Query: An environmental energy harvesting framework for sensor networks

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Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment <=SEP=> We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints connectivity and biconnectivity, and one optimization objective maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.

Directed diffusion: a scalable and robust communication paradigm for sensor networks <=SEP=> Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

Energy-Efficient Communication Protocol for Wireless Microsensor Networks <=SEP=> Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adap tive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

Parssec: A Parallel Simulation Environment for Complex Systems <=SEP=> S ystems are now being designed that can scale to very large configurations. Examples include parallel architectures with thousands of processors, wireless networks connecting tens of thousands of computers, and parallel database systems capable of processing millions of transactions a second. In some cases, systems have grown considerably larger than envisaged by their designers. Design and development costs for such systems could be significantly reduced if only there were efficient techniques for evaluating design alternatives and predicting their impact on overall system performance metrics. Due to the systems' analytical intractability, simulation is the most common performance evaluation technique for such systems. However, the long execution times needed for sequential simulation models often hamper evaluation. For instance, it can easily take weeks to simulate wireless networks with thousands of mobile nodes that are subject to the interference and signal-fading effects of a realistic environment. Also, the simulation of parallel programs typically suffers from slowdown factors of 30 to 50 per processor. 1 This means that simulation of a parallel program executing for five to 15 minutes on a 128-node multi-processor can take anywhere from a few weeks to a few months—even on state-of-the-art sequential workstations. As the size of the physical system increases, the models' memory requirements can easily exceed the workstations' capacity. The limitations of sequential model execution have led to growing interest in the use of parallel execution for simulating large-scale systems. Widespread use of parallel simulation, however, has been significantly hindered by a lack of tools for integrating parallel model execution into the overall framework of system simulation. Although a number of algorithmic alternatives exist for parallel execution of discrete-event simulation models, performance analysts not expert in parallel simulation have relatively few tools giving them flexibility to experiment with multiple algorithmic or architectural alternatives for model execution. Another drawback to widespread use of simulations is the cost of model design and maintenance. The design and development costs for detailed simulation models for complex systems can easily rival the costs for the physical systems themselves. The simulation environment we developed at UCLA attempts to address some of these issues by providing these features: • An easy path for the migration of simulation models to operational software prototypes. • Implementation on both distributed-and shared-memory platforms and support for a diverse set of parallel simulation protocols. • Support for visual and hierarchical model design. Our environment consists of three primary components: …

A transmission control scheme for media access in sensor networks <=SEP=> We study the problem of media access control in the novel regime of sensor networks, where unique application behavior and tight constraints in computation power, storage, energy resources, and radio technology have shaped this design space to be very different from that found in traditional mobile computing regime. Media access control in sensor networks must not only be energy efficient but should also allow fair bandwidth allocation to the infrastructure for all nodes in a multihop network. We propose an adaptive rate control mechanism aiming to support these two goals and find that such a scheme is most effective in achieving our fairness goal while being energy efficient for both low and high duty cycle of network traffic.

The volcanormal density for radar-based extended target tracking <=SEP=> This paper presents a novel shape model for radar-based vehicle tracking. This model is a special probability density function (pdf), designed to approximate the radar measurement spread of a vehicle more adequately than widely used models. While this pdf is used to determine the likelihood of the spatial measurements, additionally a recent Doppler measurement model is integrated in a maximum likelihood estimator. This formulation allows the estimation of a vehicle's pose, motion and axis lengths based on the radar's spatial and Doppler dimension. On this basis, an efficient computation method is derived. An evaluation of actual radar and ground truth data demonstrates the precise estimation of a vehicle's state.

Prosthetic gingival reconstruction in a fixed partial restoration. Part 1: introduction to artificial gingiva as an alternative therapy. <=SEP=> The Class III defect environment entails a vertical and horizontal deficiency in the edentulous ridge. Often, bone and soft tissue surgical procedures fall short of achieving a natural esthetic result. Alternative surgical and restorative protocols for these types of prosthetic gingival restorations are presented in this three-part series, which highlights the diagnostic and treatment aspects as well as the lab and maintenance challenges. A complete philosophical approach involves both a biologic understanding of the limitations of the hard and soft tissue healing process as well as that of multiple adjacent implants in the esthetic zone. These limitations may often necessitate the use of gingiva-colored "pink" restorative materials and essential preemptive planning via three-dimensional computer-aided design/computer-assisted manufacture to achieve the desired esthetic outcome. The present report outlines a rationale for consideration of artificial gingiva when planning dental prostheses. Prosthetic gingiva can overcome the limitations of grafting and should be a consideration in the initial treatment plan. (Int J Periodontics Restorative Dent 2009;29:471-477.).

The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data <=SEP=> Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing bias towards the majority class. In this study, we seek to determine if the inclusion of data sampling will improve the performance of the Random Forest classifier. In order to test the effect of data sampling, we used Random Undersampling along with two post-sampling class distribution ratios: 35:65 and 50:50 (minority:majority). Additionally, we also built inductive models with Random Forest when no data sampling technique was applied, so we can observe the true effect of the data sampling. All three options were tested on a series of fifteen imbalanced bioinformatics datasets. Our results show that data sampling does improve the classification performance of Random Forest, especially when using the 50:50 post-sampling class distribution ratio. However, statistical analysis shows that the increase in performance is not statistically significant. Thus, we can state that while data sampling does improve the classification performance of Random Forest, it is not a necessary step as the classifier is fairly robust to imbalanced data on its own.

Face recognition based on convolutional neural network and support vector machine <=SEP=> Face recognition is an important embodiment of human-computer interaction, which has been widely used in access control system, monitoring system and identity verification. However, since face images vary with expressions, ages, as well as poses of people and illumination conditions, the face images of the same sample might be different, which makes face recognition difficult. There are two main requirements in face recognition, the high recognition rate and less training time. In this paper, we combine Convolutional Neural Network (CNN) and Support Vector Machine (SVM) to recognize face images. CNN is used as a feature extractor to acquire remarkable features automatically. We first pre-train our CNN by ancillary data to get the updated weights, and then train the CNN by the target dataset to extract more hidden facial features. Finally we use SVM as our classifier instead of CNN to recognize all the classes. With the input of facial features extracted from CNN, SVM will recognize face images more accurately. In our experiments, some face images in the Casia-Webfaces database are used for pre-training, and FERET database is used for training and testing. The results in experiments demonstrate the efficiency with high recognition rate and less training time.

A 1.7 ps Equivalent Bin Size and 4.2 ps RMS FPGA TDC Based on Multichain Measurements Averaging Method <=SEP=> A high precision and high resolution time-to-digital converter (TDC) based on multichain measurements averaging method is implemented in a 40 nm fabrication process Virtex-6 FPGA. The results of the detailed theoretical analysis and the simulation with the MATLAB tool based on a complete TDC module show that the resolution limitation determined by the intrinsic cell delay of plain tapped-delay chain can be overcame, which results in an improvement on both resolution and precision without increasing the dead time. The test results agree with the simulation results quite well. In such a TDC, the input signal is connected to multiple tapped-delay chains simultaneously (the number of the chains is M), and each chain is just a plain TDC and generates a timestamp for a hit signal. Therefore, M timestamps should be obtained in total, which, after averaging, give the final timestamp. A TDC with 1.7 ps equivalent bin size, 1.5 ps averaged bin size and 4.2 ps RMS has been implemented with M being 16, which performs much better than the plain TDC constructed of a single tapped delay chain having 42.3 ps equivalent bin size, 24.0 ps averaged bin size resolution and 13.2 ps RMS precision. The comparisons of equivalent bin size and averaged bin size show that the nonlinearity is improved with a larger M. Due to the real time integral nonlinearity (INL) calibration and averaging calculation, the multichain TDC is almost insensitive to the process voltage and temperature (PVT) variations.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis <=SEP=> Accurate and early diagnosis of Alzheimer’s disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.

Smart home design based on ZigBee wireless sensor network <=SEP=> This work is based on the Internet of Things and ZigBee wireless sensor network technology. A kind of smart home design based on ZigBee wireless sensor network was proposed in this paper. Texas Instruments MCU device LM3S9B96, which is the ARM Cortex-M3 based controllers, was used in this system. The entire system is running on the μC/OS-II embedded real-time multitasking operating system. Users can access this system by a dynamic webpage of LwIP TCP/IP protocol stack or GSM SMS. Using this system users can conveniently know the environment parameters of home, such as temperature, humidity, meter readings, light, and control the home electronic equipments, such as light, aircondition, heater, by ZigBee wireless sensor network.

Analytics using R for predicting credit defaulters <=SEP=> Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims to extract useful knowledge from tremendous amount of complex data sets. This work aims to develop a model and construct a prototype for the same using a data set available in the UCI repository. The model is a decision tree based classification model that uses the functions available in the R Package. Prior to building the model, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The final model is used for prediction with the test dataset and the experimental results prove the efficiency of the built model.

Two Methods for Semi-automatic Image Segmentation based on Fuzzy Connectedness and Watersheds <=SEP=> At the present time, one of the best methods for semiautomatic image segmentation seems to be the approach based on the fuzzy connectedness principle. First, we identify some deficiencies of this approach and propose a way to improve it, through the introduction of competitive learning. Second, we propose a different approach, based on watersheds. We show that the competitive fuzzy connectedness-based method outperforms the noncompetitive variant and generally (but not always) outperforms the watershed-based approach. The competitive variant of the fuzzy connectedness-based method can be a good alternative to the watersheds.

Generalized Semi-Orthogonal Multiple-Access for Massive MIMO <=SEP=> We propose a novel framework to enable concurrent transmission of multiple users to an access node equipped with a massive multiple-input multiple-output (mMIMO), which encompasses and extends the conventional time-division duplex (TDD) protocol and the recently proposed semi-orthogonal multiple-access (SOMA). The new solution is referred to generalized semi-orthogonal multiple-access (GSOMA). To enable GSOMA, the users are grouped such that the users within each group are assigned mutually orthogonal pilot sequences. However the pilot sequences can be reused in different groups and the users in different group are coordinated such that their pilot sequences do not interfere by a semi-orthogonal mechanism enabled by a resource-offset sequence. We describe the general framework and analyze a case of GSOMA and show that a properly designed GSOMA can outperform the conventional TDD and SOMA.

A survey of top-k query processing techniques in relational database systems <=SEP=> Efficient processing of top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-k processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top-k queries in XML domain, and show their connections to relational approaches.

Quaternion Domain  $k$ -Means Clustering for Improved Real Time Classification of E-Nose Data <=SEP=> This paper proposes a novel clustering technique implemented in the quaternion domain for qualitative classification of E-nose data. The proposed technique is in many ways similar to the popular k-means clustering algorithm. However, computations carried out in the quaternion space have yielded better class separability and higher cluster validity. A pool of possible cluster centers was created by subjecting each initial center to a fixed rotation in the quaternion space. The test samples were then compared with each of the centers in the pool and assigned to an appropriate center using minimum Euclidean distance criterion. The evolving clusters have been evaluated periodically for their compactness and interclass separation using the Davis-Bouldin (DB) index. The set of clusters having minimum DB index was chosen as an optimal one. It was observed that using the proposed technique the inverse DB index remains significantly higher with successive iterations implying a consistent performance on the cluster validity front. Furthermore, clusters formed using quaternion algebra have been observed to have a smaller DB index. Finally, when compared with the traditional k-means algorithm, the proposed technique performed significantly better in terms of percentage classification of unlabeled samples.

On Summarizing Large-Scale Dynamic Graphs <=SEP=> How can we describe a large, dynamic graph over time? Is it random? If not, what are the most apparent deviations from randomness – a dense block of actors that persists over time, or perhaps a star with many satellite nodes that appears with some fixed periodicity? In practice, these deviations indicate patterns – for example, research collaborations forming and fading away over the years. Which patterns exist in real-world dynamic graphs, and how can we find and rank their importance? These are exactly the problems we focus on. Our main contributions are (a) formulation: we show how to formalize this problem as minimizing an information theoretic encoding cost, (b) algorithm: we propose TIMECRUNCH, an effective and scalable method for finding coherent, temporal patterns in dynamic graphs and (c) practicality: we apply our method to several large, diverse real-world datasets with up to 36 million edges and introduce our auxiliary ECOVIZ framework for visualizing and interacting with dynamic graphs which have been summarized by TIMECRUNCH. We show that TIMECRUNCH is able to compress these graphs by summarizing important temporal structures and finds patterns that agree with intuition.

Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization <=SEP=> Bilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for generic bilevel problems. The efficacy of the algorithm has been shown on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been evaluated against two benchmarks, and the performance gain is observed to be significant.

A new method for pulse oximetry possessing inherent insensitivity to artifact <=SEP=> A new method for pulse oximetry is presented that possesses an inherent insensitivity to corruption by motion artifact, a primary limitation in the practical accuracy and clinical applicability of current technology. Artifact corruption of the underlying photoplethysmographic signals is reduced in real time, using an electronic processing methodology that is based upon inversion of a physical artifact model. This fundamental approach has the potential to provide uninterrupted output and superior accuracy under conditions of sustained subject motion, therefore, widening the clinical scope of this useful measurement. A new calibration technique for oxygen saturation is developed for use with these processed signals, which is shown to be a generalization of the classical Interpretation. The detailed theoretical and practical issues of Implementation are then explored, highlighting important engineering simplifications implicit in this new approach. A quantitative investigation of the degree of insensitivity to artifact is also undertaken, with the aid of a custom electronic system and commercial pulse oximeter probes, which is compared and contrasted with the performance of a conventional implementation. It is demonstrated that this new methodology results in a reduced sensitivity to common classes of motion artifact, while retaining the generality to be combined with conventional signal processing techniques.

Intrinsic Motivation in Museums : Why Does One Want to Learn ? <=SEP=> One often meets successful adults, professionals, or scientists who recall that their lifelong vocational interest was first sparked by a visit to a museum. In these accounts the encounter with a real, concrete object from a different world an exotic animal, a strange dress, a beautiful artifact is the kernel from which a n entire career of learning grew For others with an already developed curiosity about some field such as zoology, anthropology, or art, the museum provided an essential link in the cultivation of knowledge a place where information lo'it its abstractness and became concrete. In either case, many people ascribe powerful motivation to a museum visit, claiming that their desire to learn more about some aspect of the world was directly caused by it. Granted that these accounts of "crystallizing experiences" (Waiters and Gaidner 1986) attributed to museums might often be embellished and exaggerated in retrospect, i t would be rash to dismiss them entirely, for the fascination of museums seems to be a very real psychological phenomenon. The question rather becomes. How do museums motivate viewers to learn? Is there a unique, sui generis "museum experience" that helps viewers start on the long journey of learning? How do museums present information in a meaningful way, a way that deepens a person's experience and promotes further learning? To begin answering these questions, it will be useful to review what we know about human motivation in relation to learning. Children are born with a desire for knowledge, and some of the most stupendous feats of learning to walk, talk, get along with others, to take care of oneself are accomplished without seeming effort in the first few years of life. It would be difficult to see how a species as dependent on learning as we are could have

The Language of Deceivers: Linguistic Features of Crowdfunding Scams <=SEP=> Crowdfunding sites with recent explosive growth are equally attractive platforms for swindlers or scammers. Though the growing number of articles on crowdfunding scams indicate that the fraud threats are accelerating, there has been little knowledge on the scamming practices and patterns. The key contribution of this research is to discover the hidden clues in the text by exploring linguistic features to distinguish scam campaigns from non-scams. Our results indicate that by providing less information and writing more carefully (and less informally), scammers deliberately try to deceive people; (i) they use less number of words, verbs, and sentences in their campaign pages. (ii) scammers make less typographical errors, 4.5-4.7 times lower than non-scammers.(iii) Expressivity of scams is 2.6-8.5 times lower as well.

Distributed-system Features and Challenges Shiviz Is a New Distributed System Debugging Visualization Tool Debugging Distributed Systems <=SEP=> D istributed systems pose unique challenges for software developers. Reasoning about concurrent activities of system nodes and even understanding the system’s communication topology can be difficult. A standard approach to gaining insight into system activity is to analyze system logs. Unfortunately, this can be a tedious and complex process. This article looks at several key features and debugging challenges that differentiate distributed systems from other kinds of software. The article presents several promising tools and ongoing research to help resolve these challenges.

Wireless Industrial Monitoring and Control Networks: The Journey So Far and the Road Ahead <=SEP=> While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks.

Trend Analysis in Social Networking using Opinion Mining A Survey <=SEP=> The popularity of social media in recent years has increased a lot and created new opportunities to study the interactions of different groups of people. Because of this it comes to possible to study the user‟s opinion. Two popular topics in the study of social networks are community detection and finding trends. Trend Analysis is an ongoing field of research in Data mining field, May referred as an “Opinion Mining” by some researchers. Trend Analysis is the computational treatment of opinions, sentiments and subjectivity of text. The related field to Trend Analysis contains finding emotions in a statement which attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Trend Analysis along with the community detection and their related fields with brief details. The main contributions of this paper include the overall analysis to bridge the concept between trend analyses with community detection. To find out the anomaly detection in a community with using the concept of trend analysis is a difficult task.

Emergency department management of vaginal bleeding in the nonpregnant patient. <=SEP=> Abnormal uterine bleeding is the most common reason women seek gynecologic care, and many of these women present to an emergency department for evaluation. It is essential that emergency clinicians have a thorough understanding of the underlying physiology of the menstrual cycle to appropriately manage a nonpregnant woman with abnormal bleeding. Evidence to guide the management of nonpregnant patients with abnormal bleeding is limited, and recommendations are based mostly on expert opinion. This issue reviews common causes of abnormal bleeding, including anovulatory, ovulatory, and structural causes in both stable and unstable patients. The approach to abnormal bleeding in the prepubertal girl is also discussed. Emergency clinicians are encouraged to initiate treatment to temporize an acute bleeding episode until timely follow-up with a gynecologist can be obtained.

Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. <=SEP=> The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. The availability of a noninvasive technique that can visualize the neuronal projections connecting the functional centers should therefore provide new keys to the understanding of brain function. By using high-resolution three-dimensional diffusion magnetic resonance imaging and a newly designed tracking approach, we show that neuronal pathways in the rat brain can be probed in situ. The results are validated through comparison with known anatomical locations of such fibers.

An aspect representation for object manipulation based on convolutional neural networks <=SEP=> We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.

Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis <=SEP=> The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the dependency relation between words in a review, the multiple aspects and its corresponding sentiment can be identified. However, not all types of dependency relation patterns are able to extract candidate aspect and sentiment word pairs. In this paper, a preliminary study was performed on the performance of different type of dependency relation with different POS tag patterns in pre-extracting candidate aspect from customer review. The result contributes to the identification of the specific type dependency relation with it POS tag pattern that lead to high aspect extraction performance. The combination of these dependency relations offers a solution for single aspect single sentiment and multi aspect multi sentiment cases.

Diversifying Web Service Recommendation Results via Exploring Service Usage History <=SEP=> The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users' potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.