title: Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

abstract: The framework of dynamic movement primitives (DMPs) contains many favorable properties for the execution of robotic trajectories, such as indirect dependence on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper, we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion and, thus, modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of DMPs, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task.


title: Patch-based Face Recognition using a Hierarchical Multi-label Matcher

abstract: This paper proposes a hierarchical multi-label matcher for patch-based face recognition. In signature generation, a face image is iteratively divided into multi-level patches. Two different types of patch divisions and signatures are introduced for 2D facial image and texture-lifted image, respectively. The matcher training consists of three steps. First, local classifiers are built to learn the local matching of each patch. Second, the hierarchical relationships defined between local patches are used to learn the global matching of each patch. Three ways are introduced to learn the global matching: majority voting, l1-regularized weighting, and decision rule. Last, the global matchings of different levels are combined as the final matching. Experimental results on different face recognition tasks demonstrate the effectiveness of the proposed matcher at the cost of gallery generalization. Compared with the UR2D system, the proposed matcher improves the Rank-1 accuracy significantly by 3% and 0.18% on the UHDB31 dataset and IJB-A dataset, respectively.


title: Unconstrained Multimodal Multi-Label Learning

abstract: Multimodal learning has been mostly studied by assuming that multiple label assignments are independent of each other and all the modalities are available. In this paper, we consider a more general problem where the labels contain dependency relationships and some modalities are likely to be missing. To this end, we propose a multi-label conditional restricted Boltzmann machine (ML-CRBM), which handles modality completion , fusion, and multi-label prediction in a unified framework. The proposed model is able to generate missing modalities based on observed ones, by explicitly modelling and sampling their conditional distributions. After that, it can discriminatively fuse multiple modalities to obtain shared representations under the supervision of class labels. To consider the co-occurrence of the labels, the proposed model formulates the multi-label prediction as a max-margin-based multi-task learning problem. Model parameters can be jointly learned by seeking a balance between being generative for modality generation and being discriminative for label prediction. We perform a series of experiments in terms of classification, visualization, and retrieval, and the experimental results clearly demonstrate the effectiveness of our method.


title: IBA analyses and lead concentration measurements of AMS-14C dated bones from two medieval sites in Italy

abstract: Abstract AMS (accelerator mass spectrometry) radiocarbon dated human bone samples recovered in the deserted medieval village of Quattro Macine, Lecce, in Southern Italy, were analyzed by PIXE (particle induced X-ray emission) and PIGE (particle induced gamma ray emission). The optimization of the PIXE–PIGE experimental set-up in term of beam energy and detection geometry resulted in the possibility to obtain information about major, minor and trace elements of the samples. Unexpectedly, all the analyzed samples showed a Pb concentration higher than 50 ppm. Two possible explanations of the measured high Pb concentration are discussed in the paper: the pre-mortem intake associated with the use of Pb-rich glazed pottery and the post-mortem intake from soil as the result of diagenetic processes.


title: A Theory of Questions and Question Asking

abstract: This article focuses on knowledge goals, that is, the goals of a reasoner to acquire or reorganize knowledge. Knowledge goals, often expressed as questions, arise when the reasoner's model of the domain is inadequate in some reasoning situation. This leads the reasoner to focus on the knowledge it needs, to formulate questions to acquire this knowledge, and to learn by pursuing its questions. I develop a theory of questions and of question asking, motivated both by cognitive and computational considerations, and I discuss the theory in the context of the task of story understanding. I present a computer model of an active reader that learns about novel domains by reading newspaper stories.


title: Convolutional Neural Network with Word Embedding Based Approach for Resume Classification

abstract: Document Classification is a very prominent area, it isapplicableforadiversityofnovelapplications.lnthisstudy, we focussed on classifying resumes to different classes. The proposed approach is for classifying resumes using Convolutional Neural Network with Glove-Word Embedding. We have segmented resumes into various levels and a hierarchy of classification levels is created. For each level, the CNN with word embedding model is used for classification. The output of each classifier is later combined to define the overall hierarchy of the resume category. Results are evaluated using the performance measures such as precision, recall, and f-score. The results obtained are promising and the proposed system is helpful in the recruitment and selection process of candidates.


title: Sparse Ensemble Learning for Concept Detection

abstract: This work presents a novel sparse ensemble learning scheme for concept detection in videos. The proposed ensemble first exploits a sparse non-negative matrix factorization (NMF) process to represent data instances in parts and partition the data space into localities, and then coordinates the individual classifiers in each locality for final classification. In the sparse NMF, data exemplars are projected to a set of locality bases, in which the non-negative superposition of basis images reconstructs the original exemplars. This additive combination ensures that each locality captures the characteristics of data exemplars in part, thus enabling the local classifiers to hold reasonable diversity in their own regions of expertise. More importantly, the sparse NMF ensures that an exemplar is projected to only a few bases (localities) with non-zero coefficients. The resultant ensemble model is, therefore, sparse, in the way that only a small number of efficient classifiers in the ensemble will fire on a testing sample. Extensive tests on the TRECVid 08 and 09 datasets show that the proposed ensemble learning achieves promising results and outperforms existing approaches. The proposed scheme is feature-independent, and can be applied in many other large scale pattern recognition problems besides visual concept detection.


title: Ontology-guided feature engineering for clinical text classification

abstract: Graphical abstractDisplay Omitted Highlights? Leveraged the structure of the UMLS to rank concepts for text classification. ? Used document class labels to define a context-sensitive semantic similarity measure. ? Classified clinical text with SVMs using a semantic kernel. ? Improved the top machine-learning system from I2B2 2008 challenge. In this study we present novel feature engineering techniques that leverage the biomedical domain knowledge encoded in the Unified Medical Language System (UMLS) to improve machine-learning based clinical text classification. Critical steps in clinical text classification include identification of features and passages relevant to the classification task, and representation of clinical text to enable discrimination between documents of different classes. We developed novel information-theoretic techniques that utilize the taxonomical structure of the Unified Medical Language System (UMLS) to improve feature ranking, and we developed a semantic similarity measure that projects clinical text into a feature space that improves classification. We evaluated these methods on the 2008 Integrating Informatics with Biology and the Bedside (I2B2) obesity challenge. The methods we developed improve upon the results of this challenge's top machine-learning based system, and may improve the performance of other machine-learning based clinical text classification systems. We have released all tools developed as part of this study as open source, available at http://code.google.com/p/ytex.


title: Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

abstract: Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.


title: Side-Channel Leaks in Web Applications: A Reality Today, a Challenge Tomorrow

abstract: With software-as-a-service becoming mainstream, more and more applications are delivered to the client through the Web. Unlike a desktop application, a web application is split into browser-side and server-side components. A subset of the application’s internal information flows are inevitably exposed on the network. We show that despite encryption, such a side-channel information leak is a realistic and serious threat to user privacy. Specifically, we found that surprisingly detailed sensitive information is being leaked out from a number of high-profile, top-of-the-line web applications in healthcare, taxation, investment and web search: an eavesdropper can infer the illnesses/medications/surgeries of the user, her family income and investment secrets, despite HTTPS protection; a stranger on the street can glean enterprise employees' web search queries, despite WPA/WPA2 Wi-Fi encryption. More importantly, the root causes of the problem are some fundamental characteristics of web applications: stateful communication, low entropy input for better interaction, and significant traffic distinctions. As a result, the scope of the problem seems industry-wide. We further present a concrete analysis to demonstrate the challenges of mitigating such a threat, which points to the necessity of a disciplined engineering practice for side-channel mitigations in future web application developments.


title: A Comparative Study on Regularization Strategies for Embedding-based Neural Networks

abstract: This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neural models and tasks as our testbed. We tried several frequently applied or newly proposed regularization strategies, including penalizing weights (embeddings excluded), penalizing embeddings, re-embedding words, and dropout. We also emphasized on incremental hyperparameter tuning, and combining different regularizations. The results provide a picture on tuning hyperparameters for neural NLP models.


title: The Effect of Slice Order and Thickness on fMRI Activation Data Using Multislice Echo-Planar Imaging

abstract: Multislice echo-planar imaging (EPI) is a commonly used technique for fMRI studies. Brain activation images acquired using fMRI are sensitive to T2* changes, reflecting the level of blood oxygenation (BOLD contrast), and may also contain an element of T1contrast which detects blood flow changes in large vessels. If slice inflow (T1) effects are significant in multislice EPI, then as the order in which the slices are acquired is changed, differences in the activation maps are predicted. However, in experiments presented here using visual stimulation, the data demonstrate that highly consistent results can be achieved for repetition times (TR) of 6.0, 3.0, and 1.5 s. This suggests that, for whole-brain multislice EPI, fMRI activation is dominated by T2*, BOLD contrast. The thickness of the imaging slice is also an important parameter in these studies, having implications for spatial resolution, sensitivity, and acquisition time. In separate visual cortex experiments the effect on the values of the fMRIZscores and the number of activated voxels is investigated as a function of slice thickness (from 1 to 8 mm). The maximumZscores in the data are similar for all slice thicknesses and, after resampling to allow a direct comparison to be made, the volume of visual cortex detected as significantly activated increases with slice thickness.


title: Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles

abstract: We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.


abstract: An isolated high-voltage high-frequency pulsed power converter for non-thermal plasma ozone generation is proposed in this paper. With an input of 500 VDC, the output pulsed voltage can reach −12 kV through the inductor-capacitor resonance together with a step-up transformer. The pulse duration is designed as 1.66 μs. An anti-series connected switch pair at the input terminal enables the energy stored in the passive component to be recovered and transferred back to the DC source which results in great energy saving. A hardware prototype with 500 VDC input and −12 kV pulsed voltage output has been developed. The pulse generating frequency can go up to 15 kHz. Both the simulation and experimental results are presented to validate the proposed pulsed power converter and its operating principle.


title: Stochastic gradient descent with differentially private updates

abstract: Differential privacy is a recent framework for computation on sensitive data, which has shown considerable promise in the regime of large datasets. Stochastic gradient methods are a popular approach for learning in the data-rich regime because they are computationally tractable and scalable. In this paper, we derive differentially private versions of stochastic gradient descent, and test them empirically. Our results show that standard SGD experiences high variability due to differential privacy, but a moderate increase in the batch size can improve performance significantly.


title: Wireless Wearable Magnetometer-Based Sensor for Sleep Quality Monitoring

abstract: Obstructive sleep apnoea is one of the most common sleep disorders, which is characterized by the repetitive cessations of respiratory flow during sleep. This paper describes an alternative method to detect sleep parameters by measuring a variation in the earth’s magnetic field. A magnetometer sensor, laid onto the body, detects millimeter night-time breathing movements by measuring the change in the magnetic vectors. A soft, non-invasive wearable sensor is designed to be printed on a miniature printed circuit board, packaged in a minimal invasive board, which includes a wireless Bluetooth low-energy (BLE) module and a low-power microcontroller. A very low power consumption is achieved by applying a smart processing algorithm, which is also described. The algorithm delivers respiration rate, apnoea time, and time in movement. A comparison with a well-known air-flow sensor is also discussed.


title: Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization

abstract: Given a network with node attributes, how can we identify communities and spot anomalies? How can we characterize, describe, or summarize the network in a succinct way? Community extraction requires a measure of quality for connected subgraphs (e.g., social circles). Existing subgraph measures, however, either consider only the connectedness of nodes inside the community and ignore the cross-edges at the boundary (e.g., density) or only quantify the structure of the community and ignore the node attributes (e.g., conductance). In this work, we focus on node-attributed networks and introduce: (1) a new measure of subgraph quality for attributed communities called normality, (2) a community extraction algorithm that uses normality to extract communities and a few characterizing attributes per community, and (3) a summarization and interactive visualization approach for attributed graph exploration. More specifically, (1) we first introduce a new measure to quantify the normality of an attributed subgraph. Our normality measure carefully utilizes structure and attributes together to quantify both the internal consistency and external separability. We then formulate an objective function to automatically infer a few attributes (called the “focus”) and respective attribute weights, so as to maximize the normality score of a given subgraph. Most notably, unlike many other approaches, our measure allows for many cross-edges as long as they can be “exonerated;” i.e., either (i) are expected under a null graph model, and/or (ii) their boundary nodes do not exhibit the focus attributes. Next, (2) we propose AMEN (for Attributed Mining of Entity Networks), an algorithm that simultaneously discovers the communities and their respective focus in a given graph, with a goal to maximize the total normality. Communities for which a focus that yields high normality cannot be found are considered low quality or anomalous. Last, (3) we formulate a summarization task with a multi-criteria objective, which selects a subset of the communities that (i) cover the entire graph well, are (ii) high quality and (iii) diverse in their focus attributes. We further design an interactive visualization interface that presents the communities to a user in an interpretable, user-friendly fashion. The user can explore all the communities, analyze various algorithm-generated summaries, as well as devise their own summaries interactively to characterize the network in a succinct way. As the experiments on real-world attributed graphs show, our proposed approaches effectively find anomalous communities and outperform several existing measures and methods, such as conductance, density, OddBall, and SODA. We also conduct extensive user studies to measure the capability and efficiency that our approach provides to the users toward network summarization, exploration, and sensemaking.


Digital Signal Controller Based Automatic Transfer Switch


title: Analysis for the power loss of electro hydrostatic actuator and hydraulic actuator

abstract: This article describes the difference between traditional airborne hydraulic actuator and electro hydrostatic actuator in heat dissipation. The power loss of the two systems are analyzed based on the method of energy transfer under different working conditions. To decrease the heating loss of electro-hydrostatic actuator, this paper designs an efficient component of the motor-pump. Theoretical and experimental analysis show that electro hydrostatic actuator is more efficient, and it is available for power saving design according to the need for energy.


title: Model-Checking for Real-Time Systems

abstract: Efficient automatic model-checking algorithms for real-time systems have been obtained in recent years based on the state-region graph technique of Alur, Courcoubetis and Dill. However, these algorithms are faced with two potential types of explosion arising from parallel composition: explosion in the space of control nodes, and explosion in the region space over clock-variables.


title: Automatic repair for multi-threaded programs with Deadlock/Livelock using maximum satisfiability

abstract: Deadlock-freedom is a major challenge in developing multi-threaded programs, as a deadlock cannot be resolved until one restarts the program (mostly by using manual intervention). To avoid the risk of blocking, a program may use the trylock operations rather than lock operations. In this case, if a thread fails to acquire a lock using trylock, since trylock is non-blocking, the thread can release acquired locks to avoid a deadlock after trylock returns. Although this approach avoids deadlocks, it may also introduce bugs such as livelock and deadlivelock. Moreover, when such bugs are identified in a program, revising the program manually is error-prone. With this motivation, in this paper, we propose an approach for avoiding deadlocks, livelocks and deadlivelocks in the given multi-threaded program. In our approach, we first identify cyclic lock dependencies that may lead to deadlocks, livelocks or deadlivelocks. Subsequently, we map the problem of ensuring freedom from deadlocks, livelocks and deadlivelocks to the weighted partial maximum satisfiability problem. To ensure that the repaired program preserves most of original design, our approach attempts to make minimal changes to the original program.


title: Automated annotation of coral reef survey images

abstract: With the proliferation of digital cameras and automatic acquisition systems, scientists can acquire vast numbers of images for quantitative analysis. However, much image analysis is conducted manually, which is both time consuming and prone to error. As a result, valuable scientific data from many domains sit dormant in image libraries awaiting annotation. This work addresses one such domain: coral reef coverage estimation. In this setting, the goal, as defined by coral reef ecologists, is to determine the percentage of the reef surface covered by rock, sand, algae, and corals; it is often desirable to resolve these taxa at the genus level or below. This is challenging since the data exhibit significant within class variation, the borders between classes are complex, and the viewpoints and image quality vary. We introduce Moorea Labeled Corals, a large multi-year dataset with 400,000 expert annotations, to the computer vision community, and argue that this type of ecological data provides an excellent opportunity for performance benchmarking. We also propose a novel algorithm using texture and color descriptors over multiple scales that outperforms commonly used techniques from the texture classification literature. We show that the proposed algorithm accurately estimates coral coverage across locations and years, thereby taking a significant step towards reliable automated coral reef image annotation.


title: High-Performance Work Systems and Organizational Performance: The Mediating Role of Internal Social Structure

abstract: This study provides a theoretical framework illustrating how the internal social structure of the organization can mediate the relationship between high-performance work systems (HPWS) and organizational performance. HPWS positively influence the internal social structure by facilitating bridging network ties, generalized norms of reciprocity, shared mental models, role making, and organizational citizenship behavior. Although HPWS are conceptualized as a system of human resource (HR) practices, each category ofHRpractices has a differential relationship with the mediating variables. HPWS lead to (a) financial performance via administrative efficiency and (b) sustainable performance via flexibility arising from the coordination and exploitation of knowledge resources.


title: Time to give up on a single explanation for autism

abstract: We argue that there will be no single (genetic or cognitive) cause for the diverse symptoms defining autism. We present recent evidence of behavioral fractionation of social impairment, communication difficulties and rigid and repetitive behaviors. Twin data suggest largely nonoverlapping genes acting on each of these traits. At the cognitive level, too, attempts at a single explanation for the symptoms of autism have failed. Implications for research and treatment are discussed.


title: Learning to close loops from range data

abstract: In this paper we address the loop closure detection problem in simultaneous localization and mapping ( slam ), and present a method for solving the problem using pairwise comparison of point clouds in both two and three dimensions. The point clouds are mathematically described using features that capture important geometric and statistical properties. The features are used as input to the machine learning algorithm AdaBoost, which is used to build a non-linear classifier capable of detecting loop closure from pairs of point clouds. Vantage point dependency in the detection process is eliminated by only using rotation invariant features, thus loop closure can be detected from an arbitrary direction. The classifier is evaluated using publicly available data, and is shown to generalize well between environments. Detection rates of 66%, 63% and 53% for 0% false alarm rate are achieved for 2D outdoor data, 3D outdoor data and 3D indoor data, respectively. In both two and three dimensions, experiments are performed using publicly available data, showing that the proposed algorithm compares favourably with related work.


The German Translation of Royce's Epistemology by Husserl's Student Winthrop Bell: A Neglected Bridge of Pragmatic-Phenomenological Interpretation?


title: Interpretable Counting for Visual Question Answering

abstract: 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.


title: Cascaded H-Bridge Multilevel PV Topology for Alleviation of Per-Phase Power Imbalances and Reduction of Second Harmonic Voltage Ripple

abstract: The cascaded H-bridge (CHB) topology is ideal for implementing large-scale converters for photovoltaic (PV) applications. The improved quality of output voltage waveforms, high efficiency due to transformer-less connection, and ability to employ multiple instances of a maximum power point tracking (MPPT) algorithm are just some advantages. An important disadvantage is the required over-rating to ensure balanced three-phase currents at times of unequal PV generation. Unequal generation occurs due to shading, temperature inhomogeneity, faulty H-bridges, etc. Capacitor voltage balancing under such conditions requires zero-sequence voltage injection which increases the required number of series connected H-bridges. However, leakage current and safety requirements often dictate a need for isolation between PV arrays and the cascaded converter. Therefore, this paper proposes a converter topology that avoids the cost of extra series connected H-bridges by extending the function of dc–dc converters that provide isolation. Second harmonic power oscillations seen in typical cascaded topologies can also be eliminated or reduced through use of the proposed topology. Simulation and experimental results are presented that confirm correct operation of the proposed approach.


title: A comparative study of handheld and non-handheld traffic in campus wifi networks

abstract: Handheld devices such as smartphones have become a major platform for accessing Internet services. The small, mobile nature of these devices results in a unique mix of network usage. Other studies have used Wi-Fi and 3G wireless traces to analyze session, mobility, and performance characteristics for handheld devices.We complement these studies through our unique study of the differences in the content and flow characteristics of handheld versus non-handheld traffic. We analyze packet traces from two separate campus wireless networks, with 3 days of traffic for 32, 278 unique devices. Trends for handhelds include low UDP usage, high volumes of HTTP traffic, and a greater proportion of video traffic. Our observations can inform network management and mobile system design.


title: Pattern-matching spi-calculus

abstract: Cryptographic protocols often make use of nested cryptographic primitives, for example signed message digests, or encrypted signed messages. Gordon and Jeffrey's prior work on types for authenticity did not allow for such nested cryptography. In this work, we present the pattern-matching spi-calculus, which is an obvious extension of the spi-calculus to include pattern-matching as primitive. The novelty of the language is in the accompanying type system, which uses the same language of patterns to describe complex data dependencies which cannot be described using prior type systems. We show that any appropriately typed process is guaranteed to satisfy a strong robust safety property.


title: Construction of Myanmar WordNet lexical database

abstract: A lexical resource is the central repository of data for all language processing applications. It contains information for human consumption as well as computer programs. A significant increase in the use of lexical databases has led WordNet to become one of the most widely used lexical information source for Natural Languages Processing (NLP) applications. This paper presents a methodology of constructing Myanmar WordNet lexical database from WordNet and Myanmar<->English Machine Readable Dictionaries (MRDs). This system consists of three phases which are (i) data extraction phase (ii) link analyzing phase and (iii) construction phase. In the data extraction phase, the lexical information is extracted from available resources. Then sets of candidate links between Myanmar words and synsets are derived from lexical information and that are verified for the lexical gap between two languages in the link analyzing phase. Finally, a set of the verified candidate link is used to automatically construct the Myanmar WordNet. The proposed Myanmar WordNet has coverage of 173853 links, 38142 synsets and 25865 words.


title: Region Priority Based Adaptive 360-Degree Video Streaming Using DASH

abstract: With the continuous improvement of Virtual Reality (VR) hardware and software facilities and development of VR streaming platform, the VR industry ushered in a period of rapid development. VR makes use of 360-degree panoramic or omnidirectional video with high resolution and high frame rate in order to create the immersive experience to the user. However those characteristics cause the big volume of 360-degree video and bandwidth intensive during transmission. Due to the limitation of the human eye vision, the user can only watch the part of the 360-degree video in a head-mounted display (HMD) at one time. Hence, streaming the VR video by the traditional video transmission method causes bandwidth waste. In view of this, this paper proposed a 360-degree video adaptive transmission method based on user viewport. Firstly, a 360-degree video region prioritization scheme is proposed based on user vision characteristics and head motion features. Different priority regions transmit 360-degree video content with different quality levels as the format of tiles. Then, based on the bandwidth estimation, buffer status and user viewport prediction, a 360-degree video adaptive transmission decision strategy is given. According to the predicted available bandwidth and viewport, the quality combination of the 360-degree video tiles to be transmitted in the near future is determined to implement adaptive transmission. The test results of the 360-degree video transmission experiment system based on the DASH standard show that the proposed 360-degree video adaptive transmission strategy can effectively reduce the bandwidth consumption on the basis of guaranteeing the user Quality of Experience(QoE).


title: IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?

abstract: The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.


title: Fully Connected Network for HEVC CU Split Decision equipped with Laplacian Transparent Composite Model

abstract: High Efficiency Video Coding (HEVC) improves rate distortion (RD) performance significantly, but at the same time is computationally expensive due to the adoption of a large variety of coding unit (CU) sizes in its RD optimization. In this paper, we investigate the application of fully connected neural networks (NNs) to this time-sensitive application to improve its time complexity, while controlling the resulting bitrate loss. Specifically, four NNs are introduced with one NN for each depth of the coding tree unit. These NNs either split the current CU or terminate the CU search algorithm. Because training of NNs is time-consuming and requires large training data, we further propose a novel training strategy in which offline training and online adaptation work together to overcome this limitation. Our features are extracted from original frames based on the Laplacian Transparent Composite Model (LPTCM). Experiments carried out on all-intra configuration for HEVC reveal that our method is among the best NN methods, with an average time saving of 38% and an average controlled bitrate loss of 1.6%, compared to original HEVC.


title: Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre

abstract: Theatrical improvisation (impro or improv) is a demanding form of live, collaborative performance. Improv is a humorous and playful artform built on an open-ended narrative structure which simultaneously celebrates effort and failure. It is thus an ideal test bed for the development and deployment of interactive artificial intelligence (AI)-based conversational agents, or artificial improvisors. This case study introduces an improv show experiment featuring human actors and artificial improvisors. We have previously developed a deep-learning-based artificial improvisor, trained on movie subtitles, that can generate plausible, context-based, lines of dialogue suitable for theatre (Mathewson and Mirowski 2017). In this work, we have employed it to control what a subset of human actors say during an improv performance. We also give human-generated lines to a different subset of performers. All lines are provided to actors with headphones and all performers are wearing headphones. This paper describes a Turing test, or imitation game, taking place in a theatre, with both the audience members and the performers left to guess who is a human and who is a machine. In order to test scientific hypotheses about the perception of humans versus machines we collect anonymous feedback from volunteer performers and audience members. Our results suggest that rehearsal increases proficiency and possibility to control events in the performance. That said, consistency with real world experience is limited by the interface and the mechanisms used to perform the show. We also show that human-generated lines are shorter, more positive, and have less difficult words with more grammar and spelling mistakes than the artificial improvisor generated lines.


title: CLUSTAL W : improving the sensitivity of progressive multiple sequence alignment through sequence weighting , position-specific gap penalties and weight matrix choice

abstract: Thesensitivity ofthecommonlyusedprogressive multiple sequence alignment methodhasbeengreatly improved forthealignment ofdivergent protein sequences. Firstly, individual weights areassigned to eachsequence inapartial alignment inorder todownweightnear-duplicate sequences andup-weight the mostdivergent ones. Secondly, aminoacid substitution matrices arevaried atdifferent alignment stages according tothedivergence ofthesequences tobe aligned. Thirdly, residue-specific gappenalties and locally reduced gappenalties inhydrophilic regions encourage newgapsinpotential loopregions rather thanregular secondary structure. Fourthly, positions inearly alignments wheregapshavebeenopened receive locally reduced gappenalties toencourage the opening upofnewgapsatthesepositions. These modifications areincorporated intoanewprogram, CLUSTALW whichisfreely available.


title: Building Community Social Capital: The Potential and Promise of Information and Communications Technologies

abstract: Increasing community social capital is widely viewed as one of the benefits of the deployment of information and communications technologies. A meta-analysis of the literature related to social capital and ICTs is completed. Using the five elements of social capital generally described in the research literature, the meta-analysis examines the empirical findings related to each of these five elements. The results indicate that much work remains to be done before it can be said with any validity that ICTs can, in fact, create community social capital.


title: Intelligent Parkinson Disease Prediction Using Machine Learning Algorithms

abstract: Diagnosis of the Parkinson disease through machine learning approache provides better understanding from PD dataset in the present decade. Orange v2.0b and weka v3.4.10 has been used in the present experimentation for the statistical analysis, classification, Evaluation and unsupervised learning methods. Voice dataset for Parkinson disease has been retrieved from UCI Machine learning repository from Center for Machine Learning and Intelligent Systems. The dataset contains name, MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz), MDVP:Jitter(%), MDVP: Jitter(Abs), MDVP:RAP, MDVP:PPQ, Jitter:DDP, MDVP:Shimmer, MDVP:Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5, MDVP:APQ, Shimmer:DDA, NHR, HNR, status, RPDE, DFA, spread1, spread2, D2, PPE attributes. The parallel coordinates shows higher variation in Parkinson disease dataset. SVM has shown good accuracy (88.9%) compared to Majority and k-NN algorithms. Classification algorithm like Random Forest has shown good accuracy (90.26) and Naive Bayes has shown least accuracy (69.23. Higher number of clusters in healthy dataset in Fo and less number in diseased data has been predicted by Hierarchal clustering and SOM.


title: Finger vein recognition with superpixel-based features

abstract: Finger veins based biometrics, as a new approach to personal identification, has received much attention in recent years. The methods based on low level feature, for instance the gray, texture of finger vein, are the mainstream, but they are usually faced with many challenges, such as sensitivity to noise and low local consistency. In fact, finger vein recognition based on high level feature representation has been proved to be a promising way to effectively overcome the above limitations and improve the system performance. Thus, in this paper, we present a novel identification framework, which utilizes superpixel-based features (SPFs) of finger vein for high level feature representation. When comparing two finger veins, the features of each pixel are firstly extracted as base attributes by traditional way. Then, after superpixel over-segmentation, the SPF of each finger vein can be obtained based on its base attributes by some statistical techniques. Lastly, a weighted spatial pyramid matching (WSPM) scheme is utilized to implement matching. Our experiments have yielded some very good results evidenced by an EER of 0.0147 on the benchmark database PolyU.


title: Sustained attention in children with specific language impairment

abstract: Purpose Information-processing limitations have been associated with language problems in children with specific language impairment (SLI). These processing limitations may be associated with limitations in attentional capacity, even in the absence of clinically significant attention deficits. In this study, the authors examined the performance of 4- to 6-year-old children with SLI and their typically developing (TD) peers on a visual sustained attention task. It was predicted that the children with SLI would demonstrate lower levels of performance in the absence of clinically significant attention deficits. Method A visual continuous performance task (CPT) was used to assess sustained attention in 13 children with SLI (M = 62.07 months) and 13 TD age-matched controls (M = 62.92 months). All children were screened for normal vision, hearing, and attention. Accuracy (d') and response time were analyzed to see if this sustained attention task could differentiate between the 2 groups. Results The children wi...


title: Recurrent Dropout without Memory Loss

abstract: This paper presents a novel approach to recurrent neural network (RNN) regularization. Differently from the widely adopted dropout method, which is applied to \textit{forward} connections of feed-forward architectures or RNNs, we propose to drop neurons directly in \textit{recurrent} connections in a way that does not cause loss of long-term memory. Our approach is as easy to implement and apply as the regular feed-forward dropout and we demonstrate its effectiveness for Long Short-Term Memory network, the most popular type of RNN cells. Our experiments on NLP benchmarks show consistent improvements even when combined with conventional feed-forward dropout.


title: Approaches and issues in view selection for materialising in data warehouse

abstract: To facilitate efficient query processing in data warehouses and develop online analytical processing OLAP for decision support systems, intermediate data derived in the middle of complex processing may be stored as a set of materialised views. It is not possible to save each and every intermediate query result due to limitation of space and updating cost. Therefore, an optimum set of views need to be selected for materialisation and, this requires a good optimisation technique. Many approaches have been presented in the literature to achieve good solutions to this problem. In this paper, we attempt to provide a comprehensive survey of the approaches and algorithms introduced to address the issue. We also attempt to identify the key issues and research challenges in this area.


title: HEMD: a highly efficient random forest-based malware detection framework for Android

abstract: Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google’s Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection.


title: Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges

abstract: Cyber-physical systems (CPSs) integrate the computation with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. CPS was identified as one of the eight research priority areas in the August 2007 report of the President's Council of Advisors on Science and Technology, as CPS will be the core component of many critical infrastructures and industrial control systems in the near future. However, a variety of random failures and cyber attacks exist in CPS, which greatly restrict their growth. Fortunately, an intrusion detection mechanism could take effect for protecting CPS. When a misbehavior is found by the intrusion detector, the appropriate action can be taken immediately so that any harm to the system will be minimized. As CPSs are yet to be defined universally, the application of the instruction detection mechanism remain open presently. As a result, the effort will be made to discuss how to appropriately apply the intrusion detection mechanism to CPS in this paper. By examining the unique properties of CPS, it intends to define the specific requirements first. Then, the design outline of the intrusion detection mechanism in CPS is introduced in terms of the layers of system and specific detection techniques. Finally, some significant research problems are identified for enlightening the subsequent studies.


title: Self-Supervised Intrinsic Image Decomposition

abstract: Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic image decomposition by explaining the input image. Our model, the Rendered Intrinsics Network (RIN), joins together an image decomposition pipeline, which predicts reflectance, shape, and lighting conditions given a single image, with a recombination function, a learned shading model used to recompose the original input based off of intrinsic image predictions. Our network can then use unsupervised reconstruction error as an additional signal to improve its intermediate representations. This allows large-scale unlabeled data to be useful during training, and also enables transferring learned knowledge to images of unseen object categories, lighting conditions, and shapes. Extensive experiments demonstrate that our method performs well on both intrinsic image decomposition and knowledge transfer.


title: SnO2 Nanorods on ZnO Nanofibers: A New Class of Hierarchical Nanostructures Enabled by Electrospinning as Anode Material for High-Performance Lithium-Ion Batteries

abstract: Abstract The ZnO/SnO 2 heterogeneous nanofibers (HNFs) have been prepared via an electrospinning method followed by calcination at 500 °C in air. XRD, BET, SEM, TEM and electrochemical measurements were performed to characterize the new material, including structure, morphology and electrochemical properties. The spacing between adjacent SnO 2 nanorods on mesoporous ZnO nanofibers is about twice the diameter of each SnO 2 nanorod. The SnO 2 /ZnO11 (molar ratio of Sn: Zn = 1:1) HNFs based electrodes exhibit excellent cycle performance and rate performance, which is attributed to the heterogeneous and mesoporous structure as well as the ultrafine ZnO NPs embedded in the HNFs matrix. Outstanding electrochemical performance as anode material for LIBs together with low cost, facile procedures and high reproducibility make the SnO 2 /ZnO HNFs have a prospect in the field of energy storage.


title: Objective cosmetic and anatomical outcomes at adolescence of feminising surgery for ambiguous genitalia done in childhood

abstract: Summary There are few, if any data on the long-term outcome of feminising genital surgery for children with ambiguous genitalia. We present a retrospective study of cosmetic and anatomical outcomes in 44 adolescent patients who had ambiguous genitalia in childhood and underwent feminising genital surgery. Cosmetic result was judged as poor in 18 (41%) of these patients. 43 (98%) of 44 needed further treatment to the genitalia for cosmesis, tampon use, or intercourse. 23 (89%) of 26 of genitoplasties planned as one-stage procedures required further major surgery. This information must be available to parents and clinicians planning such surgery. Cosmetic genital surgery in infancy needs to be reassessed in the light of these results.


AIDA - Abstraction for Advanced In-Database Analytics


title: Efficient public integrity checking for cloud data sharing with multi-user modification

abstract: In past years a body of data integrity checking techniques have been proposed for securing cloud data services. Most of these works assume that only the data owner can modify cloud-stored data. Recently a few attempts started considering more realistic scenarios by allowing multiple cloud users to modify data with integrity assurance. However, these attempts are still far from practical due to the tremendous computational cost on cloud users. Moreover, collusion between misbehaving cloud servers and revoked users is not considered. This paper proposes a novel data integrity checking scheme characterized by multi-user modification, collusion resistance and a constant computational cost of integrity checking for cloud users, thanks to our novel design of polynomial-based authentication tags and proxy tag update techniques. Our scheme also supports public checking and efficient user revocation and is provably secure. Numerical analysis and extensive experimental results show the efficiency and scalability of our proposed scheme.


title: Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training

abstract: In this paper, we present multiple approaches to improve sentiment analysis on Twitter data. We first establish a state-of-the-art baseline with a rich feature set. Then we build a topic-based sentiment mixture model with topic-specific data in a semi-supervised training framework. The topic information is generated through topic modeling based on an efficient implementation of Latent Dirichlet Allocation (LDA). The proposed sentiment model outperforms the top system in the task of Sentiment Analysis in Twitter in SemEval-2013 in terms of averaged F scores.


title: Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation

abstract: As mixed reality is gaining increased momentum, the development of effective and efficient solutions to egocentric hand segmentation is becoming critical. Traditional segmentation techniques typically follow a one-shot approach, where the image is passed forward only once through a model that produces a segmentation mask. This strategy, however, does not reflect the perception of humans, who continuously refine their representation of the world. In this paper, we therefore introduce a novel gated recurrent architecture. It goes beyond both iteratively passing the predicted segmentation mask through the network and adding a standard recurrent unit to it. Instead, it incorporates multiple encoder-decoder layers of the segmentation network, so as to keep track of its internal state in the refinement process. As evidenced by our results on standard hand segmentation benchmarks and on our own dataset, our approach outperforms these other, simpler recurrent segmentation techniques, as well as the state-of-the-art hand segmentation one. Furthermore, we demonstrate the generality of our approach by applying it to road segmentation, where it also outperforms other baseline methods.


title: Cloud Based Web Scraping for Big Data Applications

abstract: With the penetration of new technologies, there is a rapid growth of internet users and data (mostly unstructured) generated by those users on the internet. As scraping is one of the major sources for extraction of unstructured data from the Internet, we have analyzed the scraping process when introduced to a bulk of data extraction. We faced several challenges while scraping large amount of data, such as encountering captcha, storage issue for a large volume of data, need for intensive computation capacity and reliability of data extraction. In this paper, we investigate cloud-based web scraping architecture able to handle storage and computing resources with elasticity on demand using Amazon Web Services(Elastic Compute Cloud and DynamoDB). Our solution tries to address both scraping and feasibility for big data applications in a single cloud-based architecture for data-based industries. We discuss selenium as one of our tool for web scraping because of web drivers it supports which simulates a real user working with a browser. We also analyze the scalability and performance of the proposed cloud-based scrapper and describe the advantages of the proposed cloud-based scraping over other cloud-based scrapers.


title: Water-Saving Irrigation System Based on Automatic Control by Using GSM Technology

abstract: This project proposed an embedded system for automatic control of irrigation. This project has wireless sensor network for real-time sensing and control of an irrigation system. This system provides uniform and required level of water for the agricultural farm and it avoids water wastage. These paper have real time sensing and control of an irrigation system. When the condition of waterin the agricultural farm is abnormal then the system automatically switches ON the motor. When the water level reaches normal level the motor automatically switch OFF. In this project we are interfacing microcontroller through temperature sensor, humiditysensor and also interfacing to GSM through MAX 232.In this we set specified values of temperature, humidity and the conditioned is uniformly monitored by VB.NET. As we know that India is a developing country and and more wired connection. We introduce a new the major part of our GDP growth rate belongs to system which will have wireless connection between agriculture alone. So we can say that agriculture is the server and nodes. We introduce a new design of backbone of India and irrigation is called the lifeline. So, embedded web server making use of GSM network agriculture in India has been the most important priority technology in the paper. Compared to the wired link web in the economic development of country since the server system. This system is characterised by having no independence. Major part of our expenditure is spent on wires between the web server and terminal nodes. These agriculture alone and inspite of that we not getting systems have lower cost and having more flexibility of the required output. In India, there is uneven biological network topology. diversity cause, some part experience droughts while For every node we will use separate GSM trans- some parts flood, so there is always scarcity of water receiver to transmit the details to server nodes. Water available for the irrigation. Farmer in rural area severally irrigation control based on microcontroller and internet of affected by this condition. New technologies coming but things. By internet of things we mean that it has the they are too expensive for the common farmer. The project ability to analyse and distribute data that can be used as offers a cheaper and simpler solution to this problem by information and knowledge. Internet of things improves developing automated microclimate irrigation controllers distribution of world's resources to those who need it the with wireless capability assisted with low cost wireless most. In agricultural farm we use water irrigation is sensor nodes. Like temperature sensor, humidity sensor monitored by using sensor like temperature, humidity. which senses the level of moisture of the soil. The land or Then controller sends sensor to pc using GSM wireless firm is divided into microclimatic regions equipped with technology and updated on the internet using VB.NET. smart specified sensors and integrated wirelessly into automated irrigation controller with wireless networking Functional Blocks Diagram: The functional block


title: Stable image reconstruction using total variation minimization

abstract: This paper presents near-optimal guarantees for stable and robust image recovery from undersampled noisy measurements using total variation minimization. In particular, we show that from $O(s\log(N))$ nonadaptive linear measurements, an image can be reconstructed to within the best $s$-term approximation of its gradient up to a logarithmic factor, and this factor can be removed by taking slightly more measurements. Along the way, we prove a strengthened Sobolev inequality for functions lying in the null space of a suitably incoherent matrix.


title: A Reinforcement Learning Approach to the View Planning Problem

abstract: We present a Reinforcement Learning (RL) solution to the view planning problem (VPP), which generates a sequence of view points that are capable of sensing all accessible area of a given object represented as a 3D model. In doing so, the goal is to minimize the number of view points, making the VPP a class of set covering optimization problem (SCOP). The SCOP is NP-hard, and the inapproximability results tell us that the greedy algorithm provides the best approximation that runs in polynomial time. In order to find a solution that is better than the greedy algorithm, (i) we introduce a novel score function by exploiting the geometry of the 3D model, (ii) we device an intuitive approach to VPP using this score function, and (iii) we cast VPP as a Markovian Decision Process (MDP), and solve the MDP in RL framework using well-known RL algorithms. In particular, we use SARSA, Watkins-Q and TD with function approximation to solve the MDP. We compare the results of our method with the baseline greedy algorithm in an extensive set of test objects, and show that we can outperform the baseline in almost all cases.


title: Threat assessment design for driver assistance system at intersections

abstract: This paper considers the decision-making problem for a human-driven vehicle crossing a road intersection in the presence of other, potentially errant, drivers. Our approach relies on a novel threat assessment module, which combines an intention predictor based on support vector machines with an efficient threat assessor using rapidly-exploring random trees. This module warns the host driver with the computed threat level and the corresponding best “escape maneuver” through the intersection, if the threat is sufficiently large. Through experimental results with small autonomous and human-driven vehicles, we demonstrate that this threat assessment module can be used in real-time to minimize the risk of collision.


title: Geological significance of new zircon U–Pb geochronology and geochemistry: Niuxinshan intrusive complex, northern North China Craton

abstract: The Huajian gold deposit is one of the largest hydrothermal intrusion-related gold deposits in eastern Hebei Province, located in the northern margin of the North China Craton (NCC). The mineralization in this district displays a close spatial association with the shoshonitic Niuxinshan intrusive complex (NIC), which contributes to the characterization of the metallogeny associated with convergent margin magmatism. In the current study, new geochronological and geochemical data are combined with previously published isotopic data, obtained from the granitic rocks in the NIC, to constrain the timing of the district’s tectonic setting transformation and determine its bearing on regional metallogeny. The new geochronological data constrain the timing of the tectonic transformation between 155 and 185 Ma. The NIC’s granitic rocks can be geochemically subdivided into two groups. One group’s geochemical signature exhibits steep rare earth element (REE) patterns with negligible Eu anomalies, lower Yb, higher Sr, and negative Nb–Ta–Ti (NTT) anomalies, which indicate a volcanic-arc environment with a thickened crust in a convergent setting. The other group exhibits flat REE patterns with obvious negative Eu anomalies, higher Yb, lower Sr, and weak NTT anomalies, which indicate an intra-plate extensional environment with a thinning crust. Combining geochronologic and isotopic data, the mineralization is Late Jurassic (~155 Ma). This is interpreted to be genetically related to the crystallization of the shallow crustal-sourced portions of this complex. Additionally, a tectonic model is presented that provides a plausible explanation for the abundant polymetallic mineralization that occurs in the northern margin of the NCC after 155 Ma.


title: An iBeacon based Proximity and Indoor Localization System

abstract: Indoor localization and Location Based Services (LBS) can greatly benefit from the widescale proliferation of communication devices. The basic requirements of a system that can provide the aforementioned services are energy efficiency, scalability, lower costs, wide reception range, high localization accuracy and availability. Different technologies such as WiFi, UWB, RFID have been leveraged to provide LBS and Proximity Based Services (PBS), however they do not meet the aforementioned requirements. Apple's Bluetooth Low Energy (BLE) based iBeacon solution primarily intends to provide Proximity Based Services (PBS). However, it suffers from poor proximity detection accuracy due to its reliance on Received Signal Strength Indicator (RSSI) that is prone to multipath fading and drastic fluctuations in the indoor environment. Therefore, in this paper, we present our iBeacon based accurate proximity and indoor localization system. Our two algorithms Server-Side Running Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximity detection accuracy of iBeacons by 29% and 32% respectively, when compared with Apple's current moving average based approach. We also present our novel cascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoor localization. Our cascaded filter approach uses a Kalman Filter (KF) to reduce the RSSI fluctuation and then inputs the filtered RSSI values into a Particle Filter (PF) to improve the accuracy of indoor localization. Our experimental results, obtained through experiments in a space replicating real-world scenario, show that our cascaded filter approach outperforms the use of only PF by 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environments respectively, and achieves a localization error as low as 0.70 meters in 2D environment and 0.947 meters in 3D environment.


Uncertainty envelopes


title: Relaxed pairwise learned metric for person re-identification

abstract: Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting state-of-the-art classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that state-of-the-art results can be obtained at much lower computational costs.


title: Joint acoustic modeling of triphones and trigraphemes by multi-task learning deep neural networks for low-resource speech recognition

abstract: It is well-known in machine learning that multitask learning (MTL) can help improve the generalization performance of singly learning tasks if the tasks being trained in parallel are related, especially when the amount of training data is relatively small. In this paper, we investigate the estimation of triphone acoustic models in parallel with the estimation of trigrapheme acoustic models under the MTL framework using deep neural network (DNN). As triphone modeling and trigrapheme modeling are highly related learning tasks, a better shared internal representation (the hidden layers) can be learned to improve their generalization performance. Experimental evaluation on three low-resource South African languages shows that triphone DNNs trained by the MTL approach perform significantly better than triphone DNNs that are trained by the single-task learning (STL) approach by ~3-13%. The MTL-DNN triphone models also outperform the ROVER result that combines a triphone STL-DNN and a trigrapheme STL-DNN.


title: The effect of short-term treatments of a gonadotropin- releasing hormone analog (buserelin) on sciatic nerve regeneration

abstract: Summary Gonadotropin-releasing hormone (GnRH) has neuromodulatory roles in central and peripheral nervous systems. The purpose of this study is to evaluate the effect of GnRH analog (buserelin) on peripheral nerve regeneration. Forty adult male rats were divided into buserelin-treated, normal saline, sham surgery, and castrated + buserelin groups. The left sciatic nerve was crushed by a fine forceps and all animals were evaluated by sciatic functional index (SFI), electrophysiology, histology and immunohistochemistry testing. On post operation days 21 and 28, the difference between buserelin and normal saline groups was statistically significant (P 0.05). At the 28th day after operation, the diameters (µm) of the regenerated myelinated fibers of the buserelin group were significantly greater than those of the normal saline group (P<0.05). Although nerve conduction velocity (NCV) of the buserelin group was faster than the normal saline group, the difference was not statistically significant. The present study suggests that buserelin treatment might accelerate peripheral nerve regeneration.


title: Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA

abstract: Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems.


title: Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition

abstract: One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the registered faces. In this paper, we propose a high-level feature learning scheme to extract pose-invariant identity feature for face recognition. First, we build a single-hidden-layer neural network with sparse constraint, to extract pose-invariant feature in a supervised fashion. Second, we further enhance the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders. By enforcing the target values to be unique for input faces over different poses, the learned high-level feature that is represented by the neurons in the hidden layer is pose free and only relevant to the identity information. Finally, we conduct face identification on CMU Multi-PIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve are reported. These experiments demonstrate that our model is superior to other state-of-the-art approaches on handling pose variations.


title: WiSLAM: Improving FootSLAM with WiFi

abstract: We address Simultaneous Localization and Mapping (SLAM) for pedestrians by means of WiFi signal strength measurements. In our system odometric data from foot mounted Inertial Measurements Units are fused with received signal strength (RSS) measurements of IEEE 802.11. To do this, we assign a probabilistic model to RSS measurements, and adopt the Bayesian framework on which FootSLAM and PlaceSLAM are based. Computational aspects are also accounted in order to provide a practical implementation of the algorithm. Simulative and experimental examples of WiSLAM are shown to underline the effectiveness of our proposal.


title: The Treatment of Adolescent Transsexuals: Changing Insights

abstract: ABSTRACT Introduction Treatment of individuals with gender identity disorder (GID) has in medicine nearly always met with a great deal of skepticism. Professionals largely follow the Standards of Care of the World Professional Association for Transgender Health. For adolescents, specific guidelines have also been issued by the British Royal College of Psychiatrists. Aim To describe the stepwise changes in treatment policy which, in recent years, have been made by the team of the Gender Identity Clinic at the VU University Medical Center in Amsterdam, The Netherlands. Methods The first step taken to treat adolescents was that, after careful evaluation, (cross‐sex hormone) treatment could start between the ages of 16 and 18 years. A further step was the suppression of puberty by means of gonadotropin‐releasing hormone analogs in 12–16 year olds; the latter serves also as a diagnostic tool. Very recently, other clinics in Europe and North America have followed this policy. Results The first results from the Amsterdam clinic show that this policy is promising. Conclusion Professionals who take responsibility for these youth and are willing to help should yet be fully aware of the impact of their interventions. In this article, the pros and cons of the various approaches to youngsters with GID are presented, hopefully inciting a sound scientific discussion of the issue. Cohen‐Kettenis PT, Delemarre‐van de Waal HA, and Gooren LJG. The treatment of adolescent transsexuals: Changing insights. J Sex Med 2008;5:1892–1897.


title: Deep Multi-Task Learning for Joint Prediction of Heterogeneous Face Attributes

abstract: Face attribute prediction has important applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute prediction, most of them did not explicitly consider the attribute correlation and heterogeneity during feature learning. In this paper, we propose a Deep Multi-Task Learning (DMTL) network to jointly learn multiple models; each addresses the prediction of one category of homogenous attributes. Specifically, we group the heterogeneous face attributes into two categories (i.e., nominal and ordinal), and design corresponding prediction models. At the same time, we use a convolutional neural network (CNN) for early stage feature learning, which is shared by all the attributes. Experiments on the publicdomain MORPH II, CelebA, and LFWA databases show that the proposed approach outperforms the state of the art in joint face attribute prediction, and has good generalization ability.


title: Learning Motor Skills: From Algorithms to Robot Experiments

abstract: This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor.skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first authors doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.


title: DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation

abstract: DBSCAN is a popular method for clustering multi-dimensional objects. Just as notable as the method's vast success is the research community's quest for its efficient computation. The original KDD'96 paper claimed an algorithm with O(n log n) running time, where n is the number of objects. Unfortunately, this is a mis-claim; and that algorithm actually requires O(n2) time. There has been a fix in 2D space, where a genuine O(n log n)-time algorithm has been found. Looking for a fix for dimensionality d ≥ 3 is currently an important open problem. In this paper, we prove that for d ≥ 3, the DBSCAN problem requires Ω(n4/3) time to solve, unless very significant breakthroughs---ones widely believed to be impossible---could be made in theoretical computer science. This (i) explains why the community's search for fixing the aforementioned mis-claim has been futile for d ≥ 3, and (ii) indicates (sadly) that all DBSCAN algorithms must be intolerably slow even on moderately large n in practice. Surprisingly, we show that the running time can be dramatically brought down to O(n) in expectation regardless of the dimensionality d, as soon as slight inaccuracy in the clustering results is permitted. We formalize our findings into the new notion of ρ-approximate DBSCAN, which we believe should replace DBSCAN on big data due to the latter's computational intractability.


title: Assessing the area/power/performance tradeoffs for an integrated fully-digital, large-scale 3D-ultrasound beamformer

abstract: High-frame-rate and high-resolution 3D medical ultrasound imaging imposes high requirements on the involved processing hardware. Several thousands of analog signals need to be processed in many steps to obtain a final image. Fully digital beamforming makes it possible to achieve high image quality coupled with extreme flexibility. Unfortunately, digital beamforming imposes staggering requirements on main memory bandwidth caused by the loading of off-chip stored beamforming delays. In this paper we present the first fully-digital integrated beamformer that is able to compute 269.3 M focal points (FP) per second from 10 000 receive channels, and which does not require off-chip main memory. This is enabled by our novel delay approximation circuit that exploits temporal correlation between subsequent computations and thereby allows to compute the delays for beamforming online. To estimate the area and power requirements, the complete system was designed and the beamformer core was evaluated for a 130 nm CMOS technology. The estimated complexity per channel is 37.2 kGE and the corresponding power dissipation was estimated with 48 mW.


title: Study on Aspergillus Species and Aflatoxin Levels in Sorghum (Sorghum bicolor L.) Stored for Different Period and Storage System in Kewet Districts, Northern Shewa, Ethiopia

abstract: Sorghum serves as staple food for over 100 million people in ::: Sub-Saharan African countries. It is the most important nutritional ::: security crop and ranks third among major cereal crops in terms of ::: area and production next to teff and maize in Ethiopia. However, ::: Sorghum is susceptible to contamination by molds that produces ::: aflatoxin that causes hepatotoxic and carcinogenic effects on ::: humans and animals. This study was conducted to assess Aspergillus ::: species and aflatoxin level in Sorghum (Sorghum bicolor L.) ::: stored under different storage system for different storage period. ::: Thirty samples were analyzed for aflatoxin contamination using ::: high performance liquid chromatography equipped with fluorescent ::: detector and Aspergillus species were isolated and identified using ::: phenotypic features in a potato dextrose agar culture media. About ::: 56.7%, 16.7%, and 23.3% of the Sorghum samples were found ::: to be contaminated with Aspergillus flavus, Aspergillus niger and ::: Aspergillus parasiticus, respectively. The level of total aflatoxin, ::: aflatoxin B1, aflatoxin B2, aflatoxin G1, and aflatoxin G2 were in the ::: range of 11.44 to 344.26μg/kg, 3.95 to 153.72μg/kg, 1.17 to 91.82μg/ ::: kg, 9.87 to 139.64μg/kg, and 3.22 to 52.02μg/kg, respectively. The concentration of aflatoxin in all Sorghum samples surpassed the ::: maximum level set by the European commission and therefore, ::: deserves attention to control them across the Sorghum value-chain.


title: Ship Classification Using Deep Learning Techniques for Maritime Target Tracking

abstract: In the last five years, the state-of-the-art in computer vision has improved greatly thanks to an increased use of deep convolutional neural networks (CNNs), advances in graphical processing unit (GPU) acceleration and the availability of large labelled datasets such as ImageNet. Obtaining datasets as comprehensively labelled as ImageNet for ship classification remains a challenge. As a result, we experiment with pre-trained CNNs based on the Inception and ResNet architectures to perform ship classification. Instead of training a CNN using random parameter initialization, we use transfer learning. We fine-tune pre-trained CNNs to perform maritime vessel image classification on a limited ship image dataset. We achieve a significant improvement in classification accuracy compared to the previous state-of-the-art results for the Maritime Vessel (Marvel) dataset.


title: Detection of Spam Reviews and Spammers in E-Commerce Sites

abstract: E-services have grown tremendously in the last ten years, as people prefer online shopping. This is because they no more have time to go to real markets, to bargain with sellers or to negotiate. In recent years, online reviews have become the most significant means of customers’ opinions. However, driven by the desire for profit or publicity, fraudsters have produced deceptive (spam) reviews affecting the organization’s business. Filtering out this spam reviews ensures trust and credibility among the users. The present research proposes a systematic pproach for detection of spam reviews and spammers using a fuzzy based approach. Along with review text mining, customer loyalty based on purchasing behavior is considered as input to the fuzzy inference system. After filtration of spam reviews and spammers based on spam degree assigned to the reviews the new overall score for the product is calculated.


title: Design of a new counter-balancing stackable mechanism

abstract: This paper describes design and application of a novel counter-balancing stackable parallel mechanism. The stackable mechanism is designed by combining a five-bar and a couple of parallelograms. This design allows a compact design and wide workspace as compared to prior parallel type counter-balancing mechanisms. A linear geometry of this mechanism guarantees static balancing in the whole workspace. This mechanism was successfully applied to otologic.


title: CUDA accelerated iris template matching on Graphics Processing Units (GPUs)

abstract: In this paper we develop a parallelized iris template matching implementation on inexpensive Graphics Processing Units (GPUs) with Nvidia's CUDA programming model to achieve matching rates of 44 million iris template comparisons per second without rotation invariance. With tolerance to head tilt, we achieve 4.2 million matches per second and compare our implementation to state of the art prior work performed on GPU and FPGA, emphasizing our improvements. Additionally a comparison to highly optimized CPU implementations of iris template matching is performed, showing a 14X speedup using our approach. In contrast to other published work, we develop an implementation for parallel iris template matching that incorporates iris code shifting for rotation invariance and provide timing data showing our proposed architecture is efficiently implemented, capitalizing on shared and texture memory to speedup the bit shifting process beyond current prior art.


title: Is the mirror neuron system involved in imitation? A short review and meta-analysis

abstract: Abstract It has been suggested that the mirror neuron system provides an important neural substrate for humans’ ability to imitate. Mirror neurons have been found during single-cell recordings in monkeys in area F5 and PF. It is believed that the human equivalent of this mirror system in humans is the pars opercularis of the inferior frontal gyrus (area 44) and the rostral part of the inferior parietal lobule. This article critically reviews published fMRI studies that examined the role of frontal and parietal brain regions in imitation. A meta-analysis using activation likelihood estimation (ALE) revealed that the superior parietal lobule, inferior parietal lobule, and the dorsal premotor cortex but not the inferior frontal gyrus, are all commonly involved in imitation. An additional meta-analysis using a label-based review confirmed that in the frontal lobe, the premotor cortex rather than the inferior frontal gyrus is consistently active in studies investigating imitation. In the parietal region the superior and inferior parietal lobules are equally activated during imitation. Our results suggest that parietal and frontal regions which extend beyond the classical mirror neuron network are crucial for imitation.


title: Socially Motivated Data Caching in Ultra-Dense Small Cell Networks

abstract: UDN is considered as a promising technique to boost the network capacity by reducing the path loss between the base station and UE for the upcoming 5G mobile communication, while the backhaul capacity has become the bottleneck in huge data traffic generated by UEs. One effective approach to coping with the bottleneck is caching the content at the edge of UDNs. Taking advantage of D2D communication, not only SBSs but also UE can serve as cache nodes to help others for data access by sharing a channel to directly communicate with each other, thus tackling the bandwidth limitation of backhaul links. However, due to the selfish nature of mobile users in the network, incentive is needed for UEs to cache for others. In this article, the data caching incentive problem in UDNs is studied by exploring caching cost of SBS and social factors among UEs. A social group utility maximization (SGUM) game is exploited to motivate data caching among SBSs and UEs. Based on SGUM, a socially motivated caching approach is proposed. The evaluation of the proposed socially motivated caching approach is provided and compared to baseline caching schemes. The corresponding results and observations support the vision of a socially motivated caching paradigm for the future UDNs.


title: Investigation of Galvanic-Coupled Intrabody Communication Using the Human Body Circuit Model

abstract: Intrabody Communication (IBC) is a technique that uses the human body as a transmission medium for electrical signals to connect wearable electronic sensors and devices. Understanding the human body as the transmission medium in IBC paves way for practical implementation of IBC in body sensor networks. In this study, we propose a model for galvanic coupling-type IBC based on a simplified equivalent circuit representation of the human upper arm. We propose a new way to calculate the electrode-skin contact impedance. Based on the model and human experimental results, we discuss important characteristics of galvanic coupling-type IBC, namely, the effect of tissues, anthropometry of subjects, and electrode configuration on signal propagation. We found that the dielectric properties of the muscle primarily characterize the received signal when receiver electrodes are located close to transmitter electrodes. When receiver and transmitter electrodes are far apart, the skin dielectric property affects the received signal.


title: A New Boost Switched-Capacitor Multilevel Converter With Reduced Circuit Devices

abstract: In this paper, a novel platform for the single phase switched-capacitor multilevel inverters (SCMLIs) is presented. It has several advantages over the classical topologies, such as an appropriate boosting property, higher efficiency, lower number of required dc voltage sources, and other accompanying components with less complexity and lower cost. The basic structure of the proposed converter is capable of making nine-level of the output voltage under different kinds of loading conditions. Hereby, by using the same two capacitors paralleled to a single dc source, a switched-capacitor (SC) cell is made that contributes to boosting the value of the input voltage. In this case, the balanced voltage of the capacitors can be precisely provided on the basis of the series–parallel technique and the redundant switching states. Afterward, to reach the higher number of output voltage levels, two suggested SC cells are connected to each other with a new extended configuration. Therefore, by the use of a reasonable number of required power electronic devices, and also by utilizing only two isolated dc voltage sources, which their magnitudes can be designed based on either symmetric or asymmetric types, a 17- and 49-level of the output voltage are obtained. Based on the proposed extended configuration, a new generalized version of SCMLIs is also derived. To confirm the precise performance of the proposed topologies, apart from the theoretical analysis and a complete comparison, several simulation and experimental results are also given.


title: A Novel Integrated UWB–UHF One-Port Antenna for Localization and Energy Harvesting

abstract: In this work, a novel, compact, single-port antenna combining both UWB and UHF bands is presented. This solution is proposed for next-generation passive RFID tags, performing communication and localization through UWB technology and efficient energy harvesting in the UHF band, but it is also designed to keep compatibility with previous tags generations. The UWB communication is deployed by means of an Archimedean spiral topology, while energy harvesting at UHF is obtained by suitably extending the spiral outer arms, thus realizing a meandered dipole, without affecting the UWB behavior. The single-port architecture provides an overall size reduction, while preserving the radiation performance in both the operating bands. This solution allows a direct connection to future integrated UWB–UHF chips, to standard RFID chips, or to a diplexer for suitably combining the UWB and the UHF functionalities. A prototype of this new antenna, realized on a standard FR-4 substrate, is first presented to validate the proposed novel design. As a second step, the same architecture is scaled on a paper substrate in view of its fully ecocompatible realization to be exploited by future pervasive RFID applications. The compact design of the antenna-feeding network in discrete technology is also presented.


title: When is Temporal Planning Really Temporal?

abstract: While even STRIPS planners must search for plans of unbounded length, temporal planners must also cope with the fact that actions may start at any point in time. Most temporal planners cope with this challenge by restricting action start times to a small set of decision epochs, because this enables search to be carried out in state-space and leverages powerful state-based reachability heuristics, originally developed for classical planning. Indeed, decision-epoch planners won the International Planning Competition's Temporal Planning Track in 2002, 2004 and 2006. ::: ::: However, decision-epoch planners have a largely unrecognized weakness: they are incomplete. In order to characterize the cause of incompleteness, we identify the notion of required concurrency, which separates expressive temporal action languages from simple ones. We show that decisionepoch planners are only complete for languages in the simpler class, and we prove that the simple class is 'equivalent' to STRIPS! Surprisingly, no problems with required concurrency have been included in the planning competitions. We conclude by designing a complete state-space temporal planning algorithm, which we hope will be able to achieve high performance by leveraging the heuristics that power decision epoch planners.


title: Pagination versus Scrolling in Mobile Web Search

abstract: Vertical scrolling is the standard method of exploring search results pages. For touch-enabled mobile devices that are not equipped with a mouse or keyboard, we adopt other methods of controlling the viewport with the aim of investigating user interaction. From the intuition that people are used to reading books by turning pages horizontally, we conducted a user experiment to investigate the effects of horizontal and vertical control types (pagination versus scrolling) on a touch-enabled mobile phone. Our findings suggest that participants using pagination were more likely to find relevant documents, especially those over the fold; spent more time attending to relevant results; and were faster to click while spending less time on the search result pages overall. We also found that the main reason for the difference in search speed is the time taken for the scroll itself. We conclude that search engines need to provide different viewport controls to allow better search experiences on touch-enabled mobile devices.


title: Compression-Based Averaging of Selective Naive Bayes Classifiers

abstract: The naive Bayes classifier has proved to be very effective on many real data applications. Its performance usually benefits from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a Bayesian regularization technique to select the most probable subset of variables compliant with the naive Bayes assumption. We also study the limits of Bayesian model averaging in the case of the naive Bayes assumption and introduce a new weighting scheme based on the ability of the models to conditionally compress the class labels. The weighting scheme on the models reduces to a weighting scheme on the variables, and finally results in a naive Bayes classifier with "soft variable selection". Extensive experiments show that the compression-based averaged classifier outperforms the Bayesian model averaging scheme.


title: Power inverter topologies for photovoltaic modules-a review

abstract: This review-paper focuses on the latest development of inverters for photovoltaic AC-modules. The power range for these inverters is usually within 90 Watt to 500 Watt, which covers the most commercial photovoltaic-modules. Self-commutated inverters have replaced the grid-commutated ones. The same is true for the bulky low-frequency transformers versus the high-frequency transformers, which are used to adapt the voltage level. The AC-module provides a modular design and a flexible behaviour in various grid conditions. It hereby opens the market for photovoltaic-power for everyone at a low cost due to the plug and play concept, which also makes a further enlargement of the system possible.


title: Reaping benefits from intellectual capital.

abstract: The wealth and value of organizations are increasingly based on intellectual capital. Although acquiring talented individuals and investing in employee learning adds value to the organization, reaping the benefits of intellectual capital involves translating the wisdom of employees into reusable and sustained actions. This requires a culture that creates employee commitment, encourages learning, fosters sharing, and involves employees in decision making. An infrastructure to recognize and embed promising and best practices through social networks, evidence-based practice, customization of innovations, and use of information technology results in increased productivity, stronger financial performance, better patient outcomes, and greater employee and customer satisfaction.


title: Inferring solutions of differential equations using noisy multi-fidelity data

abstract: For more than two centuries, solutions of differential equations have been obtained either analytically or numerically based on typically well-behaved forcing and boundary conditions for well-posed problems. We are changing this paradigm in a fundamental way by establishing an interface between probabilistic machine learning and differential equations. We develop data-driven algorithms for general linear equations using Gaussian process priors tailored to the corresponding integro-differential operators. The only observables are scarce noisy multi-fidelity data for the forcing and solution that are not required to reside on the domain boundary. The resulting predictive posterior distributions quantify uncertainty and naturally lead to adaptive solution refinement via active learning. This general framework circumvents the tyranny of numerical discretization as well as the consistency and stability issues of time-integration, and is scalable to high-dimensions.


title: Serotonin receptors and their role in the pathophysiology and therapy of irritable bowel syndrome

abstract: BACKGROUND ::: Irritable bowel syndrome (IBS) is a functional disorder of the gastrointestinal tract characterized by abdominal discomfort, pain and changes in bowel habits, often associated with psychological/psychiatric disorders. It has been suggested that the development of IBS may be related to the body's response to stress, which is one of the main factors that can modulate motility and visceral perception through the interaction between brain and gut (brain-gut axis). The present review will examine and discuss the role of serotonin (5-hydroxytryptamine, 5-HT) receptor subtypes in the pathophysiology and therapy of IBS. ::: ::: ::: METHODS ::: Search of the literature published in English using the PubMed database. ::: ::: ::: RESULTS ::: Several lines of evidence indicate that 5-HT and its receptor subtypes are likely to have a central role in the pathophysiology of IBS. 5-HT released from enterochromaffin cells regulates sensory, motor and secretory functions of the digestive system through the interaction with different receptor subtypes. It has been suggested that pain signals originate in intrinsic primary afferent neurons and are transmitted by extrinsic primary afferent neurons. Moreover, IBS is associated with abnormal activation of central stress circuits, which results in altered perception during visceral stimulation. ::: ::: ::: CONCLUSIONS ::: Altered 5-HT signaling in the central nervous system and in the gut contributes to hypersensitivity in IBS. The therapeutic effects of 5-HT agonists/antagonists in IBS are likely to be due also to the ability to modulate visceral nociception in the central stress circuits. Further studies are needed in order to develop an optimal treatment.