Query: Two privacy-preserving approaches for data publishing with identity reservation

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Anonymizing sequential releases <=SEP=> An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.

Protecting Respondents' Identities in Microdata Release <=SEP=> ÐToday's globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of the respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal

Generalizing Data to Provide Anonymity when Disclosing Information (Abstract) <=SEP=> The proliferation of information on the Internet and access to fast computers with large storage capacities has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private information is released in data believed to be anonymous. At the same time, increasing demands are made on organizations to release individualized data rather than aggregate statistical information. Even when explicit identi ers, such as name and phone number, are removed or encrypted when releasing individualized data, other characteristic data, which we term quasi-identi ers, can exist which allow the data recipient to re-identify individuals to whom the data refer. In this paper, we provide a computational disclosure technique for releasing information from a private table such that the identity of any individual to whom the released data refer cannot be de nitively recognized. Our approach protects against linking to other data. It is based on the concepts of generalization, by which stored values can be replaced with semantically consistent and truthful but less precise alternatives, and of k-anonymity . A table is said to provide k-anonymity when the contained data do not allow the recipient to associate the released information to a set of individuals smaller than k. We introduce the notions of generalized table and of minimal generalization of a table with respect to a k-anonymity requirement. As an optimization problem, the objective is to minimally distort the data while providing adequate protection. We describe an algorithm that, given a table, e ciently computes a preferred minimal generalization to provide anonymity.

Bottom-Up Generalization : A Data Mining Solution to Privacy Protection <=SEP=> In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. This paper investigates data mining as a technique for masking data; therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The following privacy problem is considered in this paper: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. An iterative bottom-up generalization is adapted from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration.

M-invariance: towards privacy preserving re-publication of dynamic datasets <=SEP=> The previous literature of privacy preserving data publication has focused on performing "one-time" releases. Specifically, none of the existing solutions supports re-publication of the microdata, after it has been updated with insertions <u>and</u> deletions. This is a serious drawback, because currently a publisher cannot provide researchers with the most recent dataset continuously.
 This paper remedies the drawback. First, we reveal the characteristics of the re-publication problem that invalidate the conventional approaches leveraging k-anonymity and l-diversity. Based on rigorous theoretical analysis, we develop a new generalization principle m-invariance that effectively limits the risk of privacy disclosure in re-publication. We accompany the principle with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata. Our theoretical results are confirmed by extensive experiments with real data.

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty <=SEP=> We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.

An Architecture for Local Energy Generation, Distribution, and Sharing <=SEP=> The United States electricity grid faces significant problems resulting from fundamental design principles that limit its ability to handle the key energy challenges of the 21st century. We propose an innovative electric power architecture, rooted in lessons learned from the Internet and microgrids, which addresses these problems while interfacing gracefully into the current grid to allow for non-disruptive incremental adoption. Such a system, which we term a "Local" grid, is controlled by intelligent power switches (IPS), and can consist of loads, energy sources, and energy storage. The desired result of the proposed architecture is to produce a grid network designed for distributed renewable energy, prevalent energy storage, and stable autonomous systems. We will describe organizing principles of such a system that ensure well-behaved operation, such as requirements for communication and energy transfer protocols, regulation and control schemes, and market-based rules of operation.

A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences <=SEP=> In this paper, we propose a sequential neural encoder with latent structured description SNELSD for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory LSTM units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.

Advanced Gate Drive Unit With Closed-Loop $di_{{C}}/dt$  Control <=SEP=> This paper describes the design and the experimental investigation of a gate drive unit with closed-loop control of the collector current slope diC/dt for multichip insulated-gate bipolar transistors (IGBTs). Compared to a pure resistive gate drive, the proposed diC/dt control offers the ability to adjust the collector current slope freely which helps to find an optimized relation between switching losses and secure operation of the freewheeling diode for every type of IGBT. Based on the description of IGBT's switching behavior, the design and the realization of the gate drive are presented. The test setup and the comparison of switching tests with and without the proposed diC/dt control are discussed.

3-Dimensional Localization via RFID Tag Array <=SEP=> In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.

Inductive Learning of Answer Set Programs from Noisy Examples <=SEP=> In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.

On Formal and Cognitive Semantics for Semantic Computing <=SEP=> Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

GENDER RECOGNITION FROM FACES USING BANDLET TRANSFORMATION <=SEP=> Gender Recognition is important in different commercial and law enforcement applications. In this paper we have proposed a gender recognition system through facial images. We have used a different technique that involves Bandlet Transform instead of previously used Wavelet Transform, which is a multi-resolution technique and more efficiently provides the edges of images, and then mean is combined to create the feature vectors of the images. To classify the images for gender, we have used fuzzy c mean clustering. Experimental results have shown that average 97.1% accuracy have been achieved using this technique when SUMS database was used and 93.3% was achieved when FERET database was used. Keywords----Bandlet, Gender Recognition, Fuzzy C-mean, Multi Resolution.

Evolution of the internal fixation of long bone fractures. The scientific basis of biological internal fixation: choosing a new balance between stability and biology. <=SEP=> The advent of 'biological internal fixation' is an important development in the surgical management of fractures. Locked nailing has demonstrated that flexible fixation without precise reduction results in reliable healing. While external fixators are mainly used today to provide temporary fixation in fractures after severe injury, the internal fixator offers flexible fixation, maintaining the advantages of the external fixator but allowing long-term treatment. The internal fixator resembles a plate but functions differently. It is based on pure splinting rather than compression. The resulting flexible stabilisation induces the formation of callus. With the use of locked threaded bolts, the application of the internal fixator foregoes the need of adaptation of the shape of the splint to that of the bone during surgery. Thus, it is possible to apply the internal fixator as a minimally invasive percutaneous osteosynthesis (MIPO). Minimal surgical trauma and flexible fixation allow prompt healing when the blood supply to bone is maintained or can be restored early. The scientific basis of the fixation and function of these new implants has been reviewed. The biomechanical aspects principally address the degree of instability which may be tolerated by fracture healing under different biological conditions. Fractures may heal spontaneously in spite of gross instability while minimal, even non-visible, instability may be deleterious for rigidly fixed small fracture gaps. The theory of strain offers an explanation for the maximum instability which will be tolerated and the minimal degree required for induction of callus formation. The biological aspects of damage to the blood supply, necrosis and temporary porosity explain the importance of avoiding extensive contact of the implant with bone. The phenomenon of bone loss and stress protection has a biological rather than a mechanical explanation. The same mechanism of necrosis-induced internal remodelling may explain the basic process of direct healing.

A Survey on Clustering Algorithms for Wireless Sensor Networks <=SEP=> A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.

80ºC, 50-Gb/s Directly Modulated InGaAlAs BH-DFB Lasers <=SEP=> Direct modulation at 50 Gb/s of 1.3-μm InGaAlAs DFB lasers operating at up to 80°C was experimentally demonstrated by using a ridge-shaped buried heterostructure.

Platform-as-a-Service (PaaS): The Next Hype of Cloud Computing <=SEP=> Cloud Computing is expected to become the driving force of information technology to revolutionize the future. Presently number of companies is trying to adopt this new technology either as service providers, enablers or vendors. In this way the cloud market is estimated be likely to emerge at a remarkable rate. Under the whole cloud umbrella, PaaS seems to have a relatively small market share. However, it is expected to offer much more as it is compared with its counterparts SaaS and IaaS. This paper is aimed to assess and analyze the future of PaaS technology. Year 2015 named as “the year of PaaS”. It means that PaaS technology has established strong roots and ready to hit the market with better technology services. This research will discuss future PaaS market trends, growth and business competitors. In the current dynamic era, several companies in the market are offering PaaS services. This research will also outline some of the top service providers (proprietary & open source) to discuss their current technology status and present a futuristic look into their services and business strategies. Analysis of the present and future PaaS technology infrastructure will also be a major discussion in this paper.

Hybrid CMOS/memristor circuits <=SEP=> This is a brief review of recent work on the prospective hybrid CMOS/memristor circuits. Such hybrids combine the flexibility, reliability and high functionality of the CMOS subsystem with very high density of nanoscale thin film resistance switching devices operating on different physical principles. Simulation and initial experimental results demonstrate that performance of CMOS/memristor circuits for several important applications is well beyond scaling limits of conventional VLSI paradigm.

Automatic Face Image Quality Prediction <=SEP=> Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcherindependent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.

The Neural Architecture of the Language Comprehension Network: Converging Evidence from Lesion and Connectivity Analyses <=SEP=> While traditional models of language comprehension have focused on the left posterior temporal cortex as the neurological basis for language comprehension, lesion and functional imaging studies indicate the involvement of an extensive network of cortical regions. However, the full extent of this network and the white matter pathways that contribute to it remain to be characterized. In an earlier voxel-based lesion-symptom mapping analysis of data from aphasic patients (Dronkers et al., 2004), several brain regions in the left hemisphere were found to be critical for language comprehension: the left posterior middle temporal gyrus, the anterior part of Brodmann's area 22 in the superior temporal gyrus (anterior STG/BA22), the posterior superior temporal sulcus (STS) extending into Brodmann's area 39 (STS/BA39), the orbital part of the inferior frontal gyrus (BA47), and the middle frontal gyrus (BA46). Here, we investigated the white matter pathways associated with these regions using diffusion tensor imaging from healthy subjects. We also used resting-state functional magnetic resonance imaging data to assess the functional connectivity profiles of these regions. Fiber tractography and functional connectivity analyses indicated that the left MTG, anterior STG/BA22, STS/BA39, and BA47 are part of a richly interconnected network that extends to additional frontal, parietal, and temporal regions in the two hemispheres. The inferior occipito-frontal fasciculus, the arcuate fasciculus, and the middle and inferior longitudinal fasciculi, as well as transcallosal projections via the tapetum were found to be the most prominent white matter pathways bridging the regions important for language comprehension. The left MTG showed a particularly extensive structural and functional connectivity pattern which is consistent with the severity of the impairments associated with MTG lesions and which suggests a central role for this region in language comprehension.

Distribution of Eigenvalues for 2x2 MIMO Channel Capacity Based on Indoor Measurements <=SEP=> Based on the non line-of-sight (NLOS) indoor measurements performed in the corridors on the third floor of the Electronics and Telecommunications Research Institute (ETRI) building in Daejeon city, Republic of Korea, we investigated the distribution of the eigenvalues of HH*, where H denotes a 2×2 multiple-input multiple-output (MIMO) channel matrix. Using the observation that the distribution of the measured eigenvalues matches well with the Gamma distribution, we propose a model of eigenvalues as Gamma distributed random variables that prominently feature both transmitting and receiving correlations. Using the model with positive integer k_i, i=1, 2, which is the shape parameter of a Gamma distribution, we derive the closed-form ergodic capacity of the 2×2 MIMO channel. Validation results show that the proposed model enables the evaluation of the outage and ergodic capacities of the correlated 2×2 MIMO channel in the NLOS indoor corridor environment.

Speech recognition of Malayalam numbers <=SEP=> Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.

Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval <=SEP=> We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.

Kaleido: You Can Watch It But Cannot Record It <=SEP=> Recently a number of systems have been developed to implement and improve the visual communication over screen-camera links. In this paper we study an opposite problem: how to prevent unauthorized users from videotaping a video played on a screen, such as in a theater, while do not affect the viewing experience of legitimate audiences. We propose and develop a light-weight hardware-free system, called Kaleido, that ensures these properties by taking advantage of the limited disparities between the screen-eye channel and the screen-camera channel. Kaleido does not require any extra hardware and is purely based on re-encoding the original video frame into multiple frames used for displaying. We extensively test our system Kaleido using a variety of smartphone cameras. Our experiments confirm that Kaleido preserves the high-quality screen-eye channel while reducing the secondary screen-camera channel quality significantly.

A new design of XOR-XNOR gates for low power application <=SEP=> XOR and XNOR gate plays an important role in digital systems including arithmetic and encryption circuits. This paper proposes a combination of XOR-XNOR gate using 6-transistors for low power applications. Comparison between a best existing XOR-XNOR have been done by simulating the proposed and other design using 65nm CMOS technology in Cadence environment. The simulation results demonstrate the delay, power consumption and power-delay product (PDP) at different supply voltages ranging from 0.6V to 1.2V. The results show that the proposed design has lower power dissipation and has a full voltage swing.

Twenty-Five Years of Research on Women Farmers in Africa : Lessons and Implications for Agricultural Research Institutions with an Annotated Bibliography <=SEP=> .................................................................................................................... iv Acknowledgments ...................................................................................................... iv Introduction ............................................................................................................... 1 Labor ..........................................................................................................................2 Gender Division of Labor .................................................................................... 2 Household Labor Availability ............................................................................... 6 Agricultural Labor Markets .................................................................................. 8 Conclusions: Labor and Gender .......................................................................... 9 Land ........................................................................................................................ 10 Access to Land ................................................................................................... 10 Security of Land ................................................................................................ 11 Changing Access to Land ................................................................................... 11 Access to Other Inputs .............................................................................................. 12 Access to Credit ................................................................................................. 13 Access to Fertilizer ............................................................................................. 14 Access to Extension and Information ................................................................. 15 Access to Mechanization .................................................................................... 16 Gender Issues in Access to Inputs: Summary...................................................... 16 Outputs .................................................................................................................... 17 Household Decision-Making .................................................................................... 18 Cooperative Bargaining and Collective Models .................................................. 19 Noncooperative Bargaining Models ................................................................... 19 Conclusions .............................................................................................................. 21 References ................................................................................................................. 23 Annotated Bibliography ............................................................................................ 27

Sensor Based PUF IoT Authentication Model for a Smart Home with Private Blockchain <=SEP=> With ubiquitous adoption of connected sensors, actuators and smart devices are finding inroads into daily life. Internet of Things (IoT) authentication is rapidly transforming from classical cryptographic user-centric knowledge based approaches to device signature based automated methodologies to corroborate identity between claimant and a verifier. Physical Unclonable Function (PUF) based IoT authentication mechanisms are gaining widespread interest as users are required to access IoT devices in real time while also expecting execution of sensitive (even physical) IoT actions immediately. This paper, delineates combination of BlockChain and Sensor based PUF authentication mechanism for solving real-time but non-repudiable access to IoT devices in a Smart Home by utilizing a mining less consensus mechanism for the provision of immutable assurance to users' and IoT devices' transactions i.e. commands, status alerts, actions etc.

Defending the morality of violent video games <=SEP=> The effect of violent video games is among the most widely discussed topics in media studies, and for good reason. These games are immensely popular, but many seem morally objectionable. Critics attack them for a number of reasons ranging from their capacity to teach players weapons skills to their ability to directly cause violent actions. This essay shows that many of these criticisms are misguided. Theoretical and empirical arguments against violent video games often suffer from a number of significant shortcomings that make them ineffective. This essay argues that video games are defensible from the perspective of Kantian, Aristotelian, and utilitarian moral theories.

Education-specific Tag Recommendation in CQA Systems <=SEP=> Systems for Community Question Answering (CQA) are well-known on the open web (e.g. Stack Overflow or Quora). They have been recently adopted also for use in educational domain (mostly in MOOCs) to mediate communication between students and teachers. As students are only novices in topics they learn about, they may need various scaffoldings to achieve effective question answering. In this work, we focus specifically on automatic recommendation of tags classifying students' questions. We propose a novel method that can automatically analyze a text of a question and suggest appropriate tags to an asker. The method takes specifics of educational domain into consideration by a two-step recommendation process in which tags reflecting course structure are recommended at first and consequently supplemented with additional related tags.
 Evaluation of the method on data from CS50 MOOC at Stack Exchange platform showed that the proposed method achieved higher performance in comparison with a baseline method (tag recommendation without taking educational specifics into account).

Teaching a Machine to Read Maps With Deep Reinforcement Learning <=SEP=> The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-theart methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and naviga-