title: A strategy to perform coverage testing of mobile applications

abstract: The development of wireless application has recently received more attention due to the increment in the number and in the power of mobile devices such as PDA's and cellular phones. Different methods and techniques have been developed to ease the design and development of applications for these kind of devices. Also, different languages have been proposed to provide support for such platform, such as J2ME and Brew. On the other hand, few attention has been given to testing activity in this scenario. Some works try to test the functional aspects of a given application, others try to perform load, usability and stress testing. In this article we present a strategy to support coverage testing for mobile device software in such a way that the applications can be tested not only on emulators, but also on their real target mobile devices with the aid of structural coverage assessment. We also present an environment which supports the proposed strategy. Such environment is implemented in a tool, named JaBUTi/ME. A simple case illustrating how JaBUTi/ME can be used is also presented.


title: Principal components analysis corrects for stratification in genome-wide association studies

abstract: Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.


title: Consensus protocols for distributed tracking in wireless camera networks

abstract: Consensus-based target tracking in camera networks faces three major problems: non-linearity in the measurement model, temporary lack of measurements (naivety) due to the limited field of view (FOV) and redundancy in the iterative exchange of information. In this paper we propose two consensus-based distributed algorithms for non-linear systems using the Extended Information Filter as underlying filter to handle the non-linearity in the camera measurement model. The first algorithm is an Extended Information Consensus Filter (EICF) that overcomes the effect of naivety and non-linearity without requiring knowledge of other nodes in the network. The second algorithm is an Extended Information Weighted Consensus Filter (EIWCF) that overcomes all the three major problems (naivety, redundancy and non-linearity) but requires knowledge of the number of cameras (Nc) in the network. The basic principle of these algorithms is weighting node estimates based on their covariance information. When Nc is not available, EICF can be used at the cost of not handling the redundancy problem. Simulations with highly maneuvering targets show that the two proposed distributed non-linear consensus filters outperform the related state of the art by achieving higher accuracy and faster convergence to the centralised estimates computed by simultaneously considering the information from all the nodes.


title: A modified fuzzy C-means algorithm for collaborative filtering

abstract: Two major challenges for collaborative filtering problems are scalability and sparseness. Some powerful approaches have been developed to resolve these challenges. Two of them are Matrix Factorization (MF) and Fuzzy C-means (FCM). In this paper we combine the ideas of MF and FCM, and propose a new clustering model --- Modified Fuzzy C-means (MFCM). MFCM has better interpretability than MF, and better accuracy than FCM. MFCM also supplies a new perspective on MF models. Two new algorithms are developed to solve this new model. They are applied to the Netflix Prize data set and acquire comparable accuracy with that of MF.


title: Designing a Robot Cognitive Architecture with Concurrency and Active Perception

abstract: We are implementing ADAPT, a cognitive architecture for a Pioneer mobile robot, to give the robot the full range of cognitive abilities including perception, use of natural language, learning and the ability to solve complex problems. Our perspective is that an architecture based on a unified theory of robot cognition has the best chance of attaining human-level performance. Existing work in cognitive modeling has accomplished much in the construction of such unified cognitive architectures in areas other than robotics; however, there are major respects in which these architectures are inadequate for robot cognition. This paper examines two major inadequacies of current cognitive architectures for robotics: the absence of support for true concurrency and for active


title: Gender differences in empathy: The role of the right hemisphere

abstract: The relationship between activation of the right cerebral hemisphere (RH) and empathy was investigated. Twenty-two men and 73 women participated by completing a chimeric face task and empathy questionnaire. For the face task, participants were asked to pick which of the two chimeric faces looked happier. Both men and women were significantly more likely to say the chimera with the smile to their left was happier, suggesting activation of the RH. As expected, men scored significantly lower than women on the empathy questionnaire, p = .003. A correlation was found between RH activation on the face task and empathy for women only, p = .037, suggesting a possible neural basis for gender differences in empathy.


title: Towards a Quantum World Wide Web

abstract: We elaborate a quantum model for corpora of written documents, like the pages forming the World Wide Web. To that end, we are guided by how physicists constructed quantum theory for microscopic entities, which unlike classical objects cannot be fully represented in our spatial theater. We suggest that a similar construction needs to be carried out by linguists and computational scientists, to capture the full meaning content of collections of documental entities. More precisely, we show how to associate a quantum-like 'entity of meaning' to a 'language entity formed by printed documents', considering the latter as the collection of traces that are left by the former, in specific results of search actions that we describe as measurements. In other words, we offer a perspective where a collection of documents, like the Web, is described as the space of manifestation of a more complex entity - the QWeb - which is the object of our modeling, drawing its inspiration from previous studies on operational-realistic approaches to quantum physics and quantum modeling of human cognition and decision-making. We emphasize that a consistent QWeb model needs to account for the observed correlations between words appearing in printed documents, e.g., co-occurrences, as the latter would depend on the 'meaning connections' existing between the concepts that are associated with these words. In that respect, we show that both 'context and interference (quantum) effects' are required to explain the probabilities calculated by counting the relative number of documents containing certain words and co-ocurrrences of words.


title: Corporate social responsibilities and marketing

abstract: In today's competitive market, Corporate Social Responsibility (CSR) is proposed as one of the main and strategic concepts for several companies so that in recent years, 90% of companies that have been among the top 500 companies of Fortune, have used social-responsibility based activities. As the new paradigms of marketing (including third-generation marketing from Kotler's view), the convergence of marketing thought process toward social marketing and issues related to social responsibility show the aim of third-generation marketing is to change the world to the best place for living. Therefore, in this study, a meta-analysis was provided about the impact of CSR on two main areas of marketing namely consumer behavior and corporate marketing. Concerning the consequences of CSR on consumer behavior, it can be referred to issues such as customer-company identification, customer response, customer satisfaction, positive evaluation of product and company, positive attitude toward product and company, acceptation of suggestions, agree to pay more prices by customers, and loyalty, which play a key and effective role on consumer behavior. Regarding the corporate marketing, it can be referred to issues such as the creation of corporate image, better financial performance, the improvement of corporate reputation, the increase of brand equity and market stock value.


title: Pyrosequencing Reveals High-Temperature Cellulolytic Microbial Consortia in Great Boiling Spring after In Situ Lignocellulose Enrichment

abstract: To characterize high-temperature cellulolytic microbial communities, two lignocellulosic substrates, ammonia fiber-explosion-treated corn stover and aspen shavings, were incubated at average temperatures of 77 and 85°C in the sediment and water column of Great Boiling Spring, Nevada. Comparison of 109,941 quality-filtered 16S rRNA gene pyrosequences (pyrotags) from eight enrichments to 37,057 quality-filtered pyrotags from corresponding natural samples revealed distinct enriched communities dominated by phylotypes related to cellulolytic and hemicellulolytic Thermotoga and Dictyoglomus, cellulolytic and sugar-fermenting Desulfurococcales, and sugar-fermenting and hydrogenotrophic Archaeoglobales. Minor enriched populations included close relatives of hydrogenotrophic Thermodesulfobacteria, the candidate bacterial phylum OP9, and candidate archaeal groups C2 and DHVE3. Enrichment temperature was the major factor influencing community composition, with a negative correlation between temperature and richness, followed by lignocellulosic substrate composition. This study establishes the importance of these groups in the natural degradation of lignocellulose at high temperatures and suggests that a substantial portion of the diversity of thermophiles contributing to consortial cellulolysis may be contained within lineages that have representatives in pure culture.


title: Improvising Organizational Transformation Over Time: A Situated Change Perspective

abstract: In this paper, I outline a perspective on organizational transformation which proposes change as endemic to the practice of organizing and hence as enacted through the situated practices of organizational actors as they improvise, innovate, and adjust their work routines over time. I ground this perspective in an empirical study which examined the use of a new information technology within one organization over a two-year period. In this organization, a series of subtle but nonetheless significant changes were enacted over time as organizational actors appropriated the new technology into their work practices, and then experimented with local innovations, responded to unanticipated breakdowns and contingencies, initiated opportunistic shifts in structure and coordination mechanisms, and improvised various procedural, cognitive, and normative variations to accommodate their evolving use of the technology. These findings provide the empirical basis for a practice-based perspective on organizational transfor...


title: Mltistage bilateral noise filtering and edge detection for color image enhancement

abstract: Removing noise while preserving and enhancing edges is one of the most fundamental operations of image/video processing. When taking pictures with digital cameras, it is frequently found that the color images are corrupted by miscellaneous noise and, hence, noise filtering is necessary. The difficulty is that usually the filtering will reduce the sharpness of the image. On the other hand, optical lens imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increase the noise level as a by-product. In this paper, we present a new integrated approach to address these issues. The proposed approach combines color edge detection, bilateral noise filter, and edge enhancement based on suitable color spaces. The experimental results show that the proposed approach can effectively reduce the noise while preserving and enhancing edges.


title: Turning a Blind Eye to Temptation: How Cognitive Load Can Facilitate Self-Regulation

abstract: The present research shows in 4 studies that cognitive load can reduce the impact of temptations on cognition and behavior and, thus, challenges the proposition that distraction always hampers self-regulation. Participants performed different speeded categorization tasks with pictures of attractive and neutral food items (Studies 1-3) and attractive and unattractive female faces (Study 4), while we assessed their reaction times as an indicator of selective attention (Studies 1, 3, and 4) or as an indicator of hedonic thoughts about food (Study 2). Cognitive load was manipulated by a concurrent digit span task. Results show that participants displayed greater attention to tempting stimuli (Studies 1, 3, and 4) and activated hedonic thoughts in response to palatable food (Study 2), but high cognitive load completely eliminated these effects. Moreover, cognitive load during the exposure to attractive food reduced food cravings (Study 1) and increased healthy food choices (Study 3). Finally, individual differences in sensitivity to food temptations (Study 3) and interest in alternative relationship partners (Study 4) predicted selective attention to attractive stimuli, but again, only when cognitive load was low. Our findings suggest that recognizing the tempting value of attractive stimuli in our living environment requires cognitive resources. This has the important implication that, contrary to traditional views, performing a concurrent demanding task may actually diminish the captivating power of temptation and thus facilitate self-regulation. (PsycINFO Database Record (c) 2013 APA, all rights reserved)


title: A novel Poly(ε-caprolactone)-Pluronic-Poly(ε-caprolactone) grafted Polyethyleneimine(PCFC-g-PEI), Part 1, synthesis, cytotoxicity, and in vitro transfection study

abstract: BackgroundPolyethyleneimine (PEI), a cationic polymer, is one of the successful and widely used vectors for non-viral gene transfection in vitro. However, its in vivo application was greatly limited due to its high cytotoxicity and short duration of gene expression. To improve its biocompatibility and transfection efficiency, PEI has been modified with PEG, folic acid, and chloroquine in order to improve biocompatibility and enhance targeting.ResultsPoly(ε-caprolactone)-Pluronic-Poly(ε-caprolactone) (PCFC) was synthesized by ring-opening polymerization, and PCFC-g-PEI was obtained by Michael addition reaction with GMA-PCFC-GMA and polyethyleneimine (PEI, 25 kD). The prepared PCFC-g-PEI was characterized by 1H-NMR, SEC-MALLS. Meanwhile, DNA condensation, DNase I protection, the particle size and zeta potential of PCFC-g-PEI/DNA complexes were also determined. According to the results of flow cytometry and MTT assay, the synthesized PCFC-g-PEI, with considerable transfection efficiency, had obviously lower cytotoxicity against 293 T and A549 cell lines compared with that of PEI 25 kD.ConclusionThe cytotoxicity and in vitro transfection study indicated that PCFC-g-PEI copolymer prepared in this paper was a novel gene delivery system with lower cytotoxicity and considerable transfection efficiency compared with commercial PEI (25 kD).


Inverting Convolutional Networks with Convolutional Networks


title: Adaptive Quantization for Hashing: An Information-Based Approach to Learning Binary Codes

abstract: Large-scale data mining and retrieval applications have increasingly turned to compact binary data representations as a way to achieve both fast queries and ecient data storage; many algorithms have been proposed for learning eective binary encodings. Most of these algorithms focus on learning a set of projection hyperplanes for the data and simply binarizing the result from each hyperplane, but this neglects the fact that informativeness may not be uniformly distributed across the projections. In this paper, we address this issue by proposing a novel adaptive quantization (AQ) strategy that adaptively assigns varying numbers of bits to dierent hyperplanes based on their information content. Our method provides an information-based schema that preserves the neighborhood structure of data points, and we jointly nd the globally optimal bit-allocation for all hyperplanes. In our experiments, we compare with state-of-the-art methods on four large-scale datasets and nd that our adaptive quantization approach signicantly improves on traditional hashing methods.


title: Full band waveguide-to-microstrip probe transitions

abstract: Design data in the form of dimension tables is presented for probe transitions for microstrip substrates with 4 dielectric constants (2.2, 6.0, 10.1, and 13) in two orientations relative to the waveguide (broadside and longitudinal). These dimensions have been optimized for full waveguide bandwidth with >20 dB return loss using an electromagnetic CAD procedure. The dimensions can be applied to any size waveguide with a 2:1 width/height aspect ratio. Experimental verification for two of the designs applied to a W-band waveguide is presented.


Detecting emotion in music


title: Anterior Maxilla Augmentation Using Palatal Bone Block with Platelet-Rich Fibrin: A Controlled Trial.

abstract: PURPOSE ::: This study was conducted to characterize clinically and radiographically the effect of using platelet-rich fibrin (PRF) autologous graft on the augmentation results of autogenous palatal bone blocks. ::: ::: ::: MATERIALS AND METHODS ::: Patients who suffered from horizontal alveolar bone defects in the anterior maxillary ridge (≤ 4.5 mm) with one or two missing teeth were augmented with autogenous palatal bone blocks 4 months prior to implant placement. PRF was used to cover the block in the test group, while only the block was used in the control. Bone width was measured with a manual caliper preaugmentation (t0), and at 0 (t1) and 4 months (t2). Cone beam computed tomography (CBCT) scans were performed at t0 and t2. Nonparametric tests (Mann-Whitney, Friedman's, and Wilcoxon signed-rank tests) were used for the comparisons. The significance level was set at P ≤ .05. ::: ::: ::: RESULTS ::: In the 14 sites (12 patients), all but one autograft (from the control group) integrated successfully after 4 months. No statistically significant difference was found between demographic data in the two groups. There was a statistically significant increase in the buccopalatal bone width in both groups by time as measured by CBCT as well as the manual caliper. The test group showed statistically significantly lower mean graft resorption than the control group (test, 0.8 ± 0.6 mm; control, 1.6 ± 0.9 mm; P = .006). ::: ::: ::: CONCLUSION ::: Autogenous palatal bone block surface resorption is significantly decreased by the use of PRF coverage.


title: Privacy policies as decision-making tools: an evaluation of online privacy notices

abstract: Studies have repeatedly shown that users are increasingly concerned about their privacy when they go online. In response to both public interest and regulatory pressures, privacy policies have become almost ubiquitous. An estimated 77% of websites now post a privacy policy. These policies differ greatly from site to site, and often address issues that are different from those that users care about. They are in most cases the users' only source of information.This paper evaluates the usability of online privacy policies, as well as the practice of posting them. We analyze 64 current privacy policies, their accessibility, writing, content and evolution over time. We examine how well these policies meet user needs and how they can be improved. We determine that significant changes need to be made to current practice to meet regulatory and usability requirements.


title: Deep Learning for Real Time Crime Forecasting

abstract: Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.


title: Rocchio-based relevance feedback in video event retrieval

abstract: This paper investigates methods for user and pseudo relevance feedback in video event retrieval. Existing feedback methods achieve strong performance but adjust the ranking based on few individual examples. We propose a relevance feedback algorithm (ARF) derived from the Rocchio method, which is a theoretically founded algorithm in textual retrieval. ARF updates the weights in the ranking function based on the centroids of the relevant and non-relevant examples. Additionally, relevance feedback algorithms are often only evaluated by a single feedback mode (user feedback or pseudo feedback). Hence, a minor contribution of this paper is to evaluate feedback algorithms using a larger number of feedback modes. Our experiments use TRECVID Multimedia Event Detection collections. We show that ARF performs significantly better in terms of Mean Average Precision, robustness, subjective user evaluation, and run time compared to the state-of-the-art.


title: Discriminative Video Pattern Search for Efficient Action Detection

abstract: Actions are spatiotemporal patterns. Similar to the sliding window-based object detection, action detection finds the reoccurrences of such spatiotemporal patterns through pattern matching, by handling cluttered and dynamic backgrounds and other types of action variations. We address two critical issues in pattern matching-based action detection: 1) the intrapattern variations in actions, and 2) the computational efficiency in performing action pattern search in cluttered scenes. First, we propose a discriminative pattern matching criterion for action classification, called naive Bayes mutual information maximization (NBMIM). Each action is characterized by a collection of spatiotemporal invariant features and we match it with an action class by measuring the mutual information between them. Based on this matching criterion, action detection is to localize a subvolume in the volumetric video space that has the maximum mutual information toward a specific action class. A novel spatiotemporal branch-and-bound (STBB) search algorithm is designed to efficiently find the optimal solution. Our proposed action detection method does not rely on the results of human detection, tracking, or background subtraction. It can handle action variations such as performing speed and style variations as well as scale changes well. It is also insensitive to dynamic and cluttered backgrounds and even to partial occlusions. The cross-data set experiments on action detection, including KTH, CMU action data sets, and another new MSR action data set, demonstrate the effectiveness and efficiency of the proposed multiclass multiple-instance action detection method.


title: Location-aware associated data placement for geo-distributed data-intensive applications

abstract: Data-intensive applications need to address the problem of how to properly place the set of data items to distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those in Hadoop and Cassandra, but they do not work efficiently for the requests reading multiple data items in one transaction, especially when the source locations of requests are also distributed. Recent works proposed the managed data placement schemes for online social networks, but have a limited scope of applications due to their focuses. We propose an associated data placement (ADP) scheme, which improves the co-location of associated data and the localized data serving while ensuring the balance between nodes. In ADP, we employ the hypergraph partitioning technique to efficiently partition the set of data items and place them to the distributed nodes, and we also take replicas and incremental adjustment into considerations. Through extensive experiments with both synthesized and trace-based datasets, we evaluate the performance of ADP and demonstrate its effectiveness.


title: Efficient Parallel and Distributed Algorithms for GIS Polygonal Overlay Processing

abstract: Polygon overlay is one of the complex operations in Geographic Information Systems (GIS). In GIS, a typical polygon tends to be large in size often consisting of thousands of vertices. Sequential algorithms for this problem are in abundance in literature and most of the parallel algorithms concentrate on parallelizing edge intersection phase only. Our research aims to develop parallel algorithms to find overlay for two input polygons which can be extended to handle multiple polygons and implement it on General Purpose Graphics Processing Units (GPGPU) which offers massive parallelism at relatively low cost. Moreover, spatial data files tend to be large in size (in GBs) and the underlying overlay computation is highly irregular and compute intensive. MapReduce paradigm is now standard in industry and academia for processing large-scale data. Motivated by MapReduce programming model, we propose to develop and implement scalable distributed algorithms to solve large-scale overlay processing in this dissertation.


title: Skeletal graphs for efficient structure from motion

abstract: We address the problem of efficient structure from motion for large, unordered, highly redundant, and irregularly sampled photo collections, such as those found on Internet photo-sharing sites. Our approach computes a small skeletal subset of images, reconstructs the skeletal set, and adds the remaining images using pose estimation. Our technique drastically reduces the number of parameters that are considered, resulting in dramatic speedups, while provably approximating the covariance of the full set of parameters. To compute a skeletal image set, we first estimate the accuracy of two-frame reconstructions between pairs of overlapping images, then use a graph algorithm to select a subset of images that, when reconstructed, approximates the accuracy of the full set. A final bundle adjustment can then optionally be used to restore any loss of accuracy.


title: GLoMo: Unsupervised Learning of Transferable Relational Graphs.

abstract: Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels.


title: Experimental verification of a 3D scaling principle for low Vce(sat) IGBT

abstract: Three dimensionally (3D) scaled IGBTs that have a scaling factor of 3 (k=3) with respect to current commercial products (k=1) were fabricated for the first time. The scaling was applied to the lateral and vertical dimensions as well as the gate voltage. A significant decrease in ON resistance, — Vce(sat) reduction from 1.70 to 1.26 V — was experimentally confirmed for the 3D scaled IGBTs.


title: Robust visual tracking based on online learning sparse representation

abstract: Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via @?"1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.


title: Energy Management for MapReduce Clusters

abstract: The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload response time and overall energy consumption. Since the MapReduce framework is becoming "ubiquitous", the focus of this paper is on developing a framework for systematically considering various MapReduce node power down strategies, and their impact on the overall energy consumption and workload response time. ::: ::: We closely examine two extreme techniques that can be accommodated in this framework. The first is based on a recently proposed technique called "Covering Set" (CS) that keeps only a small fraction of the nodes powered up during periods of low utilization. At the other extreme is a technique that we propose in this paper, called the All-In Strategy (AIS). AIS uses all the nodes in the cluster to run a workload and then powers down the entire cluster. Using both actual evaluation and analytical modeling we bring out the differences between these two extreme techniques and show that AIS is often the right energy saving strategy.


Augmented Reality : Applications , Challenges and Future Trends


title: CAP twelve years later: How the "rules" have changed

abstract: The CAP theorem asserts that any networked shared-data system can have only two of three desirable properties. However, by explicitly handling partitions, designers can optimize consistency and availability, thereby achieving some trade-off of all three. The featured Web extra is a podcast from Software Engineering Radio, in which the host interviews Dwight Merriman about the emerging NoSQL movement, the three types of nonrelational data stores, Brewer's CAP theorem, and much more.


title: Active learning through notes data in Flickr: an effortless training data acquisition approach for object localization

abstract: Most of the state-of-the-art systems for object localization rely on supervised machine learning techniques, and are thus limited by the lack of labeled training data. In this paper, our motivation is to provide training dataset for object localization effectively and efficiently. We argue that the notes data in Flickr can be exploited as a novel source for object modeling. At first, we apply a text mining method to gather semantically related images for a specific class. Then a handful of images are selected manually as seed images or initial training set. At last, the training set is expanded by an incremental active learning framework. Our approach requires significantly less manual supervision compared to standard methods. The experimental results on the PASCAL VOC 2007 and NUS-WIDE datasets show that the training data acquired by our approach can complement or even substitute conventional training data for object localization.


title: Exploring Inter-Observer Differences in First-Person Object Views Using Deep Learning Models

abstract: Recent advances in wearable camera technology have led many cognitive psychologists to study the development of the human visual system by recording the field of view of infants and toddlers. Meanwhile, the vast success of deep learning in computer vision is driving researchers in both disciplines to aim to benefit from each other's understanding. Towards this goal, we set out to explore how deep learning models could be used to gain developmentally relevant insight from such first-person data. We consider a dataset of first-person videos from different people freely interacting with a set of toy objects, and train different object-recognition models based on each subject's view. We observe large inter-observer differences and find that subjects who created more diverse images of an object result in models that learn more robust object representations.


title: Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance

abstract: We address the problem of learning robust and efficient multi-view object detectors for surveillance video indexing and retrieval. Our philosophy is that effective solutions for this problem can be obtained by learning detectors from huge amounts of training data. Along this research direction, we propose a novel approach that consists of strategically partitioning the training set and learning a large array of complementary, compact, deep cascade detectors. At test time, given a video sequence captured by a fixed camera, a small number of detectors is automatically selected per image location. We demonstrate our approach on the problem of vehicle detection in challenging surveillance scenarios, using a large training dataset composed of around one million images. Our system runs at an impressive average rate of 125 frames per second on a conventional laptop computer.


title: Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches

abstract: Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR brings LDA to the forefront in the ODC field, motivating further research. This paper compares three LDA based algorithms for ODC. The first method uses the generic framework of Frank and Hall for ODC instantiated with a kernel version of LDA. Similarly, the second method is based on the also generic Data Replication framework for ODC instantiated with the same kernel version of LDA. Both the Frank and Hall and Data Replication methods address the ODC problem by the use of a base binary classifier. Finally, the third method under comparison is KDLOR. The experiments are carried out on synthetic and real datasets. A comparison between the performances of the three systems is made based on t-statistics. The performance and running time complexity of the methods do not support any advantage of KDLOR over the other two methods.


title: An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

abstract: Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM from various factors or symptoms became an issue which was not free from false presumptions accompanied by unpredictable effects. According to this context, data mining and machine learning could be used as an alternative way help us in knowledge discovery from data. We applied several learning methods, such as instance based learners, naive bayes, decision tree, support vector machines, and boosted algorithm acquire information from historical data of patient’s medical records of Mohammad Hoesin public hospital in Southern Sumatera. Rules are extracted from Decision tree to offer decision-making support through early detection of T2DM for clinicians.


title: Graph Laplacians and Stabilization of Vehicle Formations

abstract: Control of vehicle formations has emerged as a topic of significant interest to the controls community. In this paper, we merge tools from graph theory and control theory to derive stability criteria for formation stabilization. The interconnection between vehicles (i.e., which vehicles are sensed by other vehicles) is modeled as a graph, and the eigenvalues of the Laplacian matrix of the graph are used in stating a Nyquist-like stability criterion for vehicle formations. The location of the Laplacian eigenvalues can be correlated to the graph structure, and therefore used to identify desirable and undesirable formation interconnection topologies.


Stimulating effect of fermented ginseng leaf saponin on the differentiation and mineralization of murine osteoblastic MC3T3-E1 cells


title: Efficiency-Improved High-Voltage Analog Power Amplifier for Driving Piezoelectric Actuators

abstract: A novel high-voltage analog power amplifier derived from a well-known complementary class B amplifier is presented in this paper. The amplifier offers improved efficiency when driving capacitive loads such as piezoelectric multilayer actuators, while maintaining a high level of signal quality, peak power and low electromagnetic radiation. The main drawback of a class B amplifier in comparison to other power amplifiers is low efficiency, resulting in extensive power-loss in the two complementary transistors of the amplifier stage. Power-loss can be reduced by minimizing the voltage drop across the collector-emitter path of the transistors when a high collector current occurs. The proposed circuit uses an additional capacitor to recover up to one half of the charge stored in the actuator whenever the actuator is being discharged. The capacitor offers an additional power supply rail next to the positive and negative supply voltage of the amplifier stage. Switching the power supply net of the power transistors between the different supply rails, in order to reduce the voltage drop across the transistors, minimizes power-loss. The development of the amplifier was triggered by the introduction of a new piezoelectric motor and the requirements concerning signal quality and electromagnetic radiation. Therefore, in this paper, the circuit is analyzed using application-specific sinusoidal control signals which are used to drive the motor. Following the analytical investigation of the circuit, the amplifier is tested in an experiment measuring power consumption with a pure reactive load, and the level of distortion caused by the amplifier. In the experiment, a power-loss reduction of 47% was achieved in comparison with a class B stage. The amplifier processed a peak power of 160 W. The proposed amplifier showed total harmonic distortion of THD = 0.60% maximum. Distortions of a pure class B amplifier in the same setup were measured at THD = 0.25% .


title: Knowledge vault: a web-scale approach to probabilistic knowledge fusion

abstract: Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous approaches have primarily focused on text-based extraction, which can be very noisy. Here we introduce Knowledge Vault, a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories. We employ supervised machine learning methods for fusing these distinct information sources. The Knowledge Vault is substantially bigger than any previously published structured knowledge repository, and features a probabilistic inference system that computes calibrated probabilities of fact correctness. We report the results of multiple studies that explore the relative utility of the different information sources and extraction methods.


title: Toucan and hornbill beaks: a comparative study.

abstract: Abstract The structure and mechanical behavior of Toco Toucan ( Ramphastos toco ) and Wreathed Hornbill ( Rhyticeros undulatus ) beaks were compared. The beak of both species is a sandwich-structured composite, having an exterior, or rhamphotheca, consisting of multiple layers of keratin scales and a core composed of a fibrous network of bony closed-cell foam. The rhamphotheca is an arrangement of ∼50 μm diameter, overlapping, keratin tiles. The hornbill rhamphotheca exhibits a surface morphology on the ridged casque that is distinguishable from that observed on the bill proper. Intermediate filaments in the keratin matrix were observed by transmission electron microscopy. The Young’s modulus measurements of toucan rhamphotheca indicate isotropy in longitudinal and transverse directions, whereas those of hornbill rhamphotheca may suggest anisotropy. The compressive response of beak foam is governed by brittle crushing behavior. The crushing strength of hornbill foam is six times higher than that of toucan foam. Micro- and nanoindentation hardness values were measured for rhamphotheca and foam trabeculae of toucan and hornbill specimens. The sandwich design of beaks was analyzed using the Karam–Gibson and Dawson–Gibson models. The presence of a cellular core increases the bending resistance (Brazier moment) by a factor of 3–6 while decreasing the compressive strength by only 50%.


title: Big data analytics with small footprint: squaring the cloud

abstract: This paper describes the BID Data Suite, a collection of hardware, software and design patterns that enable fast, large-scale data mining at very low cost. By co-designing all of these elements we achieve single-machine performance levels that equal or exceed reported cluster implementations for common benchmark problems. A key design criterion is rapid exploration of models, hence the system is interactive and primarily single-user. The elements of the suite are: (i) the data engine, a hardware design pattern that balances storage, CPU and GPU acceleration for typical data mining workloads, (ii) BIDMat, an interactive matrix library that integrates CPU and GPU acceleration and novel computational kernels (iii), BIDMach, a machine learning system that includes very efficient model optimizers, (iv) Butterfly mixing, a communication strategy that hides the latency of frequent model updates needed by fast optimizers and (v) Design patterns to improve performance of iterative update algorithms. We present several benchmark problems to show how the above elements combine to yield multiple orders-of-magnitude improvements for each problem.


title: In the eye of the beholder: employing statistical analysis and eye tracking for analyzing abstract paintings

abstract: Most artworks are explicitly created to evoke a strong emotional response. During the centuries there were several art movements which employed different techniques to achieve emotional expressions conveyed by artworks. Yet people were always consistently able to read the emotional messages even from the most abstract paintings. Can a machine learn what makes an artwork emotional? In this work, we consider a set of 500 abstract paintings from Museum of Modern and Contemporary Art of Trento and Rovereto (MART), where each painting was scored as carrying a positive or negative response on a Likert scale of 1-7. We employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions. Additionally, we dissect the classification machinery to determine which parts of an image evokes what emotions. This opens new opportunities to research why a specific painting is perceived as emotional. We also demonstrate how quantification of evidence for positive and negative emotions can be used to predict the way in which people observe paintings.


title: The Use of Low Level Laser Therapy (LLLT) For Musculoskeletal Pain

abstract: Pain is the most common reason for physician consultation in the United States. One out of three Americans is affected by chronic pain annually. The number one reason for missed work or school days is musculoskeletal pain. Currently accepted therapies consist of non-steroidal anti-inflammatory drugs, steroid injections, opiate pain medications and surgery, each of which carries their own specific risk profiles. What is needed are effective treatments for pain which have an acceptably low risk-profile. For over forty years, low level laser (light) therapy (LLLT) and LED (light emitting diode) therapy (also known as photobiomodulation) has been shown to reduce inflammation and edema, induce analgesia, and promote healing in a range of musculoskeletal pathologies. The purpose of this paper is to review the use of LLLT for pain, the biochemical mechanisms of action, the dose response curves, and how LLLT may be employed by orthopedic surgeons to improve outcomes and reduce adverse events. With the predicted epidemic of chronic pain in developed countries, it is imperative to validate cost-effective and safe techniques for managing painful conditions which would allow people to live active and productive lives. Moreover the acceptance of LLLT (which is currently being used by many specialties around the world) into the armamentarium of the American health care provider would allow for additional treatment options for patients. A new cost-effective therapy for pain could elevate quality of life while reducing financial strains.


title: Six-Port Coupler Design for High-Power Radio Frequency Applications

abstract: Design, simulation, and implementation of six-port couplers for high-power radio frequency applications using transformer coupling is given for the first time. The complete analytical model for six-port coupler is developed, and closed-form equations using operational and scattering parameters are presented. The design is verified using time- and frequency-domain simulators. The design accuracy is increased using an accurate inductor model that is used for transformer construction. Final design is implemented and measured with network analyzer, HP8753ES. It has been observed that the analytical, simulation, and measurement results are all in agreement. It is confirmed that the proposed six-port coupler using transformer coupling gives the required coupling, isolation, and directivity levels in the forward and reverse modes, which make it an accurate power measurement device that can also be used to measure voltage, current, and impedance and hence function as a reflectometer. MATLAB Graphical User Interface (GUI) to design six-port directional couplers has been developed for academic and commercial use with implementation of the analytical model developed in this paper.


title: Java Source Code Protection Based on JNI and AES Algorithm

abstract: In the Java web security field, the encryption and decryption code of the traditional Java source code protection scheme, which is based on custom class loader encryption, can be easily decompiled. This effect results in the leakage of the source code and the low efficiency of encryption and decryption. This study proposes a scheme based on the Java Native Interface (JNI) and the Advanced Encryption Standard (AES) to protect the Java source code. The proposed scheme adopts the JNI for calling the encryption and decryption's codes implemented by C/C ++, and the encryption and decryption's codes adopt an improved AES encryption algorithm. This scheme greatly improves both the encryption and decryption of the implementation efficiency and the security of the source code.


title: Enhanced password processing scheme based on visual cryptography and OCR

abstract: Traditional password conversion scheme for user authentication is to transform the passwords into hash values. These hash-based password schemes are comparatively simple and fast because those are based on text and famed cryptography. However, those can be exposed to cyber-attacks utilizing password by cracking tool or hash-cracking online sites. Attackers can thoroughly figure out an original password from hash value when that is relatively simple and plain. As a result, many hacking accidents have been happened predominantly in systems adopting those hash-based schemes. In this work, we suggest enhanced password processing scheme based on image using visual cryptography (VC). Different from the traditional scheme based on hash and text, our scheme transforms a user ID of text type to two images encrypted by VC. The user should make two images consisted of subpixels by random function with SEED which includes personal information. The server only has user's ID and one of the images instead of password. When the user logs in and sends another image, the server can extract ID by utilizing OCR (Optical Character Recognition). As a result, it can authenticate user by comparing extracted ID with the saved one. Our proposal has lower computation, prevents cyber-attack aimed at hash-cracking, and supports authentication not to expose personal information such as ID to attackers.


title: A Compact Dual-Band Bow-Tie Slot Antenna for 900-MHz and 2400-MHz ISM Bands

abstract: In this letter, the overall size of a dual-band bow-tie slot antenna is reduced by modifying the design of the radiating slot and by extending the feeding coplanar transmission line. In particular, it is shown that by sweeping and rounding the edges of the radiating slots and by extending and terminating the feeding coplanar transmission line with a short circuit, the overall size of the antenna layout can be reduced by 47%. A dual-band prototype antenna is manufactured, and measured results are shown to compare very well with simulated values from two different commercial software packages. This letter presents two new techniques that are beneficial for reducing the overall size of a bow-tie slot antenna .


title: Time reversal based wireless events detection

abstract: In this work, we propose a novel wireless time-reversal indoor events detection system (TRIEDS). By leveraging the time-reversal (TR) technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall multiple events detection. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and a 125 MHZ bandwidth. Experiments are conducted to monitor the states of the indoor wooden doors. Experimental results show that with a single receiver (AP) and transmitter (client), TRIEDS can achieve a detection rate higher than 96:92% and a false alarm rate smaller than 3:08% under either line-of-sight (LOS) or non-LOS transmission.


title: Characterizing and modeling the distance of mobile calls: A metropolitan case study

abstract: During the past few years, with the democratization of open data, mobile network operators facilitated the sharing of mobile datasets. Such data are particularly interesting to better understand the way mobile users communicate, move and consummate services, but also to rethink the design or the evolution of current infrastructures with respects to new information and communication technologies and continuous increase of mobile usage. In this paper, we analyze a mobile dataset from a telecommunication provider in the city of Milan, Italy, and characterize mobile users' behavior. We show that, on the scale of the city, the number of short distance calls is massively higher than the number of long distance calls. Based on these observations, we propose a realistic yet simple model to describe the distance distribution of the calls in such a metropolitan environment. Finally, we focus on a specific area of the city, the soccer stadium area, and show that local events provoke regular deviation to the observed behaviors on the scale of the city. The macroscopic model we propose can be used to generate mobile datasets for any large city and thus support further simulations.


title: Grasp type understanding — classification, localization and clustering

abstract: Prehensile analysis is a research field attracting multi-disciplinary interests, including computer science, mechanology and neuroscience. For robot, grasp type recognition provides critical information for human-robot interaction and robot self-learning. One of the research direction is to discover the common modes of human hand use with first-person point-of-view wearable cameras. In contrast to previous methods based on handcraft features and multi-stage pipeline, we use a convolutional neural network to learn discriminative features of grasp types automatically, which can also achieve grasp type localization and classification simultaneously in a single-stage pipeline. Furthermore, a clustering method is also proposed to find the hierarchical relationships between different grasp types. Experiments are conducted on UT Grasp dataset and Yale human grasping dataset. The proposed method shows better accuracy and higher efficiency than traditional methods.


title: Human-Computer Co-Creativity: Blending Human and Computational Creativity

abstract: This paper describes a thesis exploring how computer programs can collaborate as equals in the artistic creative process. The proposed system, CoCo Sketch, encodes some rudimentary stylistic rules of abstract sketching and music theory to contribute supplemental lines and music while the user sketches. We describe a three-part research method that includes defining rudimentary stylistic rules for abstract line drawing, exploring the interaction design for artistic improvisation with a computer, and evaluating how CoCo Sketch affects the artistic creative process. We report on the initial results of early investigations into artistic style that describe cognitive, perceptual, and behavioral processes used in abstract artists making.


title: Projected Subgradient Methods for Learning Sparse Gaussians

abstract: Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our approach uses the l1-norm as a regularization on the inverse covariance matrix. We utilize a novel projected gradient method, which is faster than previous methods in practice and equal to the best performing of these in asymptotic complexity. We also extend the l1-regularized objective to the problem of sparsifying entire blocks within the inverse covariance matrix. Our methods generalize fairly easily to this case, while other methods do not. We demonstrate that our extensions give better generalization performance on two real domains—biological network analysis and a 2D-shape modeling image task.


title: Building Automatic Antenna Tracking system for Low Earth Orbit(LEO) satellite communications

abstract: This paper purposed to implement the Automatic Antenna Tracking system in order to establish the communication with OSCAR (Orbiting Satellite Carry Amateur Radio) for voice (FM, SSB) and data communications (AX.25, packet radio, APRS). There are more than 5 fully operational Amateur satellites both U/V mode and CUBESAT in orbit act as repeaters or Store/ Forward. The often of the activities that came from International Space Station (ISS) sent out picture to ground station with SLOW SCAN TV(SSTV) data protocol. This research areas covered include Azimuth/Elevation Rotor Design and Schematic Controller Interface design.


title: Occlusion robust face recognition based on mask learning

abstract: Face occlusion has been a long standing challenging issue in face recognition. In the state-of-the-art deep Convolutional Neural Network (CNN) face recognition models, occluded facial parts are generally embedded into the learned features together with the non-occluded parts in an equivalent manner. As such, the discriminative power of the generated face representation may be weakened for occluded face images. To address this problem, we propose the MaskNet, a trainable module which can be included in existing CNN architectures. With end-to-end training supervised by only the personal identity labels, MaskNet learns a proper way of adaptively generating different feature map masks for different occluded face images. Intuitively, MaskNet automatically assigns higher weights to the hidden units activated by the non-occluded facial parts and lower weights to those that are activated by the occluded facial parts. Experiments on datasets consisting of real-life and synthetic occluded faces demonstrate that MaskNet can effectively improve the robustness of CNN models towards occlusions in face recognition.


title: A 12.77-MHz on-chip relaxation oscillator with digital compensation for loop delay variation

abstract: This paper presents a 12.77-MHz low power, RC on-chip relaxation oscillator with self-compensation technique. A digital feedback loop is implemented to maintain the frequency stability. A replica comparator is added to generate a reference pulse width for the compensation. After compensation, the frequency is tuned by a digitally controlled delay element. The proposed design is fabricated in 0.18-μm CMOS process. The main oscillator consumes 12.8 μW at 0.9-V supply voltage. The whole system consumes 56.2 μW when the digital compensation blocks are enabled. The measured output frequency variation is less than ±0.4% across −30 to 120°C. For a supply range of 0.6 V to 1.1 V, the frequency varies less than 0.5%/V after compensation.


title: Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval

abstract: Combination of multiple evidences (multiple query formulations, multiple retrieval schemes or systems) has been shown (mostly experimentally) to be effective in data fusion in information retrieval. However, the question of why and how combination should be done still remains largely unanswered. In this paper, we provide a model for simulation and a framework for analysis in the study of data fusion in the information retrieval domain. A rank/score function is defined and the concept of a Cayley graph is used in the design and analysis of our framework. The model and framework have led us to better understanding of the data fusion phenomena in information retrieval. In particular, by exploiting the graphical properties of the rank/score function, we have shown analytically and by simulation that combination using rank performs better than combination using score under certain conditions. Moreover, we demonstrated that the rank/score function might be used as a predictive variable for the effectiveness of combination of multiple evidences.


Private vs. Business Customers in the Sharing Economy – The implications of Trust, Perceived Risk, and Social Motives on Airbnb


Topic-Sensitive PageRank : A Context-Sensitive Ranking Algorithm for Web Search


title: Yet Another Network Simulator

abstract: We report on the design objectives and initial design of a new discrete-event network simulator for the research community. Creating Yet Another Network Simulator (yans, http://yans.inria.fr/yans) is not the sort of prospect network researchers are happy to contemplate, but this effort may be timely given that ns-2 is considering a major revision and is evaluating new simulator cores. We describe why we did not choose to build on existing tools such as ns-2, GTNetS, and OPNET, outline our functional requirements, provide a high-level view of the architecture and core components, and describe a new IEEE 802.11 model provided with yans.


title: Voronoi++: A Dynamic Page Segmentation Approach Based on Voronoi and Docstrum Features

abstract: This paper presents a dynamic approach to document page segmentation. Current page segmentation algorithms lack the ability to dynamically adapt local variations in the size, orientation and distance of components within a page. Our approach builds upon one of the best algorithms, Kise et. al. work based on Area Voronoi Diagrams, which adapts globally to page content to determine algorithm parameters. In our approach, local thresholds are determined dynamically based on parabolic relations between components, and Docstrum based angular and neighborhood features are integrated to improve accuracy. Zone-based evaluation was performed on four sets of printed and handwritten documents in English and Arabic scripts and an increase of 33% in accuracy is reported.


title: CapsuleGAN: Generative Adversarial Capsule Network

abstract: We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models. We show that CapsuleGAN outperforms convolutional-GAN at modeling image data distribution on MNIST and CIFAR-10 datasets, evaluated on the generative adversarial metric and at semi-supervised image classification.


title: Cyber-physical systems: the next computing revolution

abstract: Cyber-physical systems (CPS) are physical and engineered systems whose operations are monitored, coordinated, controlled and integrated by a computing and communication core. Just as the internet transformed how humans interact with one another, cyber-physical systems will transform how we interact with the physical world around us. Many grand challenges await in the economically vital domains of transportation, health-care, manufacturing, agriculture, energy, defense, aerospace and buildings. The design, construction and verification of cyber-physical systems pose a multitude of technical challenges that must be addressed by a cross-disciplinary community of researchers and educators.


title: Finding Romanized Arabic Dialect in Code-Mixed Tweets

abstract: Recent computational work on Arabic dialect identification has focused primarily on building and annotating corpora written in Arabic script. Arabic dialects however also appear written in Roman script, especially in social media. This paper describes our recent work developing tweet corpora and a token-level classifier that identifies a romanized Arabic dialect and distinguishes it from French and English in tweets. We focus on Moroccan Darija, one of several spoken vernaculars in the family of Maghrebi Arabic dialects. Even given noisy, code-mixed tweets, the classifier achieved token-level recall of 93.2% on romanized Arabic dialect, 83.2% on English, and 90.1% on French. The classifier, now integrated into our tweet conversation annotation tool (Tratz et al. 2013), has semi-automated the construction of a romanized Arabic-dialect lexicon. Two datasets, a full list of Moroccan Darija surface token forms and a table of lexical entries derived from this list with spelling variants, as extracted from our tweet corpus collection, will be made available in the LRE MAP.


title: A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

abstract: This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.


title: Finite Control Set Model Predictive Control for Grid-Connected AC–DC Converters With LCL Filter

abstract: This paper presents two new implementations of finite control set model predictive control (FCS-MPC) methods applied to ac–dc converters with an inductive.capacitive.inductive (LCL) filter. The LCL filter, despite its advantages, can cause a strong resonance in the grid current and also pose a substantially more complex control problem. The new solutions improve the FCS-MPC with an active damping algorithm by eliminating low-order grid current harmonics and decreasing sensitivity to grid voltage distortion. The new methods, i.e., PCi1i2uc and PCi1i2uc-2steps propose multivariable approaches using converter-side current, line-side current, and capacitor voltage. Another improvement involves extending the prediction horizon in PCi1 i2uc-2steps. Both methods have been tested and compared in steady and transient states as well as under grid voltage disturbances. The methods were also compared with a predictive control algorithm with active damping. The simulation results and experimental measurements validate the developed control schemes and show high quality of grid current (low THDi value), high dynamic performance, and immunity under distorted grid voltage conditions.


title: A temperature compensated digitally trimmable on-chip IC oscillator with low voltage inhibit capability

abstract: The design of a temperature compensated IC on-chip oscillator and a low voltage detection circuitry sharing the bandgap reference is described. The circuit includes a new bandgap isolation strategy to reduce oscillator noise coupled through the current sources. The IC oscillator provides a selectable clock (11.6 MHz or 21.4 MHz) with digital trimming to minimize process variations. After fine-tuning the oscillator to the target frequency, the temperature compensated voltage and current references guarantees less than /spl plusmn/2.5% frequency variation from -40 to 125/spl deg/C, when operating from 3 V to 5 V of power supply. The low voltage detection circuit monitors the supply voltage applied to the system and generates the appropriate warning or even initiates a system shutdown before the in-circuit SoC presents malfunction. The module was implemented in a 0.5 /spl mu/m CMOS technology, occupies an area of 360 /spl times/ 530 /spl mu/m/sub 2/ and requires no external reference or components.


title: A Commutative Replicated Data Type for Cooperative Editing

abstract: A Commutative Replicated Data Type (CRDT) is one where all concurrent operations commute. The replicas of a CRDT converge automatically, without complex concurrency control. This paper describes Treedoc, a novel CRDT design for cooperative text editing. An essential property is that the identifiers of Treedoc atoms are selected from a dense space. We discuss practical alternatives for implementing the identifier space based on an extended binary tree. We also discuss storage alternatives for data and meta-data, and mechanisms for compacting the tree. In the best case, Treedoc incurs no overhead with respect to a linear text buffer. We validate the results with traces from existing edit histories.


Strategic Management of Technology and Innovation


title: Layout Dependent Effect: Impact on device performance and reliability in recent CMOS nodes

abstract: In this paper, we analyze the impact of Layout Dependent Effect (LDE) observed on MOSFETs. It is shown that changing the Layout have an impact on MOSFET device parameters and reliability. Here, we studied the Well Proximity Effect (WPE), Length of diffusion (LOD) and Oxide Spacing Effect (OSE) impacts on device MOSFET parameters and reliability. We also analyzed SiGe impacts on LDE, since it is commonly used to boost device performance.


title: Vision‐based and marker‐less surgical tool detection and tracking: a review of the literature

abstract: In recent years, tremendous progress has been made in surgical practice for example with Minimally Invasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems. In this paper, we present a review of the literature dealing with vision-based and marker-less surgical tool detection. This paper includes three primary contributions: (1) identification and analysis of data-sets used for developing and testing detection algorithms, (2) in-depth comparison of surgical tool detection methods from the feature extraction process to the model learning strategy and highlight existing shortcomings, and (3) analysis of validation techniques employed to obtain detection performance results and establish comparison between surgical tool detectors. The papers included in the review were selected through PubMed and Google Scholar searches using the keywords: “surgical tool detection”, “surgical tool tracking”, “surgical instrument detection” and “surgical instrument tracking” limiting results to the year range 2000 2015. Our study shows that despite significant progress over the years, the lack of established surgical tool data-sets, and reference format for performance assessment and method ranking is preventing faster improvement.


title: Generative Feature Language Models for Mining Implicit Features from Customer Reviews

abstract: Online customer reviews are very useful for both helping consumers make buying decisions on products or services and providing business intelligence. However, it is a challenge for people to manually digest all the opinions buried in large amounts of review data, raising the need for automatic opinion summarization and analysis. One fundamental challenge in automatic opinion summarization and analysis is to mine implicit features, i.e., recognizing the features implicitly mentioned (referred to) in a review sentence. Existing approaches require many ad hoc manual parameter tuning, and are thus hard to optimize or generalize; their evaluation has only been done with Chinese review data. In this paper, we propose a new approach based on generative feature language models that can mine the implicit features more effectively through unsupervised statistical learning. The parameters are optimized automatically using an Expectation-Maximization algorithm. We also created eight new data sets to facilitate evaluation of this task in English. Experimental results show that our proposed approach is very effective for assigning features to sentences that do not explicitly mention the features, and outperforms the existing algorithms by a large margin.


title: Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies

abstract: Electric vehicles (EVs) are regarded as one of the most effective tools to reduce the oil demands and gas emissions. And they are welcome in the near future for general road transportation. When EVs are connected to the power grid for charging and/or discharging, they become gridable EVs (GEVs). These GEVs will bring a great impact to our society and thus human life. This paper investigates and discusses the opportunities and challenges of GEVs connecting with the grid, namely, the vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G) technologies. The key is to provide the methodologies, approaches, and foresights for the emerging technologies of V2H, V2V, and V2G.


title: Physical and Mechanical Characteristics of Soft Soil in Lower Reaches of Yangtze River

abstract: Based on large numbers of geotechnical tests, the physical and mechanical characteristics of soft soil (including clay and silty clay) in the foundation of Sutong large bridge in lower reaChes of Yangtze River, Jiangsu province, China, is studied. The following conclusions can be drawn. Firstly, the variation coefficient of natural density, dry density, specific gravity of soil grain, saturation are small, the variation coefficient of natural water content, natural hole ratio, Liquid Limit, plastic Limit are generic, and the variation coefficient of plasticity index, compress module, compress coefficient, coherent strength, inner friction angle, the standard penetration test (SPT) are big. Secondly, there exist good Linear correlativity between natural density, hole ratio and natural water content, their correlativity coefficients are over 0.95. Thirdly, there is not Linear correlativity between plastic Limit and Liquid Limit, but there is better Linear correlativity between plastic Limit and Liquid Limit to clay. And the correlativity between plasticity index and Liquid Limit is good, and the correlativity coefficient is 0.90056. Fourthly, there exist good Linear correlativity between compress coefficient and natural hole ratio, natural water content, their correlativity coefficients are over 0.90. There exist good exponential correlativity between compress module and natural hole ratio, natural water content, their correlativity coefficients are over 0.85. Finally, there exist better exponential correlativity between coherent strength and natural density, natural hole ratio, the relativity between inner friction angle and natural density, natural hole ratio are not obvious.


title: Extracting knowledge from ontology using Jena for semantic web

abstract: Semantic Web extends the current web by standardizing the semantics. Purpose of Semantic Web is to make people share data and content among different applications. This is made possible by using ontologies which represents shared understanding of a domain. Semantic Web uses ontologies for representing knowledge in a uniform way that can be easily processed and shared among machine. This paper gives detail description of the role of ontologies in Semantic Web and tools required for constructing Semantic Web applications. An application is created which loads the ontology created in Protege into Eclipse and displays the properties and classes of ontology.


title: How Secure is Your Smart Watch

abstract: The Internet of Things (IoT) is a network of devices, vehicles, buildings and other items which are able to collect and exchange data. IoT devices are furnished with technologies such as electronics, software, sensors, actuators, and network connectivity in order to function as desired. A smart watch can be considered as an IoT device as they are equipped by almost all necessary technologies. In essence, smart watches are effectively wearable computers with communications and sensing capabilities. Many mobile apps run over smart watches using a mobile operating system. However, as the other IoT devices they are subject to security issues. In this paper, by surveying and reporting the security concerns and issues at smart watches, we aim to raise awareness on this matter. We also propose a road map for a more secure smart watch platform by defining the responsible actors and their responsibilities.


title: Mechanics of a mosquito bite with applications to microneedle design

abstract: The mechanics of a fascicle insertion into the skin by a mosquito of the type aedes aegypti has been studied experimentally using high-speed video (HSV) imaging, and analytically using a mathematical model. The fascicle is a polymeric microneedle composed of a ductile material, chitin. It has been proposed that the mosquito applies a non-conservative follower force component in addition to the Euler compressive load in order to prevent buckling and penetrate the skin. In addition, the protective sheath surrounding the fascicle (labium) provides lateral support during insertion. The mechanics model presented approximates the fascicle as a slender column supported on an elastic foundation (labium) subjected to non-conservative (Beck) and conservative Euler loads simultaneously at the end. Results show that the lateral support of the fascicle provided by the labium is essential for successful penetration by increasing the critical buckling load by a factor of 5. The non-conservative follower force application increases the buckling load by an additional 20% and may or may not be necessary for successful penetration. Experimental results showing the importance of the labium have been cited to validate the model predictions, in addition to the video observations presented in this work. This understanding may be useful in designing painless needle insertion systems as opposed to miniaturized hypodermic needles.


title: Development of coherent neuronal activity patterns in mammalian cortical networks: Common principles and local hetereogeneity

abstract: Many mammals are born in a very immature state and develop their rich repertoire of behavioral and cognitive functions postnatally. This development goes in parallel with changes in the anatomical and functional organization of cortical structures which are involved in most complex activities. The emerging spatiotemporal activity patterns in multi-neuronal cortical networks may indeed form a direct neuronal correlate of systemic functions like perception, sensorimotor integration, decision making or memory formation. During recent years, several studies--mostly in rodents--have shed light on the ontogenesis of such highly organized patterns of network activity. While each local network has its own peculiar properties, some general rules can be derived. We therefore review and compare data from the developing hippocampus, neocortex and--as an intermediate region--entorhinal cortex. All cortices seem to follow a characteristic sequence starting with uncorrelated activity in uncoupled single neurons where transient activity seems to have mostly trophic effects. In rodents, before and shortly after birth, cortical networks develop weakly coordinated multineuronal discharges which have been termed synchronous plateau assemblies (SPAs). While these patterns rely mostly on electrical coupling by gap junctions, the subsequent increase in number and maturation of chemical synapses leads to the generation of large-scale coherent discharges. These patterns have been termed giant depolarizing potentials (GDPs) for predominantly GABA-induced events or early network oscillations (ENOs) for mostly glutamatergic bursts, respectively. During the third to fourth postnatal week, cortical areas reach their final activity patterns with distinct network oscillations and highly specific neuronal discharge sequences which support adult behavior. While some of the mechanisms underlying maturation of network activity have been elucidated much work remains to be done in order to fully understand the rules governing transition from immature to mature patterns of network activity.


title: Anomalously low dielectric constant of confined water

abstract: The dielectric constant e of interfacial water has been predicted to be smaller than that of bulk water (e ≈ 80) because the rotational freedom of water dipoles is expected to decrease near surfaces, yet experimental evidence is lacking. We report local capacitance measurements for water confined between two atomically flat walls separated by various distances down to 1 nanometer. Our experiments reveal the presence of an interfacial layer with vanishingly small polarization such that its out-of-plane e is only ~2. The electrically dead layer is found to be two to three molecules thick. These results provide much-needed feedback for theories describing water-mediated surface interactions and the behavior of interfacial water, and show a way to investigate the dielectric properties of other fluids and solids under extreme confinement.


title: A social identity approach to identify familiar strangers in a social network

abstract: We present a novel problem of searching for ‘familiar strangers’ in a social network. Familiar strangers are individuals who are not directly connected but exhibit some similarity. The power-law nature of social networks determines that majority of individuals are directly connected with a small number of fellow individuals, and similar individuals can be largely unknown to each other. Moreover, the individuals of a social network have only a local view of the network, which makes the problem of aggregating these familiar strangers a challenge. In this work, we formulate the problem, show why it is significant to address the challenge, and present an approach that innovatively employs the social identities of the individuals with competitive approaches. A blogger and citation network are used to showcase technical details and empirical results with related issues and future work.


title: Wideband channel sounder with measurements and model for the 60 GHz indoor radio channel

abstract: A wideband channel sounder and measurement results for the short range indoor 60 GHz channel are presented. The channel sounder is based on a 1 gigasamples/s dual channel arbitrary waveform generator and A/D converter/software demodulator, which synthesize and detect a baseband PN sequence with 500 MHz bandwidth. A heterodyne transmitter and receiver translate the baseband PN sequence to and from the 60 GHz band. Ten channel measurements taken across the 59 GHz to 64 GHz range are concatenated to provide a continuous channel measurement covering 5 GHz of bandwidth, resulting in 0.2 ns time domain channel impulse response resolution. The dynamic range and maximum sensitivity performance of the channel sounder are discussed in detail. Comparisons of results with a vector network analyzer based system are shown to verify the accuracy of the sounder. In an extensive measurement campaign with vertically polarized omnidirectional antennas, several different rooms (offices, labs, conference rooms and others) in four different buildings have been investigated. Over 700 channel measurements are the basis for a comprehensive characterization of the short range 60 GHz indoor radio channel with omnidirectional antennas. Finally, a simple stochastic static multipath channel model is derived from the measurement results.


title: The effects of customer participation in co-created service recovery

abstract: The benefits of customer co-creation of value in the service context are well recognized. However, little is known about service failures in a co-creation context and the consequent roles of both firms and customers in the advent of service recovery. In conceptualizing a new construct, “customer participation in service recovery,” this study proposes a theoretical framework that delineates the consequences of the construct and empirically tests the proposed framework using role-playing experiments. The results indicate that, when customers participate in the service recovery process in self-service technology contexts, they are more likely to report higher levels of role clarity, perceived value of future co-creation, satisfaction with the service recovery, and intention to co-create value in the future. Theoretical and managerial implications of the findings are discussed.


title: The gut-brain barrier in major depression: intestinal mucosal dysfunction with an increased translocation of LPS from gram negative enterobacteria (leaky gut) plays a role in the inflammatory pathophysiology of depression.

abstract: There is now evidence that major depression (MDD) is accompanied by an activation of the inflammatory response system (IRS) and that pro-inflammatory cytokines and lipopolysacharide (LPS) may induce depressive symptoms. The aim of the present study was to examine whether an increased gastrointestinal permeability with an increased translocation of LPS from gram negative bacteria may play a role in the pathophysiology of MDD. Toward this end, the present study examines the serum concentrations of IgM and IgA against LPS of the gram-negative enterobacteria, Hafnia Alvei, Pseudomonas Aeruginosa, Morganella Morganii, Pseudomonas Putida, Citrobacter Koseri, and Klebsielle Pneumoniae in MDD patients and normal controls. We found that the prevalences and median values for serum IgM and IgA against LPS of enterobacteria are significantly greater in patients with MDD than in normal volunteers. These differences are significant to the extent that a significant diagnostic performance is obtained, i.e. the area under the ROC curve is 90.1%. The symptom profiles of increased IgM and IgA levels are fatigue, autonomic and gastro-intestinal symptoms and a subjective feeling of infection. The results show that intestinal mucosal dysfunction characterized by an increased translocation of gram-negative bacteria (leaky gut) plays a role in the inflammatory pathophysiology of depression. It is suggested that the increased LPS translocation may mount an immune response and thus IRS activation in some patients with MDD and may induce specific "sickness behaviour" symptoms. It is suggested that patients with MDD should be checked for leaky gut by means of the IgM and IgA panel used in the present study and accordingly should be treated for leaky gut.


title: Electrical Modeling of Stochastic Spin Transfer Torque Writing in Magnetic Tunnel Junctions for Memory and Logic Applications

abstract: Magnetic tunnel junctions (MTJ) are considered as one of the most promising candidates for the next generation of nonvolatile memories and programmable logic chips. Spin transfer torque (STT) in CoFeB/MgO/CoFeB MTJs with perpendicular magnetic anisotropy (PMA) exhibits noticeable performance enhancements compared to that with In-plane magnetic anisotropy, particularly in terms of thermal stability, critical current for switching, access speed and power consumption. However, the STT switching of MTJ has been revealed stochastic, which results from unavoidable thermal fluctuations of magnetization. This leads to the occurrence of write errors which deeply affects the reliability of hybrid CMOS/MTJ circuits. In this paper, we present the first spice-compact model of CoFeB/MgO/CoFeB structure PMA-MTJ integrating STT stochastic behaviors. Depending on the relative magnitude between the switching current (I) and the critical current (Ico), the STT stochastic behaviors of this PMA-MTJ can be categorized into two regions: Sun model (I > Ico) and Neel-Brown model (I <; 0.8Ico). The Monte-Carlo simulations for single cell and hybrid CMOS/MTJ circuits show the stochastic behaviors in both writing and sensing operations. This model can be very useful for investigating the reliability issues during the design and simulation before process fabrication.


title: Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

abstract: Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations, examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.


title: Early lateralization and orientation tuning for face , word , and object processing in the visual cortex

abstract: Event-related potential (ERP) studies of the human brain have shown that object categories can be reliably distinguished as early as 130-170 ms on the surface of occipito-temporal cortex, peaking at the level of the N170 component. Consistent with this finding, neuropsychological and neuroimaging studies suggest major functional distinctions within the human object recognition system, particularly in hemispheric advantage, between the processing of words (left), faces (right), and objects (bilateral). Given these observations, our aim was to (1) characterize the differential response properties of the N170 to pictures of faces, objects, and words across hemispheres; and (2) test whether an effect of inversion for highly familiar and monooriented nonface stimuli such as printed words can be observed at the level of the N170. Scalp EEG (53 channels) was recorded in 15 subjects performing an orientation decision task with pictures of faces, words, and cars presented upright or inverted. All three categories elicited at the same latency a robust N170 component associated with a positive counterpart at centro-frontal sites (vertex-positive potential, VPP). While there were minor amplitude differences at the level of the occipital medial P1 between linguistic and nonlinguistic categories, scalp topographies and source analyses indicated strong hemispheric and orientation effects starting at the level of the N170, which was right lateralized for faces, smaller and bilateral for cars, and as large for printed words in the left hemisphere as for faces. The entire N170/VPP complex was accounted for by two dipolar sources located in the lateral inferior occipital cortex/posterior fusiform gyrus. These two locations were roughly equivalent across conditions but differed in strength and lateralization. Inversion delayed the N170 (and VPP) response for all categories, with an increasing delay for cars, words, and faces, respectively, as suggested by source modeling analysis. Such results show that early processes in object recognition respond to category-specific visual information, and are associated with strong lateralization and orientation bias.


title: New Normal Mode dv/dt Filter With a Built-In Resistor Failure Detection Circuit

abstract: Insulated gate bipolar transistors (IGBTs) will soon be replaced by wide band gap devices (SiC and GaN) as the choice for power semiconductor switch in voltage source inverters. These devices have extremely fast rise time and fall time compared with IGBT devices. The high dv/dt of pulse-width modulation outputs create excessive voltage stress in the insulation system of ac motors due to voltage reflection issues associated with motors at large distances from the drive. In many oil field applications, the distance between the motor and the variable frequency drive approaches 300 m. In walking rig applications, it is common to use multiple smaller sized conductors per phase, bunched together, to achieve the desired ampacity. This practice results in higher than usual value of the cable parasitic capacitance. Traditional dv/dt filters used for mitigating over-voltage at motor terminals have been found to be inadequate in reducing the over voltage at the motor terminals in such oil field installations. The damping resistor often experiences high voltage and gets damaged. A new dv/dt filter suitable for use with high power ac motors at distances nearing 300 m with a built-in resistor failure detection circuit is proposed here. Test results are given to demonstrate its efficacy.


James Joyce's early writings and ecocritical theory: a new turn?


title: Genital findings in prepubertal girls evaluated for sexual abuse. A different perspective on hymenal measurements.

abstract: OBJECTIVE ::: To evaluate the usefulness of the horizontal transhymenal diameter as a screening parameter to differentiate between prepubertal girls with and without other definitive signs of sexual abuse. ::: ::: ::: DESIGN ::: Case comparison study using transhymenal measurements as a diagnostic screening test referenced against prior publications of criterion standards. ::: ::: ::: SETTING ::: A primary care (family practice) clinic in association with an academic program in northern California. ::: ::: ::: PATIENTS ::: A consecutive, referred sample of 1058 prepubertal girls aged 6 months to 10 years who were examined as allegedly having been sexually molested between 1987 and 1994. ::: ::: ::: RESULTS ::: Girls with no definitive signs of genital trauma exhibited a mean transhymenal diameter of 2.3 mm and in general showed an increase of approximately 1 mm per year of age. Girls with definitive signs of genital trauma exhibited a mean transhymenal diameter of 9.0 mm and no significant variance with age. Correcting for age differences, the transhymenal diameter was highly significant as a differentiating factor (F = 1079, P < .001). When compared against the criterion standard, the transhymenal measurement is 99% specific and 79% sensitive as a screening tool. ::: ::: ::: CONCLUSION ::: Although not independently diagnostic of sexual molestation, the transhymenal diameter, when compared against the criterion standard for age, is a useful screening parameter for primary care physicians evaluating children of potential sexual abuse.


title: 3D printed interactive speakers

abstract: We propose technology for designing and manufacturing interactive 3D printed speakers. With the proposed technology, sound reproduction can easily be integrated into vari-ous objects at the design stage and little assembly is required. The speaker can take the shape of anything from an abstract spiral to a rubber duck, opening new opportunities in product design. Furthermore, both audible sound and inaudible ultrasound can be produced with the same design, allowing for identifying and tracking 3D printed objects in space using common integrated microphones. The design of 3D printed speakers is based on electrostatic loudspeaker technology first explored in the early 1930s but not broadly applied until now. These speakers are simpler than common electromagnetic speakers, while allowing for sound reproduction at 60 dB levels with arbitrary directivity ranging from focused to omnidirectional. Our research of 3D printed speakers contributes to the growing body of work exploring functional 3D printing in interactive applications.


title: Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment

abstract: We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known to be predictive of lexical relations. We present a novel model which exploits this behavior as a method of feature extraction in an iterative procedure similar to Principal Component Analysis. Our model combines the extracted features with the strengths of other proposed models in the literature, and matches or outperforms prior work on multiple data sets.


title: Group delay based music source separation using deep recurrent neural networks

abstract: Deep Recurrent Neural Networks (DRNNs) have been most successfully used in solving the challenging task of separating sources from a single channel acoustic mixture. Conventionally, magnitude spectra are being used to learn the characteristics of individual sources in such monaural blind source separation (BSS) task. The phase spectra which inherently contain the timing information is often ignored. In this work, we explore the use of modified group delay (MOD-GD) function for learning the time-frequency masks of the sources in the monaural BSS problem. We demonstrate the use of MOD-GD through two music source separation tasks: singing voice separation on the MIR-1K data set and vocal-violin separation on the Carnatic music data set. We find that it outperforms the state-of-the-art feature in terms of Signal to Interference Ratio (SIR). Moreover, training and testing times are significantly reduced (by 50%) without compromising on the performance for the best performing DRNN configuration.