title: Combining Component Caching and Clause Learning for Effective Model Counting

abstract: While there has been very substantial progress in practical algorithms for satisfiability, there are many related logical problems where satisfiability alone is not enough. One particularly useful extension to satisfiability is the associated counting problem, #SAT, which requires computing the number of assignments that satisfy the input formula. #SAT’s practical importance stems in part from its very close relationship to the problem of general Bayesian inference. #SAT seems to be more computationally difficult than SAT since an algorithm for SAT can stop once it has found a single satisfying assignment, whereas #SAT requires finding all such assignments. In fact, #SAT is complete for the class #P which is at least as hard as the polynomial-time hierarchy [10]. Not only is #SAT intrinsically important, it is also an excellent test-bed for algorithmic ideas in propositional reasoning. One of these new ideas is formula caching [7, 1, 5] which seems particularly promising when performed in the form called component caching [1, 2]. In component caching, disjoint components of the formula, generated dynamically during a DPLL search, are cached so that they only have to be solved once. While formula caching in general may have theoretical value even in SAT solvers [5], component caching seems to hold great promise for the practical improvement of #SAT algorithms (and Bayes inference) where there is more of a chance to reuse cached results. In particular, Bacchus, Dalmao, and Pitassi [1] discuss three different caching schemes: simple caching, component caching, and linear-space caching and show that component caching is theoretically competitive with the best of current methods for Bayesian inference (and substantially better in some instances). It has not been clear, however, whether component caching can be as competitive in practice as it is theoretically. We provide significant evidence that it can, demonstrating that on many instances it can outperform existing algorithms for #SAT by orders of magnitude. The key to this success is carefully incorporating component caching with clause learning, one of the most important ideas used in modern SAT solvers. Although both component caching and clause learning involve recording information collected during search, the nature and use of the recorded information is radically different. In clause learning, a clause that captures the reason for failure is computed from every failed search path. Component caching, on the other hand, stores the result computed when solving a subproblem. When that subproblem is encountered again its value can be retrieved from the cache rather than having to solve it again. It is not immediately obvious how to maintain correctness as well as obtain the best performance from a combination of these techniques. In this paper we show how this combination can be achieved so as to obtain the performance improvements just mentioned. Our model-counting program is built on the ZChaff SAT solver [8, 11]. ZChaff already implements clause learning, and we have added new modules and modified many others to support #SAT and to integrate component caching with clause learning. Ours is the first implementation we are aware of that is able to benefit from both component caching and clause learning. We have tested our program against the relsat [4, 3] system, which also performs component analysis, but does not cache the computed values of these components. In most instances of both random and structured problems our new solver is significantly faster than relsat, often by up to several orders of magnitude. 3 We begin by reviewing DPLL with caching for #SAT [1], and DPLL with learning for SAT. We then outline a basic approach for efficiently integrating component caching and clause learning. With this basic


title: Transductive Zero-Shot Learning With a Self-Training Dictionary Approach

abstract: As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data. To address this problem, ZSL is usually carried out in the following two aspects: 1) capturing the domain distribution connections between seen classes data and unseen classes data and 2) modeling the semantic interactions between the image feature space and the label embedding space. Motivated by these observations, we propose a bidirectional mapping-based semantic relationship modeling scheme that seeks for cross-modal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space. Namely, we have a bidirectional connection relationship that takes place from the image feature space to the latent space as well as from the label embedding space to the latent space. To deal with the domain shift problem, we further present a transductive learning approach that formulates the class prediction problem in an iterative refining process, where the object classification capacity is progressively reinforced through bootstrapping-based model updating over highly reliable instances. Experimental results on four benchmark datasets (animal with attribute, Caltech-UCSD Bird2011, aPascal-aYahoo, and SUN) demonstrate the effectiveness of the proposed approach against the state-of-the-art approaches.


title: Truncated Power Method for Sparse Eigenvalue Problems

abstract: This paper considers the sparse eigenvalue problem, which is to extract dominant (largest) sparse eigenvectors with at most $k$ non-zero components. We propose a simple yet effective solution called truncated power method that can approximately solve the underlying nonconvex optimization problem. A strong sparse recovery result is proved for the truncated power method, and this theory is our key motivation for developing the new algorithm. The proposed method is tested on applications such as sparse principal component analysis and the densest $k$-subgraph problem. Extensive experiments on several synthetic and real-world large scale datasets demonstrate the competitive empirical performance of our method.


title: Printed MWCNT-PDMS-Composite Pressure Sensor System for Plantar Pressure Monitoring in Ulcer Prevention

abstract: In this paper, a low cost and flexible plantar pressure monitoring system is presented that is suited for everyday use to prevent pressure ulcers. To define the technical specifications of the sensor system, a gait analysis study was carried out. Analysis of measured data based on a multivariate approach and neural network classification shows that it is possible to dissociate between healthy and unhealthy rollover patterns. To implement a pressure sensor element, a multiwalled carbon nanotube (MWCNT)-polydimethylsiloxane (PDMS)-composite was selected. A sensor matrix made of the composite was fully printed to enable pressure distribution measurements. As a final application, a printed insole was fabricated with six single MWCNT-PDMS pressure sensors that were situated on characteristic points of the insole to detect the unhealthy rollover patterns. Its functionality to measure plantar pressures was proven on a human foot inside a running shoe during walking.


Exploring Critical Success Factors for Enterprise Risk Management in Chinese Construction Firms


title: Novel Visual and Statistical Image Features for Microblogs News Verification

abstract: Microblog has been a popular media platform for reporting and propagating news. However, fake news spreading on microblogs would severely jeopardize its public credibility. To identify the truthfulness of news on microblogs, images are very crucial content. In this paper, we explore the key role of image content in the task of automatic news verification on microblogs. Existing approaches to news verification depend on features extracted mainly from the text content of news tweets, while image features for news verification are often ignored. According to our study, however, images are very popular and have a great influence on microblogs news propagation. In addition, fake and real news events have different image distribution patterns. Therefore, we propose several visual and statistical features to characterize these patterns visually and statistically for detecting fake news. Experiments on a real-world multimedia dataset collected from Sina Weibo validate the effectiveness of our proposed image features. The news verification performance of our method outperforms baseline methods. To the best of our knowledge, this is the first attempt that systematically explores image features on news verification task.


title: 03-915.qxd 8/7/03 5:29 PM Page 991 Spatial Metrics and Image Texture for Mapping Urban Land Use

abstract: The arrival of new-generation, high-spatial-resolution satellite imagery (e.g., Ikonos) has opened up new opportunities for detailed mapping and analysis of urban land use. Drawing on the traditional approach used in aerial photointerpretation, this study investigates an “object-oriented” method to classify a large urban area into detailed land-use categories. Spatial metrics and texture measures are used to describe the spatial characteristics of land-cover objects within each land-use region as derived from interpreted aerial photographs. In assessing how land-use categories vary in their spatial configuration, spatial metrics were found to provide the most important information for differentiating urban land uses. A detailed land-use map with nine categories was derived for the Santa Barbara South Coast Region area. Results from our work suggest that the region-based method exploiting spatial metrics and texture measurements is a potential new avenue to extract detailed urban land-use information from highresolution satellite imagery.


title: Binarising camera images for OCR

abstract: We describe a binarisation method designed specifically for OCR of low quality camera images: background surface thresholding or BST. This method is robust to lighting variations and produces images with very little noise and consistent stroke width. BST computes a "surface" of background intensities at every point in the image and performs adaptive thresholding based on this result. The surface is estimated by identifying regions of low-resolution text and interpolating neighbouring background intensities into these regions. The final threshold is a combination of this surface and a global offset. According to our evaluation BST produces considerably fewer OCR errors than Niblack's local average method while also being more runtime efficient.


title: An abnormal lymphatic phenotype is associated with subcutaneous adipose tissue deposits in Dercum's disease

abstract: OBJECTIVE ::: Investigational, near-infrared fluorescence (NIRF) lymphatic imaging was used to assess lymphatic architecture and contractile function in participants diagnosed with Dercum's disease, a rare, poorly understood disorder characterized by painful lipomas in subcutaneous adipose tissues. ::: ::: ::: METHODS ::: After informed consent and as part of an FDA-approved feasibility study to evaluate lymphatics in diseases in which their contribution has been implicated, three women diagnosed with Dercum's disease and four control subjects were imaged. Each participant received multiple intradermal and subcutaneous injections of indocyanine green (ICG, total dose ≤400 µg) in arms, legs, and/or trunk. Immediately after injection, ICG was taken up by the lymphatics and NIRF imaging was conducted. ::: ::: ::: RESULTS ::: The lymphatics in the participants with Dercum's disease were intact and dilated, yet sluggishly propelled lymph when compared to control lymphatics. Palpation of regions containing fluorescent lymphatic pathways revealed tender, fibrotic, tubular structures within the subcutaneous adipose tissue that were associated with painful nodules, and, in some cases, masses of fluorescent tissue indicating that some lipomas may represent tertiary lymphoid tissues. ::: ::: ::: CONCLUSIONS ::: These data support the hypothesis that Dercum's disease may be a lymphovascular disorder and suggest a possible association between abnormal adipose tissue deposition and abnormal lymphatic structure and function.


title: Increased inhibitory effects on close synergists during muscle fatigue in the decerebrate cat

abstract: We compared the magnitude of reflex inhibition induced in the soleus muscle by contraction or stretch of the medial gastrocnemius (MG), before, during, and after electrically induced fatigue of the MG. Our findings are that MG fatigue is accompanied by a substantial increase in soleus inhibition, which then recovers with MG rest. This increased inhibition may explain, at least in part, the decline in motoneuron discharge rate that has been described in fatiguing human muscle.


title: Using anchor texts with their hyperlink structure for web search

abstract: As a good complement to page content, anchor texts have been extensively used, and proven to be useful, in commercial search engines. However, anchor texts have been assumed to be independent, whether they come from the same Web site or not. Intuitively, an anchor text from unrelated Web sites should be considered as stronger evidence than that from the same site. This paper proposes two new methods to take into account the possible relationships between anchor texts. We consider two relationships in this paper: links from the same site and links from related sites. The importance assigned to the anchor texts in these two situations is discounted. Experimental results show that these two new models outperform the baseline model which assumes independence between hyperlinks.


title: Fuzzy Cognitive Map to model project management problems

abstract: Project management is a complex process impacted by numerous factors either from the external environment and/or internal factors completely or partially under the project manager's control. Managing projects successfully involves a complex amalgamation of comprehensive, informed planning, dynamic assessment and analysis of changes in external and internal factors, and the development and communication of updated strategies over the life of the project. Project management involves the interaction and analysis of many systems and requires the continuous integration and evaluation of large amounts of information. Fuzzy Cognitive Maps (FCM) allow us to encode project management knowledge and experiential results to create a useful model of the interacting systems. This paper covers the representation and development of a construction project management FCM that provides an integrated view of the most important concepts affecting construction project management and risk management. This paper then presents the soft computing approach of FCM to project management (PM) modeling and analysis. The resulting PM-FCM models the interaction of internal and external factors and offers an abstract conceptual model of interacting concepts for construction project management application.


title: Analyzing Complex Network User Arrival Patterns and Their Effect on Network Topologies

abstract: Complex networks have non-trivial characteristics and appear in many real-world systems. Due to their vital importance in a large number of research fields, various studies have offered explanations on how complex networks evolve, but the full underlying dynamics of complex networks are not completely understood. Many of the barriers to better understanding the evolution process of these networks can be removed with the emergence of new data sources. ::: This study utilizes the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus, which contains detailed information on the evolution process of 11,965 social networks. We used this dataset to study the effect of the patterns in which new users join a network (referred to as user arrival curves, or UACs) on the network topology. Our results present evidence that UACs are a central factor in molding a network's topology; that is, different arrival patterns create different topological properties. Additionally, we show that it is possible to uncover the types of user arrival patterns by analyzing a social network's topology. These results imply that existing complex network evolution models need to be revisited and modified to include user arrival patterns as input to the models, in order to create models that more accurately reflect real-world complex networks.


title: WebMate: a personal agent for browsing and searching

abstract: z Searching enhancement y Parallel search, searching keywords refinement, relevant feedback z Browsing assistant y Learning, Recommending, Alias, Monitoring bookmarks, More like, Sending page to friends, Prefetching hiperlinks z Offline browsing y downloading following pages, getting references and printing it out z Filtering HTTP header, cookie, block animation to speed up z Checking html page : error finding, dead links, z Dynamically setting up : search engines, dictionary z Programming in java WebMate architecture


title: Commonsense Knowledge in Machine Intelligence

abstract: There is growing conviction that the future of computing depends on our ability to exploit big data on theWeb to enhance intelligent systems. This includes encyclopedic knowledge for factual details, common sense for human-like reasoning and natural language generation for smarter communication. With recent chatbots conceivably at the verge of passing the Turing Test, there are calls for more common sense oriented alternatives, e.g., the Winograd Schema Challenge. The Aristo QA system demonstrates the lack of common sense in current systems in answering fourth-grade science exam questions. On the language generation front, despite the progress in deep learning, current models are easily confused by subtle distinctions that may require linguistic common sense, e.g.quick food vs. fast food. These issues bear on tasks such as machine translation and should be addressed using common sense acquired from text. Mining common sense from massive amounts of data and applying it in intelligent systems, in several respects, appears to be the next frontier in computing. Our brief overview of the state of Commonsense Knowledge (CSK) in Machine Intelligence provides insights into CSK acquisition, CSK in natural language, applications of CSK and discussion of open issues. This paper provides a report of a tutorial at a recent conference with a brief survey of topics.


title: A modified Lyapunov-function based control strategy for three-phase grid-connected VSI with LCL filter

abstract: In this paper, a modified Lyapunov-function based control strategy for three-phase grid-connected LCL-filtered voltage source inverter is presented. The grid-connected system is modeled in the synchronously rotating frame. Lyapunov-function based control is derived from the Lyapunov's direct method which guarantees the global stability of the closed-loop system. Although the conventional Lyapunov-function based control strategy offers global stability, it is not able to damp the inherent oscillations that occur on the grid current. Therefore, a modified Lyapunov-function based control strategy is proposed by adding the capacitor voltage feedback into the control loop. In the proposed control strategy, the measurement of inverter currents, capacitor voltages and grid currents are essential. The generation of reference functions in the d- and q-components can be achieved by using the reference d-component grid current. Computer simulation results are presented and reported to validate the proposed control approach.


title: P-Rank: An indicator measuring prestige in heterogeneous scholarly networks

abstract: Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way. © 2011 Wiley Periodicals, Inc.


Enabling secure and resource-efficient blockchain networks with VOLT


title: Speed improvement of object recognition using Boundary-Bitmap of histogram of oriented Gradients

abstract: Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means that significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. Moreover, some areas of the foreground also can be eliminated from the processing to improve the algorithm speed and memory wise. In Boundary-Bitmap HoG approach proposed in this paper, the boundary of irregular shape of the object is represented by a bitmap to avoid processing of extra background and (partially) foreground pixels. Bitmap, derived from the training dataset, encodes those portions of an image to be used to train a classifier. Experimental results show that not only the proposed algorithm decreases the workload associated with HoG/SVM classifiers by 92.5% compared to the state-of-the-art, but also it shows an average increase about 6% in recall and a decrease about 3% in precision in comparison with standard HoG.


title: Contextual Processing of Abstract Concepts Reveals Neural Representations of Nonlinguistic Semantic Content

abstract: Concepts develop for many aspects of experience, including abstract internal states and abstract social activities that do not refer to concrete entities in the world. The current study assessed the hypothesis that, like concrete concepts, distributed neural patterns of relevant nonlinguistic semantic content represent the meanings of abstract concepts. In a novel neuroimaging paradigm, participants processed two abstract concepts convince, arithmetic and two concrete concepts rolling, red deeply and repeatedly during a concept-scene matching task that grounded each concept in typical contexts. Using a catch trial design, neural activity associated with each concept word was separated from neural activity associated with subsequent visual scenes to assess activations underlying the detailed semantics of each concept. We predicted that brain regions underlying mentalizing and social cognition e.g., medial prefrontal cortex, superior temporal sulcus would become active to represent semantic content central to convince, whereas brain regions underlying numerical cognition e.g., bilateral intraparietal sulcus would become active to represent semantic content central to arithmetic. The results supported these predictions, suggesting that the meanings of abstract concepts arise from distributed neural systems that represent concept-specific content.


title: Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

abstract: In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.


title: A survey of collision avoidance approaches for unmanned aerial vehicles

abstract: The ability to integrate unmanned and manned aircraft into airspace is a critical capability that will enable growth in wide varieties of applications. Collision avoidance is a key enabler for the integration of manned and unmanned missions in civil and military operation theaters. Large efforts have been done to address collision avoidance problem to both manned and unmanned aircraft. However, there has been little comparative discussion of the proposed approaches. This paper presents a survey of the collision avoidance approaches those deployed for aircraft, especially for unmanned aerial vehicles. The collision avoidance concept is introduced together with proposing generic functions carried by collision avoidance systems. The design factors of the sense and avoid system, which are used to categorize methods, are explained deeply. Based on the design factors, several typical approaches are categorized.


title: Blockchains: How they work and why they'll change the world

abstract: Bitcoin was hatched as an act of defiance. Unleashed in the wake of the Great Recession, the cryptocurrency was touted by its early champions as an antidote to the inequities and corruption of the traditional financial system. They cherished the belief that as this parallel currency took off, it would compete with and ultimately dismantle the institutions that had brought about the crisis. Bitcoin's unofficial catchphrase, "In cryptography we trust," left no doubt about who was to blame: It was the middlemen, the bankers, the "trusted" third parties who actually couldn't be trusted. These humans simply got in the way of other humans, skimming profits and complicating transactions.


title: Attributed Network Embedding for Learning in a Dynamic Environment

abstract: Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.


title: A High Efficiency ZCS-ZVS Buck Converter with Coupled Inductor

abstract: A novel topology for a soft-switching buck dc– dc converter with a coupled inductor is proposed. The soft-switching buck converter has advantages over the traditional hard- switching converters. The most significant advantage is that it offers a lower switching loss. This converter operates under a zero-current switching condition at turn on and a zero-voltage switching condition at turn off. It presents the circuit configuration with a least components for realizing soft switching. Because of soft switching, the proposed converter can attain a high efficiency under heavy load conditions. Likewise, a high efficiency is also attained under light load conditions, which is significantly different from other soft switching buck converters


title: An Adaptive Hierarchical Next-Best-View Algorithm for 3D Reconstruction of Indoor Scenes

abstract: We present a new and efficient next-best-view algorithm for 3D reconstruction of indoor scenes using active range sensing. To evaluate each view, we have formulated a general optimization metric function that can include many real-world acquisition constraints and quality requirements on the resulting 3D model. We overcome the computational difficulty of evaluating this function by using an adaptive hierarchical approach to exploit the various spatial coherences inherent in the acquisition constraints and quality requirements. Experimental results show large speedups over the straightforward method used by many previous algorithms. The approach allows us to compute with a highly-detailed partial scene model and to exhaustively sample the entire 3D view space at high resolution. Our hierarchical view evaluation algorithm can also take into account each view’s sensitivity to the potential scanner positioning errors. We have also developed a metric to estimate whether the scan to be acquired from each candidate view can be accurately registered to the previous scans.


title: MAC Protocols for IEEE 802.11ax: Avoiding Collisions on Dense Networks

abstract: Wireless networks have become the main form of Internet access. Statistics show that the global mobile Internet penetration should exceed 70\% until 2019. Wi-Fi is an important player in this change. Founded on IEEE 802.11, this technology has a crucial impact in how we share broadband access both in domestic and corporate networks. However, recent works have indicated performance issues in Wi-Fi networks, mainly when they have been deployed without planning and under high user density. Hence, different collision avoidance techniques and Medium Access Control protocols have been designed in order to improve Wi-Fi performance. Analyzing the collision problem, this work strengthens the claims found in the literature about the low Wi-Fi performance under dense scenarios. Then, in particular, this article overviews the MAC protocols used in the IEEE 802.11 standard and discusses solutions to mitigate collisions. Finally, it contributes presenting future trends in MAC protocols. This assists in foreseeing expected improvements for the next generation of Wi-Fi devices.


title: A Study of the Scrum Master's Role

abstract: Scrum is an increasingly common approach to software development adopted by organizations around the world. However, as organizations transition from traditional plan-driven development to agile development with Scrum, the question arises as to which Scrum role (Product Owner, Scrum Master, or Scrum Team Member) corresponds to a Project Manager, or conversely which Scrum role should the Project Managers adopt? In an attempt to answer this question, we adopted a mixed-method research approach comprising a systematic literature review and embedded case study of a commercial software development team. Our research has identified activities that comprise the Scrum Master role, and which additional roles are actually performed by Scrum Masters in practice. We found nine activities that are performed by Scrum Masters. In addition, we found that Scrum Masters also perform other roles, most importantly as Project Managers. This latter situation results in tension and conflict of interest that could have a negative impact on the performance of the team as a whole. These results point to the need to re-assess the role of Project Managers in organizations that adopt Scrum as a development approach. We hypothesize that it might be better for Project Managers to become Product Owners, as aspects of this latter role are more consistent with the traditional responsibilities of a Project Manager.


title: Evaluating the word-expert approach for Named-Entity Disambiguation

abstract: Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia. This task is closely related to word-sense disambiguation (WSD), where the supervised word-expert approach has prevailed. In this work we present the results of the word-expert approach to NED, where one classifier is built for each target entity mention string. The resources necessary to build the system, a dictionary and a set of training instances, have been automatically derived from Wikipedia. We provide empirical evidence of the value of this approach, as well as a study of the differences between WSD and NED, including ambiguity and synonymy statistics.


title: On the segmentation of 3D LIDAR point clouds

abstract: This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.


title: Forecasting exchange rate with deep belief networks

abstract: Forecasting exchange rates is an important financial problem which has received much attention. Nowadays, neural network has become one of the effective tools in this research field. In this paper, we propose the use of a deep belief network (DBN) to tackle the exchange rate forecasting problem. A DBN is applied to predict both British Pound/US dollar and Indian rupee/US dollar exchange rates in our experiments. We use six evaluation criteria to evaluate its performance. We also compare our method to a feedforward neural network (FFNN), which is the state-of-the-art method for forecasting exchange rate with neural networks. Experiments indicate that deep belief networks (DBNs) are applicable to the prediction of foreign exchange rate, since they achieve better performance than feedforward neural networks (FFNNs).


title: An overview of issues and application in credit scoring

abstract: Innovative storage technology and the rising popularity of the Internet have generated an ever-growing amount of data. In this vast amount of data much valuable knowledge is available, yet it is hidden. The Support Vector Machine (SVM) is a state-of-the-art classification technique that generally provides accurate models, as it is able to capture non-linearities in the data. However, this strength is also its main weakness, as the generated non-linear models are typically regarded as incomprehensible black-box models. By extracting rules that mimic the black box as closely as possible, we can provide some insight into the logics of the SVM model. This explanation capability is of crucial importance in any domain where the model needs to be validated before being implemented, such as in credit scoring (loan default prediction) and medical diagnosis. If the SVM is regarded as the current state-of-the-art, SVM rule extraction can be the state-of-the-art of the (near) future. This chapter provides an overview of recently proposed SVM rule extraction techniques, complemented with the pedagogical Artificial Neural Network (ANN) rule extraction techniques which are also suitable for SVMs. Issues related to this topic are the different rule outputs and corresponding rule expressiveness; the focus on high dimensional data as SVM models typically perform well on such data; and the requirement that the extracted rules are in line with existing domain knowledge. These issues are explained and further illustrated with a credit scoring case, where we extract a Trepan tree and a RIPPER rule set from the generated SVM model. The benefit of decision tables in a rule extraction context is also demonstrated. Finally, some interesting alternatives for SVM rule extraction are listed.


title: Ontologies in Ubiquitous Computing

abstract: Ontologies are an explicit specification of a conceptualization, that is understood to be an abstract and simplified version of the world to be represented. In recent years, ontologies have been used in Ubiquitous Computing, especially for the development of context-aware applications. In this paper, we offer a taxonomy for classifying ontologies used in Ubiquitous Computing, in which two main categories are distinguished: Domain ontologies, created to represent and communicate agreed knowledge within some sub-domain of Ubiquitous Computing; and Ontologies as software artifacts, when ontologies play the role of an additional type of artifact in ubiquitous computing applications. The latter category is subdivided according with the moment in that ontologies are used: at development time or at run time. Also, we analyze and classify (based on this taxonomy) some recently published works.


title: Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions

abstract: Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability


title: Abstract Empirical evaluation of the revised end user computing acceptance model

abstract: This paper proposed a revised technology acceptance model for measuring end user computing (EUC) acceptance. An empirical study was conducted to collect data. This data was empirically used to test the proposed research model. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. The results demonstrate that the model explains 56% of the variance. This finding contributes to an expanded understanding of the factors that promote EUC acceptance. The implication of this work to both researchers and practitioners is discussed.


title: Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links

abstract: This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.


title: Fast Visualization of Complex 3D Models Using Displacement Mapping The-Kiet Lu ∗ Nanyang Technological University

abstract: We present a simple method to render complex 3D models at interactive rates using real-time displacement mapping. We use an octree to decompose the 3D model into a set of height fields and display the model by rendering the height fields using per-pixel displacement mapping. By simply rendering the faces of the octree voxels to produce fragments for ray-casting on the GPU, and with straightforward transformation of view rays to the displacement map's local space, our method is able to accurately render the object's silhouettes with very little special handling. The algorithm is especially suitable for fast visualization of high-detail point-based models, and models made up of unprocessed triangle meshes that come straight from range scanning. This is because our method requires much less preprocessing time compared to the traditional triangle-based rendering approach, which usually needs a large amount of computation to preprocess the input model into one that can be rendered more efficiently. Unlike the point-based rendering approach, the rendering efficiency of our method is not limited by the number of input points. Our method can achieve interactive rendering of models with more than 300 millions points on standard graphics hardware.


title: Do people use reverse psychology? An exploration of strategic self-anticonformity

abstract: This research examined reports of the real-world use of reverse psychology, or what we term strategic self-anticonformity (SSA). In Study 1, participants reported examples in which they engaged in SSA and rated the success and frequency of use of this influence tactic. These data suggested the existence of two forms of SSA, one used as a general persuasion tactic and one used specifically to garner interpersonal reassurance. Study 2 compared the prevalence of these two forms of SSA with the use of three previously established influence tactics (i.e., door-in-the-face, foot-in-the-door, and disrupt-then-reframe). The results suggest that SSA is a prevalent, real-world influence tactic deserving of further study.


title: Millimeter-Wave MIMO Balanced Antipodal Vivaldi Antenna Design for Autonomous Cars

abstract: This paper introduces a millimeter-wave multiple-input-multiple-output (MIMO) antenna for autonomous (self-driving) cars. The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view. By using four antennas of this kind on four corners of the car's bumper, it is possible to have a full 360 deg view around the car. The designed antenna is simulated by two commercially full-wave packages and the results indicate that the proposed method can successfully bring the required 90 deg angle of view.


title: Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

abstract: Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator's driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity.


title: Is memory schematic

abstract: ion Information that has been selected because it is important and/or relevant to the schema is further reduced during the encoding process by abstraction. This process codes the meaning but not the format of a message (e.g., Bobrow, 1970; Bransford, Barclay, & Franks, 1972). Thus, details such as the lexical form of an individual word (e.g., Schank, 1972, 1976) and the syntactic form of a sentence (e.g., Sachs, 1967) will not be preserved in memory. Because memory for syntax appears to be particularly sparce as well as brief (e.g., J. R. Anderson, 1974; Begg & Wickelgren, 1974; Jarvella, 1971; Sachs, 1967, 1974), the abstraction process is thought to operate during encoding. Additional support for the notion that what is stored is an abstracted representation of the original stimulus comes from studies that demonstrate that after a passage is read, it takes subjects the same amount of time to verify information originally presented in a complex linguistic format as it does to verify that same information presented in a simpler format (e.g., King & Greeno, 1974; Kintsch M Bransford, et al., 1972; Brewer, 1975; Frederiksen, 1975a; Kintsch, 1974; Norman & Rumelhart, 1975; Schank, 1972, 1976). One formalized presentation of this idea is Schank's conceptual dependency theory (1972). The theory asserts that all propositions can be expressed by a small set of primitive concepts. All lexical expressions that share an identical meaning will be represented in one way (and so stored economically) regardless of their presentation format. As a result people should often incorrectly recall or misrecognize synonyms of originally presented words, and they do (e.g., Anderson & Bower, 1973; R. C. Anderson, 1974; Anisfeld & Knapp, 1968; Brewer, 1975; Graesser, 1978b; Sachs, 1974). Abstraction and memory theories. Since considerable detail is lost via the abstraction process, this process can easily account for the incompleteness that is characteristic of people's recall of complex events. In light of the abstraction process, the problem for schema theories becomes one of accounting for accurate recall. Schema theories do this by borrowing a finding from psycholinguistic research, to wit, that speakers of a language share preferred ways of expressing information. If both the creator and perceiver of a message are operating with the same preferences or under the same biases, the perceiver's reproduction of the input may appear to be accurate. The accuracy, however, is the product of recalling the semantic content of the message and imposing the preferred structure onto it. Thus, biases operate in a manner that is similar to the "probable detail" reconstruction process. Biases have been documented for both syntactic information (J. R. Anderson, 1974; Bock, 1977; Bock & Brewer, 1974; Clark & Clark, 1968; James, Thompson, & Baldwin, 1973) and lexical information (Brewer, 1975; Brewer & Lichtenstein, 1974). Distortions may result from the abstraction process if biases are not shared by the person who creates the message and the one who receives it. More importantly, the ab-


title: Transcriptional fingerprints of antigen-presenting cell subsets in the human vaginal mucosa and skin reflect tissue-specific immune microenvironments

abstract: BackgroundDendritic cells localize throughout the body, where they can sense and capture invading pathogens to induce protective immunity. Hence, harnessing the biology of tissue-resident dendritic cells is fundamental for the rational design of vaccines against pathogens.MethodsHerein, we characterized the transcriptomes of four antigen-presenting cell subsets from the human vagina (Langerhans cells, CD14- and CD14+ dendritic cells, macrophages) by microarray, at both the transcript and network level, and compared them to those of three skin dendritic cell subsets and blood myeloid dendritic cells.ResultsWe found that genomic fingerprints of antigen-presenting cells are significantly influenced by the tissue of origin as well as by individual subsets. Nonetheless, CD14+ populations from both vagina and skin are geared towards innate immunity and pro-inflammatory responses, whereas CD14- populations, particularly skin and vaginal Langerhans cells, and vaginal CD14- dendritic cells, display both Th2-inducing and regulatory phenotypes. We also identified new phenotypic and functional biomarkers of vaginal antigen-presenting cell subsets.ConclusionsWe provide a transcriptional database of 87 microarray samples spanning eight antigen-presenting cell populations in the human vagina, skin and blood. Altogether, these data provide molecular information that will further help characterize human tissue antigen-presenting cell lineages and their functions. Data from this study can guide the design of mucosal vaccines against sexually transmitted pathogens.


title: Synthesis of core-shell poly(divinylbenzene) microspheres via reversible addition fragmentation chain transfer graft polymerization of styrene

abstract: Styrene has been grafted from crosslinked poly(divinylbenzene) core microspheres by both reversible addition fragmentation chain transfer (RAFT) polymerization and conventional free radical polymerization. The core microspheres were prepared by precipitation polymerization. Crosslinked poly(DVB) core microspheres containing double bonds on the particle surface can be used directly to graft polymers from the surface by RAFT without prior modification of the core microspheres. The RAFT agent 1-phenylethyl dithiobenzoate (PEDB) was used: Particle sizes increased from 2 mum up to 3.06 mum, and the particle weight increased by up to 6.5%. PEDB controls the particle weight gain, the particle volume, and the molecular weight of the soluble polymer. PEDB was also used to synthesize core poly(DVB) RAFT microspheres that contain residual RAFT end groups on the surface and within the particle. Styrene was subsequently grafted from the surface of these core poly(DVB) RAFT microspheres. The generated microspheres were characterized by H-1-NMR spectroscopy, focused ion beam (FIB) milling, Coulter particle sizing, and size exclusion chromatography. (C) 2004 Wiley Periodicals, Inc.


title: Light load efficiency improvement for PFC

abstract: With the fast growing information technologies, high efficiency AC-DC front-end power supplies are becoming more and more desired in all kinds of distributed power system applications due to the energy conservation consideration. For the power factor correction (PFC) stage, the conventional constant frequency average current mode control has very low efficiency at light load due to high switching frequency related loss. The constant on-time control for PFC features the automatic reduction of switching frequency at light load, resulting improved light load efficiency. However, lower heavy load efficiency of the constant on-time control is observed because of very high frequency at Continuous Conduction Mode (CCM). By carefully comparing the on-time and frequency profiles between constant on-time and constant frequency control, a novel adaptive on-time control is proposed to improve the light load efficiency without sacrificing the heavy load efficiency. The performance of the adaptive on-time control is verified by experiment.


title: Design of three-phase line impedance stabilization networks for EMC pre-compliance testing laboratories

abstract: In this paper, the design procedure of three-phase line impedance stabilization networks (LISN) for EMC pre-compliance testing laboratories is proposed. The three-phase LISN schematics and the detail of its components are provided. Finally, the experimental results demonstrating the performances of proposed three-phase LISN are given.


title: Algorithmic Trading

abstract: In electronic financial markets, algorithmic trading refers to the use of computer programs to automate one or more stages of the trading process: pretrade analysis (data analysis), trading signal generation (buy and sell recommendations), and trade execution. Trade execution is further divided into agency/broker execution (when a system optimizes the execution of a trade on behalf of a client) and principal/proprietary trading (where an institution trades on its own account). Each stage of this trading process can be conducted by humans, by humans and algorithms, or fully by algorithms.


title: Discovery of the Twitter Bursty Botnet

abstract: Many Twitter users are bots. They can be used for spamming, opinion manipulation and online fraud. Recently we discovered the Star Wars botnet, consisting of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The bots were exposed because they tweeted uniformly from any location within two rectangle-shaped geographic zones covering Europe and the USA, including sea and desert areas in the zones. In this paper, we report another unusual behaviour of the Star Wars bots, that the bots were created in bursts or batches, and they only tweeted in their first few minutes since creation. Inspired by this observation, we discovered an even larger Twitter botnet, the Bursty botnet with more than 500,000 bots. Our preliminary study showed that the Bursty botnet was directly responsible for a large-scale online spamming attack in 2012. Most bot detection algorithms have been based on assumptions of `common' features that were supposedly shared by all bots. Our discovered botnets, however, do not show many of those features; instead, they were detected by their distinct, unusual tweeting behaviours that were unknown until now.


title: CMOS compatible thin wafer processing using temporary mechanical wafer, adhesive and laser release of thin chips/wafers for 3D integration

abstract: This paper reports a thin wafer handling technology that is compatible to CMOS processing conditions to enable 3D integration and assembly with high throughput at low cost. Using pulsed ultraviolet (UV) radiation from excimer lasers, device wafers as thin as 50µm can be released from the temporary mechanical handler wafer in less than 1min. Bonding, adhesive, debonding and post debond clean processes were demonstrated. CMOS circuit test vehicles were shown to be compatible with this temporary bonding and debonding processes.


title: Reliability of Solid-State Drives Based on NAND Flash Memory

abstract: This paper reviews the reliability of solid-state drives (SSDs) based on NAND Flash memory from the perspectives of failure mechanisms, design mitigations, qualification methods, and field failure rates. NAND reliability is dominated by gradual memory-cell degradation in late life and defects such as interconnect shorts earlier in life. Design mitigations exist for these mechanisms. Qualification methods standardized in JEDEC JESD218 are designed to evaluate the mechanisms and mitigations, over a full drive lifetime, in the laboratory. Full-lifetime qualification provides confidence in the long-term reliability of SSDs that cannot be achieved by the early-life qualifications performed on hard disk drives. If NAND mechanisms are sufficiently suppressed, field reliability will be dominated by non-NAND mechanisms such as firmware bugs, power-loss events, radiation-induced soft errors, and failures in non-NAND components. A wide range of design validation and qualification tests are necessary to evaluate these non-NAND mechanisms. Published field reliability statistics indicate that SSDs are more reliable on average than HDDs, but they are not immune to failure, and there is wide variation among models. The NAND and non-NAND mechanisms are illustrated through new case studies of SSD internal qualification and field reliability data.


title: Study of a 6DOF robot assisted ultrasound scanning system and its simulated control handle

abstract: Ultrasound scanning is a commonly used diagnostic method in hospital because of its' high efficiency and limited side effects. In this paper, a robot assisted ultrasound scanning system was developed to help doctors do the remote diagnosis. Doctor could control the ultrasound probe through the Internet from a remote place and receive the ultrasound image in real time. Simultaneously, the robot system does not need to change the doctors' habits for the ultrasound diagnosis and it could be used by doctors quickly.


title: Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges

abstract: To achieve the expected 1000x data rates under the exponential growth of traffic demand, a large number of BSs or APs will be deployed in 5G wireless systems to support high data rate services and to provide seamless coverage. Although such BSs are expected to be small-scale with lower power, the aggregated energy consumption of all BSs would be remarkable, resulting in increased environmental and economic concerns. In existing cellular networks, turning off the underutilized BSs is an efficient approach to conserve energy while preserving the QoS of mobile users. However, in 5G systems with new physical layer techniques and highly heterogeneous network architecture, new challenges arise in the design of BS ON-OFF switching strategies. In this article, we begin with a discussion of the inherent technical challenges of BS ON-OFF switching. We then provide a comprehensive review of recent advances on switching mechanisms in different application scenarios. Finally, we present open research problems and conclude the article.


The effect of mechanical action on wood and fiber components


title: The Numeric Rating Scale for Clinical Pain Measurement: A Ratio Measure?

abstract: ▪ Abstract: The Numeric Rating Scale (NRS-11) has been widely used clinically for the assessment of pain. Its use for clinical research is controversial. Reports differ as to whether or not the NRS-11 should be treated as a ratio pain measurement tool. This study compared the NRS-11 to a ratio measure for pain assessment: the visual analog scale (VAS). Simultaneous pain measurements using these 2 scales were compared in clinical situations commonly encountered in a tertiary community hospital. Whereas linear relationships were noted in laboring patients and in postoperative patients with thoracic or abdominal incisions during cough, no such correlations were noted for the same postoperative patients at rest or for postoperative orthopedic patients. The NRS-11 should not be considered to be interchangeable with the VAS. Its use for clinical research should be limited to situations where it has specifically demonstrated linear properties. ▪


title: On the Security of an Efficient Mobile Authentication Scheme for Wireless Networks

abstract: Tang and Wu proposed an efficient mobile authentication scheme for wireless networks, and claimed the scheme can effectively defend all known attacks to mobile networks including the denial-of-service attack. This article shows an existential replication attack on the scheme and, as a result, an attacker can obtain the communication key between a mobile user and the accessed VLR. Fortunately, we improve its security in this paper.


title: TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering

abstract: Vision and language understanding has emerged as a subject undergoing intense study in Artificial Intelligence. Among many tasks in this line of research, visual question answering (VQA) has been one of the most successful ones, where the goal is to learn a model that understands visual content at region-level details and finds their associations with pairs of questions and answers in the natural language form. Despite the rapid progress in the past few years, most existing work in VQA have focused primarily on images. In this paper, we focus on extending VQA to the video domain and contribute to the literature in three important ways. First, we propose three new tasks designed specifically for video VQA, which require spatio-temporal reasoning from videos to answer questions correctly. Next, we introduce a new large-scale dataset for video VQA named TGIF-QA that extends existing VQA work with our new tasks. Finally, we propose a dual-LSTM based approach with both spatial and temporal attention, and show its effectiveness over conventional VQA techniques through empirical evaluations.


Beyond Autograding: Advances in Student Feedback Platforms


title: Preventing Private Information Inference Attacks on Social Networks

abstract: Online social networks, such as Facebook, are increasingly utilized by many people. These networks allow users to publish details about themselves and to connect to their friends. Some of the information revealed inside these networks is meant to be private. Yet it is possible to use learning algorithms on released data to predict private information. In this paper, we explore how to launch inference attacks using released social networking data to predict private information. We then devise three possible sanitization techniques that could be used in various situations. Then, we explore the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. We show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described.


title: Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

abstract: SGD is the widely adopted method to train CNN. Conceptually it approximates the population with a randomly sampled batch; then it evenly trains batches by conducting a gradient update on every batch in an epoch. In this paper, we demonstrate Sampling Bias, Intrinsic Image Difference and Fixed Cycle Pseudo Random Sampling differentiate batches in training, which then affect learning speeds on them. Because of this, the unbiased treatment of batches involved in SGD creates improper load balancing. To address this issue, we present Inconsistent Stochastic Gradient Descent (ISGD) to dynamically vary training effort according to learning statuses on batches. Specifically ISGD leverages techniques in Statistical Process Control to identify a undertrained batch. Once a batch is undertrained, ISGD solves a new subproblem, a chasing logic plus a conservative constraint, to accelerate the training on the batch while avoid drastic parameter changes. Extensive experiments on a variety of datasets demonstrate ISGD converges faster than SGD. In training AlexNet, ISGD is 21.05\% faster than SGD to reach 56\% top1 accuracy under the exactly same experiment setup. We also extend ISGD to work on multiGPU or heterogeneous distributed system based on data parallelism, enabling the batch size to be the key to scalability. Then we present the study of ISGD batch size to the learning rate, parallelism, synchronization cost, system saturation and scalability. We conclude the optimal ISGD batch size is machine dependent. Various experiments on a multiGPU system validate our claim. In particular, ISGD trains AlexNet to 56.3% top1 and 80.1% top5 accuracy in 11.5 hours with 4 NVIDIA TITAN X at the batch size of 1536.


Configuration of Flight Test Telemetry Frame Formats


N ov 2 01 8 Adaptive Task Allocation for Mobile Edge Learning


title: Towards Automated Semi-Supervised Learning

abstract: Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i.e., without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.


title: The impact of lossless image compression to radiographs

abstract: The increasing number of digital imaging modalities results in data volumes of several Tera Bytes per year that must be ::: transferred and archived in a common-sized hospital. Hence, data compression is an important issue for picture ::: archiving and communication systems (PACS). The effect of lossy image compression is frequently analyzed with ::: respect to images from a certain modality supporting a certain diagnosis. However, novel compression schemes have ::: been developed recently allowing efficient but lossless compression. ::: In this study, we compare the lossless compression schemes embedded in the tagged image file format (TIFF), graphics ::: interchange format (GIF), and Joint Photographic Experts Group (JPEG 2000 II) with the Borrows-Wheeler ::: compression algorithm (BWCA) with respect to image content and origin. Repeated measures ANOVA was based on ::: 1.200 images in total. ::: Statistically significant effects (p < 0,0001) of compression scheme, image content, and image origin were found. Best ::: mean compression factor of 3.5 (2.272 bpp) is obtained applying BTW to secondarily digitized radiographs of the head, ::: while the lowest factor of 1,05 (7.587 bpp) resulted from the TIFF packbits algorithm applied to pelvis images captured ::: digitally. Over all, the BWCA is slightly but significantly more effective than JPEG 2000. Both compression schemes ::: reduce the required bits per pixel (bpp) below 3. Also, secondarily digitized images are more compressible than the ::: directly digital ones. Interestingly, JPEG outperforms BWCA for directly digital images regardless of image content, ::: while BWCA performs better than JPEG on secondarily digitized radiographs. In conclusion, efficient lossless image ::: compression schemes are available for PACS.


title: Fast switching of drift step recovery diodes based on all epi-Si growth

abstract: DSRDs are fast HV opening switching devices. Traditionally, these deep junction devices are fabricated on silicon wafers by deep diffusion. We present DSRD results based on silicon epitaxial layers with as-grown junctions. Static measurements showed a rectifying behavior with leakage currents proportional to device dimension. Pulsed power measurements showed that the switching rate was dependant on the DSRD current density.


title: Articulated Multi-person Tracking in the Wild

abstract: In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is several orders of magnitude faster. We achieve this in two ways: (1) by simplifying and sparsifying the body-part relationship graph and leveraging recent methods for faster inference, and (2) by offloading a substantial share of computation onto a feed-forward convolutional architecture that is able to detect and associate body joints of the same person even in clutter. We use this model to generate proposals for body joint locations and formulate articulated tracking as spatio-temporal grouping of such proposals. This allows to jointly solve the association problem for all people in the scene by propagating evidence from strong detections through time and enforcing constraints that each proposal can be assigned to one person only. We report results on a public MPII Human Pose benchmark and on a new dataset of videos with multiple people. We demonstrate that our model achieves state-of-the-art results while using only a fraction of time and is able to leverage temporal information to improve state-of-the-art for crowded scenes.


title: Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China

abstract: The boom in bike-sharing is receiving growing attention as societies become more aware of the importance of active non-motorized traffic modes. However, the low usage of this transport mode in China raises concerns. The primary objective of this study is to explore factors affecting bike-sharing usage and satisfaction degree of bike-sharing among the bike-sharing user population in China. Data were collected by a questionnaire survey in Ningbo. A bivariate ordered probit (BOP) model was developed to examine simultaneously those factors associated with both bike-sharing usage and satisfaction degree of bike-sharing among users. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results showed that the BOP model can account for commonly shared unobserved characteristics within usage and satisfaction of bike-sharing. The BOP model results showed that the usage of bike-sharing was affected by gender, household bicycle/e-bike ownership, trip model, travel time, bike-sharing stations location, and users’ perception of bike-sharing. The satisfaction degree of bike-sharing was affected by household income, bike-sharing stations location, and users’ perception of bike-sharing. It is also found that bike-sharing usage and satisfaction degree are strongly correlated and positive in direction. The results can enhance our comprehension of the factors that affect usage and satisfaction degree of bike-sharing. Based on the results, some suggestions regarding planning, engineering, and public advocacy were discussed to increase the usage of bike-sharing in Ningbo, China.


title: Working memory,short-term memory, and general fluid intellegence : A latent-variable approach

abstract: A study was conducted in which 133 participants performed 11 memory tasks (some thought to reflect working memory and some thought to reflect short-term memory), 2 tests of general fluid intelligence, and the Verbal and Quantitative Scholastic Aptitude Tests. Structural equation modeling suggested that short-term and working memories reflect separate but highly related constructs and that many of the tasks used in the literature as working memory tasks reflect a common construct. Working memory shows a strong connection to fluid intelligence, but short-term memory does not. A theory of working memory capacity and general fluid intelligence is proposed: The authors argue that working memory capacity and fluid intelligence reflect the ability to keep a representation active, particularly in the face of interference and distraction. The authors also discuss the relationship of this capability to controlled attention, and the functions of the prefrontal cortex.


title: Personalizing automated image annotation using cross-entropy

abstract: Annotating the increasing amounts of user-contributed images in a personalized manner is in great demand. However, this demand is largely ignored by the mainstream of automated image annotation research. In this paper we aim for personalizing automated image annotation by jointly exploiting personalized tag statistics and content-based image annotation. We propose a cross-entropy based learning algorithm which personalizes a generic annotation model by learning from a user's multimedia tagging history. Using cross-entropy-minimization based Monte Carlo sampling, the proposed algorithm optimizes the personalization process in terms of a performance measurement which can be flexibly chosen. Automatic image annotation experiments with 5,315 realistic users in the social web show that the proposed method compares favorably to a generic image annotation method and a method using personalized tag statistics only. For 4,442 users the performance improves, where for 1,088 users the absolute performance gain is at least 0.05 in terms of average precision. The results show the value of the proposed method.


title: A pediatric specific shock index in combination with GMS identifies children with life threatening or severe traumatic brain injury

abstract: Purpose ::: We have previously demonstrated that a shock index, pediatric age adjusted (SIPA) accurately identifies severely blunt injured children. We aimed to determine if SIPA could more accurately identify children with severe traumatic brain injury (TBI) than hypotension alone.


title: A cryptanalytic attack on Knapsack cipher using Differential Evolution algorithm

abstract: The paper presents a cryptanalytic attack on Merkle-Hellman Knapsack cipher using Differential Evolution. The Differential Evolution is a stochastic, population based optimization search strategy and uses three typical operators, mutation, crossover and selection, to search the solution space. An initial population is created and a new generation is generated by applying the operators until the solution is obtained. The results of Differential Evolution are compared with the results of Genetic Algorithm (GA) for cryptanalysis of the knapsack cipher. Experimental results show that cryptanalysis of Merkle Hellman Knapsack cipher using Differential Evolution is a better technique than using Genetic Algorithm for this purpose.


title: Survey Paper: A survey on the communication architectures in smart grid

abstract: The next-generation electric power systems (smart grid) are studied intensively as a promising solution for energy crisis. One important feature of the smart grid is the integration of high-speed, reliable and secure data communication networks to manage the complex power systems effectively and intelligently. We provide in this paper a comprehensive survey on the communication architectures in the power systems, including the communication network compositions, technologies, functions, requirements, and research challenges. As these communication networks are responsible for delivering power system related messages, we discuss specifically the network implementation considerations and challenges in the power system settings. This survey attempts to summarize the current state of research efforts in the communication networks of smart grid, which may help us identify the research problems in the continued studies.


title: Autonomously constructing hierarchical task networks for planning and human-robot collaboration

abstract: Collaboration between humans and robots requires solutions to an array of challenging problems, including multi-agent planning, state estimation, and goal inference. There already exist feasible solutions for many of these challenges, but they depend upon having rich task models. In this work we detail a novel type of Hierarchical Task Network we call a Clique/Chain HTN (CC-HTN), alongside an algorithm for autonomously constructing them from topological properties derived from graphical task representations. As the presented method relies on the structure of the task itself, our work imposes no particular type of symbolic insight into motor primitives or environmental representation, making it applicable to a wide variety of use cases critical to human-robot interaction. We present evaluations within a multi-resolution goal inference task and a transfer learning application showing the utility of our approach.


title: A Scan Statistics Based Suspicious Transactions Detection Model for Anti-money Laundering (AML) in Financial Institutions

abstract: Developing effective suspicious activity detection models has drawn more and more interests for supervision agencies and financial institutions in their efforts to combat money laundering. Most previous AML systems were mainly rule-based which suffered from low efficiency and also could be easily learned and evaded by money launders. While most machine learning models for AML were focused on individual level. Our paper proposes a suspicious activity recognition method basing on scan statistics, it aims to identify suspicious sequences on transaction level for financial institutions. In the end, we evaluate our algorithm using real financial data from commercial banks. And the initial experiment results demonstrate the efficiency of our approach.


title: IDENTIFYING GENERIC AND LOCAL CRITICAL SUCCESS FACTORS FOR ENTERPRISE RESOURCE PLANNING (ERP) SYSTEM IMPLEMENTATION IN PAKISTAN

abstract: The main purpose of this article is to empirically identify and evaluate generic and local critical success factors (CSFs) important for enterprise resource planning (ERP) system implementation in Pakistan. To conduct this research, we have used a three-tiered approach. In the first tier, generic critical success factors mentioned in exiting literature are reviewed. In the second tier, based on primary empirical work, we have identified 17 critical success factors from the aforementioned generic factors. In the third tier, we have added a local factor of “Computer literacy training to illiterate end user” to these 17 generic factor and have conducted secondary empirical work. A 10 point Likert rating scale was used to collect degree of agreement from 25 project participants. As a major finding, the five most important factors, according to Pakistani organisations, were top management support, project management, clear goals and objectives, careful selection of ERP software, and data accuracy. However, the local factor “computer literacy training to illiterate end users” was also critical and was more important compared to factors such as steering committee, management of expectation, ERP vendor support, and project champion. The least important factors were project champion, use of steering committee, and ERP vendor support. Keyword: Enterprise resource planning (ERP), Critical success factor, Usability


title: A Wideband Circularly Polarized Rectangular Dielectric Resonator Antenna Excited by an Archimedean Spiral Slot

abstract: A wideband circularly polarized (CP) rectangular dielectric resonator antenna (DRA) is presented. An Archimedean spiral slot is used to excite the rectangular DRA for wideband CP radiation. The operating principle of the proposed antenna is based on using a broadband feeding structure to excite the DRA. A prototype of the proposed antenna is designed, fabricated, and measured. Good agreement between the simulated and measured results is attained, and a wide 3-dB axial-ratio (AR) bandwidth of 25.5% is achieved.


title: Social Robots and Virtual Humans as Assistive Tools for Improving Our Quality of Life

abstract: Growing numbers of aging people result in great demand for social robots and virtual humans to support health care and independent life for a long time at home. We provide an overview of various actual potential applications at home in the literature concerning social robots and virtual humans. In addition, we report our latest research progress in our institute, i.e., Social communication, falling-down detection, affect computing, attention capture. All the research and applications demonstrate that the social robots and virtual humans can provide a better daily life in home environment.


title: A Survey of Surface-Based Illustrative Rendering for Visualization

abstract: In this article we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub-area of visualization. For the remainder of the survey we then focus on surface-based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods' implementations. We then provide an overview of low-level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.


Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation


title: Bringing impressionism to life with neural style transfer in Come Swim

abstract: Neural Style Transfer is a striking, recently-developed technique that uses neural networks to artistically redraw an image in the style of a source style image. This paper explores the use of this technique in a production setting, applying Neural Style Transfer to redraw key scenes in 'Come Swim' in the style of the impressionistic painting that inspired the film. We document how the technique can be driven within the framework of an iterative creative process to achieve a desired look, and propose a mapping of the broad parameter space to a key set of creative controls. We hope that this mapping can provide insights into priorities for future research.


title: Summarizing Neonatal Time Series Data

abstract: We describe our investigations in generating textual summaries of physiological time series data to aid medical personnel in monitoring babies in neonatal intensive care units. Our studies suggest that summarization is a communicative task that requires data analysis techniques for determining the content of the summary. We describe a prototype system that summarizes physiological time series.


title: Three-Level Active Neutral-Point-Clamped (ANPC) Converter with Fault Tolerant Ability

abstract: Compared with traditional three-level neutral-point-clamped converter, the recently proposed three-level active neutral-point-clamped (ANPC) converter can overcome the unequal loss distribution among semiconductor devices, and therefore result in increased output power or switching frequency. In industrial applications, fault tolerance of power electronics converters is very important considering system availability, safety and reliability. This paper presents the fault tolerant ability of three-level ANPC converter. Control schemes are proposed to keep the neutral-point voltage balanced and get stable and continuous output under single device fault for both open-circuit fault and short-circuit fault cases. Simulation results show the effectiveness of the fault tolerant ability of the three-level ANPC inverter under the presented control strategies, which are promising to carry on further experimental validation.


title: Fast Fight Detection

abstract: Action recognition has become a hot topic within computer vision. However, the action recognition community has focused mainly on relatively simple actions like clapping, walking, jogging, etc. The detection of specific events with direct practical use such as fights or in general aggressive behavior has been comparatively less studied. Such capability may be extremely useful in some video surveillance scenarios like prisons, psychiatric centers or even embedded in camera phones. As a consequence, there is growing interest in developing violence detection algorithms. Recent work considered the well-known Bag-of-Words framework for the specific problem of fight detection. Under this framework, spatio-temporal features are extracted from the video sequences and used for classification. Despite encouraging results in which high accuracy rates were achieved, the computational cost of extracting such features is prohibitive for practical applications. This work proposes a novel method to detect violence sequences. Features extracted from motion blobs are used to discriminate fight and non-fight sequences. Although the method is outperformed in accuracy by state of the art, it has a significantly faster computation time thus making it amenable for real-time applications.


title: Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment

abstract: Monolingual alignment is the task of pairing semantically similar units from two pieces of text. We report a top-performing supervised aligner that operates on short text snippets. We employ a large feature set to (1) encode similarities among semantic units (words and named entities) in context, and (2) address cooperation and competition for alignment among units in the same snippet. These features are deployed in a two-stage logistic regression framework for alignment. On two benchmark data sets, our aligner achieves F1 scores of 92.1% and 88.5%, with statistically significant error reductions of 4.8% and 7.3% over the previous best aligner. It produces top results in extrinsic evaluation as well.


title: Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review

abstract: Continuous active learning achieves high recall for technology-assisted review, not only for an overall information need, but also for various facets of that information need, whether explicit or implicit. Through simulations using Cormack and Grossman's TAR Evaluation Toolkit (SIGIR 2014), we show that continuous active learning, applied to a multi-faceted topic, efficiently achieves high recall for each facet of the topic. Our results assuage the concern that continuous active learning may achieve high overall recall at the expense of excluding identifiable categories of relevant information.


title: Stepper motor modeling and control design for a 1.5 square meters heliostat prototype

abstract: This paper presents the modeling, closed loop control and simulation of a stepper motor actuator, which is used to control an arrangement of miniheliostats in a solar tower plant located outside of Hermosillo, Sonora desert. The real time results show that the proposed control strategy is able to track the solar sun position. The controller is implemented in real time via LabView computational environment and applied in the solar tower plant facility.


title: Oily nanosuspension for long-acting intramuscular delivery of curcumin didecanoate prodrug: preparation, characterization and in vivo evaluation.

abstract: The objective of this study was to prepare the nanocrystals of curcumin didecanoate (CurDD) by wet ball milling and to investigate the comparative pharmacokinetics of oily nano- and micro-suspensions after intramuscular (i.m.) administration to rats. Upon optimizing the wet ball milling parameters, CurDD nanocrystals were produced with median particle size of ~500 nm and the freeze-dried nanocrystals were readily dispersed in peanut oil to form stable nanosuspensions. Although the nanosuspension appeared to exhibit slower clearance from the injection site after i.m. injection, compared to microsuspension (~5 μm), a significantly higher maximum plasma curcumin concentration (69.0 ng/ml) was observed for the former than that for the latter (18.5 ng/ml). In addition, the nanosuspension provided significant higher plasma curcumin concentrations and brain CurDD contents for at least 15 days than the microsuspension, except for the initial times. A single i.m. injection of nanosuspension appeared to achieve reversal effect on reserpine-induced hypothermia for at least 13 days. This study demonstrates that CurDD nanosuspension may act as a long-acting i.m. injectable for sustained delivery of curcumin, potentially applicable to elicit a long-lasting antidepressant effect.


title: Customer Equity Drivers and Future Sales

abstract: Research on linking operational marketing inputs to customer attitudes and customer behavior has been gaining significance concomitant with the growing recognition that customers are market-based assets. In response to this, researchers and practitioners have proposed several conceptual models. Despite recent advances in research, the results are still inconclusive as to the relationship between customer attitude and future sales. A reason for this could be due to the paucity of studies combining survey-based data with behavioral data to understand better the drivers of customer behavior. With that in mind, the authors investigate the effects of customer perceptions of key marketing actions on customer attitudes and actual customer behavior as reflected by future sales. The authors propose that customer perceptions of value, brand, and relationship—“customer equity drivers”—affect loyalty intentions and future sales. The results of the study, which is based on a sample of 5694 customers of a large European do-it-yourself retailer, suggest that customer equity drivers can significantly predict future sales, even after the authors control for the current sales level.


title: Job scheduling algorithm based on Berger model in cloud environment

abstract: Considered the commercialization and the virtualization characteristics of cloud computing, the paper proposed for the first time an algorithm of job scheduling based on Berger model. In the job scheduling process, the algorithm establishes dual fairness constraint. The first constraint is to classify user tasks by QoS preferences, and establish the general expectation function in accordance with the classification of tasks to restrain the fairness of the resources in selection process. The second constraint is to define resource fairness justice function to judge the fairness of the resources allocation. We have expanded simulation platform CloudSim, and have implemented the job scheduling algorithm proposed in this paper. The experimental results show that the algorithm can effectively execute the user tasks and manifests better fairness.


title: Improvements to Series Elastic Actuators

abstract: Actuators must be safe when interacting with humans, even in unexpected situations. Safety requires low impedance (low forces for a given perturbation) at all frequencies, not only in the actuator's stable bandwidth. Series Elastic Actuators (SEAs) are capable of achieving low impedance across all frequencies, though their force-frequency saturation envelope is decreased as a result. This paper examines ways to increase the force fidelity of SEAs by introducing inner control feedback loops and using appropriate sensor location. Findings include the superiority of a low fidelity sensor distal to stiction sources over a high fidelity sensor proximal to stiction and the superiority of an inner position feedback loop over an inner velocity feedback loop.


title: Fog Data: Enhancing Telehealth Big Data Through Fog Computing

abstract: The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper proposes, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various data mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture.


Brain-Based Education 1 Brain-Based Education: A Reply to Bruer


Non-invasive detection of vascular function and dysfunction


title: VidLoc: 6-DoF Video-Clip Relocalization.

abstract: Machine learning techniques, namely convolutional neural ::: networks (CNN) and regression forests, have recently ::: shown great promise in performing 6-DoF localization ::: of monocular images. However, in most cases imagesequences, ::: rather only single images, are readily available. ::: To this extent, none of the proposed learning-based ::: approaches exploit the valuable constraint of temporal ::: smoothness, often leading to situations where the per-frame ::: error is larger than the camera motion. In this paper we ::: propose a recurrent model for performing 6-DoF localization ::: of video-clips. We find that, even by considering ::: only short sequences (20 frames), the pose estimates are ::: smoothed and the localization error can be drastically reduced. ::: Finally, we consider means of obtaining probabilistic ::: pose estimates from our model. We evaluate our method ::: on openly-available real-world autonomous driving and indoor ::: localization datasets.


title: CG_Hadoop: computational geometry in MapReduce

abstract: Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not truly exploited towards processing large-scale computational geometry operations. This paper introduces CG_Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry problems, namely, polygon union, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG_Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system; a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form a nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results on a cluster of 25 machines of datasets up to 128GB show that CG_Hadoop achieves up to 29x and 260x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.


title: A dictionary learning approach for classification: separating the particularity and the commonality

abstract: Empirically, we find that, despite the class-specific features owned by the objects appearing in the images, the objects from different categories usually share some common patterns, which do not contribute to the discrimination of them. Concentrating on this observation and under the general dictionary learning (DL) framework, we propose a novel method to explicitly learn a common pattern pool (the commonality) and class-specific dictionaries (the particularity) for classification. We call our method DL-COPAR, which can learn the most compact and most discriminative class-specific dictionaries used for classification. The proposed DL-COPAR is extensively evaluated both on synthetic data and on benchmark image databases in comparison with existing DL-based classification methods. The experimental results demonstrate that DL-COPAR achieves very promising performances in various applications, such as face recognition, handwritten digit recognition, scene classification and object recognition.


title: Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition

abstract: Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech. However, studies have suggested to favor a certain type of convolutional operations when building a deep convolutional neural network for speech applications although there has been promising results using different types of convolutional operations. In this work, we study four types of convolutional operations on different input features for speech emotion recognition under noisy and clean conditions in order to derive a comprehensive understanding. Since affective behavioral information has been shown to reflect temporally varying of mental state and convolutional operation are applied locally in time, all deep neural networks share a deep recurrent sub-network architecture for further temporal modeling. We present detailed quantitative module-wise performance analysis to gain insights into information flows within the proposed architectures. In particular, we demonstrate the interplay of affective information and the other irrelevant information during the progression from one module to another. Finally we show that all of our deep neural networks provide state-of-the-art performance on the eNTERFACE'05 corpus.