title: A Preliminary Poetics for Interactive Drama and Games

abstract: Interactive drama has been discussed for a number of years as a new AI-based interactive experience. While there has been substantial technical progress in building believable agents and some technical progress in interactive plot, no work has yet been completed that combines plot and character into a full-fledged dramatic experience. Part of the difficulty in achieving interactive drama is due to the lack of a theoretical framework guiding the exploration of the technological and design issues surrounding it. This paper proposes a theory of interactive drama integrating Laurel's Aristotelian structural model with Murray's concept of agency. The resulting theory illuminates the general conditions under which a user will experience agency in any interactive experience and provides design and technology guidance for the particular case of building interactive dramatic worlds.


title: Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

abstract: The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods.


title: Self-adaptation of parameters in a learning classifier system ensemble machine

abstract: Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.


title: SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning

abstract: We describe a Twitter sentiment analysis system developed by combining a rule-based classifier with supervised learning. We submitted our results for the message-level subtask in SemEval 2015 Task 10, and achieved a F 1 -score of 57.06%. The rule-based classifier is based on rules that are dependent on the occurrences of emoticons and opinion words in tweets. Whereas, the Support Vector Machine (SVM) is trained on semantic, dependency, and sentiment lexicon based features. The tweets are classified as positive, negative or unknown by the rule-based classifier, and as positive, negative or neutral by the SVM. The results we obtained show that rules can help refine the SVM’s predictions.


title: Where you Instagram?: Associating Your Instagram Photos with Points of Interest

abstract: Instagram, an online photo-sharing platform, has gained increasing popularity. It allows users to take photos, apply digital filters and share them with friends instantaneously by using mobile devices.Instagram provides users with the functionality to associate their photos with points of interest, and it thus becomes feasible to study the association between points of interest and Instagram photos. However, no previous work studies the association. In this paper, we propose to study the problem of mapping Instagram photos to points of interest. To understand the problem, we analyze Instagram datasets, and report our findings, which also characterize the challenges of the problem. To address the challenges, we propose to model the mapping problem as a ranking problem, and develop a method to learn a ranking function by exploiting the textual, visual and user information of photos. To maximize the prediction effectiveness for textual and visual information, and incorporate the users' visiting preferences, we propose three subobjectives for learning the parameters of the proposed ranking function. Experimental results on two sets of Instagram data show that the proposed method substantially outperforms existing methods that are adapted to handle the problem.


title: An Automatic Recognition of the Frontal Sinus in X-Ray Images of Skull

abstract: In the present paper, a procedure is developed for fully automatic recognition of the frontal sinus in cranial radiographs. An X-ray image of a whole skull is required at the input of the procedure, which consists of three subsequent steps: the selection of a rectangular region of interest, containing the sinus; detection of the line of brow ridges; and fronto-nasal suture, detection of the borders of the frontal sinus. The recognition algorithm is based on a method of connectivity-preserving thresholding, introduced in the present study, and on watersheds from markers. Totally, 50 X-ray images have been analyzed. The frontal sinus borders were recognized correctly in 41 cases.


The development of an online success story: A case from the magazine publishing industry


title: A comparison of mixture models for density estimation

abstract: Gaussian mixture models (GMMs) are a popular tool for density estimation. However, these models are limited by the fact that they either impose strong constraints on the covariance matrices of the component densities or no constraints at all. This paper presents an experimental comparison of GMMs and the recently introduced mixtures of linear latent variable models. It is shown that the latter models are a more flexible alternative for GMMs and often lead to improved results.


title: Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks

abstract: Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper introduces a novel model, knowledge-guided structural attention networks (K-SAN), a generalization of RNN to additionally incorporate non-flat network topologies guided by prior knowledge. There are two characteristics: 1) important substructures can be captured from small training data, allowing the model to generalize to previously unseen test data; 2) the model automatically figures out the salient substructures that are essential to predict the semantic tags of the given sentences, so that the understanding performance can be improved. The experiments on the benchmark Air Travel Information System (ATIS) data show that the proposed K-SAN architecture can effectively extract salient knowledge from substructures with an attention mechanism, and outperform the performance of the state-of-the-art neural network based frameworks.


Application of Image Processing and Computer Vision on Rice Seed Germination Analysis


title: Children's Comprehension of Metaphor: A Piagetian Interpretation.

abstract: Metaphors selected from fifth grade readers were presented to 80 upper socio-economic status grade six and grade eight children. A descriptive interpretation of responses from the highest and lowes...


title: Comparison of neural-network learning algorithms for time-series prediction

abstract: Deep learning has received considerable attention in recent years. In this paper, we compare the long short-term memory, a deep learning technique, with other learning techniques such as the back propagation algorithm and the more recently proposed online sequential learning algorithm in the context of time-series prediction. The effectiveness of these learning algorithms is compared using a variety of datasets, univariate and multivariate. We demonstrate that the online sequential learning algorithm is more reliable and provides faster convergence resulting in better prediction performance.


title: IOrchestra: supporting high-performance data-intensive applications in the cloud via collaborative virtualization

abstract: Multi-tier data-intensive applications are widely deployed in virtualized data centers for high scalability and reliability. As the response time is vital for user satisfaction, this requires achieving good performance at each tier of the applications in order to minimize the overall latency. However, in such virtualized environments, each tier (e.g., application, database, web) is likely to be hosted by different virtual machines (VMs) on multiple physical servers, where a guest VM is unaware of changes outside its domain, and the hypervisor also does not know the configuration and runtime status of a guest VM. As a result, isolated virtualization domains lend themselves to performance unpredictability and variance. In this paper, we propose IOrchestra, a holistic collaborative virtualization framework, which bridges the semantic gaps of I/O stacks and system information across multiple VMs, improves virtual I/O performance through collaboration from guest domains, and increases resource utilization in data centers. We present several case studies to demonstrate that IOrchestra is able to address numerous drawbacks of the current practice and improve the I/O latency of various distributed cloud applications by up to 31%.


title: Distributed sensor calibration and least-square parameter identification in WSNs using consensus algorithms

abstract: In this paper we study the problem of estimating the channel parameters for a generic wireless sensor network (WSN) in a completely distributed manner, using consensus algorithms. Specifically, we first propose a distributed strategy to minimize the effects of unknown constant offsets in the reading of the radio strength signal indicator (RSSI) due to uncalibrated sensors. Then we show how the computation of the optimal wireless channels parameters, which are the solution of a global least-square optimization problem, can be obtained with a consensus-based algorithm. The proposed algorithms are general algorithms for sensor calibration and distributed least-square parameter identification, and do not require any knowledge on the global topology of the network nor the total number of nodes. Finally, we apply these algorithms to experimental data collected from an indoor WSN.


title: Predicting IMDB movie ratings using social media

abstract: We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data from either of these channels can help to extract a better set of textual feature for prediction. Our best performing model is able to rate movies very close to the observed values.


title: A Virtual Human Dialogue Model for Non-Team Interaction

abstract: We describe the dialogue model for the virtual humans developed at the Institute for Creative Technologies at the University of Southern California. The dialogue model contains a rich set of information state and dialogue moves to allow a wide range of behaviour in multimodal, multiparty interaction. We extend this model to enable non-team negotiation, using ideas from social science literature on negotiation and implemented strategies and dialogue moves for this area. We present a virtual human doctor who uses this model to engage in multimodal negotiation dialogue with people from other organisations. The doctor is part of the SASO-ST system, used for training for non-team interactions.


title: Annotation of collocations in a learner corpus for building a learning environment

abstract: Collocations in the sense of idiosyncratic lexical co-occurrences are one of the main barriers and challenges for any second language (L2) learner. In Computer Assisted Language Learning (CALL), a number of works deal with the automatic recognition of collocation errors and compilation of candidate lists for their correction. However, this is not sufficient. Firstly, to obtain a clear picture of the difficulties experienced by learners in order to be able to offer targeted aid to learners, a fine-grained linguistic analysis of collocation errors and their annotation in learner corpora is necessary. Secondly, programs must be developed that make concrete correction suggestions, besides providing correction candidate lists, and supply a learner with illustration and didactic material that is oriented towards the types of collocations with which this learner has difficulties. In our work, we attempt to push the state of the art one step further in both of these strands of research, focusing on Spanish as L2. Within the first strand, we carry out a detailed collocation-oriented annotation of a fragment of the corpus of learners of Spanish (CEDEL2). Within the second strand, we experiment with a number of strategies for choosing the most likely correction of a collocation error.


title: Introduction to Windows Mobile Forensics

abstract: Windows Mobile devices are becoming more widely used and can be a valuable source of evidence in a variety of investigations. These portable devices can contain details about an individual's communications, contacts, calendar, online activities, and whereabouts at specific times. Although forensic analysts can apply their knowledge of other Microsoft operating systems to Windows Mobile devices, there are sufficient differences that require specialized knowledge and tools to locate and interpret digital evidence on these systems. This paper provides an overview of Windows Mobile Forensics, describing various methods of acquiring and examining data on Windows Mobile devices. The locations and data formats of useful information on these systems are described, including text messages, multimedia, e-mail, Web browsing artifacts, and Registry entries. This paper concludes with an illustrative scenario involving MobileSpy monitoring software.


title: Understanding Vaccines: A Public Imperative

abstract: Though once a discovery greatly celebrated by the nation, the vaccine has come under fire in recent decades from skeptics, critics, and a movement set into motion by fraudulent scientists and fueled by frustrated parents looking for answers to the autism conundrum. There is enough denialist resistance to vaccination to bring upon renewed fear of young children and infants becoming infected with diseases, the threats of which had been functionally eradicated from the United States. In more recent years, the surge in independent online journalism and blogging has invited many to rapidly share their opinions with millions of readers and, importantly, has appeared to open the door for opinion to be portrayed as fact. As a result, many parents are inundated with horror stories of vaccine dangers, all designed to eat away at them emotionally while the medical and scientific communities have mounted their characteristic response by sharing the facts, the data, and all of the reliable peer-reviewed and well-cited research to show that vaccines are safe and effective. It has become clear to me that facts are no match for emotion, but perhaps an understanding behind vaccine methodology will help parents overcome these fears of vaccinating. By helping those who doubt vaccines better understand what vaccines really are and how they work in such an incredibly engineered fashion, we may have a stronger weapon than we realize in battling the emotional arsenal that comes from the fear and skepticism of vaccinating.


title: AWARD: Approximation-aWAre Restore in Further Scaling DRAM

abstract: DRAM further scaling becomes more and more challenging, making restore operation an serious issue in the near future. Fortunately, a wide range of modern applications are able to tolerate error or inexactness, providing a new dimension to mitigate the slow-restore issue. And thus, we can trade-off acceptable QoS loss in those applications to accelerate restore operations, and further to achieve performance and energy improvements. In this extended research abstract, we briefly explore DRAM restore-based approximate computing, and present a preliminary evaluation on impacts of quality-of-service (QoS) degradation and performance speedup. We show that restore-based approximate computing is a challenging work, and dedicated error correction/tolerance techniques are needed to balance QoS and performance.


title: Becoming Wikipedian: transformation of participation in a collaborative online encyclopedia

abstract: Traditional activities change in surprising ways when computer-mediated communication becomes a component of the activity system. In this descriptive study, we leverage two perspectives on social activity to understand the experiences of individuals who became active collaborators in Wikipedia, a prolific, cooperatively-authored online encyclopedia. Legitimate peripheral participation provides a lens for understanding participation in a community as an adaptable process that evolves over time. We use ideas from activity theory as a framework to describe our results. Finally, we describe how activity on the Wikipedia stands in striking contrast to traditional publishing and suggests a new paradigm for collaborative systems.


title: Learning High Dynamic Range from Outdoor Panoramas

abstract: Outdoor lighting has extremely high dynamic range. This makes the process of capturing outdoor environment maps notoriously challenging since special equipment must be used. In this work, we propose an alternative approach. We first capture lighting with a regular, LDR omnidirectional camera, and aim to recover the HDR after the fact via a novel, learning-based inverse tonemapping method. We propose a deep autoencoder framework which regresses linear, high dynamic range data from non-linear, saturated, low dynamic range panoramas. We validate our method through a wide set of experiments on synthetic data, as well as on a novel dataset of real photographs with ground truth. Our approach finds applications in a variety of settings, ranging from outdoor light capture to image matching.


title: Charm++ and MPI: Combining the Best of Both Worlds

abstract: Charm++ and MPI embody two distinct perspectives for writing parallel programs. While MPI provides a process-centric, user-driven model for developing parallel codes, Charm++ supports work-centric, system-driven parallel programming. One of them might be a better or more natural fit for individual modules that constitute a parallel application. In this paper, we present a framework that enables hybrid parallel programming with Charm++ and MPI, and allows programmers to develop different modules of a parallel application in these two languages while facilitating smooth interoperation. We describe the challenges in enabling interoperation between Charm++ and MPI, and present techniques for managing the control flow and resource sharing between the two. Finally, we demonstrate the benefits of interoperation between Charm++ and MPI through several case studies that use production applications and libraries, including CHARM/Chombo, EpiSimdemics, NAMD, FFTW, MPI-IO and ParMETIS.


title: High Performance Computing for Hyperspectral Remote Sensing

abstract: Advances in sensor and computer technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require real- or near real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. A relevant example of a remote sensing application in which the use of HPC technologies (such as parallel and distributed computing) is becoming essential is hyperspectral remote sensing, in which an imaging spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth. In this paper, we review recent developments in the application of HPC techniques to hyperspectral imaging problems, with particular emphasis on commodity architectures such as clusters, heterogeneous networks of computers, and specialized hardware devices such as field programmable gate arrays (FPGAs) and commodity graphic processing units (GPUs). A quantitative comparison across these architectures is given by analyzing performance results of different parallel implementations of the same hyperspectral unmixing chain, delivering a snapshot of the state-of-the-art in this area and a thoughtful perspective on the potential and emerging challenges of applying HPC paradigms to hyperspectral remote sensing problems.


title: Learning of structured graph dictionaries

abstract: We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that their structure is dependent on the graph information and its spectral representation. We first show how operators can be defined for capturing different spectral components of signals on graphs. We then propose a dictionary learning algorithm built on a sparse approximation step and a dictionary update function, which iteratively leads to adapting the structured dictionary to the class of target signals. Experimental results on synthetic and natural signals on graphs demonstrate the efficiency of the proposed algorithm both in terms of sparse approximation and support recovery performance.


title: Three-Dimensional Bipedal Walking Control Based on Divergent Component of Motion

abstract: In this paper, the concept of divergent component of motion (DCM, also called “Capture Point”) is extended to 3-D. We introduce the “Enhanced Centroidal Moment Pivot point” (eCMP) and the “Virtual Repellent Point” (VRP), which allow for the encoding of both direction and magnitude of the external forces and the total force (i.e., external plus gravitational forces) acting on the robot. Based on eCMP, VRP, and DCM, we present methods for real-time planning and tracking control of DCM trajectories in 3-D. The basic DCM trajectory generator is extended to produce continuous leg force profiles and to facilitate the use of toe-off motion during double support. The robustness of the proposed control framework is thoroughly examined, and its capabilities are verified both in simulations and experiments.


title: Learning Arguments and Supertypes of Semantic Relations Using Recursive Patterns

abstract: A challenging problem in open information extraction and text mining is the learning of the selectional restrictions of semantic relations. We propose a minimally supervised bootstrapping algorithm that uses a single seed and a recursive lexico-syntactic pattern to learn the arguments and the supertypes of a diverse set of semantic relations from the Web. We evaluate the performance of our algorithm on multiple semantic relations expressed using "verb", "noun", and "verb prep" lexico-syntactic patterns. Human-based evaluation shows that the accuracy of the harvested information is about 90%. We also compare our results with existing knowledge base to outline the similarities and differences of the granularity and diversity of the harvested knowledge.


title: Reducing idling at red lights based on probabilistic prediction of traffic signal timings

abstract: A predictive optimal velocity planning algorithm is proposed in this paper that uses traffic Signal Phase And Timing (SPAT) information to increase a vehicle's energy efficiency. Encouraged by positive results based on full SPAT information in [1], [2], this current paper focuses on benefits attainable with partial probabilistic information. Availability of signal phase data is categorized into none, real-time only, and full-future knowledge. Dynamic Programming (DP) with full future knowledge of SPAT provides an energy efficiency maximum. The case with no knowledge of phase or timing represents an uninformed driver, and provides an energy efficiency minimum. In between, a signal phase prediction model which could use historically-averaged timing data and real-time phase data is evaluated, as it represents a technology which is available today. Results from a multi-signal simulation indicate that energy efficiency can be increased with probabilistic timing data and real-time phase data.


title: Δ9-THC Intoxication by Cannabidiol-Enriched Cannabis Extract in Two Children with Refractory Epilepsy: Full Remission after Switching to Purified Cannabidiol

abstract: Animal studies and preliminary clinical trials have shown that cannabidiol (CBD)-enriched extracts may have beneficial effects for children with treatment-resistant epilepsy. However, these compounds are not yet registered as medicines by regulatory agencies. We describe the cases of two children with treatment-resistant epilepsy (Case A with left frontal dysplasia and Case B with Dravet Syndrome) with initial symptom improvement after the introduction of CBD extracts followed by seizure worsening after a short time. The children presented typical signs of intoxication by Δ9-THC (inappropriate laughter, ataxia, reduced attention, and eye redness) after using a CBD-enriched extract. The extract was replaced by the same dose of purified CBD with no Δ9-THC in both cases, which led to improvement in intoxication signs and seizure remission. These cases support pre-clinical and preliminary clinical evidence suggesting that CBD may be effective for some patients with epilepsy. Moreover, the cases highlight the need for randomized clinical trials using high-quality and reliable substances to ascertain the safety and efficacy of cannabinoids as medicines.


title: A Survey of e-Commerce Recommender Systems

abstract: Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems.


title: Reciprocal Velocity Obstacles for real-time multi-agent navigation

abstract: In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.


title: Some simplified NP-complete graph problems

abstract: Abstract It is widely believed that showing a problem to be NP -complete is tantamount to proving its computational intractability. In this paper we show that a number of NP -complete problems remain NP -complete even when their domains are substantially restricted. First we show the completeness of Simple Max Cut (Max Cut with edge weights restricted to value 1), and, as a corollary, the completeness of the Optimal Linear Arrangement problem. We then show that even if the domains of the Node Cover and Directed Hamiltonian Path problems are restricted to planar graphs, the two problems remain NP -complete, and that these and other graph problems remain NP -complete even when their domains are restricted to graphs with low node degrees. For Graph 3-Colorability, Node Cover, and Undirected Hamiltonian Circuit, we determine essentially the lowest possible upper bounds on node degree for which the problems remain NP -complete.


title: Tai Chi improves pain and functional status in adults with rheumatoid arthritis: results of a pilot single-blinded randomized controlled trial.

abstract: BACKGROUND/AIMS ::: Rheumatoid arthritis (RA) is a serious health problem resulting in significant morbidity and disability. Tai Chi may be beneficial to patients with RA as a result of effects on muscle strength and 'mind-body' interactions. To obtain preliminary data on the effects of Tai Chi on RA, we conducted a pilot randomized controlled trial. Twenty patients with functional class I or II RA were randomly assigned to Tai Chi or attention control in twice-weekly sessions for 12 weeks. The American College of Rheumatology (ACR) 20 response criterion, functional capacity, health-related quality of life and the depression index were assessed. ::: ::: ::: RESULTS ::: At 12 weeks, 5/10 patients (50%) randomized to Tai Chi achieved an ACR 20% response compared with 0/10 (0%) in the control (p = 0.03). Tai Chi had greater improvement in the disability index (p = 0.01), vitality subscale of the Medical Outcome Study Short Form 36 (p = 0.01) and the depression index (p = 0.003). Similar trends to improvement were also observed for disease activity, functional capacity and health-related quality of life. No adverse events were observed and no patients withdrew from the study. ::: ::: ::: CONCLUSION ::: Tai Chi appears safe and may be beneficial for functional class I or II RA. These promising results warrant further investigation into the potential complementary role of Tai Chi for treatment of RA.


title: Maté: a tiny virtual machine for sensor networks

abstract: Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopulate. Often there is no feasible method to recharge motes, so energy is a precious resource. Once deployed, a network must be reprogrammable although physically unreachable, and this reprogramming can be a significant energy cost.We present Mate, a tiny communication-centric virtual machine designed for sensor networks. Mate's high-level interface allows complex programs to be very short (under 100 bytes), reducing the energy cost of transmitting new programs. Code is broken up into small capsules of 24 instructions, which can self-replicate through the network. Packet sending and reception capsules enable the deployment of ad-hoc routing and data aggregation algorithms. Mate's concise, high-level program representation simplifies programming and allows large networks to be frequently reprogrammed in an energy-efficient manner; in addition, its safe execution environment suggests a use of virtual machines to provide the user/kernel boundary on motes that have no hardware protection mechanisms.


title: Pulses and food security: Dietary protein, digestibility, bioactive and functional properties

abstract: Abstract Background Food security is a current major concern. Protein malnutrition, in particular, must be overcome urgently. Simultaneously, food manufacturers are increasingly interested proteins from plant sources. Hence, pulse crops could be exploited as sustainable, climate change resistant and high quality protein sources. Scope and approach Pulses, such as peas, common beans, cowpea, fava beans, chickpea, lentil, or lupin, contain high levels of globulins and albumins, providing around 33% of dietary needs. Nevertheless, there are different compositional and technological problems (e.g., digestibility, allergenicity, or antinutritional factors: ANF) that need to be overcome to consider pulse proteins as feasible alternative sources of protein. Key findings and conclusions The ANF might be highly decreased by adequate processing conditions, increasing nutrients bioavailability and protein digestibility. Curiously, ANF might also be associated with the positive physiologic effects commonly attributed to pulses. In turn, the technological and functional properties (water and fat absorption, solubility, gel forming, emulsifying activity, foaming capacity and foam stability) of pulse proteins should be considered in food formulation and processing, particularly in the development of innovative food products, as already validated at industrial level. In either case, the potential allergenicity of pulse proteins (such as exemplified by lupin), or cross reactivity, should not be neglected throughout the complete production chain, including downstream concerns such as safe labelling. Despite the highlighted limitations, it seems evident that pulse proteins will have a major role in fulfilling food protein demands, contributing to food security worldwide, besides being achievable in more sustainable exploration processes.


title: Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

abstract: Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.


title: An E-band slotted waveguide monopulse array antenna with corporate-feed using diffusion bonding of laminated plates

abstract: In this paper, we demonstrate an E-band plate-laminated 16 × 16-element slotted waveguide monopulse array antenna with corporate-feed using the diffusion bonding technology. The proposed antenna architecture combines radiating elements, a corporate-feed circuit and a comparator, which are vertically interconnected to achieve a compact size and a reduced loss. The design, characterization, and experimental results are presented.


title: Distribution of 5-methylcytosine in chromatin.

abstract: Abstract ::: The content of 5-methylcytosine in eukaryotic DNA was measured by mass spectrometry. Almost equal amounts of methylated cytosine were found in the DNA of various tissues of the chicken. When chromatin or nuclei were digested with micrococcal nuclease, 50% of the DNA was found to be nuclease resistant. In contrast to this, over 75% of the 5-methylcytosine was protected from nuclease digestion by chromatin proteins. These results suggest that 5-methylcytosine is nonrandomly distributed with respect to the nucleoproteins.


title: Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration

abstract: Q-learning in single-agent environments is known to converge in the limit given sufficient exploration. The same algorithm has been applied, with some success, in multi-agent environments, where traditional analysis techniques break down. Using established dynamical systems methods, we derive and study an idealization of Q-learning in 2-player 2-action repeated general-sum games. In particular, we address the discontinuous case of e-greedy exploration and use it as a proxy for value-based algorithms to highlight a contrast with existing results in policy search. Analogously to previous results for gradient ascent algorithms, we provide a complete catalog of the convergence behavior of the e-greedy Q-learning algorithm by introducing new subclasses of these games. We identify two subclasses of Prisoner's Dilemma-like games where the application of Q-learning with e-greedy exploration results in higher-than-Nash average payoffs for some initial conditions.


title: Term Set Expansion based NLP Architect by Intel AI Lab

abstract: We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to-end workflow. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes.SetExpander has been used successfully in real-life use cases including integration into an automated recruitment system and an issues and defects resolution system. A video demo of SetExpander is available at this https URL (some images were blurred for privacy reasons)


title: IOT Based Automation and Solution for Medical Drug Storage: Smart Drug Store

abstract: The increase in the work stress and decrease in the time for oneself has led to the rise in the dependency on the medicines and drugs. The drugs and medicines are the key sources for saving the human life when the patient is in the danger. In order to maintain regular and quality supply of the drugs and medicines has to monitor on the regular basis. There are numerous medicines and drugs brought in the store but usually drugs and medicines are stolen to satisfy one's greed, get expired or placed at unknown locations in the store. So to prevent such situation and saving the life of the patient Drug and Medicine Monitoring Model can be used. The model uses the RFID and IoT technology in order to monitor the drugs and medicines in the store. With the help of this system the chemist or the hospital will have the knowledge of the medicines and drugs available, the medicines and drugs quality, their location and their safety.


title: Critical success factors (CSFs) for information technology governance (ITG)

abstract: The main studys aim is to investigate and identify the factors that encourage the successful implementation of ITG which will be called in this paper Critical Success Factors (CSFs).The literature has been thoroughly reviewed and analysed to identify these CSFs.The literature includes IT Governance standards and best practices; ITG frameworks and models; and related studies.The extracted, analysed and modified factors have been summarised, classified and sorted based on their importance, relativity and the proper sequence.Finding of these factors went through different steps in order to have the solution framework, Success Factors for IT Governance (SFITG). With the rapid evolution of Information Technology (IT) applications and practices across the organization, appropriate IT Governance (ITG) has become essential to an organizations success. As IT is associated with risk and value opportunities, a comprehensive, high-level system is required in each organization to minimise the associated risks and optimize value. This requirement triggered the emergence of ITG. Many researchers have addressed this field; however, the role played by critical success factors (CSFs) in the successful implementation of ITG has not yet received adequate attention. This gap in the research motivated the present study, with the main aim of defining the CSFs needed for the successful implementation of ITG. CSFs were studied and extracted from the literature review and then analysed, categorised, and synthesized to create the Success Factors for IT Governance Framework.


title: Fear and Misperception of Los Angeles Urban Space: A Spatial-Statistical Study of Communication-Shaped Mental Maps

abstract: Imagining urban space as being comfortable or fearful is studied as an effect of people's connections to their residential area communication infrastructure. Geographic Information System (GIS) modeling and spatial-statistical methods are used to process 215 mental maps obtained from respondents to a multilingual survey of seven ethnically marked residential communities of Los Angeles. Spatial-statistical analyses reveal that fear perceptions of Los Angeles urban space are not associated with commonly expected causes of fear, such as high crime victimization likelihood. The main source of discomfort seems to be presence of non-White and non-Asian populations. Respondents more strongly connected to television and interpersonal communication channels are relatively more fearful of these populations than those less strongly connected. Theoretical, methodological, and community-building policy implications are discussed.


title: Facade Segmentation in the Wild

abstract: Urban facade segmentation from automatically acquired imagery, in contrast to traditional image segmentation, poses several unique challenges. 360-degree photospheres captured from vehicles are an effective way to capture a large number of images, but this data presents difficult-to-model warping and stitching artifacts. In addition, each pixel can belong to multiple facade elements, and different facade elements (e.g., window, balcony, sill, etc.) are correlated and vary wildly in their characteristics. In this paper, we propose three network architectures of varying complexity to achieve multilabel semantic segmentation of facade images while exploiting their unique characteristics. Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to 'see' intermediate output probabilities of the various facade element classes. Our results on benchmark datasets show significant improvements over existing facade segmentation approaches for the typical facade elements. For example, on one commonly used dataset, the accuracy scores for window(the most important architectural element) increases from 0.91 to 0.97 percent compared to the best competing method, and comparable improvements on other element types.


title: Inflammatory Mechanisms in Obesity

abstract: The modern rise in obesity and its strong association with insulin resistance and type 2 diabetes have elicited interest in the underlying mechanisms of these pathologies. The discovery that obesity itself results in an inflammatory state in metabolic tissues ushered in a research field that examines the inflammatory mechanisms in obesity. Here, we summarize the unique features of this metabolic inflammatory state, termed metaflammation and defined as low-grade, chronic inflammation orchestrated by metabolic cells in response to excess nutrients and energy. We explore the effects of such inflammation in metabolic tissues including adipose, liver, muscle, pancreas, and brain and its contribution to insulin resistance and metabolic dysfunction. Another area in which many unknowns still exist is the origin or mechanism of initiation of inflammatory signaling in obesity. We discuss signals or triggers to the inflammatory response, including the possibility of endoplasmic reticulum stress as an important contributor to metaflammation. Finally, we examine anti-inflammatory therapies for their potential in the treatment of obesity-related insulin resistance and glucose intolerance.


title: Footprints preserve terminal Pleistocene hunt? Human-sloth interactions in North America

abstract: Predator-prey interactions revealed by vertebrate trace fossils are extremely rare. We present footprint evidence from White Sands National Monument in New Mexico for the association of sloth and human trackways. Geologically, the sloth and human trackways were made contemporaneously, and the sloth trackways show evidence of evasion and defensive behavior when associated with human tracks. Behavioral inferences from these trackways indicate prey selection and suggest that humans were harassing, stalking, and/or hunting the now-extinct giant ground sloth in the terminal Pleistocene.


title: Improving Neural Machine Translation Models with Monolingual Data

abstract: Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and we investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic back-translation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English->German.


title: Survey of real-time processing systems for big data

abstract: In recent years, real-time processing and analytics systems for big data--in the context of Business Intelligence (BI)--have received a growing attention. The traditional BI platforms that perform regular updates on daily, weekly or monthly basis are no longer adequate to satisfy the fast-changing business environments. However, due to the nature of big data, it has become a challenge to achieve the real-time capability using the traditional technologies. The recent distributed computing technology, MapReduce, provides off-the-shelf high scalability that can significantly shorten the processing time for big data; Its open-source implementation such as Hadoop has become the de-facto standard for processing big data, however, Hadoop has the limitation of supporting real-time updates. The improvements in Hadoop for the real-time capability, and the other alternative real-time frameworks have been emerging in recent years. This paper presents a survey of the open source technologies that support big data processing in a real-time/near real-time fashion, including their system architectures and platforms.


title: Soft pneumatic gelatin actuator for edible robotics

abstract: We present a fully edible pneumatic actuator based on gelatin-glycerol composite. The actuator is monolithic, fabricated via a molding process, and measures 90 mm in length, 20 mm in width, and 17 mm in thickness. Thanks to the composite mechanical characteristics similar to those of silicone elastomers, the actuator exhibits a bending angle of 170.3 {\deg} and a blocked force of 0.34 N at the applied pressure of 25 kPa. These values are comparable to elastomer based pneumatic actuators. As a validation example, two actuators are integrated to form a gripper capable of handling various objects, highlighting the high performance and applicability of the edible actuator. These edible actuators, combined with other recent edible materials and electronics, could lay the foundation for a new type of edible robots.


title: A Physics-Based Deep Learning Approach to Shadow Invariant Representations of Hyperspectral Images

abstract: This paper proposes the Relit Spectral Angle-Stacked Autoencoder, a novel unsupervised feature learning approach for mapping pixel reflectances to illumination invariant encodings. This work extends the Spectral Angle-Stacked Autoencoder so that it can learn a shadow-invariant mapping. The method is inspired by a deep learning technique, Denoising Autoencoders, with the incorporation of a physics-based model for illumination such that the algorithm learns a shadow invariant mapping without the need for any labelled training data, additional sensors, a priori knowledge of the scene or the assumption of Planckian illumination. The method is evaluated using datasets captured from several different cameras, with experiments to demonstrate the illumination invariance of the features and how they can be used practically to improve the performance of high-level perception algorithms that operate on images acquired outdoors.


title: Mobile Device Identification via Sensor Fingerprinting

abstract: We demonstrate how the multitude of sensors on a smartphone can be used to construct a reliable hardware fingerprint of the phone. Such a fingerprint can be used to de-anonymize mobile devices as they connect to web sites, and as a second factor in identifying legitimate users to a remote server. We present two implementations: one based on analyzing the frequency response of the speakerphone-microphone system, and another based on analyzing device-specific accelerometer calibration errors. Our accelerometer-based fingerprint is especially interesting because the accelerometer is accessible via JavaScript running in a mobile web browser without requesting any permissions or notifying the user. We present the results of the most extensive sensor fingerprinting experiment done to date, which measured sensor properties from over 10,000 mobile devices. We show that the entropy from sensor fingerprinting is sufficient to uniquely identify a device among thousands of devices, with low probability of collision.


title: “Suddenly, we got to become therapists for each other”: Designing Peer Support Chats for Mental Health

abstract: Talk therapy is a common, effective, and desirable form of mental health treatment. Yet, it is inaccessible to many people. Enabling peers to chat online using effective principles of talk therapy could help scale this form of mental health care. To understand how such chats could be designed, we conducted a two-week field experiment with 40 people experiencing mental illnesses comparing two types of online chats-chats guided by prompts, and unguided chats. Results show that anxiety was significantly reduced from pre-test to post-test. User feedback revealed that guided chats provided solutions to problems and new perspectives, and were perceived as "deep," while unguided chats offered personal connection on shared experiences and were experienced as "smooth." We contribute the design of an online guided chat tool and insights into the design of peer support chat systems that guide users to initiate, maintain, and reciprocate emotional support.


title: 2D Color Barcodes for Mobile Phones

abstract: We propose a new high capacity color barcode, named HCC2D (High Capacity Colored 2-Dimensional), which use colors to increase the barcode data density. The introduction and recognition of colored modules poses some new and non-trivial computer vision challenges, such as handling the color distortions introduced by the hardware equipment that realizes the Print&Scan process. We developed a prototype for generating and reading the HCC2D code format, both on desktops (Linux and Windows platforms) and on mobile phones (Android platforms). We tested this prototype in many experiments considering different operating scenarios and data densities, and compared it to known 2-dimensional barcodes.


title: Effectiveness of domestic wastewater treatment using microbial fuel cells at ambient and mesophilic temperatures

abstract: Domestic wastewater treatment was examined under two different temperature (23+/-3 degrees C and 30+/-1 degrees C) and flow modes (fed-batch and continuous) using single-chamber air-cathode microbial fuel cells (MFCs). Temperature was an important parameter for treatment efficiency and power generation. The highest power density of 422 mW/m(2) (12.8 W/m(3)) was achieved under continuous flow and mesophilic conditions, at an organic loading rate of 54 g COD/L-d, achieving 25.8% COD removal. Energy recovery was found to depend significantly on the operational conditions (flow mode, temperature, organic loading rate, and HRT) as well as the reactor architecture. The results demonstrate that the main advantages of using temperature-phased, in-series MFC configurations for domestic wastewater treatment are power savings, low solids production, and higher treatment efficiency.


title: A layout algorithm for data flow diagrams

abstract: A layout algorithm is presented that allows the automatic drawing of data flow diagrams, a diagrammatic representation widely used in the functional analysis of information systems. A grid standard is defined for such diagrams, and aesthetics for good readability are identified. The layout algorithm receives as input an abstract graph specifying connectivity relations between the elements of the diagram, and produces as output a corresponding diagram according to the aesthetics. The basic strategy is to build incrementally the layout; first, a good topology is constructed with few crossings between edges; subsequently, the shape of the diagram is determined in terms of angles appearing along edges. and finally dimensions are given to the graph, obtaining a grid skeleton for the diagram.


A Novel Performance Analysis of Multiplexer Based Convolutional Encoder


title: Real-time Algorithms for Facial Emotion Recognition: A Comparison of Different Approaches

abstract: Emotion recognition has application in various fields such as medicine (rehabilitation, therapy, counseling, etc.), e-learning, entertainment, emotion monitoring, marketing, law. Different algorithms for emotion recognition include feature extraction and classification based on physiological signals, facial expressions, body movements. In this paper, we present a comparison of five different approaches for real-time emotion recognition of four basic emotions (happiness, sadness, anger and fear) from facial images. We have compared three deep-learning approaches based on convolutional neural networks (CNN) and two conventional approaches for classification of Histogram of Oriented Gradients (HOG) features: 1) AlexNet CNN, 2) commercial Affdex CNN solution, 3) custom made FER-CNN, 4) Support Vector Machine (SVM) of HOG features, 5) Multilayer Perceptron (MLP) artificial neural network of HOG features. The result of real-time testing of five different algorithms on the group of eight volunteers is presented.


title: Legal and institutional tools to mitigate plastic pollution affecting marine species: Argentina as a case study.

abstract: Plastics are the most common form of debris found along the Argentine coastline. The Río de la Plata estuarine area is a relevant case study to describe a situation where ample policy exists against a backdrop of plastics disposed by populated coastal areas, industries, and vessels; with resultant high impacts of plastic pollution on marine turtles and mammals. Policy and institutions are in place but the impact remains due to ineffective waste management, limited public education and awareness, and weaknesses in enforcement of regulations. This context is frequently repeated all over the world. We list possible interventions to increase the effectiveness of policy that require integrating efforts among governments, the private sector, non-governmental organizations and the inhabitants of coastal cities to reduce the amount of plastics reaching the Río de la Plata and protect threatened marine species. What has been identified for Argentina applies to the region and globally.


title: Towards smart wearable real-time airport luggage tracking

abstract: In this paper, we propose a smart system for realtime tracking of airport luggage using mobile applications and smartwatches. We track using Kalman-filtered Wi-Fi fingerprints collected by active tags. Information about the flights and association with different luggage pieces is inputted pre-flight using QR codes. Our system uses a smart power management scheme fusing multi-sensor and flight data and assesses the risk of a battery drain to warn the user about the need for recharging. A mobile phone application is used to track the arrival of the luggage allowing passengers to rest after a long flight. We tested our proposed system's impact on the airport wireless network and the random occurrence of different travel delays on the tag energy and found it to be able to efficiently deliver real-time luggage tracking information right to passenger smartwatches.


title: Artificial annelid robot driven by soft actuators

abstract: The annelid provides a biological solution of effective locomotion adaptable to a large variety of unstructured environmental conditions. The undulated locomotion of the segmented body in the annelid is characterized by the combination of individual motion of the muscles distributed along the body, which has been of keen interest in biomimetic investigation. In this paper, we present an annelid-like robot driven by soft actuators based on dielectric elastomer. To mimic the unique motion of the annelid, a novel actuation method employing dielectric elastomer is developed. By using the actuator, a three-degree-of-freedom actuator module is presented, which can provide up–down translational motion, and two rotational degree-of-freedom motion. The proposed actuation method provides advantageous features of reduction in size, fast response and ruggedness in operation. By serially connecting the actuator modules, a micro-robot mimicking the motion of the annelid is developed and its effectiveness is experimentally demonstrated.


title: A Universal BCD-on-SOI Based High Temperature Short Circuit Protection for SiC Power Switches

abstract: In recent years, the rapid increase in the market for hybrid electric vehicles has generated great demand for low-cost, high-volume, high-temperature power converters that can work in harsh environment (temperature ≥ 150°C) conditions. Most of the commercially available power semiconductor devices and associated control electronics are rated for maximum of 85°C ambient temperature. Under this circumstance, wide bandgap (WBG) semiconductors have become a better alternative due to their ability to operate at much higher temperatures (≥500°C) than conventional bulk silicon based devices. As with any other power devices, SiC switches also require fault detection and protection mechanisms for their reliable application to real systems. One severe fault situation is the short circuit at the load end, which can cause very high surge currents that flow through the power switches. Quick detection and removal of the short circuit fault current by external circuitry is required to protect the power switch as well as...


title: Simulation of temperature dependent materials in high-frequency filters using CST STUDIO SUITE™2011

abstract: Elements of high-frequency circuits are often subject to high temperatures caused by power losses. If properties of materials comprising these elements depend on temperature, this heating may modify the spectral characteristics of these elements considerably.


title: Legal Personhood for Artificial Intelligences

abstract: Could an artificial intelligence become a legal person? As of today, this question is only theoretical. No existing computer program currently possesses the sort of capacities that would justify serious judicial inquiry into the question of legal personhood. The question is nonetheless of some interest. Cognitive science begins with the assumption that the nature of human intelligence is computational, and therefore, that the human mind can, in principle, be modelled as a program that runs on a computer. Artificial intelligence (AI) research attempts to develop such models. But even as cognitive science has displaced behavioralism as the dominant paradigm for investigating the human mind, fundamental questions about the very possibility of artificial intelligence continue to be debated. This Essay explores those questions through a series of thought experiments that transform the theoretical question whether artificial intelligence is possible into legal questions such as, "Could an artificial intelligence serve as a trustee?" What is the relevance of these legal thought experiments for the debate over the possibility of artificial intelligence? A preliminary answer to this question has two parts. First, putting the AI debate in a concrete legal context acts as a pragmatic Occam's razor. By reexamining positions taken in cognitive science or the philosophy of artificial intelligence as legal arguments, we are forced to see them anew in a relentlessly pragmatic context. Philosophical claims that no program running on a digital computer could really be intelligent are put into a context that requires us to take a hard look at just what practical importance the missing reality could have for the way we speak and conduct our affairs. In other words, the legal context provides a way to ask for the "cash value" of the arguments. The hypothesis developed in this Essay is that only some of the claims made in the debate over the possibility of AI do make a pragmatic difference, and it is pragmatic differences that ought to be decisive. Second, and more controversially, we can view the legal system as a repository of knowledge-a formal accumulation of practical judgments. The law embodies core insights about the way the world works and how we evaluate it. Moreover, in common-law systems judges strive to decide particular cases in a way that best fits the legal landscape-the prior cases, the statutory law, and the constitution. Hence, transforming the abstract debate over the possibility of AI into an imagined hard case forces us to check our intuitions and arguments against the assumptions that underlie social decisions made in many other contexts. By using a thought experiment that explicitly focuses on wide coherence, we increase the chance that the positions we eventually adopt will be in reflective equilibrium with our views about related matters. In addition, the law embodies practical knowledge in a form that is subject to public examination and discussion. Legal materials are published and subject to widespread public scrutiny and discussion. Some of the insights gleaned in the law may clarify our approach to the artificial intelligence debate.


title: Large-scale learning with AdaGrad on Spark

abstract: Stochastic Gradient Descent (SGD) is a simple yet very efficient online learning algorithm for optimizing convex (and often non-convex) functions and one of the most popular stochastic optimization methods in machine learning today. One drawback of SGD is that it is sensitive to the learning rate hyper-parameter. The Adaptive Sub-gradient Descent, AdaGrad, dynamically incorporates knowledge of the geometry of the data observed in earlier iterations to calculate a different learning rate for every feature. In this work, we implement a distributed version of AdaGrad for large-scale machine learning tasks using Apache Spark. Apache Spark is a fast cluster computing engine that provides similar scalability and fault tolerance properties to MapReduce, but in contrast to Hadoop's two-stage disk-based MapReduce paradigm, Spark's multi-stage in-memory primitives allow user programs to load data into a cluster's memory and query it repeatedly, which makes it ideal for building scalable machine learning applications. We empirically evaluate our implementation on large-scale real-world problems in the machine learning canonical tasks of classification and regression. Comparing our implementation of AdaGrad with the SGD scheduler currently available in Spark's Machine Learning Library (MLlib), we experimentally show that AdaGrad saves time by avoiding manually setting a learning-rate hyperparameter, converges fast and can even achieve better generalization errors.


title: SERCID: A Secure Mechanism for Data Collection in Wireless Sensor Networks

abstract: The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. An adversary can physically compromise a subset of sensor nodes in a wireless sensor network to eavesdrop information. Due to some intrinsic features of wireless sensor network, it is difficult to perform efficient intrusion detection against malicious nodes in such a resource-restricted environment. In this paper, we propose a novel secure mechanism, SERCID for wireless sensor networks. The mechanism consists of a key-based secure routing algorithm and a counter-based intrusion detection algorithm. The approach is able to protect the data collection in a wireless sensor network even if there are malicious nodes in the network. The major contribution of our work is that we present an original approach to defend wireless sensor networks against both tampering and packet-dropping attacks, for the first time. We provide a relatively simple but reliable approach to support secure data collection in wireless sensor networks. Compared with existing research efforts, the proposed approach is easy to be implemented and performed in resource- constrained wireless sensor networks.


title: ActNeT: Active Learning for Networked Texts in Microblogging

abstract: Supervised learning, e.g., classification, plays an important role in processing and organizing microblogging data. In microblogging, it is easy to mass vast quantities of unlabeled data, but would be costly to obtain labels, which are essential for supervised learning algorithms. In order to reduce the labeling cost, active learning is an effective way to select representative and informative instances to query for labels for improving the learned model. Different from traditional data in which the instances are assumed to be independent and identically distributed (i.i.d.), instances in microblogging are networked with each other. This presents both opportunities and challenges for applying active learning to microblogging data. Inspired by social correlation theories, we investigate whether social relations can help perform effective active learning on networked data. In this paper, we propose a novel Active learning framework for the classification of Networked Texts in microblogging (ActNeT). In particular, we study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from microblogging for labeling by taking advantage of social network structure. Experimental results on Twitter datasets show the benefit of incorporating network information in active learning and that the proposed framework outperforms existing state-of-the-art methods.


title: Identification of a satellite RNA associated with turnip crinkle virus

abstract: Abstract Turnip plants infected with turnip crinkle virus (TCV) contain four major RNA species which are not found in uninfected plants (A = 1.3 × 10 6 MW, B = 0.28 × 10 6 MW, C = 0.17 × 10 6 MW, and D = 0.13 × 10 6 MW). At least two of these RNAs, RNA A and RNA C, are packaged in the mature virion, but only the large RNA A is required for infection. Plants infected with RNA A alone produce neither the small virion RNA C nor the small nonvirion RNAs B and D. The small virion RNA C is not infective by itself, but requires coinfection with RNA A to replicate in plants. RNA C increases the severity of symptoms in plants infected with RNA A and restores the production of the nonvirion RNAs B and D. T1 RNase oligonucleotide mapping and copy DNA hybridization analysis indicate that the virion RNAs A and C do not have extensive homology. These data suggest that the large virion RNA A contains the full TCV genome and that the smaller virion RNA C is a dispensible satellite, designated S-TCV.


title: An Approach to Evaluate Data Trustworthiness Based on Data Provenance

abstract: Today, with the advances of information technology, individual people and organizations can obtain and process data from different sources. It is critical to ensure data integrity so that effective decisions can be made based on these data. An important component of any solution for assessing data integrity is represented by techniques and tools to evaluate the trustworthiness of data provenance. However, few efforts have been devoted to investigate approaches for assessing how trusted the data are, based in turn on an assessment of the data sources and intermediaries. To bridge this gap, we propose a data provenance trust model which takes into account various factors that may affect the trustworthiness and, based on these factors, assigns trust scores to both data and data providers. Such trust scores represent key information based on which data users may decide whether to use the data and for what purposes.


title: Chatting with teenagers: Considering the place of chat technologies in teen life

abstract: In the last few years, teenagers have been on the forefront of adopting short message service (SMS), a mobile phone-based text messaging system, and instant messaging (IM), a computer-based text chat system. However, while teenage adoption of SMS had led to a series of studies examining the reasons for its popularity, IM use in the teenage population remains understudied. This omission becomes significant given the increasing interest in domestic computing among human-computer interaction (HCI) and computer-supported cooperative work (CSCW) researchers. Further, because of the dearth of empirical work on teenage use of IM, we find that IM and SMS are sometimes incorrectly assumed to share the same features of use. To address these concerns, we revisit our own studies of SMS and IM use and reexamine them in tandem with other published studies on teenage chat. We consider similarities and differences in styles of SMS and IM use and how chat technologies enable the pursuit of teenage independence. We examine how differences are born out of technological differences and financial cost structures. We discuss how SMS and IM are used in concert to provide increased awareness and to coordinate inter-household communications, and how privacy is regulated within the individual household as a means of maintaining these communications.


title: From Alice to Python. Introducing Text-based Programming in Middle Schools

abstract: In this paper, we present our experience in designing computing curriculum for Middle School students. Computing education is becoming an important part of STEM education and several national curriculums are incorporating computing education in their core curriculums. We design a curriculum that uses Alice -- an interactive drag-and-drop interface that provides a simple interface to learn programming concepts. Once the students are comfortable in these concepts, they take Python in higher classes to learn computing concepts in the context of a text-based programming language. This study attempts to provide "mediated transfer" for applying concepts learned in Alice, to programming in Python. We present the results of our study in applying this curriculum in a local school.


In: Lifelong Engagement with Music MUSIC AND NEURAL PLASTICITY


title: 28 GHz Angle of Arrival and Angle of Departure Analysis for Outdoor Cellular Communications Using Steerable Beam Antennas in New York City

abstract: Propagation measurements at 28 GHz were conducted in outdoor urban environments in New York City using four different transmitter locations and 83 receiver locations with distances of up to 500 m. A 400 mega- chip per second channel sounder with steerable 24.5 dBi horn antennas at the transmitter and receiver was used to measure the angular distributions of received multipath power over a wide range of propagation distances and urban settings. Measurements were also made to study the small-scale fading of closely-spaced power delay profiles recorded at half-wavelength (5.35 mm) increments along a small-scale linear track (10 wavelengths, or 107 mm) at two different receiver locations. Our measurements indicate that power levels for small- scale fading do not significantly fluctuate from the mean power level at a fixed angle of arrival. We propose here a new lobe modeling technique that can be used to create a statistical channel model for lobe path loss and shadow fading, and we provide many model statistics as a function of transmitter- receiver separation distance. Our work shows that New York City is a multipath-rich environment when using highly directional steerable horn antennas, and that an average of 2.5 signal lobes exists at any receiver location, where each lobe has an average total angle spread of 40.3° and an RMS angle spread of 7.8°. This work aims to create a 28 GHz statistical spatial channel model for future 5G cellular networks.


title: Snowflake: A Model Agnostic Accelerator for Deep Convolutional Neural Networks

abstract: Deep convolutional neural networks (CNNs) are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processing tasks. CNNs require billions of operations to process a frame. This computational complexity, combined with the inherent parallelism of the convolution operation make CNNs an excellent target for custom accelerators. However, when optimizing for different CNN hierarchies and data access patterns, it is difficult for custom accelerators to achieve close to 100% computational efficiency. In this work, we present Snowflake, a scalable and efficient accelerator that is agnostic to CNN workloads, and was designed to always perform at near-peak hardware utilization. Snowflake is able to achieve a computational efficiency of over 91% on modern CNN models. Snowflake, implemented on a Xilinx Zynq XC7Z045 SoC is capable of achieving a peak throughput of 128G-ops/s and a measured throughput of 100 frames per second and 120 G-ops/s on the AlexNet CNN model, 36 frames per second and 116G- ops/s on the GoogLeNet CNN model and 17 frames per second and 122 G-ops/s on the ResNet-50 CNN model. To the best of our knowledge, Snowflake is the only implemented system capable of achieving over 91% efficiency on modern CNNs and the only implemented system with GoogLeNet and ResNet as part of the benchmark suite.


title: Overcoming the challenge for text classification in the open world

abstract: Classification is often referred to as the task of discriminating one class from others in a given set of classes. Traditionally, classifiers work well assuming that a priori knowledge of all classes are given. Unfortunately, a presenting of unknown class during testing can lead to poor performance of even state-of-the-art classifiers due to observed classes being incorrectly identified to other classes. Recent proposed open world recognition framework provides a promising venue for tackling this challenge. While the majority of works in this relative new field is in computer vision, the rare work in Natural Language Processing shows its instability in its performance and is not based on the open world recognition framework. To tackle this problem, we represent our Nearest Centroid Class (NCC) which is incremental learning and able to detect unknown class during testing. Our model yields promising results in a document classification on text classification domains among current state-of-the-art models.


title: Attested append-only memory: making adversaries stick to their word

abstract: Researchers have made great strides in improving the fault tolerance of both centralized and replicated systems against arbitrary (Byzantine) faults. However, there are hard limits to how much can be done with entirely untrusted components; for example, replicated state machines cannot tolerate more than a third of their replica population being Byzantine. In this paper, we investigate how minimal trusted abstractions can push through these hard limits in practical ways. We propose Attested Append-Only Memory (A2M), a trusted system facility that is small, easy to implement and easy to verify formally. A2M provides the programming abstraction of a trusted log, which leads to protocol designs immune to equivocation -- the ability of a faulty host to lie in different ways to different clients or servers -- which is a common source of Byzantine headaches. Using A2M, we improve upon the state of the art in Byzantine-fault tolerant replicated state machines, producing A2M-enabled protocols (variants of Castro and Liskov's PBFT) that remain correct (linearizable) and keep making progress (live) even when half the replicas are faulty, in contrast to the previous upper bound. We also present an A2M-enabled single-server shared storage protocol that guarantees linearizability despite server faults. We implement A2M and our protocols, evaluate them experimentally through micro- and macro-benchmarks, and argue that the improved fault tolerance is cost-effective for a broad range of uses, opening up new avenues for practical, more reliable services.


title: Wireless and Non-contact ECG Measurement System – the “Aachen SmartChair”

abstract: This publication describes a measurement system that obtains an electrocardiogram (ECG) by capacitively coupled electrodes. For demonstration purposes, this measurement system was integrated into an off-the-shelf office chair (so-called “Aachen SmartChair”). Whereas in usual clinical applications adhesive, conductively-coupled electrodes have to be attached to the skin, the described system is able to measure an ECG without direct skin contact through the cloth. A wireless communication module was integrated for transmitting the ECG data to a PC or to an ICU patient monitor. For system validation, a classical ECG with conductive electrodes and an oxygen saturation signal (SpO 2 ) were obtained simultaneously. Finally, system-specific problems of the presented device are discussed.


title: Supplementing Modern Software Defenses with Stack-Pointer Sanity

abstract: The perpetual cat-and-mouse game between attackers and software defenders has highlighted the need for strong and robust security. With performance as a key concern, most modern defenses focus on control-flow integrity (CFI), a program property that requires runtime execution of a program to adhere to a statically determined control-flow graph (CFG). Despite its success in preventing traditional return-oriented programming (ROP), CFI is known to be ineffective against modern attacks that adhere to a statically recovered CFG (e.g., COOP). This paper introduces stack-pointer integrity (SPI) as a means to supplement CFI and other modern defense techniques. Due to its ability to influence indirect control targets, stack pointer is a key artifact in attacks. We define SPI as a property comprising of two key sub-properties - Stack Localization and Stack Conservation - and implement a LLVM-based compiler prototype codenamed SPIglass that enforces SPI. We demonstrate a low implementation overhead and incremental deployability, two of the most desirable features for practical deployment. Our performance experiments show that the overhead of our defense is low in practice. We opensource SPIglass for the benefit of the community.


title: [Self-care and demand for social support in women with infertility].

abstract: INTRODUCTION ::: The prevalence of infertility among married couples is growing. It is estimated that every fifth married couple in Poland is infertile. Infertility is often a source of significant stress for the spouses. It is therefore important to provide professional support, demonstrate interest, listen to the problem, and be present in difficult moments. Social support considerably reduces the feeling of abandonment, fosters self-care, accelerates recovery, strengthens coping, and helps modify views and habits or attitudes towards treatment. The aim of this study was to assess self-care in women with infertility and to identify the most common sources of social support. ::: ::: ::: MATERIAL AND METHODS ::: The study group consisted of 100 women diagnosed or treated for infertility at the Department of Reproduction and Gynecology, Pomeranian Medical University in Szczecin. The diagnostic poll was done with the aid of a standardised research tool--the Social Support Scale Questionnaire by Danuta Zarzycka. The study protocol was approved by the Bioethics Committee of the Pomeranian Medical University. The study was performed between July 2005 and March 2007. ::: ::: ::: CONCLUSIONS ::: (1) Self-care in women with infertility is adequate. (2) Women with infertility usually expect professional support from a physician; the role of the nurse is undervalued. Thus, the role of the nurse and midwife needs further promotion.


title: Autonomous battery swapping system for small-scale helicopters

abstract: A large focus of the Unmanned Aerial Vehicle (UAV) community has been shifted to addressing the requirements necessary for managing systems of UAVs. The ability to automate the process of tracking and responding to the health of UAVs contributes to the reliable and persistent operation of multiple UAV systems. In particular, the automation of managing UAVs and their resources removes a critical, frequent, and time consuming task from an operator's workload. We have developed a battery swapping mechanism capable of ‘refueling’ UAVs autonomously. This paper presents an automated battery swapping system for multiple small-scale UAVs. The system includes a battery swapping mechanism and online algorithms to address resource management, vehicle health monitoring, and precision landing onto the battery swapping mechanism's landing platform.


title: Compiling for stream processing

abstract: This paper describes a compiler for stream programs that efficiently schedules computational kernels and stream memory operations, and allocates on-chip storage. Our compiler uses information about the program structure and estimates of kernel and memory operation execution times to overlap kernel execution with memory transfers, maximizing performance, and to optimize use of scarce on-chip memory, significantly reducing external memory bandwidth. Our compiler applies optimizations such as strip-mining, loop unrolling, and software pipelining, at the level of kernels and stream memory operations. We evaluate the performance of our compiler on a suite of media and scientific benchmarks. Our results show that compiler management of on-chip storage reduces external memory bandwidth by 35% to 93% and reduces execution time by 23% to 72% compared to cache like LRU management of the same storage. We show that strip-mining stream applications enables producer-consumer locality to be captured in on-chip storage reducing external bandwidth by 50% to 80%. We also evaluate the sensitivity of performance to the scheduling methods used and to critical resources. Overall, our compiler is able to overlap memory operations and manage local storage so that 78% to 96% of program execution time is spent in running computational kernels.


title: The One-Piece Orbitozygomatic Approach: The MacCarty Burr Hole and the Inferior Orbital Fissure as Keys to Technique and Application

abstract: OBJECTIVE ::: Use of the MacCarty keyhole burr hole and the inferior orbital fissure provides simplicity and safety to perform the one-piece frontotemporal orbitozygomatic (FTOZ1) approach. ::: ::: ::: METHODS ::: We performed the FTOZ1 approach with its three subtypes (i.e., total, temporal, and frontal) in cadaveric head specimens in the Goodyear Laboratory and subsequently in surgical cases. ::: ::: ::: RESULTS ::: The orbitozygomatic osteotomy, when added to a frontotemporal craniotomy, comprises the frontotemporal orbitozygomatic (FTOZ) approach, provides an expanded exposure to the anterior and middle cranial fossae, and enables the surgeon to create a window to the posterior cranial fossa. The MacCarty burr hole is used to facilitate orbital cuts, and the anterolateral portion of the inferior orbital fissure connects the orbital cuts to the zygomatic cuts. This allows the FTOZ1 craniotomy flap to be "out-fractured" with ease. The three types of FTOZ1 approach, i.e., the total, the temporal, and the frontal, are described step by step. ::: ::: ::: CONCLUSIONS ::: Understanding the MacCarty keyhole burr hole and the microsurgical anatomy of the inferior orbital fissure is essential to performing the FTOZ1 approach. The three types of FTOZ1 approach enable the surgeon to tailor the approach according to the surgical exposure needed for each lesion.


title: Text Detection and Recognition in Imagery: A Survey

abstract: This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field.


title: Temporal Lift by Galeapexy: A Review of 270 Cases

abstract: The purpose of this technique is to correct the ptotic tail of the brow and crow’s feet as well as to redrape the premalar skin to allow us to make a greater skin resection in the lower lid. The purpose of this presentation is to expose the advantages, the inconveniences, and the complications of this technique. We reviewed the charts of 270 patients who were operated on between June 1996 and January 2002. The average age of the patients was 45 years old. Temporal lifts were performed during a frontotemporal lift in 92 cases, and during a three-stage lift (frontal, facial, and cervical) in 170 cases. In eight cases, an isolated temporal lift was performed. An upper blepharoplasty was performed in 252 cases and transconjunctival fat pad excision was performed in 188 cases. Finally, a lower lid skin resection was done following a galeapexy in 241 cases. The surgical approach consists of a temporal incision, followed by a subgaleal dissection, and then a subcutaneous dissection that allows the cephalic galea, previously incised, to be anchored to the temporal aponeurosis. This allows good cutaneous redraping that raises the brow tail and a detachment of the orbicularis fibers. The suturing of the scalp is done without tension and without eliminating hair. The results of the technique with a follow-up period of more than five years for the earlier cases and six months for the last cases, were evaluated with three criteria: (1) the distance between the brow and the eyelashes, (2) the reduction of crow’s feet, (3) the reduction of wrinkles at the level of the lower lid and the malar region. Overall satisfaction was also rated. Of 270 patients, 225 had ratings of very good to good, 13 had average results, and 13 had unsatisfactory results. As far as complications, five had unilateral hematoma requiring an evacuation, four patients had temporary alopecia, and eight patients had temporary frontal muscle weakness that resolved within one to three months. This technique, performed under local anesthesia, is very efficient, reproducible, and simple. The incidence of complications can be reduced when one is meticulous during dissection.


title: Which documentation for software maintenance?

abstract: Software engineering has been striving for years to improve the practice of software development and maintenance. Documentation has long been prominent on the list of recommended practices to improve development and help maintenance. Recently however, agile methods started to shake this view, arguing that the goal of the game is to produce software and that documentation is only useful as long as it helps to reach this goal. On the other hand, in the re-engineering field, people wish they could re-document useful legacy software so that they may continue maintain them or migrate them to new platform. In these two case, a crucial question arises: “How much documentation is enough?” In this article, we present the results of a survey of software maintainers to try to establish what documentation artifacts are the most important to them.


title: Compact Coplanar Waveguide (CPW)-Fed Zeroth-Order Resonant Antennas With Extended Bandwidth and High Efficiency on Vialess Single Layer

abstract: This paper presents the design and analysis of compact coplanar waveguide (CPW)-fed zeroth-order resonant (ZOR) antennas. They are designed on a CPW single layer where vias are not required. The ZOR phenomenon is employed to reduce the antenna size. The novel composite right/left-handed (CRLH) unit cell on a vialess single layer simplifies the fabrication process. In addition, the CPW geometry provides high design freedom, so that bandwidth-extended ZOR antennas can be designed. The antenna's bandwidth is characterized by the circuit parameters. Based on the proposed bandwidth extension technique, symmetric, asymmetric, and chip-loaded antennas are designed. The ZOR characteristic and bandwidth extension are verified by a commercial EM simulator. Their performances are compared with those of previously reported metamaterial resonant antennas. They provide further size reduction, higher efficiency, easier manufacturing, and extended bandwidth.


title: A 0.5 V feedforward delta-sigma modulator with inverter-based integrator

abstract: This paper presents an inverter-based switched-capacitor integrator for 0.5 V low-voltage applications. The proposed integrator utilizing floating voltage source and forward body bias obtains high performance as well as good independence of variations in process and temperature. It is applied to a 0.5 V feedforward AD modulator. The test results indicate that the designed AD modulator achieves a peak SNDR of 71 dB in a 78 kHz bandwidth, and the core power consumption is only 860 µW. This work is designed in a standard 0.18 µm CMOS process.


title: Choosing DBSCAN Parameters Automatically using Differential Evolution

abstract: Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DBSCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.


title: FABC: Retinal Vessel Segmentation Using AdaBoost

abstract: This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy (0.9597 versus 0.9473 for the nearest performer).


title: Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

abstract: Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. ::: Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. ::: The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at https://sites.google.com/view/struct2depth.


title: A deep reinforcement learning-based framework for content caching

abstract: Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime.


Common ground in dialogue with a gendered humanoid robot