title: Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach

abstract: Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model. This is particularly inconvenient for language pairs for which enough parallel text is not available. In this paper, we use monolingual linguistic resources in the source side to address this challenging problem based on a multi-task learning approach. More specifically, we scaffold the machine translation task on auxiliary tasks including semantic parsing, syntactic parsing, and named-entity recognition. This effectively injects semantic and/or syntactic knowledge into the translation model, which would otherwise require a large amount of training bitext. We empirically evaluate and show the effectiveness of our multi-task learning approach on three translation tasks: English-to-French, English-to-Farsi, and English-to-Vietnamese.


title: Where Do Features Come From

abstract: It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights.


title: Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

abstract: Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network .


title: 3D Object Pose Refinement in Range Images

abstract: Estimating the pose of objects from range data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from 2.5D range images. Initialized with an approximate pose estimate, the proposed approach refines it so that it accurately accounts for an acquired range image. This is achieved by using a hypothesize-and-test scheme that combines Particle Swarm Optimization PSO and graphics-based rendering to minimize a cost function of object pose that quantifies the misalignment between the acquired and a hypothesized, rendered range image. Extensive experimental results demonstrate the superior performance of the approach compared to the Iterative Closest Point ICP algorithm that is commonly used for pose refinement.


title: Precision and Recall for Time Series

abstract: Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences.


title: MoCFI: A framework to mitigate control-flow attacks on smartphones

abstract: Runtime and control-flow attacks (such as code injection or return-oriented programming) constitute one of the most severe threats to software programs. These attacks are prevalent and have been recently applied to smartphone applications as well, of which hundreds of thousands are downloaded by users every day. While a framework for control-flow integrity (CFI) enforcement, an approach to prohibit this kind of attacks, exists for the Intel x86 platform, there is no such a solution for smartphones. In this paper, we present a novel framework, MoCFI (Mobile CFI), that provides a general countermeasure against control-flow attacks on smartphone platforms by enforcing CFI. We show that CFI on typical smartphone platforms powered by an ARM processor is technically involved due to architectural differences between ARM and Intel x86, as well as the specifics of smartphone OSes. Our framework performs CFI on-the-fly during runtime without requiring the application's source code. For our reference implementation we chose Apple's iOS, because it has been an attractive target for control-flow attacks. Nevertheless, our framework is also applicable to other ARM-based devices such as Google's Android. Our performance evaluation demonstrates that MoCFI is efficient and does not induce notable overhead when applied to popular iOS applications.


title: Audience engagement in multimedia presentations

abstract: Engagement in training and educational presentations facilitates learning. This paper describes engagement and proposes that it is conceptually similar to the state of playfulness, a construct increasingly applied to studies of human-computer interactions. It then describes how multimedia technologies can influence listeners' engagement during presentations. Results from pilot and main studies suggest that students' engagement in multimedia presentations can be increased by developing presentations that provide more challenge, feedback, presenter control, and variety. The paper concludes by discussing implications for research and practice.


title: Design and comparison of outer rotor bonded magnets Halbach motor with different topologies

abstract: The outer rotor Halbach cylinder motor is designed for hybrid electric vehicle application. The Halbach cylinder is made of bonded magnet and hence the design presented is high torque density motor with low remanence magnet. Different slots poles combinations for double layer tooth coil winding is also evaluated to find the best combination. The Halbach cylinder motor is also compared with different other motor topologies. The outer rotor Halbach motor has the highest torque compared to other topologies over the whole speed range. Variations of inductance and flux linkage of different topologies are also studied and are presented in the article.


title: Resource optimization in fog enabled IoT deployments

abstract: Internet of Things (IoT) devices are typically deployed in resource (energy, computational capacity) constrained environments. Connecting such devices to the cloud is not practical due to variable network behavior as well as high latency overheads. Fog computing refers to a scalable, distributed computing architecture which moves computational tasks closer to Edge devices or smart gateways. As an example of mobile IoT scenarios, in robotic deployments, computationally intensive tasks such as run time mapping may be performed on peer robots or smart gateways. Most of these computational tasks involve running optimization algorithms inside compute nodes at run time and taking rapid decisions based on results. In this paper, we incorporate optimization libraries within the Robot Operating System (ROS) deployed on robotic sensor-actuators. Using the ROS based simulation environment Gazebo, we demonstrate case-study scenarios for runtime optimization. The use of optimized distributed computations are shown to provide significant improvement in latency and battery saving for large computational loads. The possibility to perform run time optimization opens up a wide range of use-cases in mobile IoT deployments.


title: Comparing discrimination powers of text and citation-based context types

abstract: Different context types have different potentialities in representing subjects of documents. However, little is known on their probable differences regarding discriminating documents with different subjects. The present study aimed to compare the discrimination powers of five context types (i.e. title, textual, text-citation, reference, and reference-title contexts). Using a content analysis method, a test collection consisted of 3637 papers containing 15 English homographs in 54 subject clusters were analyzed by discriminant analysis to determine the discrimination among the clusters. The results confirmed that the homograph context types significantly discriminate between the clusters. The contexts can correctly cluster the documents from about 40 to 80%, with text and reference-title context types being stronger in differentiating the clusters. This is the first study to compare the powers of different textual contexts kinds and recommend to concentrate on context types to discriminate between documents with homographs to improve the effectiveness of their retrieval. It is suggested that a comparison of the context types be used as a tool in re-ranking or clustering of the retrieved results.


title: Broadband SOI PA with tunable matching network for improved LTE performances under high VSWR

abstract: This paper describes the design of a broadband power amplifier (PA) for LTE PMR handheld applications using a 130nm SOI CMOS technology from ST Microelectronics with high efficiency LDMOS power transistors. The PA has two power modes (3GPP and PMR) and covers the 380–450MHz and 698–862MHz frequency bands thanks to the use of a low-loss SOI Tunable Output Matching Network (TOMN) presenting the optimal load impedance for each power mode and frequency band. The TOMN is used for load tuning purpose and is able to recover any impedance mismatch up to 4:1 VSWR with less than 2dB of insertion loss. The design procedure of the TOMN is detailed in this paper. In 3GPP/PMR modes, the proposed PA provides up to 28.5dBm/31dBm of linear output power in the different frequency bands while keeping an adjacent channel leakage power ratio (ACLR) less than −35dBc with a LTE signal. Corresponding efficiency (PAE) is higher than 35% in the different modes and bands.


title: An Information System Design Theory for Green Information Systems for Sustainability Reporting - Integrating Theory with Evidence from Multiple Case studies.

abstract: Due to increasingly noticeable environmental impacts of business activities and consequently rising demands for environmental information by organizational stakeholders, reliable sustainability reporting (SR) is ever more important for firms. As the task of detailed sustainability reporting is complex and involves gathering and processing of a considerable amount of data, green information systems (Green IS) are seen as suitable to support this task. While some Green IS for SR are commercially available, their adoption is low. One reason is that there is a lack of knowledge of how to design these IS. This paper seeks to provide guidance for the design of Green IS for SR by suggesting an information system design theory (ISDT), which is a set of primarily prescriptive statements describing how to construct the class of Green IS for SR. Therefore, we synthesize knowledge gained from organizational and management theories with insights from 29 case studies conducted in a variety of industries. In result we derived a specific ISDT for Green IS for SR, that contributes to solve the trade-offs between environmental data transparency, complexity and data collection effort. Thus, the proposed ISDT paves the way for future improved Green IS for SR and sustainable development.


title: EMI: Exploration with Mutual Information

abstract: Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show competitive results on challenging locomotion tasks with continuous control and on image-based exploration tasks with discrete actions on Atari. The source code is available at this https URL .


Analysis and Stabilization of a Parallel-Type Double Inverted Pendulum System


title: Deep Learning on FPGAs: Past, Present, and Future

abstract: The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning, and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward.


title: Engineering Analysis and Recognition of Nigerian English: An Insight into Low Resource Languages

abstract: A comparative analysis between Nigerian English (NE) and American English (AE) is presented in this article. The study is aimed at highlighting differences in the speech parameters, and how they influence speech processing and automatic speech recognition (ASR). The UILSpeech corpus of Nigerian-Accented English isolated word recordings, read speech utterances, and video recordings is used as a reference for Nigerian English. The corpus captures the linguistic diversity of Nigeria with data collected from native speakers of Hausa, Igbo, and Yoruba languages. The UILSpeech corpus is intended to provide a unique opportunity for application and expansion of speech processing techniques to a limited resource language dialect. The acoustic-phonetic differences between American English (AE) and Nigerian English (NE) are studied in terms of pronunciation variations, vowel locations in the formant space, mean fundamental frequency, and phone model distances in the acoustic space, as well as through visual speech analysis of the speakers’ articulators. A strong impact of the AE–NE acoustic mismatch on ASR is observed. A combination of model adaptation and extension of the AE lexicon for newly established NE pronunciation variants is shown to substantially improve performance of the AE-trained ASR system in the new NE task. This study is a part of the pioneering efforts towards incorporating speech technology in Nigerian English and is intended to provide a development basis for other low resource language dialects and languages.


title: I-vector based speaker gender recognition

abstract: Automatic gender recognition has been becoming very important in potential applications. Many state-of-the-art gender recognition approaches based on a variety of biometrics, such as face, body shape, voice, are proposed recently. Among them, relying on voice is suboptimal due to significant variations in pitch, emotion, and noise in real-world speech. Inspired from the speaker recognition approaches relying on i-vector presentation in NIST SRE, it's believed that i-vector contains information about gender as a part of speaker's characters, and works for speaker recognition as well as for gender recognition in complex environments. So, we apply the total variability space analysis to gender classification and propose i-vector based discrimination for speaker gender recognition. The results of experiments on TIMIT corpus and NUST603_2014 database show that the proposed i-vector based speaker gender recognition improves the performance up to 99.9%, and surpasses the pitch method and UBM-SVM baseline subsystems in term of accuracy comparatively.


title: Game-Enhanced Second Language Vocabulary Acquisition Strategies: A Systematic Review

abstract: This paper is a synthesis of 17 qualitative, quantitative, and mixed-method studies published over the last decade. The synthesis suggests that game-enhanced learning provides a set of effective strategies such as language repetitions, contextual clues, interaction with native speakers and peers, and imagery to practice and use second language vocabulary in the authentic context. Some of the strategies such as word lists, dictionaries, and vocabulary exercises are more beneficial when combined with native speakers or peers’ interactions. Due to high interactivity of games, note taking and media strategies provide less support for vocabulary learning. The lack of high quality studies and empirical data makes it difficult to draw conclusions about which video games strategies provide the most benefit. The synthesis of research identifies that generally game-enhanced practices are helpful for second language vocabulary enhancement.


title: A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment

abstract: We present a wavelet-based, high performance, hierarchical scheme for image matching which includes (1) dynamic detection of interesting points as feature points at different levels of subband images via the wavelet transform, (2) adaptive thresholding selection based on compactness measures of fuzzy sets in image feature space, and (3) a guided searching strategy for the best matching from coarse level to fine level. In contrast to the traditional parallel approaches which rely on specialized parallel machines, we explored the potential of distributed systems for parallelism. The proposed image matching algorithms were implemented on a network of workstation clusters using parallel virtual machine (PVM). The results show that our wavelet-based hierarchical image matching scheme is efficient and effective for object recognition.


title: Doing Unto Future Selves As You Would Do Unto Others: Psychological Distance and Decision Making

abstract: Four experiments showed that the decisions people make for future selves and other people are similar to each other and different from their decisions for present selves. Experiments involved decisions to drink a disgusting liquid for scientific purposes (Experiment 1), tutor peers during exam week (Experiment 2), receive e-mails for charity (Experiment 3), and defer a lottery prize for a larger one (Experiment 4). These findings seemed to be at least partially rooted in the tendency for decisions regarding the ongoing, present self to be uniquely influenced by internal subjective experience. Specifically, these effects emerged for real, but not hypothetical, decisions. Also, they were mitigated by manipulations that altered participants' attention to present or future subjective experience. In addition, when participants' subjective experience primarily involved empathy for others (Experiment 3), their decisions on behalf of present selves were more generous than their decisions for future selves and others. Applications are discussed.


title: Using social media sentiment analysis for interaction design choices: an exploratory framework

abstract: Social media analytics is an emerging skill for organizations. Currently, developers are exploring ways to create tools for simplifying social media analysis. These tools tend to focus on gathering data, and using systems to make it meaningful. However, we contend that making social media data meaningful is by nature a human-computer interaction problem. We examine this problem around the emerging field of sentiment analysis, exploring criteria for designing sentiment analysis systems based in Human Computer interaction, HCI. We contend that effective sentiment analysis affects audience analysis, and can serve as a basis for communication design choices that support strategic relationship goals for organizations


title: Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure

abstract: We investigate how neural networks can learn and process languages with hierarchical, compositional semantics. To this end, we define the artificial task of processing nested arithmetic expressions, and study whether different types of neural networks can learn to compute their meaning. We find that recursive neural networks can find a generalising solution to this problem, and we visualise this solution by breaking it up in three steps: project, sum and squash. As a next step, we investigate recurrent neural networks, and show that a gated recurrent unit, that processes its input incrementally, also performs very well on this task. To develop an understanding of what the recurrent network encodes, visualisation techniques alone do not suffice. Therefore, we develop an approach where we formulate and test multiple hypotheses on the information encoded and processed by the network. For each hypothesis, we derive predictions about features of the hidden state representations at each time step, and train 'diagnostic classifiers' to test those predictions. Our results indicate that the networks follow a strategy similar to our hypothesised 'cumulative strategy', which explains the high accuracy of the network on novel expressions, the generalisation to longer expressions than seen in training, and the mild deterioration with increasing length. This is turn shows that diagnostic classifiers can be a useful technique for opening up the black box of neural networks. We argue that diagnostic classification, unlike most visualisation techniques, does scale up from small networks in a toy domain, to larger and deeper recurrent networks dealing with real-life data, and may therefore contribute to a better understanding of the internal dynamics of current state-of-the-art models in natural language processing.


title: Simple Black-Box Adversarial Perturbations for Deep Networks

abstract: Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to carefully crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world. ::: ::: In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our first attack is based on a simple idea of adding perturbation to a randomly selected single pixel or a small set of them. We then improve the effectiveness of this attack by carefully constructing a small set of pixels to perturb by using the idea of greedy local-search. Our proposed attacks also naturally extend to a stronger notion of misclassification. Our extensive experimental results illustrate that even these elementary attacks can reveal a deep neural network's vulnerabilities. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test while designing robust networks.


Intention-based walking support for paraplegia patients with Robot Suit HAL


title: Engineering a Compressed Suffix Tree Implementation

abstract: Suffix tree is one of the most important data structures in string algorithms and biological sequence analysis. Unfortunately, when it comes to implementing those algorithms and applying them to real genomic sequences, often the main memory size becomes the bottleneck. This is easily explained by the fact that while a DNA sequence of length n from alphabet Σ = {A, C, G, T} can be stored in n log |Σ| = 2n bits, its suffix tree occupies O(n log n) bits. In practice, the size difference easily reaches factor 50. ::: ::: We report on an implementation of the compressed suffix tree very recently proposed by Sadakane (Theory of Computing Systems, in press). The compressed suffix tree occupies space proportional to the text size, i.e. O(n log |Σ|) bits, and supports all typical suffix tree operations with at most log n factor slowdown. Our experiments show that, e.g. on a 10 MB DNA sequence, the compressed suffix tree takes 10% of the space of normal suffix tree. At the same time, a representative algorithm is slowed down by factor 30. ::: ::: Our implementation follows the original proposal in spirit, but some internal parts are tailored towards practical implementation. Our construction algorithm has time requirement O(n log n log |Σ|) and uses closely the same space as the final structure while constructing it: on the 10 MB DNA sequence, the maximum space usage during construction is only 1.4 times the final product size.


title: Deformable Stacked Structure for Named Entity Recognition

abstract: Neural architecture for named entity recognition has achieved great success in the field of natural language processing. Currently, the dominating architecture consists of a bi-directional recurrent neural network (RNN) as the encoder and a conditional random field (CRF) as the decoder. In this paper, we propose a deformable stacked structure for named entity recognition, in which the connections between two adjacent layers are dynamically established. We evaluate the deformable stacked structure by adapting it to different layers. Our model achieves the state-of-the-art performances on the OntoNotes dataset.


title: Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements

abstract: Positioning in wireless networks is mainly used for safety, gaming, and commercial services. It is expected to increase in popularity when emergency call services become mandatory as well as with the advent of more advanced location-based services and mobile gaming. In this article, we discuss and illustrate the possibilities and fundamental limitations associated with mobile positioning based on available wireless network measurements. The possibilities include a sensor fusion approach and model-based filtering, while the fundamental limitations provide hard bounds on the accuracy of position estimates, given the information in the measurements in the most favorable situation. The focus of this article is to illustrate the relation between performance requirements, such as those stated by the Federal Communications Commission (FCC), and the available measurements. Specific issues on accuracy limitation in each measurement, such as synchronization and multipath problems, are briefly commented upon. A geometrical example, as well as a realistic example adopted from a cell planning tool, are used for illustration.


title: A Mathematical Theory for Clustering in Metric Spaces

abstract: Clustering is one of the most fundamental problems in data analysis and it has been studied extensively in the literature. Though many clustering algorithms have been proposed, clustering theories that justify the use of these clustering algorithms are still unsatisfactory. In particular, one of the fundamental challenges is to address the following question: What is a cluster in a set of data points? In this paper, we make an attempt to address such a question by considering a set of data points associated with a distance measure (metric). We first propose a new cohesion measure in terms of the distance measure. Using the cohesion measure, we define a cluster as a set of points that are cohesive to themselves. For such a definition, we show there are various equivalent statements that have intuitive explanations. We then consider the second question: How do we find clusters and good partitions of clusters under such a definition? For such a question, we propose a hierarchical agglomerative algorithm and a partitional algorithm. Unlike standard hierarchical agglomerative algorithms, our hierarchical agglomerative algorithm has a specific stopping criterion and it stops with a partition of clusters. Our partitional algorithm, called the K-sets algorithm in the paper, appears to be a new iterative algorithm. Unlike the Lloyd iteration that needs two-step minimization, our K-sets algorithm only takes one-step minimization. One of the most interesting findings of our paper is the duality result between a distance measure and a cohesion measure. Such a duality result leads to a dual K-sets algorithm for clustering a set of data points with a cohesion measure. The dual K-sets algorithm converges in the same way as a sequential version of the classical kernel K-means algorithm. The key difference is that a cohesion measure does not need to be positive semi-definite.


title: Relation Schema Induction using Tensor Factorization with Side Information

abstract: Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of relations and their schemas, i.e., type signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an important first step towards this goal. We refer to this problem as Relation Schema Induction (RSI). In this paper, we propose Schema Induction using Coupled Tensor Factorization (SICTF), a novel tensor factorization method for relation schema induction. SICTF factorizes Open Information Extraction (OpenIE) triples extracted from a domain corpus along with additional side information in a principled way to induce relation schemas. To the best of our knowledge, this is the first application of tensor factorization for the RSI problem. Through extensive experiments on multiple real-world datasets, we find that SICTF is not only more accurate than state-of-the-art baselines, but also significantly faster (about 14x faster).


title: Advanced Local Binary Patterns for Remote Sensing Image Retrieval

abstract: The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time.


title: The Multi-user Security of GCM, Revisited: Tight Bounds for Nonce Randomization

abstract: Multi-user (mu) security considers large-scale attackers (e.g., state actors) that given access to a number of sessions, attempt to compromise at least one of them. Mu security of authenticated encryption (AE) was explicitly considered in the development of TLS 1.3. This paper revisits the mu security of GCM, which remains to date the most widely used dedicated AE mode. We provide new concrete security bounds which improve upon previous work by adopting a refined parameterization of adversarial resources that highlights the impact on security of (1) nonce re-use across users and of (2) re-keying. As one of the main applications, we give tight security bounds for the nonce-randomization mechanism adopted in the record protocol of TLS 1.3 as a mitigation of large-scale multi-user attacks. We provide tight security bounds that yield the first validation of this method. In particular, we solve the main open question of Bellare and Tackmann (CRYPTO '16), who only considered restricted attackers which do not attempt to violate integrity, and only gave non-tight bounds.


title: Frontosphenoidal synostosis: a rare cause of unilateral anterior plagiocephaly

abstract: Introduction ::: When a child walks in the clinic with a unilateral frontal flattening, it is usually associated in our minds with unilateral coronal synostosis. While the latter might be the most common cause of anterior plagiocephaly, it is not the only one. A patent coronal suture will force us to consider other etiologies, such as deformational plagiocephaly, or synostosis of another suture. To understand the mechanisms underlying this malformation, the development and growth of the skull base must be considered.


title: Feral Cats and Biodiversity Conservation: The Urgent Prioritization of Island Management

abstract: A great part of the Earth's biodiversity occurs on islands, to which humans have brought a legion of invasive species that have caused population declines and even extinctions. The domestic cat is one of the most damaging species introduced to islands, being a primary extinction driver for at least 33 insular endemic vertebrates. Here, we examine the role of feral cats in the context of the island biodiversity crisis, by combining data from reviews of trophic studies, species conservation status reports, and eradication campaigns. The integration of these reviews permits us to identify priority islands where feral cat eradications are likely to be feasible and where cats are predicted to cause the next vertebrate extinctions. Funding agencies and global conservation organizations can use these results to prioritize scarce conservation funds, and national and regional natural resource management agencies can rank their islands in need of feral cat eradication within a global context.


title: Automatic verification of design patterns in Java

abstract: Design patterns are widely used by designers and developers for building complex systems in object-oriented programming languages such as Java. However, systems evolve over time, increasing the chance that the pattern in its original form will be broken.To verify that a design pattern has not been broken requires specifying the original intent of the design pattern. Whilst informal descriptions of design patterns exist, no formal specifications are available due to differences in implementations between programming languages.We present a pattern specification language, S pine , that allows patterns to be defined in terms of constraints on their implementation in Java. We also present some examples of patterns defined in S pine and show how they are processed using a proof engine called H edgehog .The conclusion discusses the type of patterns that are amenable to defining in S pine , and highlights some repeated mini-patterns discovered in the formalisation of these design patterns.


title: Aligning Packed Dependency Trees: A Theory of Composition for Distributional Semantics

abstract: We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees. We show that these structures have the potential to capture the full sentential contexts of a lexeme and provide a uniform basis for the composition of distributional knowledge in a way that captures both mutual disambiguation and generalization.


title: Review of maglev train technologies

abstract: This paper reviews and summarizes Maglev train technologies from an electrical engineering point of view and assimilates the results of works over the past three decades carried out all over the world. Many researches and developments concerning the Maglev train have been accomplished; however, they are not always easy to understand. The purpose of this paper is to make the Maglev train technologies clear at a glance. Included are general understandings, technologies, and worldwide practical projects. Further research needs are also addressed.


title: Classifying and visualizing motion capture sequences using deep neural networks

abstract: The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative positions of joints (PO), temporal differences (TD), and normalized trajectories of motion (NT). Given these features a hybrid multi-layer perceptron is trained, which simultaneously classifies and reconstructs input data. We use deep autoencoder to visualize learnt features. The experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can. We test our system on a public database with 65 classes and more than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our knowledge, the state of the art result for such a large dataset.


title: High Accuracy Android Malware Detection Using Ensemble Learning

abstract: With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature-based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3-99% detection accuracy with very low false positive rates.


title: What is the importance of second-trimester "soft markers" for trisomy 21 after an 11- to 14-week aneuploidy screening scan?

abstract: OBJECTIVES ::: The purpose of this study was to evaluate the importance of second-trimester "soft markers" for trisomy 21 after an 11- to 14-week aneuploidy screening scan. ::: ::: ::: METHODS ::: We conducted a retrospective cohort study of consecutive patients referred for measurement of the nuchal translucency (NT) as part of a screening protocol for aneuploidy. Patients who returned for an anatomic survey between 16 and 20 weeks' gestation were evaluated. The sonographic markers and anomalies associated with the detection of trisomy 21 in the second trimester were analyzed. ::: ::: ::: RESULTS ::: There were 42 fetuses (0.4%) with trisomy 21 identified in the study cohort of 9692 patients. Trisomy 21 was suspected at the NT scan in 28 fetuses (67%) and at the second-trimester anatomic survey in 14 (33%). In fetuses first suspected of having trisomy 21 in the second trimester, 9 of 14 had normal anatomic survey results, and 5 of 14 had congenital malformations. All 14 fetuses had soft markers for aneuploidy. A thickened nuchal fold was identified in 5 of 9 fetuses with trisomy 21 and normal anatomic survey results, all of whom had an NT of less than 3.0 mm at the initial screening scan. ::: ::: ::: CONCLUSIONS ::: Second-trimester soft markers, especially a thickened nuchal fold, remain important observations in the detection of trisomy 21 by sonography among fetuses who have had first-trimester sonographic screening for aneuploidy.


title: Detecting Webshell Based on Random Forest with FastText

abstract: Web-based remote access Trojan (or webshell) is a kind of tool for network intrusion, which can be uploaded to a website to access web service management authority. Once attacker injected successfully, it can cause great damage so that it is crucial to detect webshell effectively. Webshells are flexible and changeable by using of obfuscation techniques, which compounds the difficulties of detecting. A PHP webshell detection model is proposed in this paper, which based on a combination of fastText and random forest algorithm and called FRF-WD. The PHP opcode sequences as an important feature applied for webshell detection. The experimental results show that the model can provide high detection rate and low false alarm rate, which proved the feasibility and validity of the model.


HICCUPS: Hidden communication system for corrupted networks


title: Satellite imagery segmentation: a region growing approach

abstract: This work presents a segmentation method based on a region growing approach. It has been implemented in the geographic information and image processing system (SPRING) which has been developed at INPE. The technique is applied to segment images which are being used to assess land use changes in the Amazon region. Segmented Landsat-TM images are shown to illustrate the technique.


title: Closed-form stereo image rectification

abstract: Stereo image rectification transforms a pair of images into a new pair such that the epipolar lines in the transformed images have the same direction as the image rows and matching epipolar lines in different images have the same row index to enable an efficient and reliable dense stereo matching. In this paper we propose a first closed-form algorithm to automatically rectify stereo images just using the fundamental matrix. The algorithm involves only direct and purely geometric transformation processes. There is no iterative or optimization processes for the rectification parameter estimation in our method. Real images have been used for testing purposes, and convincing results have been obtained from our algorithm. Comparison with the state-of-the-art method shows that our method produces better rectification results.


title: KGAN: How to Break The Minimax Game in GAN

abstract: Generative Adversarial Networks (GANs) were intuitively and attractively explained under the perspective of game theory, wherein two involving parties are a discriminator and a generator. In this game, the task of the discriminator is to discriminate the real and generated (i.e., fake) data, whilst the task of the generator is to generate the fake data that maximally confuses the discriminator. In this paper, we propose a new viewpoint for GANs, which is termed as the minimizing general loss viewpoint. This viewpoint shows a connection between the general loss of a classification problem regarding a convex loss function and a f-divergence between the true and fake data distributions. Mathematically, we proposed a setting for the classification problem of the true and fake data, wherein we can prove that the general loss of this classification problem is exactly the negative f-divergence for a certain convex function f. This allows us to interpret the problem of learning the generator for dismissing the f-divergence between the true and fake data distributions as that of maximizing the general loss which is equivalent to the min-max problem in GAN if the Logistic loss is used in the classification problem. However, this viewpoint strengthens GANs in two ways. First, it allows us to employ any convex loss function for the discriminator. Second, it suggests that rather than limiting ourselves in NN-based discriminators, we can alternatively utilize other powerful families. Bearing this viewpoint, we then propose using the kernel-based family for discriminators. This family has two appealing features: i) a powerful capacity in classifying non-linear nature data and ii) being convex in the feature space. Using the convexity of this family, we can further develop Fenchel duality to equivalently transform the max-min problem to the max-max dual problem.


title: Artificial Intelligence in Geography

abstract: From the Publisher: ::: The text provides an introduction to expert systems, neural nets, genetic algorithms, smart systems and artificial life and shows how they are likely to transform geographical enquiry. An integral disk provides examples and exercises for the readers to try themselves, which is a major methodological milestone in geography. The authors provide an easy to understand basic introduction to AI relevant to Geography.


title: Towards Non-Parametric Learning to Rank

abstract: This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with $n$ agents (users) $\{x_i\}_{i \in [n]}$ and $m$ alternatives (items) $\{y_j\}_{j \in [m]}$, each of which is associated with a latent feature vector. Agents rank items nondeterministically according to the Plackett-Luce model, where the higher the utility of an item to the agent, the more likely this item will be ranked high by the agent. Our goal is to find neighbors of an arbitrary agent or alternative in the latent space. ::: We first show that the Kendall-tau distance based kNN produces incorrect results in our model. Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix. Our approach is in sharp contrast to most existing feature engineering methods. Finally, we design another new algorithm identifying similar alternatives. The construction of alternative features can be done using "local information," highlighting the algorithmic difference between finding similar agents and similar alternatives.


Teaching Dual-arm Robots to Do Object Assembly using Multi-modal 3D Vision


Sustainable Interaction Design : Invention & Disposal , Renewal


title: Accelerated Gradient Temporal Difference Learning

abstract: The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD({\lambda}) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradient TD (ATD) methods that (1) provide similar data efficiency benefits to least-squares methods, at a fraction of the computation and storage (2) significantly reduce parameter sensitivity compared to linear TD methods, and (3) are asymptotically unbiased. We illustrate these claims with a proof of convergence in expectation and experiments on several benchmark domains and a large-scale industrial energy allocation domain.


title: A Survey on Data Integrity Techniques in Cloud Computing

abstract: Cloud computing which is envisioned as the next generation architecture of IT Enterprise comes into focus when someone thinks about what IT always needs. It is way to increase capacity or add capabilities without investing in infrastructure as well as licensing cost on new software. Besides of this advantage there is one major problem that needs to face while keeping sensitive data in cloud, Assurance of data integrity that is data remain as it is on server for long time. Client cannot physically access the data from the cloud server directly, without client’s knowledge, Cloud Service Provider (CSP) can alter or delete data which are either unused by client from a long a time or takes large memory space. Hence, there is need of checking the data periodically for its integrity, checking data for correction is called data integrity. To overcome data integrity problem, many techniques are proposed under different systems and security models. This paper will focus on some of the integrity proving techniques in detail along with their limitations.


title: Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning

abstract: Governments and businesses increasingly rely on data analytics and machine learning (ML) for improving their competitive edge in areas such as consumer satisfaction, threat intelligence, decision making, and product efficiency. However, by cleverly corrupting a subset of data used as input to a target's ML algorithms, an adversary can perturb outcomes and compromise the effectiveness of ML technology. While prior work in the field of adversarial machine learning has studied the impact of input manipulation on correct ML algorithms, we consider the exploitation of bugs in ML implementations. In this paper, we characterize the attack surface of ML programs, and we show that malicious inputs exploiting implementation bugs enable strictly more powerful attacks than the classic adversarial machine learning techniques. We propose a semi-automated technique, called steered fuzzing, for exploring this attack surface and for discovering exploitable bugs in machine learning programs, in order to demonstrate the magnitude of this threat. As a result of our work, we responsibly disclosed five vulnerabilities, established three new CVE-IDs, and illuminated a common insecure practice across many machine learning systems. Finally, we outline several research directions for further understanding and mitigating this threat.


title: Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings

abstract: We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. To tune the parameters of this model, we generate self-induced spelling error corpora. We perform our experiments for two languages. For English, we greatly outperform off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of a noisy channel model, showing that neural embeddings can be successfully exploited to improve upon the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling correction tool on manually annotated clinical records from the Antwerp University Hospital, but can offer no empirical evidence that our method counters the frequency bias of a noisy channel model in this case as well. However, both our context-sensitive model and our implementation of the noisy channel model obtain high scores on the test set, establishing a state-of-the-art for Dutch clinical spelling correction with the noisy channel model.


title: A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews

abstract: Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.


title: Perceived Urgency Mapping Across Modalities within a Driving Context

abstract: Hazard mapping is essential to effective driver-vehicle interface (DVI) design. Determining which modality to use for situations of different criticality requires an understanding of the relative impact of signal parameters within each modality on perceptions of urgency and annoyance. Towards this goal we obtained psychometric functions for visual, auditory and tactile interpulse interval (IPI), visual color, signal word, and auditory fundamental frequency on perceptions of urgency, annoyance, and acceptability. Results indicate that manipulation of IPI in the tactile modality, relative to visual and auditory, has greater utility (greater impact on urgency than annoyance). Manipulations of color were generally rated as less annoying and more acceptable than auditory and tactile stimuli; but they were also rated as lower in urgency relative to other modality manipulations. Manipulation of auditory fundamental frequency resulted in high ratings of both urgency and annoyance. Results of the current investigation can be used to guide DVI design and evaluation.


title: Active dv/dt control of 600V GaN transistors

abstract: With the fast-switching devices like GaN HEMT applying in power converters, the converters achieve higher switching frequency, higher efficiency and higher power density. As a result of the fast switching edge and high commutation speed, the issues like electromagnetic interference (EMI), overvoltage, gate protection become daunting tasks. The active gate control technique has been verified on the Si device to be an effective tool to relieve the challenges above, especially to reduce EMI noise by slowing down dv/dt with less penalty of switching loss. The paper proposes a new active dv/dt control circuit with fast response to change the 600 V GaN HEMT turn-off and turn-on dv/dt slew rate freely and independently, while the converter is running. To achieve this, simulations are first performed to verify the circuit function, considering all the possible parasitics distributed on the experimental setup, and a detailed circuit design is followed. Experimental results obtained on the 300V dc 15 A load current double pulse tester, composed by a GaN HEMT phase leg, validates the proposed method by varying turn-on dv/dt slew rate from 27.1 V/ns to 8.8 V/ns, turn-off dv/dt from 34.6 V/ns to 7.6 V/ns. Finally comparison with different gate resistors is provided, showing the proposed method has a smaller switching loss under the same dv/dt condition than using a large gate resistor.


title: Relative Impedance Control for Dual-Arm Robots Performing Asymmetric Bimanual Tasks

abstract: This paper presents a method of implementing impedance control (with inertia, damping, and stiffness terms) on a dual-arm system by using the relative Jacobian technique. The proposed method significantly simplifies the control implementation because the dual arm is treated as a single manipulator, whose end-effector motion is defined by the relative motion between the two end effectors. As a result, task description becomes simpler and more intuitive when specifying the desired impedance and the desired trajectories. This is the basis for the relative impedance control. In addition, the use of time-delay estimation enhances ease of implementation of our proposed method to a physical system, which would have been otherwise a very tedious and complicated process.


title: Design of a Ten-Way Conical Transmission Line Power Combiner

abstract: Axially symmetric power combiners, such as radial line and conical line combiners, are very effective in combining the output signals from a large number of power amplifiers over a wide band with low losses. The main problem with radial lines is the behavior of the characteristic impedance against radial distance, which makes design of radial combiners difficult and normally optimization based. In this paper, a step-by-step design procedure is presented for the design of a conical line combiner. The design strategy relies on the transverse electromagnetic properties of the conical line to eliminate the need for complex full-wave optimization in the design process. Circuit models are instead employed and optimized to achieve a wide matched bandwidth. A ten-way prototype was developed at X-band, which displayed more than an octave matched bandwidth with low insertion loss


title: Phylogeny and dating of divergences within the genus Thymallus (Salmonidae: Thymallinae) using complete mitochondrial genomes.

abstract: The genus Thymallus has attracted increasing attention in recent years because of its sharp demographic decline. In this study, we reported four complete mitochondrial genomes in the Thymallus genus: Baikal-Lena grayling (T. arcticus baicalolenensis), lower Amur grayling (T. tugarinae), Yalu grayling (T. a. yaluensis), and Mongolian grayling (T. brevirostris). The total length of the four new grayling mtDNAs ranged from 16 658 to 16 663 bp, all of which contained 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and one control region. The results suggested that mitochondrial genomes could be a powerful marker for resolving the phylogeny within Thymallinae. Our study validated that the Yalu grayling should be a synonym of the Amur grayling (T. grubii) at the whole mitogenome level. The phylogenetic and dating analyses placed the Amur grayling at the deepest divergence node within Thymallus, diverging at ∼14.95 Ma. The lower Amur grayling diverged at the next deepest node (∼12.14 Ma). This was followed by T. thymallus, which diverged at ∼9.27 Ma. The Mongolian grayling and the ancestor of the sister species, T. arcticus and T. arcticus baicalolenensis, diverged at ∼7.79 Ma, with T. arcticus and T. arcticus baicalolenensis separating at ∼6.64 Ma. Our study provides far better resolution of the phylogenetic relationships and divergence dates of graylings than previous studies.


title: Bayesian network learning with cutting planes

abstract: The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisation problem where the goal is to find a BN structure which maximises log marginal likelihood (BDe score). Integer programming, specifically the SCIP framework, is used to solve this optimisation problem. Acyclicity constraints are added to the integer program (IP) during solving in the form of cutting planes. Finding good cutting planes is the key to the success of the approach -the search for such cutting planes is effected using a sub-IP. Results show that this is a particularly fast method for exact BN learning.


title: Millimeter-Wave Planar Broadband Circularly Polarized Antenna Array Using Stacked Curl Elements

abstract: Using aperture-fed stacked curl elements, a full corporate-fed millimeter-wave planar broadband circularly polarized antenna array is proposed. First, the stacked curl element is aperture-fed, using the substrate-integrated waveguide (SIW) feeding network. This element is designed under a specific boundary condition, which leads to an excellent agreement of impedance bandwidth and axial ratio (AR) bandwidth between the designed element and the corresponding antenna array. Simulation results show that the antenna element achieves 35.8% impedance bandwidth and 37.8% 3 dB AR bandwidth with a maximum gain of 8.2 dBic. Then, a $2\times 2$ subarray is designed to achieve 33.1% impedance bandwidth. Next, the full-corporate SIW feeding scheme is utilized to construct the $8\times 8$ broadband antenna array. Finally, the $8\times 8$ antenna array is fabricated and its performance is evaluated. Measurement results show that its impedance bandwidth is 35.4%, 3 dB AR bandwidth is 33.8%, and 3 dB gain bandwidth is 32.2% with a peak gain of 23.5 dBic. Considering these three types of bandwidth together, its overall bandwidth is 30.6%. Good efficiency of about 70% is obtained because of the low-loss full-corporate SIW feeding scheme. A $4\times 4$ multibeam array is also simulated to verify the multibeam potential of the proposed antenna array.


title: Embedded joint-space control of a series elastic humanoid

abstract: This paper provides an overview of the embedded joint-space control approach developed for THOR, a new series elastic humanoid. The 60 kg robot features electromechanical linear series elastic actuators (SEAs), enabling low-impedance control of each joint in the lower body via linear to rotary and parallel mechanisms. We present a distributed joint impedance control framework that leverages a custom dual-axis motor controller to track position, velocity, and torque setpoints for each pair of joints. The required actuator forces are tracked using an inner force control loop combining feedforward and PID control with a model-based disturbance observer (DOB). Unlike previous approaches, we utilize an inverse plant model based on the open-loop actuator dynamics to simplify tuning of the cascaded controller by decoupling DOB estimates from the inner loop gains. The effectiveness of the proposed approach is verified through trajectory tracking and dynamic walking experiments conducted on the THOR humanoid utilizing a complementary optimization-based whole-body controller.


title: Software inspections: an effective verification process

abstract: The authors explain how to perform software inspections to locate defects. They present metrics for inspection and examples of its effectiveness. The authors contend, on the basis of their experiences and those reported in the literature, that inspections can detect and eliminate faults more cheaply than testing. >


title: Speech emotion recognition using convolutional and Recurrent Neural Networks

abstract: With rapid developments in the design of deep architecture models and learning algorithms, methods referred to as deep learning have come to be widely used in a variety of research areas such as pattern recognition, classification, and signal processing. Deep learning methods are being applied in various recognition tasks such as image, speech, and music recognition. Convolutional Neural Networks (CNNs) especially show remarkable recognition performance for computer vision tasks. In addition, Recurrent Neural Networks (RNNs) show considerable success in many sequential data processing tasks. In this study, we investigate the result of the Speech Emotion Recognition (SER) algorithm based on CNNs and RNNs trained using an emotional speech database. The main goal of our work is to propose a SER method based on concatenated CNNs and RNNs without using any traditional hand-crafted features. By applying the proposed methods to an emotional speech database, the classification result was verified to have better accuracy than that achieved using conventional classification methods.


title: Recognition of Human Motions for Imitation and Control of a Humanoid Robot

abstract: Most of the works in the field of teleoperation of humanoid robots do not use the whole-body of the robot to perform some task. This work treats the teleoperation of a humanoid robot through the recognition of human motions by using a natural interface mechanism. We propose the use of simple mathematical techniques to allow, in a satisfactory level, the user order in real-time a robot to mimic a person, to walk, to manipulate the environment with its hands and to perform some predetermined behaviors. Also, we provide an appropriate discussion around the findings of this research. The results of the experiments show a satisfactory performance for the system developed.


title: Common mechanisms of human perceptual and motor learning

abstract: The adult mammalian brain has a remarkable capacity to learn in both the perceptual and motor domains through the formation and consolidation of memories. Such practice-enabled procedural learning results in perceptual and motor skill improvements. Here, we examine evidence supporting the notion that perceptual and motor learning in humans exhibit analogous properties, including similarities in temporal dynamics and the interactions between primary cortical and higher-order brain areas. These similarities may point to the existence of a common general mechanism for learning in humans.


title: A comparison of the governance landscape of earthquake risk reduction in Nepal and the Indian State of Bihar

abstract: On 25 April 2015, a Mw 7.8 earthquake struck central Nepal, killing more than 8700 people. An earthquake of this magnitude has long been anticipated in Nepal and the neighbouring northern Indian state of Bihar, which straddle the active Himalayan frontal fault system. Drawing on field research undertaken before the earthquake, this paper traces the progress made in earthquake risk reduction efforts at the national scale in Nepal and at the sub-national scale in Bihar. With their contrasting ‘governance landscapes’, we examine the political and institutional context and power relations among different stakeholder groups, as well as the interests and political will motivating earthquake risk reduction. Nepal is a post-conflict country, with a weak legislative and institutional setting for earthquake risk reduction, and a multitude of different stakeholders (government, multi and bi-lateral donors, UN organisations, and national and international NGOs) engaged in the disaster risk reduction process. Bihar, by comparison, has a strong, hierarchical, sub-national government system with minimal influence of non-government stakeholders in earthquake risk reduction. While Nepal appears to have progressed further in strengthening earthquake resilience, the institutional structures in Bihar are stronger and could potentially support more sustainable resilience building in the long-term. The role of individual ‘champions’ in both instances (in Nepal among a national NGO, donors and multilateral agencies, and in Bihar within the government) has been instrumental in shaping the earthquake risk reduction agenda and initiatives.


title: Advances in deep learning approaches for image tagging

abstract: The advent of mobile devices and media cloud services has led to the unprecedented growth of personal photo collections. One of the fundamental problems in managing the increasing number of photos is automatic image tagging. Image tagging is the task of assigning human-friendly tags to an image so that the semantic tags can better reflect the content of the image and therefore can help users better access that image. The quality of image tagging depends on the quality of concept modeling which builds a mapping from concepts to visual images. While significant progresses are made in the past decade on image tagging, the previous approaches can only achieve limited success due to the limited concept representation ability from hand-crafted features (e.g., Scale-Invariant Feature Transform, GIST, Histogram of Oriented Gradients, etc.). Further progresses are made, since the efficient and effective deep learning algorithms have been developed. The purpose of this paper is to categorize and evaluate different image tagging approaches based on deep learning techniques. We also discuss the relevant problems and applications to image tagging, including data collection, evaluation metrics, and existing commercial systems. We conclude the advantages of different image tagging paradigms and propose several promising research directions for future works.


656: PATIENT FACTORS CONTRIBUTING TO THE COST OF INTENSIVE CARE


title: Community Detection in Networks with Node Features

abstract: Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that uses both the network edge information and the node features to detect community structures. One advantage our method has over existing joint detection approaches is the flexibility of learning the impact of different features which may differ across communities. Another advantage is the flexibility of choosing the amount of influence the feature information has on communities. The method is asymptotically consistent under the block model with additional assumptions on the feature distributions, and performs well on simulated and real networks.


title: Comparison of triangular geometries of Y-junction in co-sintered LTCC based microstrip circulator

abstract: This paper aims at comparison of transmission characteristics of circulator with triangular ferrite geometry to that of previously reported ferrite disk geometry. Co-sintering of commercially available Low Temperature Co-fired Ceramic (LTCC) dielectric tapes with pre-sintered ferrite tapes has been undertaken towards fabrication of Y-junction microstrip circulator using triangular and circular geometry. A number of samples have been fabricated to ascertain variation in transmission characteristics.


title: A survey of research on text simplification

abstract: Text simplification, defined narrowly, is the process of reducing the linguistic complexity of a text, while still retaining the original information and meaning. More broadly, text simplification encompasses other operations; for example, conceptual simplification to simplify content as well as form, elaborative modification, where redundancy and explicitness are used to emphasise key points, and text summarisation to omit peripheral or inappropriate information. There is substantial evidence that manual text simplification is an effective intervention for many readers, but automatic simplification has only recently become an established research field. There have been several recent papers on the topic, however, which bring to the table a multitude of methodologies, each with their strengths and weaknesses. The goal of this paper is to summarise the large interdisciplinary body of work on text simplification and highlight the most promising research directions to move the field forward.


An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge


title: Optimization, Maxent Models, And Conditional Estimation Without Magic

abstract: This tutorial aims to cover the basic ideas and algorithms behind techniques such as maximum entropy modeling, conditional estimation of generative probabilistic models, and issues regarding the use of models more complex than simple Naive Bayes and Hidden Markov Models. In recent years, these sophisticated probabilistic methods have been used with considerable success on most of the core tasks of natural language processing, for speech language models, and for IR tasks such as text filtering and categorization, but the methods and their relationships are often not well understood by practitioners. Our focus is on insight and understanding, using graphical illustrations rather than detailed derivations whenever possible. The goal of the tutorial is that the inner workings of these modeling and estimation techniques be transparent and intuitive, rather than black boxes labeled "magic here".The tutorial decomposes these methods into optimization problems on the one side, and optimization methods on the other. The first hour of the tutorial presents the basics of non-linear optimization, assuming only knowledge of basic calculus. We begin with a discussion of convexity and unconstrained optimization, focusing on gradient methods. We discuss in detail both simple gradient descent and the much more practical conjugate gradient descent. The key ideas are presented, including a comparison/contrast with alternative methods. Next, the case of constrained optimization is presented, highlighting the method of Lagrange multipliers and presenting several ways of translating the abstract ideas into a concrete optimization method. The principal goal, again, is to make Lagrange methods appear as intuitively natural, rather than as mathematical sleight-of-hand.The second part of the tutorial begins with a presentation of maximum entropy models from first principles, showing their equivalence to exponential models (also known as loglinear models, and particular versions of which give logistic regression, and conditional random fields). We present many simple examples to build intuition for what maxent models can and cannot do. Finally, we discuss how to find parameters for maximum entropy models using the previously presented optimization methods. By this point in the tutorial, audience members should have a clear understanding of how to build a system for estimating maxent models. We conclude with a discussion of issues specific to the language technology domain, including conditional estimation of generative models, and the issues involved in choosing model structure (such as independence, label and observation biases, and so on). We also discuss methods of smoothing, focusing on how smoothing works differently for maxent models than for standard relative-frequency-based distributions.The tutorial will run 3 hours, with a break in the middle. Participants will be assumed to know basic calculus and probability theory, and to have some exposure to models such as Naive Bayes and HMMs, but need only have a basic awareness of language technology problems.


title: Age and gender recognition in the wild with deep attention

abstract: A novel feedforward attention mechanism for CNNs is proposed.The mechanism increases CNNs robustness to image deformations and clutter.The proposed mechanism increases CNNs performance for age and gender recognition. Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy.


title: The Development of Coping

abstract: AbstractResearch on coping during childhood and adolescence is distinguished by its focus on how children deal with actual stressors in real-life contexts. Despite burgeoning literatures within age groups, studies on developmental differences and changes have proven difficult to integrate. Two recent advances promise progress toward a developmental framework. First, dual-process models that conceptualize coping as “regulation under stress” establish links to the development of emotional, attentional, and behavioral self-regulation and suggest constitutional underpinnings and social factors that shape coping development. Second, analyses of the functions of higher-order coping families allow identification of corresponding lower-order ways of coping that, despite their differences, are developmentally graded members of the same family. This emerging framework was used to integrate 44 studies reporting age differences or changes in coping from infancy through adolescence. Together, these advances outline a ...


title: A Review of Compressive Sensing in Information Security Field

abstract: The applications of compressive sensing (CS) in the field of information security have captured a great deal of researchers’ attention in the past decade. To supply guidance for researchers from a comprehensive perspective, this paper, for the first time, reviews CS in information security field from two aspects: theoretical security and application security. Moreover, the CS applied in image cipher is one of the most widespread applications, as its characteristics of dimensional reduction and random projection can be utilized and integrated into image cryptosystems, which can achieve simultaneous compression and encryption of an image or multiple images. With respect to this application, the basic framework designs and the corresponding analyses are investigated. Specifically, the investigation proceeds from three aspects, namely, image ciphers based on chaos and CS, image ciphers based on optics and CS, and image ciphers based on chaos, optics, and CS. A total of six frameworks are put forward. Meanwhile, their analyses in terms of security, advantages, disadvantages, and so on are presented. At last, we attempt to indicate some other possible application research topics in future.


title: Simplified modeling and identification approach for model-based control of parallel mechanism robot leg

abstract: Recently, new configurations of robot legs using a parallel mechanism have been studied for improving the locomotion ability in four-legged robots. However, it is difficult to obtain full dynamics of the parallel-mechanism robot legs because this mechanism has many links and complex constraint conditions, which make it difficult to design a modelbased controller. Here, we propose the simplified modeling of a parallel-mechanism robot leg with two degrees-of-freedom (2DOF), which can be used instead of complex full dynamics for model-based control. The new modeling approach considers the robot leg as a 2DOF Revolute and Prismatic(RP) manipulator, inspired by the actuation mechanism of robot legs, for easily designing a nominal model of the controller. To verify the effectiveness of the new modeling approach experimentally, we conducted dynamic simulations using a commercial multi-dynamics simulator. The simulation results confirmed that the proposed modeling approach could be an alternative modeling method for parallel-mechanism robot legs.


title: Risk prediction of post‐hepatectomy liver failure in patients with perihilar cholangiocarcinoma

abstract: BACKGROUND AND AIM ::: In most patients with perihilar cholangiocarcinoma (PHCC), major hepatectomy and extrahepatic bile duct resection are needed for surgical radicality, and a high risk of hepatic insufficiency exists. This study aims to develop a prediction model for post-hepatectomy liver failure (PHLF) in patients with PHCC. ::: ::: ::: METHODS ::: A total of 143 patients who underwent major liver resection and extrahepatic bile duct resection for PHCC between October 2001 and December 2013 were included. Clinically relevant PHLF was defined as liver failure corresponding to grade B or C of the International Study Group of Liver Surgery criteria. Multivariate logistic regression was used to develop the PHLF risk model. Model performance was evaluated internally using the area under the curve analysis (discrimination) after 1000 bootstrap resampling and the Hosmer-Lemeshow goodness-of-fit test (calibration). ::: ::: ::: RESULTS ::: Post-hepatectomy liver failure occurred in 43.4% of patients (n = 62). In multivariate analysis, PHLF was significantly associated with future liver remnant ratio (odds ratio [OR] per 10% = 0.68, 95% confidence interval [CI] 0.51-0.88), intraoperative blood loss (OR per 1 L = 1.82, 95% CI 1.11-3.17), and preoperative prothrombin time > 1.20 (OR = 3.22, 95% CI 1.15-9.97). The PHLF risk score model showed good discrimination (area under the curve = 0.708, 95% CI 0.623-0.793) and calibration (P = 0.227). ::: ::: ::: CONCLUSIONS ::: The risk model proposed in this study accurately predicted PHLF in patients with PHCC. This offers surgeons a practical guide to quantitative risk assessment of hepatic insufficiency and aids decision-making in surgical treatment and perioperative management.


title: Representation and analysis of enterprise models with semantic techniques: an application to ArchiMate, e3value and business model canvas

abstract: Enterprise models assist the governance and transformation of organizations through the specification, communication and analysis of strategy, goals, processes, information, along with the underlying application and technological infrastructure. Such models cross-cut different concerns and are often conceptualized using domain-specific modelling languages. This paper explores the application of graph-based semantic techniques to specify, integrate and analyse multiple, heterogeneous enterprise models. In particular, the proposal described in this paper (1) specifies enterprise models as ontological schemas, (2) uses transformation mapping functions to integrate the ontological schemas and (3) analyses the integrated schemas with graph querying and logical inference. The proposal is evaluated through a scenario that integrates three distinct enterprise modelling languages: the business model canvas, e3value, and the business layer of the ArchiMate language. The results show, on the one hand, that the graph-based approach is able to handle the specification, integration and analysis of enterprise models represented with different modelling languages and, on the other, that the integration challenge resides in defining appropriate mapping functions between the schemas.


title: Improved Training of Wasserstein GANs

abstract: Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.


title: Superneurons: dynamic GPU memory management for training deep neural networks

abstract: Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis}, \textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.


title: The devil is in the (implementation) details: an empirical analysis of OAuth SSO systems

abstract: Millions of web users today employ their Facebook accounts to sign into more than one million relying party (RP) websites. This web-based single sign-on (SSO) scheme is enabled by OAuth 2.0, a web resource authorization protocol that has been adopted by major service providers. The OAuth 2.0 protocol has proven secure by several formal methods, but whether it is indeed secure in practice remains an open question. We examine the implementations of three major OAuth identity providers (IdP) (Facebook, Microsoft, and Google) and 96 popular RP websites that support the use of Facebook accounts for login. Our results uncover several critical vulnerabilities that allow an attacker to gain unauthorized access to the victim user's profile and social graph, and impersonate the victim on the RP website. Closer examination reveals that these vulnerabilities are caused by a set of design decisions that trade security for implementation simplicity. To improve the security of OAuth 2.0 SSO systems in real-world settings, we suggest simple and practical improvements to the design and implementation of IdPs and RPs that can be adopted gradually by individual sites.


title: Automatic determination of facial muscle activations from sparse motion capture marker data

abstract: We built an anatomically accurate model of facial musculature, passive tissue and underlying skeletal structure using volumetric data acquired from a living male subject. The tissues are endowed with a highly nonlinear constitutive model including controllable anisotropic muscle activations based on fiber directions. Detailed models of this sort can be difficult to animate requiring complex coordinated stimulation of the underlying musculature. We propose a solution to this problem automatically determining muscle activations that track a sparse set of surface landmarks, e.g. acquired from motion capture marker data. Since the resulting animation is obtained via a three dimensional nonlinear finite element method, we obtain visually plausible and anatomically correct deformations with spatial and temporal coherence that provides robustness against outliers in the motion capture data. Moreover, the obtained muscle activations can be used in a robust simulation framework including contact and collision of the face with external objects.


title: Securing the Internet of Things in a Quantum World

abstract: Currently, we rely on cryptographic algorithms such as elliptic curve cryptosystems (ECCs) as basic building blocks to secure the communication in the IoT. However, public key schemes like ECC can easily be broken by the upcoming quantum computers. Due to recent advances in quantum computing, we should act now to prepare the IoT for the quantum world. In this article, we focus on the current state of the art and recent developments in the area of quantum-resistant cryptosystems for securing the IoT. We first demonstrate the impacts of quantum computers on the security of the cryptographic schemes used today, and then give an overview of the recommendations for cryptographic schemes that can be secure under the attacks of both classical and quantum computers. After that, we present the existing implementations of quantum-resistant cryptographic schemes on constrained devices suitable for the IoT. Finally, we give an introduction to ongoing projects for quantum-resistant schemes that will help develop future security solutions for the IoT.


title: Variable inductance multilayer inductor with MOSFET switch control

abstract: A variable monolithic inductor having a stacked spiral inductor connected with MOSFET switches is proposed and fabricated in a 0.18 /spl mu/m, one-poly-six-metal (1P6M) standard CMOS process. By controlling a voltage of the MOSFET switch, the proposed three-stacked inductor demonstrates a continuously variable inductance of from 8 to 23 nH at 2.4 GHz, and due to its stacked structure, it takes less than 50% of the chip area compared with conventional single layer inductors. With its compact size and variable inductance feature, the proposed variable inductor is a prospective key component for the multiband RF circuits such as electrically controllable matching circuits and wide tuning range voltage controlled oscillators (VCOs).


title: Entropy-Based Dynamic Complexity Metrics for Service-Oriented Systems

abstract: The complexity analysis of service systems is mainly from the static aspect, and the dynamic complexity measurement has not attracted enough attention yet. In this paper, we attempt to provide some dynamic complexity metrics for service systems through measuring their execution behaviors. At first, two models, i.e., service unit vector and service pair matrix, are presented to represent the execution traces. Meanwhile, distance entropy is adapted in our work for analyzing execution traces. Based on the modeling representation of execution traces, the basic Shannon entropy and the adapted distance entropy are used to measure the dynamic complexity of a service system. In addition, the effectiveness of our proposed entropy-based metrics of dynamic complexity is validated by several examples of service systems and a real application. Particularly, the metric of combining the adapted distance entropy and service pair matrix has the strongest ability to depict the dynamic complexity.


title: Modeling and Prediction of Human Behavior

abstract: We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements. Language: en


title: Using Repertory Grid Analysis to gather qualitative data for information systems research

abstract: Repertory Grid Analysis (RepGrid) is a research method commonly used in a quantitative manner. This paper supports the argument that RepGrid is a suitable tool for information systems researchers to use in gathering qualitative data. This argument is based on experiences with using RepGrid as a qualitative tool in the preliminary stages of an information systems research project and concurs with the findings of two previous authors. The paper begins by describing the method and its potential applications. It then describes the specifics of the research setting and how and why RepGrid was utilised. The authors’ findings in relation to the methods suitability are then presented; these are classified as either ‘limitations’ or ‘strengths’. Mechanisms for overcoming the limitations are detailed. These findings support the conclusion that RepGrid is a suitable tool for gathering qualitative data. The paper is of value to information systems researchers considering various qualitative data gathering techniques. It adds further weight to the findings of previous authors supporting the use of RepGrid and presents materials to assist those considering its use.


title: A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content

abstract: Malicious web content is a serious problem on the Internet today. In this paper we propose a deep learning approach to detecting malevolent web pages. While past work on web content detection has relied on syntactic parsing or on emulation of HTML and Javascript to extract features, our approach operates directly on a language-agnostic stream of tokens extracted directly from static HTML files with a simple regular expression. This makes it fast enough to operate in high-frequency data contexts like firewalls and web proxies, and allows it to avoid the attack surface exposure of complex parsing and emulation code. Unlike well-known approaches such as bag-of-words models, which ignore spatial information, our neural network examines content at hierarchical spatial scales, allowing our model to capture locality and yielding superior accuracy compared to bag-of-words baselines. Our proposed architecture achieves a 97.5% detection rate at a 0.1% false positive rate, and classifies small-batched web pages at a rate of over 100 per second on commodity hardware. The speed and accuracy of our approach makes it appropriate for deployment to endpoints, firewalls, and web proxies.


title: Autonomous operation of novel elevators for robot navigation

abstract: Although robot navigation in indoor environments has achieved great success, robots are unable to fully navigate these spaces without the ability to operate elevators, including those which the robot has not seen before. In this paper, we focus on the key challenge of autonomous interaction with an unknown elevator button panel. A number of factors, such as lack of useful 3D features, variety of elevator panel designs, variation in lighting conditions, and small size of elevator buttons, render this goal quite difficult. To address the task of detecting, localizing, and labeling the buttons, we use state-of-the-art vision algorithms along with machine learning techniques to take advantage of contextual features. To verify our approach, we collected a dataset of 150 pictures of elevator panels from more than 60 distinct elevators, and performed extensive offline testing. On this very diverse dataset, our algorithm succeeded in correctly localizing and labeling 86.2% of the buttons. Using a mobile robot platform, we then validate our algorithms in experiments where, using only its on-board sensors, the robot autonomously interprets the panel and presses the appropriate button in elevators never seen before by the robot. In a total of 14 trials performed on 3 different elevators, our robot succeeded in localizing the requested button in all 14 trials and in pressing it correctly in 13 of the 14 trials.


title: Incremental Learning of Concept Drift from Streaming Imbalanced Data

abstract: Learning in nonstationary environments, also known as learning concept drift, is concerned with learning from data whose statistical characteristics change over time. Concept drift is further complicated if the data set is class imbalanced. While these two issues have been independently addressed, their joint treatment has been mostly underexplored. We describe two ensemble-based approaches for learning concept drift from imbalanced data. Our first approach is a logical combination of our previously introduced Learn++.NSE algorithm for concept drift, with the well-established SMOTE for learning from imbalanced data. Our second approach makes two major modifications to Learn++.NSE-SMOTE integration by replacing SMOTE with a subensemble that makes strategic use of minority class data; and replacing Learn++.NSE and its class-independent error weighting mechanism with a penalty constraint that forces the algorithm to balance accuracy on all classes. The primary novelty of this approach is in determining the voting weights for combining ensemble members, based on each classifier's time and imbalance-adjusted accuracy on current and past environments. Favorable results in comparison to other approaches indicate that both approaches are able to address this challenging problem, each with its own specific areas of strength. We also release all experimental data as a resource and benchmark for future research.


A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap