Query: Alternating Optimisation and Quadrature for Robust Reinforcement Learning

Relevant passages:
PILCO: A Model-Based and Data-Efficient Approach to Policy Search <=SEP=> In this paper, we introduce pilco, a practical, data-efficient model-based policy search method. Pilco reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, pilco can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-ofthe-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.

An experimental investigation of model-based parameter optimisation: SPO and beyond <=SEP=> This work experimentally investigates model-based approaches for optimising the performance of parameterised randomised algorithms. We restrict our attention to procedures based on Gaussian process models, the most widely-studied family of models for this problem. We evaluated two approaches from the literature, and found that sequential parameter optimisation (SPO) [4] offered the most robust performance. We then investigated key design decisions within the SPO paradigm, characterising the performance consequences of each. Based on these findings, we propose a new version of SPO, dubbed SPO+, which extends SPO with a novel intensification procedure and log-transformed response values. Finally, in a domain for which performance results for other (model-free) parameter optimisation approaches are available, we demonstrate that SPO+ achieves state-of-the-art performance.

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters <=SEP=> This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain's complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks.

Automatic Gait Optimization with Gaussian Process Regression <=SEP=> Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.

Efficient Global Optimization of Expensive Black-Box Functions <=SEP=> In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.

Co-regularized least square regression for multi-view multi-class classification <=SEP=> Many classification problems involve instances that are unlabeled, multi-view and multi-class. However, few technique has been benchmarked for this complex scenario, with a notable exception that combines co-trained naive bayes (CoT-NB) with BCH coding. In this paper, we benchmark the performance of co-regularized least square regression (CoR-LS) for semi-supervised multi-view multi-class classification. We find it performed consistently and significantly better than CoT-NB over eight data sets at different scales. We also find for CoR-LS identity coding is optimal on large data sets and BCH coding is optimal on small data sets. Optimal scoring, a data-dependent coding scheme, often provides near-optimal performance.

Inter-satellite links for cubesats <=SEP=> Realizing inter-satellite links is a must for ensuring the success of cubesat swarm missions. Nevertheless, it has hardly been considered until now. The communication systems for cubesats have to deal with a few peculiar demands regarding consumed power, geometry and throughput. Depending on the type of application, required data rates can go up to tens of megabits per second, while power consumption and physical size are limited by the platform. The proposed communication scheme will combine power-efficient modulation and channel coding with multiple access and spread spectrum techniques, enabling the deployment of multiple satellites. Apart from this, the antenna system has to be designed such that links can be established and maintained independent of the satellites' orientation. An electrically steerable radiation pattern is achieved by placing antennas on each face of the cube. Conformal beamforming provides the system with 5 dBi gain for any desired direction of transmission, eliminating the need for attitude control. Furthermore, using planar antennas reduces the complexity of the mechanical part as they require no deployment.

Transparent, conductive graphene electrodes for dye-sensitized solar cells. <=SEP=> Transparent, conductive, and ultrathin graphene films, as an alternative to the ubiquitously employed metal oxides window electrodes for solid-state dye-sensitized solar cells, are demonstrated. These graphene films are fabricated from exfoliated graphite oxide, followed by thermal reduction. The obtained films exhibit a high conductivity of 550 S/cm and a transparency of more than 70% over 1000-3000 nm. Furthermore, they show high chemical and thermal stabilities as well as an ultrasmooth surface with tunable wettability.

BUILDING GENERALIZABLE AGENTS <=SEP=> Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely generalization. In this work, we build House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multistoreyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, RoomNav, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g. segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D1, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community.

Online Learning for Time Series Prediction <=SEP=> In this paper we address the problem of predicting a time seri e using the ARMA (autoregressive moving average) model, under minimal assumptions on the noi se terms. Using regret minimization techniques, we develop effective online learning algorith ms for the prediction problem, withoutassuming that the noise terms are Gaussian, identically distribu ted or even independent. Furthermore, we show that our algorithm’s performances asymptotically app roaches the performance of the best ARMA model in hindsight.

Inferring the meaning of chord sequences via lyrics <=SEP=> This paper discusses how meanings associated with chord sequences can be inferred from word associations based on lyrics. The approach works by analyzing in-line chord annotations of lyrics to maintain co-occurrence statistics for chords and lyrics. This is analogous to the way parallel corpora are analyzed in order to infer translation lexicons. The result can benefit musical discovery systems by modeling how the chord structure complements the lyrics.

Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron <=SEP=> Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010–1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010–1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.

New circularly polarized slot radiator for substrate integrated waveguide (SIW) planar array <=SEP=> A circularly polarized slot radiator in millimetre band is presented. Low reflection and good polarization performance is obtained in comparison with state of art slot elements. A high gain array prototype has been implemented in substrate integrated waveguides (SIW) technology, in order to check the new element performance. A cosecant amplitude coverage in the elevation plane, as well as a 6° tilted monopulse pattern in the azimuth plane are obtained. A gain peak value of 28.6 dBi and 79% of efficiency at 36.7 GHz have been obtained in the specified operation band (36.7 – 37 GHz) for a manufactured antenna prototype. Likewise, the top measured axial ratio is 1.95 dB at 36.85 GHz.

Defects of the nose, lip, and cheek: rebuilding the composite defect. <=SEP=> BACKGROUND
The face can be divided into regions (units) with characteristic skin quality, border outline, and three-dimensional contour. A defect may lie entirely within a single major unit or encompass several adjacent units, creating unique problems for repair. Composite defects overlap two or more facial units. Nasal defects often extend into the adjacent lip and cheek. The simplest solution may appear to be to simply "fill the hole"--just replace the missing bulk. Poor contour, incorrect dimension, malposition, asymmetry, poor blending into adjacent normal tissues, and a patch-like repair often follow.


METHODS
The following principles of regional unit repair were applied to guide these complex reconstructions: (1) reconstruct units, not defects; (2) alter the wound in site, size, shape, and depth; (3) consider using separate grafts and flaps for each unit and subunit, if appropriate; (4) use "like" tissue for "like" tissue; (5) restore a stable platform; (6) build in stages; (7) use distant tissue for "invisible" needs and local skin for resurfacing; and (8) disregard old scars.


RESULTS
Clinical cases of increasing composite complexity were repaired with local, regional, and distant tissues. Excellent aesthetics and function were obtained.


CONCLUSIONS
Careful visual analysis of the normal face and the defect can direct the choice, timing, and technique of facial repair. Regional unit principles provide a coordinated approach to the vision, planning, and fabrication of these difficult wounds. The entire repair should be intellectually planned, designed step by step, and laid out in a series of coordinated steps, with general principles applied to successfully repair composite defects of the nose, lip, and cheek.

Nasal Duplication Combined with Cleft Lip and Palate: Surgical Correction and Long-Term Follow-Up <=SEP=> Background
Diprosopus dirrhinus, or nasal duplication, is a rare entity of partial craniofacial duplication.


Methods
The case we present is the first report of diprosopus dirrhinus associated with complete cleft lip and palate. The baby was born in Cambodia at full term by normal vaginal delivery with no significant perinatal and family history. Physical examination revealed significant facial deformity due to the duplicated nose and the left complete cleft lip/palate on the right subset.


Results
There were 4 nostrils; both medial apertures including the cleft site were found to be 10-15 mm deep cul-de-sac structures without communication to the nasopharynx. The upper third of the face was notable for hypertelorism with a duplication of the soft-tissue nasion and glabella. Between the 2 nasal dorsums, there was a small cutaneous depression with a lacrimal fistula in the midline. Surgical treatment included the first stage of primary lip and nose repair and the second stage of palatoplasty.


Conclusions
The patient was followed up at the age of 10 years showing satisfactory results for both aesthetic and functional aspects. Further management in the future will be required for the hypertelorism and nasal deformity.

Compositionality and the angular gyrus: A multi-voxel similarity analysis of the semantic composition of nouns and verbs <=SEP=> The cognitive and neural systems that enable conceptual processing must support the ability to combine (and recombine) concepts to form an infinite number of ideas. Two candidate neural systems for conceptual combination-the left anterior temporal lobe (ATL) and the left angular gyrus (AG)-have been characterized as "semantic hubs" due to both functional and anatomical properties; however, these two regions likely support different aspects of composition. Here we consider two hypotheses for the role of AG in conceptual combination, both of which differ from a putative role for the ATL in "feature-based" combinatorics (i.e., meaning derived by combining concepts' features). Firstly, we examine whether AG is more sensitive to function-argument relations of the sort that arise when a predicate is combined with its arguments. Secondly, we examine the non-mutually exclusive possibility that AG represents information carried on a verb in particular, whether this be information about event composition or about thematic relations denoted uniquely by verbs. We identified voxels that respond differentially to two-word versus one-word stimuli, and we measured the similarity of the patterns in these voxels evoked by (1) pairs of two-word phrases that shared a noun that was an argument, thus sharing function-argument composition (e.g. eats meat and with meat), in comparison with two-word phrases that shared only a noun, not an argument (e.g., eats meat and tasty meat); and (2) stimulus pairs that shared only an event (operationalized here as sharing a verb; e.g. eats meat and eats quickly), in comparison to both of the above. We found that activity patterns in left AG tracked information relating to the presence of an event-denoting verb in a pair of two-word phrases. We also found that the neural similarity in AG voxel patterns between two phrases sharing a verb correlated with subjects' ratings of how similar the meanings of those two verb phrases were. These findings indicate that AG represents information specific to verbs, perhaps event structure or thematic relations mediated by verbs, as opposed to argument structure in general.

A review of trisomy X (47,XXX) <=SEP=> Trisomy X is a sex chromosome anomaly with a variable phenotype caused by the presence of an extra X chromosome in females (47,XXX instead of 46,XX). It is the most common female chromosomal abnormality, occurring in approximately 1 in 1,000 female births. As some individuals are only mildly affected or asymptomatic, it is estimated that only 10% of individuals with trisomy X are actually diagnosed. The most common physical features include tall stature, epicanthal folds, hypotonia and clinodactyly. Seizures, renal and genitourinary abnormalities, and premature ovarian failure (POF) can also be associated findings. Children with trisomy X have higher rates of motor and speech delays, with an increased risk of cognitive deficits and learning disabilities in the school-age years. Psychological features including attention deficits, mood disorders (anxiety and depression), and other psychological disorders are also more common than in the general population. Trisomy X most commonly occurs as a result of nondisjunction during meiosis, although postzygotic nondisjunction occurs in approximately 20% of cases. The risk of trisomy X increases with advanced maternal age. The phenotype in trisomy X is hypothesized to result from overexpression of genes that escape X-inactivation, but genotype-phenotype relationships remain to be defined. Diagnosis during the prenatal period by amniocentesis or chorionic villi sampling is common. Indications for postnatal diagnoses most commonly include developmental delays or hypotonia, learning disabilities, emotional or behavioral difficulties, or POF. Differential diagnosis prior to definitive karyotype results includes fragile X, tetrasomy X, pentasomy X, and Turner syndrome mosaicism. Genetic counseling is recommended. Patients diagnosed in the prenatal period should be followed closely for developmental delays so that early intervention therapies can be implemented as needed. School-age children and adolescents benefit from a psychological evaluation with an emphasis on identifying and developing an intervention plan for problems in cognitive/academic skills, language, and/or social-emotional development. Adolescents and adult women presenting with late menarche, menstrual irregularities, or fertility problems should be evaluated for POF. Patients should be referred to support organizations to receive individual and family support. The prognosis is variable, depending on the severity of the manifestations and on the quality and timing of treatment.

Recognition and identification of the clothes in the photo or video using neural networks <=SEP=> We introduce our solution of the problem of clothing recognition and identification. The cause of taking convolutional neural network as a mathematic model for the further research was the result of an experimental survey of methods of keypoints detection and computing their descriptors. The convolutional neural network model was developed and trained on the Apparel Classification with Style clothing image dataset. The training image dataset was expanded with the help of augmentation methods. The computational experiment aimed at determination of neural network efficiency and stability proved its productivity. Moreover, we developed clothing color recognition, which let us perform clothing identification by 2 parameters: type and color.

A Survey: Smart agriculture IoT with cloud computing <=SEP=> IoT is a revolutionary technology that represents the future of computing and communications. Most of the people over all worlds depend on agriculture. Because of this reason smart IT technologies are needed to migrate with traditional agriculture methods. Using modern technologies can control the cost, maintenance and monitoring performance. Satellite and aerial imagery play a vital role in modern agriculture. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. so, with IoT farmers can remotely monitor their crop and equipment by phones and computers. In this paper, we surveyed some typical applications of Agriculture IoT Sensor Monitoring Network technologies using Cloud computing as the backbone. This survey is used to understand the different technologies and to build sustainable smart agriculture. Simple IoT agriculture model is addressed with a wireless network.

Efficiently indexing shortest paths by exploiting symmetry in graphs <=SEP=> Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries on-the-fly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadth-first search trees materialized is reduced from O(N) to O(|Δ|), where |Δ| ≤ N is the number of orbits in the graph. We explore orbit adjacency and local symmetry to obtain compact breadth-first-search trees (compact BFS-trees). An extensive empirical study using both synthetic data and real data shows that compact BFS-trees can be built efficiently and the space cost can be reduced substantially. Moreover, online shortest path query answering can be achieved using compact BFS-trees.

Trends in Obesity Among Adults in the United States, 2005 to 2014. <=SEP=> IMPORTANCE
Between 1980 and 2000, the prevalence of obesity increased significantly among adult men and women in the United States; further significant increases were observed through 2003-2004 for men but not women. Subsequent comparisons of data from 2003-2004 with data through 2011-2012 showed no significant increases for men or women.


OBJECTIVE
To examine obesity prevalence for 2013-2014 and trends over the decade from 2005 through 2014 adjusting for sex, age, race/Hispanic origin, smoking status, and education.


DESIGN, SETTING, AND PARTICIPANTS
Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height.


EXPOSURES
Survey period.


MAIN OUTCOMES AND MEASURES
Prevalence of obesity (body mass index ≥30) and class 3 obesity (body mass index ≥40).


RESULTS
This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21,013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity (P = .004) and for class 3 obesity (P = .01) but not among men (P = .30 for overall obesity; P = .14 for class 3 obesity).


CONCLUSIONS AND RELEVANCE
In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity in 2013-2014 was 35.0% among men and 40.4% among women. The corresponding values for class 3 obesity were 5.5% for men and 9.9% for women. For women, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014; there were no significant trends for men. Other studies are needed to determine the reasons for these trends.

Chipless RFID: Bar Code of the Future <=SEP=> Radio-frequency identification (RFID) is a wireless data capturing technique that utilizes radio frequency (RF) waves for automatic identification of objects. RFID relies on RF waves for data transmission between the data carrying device, called the RFID tag, and the interrogator.

Electronically Reconfigurable Microwave Bandpass Filter <=SEP=> A new class of reconfigurable bandpass filter based on parallel-coupled switched delay line approach is presented in this paper. The approach allows both center frequency and passband bandwidth to be reconfigured electronically. In contrast to conventional approach, the effects of the switch losses are relatively insignificant and can be treated as an auxiliary effect and therefore the filter has low passband loss. Despite the loss performance, the filter provides excellent linearity performance with large power handling capability. Theoretical analysis is presented and the feasibility of the approach has been experimentally verified with microstrip circuit prototypes. The first filter prototype provides narrowband quasi-elliptic filter response with constant bandwidth (50-MHz) tuning across 45% tuning range. The minimum stopband rejection level is 45 dB. The second filter possesses passband-width 3:1 ratio tunability. The third filter provides both functionalities which allow center frequency and passband bandwidth to be reconfigured. All filters have a < 4-dB passband loss with an OIP3 of >20 dBm and are immune from power saturation effect. The measured results demonstrate improved performance in terms of linear and nonlinear characteristic compared with prior works.

Fraud : The Affinity of Classification Techniques to Insurance Fraud Detection <=SEP=> Quite a large number of data mining techniques employed in financial fraud detection (FFD) are seen to be classification techniques. In this paper, we developed an algorithm to find the features of classification techniques (or method) that so much place it (classification techniques) in the heart of researchers in their various efforts in the study of insurance frauds detection. We also got to know the characteristics of insurance frauds data that made data mining classification techniques so much attracted to it (insurance data).

Practical steganalysis of digital images: state of the art <=SEP=> Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we classify and review current stego-detection algorithms that can be used to trace popular steganographic products. We recognize several qualitatively different approaches to practical steganalysis – visual detection, detection based on first order statistics (histogram analysis), dual statistics methods that use spatial correlations in images and higher-order statistics (RS steganalysis), universal blind detection schemes, and special cases, such as JPEG compatibility steganalysis. We also present some new results regarding our previously proposed detection of LSB embedding using sensitive dual statistics. The recent steganalytic methods indicate that the most common paradigm in image steganography – the bit-replacement or bit substitution – is inherently insecure with “safe capacities” far smaller than previously thought.

Topic-aware Social Influence Minimization <=SEP=> In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking $k$ nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.

The City Browser : Utilizing Massive Call Data to Infer City Mobility Dynamics <=SEP=> This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city’s mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.

Value creation in web services: An integrative model <=SEP=> Web services are redefining the way organizations exchange business-critical information internally and externally with customers, suppliers, and other business associates. In this paper, we develop an integrative model for understanding value creation in web services from a provider’s perspective. The model integrates the static representation of conventional business values with the fluidity of the emergent IT services domain. It captures the complexity and contradictions facing Web services providers in their drive towards market leadership, strategic differentiation and revenue generation from web services. The model comprises twelve propositions to guide our understanding and future research and practice in this increasingly important segment in the IT field. 2005 Elsevier B.V. All rights reserved.

DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks <=SEP=> Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.

Application of electrostatic separation to the recycling of plastic wastes: separation of PVC, PET, and ABS. <=SEP=> Plastics are widely used in everyday life as a useful material, and thus their consumption is growing at a rate of about 5% per year in Korea. However, the constant generation of plastic wastes and their disposal generates environmental problems along with economic loss. In particular, mixed waste plastics are difficult to recycle because of their inferior characteristics. A laboratory-scale triboelectrostatic separator unit has been designed and assembled for this study. On the basis of the control of electrostatic charge, the separation of three kinds of mixed plastics, polyvinyl chloride (PVC), poly(ethylene terephthalate) (PET), and acrylonitrile-butadiene-styrene (ABS), in a range of similar gravities has been performed through a two-stage separation process. Polypropylene (PP) and high-impact polystyrene (HIPS) were found to be the most effective materials for a tribo-charger in the separation of PVC, PET, and ABS. The charge-to-mass ratio (nC/g) of plastics increased with increasing air velocity in the tribo charger. In the first stage, using the PP cyclone charger, the separation efficiency of particles considerably depended on the air velocity (10 m/s), the relative humidity (< 30%), the electrode potential (> 20 kV), and the splitter position (+2 cm from the center) in the triboelelctrostatic separator unit. At this time, a PVC grade of 99.40% and a recovery of 98.10% have successfully been achieved. In the second stage, using the HIPS cyclone charger, a PET grade of 97.80% and a recovery of 95.12% could be obtained under conditions of 10 m/s, over 25 kV, a central splitter position, and less than 40% relative humidity. In order to obtain 99.9% PVC grade and 99.3% PET grade, their recoveries should be sacrificed by 20.9% and 27%, respectively, with moving the splitter from the center to a (+)6 cm position.