0 and its impact on enterprise Web accessibility. In the second study qualitative in nature, focus group interactions led to a set of recommendations towards greater accessibility.


title: A 4.8 GHz RF CMOS wideband LC VCO for wireless sensor networks RFICs

abstract: A 4.8GHz LC voltage controlled oscillator (VCO) for Wireless Sensor Network (WSN) SoC RFIC chipset is designed based on SMIC 0.18 μm 1P6M RF CMOS process. The core circuit adopts complementary differential negative resistance structure with resistor biasing which achieves good phase noise performance. The 2 bit switched capacitor array provides extra tuning range. The chip size is 600μm×475μm with testing pads. With a 1.8V supply voltage, the post-simulation and chipset measured results show that the achieved maximum 40% tuning range can perfectly compensating the deviation due to process corners. And the measured phase noise is −96dBc/Hz@3MHz with the carrier be 4.8GHz. Besides, the operating current of the whole circuit is less than 7mA.


title: Enterprise Cloud Service Architecture

abstract: Cloud computing, a new paradigm of distributed computing, introduces many new ideas, concepts, principals, technologies and architectural styles into enterprise service-oriented computing. The enterprise service-oriented architecture (ESOA) style is an abstraction of concrete enterprise service-orientated architectures, which includes SOA architectural elements, service design patterns as well as principles, and SOA quality attributes. It can be extended to a new style for realizing enterprise cloud computing. Meanwhile, the principles and style of enterprise service-oriented computing facilitate the enterprise-wide adoption of cloud computing. This paper extends the ESOA style to a new hybrid architectural style, Enterprise Cloud Service Architecture (ECSA). The style is described by extending enterprise service-oriented formula for ESOA. We model the style through specifying each element in the formula with both service-oriented and cloud architectural styles.


title: A Hotel Recommendation System Based on Collaborative Filtering and Rankboost Algorithm

abstract: The paper analyzed the traditional collaborative filtering technology and proposed a hotel recommendation system based on collaborative filtering method of clustering and Rankboost algorithm,which solved the cold-start and scalability problems of traditional collaborative filtering. One can find a hotel quickly and efficiently when he uses this hotel recommendation system.


title: From sensorimotor learning to memory cells in prefrontal and temporal association cortex: A neurocomputational study of disembodiment

abstract: Memory cells, the ultimate neurobiological substrates of working memory, remain active for several seconds and are most commonly found in prefrontal cortex and higher multisensory areas. However, if correlated activity in “embodied” sensorimotor systems underlies the formation of memory traces, why should memory cells emerge in areas distant from their antecedent activations in sensorimotor areas, thus leading to “disembodiment” (movement away from sensorimotor systems) of memory mechanisms? We modelled the formation of memory circuits in six-area neurocomputational architectures, implementing motor and sensory primary, secondary and higher association areas in frontotemporal cortices along with known between-area neuroanatomical connections. Sensorimotor learning driven by Hebbian neuroplasticity led to formation of cell assemblies distributed across the different areas of the network. These action-perception circuits (APCs) ignited fully when stimulated, thus providing a neural basis for long-term memory (LTM) of sensorimotor information linked by learning. Subsequent to ignition, activity vanished rapidly from APC neurons in sensorimotor areas but persisted in those in multimodal prefrontal and temporal areas. Such persistent activity provides a mechanism for working memory for actions, perceptions and symbols, including short-term phonological and semantic storage. Cell assembly ignition and “disembodied” working memory retreat of activity to multimodal areas are documented in the neurocomputational models' activity dynamics, at the level of single cells, circuits, and cortical areas. Memory disembodiment is explained neuromechanistically by APC formation and structural neuroanatomical features of the model networks, especially the central role of multimodal prefrontal and temporal cortices in bridging between sensory and motor areas. These simulations answer the “where” question of cortical working memory in terms of distributed APCs and their inner structure, which is, in part, determined by neuroanatomical structure. As the neurocomputational model provides a mechanistic explanation of how memory-related “disembodied” neuronal activity emerges in “embodied” APCs, it may be key to solving aspects of the embodiment debate and eventually to a better understanding of cognitive brain functions.


title: Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns

abstract: This paper presents a computationally efficient method for action recognition from depth video sequences. It employs the so called depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues and uses local binary patterns (LBPs) to gain a compact feature representation. Two types of fusion consisting of feature-level fusion and decision-level fusion are considered. In the feature-level fusion, LBP features from three DMMs are merged before classification while in the decision-level fusion, a soft decision-fusion rule is used to combine the classification outcomes. The introduced method is evaluated on two standard datasets and is also compared with the existing methods. The results indicate that it outperforms the existing methods and is able to process depth video sequences in real-time.


title: CARDWATCH: a neural network based database mining system for credit card fraud detection

abstract: CARDWATCH, a database mining system used for credit card fraud detection, is presented. The system is based on a neural network learning module, provides an interface to a variety of commercial databases and has a comfortable graphical user interface. Test results obtained for synthetically generated credit card data and an autoassociative neural network model show very successful fraud detection rates.


title: Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model

abstract: Automatic age estimation from real-world and unconstrained face images is rapidly gaining importance. In our proposed work, a deep CNN model that was trained on a database for face recognition task is used to estimate the age information on the Adience database. This paper has three significant contributions in this field. (1) This work proves that a CNN model, which was trained for face recognition task, can be utilized for age estimation to improve performance; (2) Over fitting problem can be overcome by employing a pretrained CNN on a large database for face recognition task; (3) Not only the number of training images and the number subjects in a training database effect the performance of the age estimation model, but also the pre-training task of the employed CNN determines the performance of the model.


title: A Survey of Shape Analysis Techniques

abstract: Abstract This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Research in shape analysis has been motivated, in part, by studies of human visual form perception systems. Several theories of visual form perception are briefly mentioned. Shape analysis methods are classified into several groups. Classification is determined according to the use of shape boundary or interior, and according to the type of result. An overview of the most representative methods is presented.


title: Analysis and design of snubber circuit for Z-source inverter

abstract: The Z-Source Inverter (ZSI) is a novel and attractive topology for an power electronic interface. Shoot-through states are utilized to boost the DC bus voltage, which also differentiates the working process of the snubber circuit from that of a Voltage Source Inverter (VSI). It was found that there would be a distortion in the DC link voltage of four traditional snubber circuits, and that this voltage would increase more than it would have without the snubber circuit. Compared with a VSI, larger energy losses would be generated in this situation due to the shoot-through state. This paper presents an improved RCD snubber circuit for ZSI applications, which resolves the problems of voltage distortion and large energy losses. These theoretical findings and design guidelines for snubber circuits with a ZSI were then verified by computer simulation and experiment.


title: A Graph Optimization-Based Indoor Map Construction Method via Crowdsourcing

abstract: Indoor mapping is an essential element of indoor navigation systems. In this paper, we present a graph optimization-based method for indoor map construction, which can be used in buildings without prior knowledge. By using crowdsourcing data on mobile phone sensors, we can derive activity landmarks where people perform different activities (turning, taking the elevator, taking the escalator, and walking up/down the stairs). Our method uses graph optimization techniques to align crowdsourcing trajectories by their intersecting landmarks. After trajectory alignment, this method applies a pose graph optimization method to construct an indoor map. Finally, our method performs a transform from relative coordinates to an absolute coordinate with reference points and reduces the redundant segments using dynamic time warping. To evaluate the performance, we implement the proposed method in an office building and a shopping mall. Experiment results show that 80th percentile error of the mapping accuracy is about 1.7–3.5 m. Moreover, the proposed method can deal with the curved routes in a building and can also decrease the amount of required data.


title: Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation

abstract: Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The "actor'' in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.


title: Sensitivity of maximum sprinting speed to characteristic parameters of the muscle force–velocity relationship

abstract: Abstract An accumulation of evidence suggests that the force–velocity relationship (FVR) of skeletal muscle plays a major role in limiting maximum human sprinting speed. However, most of the theories on this limiting role have been non-specific as to how the FVR limits speed. The FVR is characterized by three parameters that each have a different effect on its shape, and could thus limit sprinting speed in different ways: the maximum shortening velocity V max , the shape parameter A R , and the eccentric plateau C ecc . In this study, we sought to determine how specifically the FVR limits sprinting speed using forward dynamics simulations of human locomotion to examine the sensitivity of maximum speed to these three FVR parameters. Simulations were generated by optimizing the model's muscle excitations to maximize the average horizontal speed. The simulation's speed, temporal stride parameters, joint angles, GRF, and muscle activity in general compared well to data from human subjects sprinting at maximum effort. Simulations were then repeated with incremental and isolated adjustments in V max , A R , and C ecc across a physiological range. The range of speeds (5.22–6.91 m s −1 ) was most sensitive when V max was varied, but the fastest speed of 7.17 m s −1 was attained when A R was set to its maximum value, which corresponded to all muscles having entirely fast-twitch fibers. This result was explained by the muscle shortening velocities, which tended to be moderate and within the range where A R had its greatest effect on the shape of the FVR. Speed was less sensitive to adjustments in C ecc , with a range of 6.23–6.70 m s −1 . Increases in speed with parameter changes were due to increases in stride length more so than stride frequency. The results suggest that the shape parameter A R , which primarily determines the amount of muscle force that can be produced at moderate shortening velocities, plays a major role in limiting the maximum sprinting speed. Analysis of muscle force sensitivity indicated support for previous theories on the time to generate support forces in stance ( Weyand et al., 2000 , Journal of Applied Physiology, 89, 1991–1999) and energy management of the leg in swing ( Chapman & Caldwell, 1983 , Journal of Biomechanics 16, 79–83) as important factors in limiting maximum speed. However, the ability of the knee flexors to slow the rotational velocity of the leg in preparation for footstrike did not appear to play a major role in limiting speed.


Antecedents of Turnover Intentions : A Literature Review


title: A Systematic Approach for Privilege Escalation Prevention

abstract: Information systems are designed to present services and functionalities for multiple users. Thus, it is used to have on one information system different levels of privilege for different users. Privileges describe what a user is permitted to do such as viewing files, modifying or deleting data. Privilege escalation takes place when a user gets access to more resources or services than they are normally allowed to perform unauthorized actions. Many studies have been presented to detect anomalies and vulnerabilities in information systems to discover security issues or attacks related to privilege escalation. In this context, we introduced a systematic methodology that uses pattern recognition and outliers identification to detect numerous abnormal events which can indicate anomalies and security issues related to privilege escalation. In this paper, we describe results from an empirical study in order to show the performance of a new outliers detection algorithm to identify unknown abnormal events and the ability of pattern recognition techniques to specify known abnormal events. Results show that the outlier detection algorithm, enables us to discover unknown abnormal events on synthetic data sets. In addition, results notice the existence and the usefulness of four patterns used the discover known privilege escalation scenarios and, potentially, reduce security issues in systems.


title: Hashing as Tie-Aware Learning to Rank

abstract: Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.


title: The Institutionalization of Institutional Theory

abstract: Excerpt] Our primary aims in this effort are twofold: to clarify the independent theoretical contributions of institutional theory to analyses of organizations, and to develop this theoretical perspective further in order to enhance its use in empirical research. There is also a more general, more ambitious objective here, and that is to build a bridge between two distinct models of social actor that underlie most organizational analyses, which we refer to as a rational actor model and an institutional model. The former is premised on the assumption that individuals are constantly engaged in calculations of the costs and benefits of different action choices, and that behavior reflects such utility-maximizing calculations. In the latter model, by contrast, 'oversocialized' individuals are assumed to accept and follow social norms unquestioningly, without any real reflection or behavioral resistance based on their own particular, personal interests. We suggest that these two general models should be treated not as oppositional but rather as representing two ends of a continuum of decisionmaking processes and behaviors. Thus, a key problem for theory and research is to specify the conditions under which behavior is more likely to resemble one end of this continuum or the other. In short, what is needed are theories of when rationality is likely to be more or less bounded. A developed conception of institutionalization processes provides a useful point of departure for exploring this issue.


title: Efficient label collection for unlabeled image datasets

abstract: Visual classifiers are part of many applications including surveillance, autonomous navigation and scene understanding. The raw data used to train these classifiers is abundant and easy to collect but lacks labels. Labels are necessary for training supervised classifiers, but the labeling process requires significant human effort. Techniques like active learning and group-based labeling have emerged to help reduce the labeling workload. However, the possibility of collecting label noise affects either the efficiency of these systems or the performance of the trained classifiers. Further, many introduce latency by iteratively retraining classifiers or re-clustering data. We introduce a technique that searches for structural change in hierarchically clustered data to identify a set of clusters that span a spectrum of visual concept granularities. This allows us to efficiently label clusters with less label noise and produce high performing classifiers. The data is hierarchically clustered only once, eliminating latency during the labeling process. Using benchmark data we show that collecting labels with our approach is more efficient than existing labeling techniques, and achieves higher classification accuracy. Finally, we demonstrate the speed and efficiency of our system using real-world data collected for an autonomous navigation task.


title: Media and Communication Studies Going Global

abstract: Media and communication studies have always been international. Still, in the era of globalization, internationalistic agendas have to be radicalized, opened up also to non-Western media thought, setting out from existing regional modernities and transformations. Thus, globalization calls for regional, not universalistic epistemologies and for cross-disciplinary, not intra-disciplinary research efforts. The chapter develops these arguments, first by recapitulating the criticism of the media and development paradigm – an early theory of media and social change. This is followed up by a summary report on media and global inequalities in the post-Cold War period, suggesting that the relations between media, politics and economy develop contrary to the prescriptions of received models. This leads to a reinforced questioning of the very distinctions between, e.g., politics, economy and culture in media studies, as illustrated by examples from case studies of recent developments in new democracies and (neo)authoritarian systems.


Shuffled Frog Leaping Algorithm Based Clustering Algorithm for Mobile Ad hoc Networks


title: STRATEGIES FOR DESIGN SCIENCE RESEARCH EVALUATION

abstract: Seminal works in the application of design science research (DSR) in IS emphasize the importance of evaluation. However, discussion of evaluation activities and methods is limited and typically assumes an ex post perspective, in which evaluation occurs after the construction of an IS artifact. Such perspectives can assume that the evaluation is an empirical process and its methods can be selected in the same way as empirical research methods. In this paper, we analyze a broader range of evaluation strategies, which includes ex ante (prior to artifact construction) evaluation. This broader view is developed as a strategic DSR evaluation framework, which expands evaluation choices for IS DSR researchers, and also adds emphasis to strategies for evaluating design processes in addition to design products, using well-known quality criteria as an important asset. The framework encompasses both ex ante and ex post orientations as well as naturalistic settings (e.g., case studies) and artificial settings (e.g., lab experiments) for DSR evaluation. The framework proposed offers a strategic view of DSR evaluation that is useful in analyzing published studies, and also in surfacing the evaluation opportunities that present themselves to IS DSR researchers.


title: Removal of beam squint in series fed array antennas using abnormal group delay phase shifters

abstract: Phase shifting networks are ubiquitous in microwave devices and applications such as antenna feed networks where precise phase shifters are necessary for radiation pattern synthesis. Ideally, a phase shifter must provide low insertion loss, good matching, and achieve the desired phase shift over the required bandwidth. For a phase shifter with an approximately linear phase response, the phase bandwidth – the frequency range over which the phase error is within a given tolerance – is inversely proportional to the group delay [1]. Therefore, a phase shifter optimized for maximal phase bandwidth should ideally exhibit zero group delay. To achieve some of the above goals designers often periodically load a matched host transmission line (TL) with lumped elements to synthesize new dispersive properties [2–3]. For example, a microstrip TL is loaded with surface-mount components to construct 2-port networks exhibiting negative group delay (NGD) [2] and negative phase delay [3].


title: The network of global corporate control

abstract: The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers.


title: MODIFIED MULTILOOK CROSS CORRELATION (MLCC) ALGORITHM FOR DOPPLER CENTROID ESTIMATION IN SYNTHETIC APERTURE RADAR SIGNAL PROCESSING

abstract: The Multilook Cross Correlation (MLCC) is one of the most reliable algorithms used for Doppler ambiguity number estimation of the Doppler centroid parameter. However, the existing MLCC algorithm is only suitable for low contrast scenes. In high contrast scenes, the estimated result is not reliable, and the error is unacceptable. Besides, the Doppler centroid estimation processing time is long and can only be used in o†ine processing. In this paper, we introduce a modifled MLCC algorithm that has better sensitivity which is suitable not only for low contrast scenes, but also for high contrast scenes. In addition, the modifled MLCC algorithm can be implemented on parallel signal processing units for better time e-ciency. Experiments with RADARSAT-1 data show that the modifled algorithm works well in both high and low contrast scenes.


title: Evaluating the performance of face-aging algorithms

abstract: An important aspect related to the development of face-aging algorithms is the evaluation of the ability of such algorithms to produce accurate age-progressed faces. In most studies reported in the literature, the performance of face-aging systems is established based either on the judgment of human observers or by using machine-based evaluation methods. In this paper we perform an experimental evaluation that aims to assess the applicability of human-based against typical machine based performance evaluation methods. The results of our experiments indicate that machines can be more accurate in determining the performance of face-aging algorithms. Our work aims towards the development of a complete evaluation framework for age progression methodologies.


title: Incentive Mechanisms for Crowdsourcing Platforms

abstract: Crowdsourcing emerged with the development of Web 2.0 technologies as a distributed online practice that harnesses the collective aptitudes and skills of the crowd in order to reach specific goals. The success of crowdsourcing systems is influenced by the users’ levels of participation and interactions on the platform. Therefore, there is a need for the incorporation of appropriate incentive mechanisms that would lead to sustained user engagement and quality contributions. Accordingly, the aim of the particular paper is threefold: first, to provide an overview of user motives and incentives, second, to present the corresponding incentive mechanisms used to trigger these motives, alongside with some indicative examples of successful crowdsourcing platforms that incorporate these incentive mechanisms, and third, to provide recommendations on their careful design in order to cater to the context and goal of the platform.


title: Broadband Planar Fully Integrated 8 $\, \times \,$8 Butler Matrix Using Coupled-Line Directional Couplers

abstract: An innovative approach that allows for realization of broadband planar fully integrated 8 × 8 Butler matrices is presented. Coupled-line directional couplers have been utilized, which enable broad operational band. A novel arrangement of the network has been proposed that allows the creation of an entirely planar design having two metallization layers and no interconnections between these layers. Four selected crossovers have been realized as a tandem connection of two 3-dB/90° coupled-line directional couplers, which, together with reference lines having appropriate electrical lengths, perform simultaneously crossovers of signal lines, and all needed broadband constant value phase shifters. Moreover, two of the needed 3-dB/90° directional couplers have been designed as tandem connections of two 8.34-dB directional couplers, acting as 3-dB/90° directional couplers having crossed outputs. With such a modification, a fully planar design with no inter-layer connections is possible. The proposed network arrangement has been experimentally tested with the design of an 8 × 8 Butler matrix operating at the center frequency of 3 GHz. The obtained measurement results fully confirm the validity of the proposed approach.


title: Short text classification using very few words

abstract: We propose a simple, scalable, and non-parametric approach for short text classification. Leveraging the well studied and scalable Information Retrieval (IR) framework, our approach mimics human labeling process for a piece of short text. It first selects the most representative and topical-indicative words from a given short text as query words, and then searches for a small set of labeled short texts best matching the query words. The predicted category label is the majority vote of the search results. Evaluated on a collection of more than 12K Web snippets, the proposed approach achieves comparable classification accuracy with the baseline Maximum Entropy classifier using as few as 3 query words and top-5 best matching search hits. Among the four query word selection schemes proposed and evaluated in our experiments, term frequency together with clarity gives the best classification accuracy.


title: Efficient Online Timed Pattern Matching by Automata-Based Skipping

abstract: The timed pattern matching problem is an actively studied topic because of its relevance in monitoring of real-time systems. There one is given a log $w$ and a specification $\mathcal{A}$ (given by a timed word and a timed automaton in this paper), and one wishes to return the set of intervals for which the log $w$, when restricted to the interval, satisfies the specification $\mathcal{A}$. In our previous work we presented an efficient timed pattern matching algorithm: it adopts a skipping mechanism inspired by the classic Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem of online timed pattern matching, towards embedded applications where it is vital to process a vast amount of incoming data in a timely manner. Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching algorithm---a recent variant of the BM algorithm---and extend it to timed pattern matching. Our experiments indicate the efficiency of our FJS-type algorithm in online and offline timed pattern matching.


title: Applicability of pocket ultrasound during the first trimester of pregnancy.

abstract: Aim: To determine the applicability of pocket ultrasound as a complementary method for clinical evaluation during the first trimester of pregnancy. Material and methods: A prospective cross-sectional study was conducted with 86 pregnant women attended in an emergency. The same operator performed the first examination with pocket device Vscan (General Electric, Vingmed Ultrasound, Horten, Norway) and then repeated the exam using a traditional handset high resolution Voluson 730 Expert (General Electric Healthcare Ultrasound, Milwaukee, WI, USA). Data were recorded with individual diagnoses by means of yes/no binary as the visualization of variables: gestational sac, embryo, embryo heartbeat, topical or ectopic pregnancy. Concordance was calculated using the kappa coefficient with its respective 95% confidential intervals (CI). Results: There was no disagreement between the methods when compared the pocket and conventional ultrasounds. The best comparative result between the devices was as the visualization of the embryo heartbeat with kappa coefficient of 0.84 (95% CI 0.76 to 0.89). However, the pocket ultrasound had a low correlation for diagnosis of ectopic pregnancy, with kappa coefficient of −0.02 (95% CI −0.23 to 0.19). The smaller structure visualized and studied using the pocket ultrasound was an embryo of 3.4 mm. Conclusions: The method of pocket ultrasound has the potential to become a complementary and easy access for diagnostic tool in obstetric patients during the first trimester. However, in ectopic pregnancy cases the diagnosis should be realized by conventional ultrasound.


title: Study Change the Demographic Situation in Egypt between Employment and Unemployment (2002-2012)

abstract: In light of the world's attention to employment and unemployment issues as one of the priorities of development plans and policies overall, as well as the improvement for its indicators will result in reinforce the levels and opportunities of human development for each country. Indeed, this study is considered important because it traces the % change in both employment and unemployment rates in Egypt according to some demographic variables and it will focuses on monitoring the gender gap with regard to employment and unemployment to highlight the status of females in Egypt. Consequence, it will depend on data issued by the statistics and databases on the national level for the sake of compiling time series for the target period (2002-2012), and then using statistical methods that be suitable to achieve the objectives of this study. The study concluded that unemployment rates in Egypt was the highest in 2012, besides there are relative differences between males and females in labor force according to economic activity and educational status, as unemployment rate was highest among those have higher education, especially females, as there were significant differences in those rates that attributable to the gender and residence, as unemployment in urban was higher than rural. Births, the males unemployed in urban and males graduates of theoretical faculties were the highest impact on increasing the No. of unemployed in Egypt (2002-2012). The study recommends the adoption of the implementation of national projects of employment in collaboration with the private sector to limit the gender gap and reinforce the participation rate of youth in the labor force, especially females whose have highest education to reduce the aggravation problem of unemployment sharply. Moreover, It tries to attract attention of officials to develop plans and policies regarding these indicators to enhance the economic status within Egypt, in favor of youth and women together.


title: Fault-tolerant control with active fault diagnosis for four-wheel independently-driven electric ground vehicles

abstract: This paper presents a fault-tolerant control approach for four-wheel independently driven (4WID) electric vehicles. An adaptive control-based passive fault-tolerant controller is designed to ensure vehicle system stability and to track the desired vehicle motion when an in-wheel motor/motor driver fault happens. Due to the system actuation redundancy, it is challenging to isolate the faulty wheel and to accurately estimate the control gain of the faulty in-wheel motor/motor driver for 4WID electric vehicles. An active fault diagnosis (FD) approach is thus proposed to explicitly isolate and evaluate the fault. Based on the estimated control gain of the faulty wheel, the control efforts of all the wheels are redistributed to relieve the torque demand on the faulty wheel. Simulations using a high-fidelity CarSim full-vehicle model show the effectiveness of the proposed in-wheel motor/motor driver active fault diagnosis and fault-tolerant control approaches in various driving scenarios.


title: The lean gap: A review of lean approaches to large-scale software systems development

abstract: Lean approaches to product development (LPD) have had a strong influence on many industries and in recent years there have been many proponents for lean in software development as it can support the increasing industry need of scaling agile software development. With it's roots in industrial manufacturing and, later, industrial product development, it would seem natural that LPD would adapt well to large-scale development projects of increasingly software-intensive products, such as in the automotive industry. However, it is not clear what kind of experience and results have been reported on the actual use of lean principles and practices in software development for such large-scale industrial contexts. This was the motivation for this study as the context was an ongoing industry process improvement project at Volvo Car Corporation and Volvo Truck Corporation. The objectives of this study are to identify and classify state of the art in large-scale software development influenced by LPD approaches and use this established knowledge to support industrial partners in decisions on a software process improvement (SPI) project, and to reveal research gaps and proposed extensions to LPD in relation to its well-known principles and practices. For locating relevant state of the art we conducted a systematic mapping study, and the industrial applicability and relevance of results and said extensions to LPD were further analyzed in the context of an actual, industrial case. A total of 10,230 papers were found in database searches, of which 38 papers were found relevant. Of these, only 42 percent clearly addressed large-scale development. Furthermore, a majority of papers (76 percent) were non-empirical and many lacked information about study design, context and/or limitations. Most of the identified results focused on eliminating waste and creating flow in the software development process, but there was a lack of results for other LPD principles and practices. Overall, it can be concluded that research in the much hyped field of lean software development is in its nascent state when it comes to large scale development. There is very little support available for practitioners who want to apply lean approaches for improving large-scale software development, especially when it comes to inter-departmental interactions during development. This paper explicitly maps the area, qualifies available research, and identifies gaps, as well as suggests extensions to lean principles relevant for large scale development of software intensive systems.


title: Low Power VLSI Design of Modified Booth Multiplier

abstract: Low power VLSI circuits became very vital criteria for designing the energy efficient electronic designs for prime performance and compact devices. Multipliers play a very important role for planning energy economical processors that decides the potency of the processor. To scale back the facility consumption of multiplier factor booth coding methodology is being employed to rearrange the input bits. The operation of the booth decoder is to rearrange the given booth equivalent. Booth decoder can increase the range of zeros in variety. Hence the switching activity are going to be reduced that further reduces the power consumption of the design. The input bit constant determines the switching activity part that's once the input constant is zero corresponding rows or column of the adder ought to be deactivated. When multiplicand contains a lot of number of zeros the higher power reduction will takes place. therefore in booth multiplier factor high power reductions are going to be achieved.


title: CubeSLAM: Monocular 3D Object Detection and SLAM without Prior Models

abstract: We present a method for single image 3D cuboid object detection and multi-view object SLAM without prior object model, and demonstrate that the two aspects can benefit each other. For 3D detection, we generate high quality cuboid proposals from 2D bounding boxes and vanishing points sampling. The proposals are further scored and selected to align with image edges. Experiments on SUN RGBD and KITTI shows the efficiency and accuracy over existing approaches. Then in the second part, multi-view bundle adjustment with novel measurement functions is proposed to jointly optimize camera poses, objects and points, utilizing single view detection results. Objects can provide more geometric constraints and scale consistency compared to points. On the collected and public TUM and KITTI odometry datasets, we achieve better pose estimation accuracy over the state-of-the-art monocular SLAM while also improve the 3D object detection accuracy at the same time.


title: LinkForecast: Cellular Link Bandwidth Prediction in LTE Networks

abstract: Accurate cellular link bandwidth prediction can benefit upper-layer protocols significantly. In this paper, we investigate how to predict cellular link bandwidth in LTE networks. We first conduct an extensive measurement study in two major commercial LTE networks in the US, and identify five types of lower-layer information that are correlated with cellular link bandwidth. We then develop a machine learning based prediction framework, LinkForecast , that identifies the most important features (from both upper and lower layers) and uses these features to predict link bandwidth in real time. Our evaluation shows that LinkForecast is lightweight and the prediction is highly accurate: At the time granularity of one second, the average prediction error is in the range of 3.9 to 17.0 percent for all the scenarios we explore. We further investigate the prediction performance when using lower-layer features obtained through standard APIs provided by the operating system, instead of specialized tools. Our results show that, while the features thus obtained have lower fidelity compared to those from specialized tools, they lead to similar prediction accuracy, indicating that our approach can be easily used over commercial off-the-shelf mobile devices.


Speech Synthesis of Code-Mixed Text.


title: Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking

abstract: Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not “realistic” for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models. In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.


A performance comparison of multi-hop wireless ad hoc network routing protocols


title: Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 2)

abstract: Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focussed on tactical combat. In this paper, we present three data-driven measures of spatio-temporal behaviour in Defence of the Ancients 2 (DotA 2): 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behaviour varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results from three analyses indicate that spatio-temporal behaviour of MOBA teams is highly related to team skill.


title: The customer lifetime value prediction in mobile telecommunications

abstract: How to treat the customer relationship is a crucial problem in the telecommunications industry. Therefore, how to measure and manage customer lifetime value (CLV) for determining the likely future profit from the customer is very important because the customer is always looking for better and cheaper products and services. The CLV value not only combines with the churn management but also considers the cross-selling and up-selling to allure customer. Earning, not just buying, customerspsila loyalty is now mandatory. Analysis and prediction of customer lifetime value (CLV) methods by using Artificial neural network (ANN) is proposed here. In this paper Multi-Layer Perceptron (MLP) network with Levenberg-Marquardt algorithm is used to predict the CLV, the strategic and operational decisions to retain a customer Lifetime Value in the Mobile Telecommunications industry.


title: Learning with Privileged Information for Multi-Label Classification

abstract: Abstract In this paper, we propose a novel approach for learning multi-label classifiers with the help of privileged information. Specifically, we use similarity constraints to capture the relationship between available information and privileged information, and use ranking constraints to capture the dependencies among multiple labels. By integrating similarity constraints and ranking constraints into the learning process of classifiers, the privileged information and the dependencies among multiple labels are exploited to construct better classifiers during training. A maximum margin classifier is adopted, and an efficient learning algorithm of the proposed method is also developed. We evaluate the proposed method on two applications: multiple object recognition from images with the help of implicit information about object importance conveyed by the list of manually annotated image tags; and multiple facial action unit detection from low-resolution images augmented by high-resolution images. Experimental results demonstrate that the proposed method can effectively take full advantage of privileged information and dependencies among multiple labels for better object recognition and better facial action unit detection.


title: Comparison of different image formats using LSB Steganography

abstract: Nowadays secure and safe transmission of data is a very important aspect for all internet users. In the data securing process the use of encryption algorithms almost make it very difficult for an eavesdropper to get access to the user's data. It helps to ensure the privacy of a company/user from others. Keeping in view the importance of steganography for secure data transmission this work incorporates the use of LSB substitution method. Simulation results show the feasibility of the proposed method. The data has been successfully embedded into different formats of images without any perceptible distortion in the image.


title: Camera tracking for augmented reality media

abstract: The paper presents a camera tracking system for the spatial stabilization of augmented reality (AR) media. Our approach integrates both artificial landmarks (fiducials) and natural features for camera tracking. Artificial landmarks are used for system initialization and computation of initial camera pose. Robust and extendible tracking is achieved by dynamically calibrating the 3D positions of a priori uncalibrated natural features. Analysis and experimental results demonstrate the effectiveness of this approach for presenting stabilized AR media in long camera-motion video sequences.


title: Biologically Inspired Mobile Robot Vision Localization

abstract: We present a robot localization system using biologically inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the ldquogistrdquo of a scene to produce a coarse localization hypothesis and (2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, thereby yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, which efficiently directs the time-consuming landmark-identification process toward the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments-building complex (38.4 m times 54.86 m area, 13 966 testing images), vegetation-filled park (82.3 m times 109.73 m area, 26 397 testing images), and open-field park (137.16 m times 178.31 m area, 34 711 testing images)-each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46 m, respectively, even with multiple kidnapped-robot instances.


title: Hebbian Learning of Context in Recurrent Neural Networks

abstract: Single electrode recordings in the inferotemporal cortex of monkeys during delayed visual memory tasks provide evidence for attractor dynamics in the observed region. The persistent elevated delay activities could be internal representations of features of the learned visual stimuli shown to the monkey during training. When uncorrelated stimuli are presented during training in a fixed sequence, these experiments display significant correlations between the internal representations. Recently a simple model of attractor neural network has reproduced quantitatively the measured correlations. An underlying assumption of the model is that the synaptic matrix formed during the training phase contains in its efficacies information about the contiguity of persistent stimuli in the training sequence. We present here a simple unsupervised learning dynamics that produces such a synaptic matrix if sequences of stimuli are repeatedly presented to the network at fixed order. The resulting matrix is then shown to convert temporal correlations during training into spatial correlations between attractors. The scenario is that, in the presence of selective delay activity, at the presentation of each stimulus, the activity distribution in the neural assembly contains information of both the current stimulus and the previous one (carried by the attractor). Thus the recurrent synaptic matrix can code not only for each of the stimuli presented to the network but also for their context. We combine the idea that for learning to be effective, synaptic modification should be stochastic, with the fact that attractors provide learnable information about two consecutive stimuli. We calculate explicitly the probability distribution of synaptic efficacies as a function of training protocol, that is, the order in which stimuli are presented to the network. We then solve for the dynamics of a network composed of integrate-and-fire excitatory and inhibitory neurons with a matrix of synaptic collaterals resulting from the learning dynamics. ::: ::: The network has a stable spontaneous activity, and stable delay activity develops after a critical learning stage. The availability of a learning dynamics makes possible a number of experimental predictions for the dependence of the delay activity distributions and the correlations between them, on the learning stage and the learning protocol. In particular it makes specific predictions for pair-associates delay experiments.


title: Equal gain transmission in multiple-input multiple-output wireless systems

abstract: Multiple-input multiple-output (MIMO) wireless systems are of interest due to their ability to provide substantial gains in capacity and quality. The paper proposes equal gain transmission (EGT) to provide diversity advantage in MIMO systems experiencing Rayleigh fading. The applications of EGT with selection diversity combining, equal gain combining, and maximum ratio combining are addressed. It is proven that systems using EGT with any of these combining schemes achieve full diversity order when transmitting over a memoryless, flat-fading Rayleigh matrix channel with independent entries. Since, in practice, full channel knowledge at the transmitter is difficult to realize, a quantized version of EGT is proposed. An algorithm to construct a beamforming vector codebook that guarantees full diversity order is presented. Monte-Carlo simulation comparisons with various beamforming and combining systems illustrate the performance as a function of quantization.


title: Synthesizing Products for Online Catalogs

abstract: A comprehensive product catalog is essential to the success of Product Search engines and shopping sites such as Yahoo! Shopping, Google Product Search, and Bing Shopping. Given the large number of products and the speed at which they are released to the market, keeping catalogs up-to-date becomes a challenging task, calling for the need of automated techniques. In this paper, we introduce the problem of product synthesis, a key component of catalog creation and maintenance. Given a set of offers advertised by merchants, the goal is to identify new products and add them to the catalog, together with their (structured) attributes. A fundamental challenge in product synthesis is the scale of the problem. A Product Search engine receives data from thousands of merchants about millions of products; the product taxonomy contains thousands of categories, where each category has a different schema; and merchants use representations for products that are different from the ones used in the catalog of the Product Search engine. ::: ::: We propose a system that provides an end-to-end solution to the product synthesis problem, and addresses issues involved in data extraction from offers, schema reconciliation, and data fusion. For the schema reconciliation component, we developed a novel and scalable technique for schema matching which leverages knowledge about previously-known instance-level associations between offers and products; and it is trained using automatically created training sets (no manually-labeled data is needed). We present an experimental evaluation using data from Bing Shopping for more than 800K offers, a thousand merchants, and 400 categories. The evaluation confirms that our approach is able to automatically generate a large number of accurate product specifications. Furthermore, the evaluation shows that our schema reconciliation component outperforms state-of-the-art schema matching techniques in terms of precision and recall.


title: Feature Selection Methods and Their Combinations in High-Dimensional Classification of Speaker Likability, Intelligibility and Personality Traits

abstract: This study focuses on feature selection in paralinguistic analysis and presents recently developed supervised and unsupervised methods for feature subset selection and feature ranking. Using the standard k-nearest-neighbors (kNN) rule as the classification algorithm, the feature selection methods are evaluated individually and in different combinations in seven paralinguistic speaker trait classification tasks. In each analyzed data set, the overall number of features highly exceeds the number of data points available for training and evaluation, making a well-generalizing feature selection process extremely difficult. The performance of feature sets on t feature selection data is observed to be a poor indicator of their performance on unseen data. The studied feature selection methods clearly outperform a standard greedy hill-climbing selection algorithm by being more robust against overfitting. When the selection methods are suitably combined with each other, the performance in the classification task can be further improved. In general, it is shown that the use of automatic feature selection in paralinguistic analysis can be used to reduce the overall number of features to a fraction of the original feature set size while still achieving a comparable or even better performance than baseline support vector machine or random forest classifiers using the full feature set. The most typically selected features for recognition of speaker likability, intelligibility and five personality traits are also reported.


title: Cell Phone-Based System (Chaak) for Surveillance of Immatures of Dengue Virus Mosquito Vectors

abstract: Capture of surveillance data on mobile devices and rapid transfer of such data from these devices into an electronic database or data management and decision support systems promote timely data analyses and public health response during disease outbreaks. Mobile data capture is used increasingly for malaria surveillance and holds great promise for surveillance of other neglected tropical diseases. We focused on mosquito-borne dengue, with the primary aims of: 1) developing and field-testing a cell phone-based system (called Chaak) for capture of data relating to the surveillance of the mosquito immature stages, and 2) assessing, in the dengue endemic setting of Merida, Mexico, the cost-effectiveness of this new technology versus paper-based data collection. Chaak includes a desktop component, where a manager selects premises to be surveyed for mosquito immatures, and a cell phone component, where the surveyor receives the assigned tasks and captures the data. Data collected on the cell phone can be transferred to a central database through different modes of transmission, including near-real time where data are transferred immediately (e.g., over the Internet) or by first storing data on the cell phone for future transmission. Spatial data are handled in a novel, semantically driven, geographic information system. Compared with a pen-and-paper-based method, use of Chaak improved the accuracy and increased the speed of data transcription into an electronic database. The cost-effectiveness of using the Chaak system will depend largely on the up-front cost of purchasing cell phones and the recurring cost of data transfer over a cellular network.


title: Analgesic Effect and Functional Improvement Caused by Radiofrequency Treatment of Genicular Nerves in Patients With Advanced Osteoarthritis of the Knee Until 1 Year Following Treatment

abstract: BACKGROUND AND OBJECTIVES ::: Radiofrequency ablation of genicular nerves has proved to be successful in relieving pain and incapacity caused by osteoarthritis of the knee. However, long-term efficacy of such a treatment remains to be assessed. The current study aimed to reproduce radiofrequency neurotomy of genicular nerves to manage gonarthrosis pain and disability and establish therapeutic response until 1 year after intervention. ::: ::: ::: METHODS ::: This single-center, prospective, observational, noncontrolled, longitudinal study included patients with grade 3 to 4 gonarthrosis suffering from intractable knee pain, scoring 5 or more on the visual analog scale (VAS) during >6 months. Therapy was based on ultrasound guided radiofrequency neurotomy of the superior medial, superior lateral and inferior medial genicular nerves. Visual analog scale and Western Ontario and McMaster Universities Osteoarthritis scores were assessed before therapy and at 1, 6, and 12 months following treatment. ::: ::: ::: RESULTS ::: Radiofrequency neurotomy of genicular nerves significantly reduced perceived pain (VAS) and disability (Western Ontario and McMaster Universities Osteoarthritis) in the majority of participants, without untoward events. The proportion of participants with improvement of 50% or greater in pretreatment VAS scores at 1, 6, and 12 months following intervention were 22/25 (88%), 16/25 (64%) and 8/25 (32%), respectively. ::: ::: ::: CONCLUSIONS ::: Ultrasound-guided radiofrequency neurotomy of genicular nerves alleviates intractable pain and disability in the majority of patients with advanced osteoarthritis of the knee. Such a treatment is safe and minimally invasive and can be performed in an outpatient setting. The beneficial effect of treatment started to decline after 6 months, but even 1 year after the intervention, 32% of patients reported 50% improvement or greater in pretreatment VAS scores.


title: The Tarpit - A general theory of software engineering

abstract: ContextRecent years have seen an increasing interest in general theories of software engineering. As in other academic fields, these theories aim to explain and predict the key phenomena of the discipline. ObjectiveThe present article proposes a general theory of software engineering that we have labeled the Tarpit theory, in reference to the 1982 epigram by Alan Perlis. MethodAn integrative theory development approach was employed to develop the Tarpit theory from four underlying theoretical fields: (i) languages and automata, (ii) cognitive architecture, (iii) problem solving, and (iv) organization structure. Its applicability was explored in three test cases. ResultsThe theory demonstrates an explanatory and predictive potential for a diverse set of software engineering phenomena. It demonstrates a capability of explaining Brooks's law, of making predictions about domain-specific languages, and of evaluating the pros and cons of the practice of continuous integration. ConclusionThe presented theory appears capable of explaining and predicting a wide range of software engineering phenomena. Further refinement and application of the theory remains as future work.


title: On visual crosswalk detection for driver assistance systems

abstract: In this paper a new crosswalk detection strategy is presented. The application area is narrowed down to driver assistance systems to guarantee a reliable detection result and to benefit from the properties of a vehicle mounted camera.


title: A multi-frame image super-resolution method

abstract: Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction.


title: A task description language for robot control

abstract: Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task tree representation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.


title: Activation of Receptor for Advanced Glycation End Products : A Mechanism for Chronic Vascular Dysfunction in Diabetic Vasculopathy and Atherosclerosis

abstract: Receptor for advanced glycation end products (RAGE) is a member of the immunoglobulin superfamily of cell surface molecules and engages diverse ligands relevant to distinct pathological processes. One class of RAGE ligands includes glycoxidation products, termed advanced glycation end products, which occur in diabetes, at sites of oxidant stress in tissues, and in renal failure and amyloidoses. RAGE also functions as a signal transduction receptor for amyloid beta peptide, known to accumulate in Alzheimer disease in both affected brain parenchyma and cerebral vasculature. Interaction of RAGE with these ligands enhances receptor expression and initiates a positive feedback loop whereby receptor occupancy triggers increased RAGE expression, thereby perpetuating another wave of cellular activation. Sustained expression of RAGE by critical target cells, including endothelium, smooth muscle cells, mononuclear phagocytes, and neurons, in proximity to these ligands, sets the stage for chronic cellular activation and tissue damage. In a model of accelerated atherosclerosis associated with diabetes in genetically manipulated mice, blockade of cell surface RAGE by infusion of a soluble, truncated form of the receptor completely suppressed enhanced formation of vascular lesions. Amelioration of atherosclerosis in these diabetic/atherosclerotic animals by soluble RAGE occurred in the absence of changes in plasma lipids or glycemia, emphasizing the contribution of a lipid- and glycemia-independent mechanism(s) to atherogenesis, which we postulate to be interaction of RAGE with its ligands. Future studies using mice in which RAGE expression has been genetically manipulated and with selective low molecular weight RAGE inhibitors will be required to definitively assign a critical role for RAGE activation in diabetic vasculopathy. However, sustained receptor expression in a microenvironment with a plethora of ligand makes possible prolonged receptor stimulation, suggesting that interaction of cellular RAGE with its ligands could be a factor contributing to a range of important chronic disorders.


title: That's What She Said: Double Entendre Identification

abstract: Humor identification is a hard natural language understanding problem. We identify a subproblem --- the "that's what she said" problem --- with two distinguishing characteristics: (1) use of nouns that are euphemisms for sexually explicit nouns and (2) structure common in the erotic domain. We address this problem in a classification approach that includes features that model those two characteristics. Experiments on web data demonstrate that our approach improves precision by 12% over baseline techniques that use only word-based features.


title: Stochastic game approach for replay attack detection

abstract: The existing tradeoff between control system performance and the detection rate for replay attacks highlights the need to provide an optimal control policy that balances the security overhead with control cost. We employ a finite horizon, zero-sum, nonstationary stochastic game approach to minimize the worst-case control and detection cost, and obtain an optimal control policy for switching between control-cost optimal (but nonsecure) and secure (but cost-suboptimal) controllers in presence of replay attacks. To formulate the game, we quantify game parameters using knowledge of the system dynamics, controller design and utilized statistical detector. We show that the optimal strategy for the system exists, and present a suboptimal algorithm used to calculate the system's strategy by combining robust game techniques and a finite horizon stationary stochastic game algorithm. Our approach can be generalized for any system with multiple finite cost, time-invariant linear controllers/estimators/intrusion detectors.


title: Liver lesion segmentation informed by joint liver segmentation

abstract: We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes. We build the model from two fully convolutional networks, connected in tandem and trained together end-to-end. We evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge, attaining competitive liver and liver lesion detection and segmentation scores across a wide range of metrics. Unlike other top performing methods, we do not post-process model outputs, we do not use data external to the challenge, and we propose a simple single-stage model that is trained end-to-end. However, our method nearly matches the top lesion segmentation performance and achieves the second highest precision for lesion detection while maintaining high recall.


Second Generation AMIRA Tools for Arabic Processing : Fast and Robust Tokenization , POS tagging , and Base Phrase Chunking


splab at the NTCIR-13 STC-2 Task


Who will Attend This Event Together? Event Attendance Prediction via Deep LSTM Networks


title: Improving Business Process Quality through Exception Understanding, Prediction, and Prevention

abstract: Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and mining t We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.


title: Lossless compression based on hierarchical extrapolation for biomedical imaging applications

abstract: The imaging systems like CT, MRI scan exhibits huge amount of digital data and therefore compression becomes crucial for storage and communication resolves. Most of the recent compression schemes offers a very high compression ratio with significant loss of image quality and do not always perform better for all sets of similar images. This work aims at resolving this issue, which serves as the motivation. This paper presents a lossless image compression based on hierarchical extrapolation for medical images using Haar transform and through Embedded Encoding technique. The compression technique proves to be lossless and as well perform better for a variety of images including CT scan, MRI and ultrasound biomedical images than the existing schemes. The performance metrics namely Peak Signal to Noise Ratio, Compression ratio and mean square error values are computed for the compressed image for evaluation. The performance metrics attained through the proposed algorithm is bench marked with JPEG 2000. The result section of this paper brings forth the relative improvement offered by the proposed logic.


title: Parallel eigenvalue calculation based on multiple shift-invert Lanczos and contour integral based spectral projection method

abstract: We discuss the possibility of using multiple shift-invert Lanczos and contour integral based spectral projection method to compute a relatively large number of eigenvalues of a large sparse and symmetric matrix on distributed memory parallel computers. The key to achieving high parallel efficiency in this type of computation is to divide the spectrum into several intervals in a way that leads to optimal use of computational resources. We discuss strategies for dividing the spectrum. Our strategies make use of an eigenvalue distribution profile that can be estimated through inertial counts and cubic spline fitting. Parallel sparse direct methods are used in both approaches. We use a simple cost model that describes the cost of computing k eigenvalues within a single interval in terms of the asymptotic cost of sparse matrix factorization and triangular substitutions. Several computational experiments are performed to demonstrate the effect of different spectrum division strategies on the overall performance of both multiple shift-invert Lanczos and the contour integral based method. We also show the parallel scalability of both approaches in the strong and weak scaling sense. In addition, we compare the performance of multiple shift-invert Lanczos and the contour integral based spectral projection method on a set of problems from density functional theory (DFT).