Query: MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification

Relevant passages:
A Convolutional Neural Network for Modelling Sentences <=SEP=> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification <=SEP=> Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on sentence classification tasks (Kim, 2014; Kalchbrenner et al., 2014). However, these models require practitioners to specify the exact model architecture and accompanying hyperparameters, e.g., the choice of filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance (Kim, 2014). We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification. One important observation borne out by our experimental results is that researchers should report performance variances, as these can be substantial due to stochastic initialization and inference.

Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

Convolutional Neural Networks for Sentence Classification <=SEP=> We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks <=SEP=> Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-ofword conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.

Improving TCP Throughput over Two-Way Asymmetric Links: Analysis and Solutions <=SEP=> The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.

Multiagent Systems Engineering <=SEP=> This paper describes the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE also provides a way to specify architecture-independent detailed definition of the internal agent design. An example of applying the MaSE methodology is also presented.

Data augmentation for deep convolutional neural network acoustic modeling <=SEP=> This paper investigates data augmentation based on label-preserving transformations for deep convolutional neural network (CNN) acoustic modeling to deal with limited training data. We show how stochastic feature mapping (SFM) can be carried out when training CNN models with log-Mel features as input and compare it with vocal tract length perturbation (VTLP). Furthermore, a two-stage data augmentation scheme with a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Improved performance has been observed in experiments conducted on the limited language pack (LLP) of Haitian Creole in the IARPA Babel program.

RoXSum: Leveraging Data Aggregation and Batch Processing for XML Routing <=SEP=> Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.

Relation Extraction Using Multi-Encoder LSTM Network on a Distant Supervised Dataset <=SEP=> Relation extraction techniques are used to find potential relational facts in textual content. Relation Extraction systems require huge amount of training data to model semantics of sentences and identify relations. Distant supervision, often used to construct training data for relation extraction, produces noisy alignments that can hurt the performance of relation extraction systems. To this end, we propose a simple, yet effective, technique to automatically compute confidence levels of alignments. We compute the confidence values of automatically labeled content using co-occurrence statistics of relations and dependency patterns of aligned sentences. Thereafter, we propose a novel multi-encoder bidirectional Long Short Term Memory (LSTM) model to identify relations in a given sentence. We use different features (words, part-of-speech (POS) tags and dependency paths) in each encoder and concatenate the hidden states of all the encoders to predict the relations. Our experiments show that a multi-encoder network can handle different features together to predict relations more accurately (~9% improvement over a single encoder model). We also conduct visualization experiments to show that our model learns intermediate representations effectively to identify relations in sentences.

REFLECTIONS ON FREQUENCY EFFECTS IN LANGUAGE PROCESSING <=SEP=> This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of “noticing”; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna-contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners’ language is a product of their history of usage in communicative interaction.

OPAQUE: An Asymmetric PAKE Protocol Secure Against Pre-Computation Attacks <=SEP=> Password-Authenticated Key Exchange (PAKE) protocols allow two parties that only share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) strengthens this notion for the more common client-server setting where the server stores a mapping of the password and security is required even upon server compromise, that is, the only allowed attack in this case is an (inevitable) offline exhaustive dictionary attack against individual user passwords. Unfortunately, current aPAKE protocols (that dispense with the use of servers’ public keys) allow for pre-computation attacks that lead to the instantaneous compromise of user passwords upon server compromise, thus forgoing much of the intended aPAKE security. Indeed, these protocols use – in essential ways – deterministic password mappings or use random “salt” transmitted in the clear from servers to users, and thus are vulnerable to pre-computation attacks. We initiate the study of Strong aPAKE protocols that are secure as aPAKE’s but are also secure against pre-computation attacks. We formalize this notion in the Universally Composable (UC) settings and present two modular constructions using an Oblivious PRF as a main tool. The first builds a Strong aPAKE from any aPAKE (which in turn can be constructed from any PAKE [23]) while the second builds a Strong aPAKE from any authenticated key-exchange protocol secure against reverse impersonation (a.k.a. KCI). Using the latter transformation, we show a practical instantiation of a UC-secure Strong aPAKE in the Random Oracle model. The protocol (“OPAQUE”) consists of 2 messages (3 with mutual authentication), requires 3 and 4 exponentiations for server and client, respectively (2 to 4 of which can be fixed-base depending on optimizations), provides forward secrecy, is PKI-free, supports user-side hash iterations, has a built-in facility for password-based storage and retrieval of secrets and credentials, and accommodates a user-transparent server-side threshold implementation.

Death by complete decapitation of motorcyclist wearing full face helmet: case report. <=SEP=> We describe a case of complete decapitation following a motorcycle accident in which the victim was wearing a full face helmet. A young man lost control of his motorcycle and was thrown about 20 m, hitting his head against the barrier separating a tramline from the road. The resulting trauma caused his decapitation, the only fatal wound ascertained by the various forensic investigations. The authors present this rare case and compare it against the other two cases reported in the literature, providing some observations on the ways in which this injury can come about. The absence of abrasions or signs that the wound edges came into contact with a metal structure, the presence of signs of impact on the side of the helmet and the finding of a transversal fracture at the base of the skull point to the violent action of a side-to-side opposite force, due to the resistance provided by the lower edge of the protective helmet.

A Regularization Method with Inference of Trust and Distrust in Recommender Systems <=SEP=> In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users’ preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users’ preferences are sparse, but also users’ social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users’ explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends’ trust and foes’ distrust degrees on users’ preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users’ and items’ latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users’ preferences and social relationships, significantly outperforming several state-of-the-art methods.

A New Algorithm of Fully-automatic Red-eye Removal <=SEP=> the redeye effect is typically formed in consumer photos taken with a built-in camera flash. The paper presents a new algorithm of the automatic removal of redeye from digital photos. The proposed algorithm can remove red-eye automatically without manual intervention. First, Detecting faces by using the AdaBoost algorithm. Second, the red-eyes are located by using operations of segmentation, morphology and geometric constraint. Finally, correcting the located red-eyes completely. The experimental results are satisfied with high correction rates, relatively low computational complexity and robustness.

Autonomous Underwater Vehicles : Trends and Transformations <=SEP=> Three examples of inter-agency cooperation utilizing current generation, individual Autonomous Underwater Vehicles (AUVs) are described consistent with recent recommendations of the U.S. Commission on Ocean Policy. The first steps in transforming individual AUVs into adaptive, networked systems are underway. To realize an affordable and deployable system, a network-class AUV must be designed with cost–size constraints not necessarily applied in developing solo AUVs. Vehicle types are suggested based on function and ocean operating regime: surface layer, interior and bottom layer. Implications for platform, navigation and control subsystems are explored and practical formulations for autonomy and intelligence are postulated for comparing performance and judging behavior. Laws and conventions governing intelligent maritime navigation are reviewed and an autonomous controller with conventional collision avoidance behavior is described. Network-class cost constraints can be achieved through economies of scale. Productivity and efficiency in AUV manufacturing will increase if constructive competition is maintained. Constructive strategies include interface and operating standards. Professional societies and industry trade groups have a leadership role to play in establishing public, open standards. cations are described at many conferences and in an expanding literature of journal publications. Griffiths (2003) provides recent developments in AUV design, construction and operation. A number of commercial manufacturers have emerged to supply the growing market. Clearly, individual AUVs are evolving into useful tools that extend current measurement methods. Three examples involving current generation, individual AUVs will serve to illustrate trends in inter-agency cooperation utilizing this technology. Following these examples, we examine factors that will transform current measurement methods. Network externalities associated with interagency cooperation will play a role in driving this transformation. Recently the Navy joined with the U. S. Environmental Protection Agency, the Narragansett Bay Estuary Program, and the Autonomous Undersea Systems Institute to demonstrate the effectiveness of using a Solar Powered Autonomous Underwater Vehicle (SAUV II) to measure dissolved oxgyen concentrations in Greenwich Bay, Rhode Island. Utilization of an AUV to rapidly move continuously-recording dissolved oxygen sensors over large spatial areas to map the frequency and extent of hypoxia demonstrates cost-effective water quality monitoring (Crimmins, 2005). Real-time monitoring of dissolved oxygen over large spatial scales is important for predicting the onset of hypoxia and mapping its extent (RI Dept Env Mgmt, 2004). The SAUV II (Figure 1) is a solar powered AUV designed for long endurance missions such as monitoring, surveillance, or station keeping where real time bi-directional communications to shore are critical. The SAUV II operates continuously for several months using solar energy to recharge its lithium ion batteries during daylight hours. The SAUV II was fitted with a fast-response galvanic oxygen micro-sensor (AMT Analysenmesstechnik GmbH). This sensor provides rapid in situ profiling of dissolved oxygen at depths of up to 100 m with a response time of a few hundred milliseconds. The SAUV II was also fitted with a NXIC conductivity-temperature-depth (CTD) sensor (Falmouth Scientific Inc.). CTD data are essential for detecting water column stratification, which affects the development and breakdown of hypoxia (Crimmins, 2005). T T R E N D S o move forward wisely with ocean science and technology, the United States needs a unified strategy that engages national and international collaboration. Part VII of the Report by the U.S. Commission on Ocean Policy, “Science-Based Decisions: Understanding of the Oceans,” not only affirms this assertion, but also identifies a framework for federal agencies to proactively develop and implement policies that embrace the spirit of inter-agency cooperation and cost-effective shared technology. The Commission states that a larger, coordinated investment in infrastructure and technology development is necessary to conduct and support ocean science (Commissioners, 2004; Crimmins, 2004a). Future ocean sampling and surveillance systems should be capable of global deployment, sustained presence, three dimensional adaptive aperture, real time control and robust performance. These requirements can be met affordably by a network of Autonomous Underwater Vehicles (AUVs). The first generation of AUVs has transitioned from dream to reality (Griffiths, 2003). Engineering progress is now regularly reported and appli-

Ensemble Classifiers for Network Intrusion Detection System <=SEP=> Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.

Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system <=SEP=> The aim of this paper is to employ fractional order proportional integral derivative ( FO-PID ) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system ( MLS ), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization ( dPSO ) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.

Buddha ' s Brain : Neuroplasticity and Meditation <=SEP=> In a recent visit to the United States, the Dalai Lama gave a speech at the Society for Neuroscience's annual meeting in Washington, D.C. Over the past several years, he has helped recruit Tibetan Buddhist monks for, and directly encouraged research on the brain and meditation in the Waisman Laboratory for Brain Imaging and Behavior at the University of Wisconsin-Madison. The findings from studies in this unusual sample as well as related research efforts, suggest that, over the course of meditating for tens of thousands of hours, the long-term practitioners had actually altered the structure and function of their brains. In this article we discuss neuroplasticity, which encompasses such alterations, and the findings from these studies. Further, we comment on the associated signal processing (SP) challenges, current status and how SP can contribute to advance these studies.

The most effective exercise for strengthening the supraspinatus muscle: evaluation by magnetic resonance imaging. <=SEP=> BACKGROUND
Electromyography has been used to determine the best exercise for strengthening the supraspinatus muscle, but conflicting results have been reported. Magnetic resonance imaging T2 relaxation time appears to be more accurate in determining muscle activation.


PURPOSE
To determine the best exercises for strengthening the supraspinatus muscle.


STUDY DESIGN
Criterion standard.


METHODS
Six male volunteers performed three exercises: the empty can, the full can, and horizontal abduction. Immediately before and after each exercise, magnetic resonance imaging examinations were performed and changes in relaxation time for the subscapularis, supraspinatus, infraspinatus, teres minor, and deltoid muscles were recorded.


RESULTS
The supraspinatus muscle had the greatest change among the studied muscles in relaxation time for the empty can (10.5 ms) and full can (10.5 ms) exercises. After the horizontal abduction exercise the change in relaxation time for the supraspinatus muscle (3.6 ms) was significantly smaller than that for the posterior deltoid muscle (11.5 ms) and not significantly different from that of the other muscles studied.


CONCLUSION
The empty can and full can exercises were most effective in activating the supraspinatus muscle.

Neural Network Model for Earthquake Prediction Using DMETER Data and Seismic Belt Information <=SEP=> The mechanism of the earthquake remains to be investigated, though some anomalies associated with earthquakes have been found by DEMETER satellite observations. The next step is to use the DEMETER data for earthquake prediction. It is a useful and feasible way to use the self-adaptive artificial neural network to construct relations between various symptom factors and earthquake occurrences. The back-propagation neural network is quite suitable to express the nonlinear relation between earthquake and various anomalies. In this paper a series of physical quantities measured by the DEMETER satellite including Electron density, Electron temperature, ions temperature and oxygen ion density, together with seismic belt information are used to form sample sets for a back-propagation neural network. The neural network model then can be used to conduct the prediction. In the end, validation tests are performed based on those important seismic events happened in 2008.

Collaborative computer-aided design - research and development status <=SEP=> In order to facilitate product design and realization processes, presently, research is actively carried out for developing methodologies and technologies of collaborative computer-aided design systems to support design teams geographically dispersed based on the quickly evolving information technologies. In this paper, the developed collaborative systems, methodologies and technologies, which are organized as a horizontal or a hierarchical manner, are reviewed. Meanwhile, a 3D streaming technology, which can effectively transmit visualization information across networks for Web applications, is highlighted and the algorithms behind it are disclosed.

Analyzing the performance differences between pattern matching and compressed pattern matching on texts <=SEP=> In this study the statistics of pattern matching on text data and the statistics of compressed pattern matching on compressed form of the same text data are compared. A new application has been developed to count the character matching numbers in compressed and uncompressed texts individually. Also a new text compression algorithm that allows compressed pattern matching by using classical pattern matching algorithms without any change is presented in this paper. In this paper while the presented compression algorithm based on digram and trigram substitution has been giving about 30-35% compression factor, the duration of compressed pattern matching on compressed text is calculated less than the duration of pattern matching on uncompressed text. Also it is confirmed that the number of character comparison on compressed texts while doing a compressed pattern matching is less than the number of character comparison on uncompressed texts. Thus the aim of the developed compression algorithm is to point out the difference in text processing between compressed and uncompressed text and to form opinions for another applications.

A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios <=SEP=> Collisions are a main cause of throughput degradation in wireless local area networks. The current contention mechanism used in the IEEE 802.11 networks is called carrier sense multiple access with collision avoidance CSMA/CA. It uses a binary exponential backoff technique to randomize each contender attempt of transmitting, effectively reducing the collision probability. Nevertheless, CSMA/CA relies on a random backoff that while effective and fully decentralized, in principle is unable to completely eliminate collisions, therefore degrading the network throughput as more contenders attempt to share the channel. To overcome these situations, carrier sense multiple access with enhanced collision avoidance CSMA/ECA is able to create a collision-free schedule in a fully decentralized manner using a deterministic backoff after successful transmissions. Hysteresis and fair share are two extensions of CSMA/ECA to support a large number of contenders in a collision-free schedule. CSMA/ECA offers better throughput than CSMA/CA and short-term throughput fairness. This paper describes CSMA/ECA and its extensions. In addition, it provides the first evaluation results of CSMA/ECA with non-saturated traffic, channel errors, and its performance when coexisting with CSMA/CA nodes. Furthermore, it describes the effects of imperfect clocks over CSMA/ECA and presents a mechanism to leverage the impact of channel errors and the addition/withdrawal of nodes over collision-free schedules. Finally, the experimental results on throughput and lost frames from a CSMA/ECA implementation using commercial hardware and open-source firmware are presented.

C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty <=SEP=> We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach.

Using Semantic Web Technologies for Exploratory OLAP: A Survey <=SEP=> This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction <=SEP=> With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.

Image-based ghostings for single layer occlusions in augmented reality <=SEP=> In augmented reality displays, X-Ray visualization techniques make hidden objects visible through combining the physical view with an artificial rendering of the hidden information. An important step in X-Ray visualization is to decide which parts of the physical scene should be kept and which should be replaced by overlays. The combination should provide users with essential perceptual cues to understand the relationship of depth between hidden information and the physical scene. In this paper we present an approach that addresses this decision in unknown environments by analyzing camera images of the physical scene and using the extracted information for occlusion management. Pixels are grouped into perceptually coherent image regions and a set of parameters is determined for each region. The parameters change the X-Ray visualization for either preserving existing structures or generating synthetic structures. Finally, users can customize the overall opacity of foreground regions to adapt the visualization.

A restricted Boltzmann machine based two-lead electrocardiography classification <=SEP=> An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.

Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing <=SEP=> For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic Voltage Scaling (DVS) to minimize energy consumption and propose two algorithms. These two algorithms use the methods of unify and double fitness to define the fitness function and select individuals. They adopt the genetic algorithm to parallel find the reasonable scheduling scheme. The simulation results demonstrate the two algorithms can efficiently find the right compromise between make span and energy consumption.