title: TransG : A Generative Mixture Model for Knowledge Graph Embedding

abstract: Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel Gaussian mixture model for embedding, TransG. The new model can discover latent semantics for a relation and leverage a mixture of relation component vectors for embedding a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, which is able to deal with multiple relation semantics. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.


title: Opinion Target Extraction Using Partially-Supervised Word Alignment Model

abstract: Mining opinion targets from online reviews is an important and challenging task in opinion mining. This paper proposes a novel approach to extract opinion targets by using partially-supervised word alignment model (PSWAM). At first, we apply PSWAM in a monolingual scenario to mine opinion relations in sentences and estimate the associations between words. Then, a graph-based algorithm is exploited to estimate the confidence of each candidate, and the candidates with higher confidence will be extracted as the opinion targets. Compared with existing syntax-based methods, PSWAM can effectively avoid parsing errors when dealing with informal sentences in online reviews. Compared with the methods using alignment model, PSWAM can capture opinion relations more precisely through partial supervision from partial alignment links. Moreover, when estimating candidate confidence, we make penalties on higher-degree vertices in our graph-based algorithm in order to decrease the probability of the random walk running into the unrelated regions in the graph. As a result, some errors can be avoided. The experimental results on three data sets with different sizes and languages show that our approach outperforms state-of-the-art methods.


title: Characterization of linux kernel behavior under errors

abstract: This paper describes an experimental study of Linux kernel behavior in the presence of errors that impact the instruction stream of the kernel code. Extensive error injection experiments including over 35,000 errors are conducted targeting the most fre- quently used functions in the selected kernel subsystems. Three types of faults/errors injection campaigns are conducted: (1) ran- dom non-branch instruction, (2) random conditional branch, and (3) valid but incorrect branch. The analysis of the obtained data shows: (i) 95% of the crashes are due to four major causes, namely, unable to handle kernel NULL pointer, unable to handle kernel paging request, invalid opcode, and general protection fault, (ii) less than 10% of the crashes are associated with fault propagation and nearly 40% of crash latencies are within 10 cycles, (iii) errors in the kernel can result in crashes that require reformatting the file system to restore system operation; the process of bringing up the system can take nearly an hour. Subsequently, over 35,000 faults/errors are injected into the kernel functions within four subsystems: architecture- dependent code (arch), virtual file system interface (fs), cen- tral section of the kernel (kernel), and memory management (mm). Three types of fault/error injection campaigns are con- ducted: random non-branch, random conditional branch, and valid but incorrect conditional branch. The data is analyzed to quantify the response of the OS as a whole based on the sub- system and to determine which functions are responsible for error sensitivity. The analysis provides a detailed insight into the OS behavior under faults/errors. The major findings in- clude: • Most crashes (95%) are due to four major causes: unable to handle kernel NULL pointer, unable to handle kernel paging request, invalid opcode, and general protection fault. • Nine errors in the kernel result in crashes (most severe crash category), which require reformatting the file system. The process of bringing up the system can take nearly an hour. • Less than 10% of the crashes are associated with fault propagation, and nearly 40% of crash latencies are within 10 cycles. The closer analysis of the propagation patterns indicates that it is feasible to identify strategic locations for embedding additional assertions in the source code of a given subsystem to detect errors and, hence, to prevent er- ror propagation.


title: 98.1%-Efficiency Hysteretic-Current-Mode Noninverting Buck–Boost DC-DC Converter With Smooth Mode Transition

abstract: A noninverting buck-boost dc-dc converter can work in buck, boost, or buck-boost mode. Hence, it provides a good solution when the input voltage may be higher or lower than the output voltage. However, a buck-boost converter requires four power transistors, rather than two. Therefore, its efficiency decreases, due to the conduction and switching losses of the two extra power transistors. Another issue of a buck-boost converter is how to smoothly switch its operational mode, when its input voltage approaches its output voltage. A hysteretic-current-mode noninverting buck-boost converter with high efficiency and smooth mode transition is proposed, and it was designed and fabricated using TSMC 0.35-μm CMOS 2P4 M 3.3 V/5V mixed-signal polycide process. The input voltage may range from 2.5 to 5 V, the output voltage is 3.3 V, and the maximal load current is 400 mA. According to the measured results, the maximal efficiency reaches 98.1%, and the efficiencies measured in the entire input voltage and loading ranges are all above 80%.


title: Impact of Advertisement on Buying Behaviours of the Consumers: Study of Cosmetic Industry in Karachi City.

abstract: Advertisements have been used for many years to influence the buying behaviors of the consumers. Advertisements are helpful in creating the awareness and perception among the customers of cosmetic products; both of these variables are lethal combination to influence the buying behaviors of the consumers. This particular research was conducted on the 200 young male or female who use different brands of cosmetics to check the influence of advertisement on their buying behavior while creating the awareness and building the perceptions. Correlation and regression analysis were used to identify the relationship between these variables. The results revealed provide the new way to managers to devise suitable strategy for the marketing of cosmetic products. These results show that advertisements are very useful in creating the awareness among the people but they are failed to build strong perceptions in the mind of consumers. Both of these variables such as consumer awareness and consumer perceptions will motivate the consumer to buy a certain product, as there is a positive relationship present in between them.


title: Spatial Transferability of Vegetation Types in Distribution Models Based on Sample Surveys from an Alpine Region

abstract: Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.


title: An online algorithm for segmenting time series

abstract: In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time-series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data-mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature.


title: Non-linear Dimensionality Reduction by Locally Linear Isomaps

abstract: Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text. How do we judge similarity? Our mental representations of the world are formed by processing large numbers of sensory in


3D-City Models: Automatic and Semiautomatic Acquisition Methods


title: A Graph-Based Approach to Skill Extraction from Text

abstract: This paper presents a system that performs skill extraction from text documents. It outputs a list of professional skills that are relevant to a given input text. We argue that the system can be practical for hiring and management of personnel in an organization. We make use of the texts and the hyperlink graph of Wikipedia, as well as a list of professional skills obtained from the LinkedIn social network. The system is based on first computing similarities between an input document and the texts of Wikipedia pages and then using a biased, hub-avoiding version of the Spreading Activation algorithm on the Wikipedia graph in order to associate the input document with skills.


title: Analyzing the energy efficiency of a database server

abstract: Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization. We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.


title: Disrupted-In-Schizophrenia 1, a candidate gene for schizophrenia, participates in neurite outgrowth

abstract: Disrupted-In-Schizophrenia 1 (DISC1) was identified as a novel gene disrupted by a (1;11)(q42.1;q14.3) translocation that segregated with schizophrenia in a Scottish family. Predicted DISC1 product has no significant homology to other known proteins. Here, we demonstrated the existence of DISC1 protein and identified fasciculation and elongation protein zeta-1 (FEZ1) as an interacting partner of DISC1 by a yeast two-hybrid study. FEZ1 and its nematode homolog are reported to represent a new protein family involved in axonal outgrowth and fasciculation. In cultured hippocampal neurons, DISC1 and FEZ1 colocalized in growth cones. Interactions of these proteins were associated with F-actin. In the course of neuronal differentiation of PC12 cells, upregulation of DISC1/FEZ1 interaction was observed as along with enhanced extension of neurites by overexpression of DISC1. The present study shows that DISC1 participates in neurite outgrowth through its interaction with FEZ1. Recent studies have provided reliable evidence that schizophrenia is a neurodevelopmental disorder. As there is a high level of DISC1 expression in developing rat brain, dysfunction of DISC1 may confer susceptibility to psychiatric illnesses through abnormal development of the nervous system.


title: The Growing Relationship Between China and Sub-Saharan Africa

abstract: China’s economic ascendance over the past two decades has generated ripple effects in the world economy. Its search for natural resources to satisfy the demands of industrialization has led it to Sub-Saharan Africa. Trade between China and Africa in 2006 totaled more than $50 billion, with Chinese companies importing oil from Angola and Sudan, timber from Central Africa, and copper from Zambia. Demand from China has contributed to an upward swing in prices, particularly for oil and metals from Africa, and has given a boost to real GDP in Sub-Saharan Africa. Chinese aid and investment in infrastructure are bringing desperately needed capital to the continent. At the same time, however, strong Chinese demand for oil is contributing to an increase in the import bill for many oil-importing Sub- Saharan African countries, and its exports of low-cost textiles, while benefiting African consumers, is threatening to displace local production. China poses a challenge to good governance and macroeconomic management ...


title: Synchronization and Frequency Regulation of DFIG-Based Wind Turbine Generators With Synchronized Control

abstract: Synchronized control (SYNC) is widely adopted for doubly fed induction generator (DFIG)-based wind turbine generators (WTGs) in microgrids and weak grids, which applies P-f droop control to achieve grid synchronization instead of phase-locked loop. The DFIG-based WTG with SYNC will reach a new equilibrium of rotor speed under frequency deviation, resulting in the WTG's acceleration or deceleration. The acceleration/deceleration process can utilize the kinetic energy stored in the rotating mass of WTG to provide active power support for the power grid, but the WTG may lose synchronous stability simultaneously. This stability problem occurs when the equilibrium of rotor speed is lost and the rotor speed exceeds the admissible range during the frequency deviations, which will be particularly analyzed in this paper. It is demonstrated that the synchronous stability can be improved by increasing the P-f droop coefficient. However, increasing the P-f droop coefficient will deteriorate the system's small signal stability. To address this contradiction, a modified synchronized control strategy is proposed. Simulation results verify the effectiveness of the analysis and the proposed control strategy.


title: Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective

abstract: With advances in tracking and database technologies, firms are increasingly able to understand their customers and translate this understanding into products and services that appeal to them. Technologies such as collaborative filtering, data mining, and click-stream analysis enable firms to customize their offerings at the individual level. While there has been a lot of hype about web personalization recently, our understanding of its effectiveness is far from conclusive. Drawing on the elaboration likelihood model (ELM) literature, this research takes the view that the interaction between a firm and its customers is one of communicating a persuasive message to the customers driven by business objectives. In particular, we examine three major elements of a web personalization strategy: level of preference matching, recommendation set size, and sorting cue. These elements can be manipulated by a firm in implementing its personalization strategy. This research also investigates a personal disposition, need for cognition, which plays a role in assessing the effectiveness of web personalization. Research hypotheses are tested using 1,000 subjects in three field experiments based on a ring-tone download website. Our findings indicate the saliency of these variables in different stages of the persuasion process. Theoretical and practical implications of the findings are discussed.


title: Household sanitation and personal hygiene practices are associated with child stunting in rural India: a cross-sectional analysis of surveys

abstract: OBJECTIVES ::: Increasing evidence suggests that water, sanitation and hygiene (WASH) practices affect linear growth in early childhood. We determined the association between household access to water, sanitation and personal hygiene practices with stunting among children aged 0-23 months in rural India. ::: ::: ::: SETTING ::: India. ::: ::: ::: PARTICIPANTS ::: A total of 10 364, 34 639 and 1282 under-2s who participated in the 2005-2006 National Family Health Survey (NFHS-3), the 2011 Hunger and Malnutrition Survey (HUNGaMA) and the 2012 Comprehensive Nutrition Survey in Maharashtra (CNSM), respectively, were included in the analysis. ::: ::: ::: PRIMARY OUTCOME MEASURES ::: The association between WASH indicators and child stunting was assessed using logistic regression models. ::: ::: ::: RESULTS ::: The prevalence of stunting ranged from 25% to 50% across the three studies. Compared with open defecation, household access to toilet facility was associated with a 16-39% reduced odds of stunting among children aged 0-23 months, after adjusting for all potential confounders (NHFS-3 (OR=0.84, 95% CI 0.71 to 0.99); HUNGaMA (OR=0.84, 95% CI 0.78 to 0.91); CNSM (OR=0.61, 95% CI 0.44 to 0.85)). Household access to improved water supply or piped water was not in itself associated with stunting. The caregiver's self-reported practices of washing hands with soap before meals (OR=0.85, 95% CI 0.76 to 0.94) or after defecation (OR=0.86, 95% CI 0.80 to 0.93) were inversely associated with child stunting. However, the inverse association between reported personal hygiene practices and stunting was stronger among households with access to toilet facility or piped water (all interaction terms, p<0.05). ::: ::: ::: CONCLUSIONS ::: Improved conditions of sanitation and hygiene practices are associated with reduced prevalence of stunting in rural India. Policies and programming aiming to address child stunting should encompass WASH interventions, thus shifting the emphasis from nutrition-specific to nutrition-sensitive programming. Future randomised trials are warranted to validate the causal association.


The potential of Chlorella vulgaris for wastewater treatment and biodiesel production.


Neural Joke Generation


title: Depositional environments of unfossiliferous sediments of the Jodhpur group, Western India

abstract: Abstract The Jodhpur Group of Rajasthan, western India, the lowest stratigraphic unit of the Marwar Supergroup (Upper Proterozoic to Lower Cambrian?), consists of unfossiliferous, mainly horizontal bedded sandstones and shales deposited in an intracratonic basin. Six lithofacies have been recognized and interpreted in terms of environmental facies as braided fluvial facies, low-energy beach facies with tidal effects during depositional regression, storm-influenced beach facies during depositional regression, low-energy beach facies during depositional transgression, lagoonal facies and shelf facies. The most common paleogeographic scenario was of braided streams flowing to the sea. Streams flowed west, northwest and north of the Pipar Road Horst Block situated in the southern part of the area of study. Low-energy or storm-influenced beaches developed along the shoreline beyond which was a wide shelf zone. The braided fluvial lithofacies exhibits characteristics intermediate between those of sediments deposited by the modern braided South Saskatchewan and Platte rivers.


title: Anti-cancer Properties of Protein Hydrolysate from the Posterior Salivary Gland of Amphioctopus membranaceus (Quoy & Gaimard, 1832)

abstract: The present study was carried out to evaluate the anti-cancer properties of protein hydrolysate from posterior salivary gland (PSG) of Amphioctopus membranaceus. The sample (octopus) was initially screened for species identification through CO-I gene. The PSG of the animal was homogenized and centrifuged at 6000 rpm for 10 min. Further, enzymatic hydrolysis was performed using papain enzyme at a dose of 2.5% w/w. Later the hydrolysate (supernatant) obtained was subjected to fractionation using gel permeation chromatography. 20 fractions were collected and the fraction 4 (OM-H) was found to have the maximum amount of protein content of 3.2 mg/mL. SDS-PAGE depicted prominent bands ranging from 14 to 25 kDa. The IC50 evaluation was studied using Vero cell lines that showed the cytotoxic effect of OM-H at 405 μg/mL and in HeLa cell lines 250 µg/mL showed the highest inhibition rate of 45.01%. Thus, the study showed that partially purified PSG hydrolysate was found to display good anti-cancer activity against human cancer cell lines.


title: Development of a Unique Triage System for Acute Care Physical Therapy and Occupational Therapy Services: An Administrative Case Report

abstract: BACKGROUND AND PURPOSE ::: This administrative case report documents the development of a mechanism by which systematic triage was used to assign patients to therapists in acute care settings. The primary objective was to develop a triage tool to improve patient access to medically necessary therapy services. ::: ::: ::: CASE DESCRIPTION ::: A unique triage tool and a decision tree were developed to determine which patients referred to therapists for acute care therapy required skilled services. The triage tool was used to examine therapy referrals for patients from 2 large academic hospitals; 6 criteria were used to determine which evaluations should be cancelled. During the trial period, the predictive ability of individual triage criterion items was analyzed, the tool was modified and validated, and a decision tree was established. Descriptive and chi-square analyses were performed on all variables of interest. ::: ::: ::: OUTCOMES ::: The systematic triage system reduced the number of therapy evaluations that were not appropriate by 29%, resulting in an improvement in the availability of therapy services for patients who required skilled care. The average number of patients per therapist per workday decreased from 18.9 to 12.1 and from 15.1 to 12.8 in the 2 hospitals. An improvement in a newly developed "workload index" related to missed patient visits also indicated the success of this project. ::: ::: ::: DISCUSSION ::: A novel systematic triage system reduced the number of therapy evaluations that were not appropriate, resulting in an improvement in the availability of therapy services for patients who require skilled intervention.


title: Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains

abstract: When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded populations of neurons can be explained by a small number of shared factors, or a low-dimensional trajectory. Recovering these latent trajectories, particularly from single-trial population recordings, may help us understand the dynamics that drive neural computation. However, due to the biophysical constraints and noise in the spike trains, inferring trajectories from data is a challenging statistical problem in general. Here, we propose a practical and efficient inference method, the variational latent gaussian process (vLGP). The vLGP combines a generative model with a history-dependent point process observation, together with a smoothness prior on the latent trajectories. The vLGP improves on earlier methods for recovering latent trajectories, which assume either observation models inappropriate for point processes or linear dynamics. We compare and validate vLGP on both simulated data sets and populat...


title: Automatically learning shape specifications

abstract: This paper presents a novel automated procedure for discovering expressive shape specifications for sophisticated functional data structures. Our approach extracts potential shape predicates based on the definition of constructors of arbitrary user-defined inductive data types, and combines these predicates within an expressive first-order specification language using a lightweight data-driven learning procedure. Notably, this technique requires no programmer annotations, and is equipped with a type-based decision procedure to verify the correctness of discovered specifications. Experimental results indicate that our implementation is both efficient and effective, capable of automatically synthesizing sophisticated shape specifications over a range of complex data types, going well beyond the scope of existing solutions.


title: Wavelet Transform-Based Analysis of QRS complex in ECG Signals

abstract: In the present paper we have reported a wavelet based time-frequency multiresolution analysis of an ECG signal. The ECG (electrocardiogram), which records hearts electrical activity, is able to provide with useful information about the type of Cardiac disorders suffered by the patient depending upon the deviations from normal ECG signal pattern. We have plotted the coefficients of continuous wavelet transform using Morlet wavelet. We used different ECG signal available at MIT-BIH database and performed a comparative study. We demonstrated that the coefficient at a particular scale represents the presence of QRS signal very efficiently irrespective of the type or intensity of noise, presence of unusually high amplitude of peaks other than QRS peaks and Base line drift errors. We believe that the current studies can enlighten the path towards development of very lucid and time efficient algorithms for identifying and representing the QRS complexes that can be done with normal computers and processors.


title: Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation

abstract: Among statistical approaches to Chinese word segmentation, the word-based n-gram (generative) model and the character-based tagging (discriminative) model are two dominant approaches in the literature. The former gives excellent performance for the in-vocabulary (IV) words; however, it handles out-of-vocabulary (OOV) words poorly. On the other hand, though the latter is more robust for OOV words, it fails to deliver satisfactory performance for IV words. These two approaches behave differently due to the unit they use (word vs. character) and the model form they adopt (generative vs. discriminative). In general, character-based approaches are more robust than word-based ones, as the vocabulary of characters is a closed set; and discriminative models are more robust than generative ones, since they can flexibly include all kinds of available information, such as future context. This article first proposes a character-based n-gram model to enhance the robustness of the generative approach. Then the proposed generative model is further integrated with the character-based discriminative model to take advantage of both approaches. Our experiments show that this integrated approach outperforms all the existing approaches reported in the literature. Afterwards, a complete and detailed error analysis is conducted. Since a significant portion of the critical errors is related to numerical/foreign strings, character-type information is then incorporated into the model to further improve its performance. Last, the proposed integrated approach is tested on cross-domain corpora, and a semi-supervised domain adaptation algorithm is proposed and shown to be effective in our experiments.


title: Attachment Styles and Intimate Television Viewing: Insecurely Forming Relationships in a Parasocial Way

abstract: Attachment theory was investigated as an alternative inter-personal theory for understanding how audience members form parasocial relationships with television personalities. Attachment theory posits that people develop relationships in either a secure or insecure fashion. We explored whether attachment styles influenced the extent to which individuals engage in parasocial interaction. A total of 115 students completed the parasocial scale and two attachment style questionnaires. Results provided evidence that attachment styles are related to parasocial behavior: Anxious-ambivalents were the most likely to form parasocial bonds, Avoidants were the least likely to develop such relationships, and Secures were in the middle, with the more mistrusting Secures showing a tendency to engage in parasocial interaction. The discussion focuses on the implications of these findings for the attachment process.


title: Transfer learning and active transfer learning for reducing calibration data in single-trial classification of visually-evoked potentials

abstract: Single-trial Event-Related Potential (ERP) classification is a key requirement for several types of Brain-Computer Interaction (BCI) technologies. However, strong individual differences make it challenging to develop a generic single-trial ERP classifier that performs well for all subjects. Usually some subject-specific training samples need to be collected in an initial calibration session to customize the classifier. However, if implemented into an actual BCI system, then this calibration process would decrease the utility of the system, potentially decreasing its usability. In this paper we propose a Transfer Learning approach for reducing the amount of subject-specific data in online single-trial ERP classifier calibration, and an Active Transfer Learning approach for offline calibration. By applying these approaches to data from a Visually-Evoked Potential EEG experiment, we demonstrate that they improve the classification performance, given the same number of labeled subject-specific training samples. In other words, these approaches can also attain a desired level of classification accuracy with less labeling effort when compared to a randomly selected training set.


title: Leaf recognition for plant classification using GLCM and PCA methods

abstract: In this paper, the image processing techniques has been used in order to classify the plants by applying on the leaves images. To extract the leaves texture features, the Gray-Level Co-occurrence matrix (GLCM) and Principal Component Analysis (PCA) algorithms have been considered. The Algorithms are trained by 390 leaves to classify 13 kinds of plants with 65 new or deformed leaves images. The result indicates that the accuracy for the GLCM method is 78% while the accuracy for the PCA method is 98%.


title: Analysis and Enhancement of Random Number Generator in FPGA Based on Oscillator Rings

abstract: A true random number generator (TRNG) is an important component in cryptographic systems. Designing a fast and secure TRNG in an FPGA is a challenging task. In this paper we analyze the TRNG designed by Sunar et al. based on XOR of the outputs of many oscillator rings. We propose an enhanced TRNG that does not require post-processing to pass statistical tests and with better randomness characteristics on the output. We have shown by experiment that the frequencies of the equal length oscillator rings in the TRNG are not identical but different due to the placement of the inverters in the FPGA. We have implemented our proposed TRNG in an Altera Cyclone II FPGA. Our implementation has passed the NIST and DIEHARD statistical tests with a throughput of 100 Mbps and with a usage of less than 100 logic elements in the FPGA.


Detecting Architecture Erosion by Design Decision of Architectural Pattern.


title: An algorithm for the construction of intrinsic delaunay triangulations with applications to digital geometry processing

abstract: The discrete Laplace-Beltrami operator plays a prominent role in many Digital Geometry Processing applications ranging from denoising to parameterization, editing, and physical simulation. The standard discretization uses the cotangents of the angles in the immersed mesh which leads to a variety of numerical problems. We advocate use of the intrinsic Laplace-Beltrami operator. It satis- fies a local maximum principle, guaranteeing, e.g., that no flipped triangles can occur in parameterizations. It also leads to better conditioned linear systems. The intrinsic Laplace-Beltrami operator is based on an intrinsic Delaunay triangulation of the surface. We give an incremental algorithm to construct such triangulations together with an overlay structure which captures the relationship between the extrinsic and intrinsic triangulations. Using a variety of example meshes we demonstrate the numerical benefits of the intrinsic Laplace-Beltrami operator.


Sign Language Recognition and Translation with Kinect


title: Top-k Query Processing in Uncertain Databases

abstract: Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty makes traditional techniques inapplicable. We introduce new probabilistic formulations for top-k queries. Our formulations are based on "marriage" of traditional top-k semantics and possible worlds semantics. In the light of these formulations, we construct a framework that encapsulates a state space model and efficient query processing techniques to tackle the challenges of uncertain data settings. We prove that our techniques are optimal in terms of the number of accessed tuples and materialized search states. Our experiments show the efficiency of our techniques under different data distributions with orders of magnitude improvement over naive materialization of possible worlds.


title: Compression of Individual Sequences via Variable-Rate Coding

abstract: Compressibility of individual sequences by the class of generalized finite-state information-lossless encoders is investigated. These encoders can operate in a variable-rate mode as well as a fixed-rate one, and they allow for any finite-state scheme of variable-length-to-variable-length coding. For every individual infinite sequence x a quantity \rho(x) is defined, called the compressibility of x , which is shown to be the asymptotically attainable lower bound on the compression ratio that can be achieved for x by any finite-state encoder. This is demonstrated by means of a constructive coding theorem and its converse that, apart from their asymptotic significance, also provide useful performance criteria for finite and practical data-compression tasks. The proposed concept of compressibility is also shown to play a role analogous to that of entropy in classical information theory where one deals with probabilistic ensembles of sequences rather than with individual sequences. While the definition of \rho(x) allows a different machine for each different sequence to be compressed, the constructive coding theorem leads to a universal algorithm that is asymptotically optimal for all sequences.


title: Clinical Approach to Motor Stereotypies in Autistic Children

abstract: This is an overview of stereotypic behavior in autistic spectrum disorder (ASD). This repetitive, nonfunctional, fixed pattern of behavior is associated with autism severity but it is not specific for ASD. There are a wide range of behaviors mentioned as stereotypies. It usually starts in early childhood and its severity is associated with outcomes and severity of autism in adolescence and adulthood. It is usually co-morbid with other psychiatric problems and its pathophysiology is not exactly known. Management is most likely behavioral. There are some reports regarding efficacy of antipsychotics for its management. Further studies should be conducted to improve our knowledge about it and our ability to differentiate it from tics.


title: Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network

abstract: The accurate localization of facial landmarks is at the core of face analysis tasks, such as face recognition and facial expression analysis, to name a few. In this work we propose a novel localization approach based on a Deep Learning architecture that utilizes dual cascaded CNN subnetworks of the same length, where each subnetwork in a cascade refines the accuracy of its predecessor. The first set of cascaded subnetworks estimates heatmaps that encode the landmarks' locations, while the second set of cascaded subnetworks refines the heatmaps-based localization using regression, and also receives as input the output of the corresponding heatmap estimation subnetwork. The proposed scheme is experimentally shown to compare favorably with contemporary state-of-the-art schemes.


title: Contour Line Extraction from Paper-based Topographic Maps

abstract: Contour line is the main linear feature on topographic maps. Extraction of contour lines is tedious and time-consuming process, and is still an interesting problem. A novel method for extraction of contour lines from paper-based topographic maps is presented in this paper. In our approach, we firstly segment color topographic maps for achieving the binary image of brown color contour lines. Then, mathematic morphology method is used to filter the binary image. Next, Utilizing c-means algorithm to look for initial seed point on thinning contour lines. Fourthly, utilizing improved active contour model to extract non- thinning contour lines. Last, we have analyzed the directional field of contour lines near the gap, and then reconnected broken contour lines. The performance of the algorithm is tested on several topographic maps and comparing with other algorithms.


title: Collaborative filtering recommender systems

abstract: One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.


title: Strength Training for Children and Adolescents What Can Physicians Recommend?

abstract: Primary care physicians who see young patients are often asked about strength training programs for children. Some parents are seeking ways to give their child a competitive edge in sports. Many parents of overweight children seek guidance about which activities are effective for weight loss. Musculoskeletal injuries and epiphyseal fractures are also a concern. Informed clinicians can reassure parents that, with adult supervision, proper equipment, and realistic expectations, strength training programs designed for children and adolescents are safe and effective.


title: High efficiency classification of children with autism spectrum disorder

abstract: Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD is diagnosed mainly with psychometric tools, often unable to provide an early and reliable diagnosis. Recently, biochemical methods are being explored as a means to meet the latter need. For example, an increased predisposition to ASD has been associated with abnormalities of metabolites in folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS). Multiple metabolites in the FOCM/TS pathways have been measured, and statistical analysis tools employed to identify certain metabolites that are closely related to ASD. The prime difficulty in such biochemical studies comes from (i) inefficient determination of which metabolites are most important and (ii) understanding how these metabolites are collectively related to ASD. This paper presents a new method based on scores produced in Support Vector Machine (SVM) modeling combined with High Dimensional Model Representation (HDMR) sensitivity analysis. The new method effectively and efficiently identifies the key causative metabolites in FOCM/TS pathways, ranks their importance, and discovers their independent and correlative action patterns upon ASD. Such information is valuable not only for providing a foundation for a pathological interpretation but also for potentially providing an early, reliable diagnosis ideally leading to a subsequent comprehensive treatment of ASD. With only tens of SVM model runs, the new method can identify the combinations of the most important metabolites in the FOCM/TS pathways that lead to ASD. Previous efforts to find these metabolites required hundreds of thousands of model runs with the same data.


title: Cross-layer optimization for routing data traffic in UWB-based sensor networks

abstract: Ultra-wideband (UWB) has great potential for wireless communications in emerging applications such as sensor networks. This paper considers UWB-based sensor networks and studies the following problem: given a set of source sensor nodes in the network each generating a certain data rate, is it possible to relay all these rates successfully to the base-station? We follow a cross-layer optimization approach, with joint consideration of link layer scheduling, power control, and network layer routing. The optimization problem is formulated as a non-linear programming problem. For small-sized networks, we develop a powerful approximation solution procedure to this problem based on the branch-and-bound approach and the novel Reformulation-Linearization Technique (RLT). For large-sized networks, we propose an efficient heuristic algorithm by partitioning the sensor network into a core centered around the base-station and an edge that is outside the core. We also provide a closed-form analysis for the maximum rate that a base-station can receive. Simulation results exhibit the efficacy of our proposed optimization solution procedure and demonstrate the importance of the cross-layer approach to UWB-based sensor networks.


title: Toward a merged approach for cross-platform applications (web, mobile and desktop)

abstract: Several approaches are available to create cross-platform applications. The majority of these approaches focus on purely mobile platforms. Their principle is to develop the application once and be able to deploy it to multiple mobile platforms with different operating systems (Android (Java), IOS (Objective C), Windows Phone 7 (C#), etc.). In this article, we propose a merged approach and cross-platform called ZCA "ZeroCouplage Approach". Merged to regroup the strong points of approaches: "Runtime", "Component-Based" and "Cloud-Based" thank to a design pattern which we created and named M2VC (Model-Virtual-View-Controller). Cross-platform allows creating a unique application that is deployable directly on many platforms: Web, Mobile and Desktop. In this article, we also compare our ZCA approach with others to approve its added value. Our idea, contrary to mobile approaches, consists of a given technology to implement cross-platform applications. To validate our approach, we have developed an open source framework named ZCF "ZeroCouplage Framework" for Java technology.


title: BabelSenticNet: A Commonsense Reasoning Framework for Multilingual Sentiment Analysis

abstract: SenticNet is a concept-level knowledge base used to develop commonsense reasoning algorithms for sentiment analysis tasks. One of the challenges that this resource must overcome is its lack of availability for languages aside from English. Prototype algorithms have been recently proposed to create non-English language concept-level knowledge databases, but they rely on a number of heterogeneous resources that complicate comparison, reproducibility and maintenance. This paper proposes an easy and replicable method to automatically generate SenticNet for a variety of languages, obtaining as a result BabelSenticNet. We use statistical machine translation tools to create a high coverage SenticNet version for the target language. We then introduce an algorithm to increase the robustness of the translated resources, relying on a mapping technique, based on WordNet and its multilingual versions. SenticNet versions for 40 languages have been made available. Human-based evaluation on languages belonging to different families, alphabets and cultures proves the robustness of the method and its potential for utility in future research on multilingual concept-level sentiment analysis.


Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams


title: Vc: A C++ library for explicit vectorization

abstract: It is an established trend that CPU development takes advantage of Moore's Law to improve in parallelism much more than in scalar execution speed. This results in higher hardware thread counts (MIMD) and improved vector units (SIMD), of which the MIMD developments have received the focus of library research and development in recent years. To make use of the latest hardware improvements, SIMD must receive a stronger focus of API research and development because the computational power can no longer be neglected and often auto-vectorizing compilers cannot generate the necessary SIMD code, as will be shown in this paper. Nowadays, the SIMD capabilities are sufficiently significant to warrant vectorization of algorithms requiring more conditional execution than was originally expected for Streaming SIMD Extension to handle. The Vc library (http://compeng.uni-frankfurt.de/?vc) was designed to support developers in the creation of portable vectorized code. Its capabilities and performance have been thoroughly tested. Vc provides portability of the source code, allowing full utilization of the hardware's SIMD capabilities, without introducing any overhead. Copyright © 2011 John Wiley & Sons, Ltd.


title: Comparative study of 2.4 GHz and 10 GHz vital signal sensing Doppler radars

abstract: Performance and implementation complexity between 2.4 GHz radar system with homodyne and 10 GHz radar system with super-heterodyne are compared and discussed. In particular, recently proposed architectures are examined, which are implemented by identical PLL and VCO. Both the operation of two different architectures and the superior performance of these radar systems can be understood by observing what level of signal is received based on same signal conditions. 10 GHz approach yields better performance in terms of RCS and antenna gain, but at the cost of increased implementation complexity.


title: Connection setup of openvibe tool with EEG headset, parsing and processing of EEG signals

abstract: Bio-signal, a signal generated by body can be used as an information source to drive and control many devices. Various such signals are as: Electroencephalogram (EEG) generated by brain, Electrocardiogram (ECG) generated by heart, Electromyogram generated by muscles and Electrooculogram generated by eyes. Here, EEG signal measured along the scalp and generated by neurons of brain has been under focus. EEG signals are highly random in nature and has unique pattern for every individual. Thus, to record and analyze EEG signal demand rigorous analysis. Many equipment for recording and software tools for analysis are available in market. Here, Neurosky B3 band Headset is used for recording and OpenVibe-a software tool for Brain computer Interface (BCI) is used for analysis. Primarily, connecting headset with the software and loading the recorded values into software need some steps to be followed which is discussed herewith. OpenVibe tool has mainly acquisition server that allows connection with external peripheral and Designer that has various processing modules. It deals with modular approach for online and offline processing of EEG signals. Secondarily, basic processing boxes of OpenVibe and the method to set its parameters are discussed. Learning to parse and process signals into Openvibe from Brain sensors allow researchers to work in various areas of BCI.


title: Push Recovery by stepping for humanoid robots with force controlled joints

abstract: In order to interact with human environments, humanoid robots require safe and compliant control which can be achieved through force-controlled joints. In this paper, full body step recovery control for robots with force-controlled joints is achieved by adding model-based feed-forward controls. Push Recovery Model Predictive Control (PR-MPC) is presented as a method for generating full-body step recovery motions after a large disturbance. Results are presented from experiments on the Sarcos Primus humanoid robot that uses hydraulic actuators instrumented with force feedback control.


title: Applying Sensor-Based Technology to Improve Construction Safety Management

abstract: Construction sites are dynamic and complicated systems. The movement and interaction of people, goods and energy make construction safety management extremely difficult. Due to the ever-increasing amount of information, traditional construction safety management has operated under difficult circumstances. As an effective way to collect, identify and process information, sensor-based technology is deemed to provide new generation of methods for advancing construction safety management. It makes the real-time construction safety management with high efficiency and accuracy a reality and provides a solid foundation for facilitating its modernization, and informatization. Nowadays, various sensor-based technologies have been adopted for construction safety management, including locating sensor-based technology, vision-based sensing and wireless sensor networks. This paper provides a systematic and comprehensive review of previous studies in this field to acknowledge useful findings, identify the research gaps and point out future research directions.


title: Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning approach on a cell phone

abstract: To date, cardiovascular disease (CVD) is the leading cause of global death. The Electrocardiogram (ECG) is the most widely adopted clinical tool that measures the electrical activities of the heart from the body surface. However, heart rhythm irregularities cannot always be detected on a standard resting ECG machine, since they may not occur during an individual’s recording session. Common Holter-based portable solutions that record ECG for up to 24 to 48 hours lack the capability to provide real-time feedback. In this research, we seek to establish a cell phone-based real-time monitoring technology for CVD, capable of performing continuous on-line ECG processing, generating a personalized cardiac health summary report in layman’s language, automatically detecting and classifying abnormal CVD conditions, all in real time. Specifically, we developed an adaptive artificial neural network (ANN)-based machine learning technique, combining both an individual’s cardiac characteristics and information from clinical ECG databases, to train the cell phone to learn to adapt to its user’s physiological conditions to achieve better ECG feature extraction and more accurate CVD classification on cell phones.


title: Getting Even for Customer Mistreatment : The Role of Moral Identity in the Relationship Between Customer Interpersonal Injustice and Employee Sabotage

abstract: Research on the “dark side” of organizational behavior has determined that employee sabotage is most often a reaction by disgruntled employees to perceived mistreatment. To date, however, most studies on employee retaliation have focused on intra-organizational sources of (in)justice. Results from this field study of customer service representatives (N � 358) showed that interpersonal injustice from customers relates positively to customer-directed sabotage over and above intra-organizational sources of fairness. Moreover, the association between unjust treatment and sabotage was moderated by 2 dimensions of moral identity (symbolization and internalization) in the form of a 3-way interaction. The relationship between injustice and sabotage was more pronounced for employees high (vs. low) in symbolization, but this moderation effect was weaker among employees who were high (vs. low) in internalization. Last, employee sabotage was negatively related to job performance ratings.


title: Nasal prosthesis rehabilitation using CAD-CAM technology after total rhinectomy: a pilot study

abstract: PurposeThis study describes a nose prosthetic rehabilitation using computer-aided design and manufacturing (CAD-CAM) technology after facial disfigurement because of a total rhinectomy.MethodsA patient with a total rhinectomy was scheduled for a nasal prosthesis. Based on the 3-D model of the patient’s face reconstructed with the CT data, a four-piece mould for the nasal prosthesis was prototyped using a CAD-CAM procedure. Conventional silicone was processed with this physical mould to fabricate the definitive nasal prosthesis.ResultsA silicone nasal prosthesis was manufactured. The size, shape, and cosmetic look of the prosthesis were satisfactory and matched the nasal defect area well.ConclusionsThe protocol presented herein illustrates favorable clinical treatment outcomes in the prosthetic rehabilitation after a total rhinectomy by means of CAD-CAM technology.


title: Contemporary Evolution Strategies

abstract: This book surveys key algorithm developments between 1990 and 2012, with brief descriptions, a unified pseudocode for each algorithm and downloadable program code. Provides a taxonomy to clarify similarities and differences as well as historical relationships.


title: SASI: a generic texture descriptor for image retrieval

abstract: In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity.


title: TMSUI: A Trust Management Scheme of USB Storage Devices for Industrial Control Systems

abstract: The security of sensitive data and the safety of control signal are two core issues in industrial control system (ICS). However, the prevalence of USB storage devices brings a great challenge on protecting ICS in those respects. Unfortunately, there is currently no solution specially for ICS to provide a complete defense against communication between untrusted USB storage devices and critical equipment without forbidding normal USB device function. This paper proposes a trust management scheme of USB storage devices for ICS (TMSUI). By fully considering application scenarios, TMSUI is designed based on security chip to flexibly achieve authorizing a certain USB storage device to only access some exact protected terminals in ICS for a particular period of time. The scheme enables administrators to revoke authorized devices. We analyze six security properties of TMSUI. The prototype system is finally implemented. The evaluation results indicate that our scheme meets the security goals with high compatibility and good efficiency.


title: Bringing semantic structures to user intent detection in online medical queries

abstract: The Internet has revolutionized healthcare by offering medical information ubiquitously to patients via web search. The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries. Aiming to better capture a focused picture of user's medical-related information search and shed insights on their healthcare information access strategies, it is challenging yet rewarding to detect structured user intentions from their diversely expressed medical text queries. We introduce a graph-based formulation to explore structured concept transitions for effective user intent detection in medical queries, where each node represents a medical concept mention and each directed edge indicates a medical concept transition. A deep model based on multi-task learning is introduced to extract structured semantic transitions from user queries, where the model extracts word-level medical concept mentions as well as sentence-level concept transitions collectively. A customized graph-based mutual transfer loss function is designed to impose explicit constraints and further exploit the contribution of mentioning a medical concept word to the implication of a semantic transition. We observe an 8% relative improvement in AUC and 23% relative reduction in coverage error by comparing the proposed model with the best baseline model for the concept transition inference task on real-world medical text queries.


Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems


title: Segmentation and Recognition using Structure from Motion Point Clouds

abstract: We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our main contribution is to show how semantic segmentation is possible based solely on motion-derived 3D world structure. Our method works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors. ::: ::: Experiments were performed on a challenging new video database containing sequences filmed from a moving car in daylight and at dusk. The results confirm that indeed, accurate segmentation and recognition are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance.


title: On Measuring the Accuracy of SLAM Algorithms

abstract: In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. ::: ::: We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.


title: Using Address Independent Seed Encryption and Bonsai Merkle Trees to Make Secure Processors OS- and Performance-Friendly

abstract: In today's digital world, computer security issues have become increasingly important. In particular, researchers have proposed designs for secure processors which utilize hardware-based mem- ory encryption and integrity verification to protect the privacy and integrity of computation even from sophisticated physical attacks. However, currently proposed schemes remain hampered by prob- lems that make them impractical for use in today's computer sys- tems: lack of virtual memory and Inter-Process Communication support as well as excessive storage and performance overheads. In this paper, we propose 1) Address Independent Seed Encryption (AISE), a counter-mode based memory encryption scheme using a novel seed composition, and 2) Bonsai Merkle Trees (BMT), a novel Merkle Tree-based memory integrity verification technique, to elim- inate these system and performance issues associated with prior counter-mode memory encryption and Merkle Tree integrity veri- fication schemes. We present both a qualitative discussion and a quantitative analysis to illustrate the advantages of our techniques over previously proposed approaches in terms of complexity, feasi- bility, performance, and storage. Our results show that AISE+BMT reduces the overhead of prior memory encryption and integrity ver- ification schemes from 12% to 2% on average, while eliminating critical system-level problems.


title: Real-Time Full-Body Motion Capture from Video and IMUs

abstract: A real-time full-body motion capture system is presented which uses input from a sparse set of inertial measurement units (IMUs) along with images from two or more standard video cameras and requires no optical markers or specialized infra-red cameras. A real-time optimization-based framework is proposed which incorporates constraints from the IMUs, cameras and a prior pose model. The combination of video and IMU data allows the full 6-DOF motion to be recovered including axial rotation of limbs and drift-free global position. The approach was tested using both indoor and outdoor captured data. The results demonstrate the effectiveness of the approach for tracking a wide range of human motion in real time in unconstrained indoor/outdoor scenes.


title: Hop-count filtering: an effective defense against spoofed DDoS traffic

abstract: IP spoofing has been exploited by Distributed Denial of Service (DDoS) attacks to (1) conceal flooding sources and localities in flooding traffic, and (2) coax legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victims is essential to their own protection as well as to their avoidance of becoming involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he or she cannot falsify the number of hops an IP packet takes to reach its destination. This hop-count information can be inferred from the Time-to-Live (TTL) value in the IP header. Using a mapping between IP addresses and their hop-counts to an Internet server, the server can distinguish spoofed IP packets from legitimate ones. Base on this observation, we present a novel filtering technique that is immediately deployable to weed out spoofed IP packets. Through analysis using network measurement data, we show that Hop-Count Filtering (HCF) can identify close to 90% of spoofed IP packets, and then discard them with little collateral damage. We implement and evaluate HCF in the Linux kernel, demonstrating its benefits using experimental measurements.


title: A view of the parallel computing landscape

abstract: Writing programs that scale with increasing numbers of cores should be as easy as writing programs for sequential computers.


title: PatchMatch: a randomized correspondence algorithm for structural image editing

abstract: This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance. This one simple algorithm forms the basis for a variety of tools -- image retargeting, completion and reshuffling -- that can be used together in the context of a high-level image editing application. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods.


title: Embodying Emotion

abstract: Recent theories of embodied cognition suggest new ways to look at how we process emotional information. The theories suggest that perceiving and thinking about emotion involve perceptual, somatovisceral, and motoric reexperiencing (collectively referred to as "embodiment") of the relevant emotion in one's self. The embodiment of emotion, when induced in human participants by manipulations of facial expression and posture in the laboratory, causally affects how emotional information is processed. Congruence between the recipient's bodily expression of emotion and the sender's emotional tone of language, for instance, facilitates comprehension of the communication, whereas incongruence can impair comprehension. Taken all together, recent findings provide a scientific account of the familiar contention that "when you're smiling, the whole world smiles with you."


title: Reject Brines from Desalination as Possible Sources for Environmental Technologies

abstract: Desalination of seawater represents the way to increase the supply of water for domestic, agricultural and industrial purposes. The potential environmental impact of desalination needs to be thoroughly evaluated, and any environmentally adverse consequences must be promptly addressed in order to assure a sustainable development of this technology. The main concern in desalination is the management of the brines whose uncontrolled discharge has significant negative impacts on the environment. Indeed, even if the production costs have decreased in the last decade, those related to the disposal of the brine have shown only limited reduction of the relative costs. New production strategies benefit from modern and efficient freshwater-generation plants, but the management strategies are based only on the few traditional options for the disposal of wastes. Sustainability is currently a main focus of high-level political discussions in the world, as it directly involves environmental pollution and global warming. Economic geologists are very well placed to understand and contribute to issues of sustainability, and to evaluate alternative resources for the future development of humanity. We, as economic geologists, are trained to unravel the complex series of events that led to the formation of natural resources. Consequently, we can use this expertise to help develop manmade processes to convert waste into resources. Today, the challenge is to consider the brine, instead of a harmful by-product, as a valuable source for several compounds to be involved in diverse industrial processes. For example, the brines could provide significant amounts of magnesium, resulting in a very promising source of this element as an alternative to traditional mining. Indeed, today there are stringent regulations concerning mining in all most all countries to prevent or at least minimize damage to exploited properties. As traditional approaches in mining are costly in time or inapplicable in abandoned and remote regions, research needs to be focused on nontraditional sources such as brines coming from desalination plants. These brines certainly have several advantages, and in time can replace, at least in part, the conventional sources with an alternative, economically attractive process. Dual benefits can be achieved in recovering and recycling Mg from reject brines: reduction of large volumes of brines to be


title: Batch zero-knowledge proof and verification and its applications

abstract: The batch verification technique of Bellare et al. is extended to verification of several frequently employed zero-knowledge proofs. The new techniques are correct, sound, efficient, and can be widely applied. Specific applications are discussed in detail, including batch ZK proof and verification of validity of encryption (or reencryption) and batch ZK proof and verification of validity of decryption. Considerable efficiency improvements are gained in these two applications without compromising security. As a result, efficiency of the practical cryptographic systems (such as mix networks) based on these two applications is dramatically improved.


title: Novel approach to nonlinear/non-Gaussian Bayesian state estimation

abstract: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.


title: The Effects of E-Government on Trust and Confidence in Government

abstract: Arguments that e-government may improve citizen trust in government have not been sufficiently tested. We are interested in exploring the potential for e-government to influence citizen attitudes about government, across various population groups, including those with limited technology access and skill. This paper surveys literature relevant to e-government and its effect on civic trust, and summarizes our previous research on citizen attitudes on e-government. We propose further research using an Internet-based experiment that will expose a random sample of respondents to government web sites that are chosen to represent best practices for different types of sites. Surveys administered before and after exposure to the sites will measure any significant changes in attitudes about government in general, as well as perceptions of e-government. Oversampling of low-income and minority respondents will allow us to explore any differential impact across demographic groups.


title: Efficient 3D Hand Tracking in Articulation Subspaces for the Manipulation of Virtual Objects

abstract: We propose an efficient method for model-based 3D tracking of hand articulations observed from an egocentric viewpoint that aims at supporting the manipulation of virtual objects. Previous model-based approaches optimize non-convex objective functions defined in the 26 Degrees of Freedom (DoFs) space of possible hand articulations. In our work, we decompose this space into six articulation subspaces (6 DoFs for the palm and 4 DoFs for each finger). We also label each finger with a Gaussian model that is propagated between successive image frames. As confirmed by a number of experiments, this divide-and-conquer approach tracks hand articulations more accurately than existing model-based approaches. At the same time, real time performance is achieved without the need of GPGPU processing. Additional experiments show that the proposed approach is preferable for supporting the accurate manipulation of virtual objects in VR/AR scenarios.


title: Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer

abstract: Summary Durable responses and encouraging survival have been demonstrated with immune checkpoint inhibitors in small-cell lung cancer (SCLC), but predictive markers are unknown. We used whole exome sequencing to evaluate the impact of tumor mutational burden on efficacy of nivolumab monotherapy or combined with ipilimumab in patients with SCLC from the nonrandomized or randomized cohorts of CheckMate 032. Patients received nivolumab (3 mg/kg every 2 weeks) or nivolumab plus ipilimumab (1 mg/kg plus 3 mg/kg every 3 weeks for four cycles, followed by nivolumab 3 mg/kg every 2 weeks). Efficacy of nivolumab ± ipilimumab was enhanced in patients with high tumor mutational burden. Nivolumab plus ipilimumab appeared to provide a greater clinical benefit than nivolumab monotherapy in the high tumor mutational burden tertile.


title: PUBLIC-KEY STEGANOGRAPHY BASED ON MODIFIED LSB METHOD

abstract: Steganography is the art and science of invisible communication. It is a technique which keeps the existence of the message secret. This paper proposed a technique to implement steganogaraphy and cryptography together to hide the data into an image. This technique describes as: Find the shared stego-key between the two communication parties by applying Diffie-Hellman Key exchange protocol, then encrypt the data using secret stego-key and then select the pixels by encryption process with the help of same secret stego- key to hide the data. Each selected pixel will be used to hide 8 bits of data by using LSB method.


title: The Prosumer: The Key Player of the Museum of the Future

abstract: This paper analyses the role of the prosumer within the cultural sector and why prosumers should be identified as key players for the museum of the future. Social platforms such as Tumblr and YouTube are presented as important tools for museums to build new communities and engage their audiences in a meaningful conversation while preserving our heritage. Fruitful examples of digital initiatives in the UK and the US such as The Space (UK) and the MoMA Teens (US) are addressed to demonstrate that heritage institutions should be focusing on digital design and production as much as enabling users to become producers. The contemporary debates on such current trends as 'the curator phenomenon' and 'Post-Internet Art' are analysed here to show how digital natives (those born during the age of digital technology) are the prosumers museums need to attract. This audience, used to self-expression via such devices as mobile phones and tablets, cannot be ignored. We should also expect to see an increasing number of artists using the internet as their medium in the future. These are not passing trends, as some might argue, but a way of life for the younger generation and its successors; this paper, therefore, builds a case that supports the integration of the prosumer with the cultural sector.


title: An API for Runtime Code Patching

abstract: The authors present a postcompiler program manipulation tool called Dyninst, which provides a C++ class library for program instrumentation. Using this library, it is possible to instrument and modify application programs during execution. A unique feature of this library is that it permits machine-independent binary instrumentation programs to be written. The authors describe the interface that a tool sees when using this library. They also discuss three simple tools built using this interface: a utility to count the number of times a function is called, a program to capture the output of an already running program to a file, and an implementation of conditional breakpoints. For the conditional breakpoint example, the authors show that by using their interface compared with gdb, they are able to execute a program with conditional breakpoints up to 900 times faster.


title: Genetic diversity and evolution of hepatitis C virus – 15 years on

abstract: In the 15 years since the discovery of hepatitis C virus (HCV), much has been learned about its role as a major causative agent of human liver disease and its ability to persist in the face of host-cell defences and the immune system. This review describes what is known about the diversity of HCV, the current classification of HCV genotypes within the family Flaviviridae and how this genetic diversity contributes to its pathogenesis. On one hand, diversification of HCV has been constrained by its intimate adaptation to its host. Despite the >30 % nucleotide sequence divergence between genotypes, HCV variants nevertheless remain remarkably similar in their transmission dynamics, persistence and disease development. Nowhere is this more evident than in the evolutionary conservation of numerous evasion methods to counteract the cell's innate antiviral defence pathways; this series of highly complex virus-host interactions may represent key components in establishing its 'ecological niche' in the human liver. On the other hand, the mutability and large population size of HCV enables it to respond very rapidly to new selection pressures, manifested by immune-driven changes in T- and B-cell epitopes that are encountered on transmission between individuals with different antigen-recognition repertoires. If human immunodeficiency virus type 1 is a precedent, future therapies that target virus protease or polymerase enzymes may also select very rapidly for antiviral-resistant mutants. These contrasting aspects of conservatism and adaptability provide a fascinating paradigm in which to explore the complex selection pressures that underlie the evolution of HCV and other persistent viruses.


title: Fast Hypervisor Recovery Without Reboot

abstract: System recovery latency is decreased by using microreboot to reboot only the failed component instead of the entire system. For large, complex components, such as hypervisors, even the latency of microreboot is unacceptably high in important deployment scenarios. We investigate an alternative component-level recovery mechanism, which we call microreset, that can achieve dramatically lower recovery latency for some such components. Instead of component reboot, microreset quickly resets the component to a quiescent state that is highly likely to be valid and where the component is ready to handle new or retried interactions with the rest of the system. We present a recovery mechanism for the Xen hypervisor, called NiLiHype, based on microreset. We show that, compared to microreboot-based hypervisor recovery, NiLiHype achieves nearly the same recovery success rate but with a recovery latency that is shorter by a factor of over 30.


title: Although unseen, chronic pain is real–A phenomenological study

abstract: a b s t r a c t Background: Research has emphasised the essential role of psychosocial risk factors in chronic pain. In practice, pain is usually verified by identifying its physical cause. In patients without any distinct pathology, pain is easily defined as imaginary pain. The aim of this qualitative study was to explore the


title: Peer effects in adolescent overweight

abstract: This study is the first to estimate peer effects for adolescent weight. We use data from the National Longitudinal Study of Adolescent Health (Add Health) and define peer groups using nominated friends within schools. Endogenous peer groups are accounted for using a combination of school fixed effects, instrumental variables, and alternative definitions of peers (i.e., grade-level peers). Mean peer weight is correlated with adolescent weight, even after controlling endogenous peer groups. The impact of peer weight is larger among females and adolescents with high body mass index. The results are consistent with social multipliers for adolescent overweight policies.


title: Street lighting control system based on large-scale WSN: A step towards a smart city

abstract: This paper presents the field testing of a street lighting monitoring and control system. The system is based on a WSN network of the large-scale type that enables the remote control of street lighting lamps. The system also enables savings in terms of the electric power and maintenance costs. The architecture uses integrated Doppler sensors that allows for vehicle detection and help complete the power efficiency objective. Thus, when a vehicle is detected the light intensity of the lamps is increased to a preset level, so as not to affect road traffic safety, and reduced in the opposite case. Moreover, the system uses current sensors so as to allow for the identification of any possible malfunctions and thus facilitate the maintenance process. According to the obtained results, the system allows for an increased performance level and can be integrated in the Smart City concept.


title: Future affective technology for autism and emotion communication

abstract: People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with challenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come 'out of the blue', but in fact may have been steadily building. Because ANS activation both influences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research.


title: A Dynamic-Adversarial Mining Approach to the Security of Machine Learning

abstract: Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers - a) harder to evade, b) easier to detect changes in the data distribution over time, and c) be able to retrain and recover from model degradation. While most works in the security of machine learning has concentrated on the evasion resistance (a) problem, there is little work in the areas of reacting to attacks (b and c). Additionally, while streaming data research concentrates on the ability to react to changes to the data distribution, they often take an adversarial agnostic view of the security problem. This makes them vulnerable to adversarial activity, which is aimed towards evading the concept drift detection mechanism itself. In this paper, we analyze the security of machine learning, from a dynamic and adversarial aware perspective. The existing techniques of Restrictive one class classifier models, Complex learning models and Randomization based ensembles, are shown to be myopic as they approach security as a static task. These methodologies are ill suited for a dynamic environment, as they leak excessive information to an adversary, who can subsequently launch attacks which are indistinguishable from the benign data. Based on empirical vulnerability analysis against a sophisticated adversary, a novel feature importance hiding approach for classifier design, is proposed. The proposed design ensures that future attacks on classifiers can be detected and recovered from. The proposed work presents motivation, by serving as a blueprint, for future work in the area of Dynamic-Adversarial mining, which combines lessons learned from Streaming data mining, Adversarial learning and Cybersecurity.


title: Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA

abstract: Admixture with other hominin species helped humans to adapt to high-altitude environments; the EPAS1 gene in Tibetan individuals has an unusual haplotype structure that probably resulted from introgression of DNA from Denisovan or Denisovan-related individuals into humans, and this haplotype is only found in Denisovans and Tibetans, and at low frequency among Han Chinese.


title: Influence of the Antennas on the Ultra-Wideband Transmission

abstract: Spectrum is presently one of the most valuable goods worldwide as the demand is permanently increasing and it can be traded only locally. Since the United States Federal Communications Commission (FCC) has opened the spectrum from 3.1 GHz to 10.6GHz, that is, a bandwidth of 7.5GHz, for unlicensed use with up to -41.25dBm/MHz EIRP, numerous applications in communications and sensor areas are showing up. Like all wireless devices, these have an antenna as an integral part of the air interface. The antennas are modeled as linear time-invariant (LTI) systems with a transfer function. The measurement of the antenna's frequency-dependent directional transfer function is described. Quality measures for the antennas like the peak value of the transient response, its width and ringing, as well as the transient gain are discussed. The application of these quality measures is shown for measurements of different UWB antennas.


title: Fostering Creativity in Tablet-Based Interactive Classrooms

abstract: Introduction With the advent of information and communication technology, educators have become increasingly interested in adapting interactive and mobile technologies for classroom curricula and learning activities (Enriquez, 2010; Ifenthaler & Schweinbenz, 2013; Koile & Singer, 2008; Loveless, 2002). Interactive technologies, including smartphones, tablets, interactive whiteboards, and classroom response systems, have generated active discussions that focus on students' learning performance and abilities (Gikas & Grant, 2013). Recently, tablet computers have caught educators' attention because their versatility and mobility may afford students active and collaborative experiences. A tablet's features and capabilities allow students to access course content and to interact with their teachers and classmates in new ways (Gikas & Grant, 2013). For example, students in a mathematics class can transmit their solutions through their individual tablets to an electronic board to review the answers with their classmates and teachers. Additionally, teachers can interact more with individual students during a limited class period, adapt activities quickly in response to pupils' responses, and use students' errors and misconceptions as a teaching moment for the entire class (Muijs & Reynolds, 2011). Recently, the education field has begun actively researching ways of leveraging technology-supported interactive environments to foster students' creative experiences during classroom activities (Koile & Singer, 2006; Koile & Singer, 2008). Students can brainstorm ideas together, develop various alternatives to solve problems, share ideas and work, and develop solutions using various technological tools, such as 3D printers, painting tools, cloud-based notebooks, composition tools, and interactive management solutions for tablets and electronic boards. In particular, the relationships between the distinctive features of interactive technologies (tablet computers) and the characteristics of creativity open up new approaches and strategies for cultivating creativity in education. Tablet-based interactive classroom technologies have the potential to generate new ways of fostering creative experiences through drawing, seeing, expressing, learning, and engaging with instructional situations. These innovative technologies help teachers offer new ways for students to practice drawing skills as a creative activity to increase their creative understanding (Ainsworth, Prain, & Tytler, 2011). Thus, in the present study, the researchers utilized an instructional model designed to foster creativity. The model allows students to develop their own drawings and interact with others in a high-tech, interactive classroom. This model seeks to help students more creatively comprehend and express what they have learned. The current study aims to examine the effects and benefits of a model entitled "Visual Thinking through Tablet-based Classroom Interaction" (VTTCI) in terms of students' creativity after one semester of use. We further seek to better understand the effects of the model on students' creativity by examining the creativity subscales of the Torrance Tests of Creative Thinking-Figural (TTCT-Figural, Torrance, 1990; Torrance, 1998) and by evaluating the differences between the treatment and control groups. This article describes the process that we follow in our evaluation of the model and the outcomes. We begin by discussing the general benefits and shortcomings of drawing as a creative activity in a tablet-based classroom interaction environment. Then, we identify the pedagogical attributes of drawing activities in classrooms that are supported by a high-tech, interactive learning environment. Next, we introduce the instructional model utilized in the study. Finally, we reveal the findings. Literature review Value of learner-generated drawings Drawing activities are known to promote students' creative imagination, i. …


title: Automated Assessment of Non-Native Learner Essays: Investigating the Role of Linguistic Features

abstract: Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question ”what are the most predictive features?” has a different answer for each dataset.


title: Visual Cryptography for Image Processing and Security: Theory, Methods, and Applications

abstract: This unique book describes the fundamental concepts, theories and practice of visual cryptography. The design, construction, analysis, and application of visual cryptography schemes (VCSs) are discussed in detail. Original, cutting-edge research is presented on probabilistic, size invariant, threshold, concolorous, and cheating immune VCS. Features: provides a thorough introduction to the field; examines various common problems in visual cryptography, including the alignment, flipping, cheating, distortion, and thin line problems; reviews a range of VCSs, including XOR-based visual cryptography and security enriched VCS; describes different methods for presenting color content using visual cryptographic techniques; covers such applications of visual cryptography as watermarking, resolution variant VCS, and multiple resolution VCS. This logically-structured and comprehensive work will serve as a helpful reference for all researchers and students interested in document authentication and cryptography.


title: Controlled release of alendronate from nitrogen-doped mesoporous carbon

abstract: Abstract We have synthesized a nitrogen doped mesoporous carbon with the BET surface area of 1066 m 2 /g, total pore volume 0.6 cm 3 /g and nitrogen content of 0.5%. Total alendronate adsorption in this carbon was ∼5%. The release experiments were designed in four different media with sequential pH values of 1.2, 4.5, 6.8 and 7.4 for 3, 1, 3 and 5 h, respectively and at 37 °C to imitate the physiological conditions of stomach, duodenum, small intestine and colon, respectively. Release of the drug demonstrated a controlled fashion; only 20% of the drug was released in the media with pH = 1.2, whereas 64% of the drug was released in pH = 7.4. This is in contrary to pure alendronate that was completely dissolved within 30 min in the first release media (pH = 1.2) only. The relatively larger uptake of alendronate in this carbon and its sustained fashion of release can be attributed to the hydrogen bonding between the drug and the nitrogen functionalities on carbon surface. Based on this result, it can be inferred that this formulation may lower the side effects of oral delivery of alendronate.


title: Prevalence of Insomnia and Its Psychosocial Correlates Among College Students in Hong Kong

abstract: Abstract Objective: This study examined the prevalence of insomnia and its psychosocial correlates among college students in Hong Kong. Participants: A total of 529 Hong Kong college students participated in the study. Methods: Participants completed a self-reported questionnaire that included the Pittsburgh Sleep Quality Index (PSQI), the Revised Life Orientation Test, the Perceived Stress Scale, and the Beck Depression Inventory, and questions about sociodemographic and lifestyle factors. A PSQI global score equal to or greater than 5 indicated insomnia. Results: Results indicated that 68.6% of the participants were insomniacs. Adjusted regression analyses revealed that optimism, stress, and depression significantly predicted insomnia (p < .01). Conclusions: These results suggest that insomnia is very common among Hong Kong college students and associated with other psychosocial factors. This study provides preliminary data on sleep quality and risk factors for insomnia, which may be used to guide sleep...


title: iKnow how you walk — A smartphone based personalized gait diagnosing system

abstract: Humans, due to aging and hormonal changes are prone to pains in their limbs. As a result of which, the fundamental activity of humans i.e., movement pattern of limbs also known as gait is affected. By exerting unequal weight on both limbs in-order to avoid pain in one leg, humans slowly develop an abnormal gait pattern consisting of limping and sideways bend in the posture. This often goes unnoticed for a long time. We propose a system using smartphones that can sense and detect abnormal walking patterns. The sensing of limb movement is performed by an embedded accelerometer in a smartphone and detection of abnormal walk pattern is performed by classifying different features such as stride length, walk velocity etc. By incorporating Naive Bayes and Decision trees classifiers, we obtained close to 89% accuracies in classifying different levels of abnormalaties. Further validation was done by implementing a decision tree based gait variation detector smartphone application across five users which resulted in 90% accuracy.


title: A multi-agent reinforcement learning model of common-pool resource appropriation

abstract: Humanity faces numerous problems of common-pool resource appropriation. This class of multi-agent social dilemma includes the problems of ensuring sustainable use of fresh water, common fisheries, grazing pastures, and irrigation systems. Abstract models of common-pool resource appropriation based on non-cooperative game theory predict that self-interested agents will generally fail to find socially positive equilibria---a phenomenon called the tragedy of the commons. However, in reality, human societies are sometimes able to discover and implement stable cooperative solutions. Decades of behavioral game theory research have sought to uncover aspects of human behavior that make this possible. Most of that work was based on laboratory experiments where participants only make a single choice: how much to appropriate. Recognizing the importance of spatial and temporal resource dynamics, a recent trend has been toward experiments in more complex real-time video game-like environments. However, standard methods of non-cooperative game theory can no longer be used to generate predictions for this case. Here we show that deep reinforcement learning can be used instead. To that end, we study the emergent behavior of groups of independently learning agents in a partially observed Markov game modeling common-pool resource appropriation. Our experiments highlight the importance of trial-and-error learning in common-pool resource appropriation and shed light on the relationship between exclusion, sustainability, and inequality.