query: SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS

positive: A Scalable Hierarchical Distributed Language Model Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used n-gram language models. The main drawback of NPLMs is their extremely long training and testing times. Morin and Bengio have proposed a hierarchical language model built around a binary tree of words, which was two orders of magnitude faster than the nonhierarchical model it was based on. However, it performed considerably worse than its non-hierarchical counterpart in spite of using a word tree created using expert knowledge. We introduce a fast hierarchical language model along with a simple feature-based algorithm for automatic construction of word trees from the data. We then show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models.

positive: Extensions of recurrent neural network language model We present several modifications of the original recurrent neural network language model (RNN LM).While this model has been shown to significantly outperform many competitive language modeling techniques in terms of accuracy, the remaining problem is the computational complexity. In this work, we show approaches that lead to more than 15 times speedup for both training and testing phases. Next, we show importance of using a backpropagation through time algorithm. An empirical comparison with feedforward networks is also provided. In the end, we discuss possibilities how to reduce the amount of parameters in the model. The resulting RNN model can thus be smaller, faster both during training and testing, and more accurate than the basic one.

positive: Learning representations by back-propagating errors We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.

positive: Long Short-Term Memory Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

positive: Distributional Clustering of English Words We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical “soft” clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.

negative: Characterization of liquid crystal polymer (LCP) material and transmission lines on LCP substrates from 30 to 110 GHz Liquid crystal polymer (LCP) is a material that has gained attention as a potential high-performance microwave substrate and packaging material. This investigation uses several methods to determine the electrical properties of LCP for millimeter-wave frequencies. Microstrip ring resonators and cavity resonators are measured in order to characterize the dielectric constant (/spl epsi//sub r/) and loss tangent (tan/spl delta/) of LCP above 30 GHz. The measured dielectric constant is shown to be steady near 3.16, and the loss tangent stays below 0.0049. In addition, various transmission lines are fabricated on different LCP substrate thicknesses and the loss characteristics are given in decibels per centimeter from 2 to 110 GHz. Peak transmission-line losses at 110 GHz vary between 0.88-2.55 dB/cm, depending on the line type and geometry. These results show, for the first time, that LCP has excellent dielectric properties for applications extending through millimeter-wave frequencies.

negative: Efficient Multi-task Feature and Relationship Learning We consider a multitask learning problem, in which several predictors are learned jointly. Prior research has shown that learning the relations between tasks, and between the input features, together with the predictor, can lead to better generalization and interpretability, which proved to be useful for applications in many domains. In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively. First, we demonstrate that existing methods proposed for this problem present an issue that may lead to ill-posed optimization. We then propose an alternative formulation, as well as an efficient algorithm to optimize it. Using ideas from optimization and graph theory, we propose an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Our experiments show that the proposed optimization method is orders of magnitude faster than its competitors. We also provide a nonlinear extension that is able to achieve better generalization than existing methods.

negative: Sensor relocation in mobile sensor networks Recently there has been a great deal of research on using mobility in sensor networks to assist in the initial deployment of nodes. Mobile sensors are useful in this environment because they can move to locations that meet sensing coverage requirements. This paper explores the motion capability to relocate sensors to deal with sensor failure or respond to new events. We define the problem of sensor relocation and propose a two-phase sensor relocation solution: redundant sensors are first identified and then relocated to the target location. We propose a Grid-Quorum solution to quickly locate the closest redundant sensor with low message complexity, and propose to use cascaded movement to relocate the redundant sensor in a timely, efficient and balanced way. Simulation results verify that the proposed solution outperforms others in terms of relocation time, total energy consumption, and minimum remaining energy.

negative: Distributed Optimization for Model Predictive Control of Linear Dynamic Networks With Control-Input and Output Constraints A linear dynamic network is a system of subsystems that approximates the dynamic model of large, geographically distributed systems such as the power grid and traffic networks. A favorite technique to operate such networks is distributed model predictive control (DMPC), which advocates the distribution of decision-making while handling constraints in a systematic way. This paper contributes to the state-of-the-art of DMPC of linear dynamic networks in two ways. First, it extends a baseline model by introducing constraints on the output of the subsystems and by letting subsystem dynamics to depend on the state besides the control signals of the subsystems in the neighborhood. With these extensions, constraints on queue lengths and delayed dynamic effects can be modeled in traffic networks. Second, this paper develops a distributed interior-point algorithm for solving DMPC optimization problems with a network of agents, one for each subsystem, which is shown to converge to an optimal solution. In a traffic network, this distributed algorithm permits the subsystem of an intersection to be reconfigured by only coordinating with the subsystems in its vicinity.

negative: Semi-Global Matching: A Principled Derivation in Terms of Message Passing Semi-global matching, originally introduced in the context of dense stereo, is a very successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field defined on a grid. We offer the first principled explanation of this empirically successful algorithm, and clarify its exact relation to belief propagation and tree-reweighted message passing. One outcome of this new connection is an uncertainty measure for the MAP label of a variable in a Markov Random Field.

negative: Challenges and implications of using ultrasonic communications in intra-body area networks Body area networks (BANs) promise to enable revolutionary biomedical applications by wirelessly interconnecting devices implanted or worn by humans. However, BAN wireless communications based on radio-frequency (RF) electromagnetic waves suffer from poor propagation of signals in body tissues, which leads to high levels of attenuation. In addition, in-body transmissions are constrained to be low-power to prevent overheating of tissues and consequent death of cells. To address the limitations of RF propagation in the human body, we propose a paradigm shift by exploring the use of ultrasonic waves as the physical medium to wirelessly interconnect in-body implanted devices. Acoustic waves are the transmission technology of choice for underwater communications, since they are known to propagate better than their RF counterpart in media composed mainly of water. Similarly, we envision that ultrasound (e.g., acoustic waves at non-audible frequencies) will provide support for communications in the human body, which is composed for 65% of water. In this paper, we first assess the feasibility of using ultrasonic communications in intra-body BANs, i.e., in-body networks where the devices are biomedical sensors that communicate with an actuator/gateway device located inside the body. We discuss the fundamentals of ultrasonic propagation in tissues, and explore important tradeoffs, including the choice of a transmission frequency, transmission power, bandwidth, and transducer size. Then, we discuss future research challenges for ultrasonic networking of intra-body devices at the physical, medium access and network layers of the protocol stack.

negative: Biobanks: transnational, European and global networks. Biobanks contain biological samples and associated information that are essential raw materials for advancement of biotechnology, human health, and research and development in life sciences. Population-based and disease-oriented biobanks are major biobank formats to establish the disease relevance of human genes and provide opportunities to elucidate their interaction with environment and lifestyle. The developments in personalized medicine require molecular definition of new disease subentities and biomarkers for identification of relevant patient subgroups for drug development. These emerging demands can only be met if biobanks cooperate at the transnational or even global scale. Establishment of common standards and strategies to cope with the heterogeneous legal and ethical landscape in different countries are seen as major challenges for biobank networks. The Central Research Infrastructure for Molecular Pathology (CRIP), the concept for a pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI), and the Organization for Economic Co-operation and Development (OECD) global Biological Resources Centres network are examples for transnational, European and global biobank networks that are described in this article.

negative: Implantable Antennas for Biomedical Applications: An Overview on Alternative Antenna Design Methods and Challenges Implanted biomedical devices are witnessing great attention in finding solutions to complex medical conditions. Many challenges face the design of implantable biomedical devices including designing and implanting antennas within hostile environment due to the surrounding tissues of human body. Implanted antennas must be compact in size, efficient, safe, and can effectively work within adequate medical frequency bands. This paper presents an overview of the major aspects related to the design and challenges of in body implanted antennas. The review includes surveying the applications, design methods, challenges, simulation tools, and testing and manufacturing of implantable biomedical antennas.

negative: Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

negative: The emotional brain The discipline of affective neuroscience is concerned with the neural bases of emotion and mood. The past 30 years have witnessed an explosion of research in affective neuroscience that has addressed questions such as: which brain systems underlie emotions? How do differences in these systems relate to differences in the emotional experience of individuals? Do different regions underlie different emotions, or are all emotions a function of the same basic brain circuitry? How does emotion processing in the brain relate to bodily changes associated with emotion? And, how does emotion processing in the brain interact with cognition, motor behaviour, language and motivation?

negative: Particle Swarm Optimization of the Multioscillatory LQR for a Three-Phase Four-Wire Voltage-Source Inverter With an  $LC$ Output Filter This paper presents evolutionary optimization of the linear quadratic regulator (LQR) for a voltage-source inverter with an LC output filter. The procedure involves particle-swarm-based search for the best weighting factors in the quadratic cost function. It is common practice that the weights in the cost function are set using the guess-and-check method. However, it becomes quite challenging, and usually very time-consuming, if there are many auxiliary states added to the system. In order to immunize the system against unbalanced and nonlinear loads, oscillatory terms are incorporated into the control scheme, and this significantly increases the number of weights to be guessed. All controller gains are determined altogether in one LQR procedure call, and the originality reported here refers to evolutionary tuning of the weighting matrix. There is only one penalty factor to be set by the designer during the controller synthesis procedure. This coefficient enables shaping the dynamics of the closed-loop system by penalizing the dynamics of control signals instead of selecting individual weighting factors for augmented state vector components. Simulational tuning and experimental verification (the physical converter at the level of 21 kVA) are included.

negative: Towards fabrication of Vertical Slit Field Effect Transistor (VeSFET) as new device for nano-scale CMOS technology This paper proposes a CMOS based process for Vertical Slit Field Effect Transistors. The central part of the device, namely, the vertical slit, is defined by using electron beam lithography and silicon dry etching. In order to verify the validity and the reproducibility of the process, devices having the slit width ranging from 16 nm to 400 nm were fabricated, with slit conductance in the range 0.6 to 3 milliSiemens, in agreement with the expected values.

negative: Interpretable Counting for Visual Question Answering Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of both the image and question or summing fractional counts estimated from each section of the image. In contrast, we treat counting as a sequential decision process and force our model to make discrete choices of what to count. Specifically, the model sequentially selects from detected objects and learns interactions between objects that influence subsequent selections. A distinction of our approach is its intuitive and interpretable output, as discrete counts are automatically grounded in the image. Furthermore, our method outperforms the state of the art architecture for VQA on multiple metrics that evaluate counting.

negative: A systematic review of the impact of the use of social networking sites on body image and disordered eating outcomes. A large body of literature has demonstrated mass media effects on body image and disordered eating. More recently, research in this area has turned to 'new' forms of media, such as the Internet, and particularly Social Networking Sites (SNSs). A systematic search for peer-reviewed articles on SNS use and body image and eating disorders resulted in 20 studies meeting specific inclusion criteria. As a whole, these articles demonstrated that use of SNSs is associated with body image and disordered eating. Specific SNS activities, such as viewing and uploading photos and seeking negative feedback via status updates, were identified as particularly problematic. A small number of studies also addressed underlying processes and found that appearance-based social comparison mediated the relationship between SNS use and body image and eating concerns. Gender was not found to be a moderating factor. It was concluded that, although there is a good deal of correlational research supporting the maladaptive effect of SNS use on body image and disordered eating, more longitudinal and experimental studies are needed.

negative: Supporting Cloud Computing in Thin-Client/Server Computing Model This paper addresses the issue of how to support cloud computing in thin-client/server computing model. We describe the design and implementation of the multiple-application-server thin-client/server computing (MAS TC/S) model that allows users with thin-client devices to roam around a wide area network whilst experiencing transparent working environments. MAS TC/S can be applied to a wide variety of applications in wide area network, such as pervasive software rental, service-oriented infrastructure for an Internet service provider, and office automation for transnational corporations. The MAS TC/S architecture model comprises five major components: the display protocol, the multiple-application-server topology, the application-server discovery protocol, the distributed file system, and the network input/output protocol. We discuss problems and present solutions in the design of each constituent component. A prototype of the MAS TC/S that spans the campuses of two universities in Taiwan has been built – we also report our experiences in constructing this prototype.

negative: Explaining Inconsistencies in OWL Ontologies Justifications play a central role as the basis for explaining entailments in OWL ontologies. While techniques for computing justifications for entailments in consistent ontologies are theoretically and practically well-understood, little is known about the practicalities of computing justifications for inconsistent ontologies. This is despite the fact that justifications are important for repairing inconsistent ontologies, and can be used as a basis for paraconsistent reasoning. This paper presents algorithms, optimisations, and experiments in this area. Surprisingly, it turns out that justifications for inconsistent ontologies are more “difficult” to compute and are often more “numerous” than justifications for entailments in consistent ontologies: whereas it is always possible to compute some justifications, it is often not possible to compute all justifications for real world inconsistent ontologies.

negative: Impact of interleaving on common-mode EMI filter weight reduction of paralleled three-phase voltage-source converters This paper presents a detailed analysis on the impact of the interleaving angle on EMI filter weight reduction in a paralleled three-phase VSI motor drive system. The EMI noise analysis equivalent circuits are given in frequency domain and EMI filter design methods for a motor drive system are discussed. Based on the analysis, design examples are given for DC and AC common-mode (CM) EMI filters showing that minimum corner frequency in this case cannot ensure minimum EMI filter weight. In effect, the EMI filter weight is also determined by the volt-seconds on the inductor. With this consideration, the impact of interleaving is analyzed based on the equivalent circuit developed. Specifically, it is shown that interleaving can either reduce the volt-second on the inductor or help reduce the inductance needed, which has a different impact on the filter weight when the design condition is different. EMI filters are designed for both AC and DC sides as examples, showing the impact that different interleaving angles can have on filter weight reduction. Verifications are carried out both in simulation and experimentally on a 2 kW converter system.

negative: A hybrid RF and vibration energy harvester for wearable devices This paper describes a hybrid radio frequency (RF) and piezoelectric thin film polyvinylidene fluoride (PVDF) vibration energy harvester for wearable devices. By exploiting the impedance characteristics of parasitic capacitances and discrete inductors, the proposed harvester not only scavenges 15 Hz vibration energy but also works as a 915 MHz flexible silver-ink RF dipole antenna. In addition, an interface circuit including a 6-stage Dickson RF-to-DC converter and a diode bridge rectifier to convert the RF and vibration outputs of the hybrid harvester into DC signals to power resistive loads is evaluated. A maximum DC output power of 20.9 μ”, when using the RF to DC converter and −8 dBm input RF power, is achieved at 36 % of the open-circuit output voltage while the DC power harvested from 3 g vibration excitation reaches a maximum of 2.8 μW at 51% of open-circuit voltage. Experimental results show that the tested hybrid harvesting system simultaneously generates 7.3 μW DC power, when the distance from the harvester to a 3 W EIRP 915 MHz transmitter is 5.5 m, and 1.8 μW DC power from a 1.8 g vibration acceleration peak.

negative: An Efficient End-to-End Neural Model for Handwritten Text Recognition Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs [10] have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss [18], an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.

negative: Supply chain information systems strategy : Impacts on supply chain performance and firm performance This paper examines the relationship between supply chain (SC) strategy and supply chain information systems (IS) strategy, and its impact on supply chain performance and firm performance. Theorizing from the supply chain and IS literatures within an overarching framework of the information processing theory (IPT), we develop hypotheses proposing a positive moderating effect of two supply chain IS strategies – IS for Efficiency and IS for Flexibility – on the respective relationships between two SC strategies – Lean and Agile, and supply chain performance. Based on confirmatory analysis and structural equation modeling of survey data from members of senior and executive management in the purchase/materials management/logistics/supply chain functions, from 205 firms, we validate these hypotheses and show that the IS for Efficiency (IS for Flexibility) IS strategy enhances the relationship between Lean (Agile) SC strategy and supply chain performance. We also show a positive association between supply chain performance and firm performance, and a full (partial) mediation effect of supply chain performance on the relation between Agile (Lean) SC strategy and firm performance. The paper contributes to the supply chain literature by providing theoretical understanding and empirical support of how SC strategies and IS strategies can work together to boost supply chain performance. In doing so, it identifies particular types of supply chain IS application portfolios that can enhance the benefits from specific SC strategies. The paper also develops and validates instruments for measuring two types of SC strategies and supply chain IS strategies. For practice, the paper offers guidance in making investment decisions for adopting and deploying IS appropriate to particular SC strategies and analyzing possible lack of alignment between applications that the firm deploys in its supply chain, and the information processing needs of its SC strategy. & 2012 Elsevier B.V. All rights reserved.

negative: CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation Weakly supervised semantic segmentation based on image-level labels aims for alleviating the data scarcity problem by training with coarse labels. State-of-the-art methods rely on image-level labels to generate proxy segmentation masks, then train the segmentation network on these masks with various constraints. These methods consider each image independently and lack the exploration of cross-image relationships. We argue the cross-image relationship is vital to weakly supervised learning. We propose an end-to-end affinity module for explicitly modeling the relationship among a group of images. By means of this, one image can benefit from the complementary information from other images, and the supervision guidance can be shared in the group. The proposed method improves over the baseline with a large margin. Our method achieves 64.1% mIOU score on Pascal VOC 2012 validation set, and 64.7% mIOU score on test set, which is a new state-of-theart by only using image-level labels, demonstrating the effectiveness of the method.

negative: Where2Stand: A Human Position Recommendation System for Souvenir Photography People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of “souvenir photo” is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.

negative: Extreme Adaptation for Personalized Neural Machine Translation Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-sizefits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.

negative: Three-Level Bidirectional Converter for Fuel-Cell/Battery Hybrid Power System A novel three-level (3L) bidirectional converter (BDC) is proposed in this paper. Compared with the traditional BDC, the inductor of the 3L BDC can be reduced significantly so that the dynamic response is greatly improved. Hence, the proposed converter is very suitable for fuel-cell/battery hybrid power systems. In addition, the voltage stress on the switch of the proposed converter is only half of the voltage on the high-voltage side, so it is also suitable for high-voltage applications. The operation principle and the implementation of the control circuit are presented in detail. This paper also proposes a novel bidirectional soft-start control strategy for the BDC. A 1-kW prototype converter is built to verify the theoretical analysis.

negative: Gamut Mapping to Preserve Spatial Luminance Variations A spatial gamut mapping technique is proposed to overcome the shortcomings encountered with standard pointwise gamut mapping algorithms by preserving spatially local luminance variations in the original image. It does so by first processing the image through a standard pointwise gamut mapping algorithm. The difference between the original image luminance Y and gamut mapped image luminance Y’ is calculated. A spatial filter is then applied to this difference signal, and added back to the gamut mapped signal Y’. The filtering operation can result in colors near the gamut boundary being placed outside the gamut, hence a second gamut mapping step is required to move these pixels back into the gamut. Finally, the in-gamut pixels are processed through a color correction function for the output device, and rendered to that device. Psychophysical experiments validate the superior performance of the proposed algorithm, which reduces many of the artifacts arising from standard pointwise techniques.

negative: Modelling out-of-vocabulary words for robust speech recognition This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. Most of today's state-of-the-art speech recognition systems can recognize only words that belong to some predefined finite word vocabulary. When encountering an OOV word, a speech recognizer erroneously substitutes the OOV word with a similarly sounding word from its vocabulary. Furthermore, a recognition error due to an OOV word tends to spread errors into neighboring words; dramatically degrading overall recognition performance. In this thesis we propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. The thesis also extends this OOV approach to modelling multiple classes of OOV words. Instead of augmenting the word search space with a single model, we add several models, one for each class of words. We present two approaches for designing the OOV word classes. The first approach relies on using common part-of-speech tags. The second approach is a data-driven two-step clustering procedure, where the first step uses agglomerative clustering to derive an initial class assignment, while the second step uses iterative clustering to move words from one class to another in order to reduce the model perplexity.

negative: A Comprehensive Survey on Bengali Phoneme Recognition  SUST, ICERIE Abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed. Comparison among these methods is discussed.

negative:
Sign language recognition with recurrent neural network using human keypoint detection We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition.
 Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.

negative: Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech Although isiZulu speakers code-switch with English as a matter of course, extremely little appropriate data is available for acoustic modelling. Recently, a small five-language corpus of code-switched South African soap opera speech was compiled. We used this corpus to evaluate the application of multilingual neural network acoustic modelling to English-isiZulu code-switched speech recognition. Our aim was to determine whether English-isiZulu speech recognition accuracy can be improved by incorporating three other language pairs in the corpus: English-isiXhosa, English-Setswana and English-Sesotho. Since isiXhosa, like isiZulu, belongs to the Nguni language family, while Setswana and Sesotho belong to the more distant Sotho family, we could also investigate the merits of additional data from within and across language groups. Our experiments using both fully connected DNN and TDNN-LSTM architectures show that English-isiZulu speech recognition accuracy as well as language identification after code-switching is improved more by the incorporation of English-isiXhosa data than by the incorporation of the other language pairs. However additional data from the more distant language group remained beneficial, and the best overall performance was always achieved with a multilingual neural network trained on all four language pairs.

negative: Autoregressive Attention for Parallel Sequence Modeling We introduce an autoregressive attention mechanism for parallelizable characterlevel sequence modeling. We use this method to augment a neural model consisting of blocks of causal convolutional layers connected by highway network skip connections. We denote the models with and without the proposed attention mechanism respectively as Highway Causal Convolution (Causal Conv) and Autoregressive-attention Causal Convolution (ARA-Conv). The autoregressive attention mechanism crucially maintains causality in the decoder, allowing for parallel implementation. We demonstrate that these models, compared to their recurrent counterparts, enable fast and accurate learning in character-level NLP tasks. In particular, these models outperform recurrent neural network models in natural language correction and language modeling tasks, and run in a fraction of the time.

negative: Recurrent Neural Network Regularization We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, and machine translation.

negative: A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.


query: Vehicle Velocity Observer Design Using 6-D IMU and Multiple-Observer Approach

positive: Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.

positive: High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

positive: Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.

positive: IMM object tracking for high dynamic driving maneuvers Classical object tracking approaches use a Kalman-filter with a single dynamic model which is therefore optimised to a single driving maneuver. In contrast the interacting multiple model (IMM) filter allows for several parallel models which are combined to a weighted estimate. Choosing models for different driving modes, such as constant speed, acceleration and strong acceleration changes, the object state estimation can be optimised for highly dynamic driving maneuvers. The paper describes the analysis of Stop&Go situations and the systematic parametrisation of the IMM method based on these statistics. The evaluation of the IMM approach is presented based on real sensor measurements of laser scanners, a radar and a video image processing unit.

negative: Overview of the Lucy Project: Dynamic Stabilization of a Biped Powered by Pneumatic Artificial Muscles This paper gives an overview of the Lucy project. What is special is that the biped is not actuated with the classical electrical drives, but with pleated pneumatic artificial muscles. In an antagonistic setup of such muscles both the torque and the compliance are controllable. From human walking there is evidence that joint compliance plays an important role in energy-efficient walking and running. To be able to walk at different walking speeds and step lengths, a trajectory generator and joint trajectory tracking controller are combined. The first generates dynamically stable trajectories based on the objective locomotion parameters which can be changed from step to step. The joint trajectory tracking unit controls the pressure inside the muscles so the desired motion is followed. It is based on a computed torque model and takes the torque– angle relation of the antagonistic muscle setup into account. With this strategy the robot is able to walk at a speed up to 0.15 m/s. A compliance controller is developed to reduce the energy consumption by combining active trajectory control with the exploitation of the natural dynamics. A mathematical formulation was developed to find an optimal compliance setting depending on the desired trajectory and physical properties of the system. This strategy is experimentally evaluated on a single pendulum structure and not implemented on the real robot because the walking speed of the robot is currently too slow. At the end a discussion is given about the pros and cons of building a pneumatic biped, and the control architecture used. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2008

negative: DeepTag: inferring diagnoses from veterinary clinical notes Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free-text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free-text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources.

negative: Multi-Objective Resource Allocation for Secure Communication in Cognitive Radio Networks with Wireless Information and Power Transfer In this paper, we study resource allocation for multiuser multiple-input single-output (MISO) secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multi-objective optimization framework for the design of a Pareto optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference power leakage-totransmit power ratio minimization. The proposed framework takes into account a quality of service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is non-convex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP relaxed problem. Besides, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes, but also unveil an interesting trade-off between the considered conflicting system design objectives.

negative: Latent Discriminant Analysis with Representative Feature Discovery Linear Discriminant Analysis (LDA) is a well-known method for dimension reduction and classification with focus on discriminative feature selection. However, how to discover discriminative as well as representative features in LDA model has not been explored. In this paper, we propose a latent Fisher discriminant model with representative feature discovery in an semi-supervised manner. Specifically, our model leverages advantages of both discriminative and generative models by generalizing LDA with data-driven prior over the latent variables. Thus, our method combines multi-class, latent variables and dimension reduction in an unified Bayesian framework. We test our method on MUSK and Corel datasets and yield competitive results compared to baselines. We also demonstrate its capacity on the challenging TRECVID MED11 dataset for semantic keyframe extraction and conduct a human-factors ranking-based experimental evaluation, which clearly demonstrates our proposed method consistently extracts more semantically meaningful keyframes than chal-

negative: Approximate Orthogonal Sparse Embedding for Dimensionality Reduction Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1 -norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.

negative: Piccolo: An Ultra-Lightweight Blockcipher We propose a new 64-bit blockcipher Piccolo supporting 80 and 128-bit keys. Adopting several novel design and implementation techniques, Piccolo achieves both high security and notably compact implementation in hardware. We show that Piccolo offers a sufficient security level against known analyses including recent related-key differential attacks and meet-in-the-middle attacks. In our smallest implementation, the hardware requirements for the 80 and the 128-bit key mode are only 683 and 758 gate equivalents, respectively. Moreover, Piccolo requires only 60 additional gate equivalents to support the decryption function due to its involution structure. Furthermore, its efficiency on the energy consumption which is evaluated by energy per bit is also remarkable. Thus, Piccolo is one of the competitive ultra-lightweight blockciphers which are suitable for extremely constrained environments such as RFID tags and sensor nodes.

negative: A Post-failure Analysis of Mobile Payment Platforms Despite on-going efforts over the past decade, mobile payments are yet to take off successfully. Repeated failures show that mobile payment platforms are complex to launch. The objective of this paper is to unveil factors explaining the failure of past mobile payment platforms. The use of a multi-level framework aims at enriching the variety of issues investigated. In order to explore the reasons of failure, we selected four cases from different countries happening at different times. Our results show that these cases share many of the same failure factors: lack of collaboration between the stakeholders, no technology standard, and low value-added for consumers and merchants compared to existing payment solutions. Even though the list is not exhaustive, the uncovered factors in our study seem to be necessary (but not sufficient) for the further developments of mobile payment platforms.

negative: Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness This paper proposes a new Multi-Dimensional SAX-GA approach to pattern discovery using genetic algorithms (GA). The approach is capable of discovering patterns in multi-dimensional financial time series. First, the several dimensions of data are converted to a Symbolic Aggregate approXimation (SAX) representation, which is, then, feed to a GA optimization kernel. The GA searches for profitable patterns occurring simultaneously in the multi-dimensional time series. Based on the patterns found, the GA produces more robust investment strategies, since the simultaneity of patterns on different dimensions of the data, reinforces the strength of the trading decisions implemented. The proposed approach was tested using stocks from S&P500 index, and is compared to previous reference works of SAX-GA and to the Buy & Hold (B&H) classic investment strategy.

negative: A survey on human activity recognition from videos Understanding the activities of human from videos is demanding task in Computer Vision. Identifying the actions being accomplished by the human in the video sequence automatically and tagging their actions is the prime functionality of intelligent video systems. The goal of activity recognition is to identify the actions and objectives of one or more objects from a series of examination on the action of object and their environmental condition. The major applications of Human Activity Recognition varies from Content-based Video Analytics, Robotics, Human-Computer Interaction, Human fall detection, Ambient Intelligence, Visual Surveillance, Video Indexing etc. This paper collectively summarizes and deciphers the various methodologies, challenges and issues of Human Activity Recognition systems. Variants of Human Activity Recognition systems such as Human Object Interactions and Human-Human Interactions are also explored. Various benchmarking datasets and their properties are being explored. The Experimental Evaluation of various papers are analyzed efficiently with the various performance metrics like Precision, Recall, and Accuracy.

negative: Towards Agile Implementation of Test Maturity Model Integration (TMMI) Level 2 using Scrum Practices The software industry has invested the substantial effort to improve the quality of its products like ISO, CMMI and TMMI. Although applying of TMMI maturity criteria has a positive impact on product quality, test engineering productivity, and cycle-time effort. But it requires heavy software development processes and large investments in terms of cost and time that medium/small companies do not deal with it. At the same time, agile methods deal with changing requirements and continuous delivery of valuable software by short time iterations. So that the aim of this paper is to improve the testing process by applying a detailed mapping between TMMI and Scrum practices and verifying this mapping with a study providing empirical evidence of the obtained results. The research has been evaluated on a sample of 2 large-sized TMMI certified companies. In conclusion, the experimental results show the effectiveness of using this integrated approach compared to other existing approaches. Keywords—Agile software development; Scrum; TMMI; Software Testing; CMMI

negative: An Experimental Study of Genetic Crossover Operators for the Job Shop Scheduling Problem The aim of this paper is to show the influence of genetic crossover operators on the performance of a genetic algorithm (GA). The GA is applied to the job shop scheduling problem (JSSP). To achieve this aim an experimental study of a set of crossover operators is presented. The experimental study is based on a decision support system (DSS). To compare the abilities of different crossover operators, the DSS was designed giving all the operators the same opportunities. The genetic crossover operators are tested on a set of standard instances taken from the literature. The makespan is the measure used to evaluate the genetic crossover operators. The main conclusion is that there is a crossover operator having the best average performance on a specific set of solved instances. The DSS developed can be utilized in a common industrial or construction environment. Key-Words: Genetic algorithms, crossover operators, scheduling, heuristics, JSSP.

negative: Text mining in healthcare. Applications and opportunities. Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.

negative: Deep Visuo-Tactile Learning: Estimation of Material Properties from Images Estimation of materials properties, such as softness or roughness from visual perception is an essential factor in deciding our way of interaction with our environment in e.g., object manipulation tasks or walking. In this research, we propose a method for deep visuo-tactile learning in which we train a encoder-decoder network with an intermediate layer in an unsupervised manner with images as input and tactile sequences as output. Materials properties are then represented in the intermediate layer as a continuous feature space and are estimated from image information. Unlike past studies utilizing tactile sensors focusing on classification for object recognition or recognizing material properties, our method does not require manually designing class labels or annotation, does not cause unknown objects to be classified into known discrete classes, and can be used without a tactile sensor after training. To collect data for training, we have attached a uSkin tactile sensor and a camera to the end-effector of a Sawyer robot to stroke surfaces of 21 different material surfaces. Our results after training show that features are indeed expressed continuously, and that our method is able to handle unknown objects in its feature space.

negative: Dynamic modeling and inverse dynamic control of mobile robot This paper presents a dynamic modeling and control studies of mobile robot. The robot is mainly built all around three wheels and platform. A mathematical model of unicycle mobile robot is determined by kinematic and dynamic model. The dynamic modelling equations are based on Lagrangian formulation. The motion control strategy is based on the inverse of dynamic control. This leads to accurate tracking of trajectory. The goal to reach in this paper is to improve the performances of inverse dynamic control of mobile robot. The validity of the proposed controller is demonstrated by the simulation of two wheels mobile robots case.

negative: A Method for Building Personalized Ontology Summaries In the context of ontology engineering, the ontology understanding is the basis for its further development and reuse. One intuitive effective approach to support ontology understanding is the process of ontology summarization which highlights the most important concepts of an ontology. Ontology summarization identifies an excerpt from an ontology that contains the most relevant concepts and produces an abridged ontology. In this article, we present a method for summarizing ontologies that represent a data source schema or describe a knowledge domain. We propose an algorithm to produce a personalized ontology summary based on user-defined parameters, e.g. summary size. The relevance of a concept is determined through user indication or centrality measures, commonly used to determine the relative importance of a vertex within a graph. The algorithm searches for the best ontology summary, i.e., the one containing the most relevant ontology concepts respecting all the original relationships between concepts and the parameters set by the user. Experiments were done comparing our generated ontology summaries against golden standard summaries as well as summaries produced by methods available in related work. We achieved in average more than 62.5% of similarity with golden standard summaries.

negative: The Influence of Information Overload on the Development of Trust and Purchase Intention Based on Online Product Reviews in a Mobile vs . Web Environment : A Research Proposal Information overload has been studied extensively by decision science researchers, particularly in the context of task-based optimization decisions. Media selection research has similarly investigated the extent to which task characteristics influence media choice and use. This paper outlines a proposed study, which would compare the effectiveness of web-based online product review systems in facilitation trust and purchase intention to those of mobile product review systems. We propose that since web-based systems are more effective in fostering focus and are less prone to navigation frustration, information overload is less likely influence the extent to which a consumer trusts an online product review.

negative: Systematic Review of Clustering High-Dimensional and Large Datasets Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.

negative: Scalable Distributed Stream Processing Many stream-based applications are naturally distributed. Applications are often embedded in an environment with numerous connected computing devices with heterogeneous capabilities. As data travels from its point of origin (e.g., sensors) downstream to applications, it passes through many computing devices, each of which is a potential target of computation. Furthermore, to cope with time-varying load spikes and changing demand, many servers would be brought to bear on the problem. In both cases, distributed computation is the norm. Abstract

negative: CLOUDS: A Decision Tree Classifier for Large Datasets Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifters, while maintaining the quality of the generated trees in terms of accuracy and tree size. We provide experimental results with a number of real and synthetic data~ets.

negative: What are the Gaps in Mobile Patient Portal? Mining Users Feedback Using Topic Modeling Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. In this article, we extend the existing literature by discovering design gaps for patient portals from a systematic analysis of negative users’ feedback from the actual use of patient portals. Specifically, we adopt topic modeling approach, LDA algorithm, to discover design gaps from online low rating user reviews of a common mobile patient portal, EPIC’s mychart. To validate the extracted gaps, we compared the results of LDA analysis with that of human analysis. Overall, the results revealed opportunities to improve collaboration and to enhance the design of portals intended for patientcentered care.

negative: Text-to-3 D Scene Generation using Semantic Parsing and Spatial Knowledge with Rule Based System Scene Generation plays an important role in digital media to represent a news or a specific domain to the viewers. It’s not easy to produce a scene from a text. Text may not completely express the whole situation in digital media. Most of the people are not conscious about the news until it's not visualized to them. Text to 3D scene generation is a process where people do not need to read a news. The 3D Scene will represent the situation. It will help people to conscious about their life. In this paper, we introduce a rule-based framework where scene generated from text using semantic parsing and spatial knowledge. Semantic parsing has identified the templates, objects, and constraints and spatial knowledge have built the relation between object and template. Our rule based framework has identified the uncountable noun and some spatial relations to generate 3D scenes.

negative: A deadlock prevention method for railway networks using monitors for colored Petri nets The real-time traffic control of railway networks authorizes movements of the trains and imposes safety constraints. The paper deals with the real time traffic control focusing on deadlock prevention problem. Colored Petri nets are used to model the dynamics of the railway network system: places represent tracks and stations, tokens are trains. The prevention policy is expressed by a set of linear inequality constraints, called colored Generalized Mutual Exclusion Constraints that are enforced by adding appropriate monitor places. Using digraph tools, deadlock situations are characterized and a strategy is established to define off-line a set of Generalized Mutual Exclusion Constraints that prevent deadlock. An example shows in detail the design of the proposed control logic.

negative: LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks (Fig. 1). Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. The source code is available online [1].

negative: User Preference Modeling and Exploitation in IoT Scenarios Recommender Systems are commonly used in web applications to support users in finding items of their interest. We here propose to use Recommender Systems in Internet of Things scenarios to support human decision making in the physical world. For instance, users' choices (visit actions) for points of interests (POIs) while they explore a sensor enabled city can be tracked and used to generate recommendations for not yet visited POIs. In this PhD research, we propose a novel learning approach for generating an explainable human behaviour model and relevant recommendations in sensor enabled areas. Moreover, we propose techniques for simulating user behaviour and analyse the collective dynamics of a population of users.

negative: The Race Between Man and Machine : Implications of Technology for Growth , Factor Shares and Employment The advent of automation and the simultaneous decline in the labor share and employment among advanced economies raises concerns that labor will be marginalized and made redundant by new technologies. This paper examines this proposition in a task-based framework wherein not only are tasks previously performed by labor automated, but also more complex versions of existing tasks, in which labor has a comparative advantage, can be created. We fully characterize the structure of equilibrium in this model, establishing how the allocation of factors to tasks and factor prices are determined by the available technology and the endogenous choices of firms between capital and labor. We then demonstrate that although automation tends to reduce employment and the share of labor in national income, the creation of more complex tasks has the opposite effect. Our full model endogenizes the direction of research and development towards automation and the creation of new complex tasks. We show that, under reasonable conditions, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Consequently, an increase in automation reduces the wage to rental rate ratio, and thus discourages further automation, encourages greater creation of more labor-intensive tasks, and restores the share of labor in national income and the employment to population ratio back towards their initial values. Though the economy is self-correcting, the equilibrium allocation of research effort is not optimal: to the extent that wages reflect quasi-rents for workers, firms will engage in too much automation. Finally, we extend the model to include workers of different skills. We find that inequality increases during transitions, but the self-correcting forces also serve to limit the increase in inequality over longer periods. Still in Progress. Comments Welcome. ∗We thank Philippe Aghion, David Autor, Erik Brynjolfsson, Chad Jones, and seminar participants at the AEA 2015, the MIT Initiative on the Digital Economy, and the NBER 2015 Economic Growth Group for their valuable comments.

negative: Synchromesh Torque Estimation in an Electric Vehicle's Clutchless Automated Manual Transmission Using Unknown Input Observer This paper studies the estimation of the frictional torque of the synchromesh during the gear shifting operation in an electric vehicle equipped with a clutchless automated manual transmission (AMT). The clutchless drivetrain of the electric vehicle is discussed and the dynamical model of the powertrain from the electric traction motor to the synchromesh system of a two-speed AMT is developed. In order to estimate the frictional torque of the synchromesh, which is indeed an unknown input to the dynamical system, it is assumed to be generated by a fictitious autonomous system. Thereafter, the augmented state- space representation of the actual and fictitious state variables, which forms the basis for the observer design, is provided and the observability of this augmented system is discussed. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed in order to estimate the frictional torque of the synchromesh. The estimation is based on the measuring angular velocities of the electric motor and synchro ring together with the known electromagnetic torque of the traction motor. The performance of the observers is assessed experimentally by means of a test rig. The results demonstrate the satisfactory performance of the stochastic observer when the system encounters process and measurement noise which are likely to happen in practice.

negative: Building a Robust Implementation of Bearing-only Inertial SLAM for a UAV This paper presents the on-going design and implementation of a robust inertial sensor based Simultaneous Localisation And Mapping (SLAM) algorithm for an Unmanned Aerial Vehicle (UAV) using bearing-only observations. A single colour vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete 6-DoF motion of the UAV along with the three-dimensional position of the features in the environment. An Extended Kalman Filter (EKF) approach is used where a technique of delayed initialisation is performed to initialise the 3D position of features from bearing-only observations. Data association is achieved using a multi-hypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off-line using inertial sensor and vision data collected during a flight test of a UAV.

negative: Model-Based Wheel Slip Detection for Outdoor Mobile Robots This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.

negative: Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels With MAVs In this paper, we address the estimation, control, navigation and mapping problems to achieve autonomous inspection of penstocks and tunnels using aerial vehicles with on-board sensing and computation. Penstocks and tunnels have the shape of a generalized cylinder. They are generally dark and featureless. State estimation is challenging because range sensors do not yield adequate information and cameras do not work in the dark. We show that the six degrees of freedom (DOF) pose and velocity can be estimated by fusing information from an inertial measurement unit (IMU), a lidar and a set of cameras. This letter discusses in detail the range-based estimation part while leaving the details of vision component to our earlier work. The proposed algorithm relies only on a model of the generalized cylinder and is robust to changes in shape of the tunnel. The approach is validated through real experiments showing autonomous and shared control, state estimation and environment mapping in the penstock at Center Hill Dam, TN. To our knowledge, this is the first time autonomous navigation and mapping has been achieved in a penstock without any external infrastructure such GPS or external cameras.

negative: Interactive ramp merging planning in autonomous driving: Multi-merging leading PGM (MML-PGM) Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles.

negative: Accurate Angle Estimator for High-Frame-Rate 2-D Vector Flow Imaging This paper presents a novel approach for estimating 2-D flow angles using a high-frame-rate ultrasound method. The angle estimator features high accuracy and low standard deviation (SD) over the full 360° range. The method is validated on Field II simulations and phantom measurements using the experimental ultrasound scanner SARUS and a flow rig before being tested in vivo. An 8-MHz linear array transducer is used with defocused beam emissions. In the simulations of a spinning disk phantom, a 360° uniform behavior on the angle estimation is observed with a median angle bias of 1.01° and a median angle SD of 1.8°. Similar results are obtained on a straight vessel for both simulations and measurements, where the obtained angle biases are below 1.5° with SDs around 1°. Estimated velocity magnitudes are also kept under 10% bias and 5% relative SD in both simulations and measurements. An in vivo measurement is performed on a carotid bifurcation of a healthy individual. A 3-s acquisition during three heart cycles is captured. A consistent and repetitive vortex is observed in the carotid bulb during systoles.

negative: Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM The fusion of inertial and visual data is widely used to improve an object’s pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object’s absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial / monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 231855 (sFly). Gabriel Nützi is currently a Master student at the ETH Zurich. Stephan Weiss is currently PhD student at the ETH Zurich. Davide Scaramuzza is currently senior researcher and team leader at the ETH Zurich. Roland Siegwart is full professor at the ETH Zurich and head of the Autonomous Systems Lab. G. Nützi · S. Weiss · D. Scaramuzza · R. Siegwart ETH Autonomous Systems Laboratory, 8092, Zurich, Switzerland, www.asl.ethz.ch E-mail: nuetzi@student.ethz.ch, stephan.weiss@mavt.ethz.ch, davide.scaramuzza@ieee.org, rsiegwart@ethz.ch 2