title: Automatic interpretation and coding of face images using flexible models

abstract: Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.


title: Low-power 1V 5.8 GHz bulk-driven mixer with on-chip balun in 0.18μm CMOS

abstract: Two bulk-driven down-conversion mixers with onchip baluns are presented. The inclusion of the passive balun improves gain and provides impedance matching. Implemented in 0.18 mum CMOS, the mixers operate in the 5.8 GHz ISM band and consume 1 mW from a 1 V supply. The mixer in which the RF signal is applied to the gates of the mixing transistors achieves a measured input-referred P1dB of -14 dBm, an IIP3 of -5.2 dBm, a gain of 13.6 dB, a NF of 26 dB, and an LO-to-RF isolation of greater than 50 dB.


title: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations

abstract: Classification of panchromatic high-resolution data from urban areas using morphological and neural approaches is investigated. The proposed approach is based on three steps. First, the composition of geodesic opening and closing operations of different sizes is used in order to build a differential morphological profile that records image structural information. Although, the original panchromatic image only has one data channel, the use of the composition operations will give many additional channels, which may contain redundancies. Therefore, feature extraction or feature selection is applied in the second step. Both discriminant analysis feature extraction and decision boundary feature extraction are investigated in the second step along with a simple feature selection based on picking the largest indexes of the differential morphological profiles. Third, a neural network is used to classify the features from the second step. The proposed approach is applied in experiments on high-resolution Indian Remote Sensing 1C (IRS-1C) and IKONOS remote sensing data from urban areas. In experiments, the proposed method performs well in terms of classification accuracies. It is seen that relatively few features are needed to achieve the same classification accuracies as in the original feature space.


title: Probability that a band-gap extremum is located on the irreducible Brillouin-zone contour for the 17 different plane crystallographic lattices

abstract: After recalling the first Brillouin zone (BZ) and the first irreducible Brillouin zone (IBZ) of a lattice in terms of its plane crystallographic group, we investigate the danger of restricting a band-gap detection to the contour of the IBZ, instead of its full IBZ. Based on hundreds of porous phononic crystal simulations, we provide for the 17 plane crystallographic groups (i) statistics of the band-gap localizations, (ii) probabilities to get non-full band-gaps, and (iii) averages of the bandwidth error made when only the IBZ contour is considered. It is found that for phononic crystals, the IBZ contour provides accurate results only for highly symmetric lattices.


title: Adaptive codebook for beamforming in limited feedback MIMO systems

abstract: We propose a new scheme for limited feedback in MIMO systems. We consider transmit beamforming and receiver maximal ratio combining as a base for our work, and propose a novel beamforming codebook to exploit the inherent correlation of the channel. This novel beamforming codebook, unlike the conventional beamforming codebooks, adaptively changes with the channel matrix. Moreover, the adaptive approach is independent of the channel model and can be applied to any general MIMO channel with temporal and spatial correlations. Simulation results show that compared to previously known beamforming schemes, this technique significantly improves the BER performance in spatio-temporally correlated channels.


title: A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification

abstract: We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume fixed-dimensional feature spaces. To address these challenges, we propose an uncertainty-aware classification framework based on a special computational layer we refer to as the Gaussian process adapter that can connect irregularly sampled time series data to any black-box classifier learnable using gradient descent. We show how to scale up the required computations based on combining the structured kernel interpolation framework and the Lanczos approximation method, and how to discriminatively train the Gaussian process adapter in combination with a number of classifiers end-to-end using backpropagation.


title: Evaluating the Quality of Web Based Sustainability Reports: A Multi-method Framework

abstract: Sustainability reports bear the potential to improve a company's image and to create competitive advantages, for they influence consumers' buying decisions and the investment decisions of potential shareholders. There are multiple standards and guidelines on sustainability reporting, which, however, mainly relate to the contents of the reports. We argue that due to the stakeholder-focused nature of sustainability reports, additional quality criteria exceeding the mere content need to be considered to improve the stakeholders' acceptance. Since these criteria are stakeholder-specific, they need to be defined and evaluated with direct involvement of the stakeholders. Thus, we propose a multi-method framework for the quality evaluation of web based sustainability reports. In order to evaluate our framework and to gain first insights into stakeholder-focused quality and acceptance criteria, we applied our framework within a preliminary study. First experiences and results are presented and implications for theory and practice are derived.


title: Development of prosocial, individualistic, and competitive orientations: Theory and preliminary evidence

abstract: The authors adopt an interdependen ce analysis of social value orientation, proposing that prosocial, individualistic, and competitive orientations are (a) partially rooted in different patterns of social interaction as experienced during the periods spanning early childhood to young adulthood and (b) further shaped by different patterns of social interaction as experienced during early adulthood, middle adulthood, and old age. Congruent with this analysis, results revealed that relative to individualists and competitors, prosocial individuals exhibited greater levels of secure attachment (Studies 1 and 2) and reported having more siblings, especially sisters (Study 3). Finally, the prevalence of prosocials increased—and the prevalence of individualist s and competitors decreased—from early adulthood to middle adulthood and old age (Study 4). Traditional theories and insights assume that the principle of rational self-interest or economic man reflects the prevailing motivation among humankind (Luce & Raiffa, 1957; Von Neuman & Morgenstern, 1947; cf. Roth, 1988). However, more recent theoretical developments have indicated that individuals systematically differ in the manner in which they approach interdependent others. Some people are inclined to give interdependent others the benefit of the doubt and approach them cooperatively, whereas other people are inclined to approach interdependent others in a less cooperative manner. Such individual differences are related to social value orientation, defined as stable preferences for certain patterns of outcomes for oneself and others (McClintock, 1978; Messick & McClintock, 1968). Although a variety of different social value orientations can be distinguished from a theoretical point of view (e.g., Knight & Dubro, 1984), in this article we address a three-category typology of social value orientation, examining differences between prosocial, individualistic, and competitive orientations. Prosocials tend to maximize outcomes for both themselves and others (i.e., cooperation) and to minimize differences between outcomes for


title: Four-year clinical evaluation of a self-adhesive luting agent for ceramic inlays

abstract: OBJECTIVES ::: The aim of this randomized controlled clinical trial was to evaluate the 4-year clinical performance of a self-adhesive resin cement, RelyX Unicem (3M ESPE), used for cementation of ceramic inlays. In addition, the influence of selectively acid-etching enamel prior to luting on the clinical performance of the restorations was assessed. ::: ::: ::: METHODS ::: Sixty-two IPS Empress 2 inlays/onlays were placed in 31 patients by two experienced clinicians. The restorations were luted with RelyX Unicem with (=experimental group: E) or without (=control group: NE) prior enamel etching with phosphoric acid. At baseline, 6 months, and 1, 2, and 4 years after placement, the restorations were assessed by two calibrated investigators using modified USPHS criteria. Ten selected samples of each group were investigated under SEM regarding morphological changes at the cement-inlay interface. ::: ::: ::: RESULTS ::: The recall rate at 4 years was 97%. Two restorations (1 E, 1 NE) were lost, and one (E) had to be replaced due to inlay and tooth fracture resulting in a survival rate of 95%. No significant differences between the experimental and control group were noticed regarding all criteria (McNemar, p < 0.05). An obvious deterioration in marginal integrity was observed after 4 years as only 5% (E = 7%; NE = 3%) of the restorations exhibited an excellent marginal adaptation. In 90% of the restorations small, still clinically acceptable marginal deficiencies were observed. SEM of the luting gap showed an increased wear of the RelyX Unicem cement over the 4-year period. ::: ::: ::: CONCLUSIONS ::: The self-adhesive luting cement RelyX Unicem can be recommended for bonding of ceramic inlays/onlays. Additional selective enamel etching does not improve the clinical performance of the restorations within the 4-year period. ::: ::: ::: CLINICAL RELEVANCE ::: The self-adhesive resin composite RelyX Unicem showed acceptable clinical performance after 4 years of clinical service.


title: Preoperative Halo-Gravity Traction for Severe Thoracic Kyphoscoliosis Patients from Tibet: Radiographic Correction, Pulmonary Function Improvement, Nursing, and Complications

abstract: BACKGROUND This study investigated the outcomes of preoperative HGT as an adjunct treatment for severe thoracic kyphoscoliosis, its role in radiographic correction, and pulmonary function improvement, together with nursing strategy and incidence of complications. MATERIAL AND METHODS Eleven patients with a mean age of 18.8 years were retrospectively reviewed. Inclusion criteria were: patients with severe kyphoscoliosis (coronal Cobb angle and kyphosis angle ≥80°); duration of HGT ≥8 weeks; patients undergoing HGT for at least 12 h per day; traction weight no less than 40% of body weight; and patients not receiving physical therapies. All patients underwent respiratory training. RESULTS The major coronal curve scoliosis averaged 114.00±24.43° and was reduced to 80.55±17.98° after HGT. The major kyphosis was 103.91±18.95° and was reduced to 80.55±17.98°. Significantly improved percent-predicted values for FVC was found after HGT (p=0.014), and significantly increased forced expiratory volume in 1 s (FEV1%) was also observed (p<0.001), with significantly improved percent-predicted values for PEF (p=0.003) after HGT. CONCLUSIONS Our data reveal that preoperative HGT can be performed safely, and can help achieve excellent curve correction in both the coronal and sagittal planes, together with improved respiratory function and no severe complications in patients with severe thoracic kyphoscoliosis.


title: Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning

abstract: Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components. In this paper, we present a novel representation learning framework that generates an end-to-end photo-sketch mapping through structure and texture decomposition. In the training stage, we first decompose the input face photo into different components according to their representational contents (i.e., structural and textural parts) by using a pre-trained convolutional neural network (CNN). Then, we utilize a branched fully CNN for learning structural and textural representations, respectively. In addition, we design a sorted matching mean square error metric to measure texture patterns in the loss function. In the stage of sketch rendering, our approach automatically generates structural and textural representations for the input photo and produces the final result via a probabilistic fusion scheme. Extensive experiments on several challenging benchmarks suggest that our approach outperforms example-based synthesis algorithms in terms of both perceptual and objective metrics. In addition, the proposed method also has better generalization ability across data set without additional training.


title: Efficient Algorithms for Citation Network Analysis

abstract: In the paper very efficient, linear in number of arcs, algorit hms for determining Hummon and Doreian’s arc weights SPLC and SPNP in citation network are proposed, and some theoretical properties of these weights are presented. The nonacyclicity problem in citation networks is discussed. An approach to identify on the basis of arc weights an important small subnetwork is proposed and illustrated on the citation networks of SOM (self organizing maps) literature and US patents.


title: An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network †

abstract: The ubiquity of Bluetooth-enabled smartphones and peripherals has brought tremendous convenience to our daily life. In recent years, Bluetooth beacons have also been gaining popularity in implementing a variety of innovative location-based services such as self-guided systems in exhibition centers. However, the broadcast-based beacon technology can only provide unidirectional communication. In case smartphone users would like to respond to the beacon messages, they have to rely on their own mobile Internet connections to send the information back to the backend system. Nevertheless, mobile Internet services may not be always available or too costly. In this work, we develop a real-time locating system based only on the Bluetooth low energy (BLE) technology to support interactive communications by combining the broadcast and mesh topology options to extend the applicability of beacon solutions. Specifically, we turn the smartphone into a beacon device and augment the beacon devices with the capability of forming a mesh network. The implementation result shows that our beacon devices can detect the presence of specific users at specific locations, and then the presence state can be sent to the application server via the relay of beacon devices. Moreover, the application server can send personalized location-based messages to the users, again via the relay of beacon devices. With the capability of relaying messages between the beacon devices, it would be convenient for developers to implement a variety of interactive applications such as tracking VIP customers at the airport, or tracking an elder with Alzheimer’s disease in the neighborhood.


An Exploratory Study of Current Information Security Training and Awareness Practices in Organizations


title: Learning Kolmogorov Models for Binary Random Variables

abstract: We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. We also propose an algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We apply the proposed algorithm to recommendation systems, although it is applicable to other scenarios. We believe that the work is a significant step toward interpretable machine learning.


title: Recurrent nets that time and count

abstract: The size of the time intervals between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While hidden Markov models tend to ignore this information, recurrent neural networks (RNN) can in principle learn to make use of it. We focus on long short-term memory (LSTM) because it usually outperforms other RNN. Surprisingly, LSTM augmented by "peephole connections" from its internal cells to its multiplicative gates can learn the fine distinction between sequences of spikes separated by either 50 or 49 discrete time steps, without the help of any short training exemplars. Without external resets or teacher forcing or loss of performance on tasks reported earlier, our LSTM variant also learns to generate very stable sequences of highly nonlinear, precisely timed spikes. This makes LSTM a promising approach for real-world tasks that require to time and count.


title: Normative and Informational Influences in Online Political Discussions

abstract: How do the statements made by people in online political discussions affect other people's willingness to express their own opinions, or argue for them? And how does group interaction ultimately shape individual opinions? We examine carefully whether and how patterns of group discussion shape (a) individuals’ expressive behavior within those discussions and (b) changes in personal opinions. This research proposes that the argumentative “climate” of group opinion indeed affects postdiscussion opinions, and that a primary mechanism responsible for this effect is an intermediate influence on individual participants’ own expressions during the online discussions. We find support for these propositions in data from a series of 60 online group discussions, involving ordinary citizens, about the tax plans offered by rival U.S. presidential candidates George W. Bush and Al Gore in 2000.


title: Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

abstract: Present object detection methods working on 3D range data are so far either optimized for unstructured offroad environments or flat urban environments. We present a fast algorithm able to deal with tremendous amounts of 3D Lidar measurements. It uses a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures. Experiments show good results in urban environments including smoothly bended road surfaces.


title: FREyA: an Interactive Way of Querying Linked Data Using Natural Language

abstract: Natural Language Interfaces are increasingly relevant for information systems fronting rich structured data stores such as RDF and OWL repositories, mainly because of the conception of them being intuitive for human. In the previous work, we developed FREyA, an interactive Natural Language Interface for querying ontologies. It uses syntactic parsing in combination with the ontology-based lookup in order to interpret the question, and involves the user if necessary. The user's choices are used for training the system in order to improve its performance over time. In this paper, we discuss the suitability of FREyA to query the Linked Open Data. We report its performance in terms of precision and recall using the MusicBrainz and DBpedia datasets.


title: A single-stage three-phase high power factor rectifier with high-frequency isolation and regulated DC-bus based on the DCM SEPIC converter

abstract: In this paper, the main concepts related to the development of a single-stage three-phase high power factor rectifier, with high-frequency isolation and regulated DC bus are described. The structure operation is presented, being based on the DC-DC SEPIC converter operating in the discontinuous conduction mode. This operational mode provides to the rectifier a high power factor feature, with sinusoidal input current, without the use of any current sensors and current loop control. A design example and simulation results are presented in order to validate the theoretical analysis.


title: Downregulation of CPE regulates cell proliferation and chemosensitivity in pancreatic cancer

abstract: Pancreatic cancer (PC) is one of the most common cancers worldwide and a leading cause of cancer-related death. Discovering novel targets is a key for its therapy. Carboxypeptidase E (CPE), a subtype of the pro-protein convertases, has been shown to be upregulated in many types of cancer, yet its function in PC remains elusive. The expressions of CPE in PC cell lines and cancer patients were investigated by Western blot and qRT-PCR. In PC cell line BX-pc-3, CPE was downregulated and its effect on cancer cell proliferation, migration, cisplatin chemosensitivity, and in vivo tumor growth was analyzed by Western blot, proliferation assay, invasion assay, and in vivo transplantation, respectively. The expression of nuclear factor-kappaB (NF-κB), a possible downstream target of CPE was examined by Western blot upon CPE regulation in PC cells, and the effects of inhibiting NF-κB on PC cell invasion and proliferation were examined. CPE was significantly upregulated in PC cell lines and tumor tissues. Proliferation and invasion assays indicated that downregulation of CPE inhibited cancer cell growth and migration and increased chemosensitivity to cisplatin. Inoculation of small interfering RNA (siRNA) transfected BX-pc-3 cells into null mice demonstrated that downregulation of CPE prevented tumor growth in vivo. NF-κB was directly regulated by CPE in pancreatic cancer, and siRNA-mediated inhibition of NF-κB exerted similar anti-tumor effect as downregulating CPE. Taken together, our results demonstrate that CPE plays an important role in pancreatic cancer. Inhibition of CPE may serve as a potential target for PC therapeutics.


title: Noise and The Reality Gap: The Use of Simulation in Evolutionary Robotics

abstract: The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artificial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using different levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very good results achieved when realistic quantities were applied. It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.


Akademia Baru The Effect of Explicit Vocabulary Application ( EVA ) on Students ’ Achievement and Acceptance in Learning Explicit English Vocabulary


title: Environmental Stimuli Shape Biofilm Formation and the Virulence of Periodontal Pathogens

abstract: Periodontitis is a common inflammatory disease affecting the tooth-supporting structures. It is initiated by bacteria growing as a biofilm at the gingival margin, and communication of the biofilms differs in health and disease. The bacterial composition of periodontitis-associated biofilms has been well documented and is under continual investigation. However, the roles of several host response and inflammation driven environmental stimuli on biofilm formation is not well understood. This review article addresses the effects of environmental factors such as pH, temperature, cytokines, hormones, and oxidative stress on periodontal biofilm formation and bacterial virulence.


title: A Dynamic Structural Model of User Learning in Mobile Media Content

abstract: Consumer adoption and usage of mobile communication and multimedia content services has been growing steadily over the past few years in many countries around the world. In this paper, we develop and estimate a structural model of user behavior and learning with regard to content generation and usage activities in mobile digital media environments. Users learn about two different categories of content: content from regular Internet social networking and community (SNC) sites and that from mobile portal sites. Then they can choose to engage in the creation (uploading) and consumption (downloading) of multi-media content from these two categories of websites. In our context, users have two sources of learning about content quality: (i) direct experience through their own content creation and usage behavior and (ii) indirect experience through word-of-mouth such as the content creation and usage behavior of their social network neighbors. Our model seeks to explicitly explain how direct and indirect experiences from social interactions influence the content creation and usage behavior of users over time. We estimate this model using a unique dataset of consumers mobile media content creation and usage behavior over a 3-month time period. Our estimates suggest that when it comes to user learning from direct experience, the content that is downloaded from mobile portals has the highest level of quality. In contrast, content that is downloaded by users from SNC websites has the lowest level of quality. Besides, the order of magnitude of signal accuracy for each content type from the direct experience is consistent with the order of true quality level. This finding implies that in the context of mobile media users make content choices based on their perception of differences in both content quality level and content quality variation. Further we find that signals about the quality of content from direct experience are more accurate than signals from indirect experiences. Potential implications for mobile phone operators and advertisers are discussed.


title: Beyond Amdahl's Law: An Objective Function That Links Multiprocessor Performance Gains to Delay and Energy

abstract: Beginning with Amdahl's law, we derive a general objective function that links parallel processing performance gains at the system level, to energy and delay in the subsystem microarchitecture structures. The objective function employs parameterized models of computation and communication to represent the characteristics of processors, memories, and communications networks. The interaction of the latter microarchitectural elements defines global system performance in terms of energy-delay cost. Following the derivation, we demonstrate its utility by applying it to the problem of Chip Multiprocessor (CMP) architecture exploration. Given a set of application and architectural parameters, we solve for the optimal CMP architecture for six different architectural optimization examples. We find the parameters that minimize the total system cost, defined by the objective function under the area constraint of a single die. The analytical formulation presented in this paper is general and offers the foundation for the quantitative and rapid evaluation of computer architectures under different constraints including that of single die area.


title: A simplified approach to alar base reduction: a review of 124 patients over 20 years.

abstract: OBJECTIVES ::: To simplify the approach and diagnosis of alar base reduction, suggest a treatment algorithm, and evaluate the long-term outcomes of 3 different techniques used separately or in conjunction with one another. ::: ::: ::: DESIGN ::: Retrospective review of 124 patients seen in a private practice by a single surgeon. Patients ranged in age from 15 to 59 years (mean age, 30.4 years). Patients were undergoing primary (83.9%) or revision (16.1%) procedures. ::: ::: ::: RESULTS ::: Of the 124 patients undergoing alar base reduction, 31 (25%) were male and 93 (75%) were female. Average follow-up was 2 years. All patients underwent wedge excision, and for 64 patients (51.6%), this was the only technique used on the alar base. Alar wedge and nasal sill excisions were performed in 21 patients (16.9%); 19 (15.3%) underwent alar wedge excision with V-Y advancement, and 20 (16.1%) underwent alar wedge excision, nasal sill excision, and V-Y advancement. Thirty-one patients (25.0%) received dermabrasion for notable postoperative incision scars. ::: ::: ::: CONCLUSIONS ::: The data represent the senior author's outcomes of alar base reductions over the past 20 years. The 3 techniques we describe have been effective when used alone or in combination in reducing alar flare and in narrowing the nasal base. Patients should be counseled that dermabrasion of the wedge excision areas in the alar-facial groove may be necessary to diminish visible scars.


title: Vegetable price prediction using data mining classification technique

abstract: Each and every sector in this digital world is undergoing a dramatic change due to the influence of IT field. The agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the characteristics of neural networks such as self-adapt, self-study and high fault tolerance, to build up the model of Back-propagation neural network to predict vegetable price. A prediction model was set up by applying the neural network. Taking tomato as an example, the parameters of the model are analyzed through experiment. At the end of the result of Back-propagation neural network shows absolute error percentage of monthly and weekly vegetable price prediction and analyze the accuracy percentage of the price prediction.


title: Video Imitation GAN: Learning control policies by imitating raw videos using generative adversarial reward estimation

abstract: Natural imitation in humans usually consists of mimicking visual demonstrations of another person by continuously refining our skills until our performance is visually akin to the expert demonstrations. In this paper, we are interested in imitation learning of artificial agents in the natural setting - acquiring motor skills by watching raw video demonstrations. Traditional methods for learning from videos rely on extracting meaningful low-dimensional features from the videos followed by a separate hand-crafted reward estimation step based on feature separation between the agent and expert. We propose an imitation learning framework from raw video demonstrations, that reduces the dependence on hand engineered reward functions, by jointly learning the feature extraction and separation estimation steps, using generative adversarial networks. Additionally, we establish the equivalence between adversarial imitation from image manifolds and low-level state distribution matching, under certain conditions. Experimental results show that our proposed imitation learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques with low-level state and action information available while outperforming existing video imitation methods. Furthermore, we show that our method can learn action policies by imitating video demonstrations available on YouTube with performance comparable to learned agents from true reward signal. Please see the video at this https URL.


title: Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy

abstract: Building effective, enjoyable, and safe autonomous vehicles is a lot harder than has historically been considered. The reason is that, simply put, an autonomous vehicle must interact with human beings. This interaction is not a robotics problem nor a machine learning problem nor a psychology problem nor an economics problem nor a policy problem. It is all of these problems put into one. It challenges our assumptions about the limitations of human beings at their worst and the capabilities of artificial intelligence systems at their best. This work proposes a set of principles for designing and building autonomous vehicles in a human-centered way that does not run away from the complexity of human nature but instead embraces it. We describe our development of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study of implementing these principles in practice.


title: Odyssey's end: lay conceptions of nostalgia reflect its original Homeric meaning.

abstract: Nostalgia fulfills pivotal functions for individuals, but lacks an empirically-derived and comprehensive definition. We examined lay conceptions of nostalgia using a prototype approach. In Study 1, participants generated open-ended features of nostalgia, which were coded into categories. In Study 2, participants rated the centrality of these categories, which were subsequently classified as central (e.g., memories, relationships, happiness) or peripheral (e.g., daydreaming, regret, loneliness). Central (compared to peripheral) features were more often recalled and falsely recognized (Study 3), were classified more quickly (Study 4), were judged to reflect more nostalgia in a vignette (Study 5), better characterized participants’ own nostalgic (vs. ordinary) experiences (Study 6), and prompted higher levels of actual nostalgia and its intrapersonal benefits when used to trigger a personal memory, regardless of age (Study 7). These findings highlight that lay people view nostalgia as a self-relevant and social blended emotional and cognitive state, featuring a mixture of happiness and loss. The findings also aid understanding of nostalgia’s functions and identify new methods for future research.


title: Miniaturized ultra-wide stopband microstrip low pass filter design

abstract: In this paper, a microstrip low pass filter (LPF) with ultra-wide stopband, low insertion loss and compact size is introduced in this work. The designed LPF is composed of two open stubs and different patterns etched in the ground plane (three bowties and two rectangles). The proposed LPF has good performances in terms of insertion loss (less than 0.1 dB), return loss (less than 17.1 dB), wide stopband at −20 dB suppression level ranging from 5 GHz up-to 20 GHz and dimensions of 29×9.2 mm2 compared to previous published works.


title: A Compact GPS Antenna for Artillery Projectile Applications

abstract: This letter presents a compact antenna suitable for the reception of GPS signals on artillery projectiles over 1.57-1.60 GHz. Four inverted-F-type elements are excited by a series feed network in equal magnitude and successive 90° phase difference. The shape and form factor of the antenna is tailored so that the antenna can be easily installed inside an artillery fuze. Measurements show that the proposed antenna has a gain of 2.90-3.77 dBic, an axial ratio of 1.9-2.86 dB, and a reflection coefficient of less than -10 dB over 1.57-1.62 GHz.


title: HSfM: Hybrid Structure-from-Motion

abstract: Structure-from-Motion (SfM) methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. While incremental system has advanced in robustness and accuracy, the efficiency remains its key challenge. To solve this problem, global reconstruction system simultaneously estimates all camera poses from the epipolar geometry graph, but it is usually sensitive to outliers. In this work, we propose a new hybrid SfM method to tackle the issues of efficiency, accuracy and robustness in a unified framework. More specifically, we propose an adaptive community-based rotation averaging method first to estimate camera rotations in a global manner. Then, based on these estimated camera rotations, camera centers are computed in an incremental way. Extensive experiments show that our hybrid method performs similarly or better than many of the state-of-the-art global SfM approaches, in terms of computational efficiency, while achieves similar reconstruction accuracy and robustness with two other state-of-the-art incremental SfM approaches.


title: The Vector representation of the Standard Quantum Process Tomography

abstract: The characterization of the evolution of a quantum system is one of the main tasks to accomplish to achieve quantum information processing. The standard quantum process tomography (SQPT) has the unique property that it can be applied without introducing any additional quantum resources. In present work, we shall focus on the following two topics about the SQPT. At first, in the SQPT protocol for a $d$-dimensional system, one should encounter a problem in solving of a set of $d^4$ linear equations in order to get the matrix containing the complete information about the unknown quantum channel. Until now, the general form of the solution is unknown. And a long existed conviction is that the solutions are not unique. Here, we shall develop a self-consistent scheme, in which bounded linear operators are presented by vectors, to construct the set of linear equations. With the famous Cramer's rule for the set of linear equations, we are able to give the general form of the solution and prove that it is unique. In second, the central idea of the SQPT is to prepare a set of linearly independent inputs and measuring the outputs via the quantum state tomography (QST). Letting the inputs and the measurements be prepared by two sets of the rank-one positive-operator-valued measures [POVMs], where each POVM is supposed to be linearly independent and informationally complete (IC), we observe that SQPT now is equivalent to deciding a unknown state with a set of product IC-POVM in the $d^2$-dimensional Hilbert space. Following the general linear state tomography theory, we show that the product symmetric IC-POVM should minimize the mean-square Hilbert-Schmidt distance between the estimator and the true states. So, an optimal SQPT can be realized by preparing both the inputs and the measurements as the symmetric IC-POVM.


title: Parameterization and Validation of an Integrated Electro-Thermal Cylindrical LFP Battery Model

abstract: In this paper, for the first time, an equivalent circuit electrical model is integrated with a two-state thermal model to form an electro-thermal model for cylindrical lithium ion batteries. The parameterization of such model for an A123 26650 LiFePO4 cylindrical battery is presented. The resistances and capacitances of the equivalent circuit model are identified at different temperatures and states of charge (SOC), for charging and discharging. Functions are chosen to characterize the fitted parameters. A two-state thermal model is used to approximate the core and surface temperatures of the battery. The electrical model is coupled with the thermal model through heat generation and the thermal states are in turn feeding a radially averaged cell temperature affecting the parameters of the electrical model. Parameters of the thermal model are identified using a least squares algorithm. The electro-thermal model is then validated against voltage and surface temperature measurements from a realistic drive cycle experiment.Copyright © 2012 by ASME


title: The Long-Standing Privacy Debate: Mobile Websites vs Mobile Apps

abstract: The vast majority of online services nowadays, provide both a mobile friendly website and a mobile application to their users. Both of these choices are usually released for free, with their developers, usually gaining revenue by allowing advertisements from ad networks to be embedded into their content. In order to provide more personalized and thus more effective advertisements, ad networks usually deploy pervasive user tracking, raising this way significant privacy concerns. As a consequence, the users do not have to think only their convenience before deciding which choice to use while accessing a service: web or app, but also which one harms their privacy the least. ::: ::: In this paper, we aim to respond to this question: which of the two options protects the users' privacy in the best way apps or browsers? To tackle this question, we study a broad range of privacy related leaks in a comparison of several popular apps and their web counterpart. These leaks may contain not only personally identifying information (PII) but also device-specific information, able to cross-application and cross-site track the user into the network, and allow third parties to link web with app sessions. ::: ::: Finally, we propose an anti-tracking mechanism that enable the users to access an online service through a mobile app without risking their privacy. Our evaluation shows that our approach is able to preserve the privacy of the user by reducing the leaking identifiers of apps by 27.41% on average, while it imposes a practically negligible latency of less than 1 millisecond per request.


title: On the wafer/pad friction of linear chemical-mechanical planarization (CMP): modeling, analysis and experiments

abstract: In this paper, we present a modeling and monitoring scheme of the friction between the wafer and polishing pad for the linear chemical-mechanical planarization (CMP) processes. Kinematic analysis of the linear CMP system is investigated and a distributed LuGre dynamic friction model is utilized to capture the friction forces generated by the wafer/pad interactions. We present an experimental validation of wafer/pad friction modeling and analysis. Pad conditioning and wafer film topography effects on the wafer/pad friction are also experimentally demonstrated. Finally, one application example is illustrated the use of friction torques for real-time monitoring the shallow trench isolation (STI) CMP processes.


title: Computational Principles Of Mobile Robotics

abstract: Mobile robotics is a multidisciplinary field involving both computer science and engineering. Addressing the design of automated systems, it lies at the intersection of artificial intelligence, computational vision, and robotics. This textbook for advanced undergraduates and graduate students emphasizes algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but discusses a variety of other propulsion systems. The new edition includes advances in robotics and intelligent machines over the last ten years, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of sample problems. Various mathematical techniques that were assumed in the first edition are now briefly introduced in appendices at the end of the text to make the book more self-contained. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies.


title: An improved NSGA-III procedure for evolutionary many-objective optimization

abstract: Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection pressure based on Pareto-dominance degrades severely with the number of objectives increasing. Very recently, a reference-point based NSGA-II, referred as NSGA-III, is suggested to deal with many-objective problems, where the maintenance of diversity among population members is aided by supplying and adaptively updating a number of well-spread reference points. However, NSGA-III still relies on Pareto-dominance to push the population towards Pareto front (PF), leaving room for the improvement of its convergence ability. In this paper, an improved NSGA-III procedure, called θ-NSGA-III, is proposed, aiming to better tradeoff the convergence and diversity in many-objective optimization. In θ-NSGA-III, the non-dominated sorting scheme based on the proposed θ-dominance is employed to rank solutions in the environmental selection phase, which ensures both convergence and diversity. Computational experiments have shown that θ-NSGA-III is significantly better than the original NSGA-III and MOEA/D on most instances no matter in convergence and overall performance.


title: Automated segmentation of RPE layer for the detection of age macular degeneration using OCT images

abstract: Macular retinal anomaly known as Drusen present in the eyes infected with Age Macular Degeneration (AMD) can be visualized with optical coherence tomography (OCT). This paper presents an algorithm to automatically segment retinal pigment epithelium (RPE) layer of an eye using spectral domain optical coherence tomography (SD-OCT) images for the detection of drusen. Segmentation framework is based on intensity level thresholding of high reflective pixels around retinal nerve fiber layer (RNFL). A dataset of 50 different B-scan is taken with having 25 AMD and 25 heathy images. Bottom layer of the retina known as RPE layer is extracted and analyzed by fitting a second order polynomial curve. The difference between both the curvess decide the class of the image. Results show an accurate detection of drusen with 96 % accuracy.


Remarks on Emotion Recognition from BioPotential Signals


title: Digital image smoothing and the sigma filter

abstract: Abstract A conceptually simple but effective noise smoothing algorithm is described. This filter is motivated by the sigma probability of the Gaussian distribution, and it smooths the image noise by averaging only those neighborhood pixels which have the intensities within a fixed sigma range of the center pixel. Consequently, image edges are preserved, and subtle details and thin lines such as roads are retained. The characteristics of this smoothing algorithm are analyzed and compared with several other known filtering algorithms by their ability to retain subtle details, preserving edge shapes, sharpening ramp edges, etc. The comparison also indicates that the sigma filter is the most computationally efficient filter among those evaluated. The filter can be easily extended into several forms which can be used in contrast enhancement, image segmentation, and smoothing signal-dependent noisy images. Several test images 128 × 128 and 256 × 256 pixels in size are used to substantiate its characteristics. The algorithm can be easily extended to 3-D image smoothing.


title: Development of a group recommender application in a Social Network

abstract: In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.


title: Understanding and Quantifying Creativity in Lexical Composition

abstract: Why do certain combinations of words such as “disadvantageous peace” or “metal to the petal” appeal to our minds as interesting expressions with a sense of creativity, while other phrases such as “quiet teenager”, or “geometrical base” not as much? We present statistical explorations to understand the characteristics of lexical compositions that give rise to the perception of being original, interesting, and at times even artistic. We first examine various correlates of perceived creativity based on information theoretic measures and the connotation of words, then present experiments based on supervised learning that give us further insights on how different aspects of lexical composition collectively contribute to the perceived creativity.


title: Network Geometry Inference using Common Neighbors

abstract: We introduce and explore a method for inferring hidden geometric coordinates of nodes in complex networks based on the number of common neighbors between the nodes. We compare this approach to the HyperMap method, which is based only on the connections (and disconnections) between the nodes, i.e., on the links that the nodes have (or do not have). We find that for high degree nodes, the common-neighbors approach yields a more accurate inference than the link-based method, unless heuristic periodic adjustments (or "correction steps") are used in the latter. The common-neighbors approach is computationally intensive, requiring O(t4) running time to map a network of t nodes, versus O(t3) in the link-based method. But we also develop a hybrid method with O(t3) running time, which combines the common-neighbors and link-based approaches, and we explore a heuristic that reduces its running time further to O(t2), without significant reduction in the mapping accuracy. We apply this method to the autonomous systems (ASs) Internet, and we reveal how soft communities of ASs evolve over time in the similarity space. We further demonstrate the method's predictive power by forecasting future links between ASs. Taken altogether, our results advance our understanding of how to efficiently and accurately map real networks to their latent geometric spaces, which is an important necessary step toward understanding the laws that govern the dynamics of nodes in these spaces, and the fine-grained dynamics of network connections.


title: A Giant Pituitary Adenoma: Surgical Excision via a Staged Endoscopic and Open Approach

abstract: The operative management of giant pituitary adenomas represents a significant challenge for neurosurgeons. This challenge is amplified by the degree of local tumour infiltration into adjacent structures such as the cavernous sinus. The degree of parasellar tumour extension can be classified according to the Knosp grading system’ while suprasellar extension is qualified in accordance with the Modified Hardys classification system. We report a 59 year male with a Knosp grade 4, Hardys C giant pituitary adenoma in which two-stage near total surgical resection via an expanded endoscopic transphenoidal approach and subsequent bifrontal craniotomy was achieved. Typically, resection rates of less than 50% have been reported following surgery on giant pituitary adenomas. Traditionally multi-modal treatment strategies with adjuvant stereotactic radiotherapy or radio surgery, has been the gold standard in the management of these locally aggressive tumours. This case serves to illustrate that even in the presence of significant suprasellar and parasellar extension, radical resection of giant pituitary adenomas can be achieved.


title: Effect of Corporate Governance Structure on the Financial Performance of Johannesburg Stock Exchange (JSE)-Listed Mining Firms

abstract: There have been many corporate collapses and financial crises in recent years linked to a lack of effective corporate governance. The South African King IV Code of Corporate Governance recommends that corporate governing bodies should be comprised of an appropriate balance of knowledge, diversity, and independence for discharging their duties objectively and more efficiently. This study examines the effect of corporate governance structures on firm financial performance. The secondary data of selected Johannesburg Stock Exchange (JSE), Socially Responsible Investment (SRI) Index-listed mining firms’ sustainability reports, and integrated annual financial statements are used. Using panel data analysis of the random effects model, we determined the relationship between board independence and board size and the return on equity (ROE) for the period 2010–2015. Results indicate a weak negative correlation between ROE and board size, and a weak, but positive, correlation between ROE and board independence. Additionally, there is a positive, but weak, correlation between ROE and sales growth, but a negative and weak relationship between ROE and firm size. The study suggests that effective corporate governance through a small effective board and monitoring by an independent board result in increased firm financial performance. We recommend that South African companies see compliance with the recommendations of the King IV Code on Corporate Governance not as a liability, but an ethical investment that may likely yield financial benefit in the long-term. Although complying with corporate governance principles does not necessarily translate into a significant economic benefit, firms should, however, continue to adopt corporate governance for ethical reasons to meet stakeholder’s social and environmental needs for sustainable development.


title: A cross-national profile of bullying and victimization among adolescents in 40 countries

abstract: Objectives: ::: (1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country.


title: Debugging the data plane with anteater

abstract: Diagnosing problems in networks is a time-consuming and error-prone process. Existing tools to assist operators primarily focus on analyzing control plane configuration. Configuration analysis is limited in that it cannot find bugs in router software, and is harder to generalize across protocols since it must model complex configuration languages and dynamic protocol behavior. ::: This paper studies an alternate approach: diagnosing problems through static analysis of the data plane. This approach can catch bugs that are invisible at the level of configuration files, and simplifies unified analysis of a network across many protocols and implementations. We present Anteater, a tool for checking invariants in the data plane. Anteater translates high-level network invariants into boolean satisfiability problems (SAT), checks them against network state using a SAT solver, and reports counterexamples if violations have been found. Applied to a large university network, Anteater revealed 23 bugs, including forwarding loops and stale ACL rules, with only five false positives. Nine of these faults are being fixed by campus network operators.


title: The use of artificial intelligence based techniques for intrusion detection: a review

abstract: The Internet connects hundreds of millions of computers across the world running on multiple hardware and software platforms providing communication and commercial services. However, this interconnectivity among computers also enables malicious users to misuse resources and mount Internet attacks. The continuously growing Internet attacks pose severe challenges to develop a flexible, adaptive security oriented methods. Intrusion detection system (IDS) is one of most important component being used to detect the Internet attacks. In literature, different techniques from various disciplines have been utilized to develop efficient IDS. Artificial intelligence (AI) based techniques plays prominent role in development of IDS and has many benefits over other techniques. However, there is no comprehensive review of AI based techniques to examine and understand the current status of these techniques to solve the intrusion detection problems. In this paper, various AI based techniques have been reviewed focusing on development of IDS. Related studies have been compared by their source of audit data, processing criteria, technique used, dataset, classifier design, feature reduction technique employed and other experimental environment setup. Benefits and limitations of AI based techniques have been discussed. The paper will help the better understanding of different directions in which research has been done in the field of IDS. The findings of this paper provide useful insights into literature and are beneficial for those who are interested in applications of AI based techniques to IDS and related fields. The review also provides the future directions of the research in this area.


title: Scaper: A library for soundscape synthesis and augmentation

abstract: Sound event detection (SED) in environmental recordings is a key topic of research in machine listening, with applications in noise monitoring for smart cities, self-driving cars, surveillance, bioa-coustic monitoring, and indexing of large multimedia collections. Developing new solutions for SED often relies on the availability of strongly labeled audio recordings, where the annotation includes the onset, offset and source of every event. Generating such precise annotations manually is very time consuming, and as a result existing datasets for SED with strong labels are scarce and limited in size. To address this issue, we present Scaper, an open-source library for soundscape synthesis and augmentation. Given a collection of iso-lated sound events, Scaper acts as a high-level sequencer that can generate multiple soundscapes from a single, probabilistically defined, “specification”. To increase the variability of the output, Scaper supports the application of audio transformations such as pitch shifting and time stretching individually to every event. To illustrate the potential of the library, we generate a dataset of 10,000 sound-scapes and use it to compare the performance of two state-of-the-art algorithms, including a breakdown by soundscape characteristics. We also describe how Scaper was used to generate audio stimuli for an audio labeling crowdsourcing experiment, and conclude with a discussion of Scaper's limitations and potential applications.


Language Runtime and Optimizations in IBM Streams


title: Multi-task Curriculum Transfer Deep Learning of Clothing Attributes

abstract: Recognising detailed clothing characteristics (finegrained attributes) in unconstrained images of people inthe-wild is a challenging task for computer vision, especially when there is only limited training data from the wild whilst most data available for model learning are captured in well-controlled environments using fashion models (well lit, no background clutter, frontal view, high-resolution). In this work, we develop a deep learning framework capable of model transfer learning from well-controlled shop clothing images collected from web retailers to in-the-wild images from the street. Specifically, we formulate a novel Multi-Task Curriculum Transfer (MTCT) deep learning method to explore multiple sources of different types of web annotations with multi-labelled fine-grained attributes. Our multi-task loss function is designed to extract more discriminative representations in training by jointly learning all attributes, and our curriculum strategy exploits the staged easy-to-hard transfer learning motivated by cognitive studies. We demonstrate the advantages of the MTCT model over the state-of-the-art methods on the X-Domain benchmark, a large scale clothing attribute dataset. Moreover, we show that the MTCT model has a notable advantage over contemporary models when the training data size is small.


title: The EigenRumor algorithm for ranking blogs

abstract: ABSTRACT The advent of easy to use blogging tools is increasing the number of bloggers leading to more diversity in the quality blogspace. The blog search technologies that help users to find “good” blogs are thus more and more important. This paper proposes a new algorithm called “EigenRumor” that scores each blog entry by weighting the hub and authority scores of the bloggers based on eigenvector calculations. This algorithm enables a higher score to be assigned to the blog entries submitted by a good blogger but not yet linked to by any other blogs based on acceptance of the blogger's prior work. General Terms Algorithms, Management, Experimentation Keywords Weblog, link-analysis, ranking, search engine. 1. INTRODUCTION Many approaches on ranking Web pages have been proposed and studied[3]. PageRank[2] and HITS[7] are most successful of these and their effectiveness has been shown in both industry and the academic world. Of course these techniques are also effective for ranking blogs. The simple adoption of these algorithms to blogs, however, induces some issues as follows: 1) The number of links to a blog entry is generally very small. As the result, the scores of blog entries calculated by PageRank, for example, are generally too small to permit blog entries to be ranked by importance. 2) Generally, some time is needed to develop a number of in-links and thus have a higher PageRank score. Since blogs are considered to be a communication tool for discussing new topics, it is desirable to assign a higher score to an entry submitted by a blogger who has been received a lot of attention in the past, even if the entry itself has no in-links at first. Considering these issues, this paper proposes a new link-analysis algorithm called “EigenRumor.” The algorithm is designed for ranking information resources provided as blogs or other cyberspace communities, in which the identities of information providers are observable. Unlike generic web pages, a blog site is constructed from a set of blog entries written by a single blogger and the quality of blog entries and topics are dominated by the ability or interests of the blogger. Using this structural characteristic of blogs, the EigenRumor algorithm rates a new blog entry or other blog entries that have no in-links according to the past behavior of the blogger. In this paper, we define a


title: Improving the High-Frequency Performance of Coaxial-to-Microstrip Transitions

abstract: This paper presents a new design to improve the transmission characteristics of coaxial-to-microstrip transitions. A metallic ring is added to a conventional coaxial structure to serve as a buffer between a coaxial line and a microstrip line. The introduction of the metallic ring reduces the insertion loss of the transition caused by the sudden changes of the electromagnetic field distributions from one transmission line to another. This improvement is more prominent at higher frequencies; hence, the 1-dB transmission passband of the transition is increased significantly. A practical design example is implemented and measured. The increase of the 1-dB passband is more than 60%. The new design is also proven to be suitable for transitions between various coaxial cables/connectors and microstrip lines on different substrates or another commonly used planar transmission line, the coplanar waveguides. The robust nature of this design makes its performance not susceptible to the fabrication and assembly errors of the transitions. These characteristics qualify the proposed design as an excellent candidate for coaxial-to-microstrip transitions at higher frequencies.


title: Evaluation of Android Malware Detection Based on System Calls

abstract: With Android being the most widespread mobile platform, protecting it against malicious applications is essential. Android users typically install applications from large remote repositories, which provides ample opportunities for malicious newcomers. In this paper, we evaluate a few techniques for detecting malicious Android applications on a repository level. The techniques perform automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the techniques in the maline tool, and performed extensive empirical evaluation on a suite of around 12,000 applications. The evaluation considers the size and type of inputs used in analyses. We show that simple and relatively small inputs result in an overall detection accuracy of 93% with a 5% benign application classification error, while results are improved to a 96% detection accuracy with up-sampling. This indicates that system-call based techniques are viable to be used in practice. Finally, we show that even simplistic feature choices are effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated.


title: A Systematic Method for Configuring VLSI Networks of Spiking Neurons

abstract: An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.


title: A 1.1- $\mu \text{m}$ 33-Mpixel 240-fps 3-D-Stacked CMOS Image Sensor With Three-Stage Cyclic-Cyclic-SAR Analog-to-Digital Converters

abstract: In this paper, a 1.1- $\mu \text{m}$ -pitch 33-Mpixel 240-fps backside-illuminated 3-D-stacked CMOS image sensor with three-stage cyclic-cyclic-successive-approximation-register (SAR) analog-to-digital converters (ADCs) is developed. The narrow-pitch interconnection technology that connects the pixels and arrayed ADCs inside the pixel area is described. The 3-D-stacked architecture, constructed using the interconnection technology, makes it possible to place a $\textsf {1932} ~{(}\textsf {H}{)} \times \textsf {4}~ {(}\textsf {V}{)}$ correlated-double-sampling/ADC array underneath the pixel area. Furthermore, the pipelined and parallel operation of the three-stage cyclic-cyclic-SAR ADC architecture effectively reduces the conversion time period and power consumption and achieves 12-b precision within one horizontal scan time of $0.92~\mu \text{s}$ . As a result, the interconnection technology and ADC architecture achieved a high frame rate of 240 fps in 33 Mpixels. Random noise of 3.6 $\text{e}^{-}$ and low power consumption of 3.0 W were attained at an extremely high pixel rate of 7.96 Gpixel/s. A good figure of merit is achieved compared with recently developed image sensors.


Infidelity in romantic relationship s


title: The Alicia/sup 3/ climbing robot: a three-module robot for automatic wall inspection

abstract: The system proposed in this article is the Alicia/sup 3/ climbing robot. The aim of this project is to develop a system that can be adopted in a variety of applications, such as maintenance, building, inspection, and safety in the process and construction industries. The system could be adopted in many places where direct access by a human operator is very expensive because of the need for scaffolding or is very dangerous due to the presence of a hostile environment.


title: Equitable Machine Learning Algorithms to Probe Over P2P Botnets

abstract: Cyber security has become very significant research area in line due to the increase in the number of malicious attacks by both state and nonstate actors. Ideally, one would like to properly secure the machines from being infected by viruses of any form. Nowadays, botnets have become an integral part of the Internet and the main drive for creating them is for financial gain. A bot conceals itself using a secret canal to communicate with its governing command-and-control server. Botnets are well-ordered from end to end using protocols such as IRC, HTTP, and P2P. Of all HTTP-based and IRC-based, P2P botnet detection became a challenging task because of its decentralized nature. The paper focuses on the techniques that are predominantly used in botnet detection and we formulate a method for detecting the P2P botnets using supervised machine learning algorithms such as random forest (RF), multilayer perceptron (MLP), and K-nearest neighbor classifier (KNN). We analyze the performance of selected algorithms there by revealing the best classification algorithm for detecting P2P botnets.


title: The Order Of Prenominal Adjectives In Natural Language Generation

abstract: The order of prenominal adjectival modifiers in English is governed by complex and difficult to describe constraints which straddle the boundary between competence and performance. This paper describes and compares a number of statistical and machine learning techniques for ordering sequences of adjectives in the context of a natural language generation system.


title: Lyapunov-function-based flux and speed observer for AC induction motor sensorless control and parameters estimation

abstract: AC induction motors have become very popular for motion-control applications due to their simple and reliable construction. Control of drives based on ac induction motors is a quite complex task. Provided the vector-control algorithm is used, not only the rotor speed but also the position of the magnetic flux inside the motor during the control process should be known. In most applications, the flux sensors are omitted and the magnetic-flux phasor position has to be calculated. However, there are also applications in which even speed sensors should be omitted. In such a situation, the task of state reconstruction can be solved only from voltage and current measurements. In the current paper, a method based on deterministic evaluation of measurement using the state observer based on the Lyapunov function is presented. The method has been proven in testing on a real ac induction machine.


title: "Making the News": Identifying Noteworthy Events in News Articles

abstract: Most events described in a news article are background events ‐ only a small number are noteworthy, and a even smaller number serve as the trigger for writing of that article. Although these events are difficult to identify, they are crucial to NLP tasks such as first story detection, document summarization and event coreference, and to many applications of event analysis that depend on event counting and identifying trends. In this work, we introduce the notion of news-peg, a concept borrowed from the political science literature, in an attempt to remedy this problem. A news-peg is an event which prompted the author to write the article, and it serves as a more fine-grained measure of noteworthiness than a summary. We describe a new task of news-peg identification and release an annotated dataset for its evaluation. We formalize an operational definition of a news-peg, on which human annotators achieve high inter-annotator agreement (over 80%), and present a rule-based system for this task, which exploits syntactic features derived from established journalistic devices.


title: High Voltage Marx Generator Implementation using IGBT Stacks

abstract: High voltage Marx generator implementation using IGBT (Insulated Gate Bipolar Transistor) stacks is proposed in this paper. To protect the Marx generator at the moment of breakdown, AOCP (Active Over-Current Protection) part is included. The Marx generator is composed of 12 stages and each stage is made of IGBT stacks, two diode stacks, and capacitors. IGBT stack is used as a single switch. Diode stacks and inductors are used to charge the high voltage capacitor at each stage without power loss. These are also used to isolate input and high voltage negative output in high voltage generation mode. The proposed Marx generator implementation uses IGBT stack with a simple driver and has modular design. This system structure gives compactness and easiness to implement the total system. Some experimental and simulated results are included to verify the system performances in this paper.


title: MVE: a multi-view reconstruction environment

abstract: We present MVE, the Multi-View Environment. MVE is an end-to-end multi-view geometry reconstruction software which takes photos of a scene as input and produces a surface triangle mesh as result. The system covers a structure-from-motion algorithm, multi-view stereo reconstruction, generation of extremely dense point clouds, and reconstruction of surfaces from point clouds. In contrast to most image-based geometry reconstruction approaches, our system is focused on reconstruction of multi-scale scenes, an important aspect in many areas such as cultural heritage. It allows to reconstruct large datasets containing some detailed regions with much higher resolution than the rest of the scene. Our system provides a graphical user interface for structure-from-motion reconstruction, visual inspection of images, depth maps, and rendering of scenes and meshes.


title: Educational Robots: Three Models for the Research of Learning Theories and Human-Robot Interaction

abstract: In the summer of 2006, the Tulane Electrical Engineering and Computer Science department teamed up with UNO graduate students in education and developmental psychology to conduct experiments in the education of young children using robotic agents. Students from the “AI Robotics” class used the Sony AIBO robotic dog to design three different educational systems geared primarily toward young physically and mentally challenged children. We consulted with UNO graduate students concerning prekindergarten education and visited a special education institution, The Chinchuba Institute. The Chinchuba Institute educates hearing- and speech-impaired children, along with children with mental disabilities. We returned with impressive results, designing three systems that focus on methods of communication with the student and usability for the student and teacher. We encountered many challenges in designing for the pre-K and K-12 classroom, such as making the robot interaction robust and intuitive to a child.


title: Malware Characterization using Behavioral Components

abstract: Over the past years, we have experienced an increase in the quantity and complexity of malware binaries. This change has been fueled by the introduction of malware generation tools and reuse of different malcode modules. Recent malware appears to be highly modular and less functionally typified. A side-effect of this "composition" of components across different malware types, a growing number of new malware samples cannot be explicitly assigned to traditional classes defined by Anti-Virus (AV) vendors. Indeed, by nature, clustering techniques capture dominant behavior that could be a manifestation of only one of the malware component failing to reveal malware similarities that depend on other, less dominant components and other evolutionary traits. ::: ::: In this paper, we introduce a novel malware behavioral commonality analysis scheme that takes into consideration component-wise grouping, called behavioral mapping. Our effort attempts to shed light to malware behavioral relationships and go beyond simply clustering the malware into a family. To this end, we implemented a method for identifying soft clusters and reveal shared malware components and traits. Using our method, we demonstrate that a malware sample can belong to several groups (clusters), implying sharing of its respective components with other samples from the groups. We performed experiments with a large corpus of real-world malware data-sets and identified that we can successfully highlight malware component relationships across the existing AV malware families and variants.


title: Blind Authentication: A Secure Crypto-Biometric Verification Protocol

abstract: Concerns on widespread use of biometric authentication systems are primarily centered around template security, revocability, and privacy. The use of cryptographic primitives to bolster the authentication process can alleviate some of these concerns as shown by biometric cryptosystems. In this paper, we propose a provably secure and blind biometric authentication protocol, which addresses the concerns of user's privacy, template protection, and trust issues. The protocol is blind in the sense that it reveals only the identity, and no additional information about the user or the biometric to the authenticating server or vice-versa. As the protocol is based on asymmetric encryption of the biometric data, it captures the advantages of biometric authentication as well as the security of public key cryptography. The authentication protocol can run over public networks and provide nonrepudiable identity verification. The encryption also provides template protection, the ability to revoke enrolled templates, and alleviates the concerns on privacy in widespread use of biometrics. The proposed approach makes no restrictive assumptions on the biometric data and is hence applicable to multiple biometrics. Such a protocol has significant advantages over existing biometric cryptosystems, which use a biometric to secure a secret key, which in turn is used for authentication. We analyze the security of the protocol under various attack scenarios. Experimental results on four biometric datasets (face, iris, hand geometry, and fingerprint) show that carrying out the authentication in the encrypted domain does not affect the accuracy, while the encryption key acts as an additional layer of security.


title: Improving code review effectiveness through reviewer recommendations

abstract: Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.


title: Privacy-Preserving Deep Learning for any Activation Function

abstract: This paper considers the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local datasets owing to privacy concerns. We design systems for the scenario that the stochastic gradient descent (SGD) algorithm is used as the machine learning method because SGD (or its variants) is at the heart of recent deep learning techniques over neural networks. Our systems differ from existing systems in the following features: {\bf (1)} any activation function can be used, meaning that no privacy-preserving-friendly approximation is required; {\bf (2)} gradients computed by SGD are not shared but the weight parameters are shared instead; and {\bf (3)} robustness against colluding parties even in the extreme case that only one honest party exists. We prove that our systems, while privacy-preserving, achieve the same learning accuracy as SGD and hence retain the merit of deep learning with respect to accuracy. Finally, we conduct several experiments using benchmark datasets, and show that our systems outperform previous system in terms of learning accuracies.


Towards Quantifying Uncertainty in Data Analysis & Exploration


title: Neural Animation and Reenactment of Human Actor Videos

abstract: We propose a method for generating (near) video-realistic animations of real humans under user control. In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person. With that, our approach significantly reduces production cost compared to conventional rendering approaches based on production-quality 3D models, and can also be used to realistically edit existing videos. Technically, this is achieved by training a neural network that translates simple synthetic images of a human character into realistic imagery. For training our networks, we first track the 3D motion of the person in the video using the template model, and subsequently generate a synthetically rendered version of the video. These images are then used to train a conditional generative adversarial network that translates synthetic images of the 3D model into realistic imagery of the human. We evaluate our method for the reenactment of another person that is tracked in order to obtain the motion data, and show video results generated from artist-designed skeleton motion. Our results outperform the state-of-the-art in learning-based human image synthesis. Project page: http://gvv.mpi-inf.mpg.de/projects/wxu/HumanReenactment/


Environmental Kuznets Curves


title: Fulminant course in a patient with anti-N-methyl-D-aspartate receptor encephalitis with bilateral ovarian teratomas: A case report and literature review

abstract: RATIONALE ::: Anti-N-methyl-D-aspartate (NMDA) receptor encephalitis is an autoimmune disorder that can be controlled and reversed by immunotherapy. The presentation of NMDA receptor encephalitis varies, but NMDA receptor encephalitis is seldom reported in patients with both bilateral teratomas and preexisting brain injury. ::: ::: ::: PATIENT CONCERNS ::: A 28-year-old female with a history of traumatic intracranial hemorrhage presented acute psychosis, seizure, involuntary movement, and conscious disturbance with a fulminant course. Anti-NMDA receptor antibody was identified in both serum and cerebrospinal fluid, confirming the diagnosis of anti-NMDA receptor encephalitis. Bilateral teratomas were also identified during tumor survey. DIAGNOSES:: anti-N-methyl-D-aspartate receptor encephalitis. ::: ::: ::: INTERVENTIONS ::: Tumor resection and immunotherapy were performed early during the course. ::: ::: ::: OUTCOMES ::: The patient responded well to tumor resection and immunotherapy. Compared with other reports in the literature, her symptoms rapidly improved without further relapse. ::: ::: ::: LESSONS ::: This case report demonstrates that bilateral teratomas may be related to high anybody titers and that the preexisting head injury may be responsible for lowering the threshold of neurological deficits. Early diagnosis and therapy are crucial for a good prognosis in such patients.


title: Comparison of semantic segmentation approaches for horizon/sky line detection

abstract: Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on user-in-the-loop skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection[2], second focused on visual geo-localization but relying on accurate detection of skyline [15] and other two proposed for general semantic segmentation — Fully Convolutional Networks (FCN) [21] and SegNet[22]. Each of the first two methods is trained on a common training set [11] comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.


PROBABILISTIC TEXTUAL ENTAILMENT: GENERIC APPLIED MODELING OF LANGUAGE VARIABILITY


title: A learning framework using Green's function and kernel regularization with application to recommender system

abstract: Green's function for the Laplace operator represents the propagation of influence of point sources and is the foundation for solving many physics problems. On a graph of pairwise similarities, the Green's function is the inverse of the combinatorial Laplacian; we resolve the zero-mode difficulty by showing its physical origin as the consequence of the Von Neumann boundary condition. We propose to use Green's function to propagate label information for both semi-supervised and unsupervised learning. We also derive this learning framework from the kernel regularization using Reproducing Kernel Hilbert Space theory at strong regularization limit. Green's function provides a well-defined distance metric on a generic weighted graph, either as the effective distance on the network of electric resistors, or the average commute time in random walks. We show that for unsupervised learning this approach is identical to Ratio Cut and Normalized Cut spectral clustering algorithms. Experiments on newsgroups and six UCI datasets illustrate the effectiveness of this approach. Finally, we propose a novel item-based recommender system using Green's function and show its effectiveness.


title: Security services as coping mechanisms: an investigation into user intention to adopt an email authentication service

abstract: Email plays an important role in the digital economy but is threatened by increasingly sophisticated cybercrimes. A number of security services have been developed, including an email authentication service designed to cope with email threats. It remains unknown how users perceive and evaluate these security services and consequently form their adoption intention. Drawing on the Technology Acceptance Model and Technology Threat Avoidance Theory, this paper investigates the factors that affect user intention to adopt an email authentication service. Our results show that user intention to adopt an email security service is contingent upon users' perception of risk and evaluation of both internal and external coping strategies. This study contributes to research in security service adoption, service success and design, and information security behaviour.


title: Word2vec based system for recognizing partial textual entailment

abstract: Recognizing textual entailment is typically considered as a binary decision task - whether a text T entails a hypothesis H. Thus, in case of a negative answer, it is not possible to express that H is “almost entailed” by T. Partial textual entailment provides one possible approach to this issue. This paper presents an attempt to use word2vec model for recognizing partial (faceted) textual entailment. The proposed approach does not rely on language dependent NLP tools and other linguistic resources, therefore it can be easily implemented in different language environments where word2vec models are available.


Differences among the data models used by the geographic information systems and atmospheric science communities


title: Parametric Human Body Reconstruction Based on Sparse Key Points

abstract: We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user’s body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.


title: Travel route recommendation using geotags in photo sharing sites

abstract: The ability to create geotagged photos enables people to share their personal experiences as tourists at specific locations and times. Assuming that the collection of each photographer's geotagged photos is a sequence of visited locations, photo-sharing sites are important sources for gathering the location histories of tourists. By following their location sequences, we can find representative and diverse travel routes that link key landmarks. In this paper, we propose a travel route recommendation method that makes use of the photographers' histories as held by Flickr. Recommendations are performed by our photographer behavior model, which estimates the probability of a photographer visiting a landmark. We incorporate user preference and present location information into the probabilistic behavior model by combining topic models and Markov models. We demonstrate the effectiveness of the proposed method using a real-life dataset holding information from 71,718 photographers taken in the United States in terms of the prediction accuracy of travel behavior.


title: Continuous queries over distributed streams of heterogeneous monitoring data in cloud datacenters

abstract: The use of stream processing for state monitoring of distributed infrastructures has been advocated by some in order to overcome the issues of traditional monitoring solutions when tasked with complex continuous queries. However, in the domain of behavior monitoring the situation gets more complicated. It is mainly because of the low-quality source of behavior-related monitoring information (natural language computer logs). Existing approaches prevalently rely on indexing and real-time data-mining of the behavior-related data rather than on using event/stream processing techniques and the many corresponding benefits. The goal of this paper is to present a general notion of Distributed Event-Driven Monitoring Architecture that will enable an easy definition of expressive continuous queries over many distributed and heterogeneous streams of behavior-related (and state-related) monitoring data.


title: Raindrop detection algorithm for ADAS

abstract: Vision based advance driver assistance system (ADAS) completely rely on the images captured by the vehicle bound camera. Vision based ADAS also referred as computer vision based ADAS depends on clear images from surroundings for its algorithm implementation. If the images are hampered from external weather conditions such as rain or fog, the ADAS functionalities are affected. In general, checking the image, whether it is free from raindrop occlusions is one of the steps in the pre-processing, as clear, noise free images are required for ADAS algorithm deployments, which would enhance the ADAS functionalities. In ADAS, generically raindrop occlusions is identified by comparing two successive images then sorting analysis using displacement formulae or by identifying them typically based on photometric or geometric properties of raindrop. The computation time and the accuracy for these approaches are trade off factors. In this paper, we propose an algorithm to detect the raindrop, where detection is performed using thresholding and feature transform only on single frame. The approach reduces the computation time and memory resource, executing only on current frame. Once the raindrop occlusion is affirmed, an interrupt is sent to the concurrent algorithm about the occlusions and abort the same. To retrieve the region from occlusion we follow one of the generic method employing Gauss filter.


title: From Data Fusion to Knowledge Fusion

abstract: The task of data fusion is to identify the true values of data items (e.g., the true date of birth for Tom Cruise) among multiple observed values drawn from different sources (e.g., Web sites) of varying (and unknown) reliability. A recent survey [20] has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: knowledge fusion. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.


title: Motor learning principles for neurorehabilitation

abstract: Abstract Neurorehabilitation is based on the assumption that motor learning contributes to motor recovery after injury. However, little is known about how learning itself is affected by brain injury, how learning mechanisms interact with spontaneous biological recovery, and how best to incorporate learning principles into rehabilitation training protocols. Here we distinguish between two types of motor learning, adaptation and skill acquisition, and discuss how they relate to neurorehabilitation. Functional recovery can occur through resolution of impairment (reacquisition of premorbid movement patterns) and through compensation (use of alternative movements or effectors to accomplish the same goal); both these forms of recovery respond to training protocols. The emphasis in current neurorehabilitation practice is on the rapid establishment of independence in activities of daily living through compensatory strategies, rather than on the reduction of impairment. Animal models, however, show that after focal ischemic damage there is a brief, approximately 3–4-week, window of heightened plasticity, which in combination with training protocols leads to large gains in motor function. Analogously, almost all recovery from impairment in humans occurs in the first 3 months after stroke, which suggests that targeting impairment in this time-window with intense motor learning protocols could lead to gains in function that are comparable in terms of effect size to those seen in animal models.