title: On Complex Word Alignment Configurations

abstract: Resources of manual word alignments contain configurations that are beyond the alignment capacity of current translation models, hence the term complex alignment configuration. They have been the matter of some debate in the machine translation community, as they call for more powerful translation models that come with further complications. In this work we investigate instances of complex alignment configurations in data sets of four different language pairs to shed more light on the nature and cause of those configurations. For the English-German alignments from Pado and Lapata (2006), for instance, we find that only a small fraction of the complex configurations are due to real annotation errors. While a third of the complex configurations in this data set could be simplified when annotating according to a different style guide, the remaining ones are phenomena that one would like to be able to generate during translation. Those instances are mainly caused by the different word order of English and German. Our findings thus motivate further research in the area of translation beyond phrase-based and context-free translation modeling.


title: Analyzing characteristics of task structures to develop GPGP coordination mechanisms

abstract: Previous research about multi-agent coordination has concentrated at a high level, e.g. developing communication protocols for coordination, constructing special purpose agents to dictate the coordination behaviors of an entire system, or associating rules or coordination mechanisms with every agent to achieve cooperative behaviors. Much less research addresses multi-agent coordination at a low level: evaluating the effects of agents' task structures upon agents' coordination behaviors. This paper presents an Extended Hierarchical Task Network (EHTN) to represent precisely those structural features that affect coordination. Using this EHTN formalism, an extended set of Generalized Partial Global Planning (GPGP) coordination mechanisms has been developed for multi-agent coordination. Each coordination mechanism is defined in terms of EHTN rewriting rules and an associated set of pre-defined EHTN behaviors. This set of GPGP coordination mechanisms has been applied to a simulated emergency medical service (EMS) system. The experimental results reveal some of the performance relationships between specific mechanisms and external environmental characteristics.


title: Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks

abstract: We address the problem of classifying sparsely labeled networks, where labeled nodes in the network are extremely scarce. Existing algorithms, such as collective classification, have been shown to be effective for jointly deriving labels of related nodes, by exploiting class label dependencies among neighboring nodes. However, when the underlying network is sparsely labeled, most nodes have too few or even no connections to labeled nodes. This makes it very difficult to leverage supervised knowledge from labeled nodes to accurately estimate label dependencies, thereby largely degrading the classification accuracy. In this paper, we propose a novel discriminative matrix factorization (DMF) based algorithm that effectively learns a latent network representation by exploiting topological paths between labeled and unlabeled nodes, in addition to nodes' content information. The main idea is to use matrix factorization to obtain a compact representation of the network that fully encodes nodes' content information and network structure, and unleash discriminative power inferred from labeled nodes to directly benefit collective classification. To achieve this, we formulate a new matrix factorization objective function that integrates network representation learning with an empirical loss minimization for classifying node labels. An efficient optimization algorithm based on conjugate gradient methods is proposed to solve the new objective function. Experimental results on real-world networks show that DMF yields superior performance gain over the state-of-the-art baselines on sparsely labeled networks.


title: Modelling and design of a synergy-based actuator for a tendon-driven soft robotic glove

abstract: The need for a means of assistance in human grasping, to compensate for weakness or to augment performance, is well documented. An appealing new way of doing so is through soft, wearable robots that work in parallel with the human muscles. In this paper we present the design and modelling of a tendon-driving unit that empowers a wearable, soft glove. Being portability one of our main objectives, we use only 1 motor to move 8 degrees of freedom of the hand. To achieve this we use an underactuation strategy based on the human hand's first postural synergy, which explains alone ≈60% of activities of daily living. The constrains imposed by the underactuation strategy are softened, to allow adaptability during grasping, by placing elastic elements in series with the tendons. A simulation of the dynamic behaviour of the glove on a human hand allows us to quantify the magnitude and distribution of the forces involved during usage. These results are used to guide design choices such as the power of the motor and the stiffness of the springs. The designed tendon-driving unit comprises a DC motor which drives an array of spools dimensioned according to the first postural synergy, an electromechanical clutch to hold the hand in position during static posture and a feeder mechanism to avoid slacking of the tendons around the spool. Finally, the tendon-driving unit is tested to verify that it satisfies motion and force characteristics required to assist its wearer in activities of daily living.


title: Point cloud segmentation towards urban ground modeling

abstract: This paper presents a new method for segmentation and interpretation of 3D point clouds from mobile LIDAR data. The main contribution of this work is the automatic detection and classification of artifacts located at the ground level. The detection is based on Top-Hat of hole filling algorithm of range images. Then, several features are extracted from the detected connected components (CCs). Afterward, a stepwise forward variable selection by using Wilk's Lambda criterion is performed. Finally, CCs are classified in four categories (lampposts, pedestrians, cars, the others) by using a SVM machine learning method.


title: A Survey on Multi-View Clustering

abstract: With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress. Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination.


title: Performance evaluation of directional MAC protocol for inter-vehicle communication

abstract: In this paper, the performance of directional MAC protocol, proposed in [T. Koriakis et al., June 2003], and IEEE 802.11 MAC protocol are compared for an inter-vehicle communication application. Actual vehicle movement in a city and highway road scenarios are used for simulating inter-vehicle communication among nodes in network simulator (ns-2). DMAC protocol was implemented in ns-2. The results obtained from simulations demonstrate the performance improvement of directional MAC protocol as compared to IEEE 802.11 MAC for inter-vehicle communication.


title: Detecting SPARQL Query Templates for Data Prefetching

abstract: Publicly available Linked Data repositories provide a multitude of information. By utilizing Sparql, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mash-ups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the Sparql endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world Sparql query logs.


title: Scrolling behaviour with single- and multi-column layout

abstract: The standard layout model used by web browsers is to lay text out in a vertical scroll using a single column. The horizontal-scroll layout model--in which text is laid out in columns whose height is set to that of the browser window and the viewer scrolls horizontally - seems well-suited to multi-column layout on electronic devices. We describe a study that examines how people read and, in particular, the strategies they use for scrolling with these two models when reading large textual documents on a standard computer monitor. We compare usability of the models and evaluate both user preferences and the effect of the model on performance. Also interesting is the description of the browser and its user interface which we used for the study.


title: Assessing Software Service Quality and Trustworthiness at Selection Time

abstract: The integration of external software in project development is challenging and risky, notably because the execution quality of the software and the trustworthiness of the software provider may be unknown at integration time. This is a timely problem and of increasing importance with the advent of the SaaS model of service delivery. Therefore, in choosing the SaaS service to utilize, project managers must identify and evaluate the level of risk associated with each candidate. Trust is commonly assessed through reputation systems; however, existing systems rely on ratings provided by consumers. This raises numerous issues involving the subjectivity and unfairness of the service ratings. This paper describes a framework for reputation-aware software service selection and rating. A selection algorithm is devised for service recommendation, providing SaaS consumers with the best possible choices based on quality, cost, and trust. An automated rating model, based on the expectancy-disconfirmation theory from market science, is also defined to overcome feedback subjectivity issues. The proposed rating and selection models are validated through simulations, demonstrating that the system can effectively capture service behavior and recommend the best possible choices.


title: Review IMPROVING PATIENT CARE Pressure Ulcer Risk Assessment and Prevention A Systematic Comparative Effectiveness Review

abstract: BACKGROUND ::: Pressure ulcers are associated with substantial health burdens but may be preventable. ::: ::: ::: PURPOSE ::: To review the clinical utility of pressure ulcer risk assessment instruments and the comparative effectiveness of preventive interventions in persons at higher risk. ::: ::: ::: DATA SOURCES ::: MEDLINE (1946 through November 2012), CINAHL, the Cochrane Library, grant databases, clinical trial registries, and reference lists. ::: ::: ::: STUDY SELECTION ::: Randomized trials and observational studies on effects of using risk assessment on clinical outcomes and randomized trials of preventive interventions on clinical outcomes. ::: ::: ::: DATA EXTRACTION ::: Multiple investigators abstracted and checked study details and quality using predefined criteria. ::: ::: ::: DATA SYNTHESIS ::: One good-quality trial found no evidence that use of a pressure ulcer risk assessment instrument, with or without a protocolized intervention strategy based on assessed risk, reduces risk for incident pressure ulcers compared with less standardized risk assessment based on nurses' clinical judgment. In higher-risk populations, 1 good-quality and 4 fair-quality randomized trials found that more advanced static support surfaces were associated with lower risk for pressure ulcers compared with standard mattresses (relative risk range, 0.20 to 0.60). Evidence on the effectiveness of low-air-loss and alternating-air mattresses was limited, with some trials showing no clear differences from advanced static support surfaces. Evidence on the effectiveness of nutritional supplementation, repositioning, and skin care interventions versus usual care was limited and had methodological shortcomings, precluding strong conclusions. ::: ::: ::: LIMITATION ::: Only English-language articles were included, publication bias could not be formally assessed, and most studies had methodological shortcomings. ::: ::: ::: CONCLUSION ::: More advanced static support surfaces are more effective than standard mattresses for preventing ulcers in higher-risk populations. The effectiveness of formal risk assessment instruments and associated intervention protocols compared with less standardized assessment methods and the effectiveness of other preventive interventions compared with usual care have not been clearly established.


title: Visual understanding environment

abstract: The Visual Understanding Environment (VUE) project at Tufts' Academic Technology department provides faculty and students with tools to successfully integrate digital resources into their teaching and learning. VUE provides a visual environment for structuring, presenting, and sharing digital information and an OKI-compliant software bridge for connecting to FEDORA-based digital repositories. Using VUE's concept mapping interface, faculty and students design customized semantic networks of digital resources drawing from digital libraries, local files and the Web. The resulting content maps can then be viewed and exchanged online.


title: Environmental Sound Classification Based on Multi-temporal Resolution CNN Network Combining with Multi-level Features.

abstract: Motivated by the fact that characteristics of different sound classes are highly diverse in different temporal scales and hierarchical levels, a novel deep convolutional neural network (CNN) architecture is proposed for the environmental sound classification task. This network architecture takes raw waveforms as input, and a set of separated parallel CNNs are utilized with different convolutional filter sizes and strides, in order to learn feature representations with multi-temporal resolutions. On the other hand, the proposed architecture also aggregates hierarchical features from multi-level CNN layers for classification using direct connections between convolutional layers, which is beyond the typical single-level CNN features employed by the majority of previous studies. This network architecture also improves the flow of information and avoids vanishing gradient problem. The combination of multi-level features boosts the classification performance significantly. Comparative experiments are conducted on two datasets: the environmental sound classification dataset (ESC-50), and DCASE 2017 audio scene classification dataset. Results demonstrate that the proposed method is highly effective in the classification tasks by employing multi-temporal resolution and multi-level features, and it outperforms the previous methods which only account for single-level features.


title: QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds

abstract: Whole brain segmentation from structural magnetic resonance imaging is a prerequisite for most morphological analyses, but requires hours of processing time and therefore delays the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolution neural network that segments a brain scan in 20 seconds. To enable training of the complex network with limited annotated data, we propose to pre-train on auxiliary labels created from existing segmentation software and to subsequently fine-tune on manual labels. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior performance to state-of-the-art methods, while being about 700 times faster. This drastic speed up greatly facilitates the processing of large data repositories and supports the translation of imaging biomarkers by making them almost instantaneously available.


title: The FactChecker: Verifying Text Summaries of Relational Data Sets

abstract: We present a novel natural language query interface, the FactChecker, aimed at text summaries of relational data sets. The tool focuses on natural language claims that translate into an SQL query and a claimed query result. Similar in spirit to a spell checker, the FactChecker marks up text passages that seem to be inconsistent with the actual data. At the heart of the system is a probabilistic model that reasons about the input document in a holistic fashion. Based on claim keywords and the document structure, it maps each text claim to a probability distribution over associated query translations. By efficiently executing tens to hundreds of thousands of candidate translations for a typical input document, the system maps text claims to correctness probabilities. This process becomes practical via a specialized processing backend, avoiding redundant work via query merging and result caching. Verification is an interactive process in which users are shown tentative results, enabling them to take corrective actions if necessary. ::: Our system was tested on a set of 53 public articles containing 392 claims. Our test cases include articles from major newspapers, summaries of survey results, and Wikipedia articles. Our tool revealed erroneous claims in roughly a third of test cases. A detailed user study shows that users using our tool are in average six times faster at checking text summaries, compared to generic SQL interfaces. In fully automated verification, our tool achieves significantly higher recall and precision than baselines from the areas of natural language query interfaces and fact checking.


title: RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows

abstract: Traditional tools for configuring cloud services can run much slower than the workflows they are trying to optimize. For example, in the case studies reported here, we find cases where (using traditional methods) it takes hours to find ways to make a workflow terminate in tens of seconds. Such slow optimizers are a poor choice of tools for reacting to changing operational environmental conditions. Hence, they are unsuited for cloud services that support rapidly changing workflows, e.g., scientific workflows or workflows from the media or telecommunication industries. ::: To solve this problem, this paper presents RIOT (Randomized Instance Order Types), a new configuration tool. RIOT has a very low optimization overhead-- often, less than 10\% of the system runtime, especially for every complex workflow. Instead of simulating many configurations, RIOT uses a novel surrogate sampling method to quickly find promising solutions. As shown by this paper, RIOT achieves comparable results to the other approaches but does so in a fraction of the time.


title: VoNet: vehicle orientation classification using convolutional neural network

abstract: This paper presents a novel convolution neural network for classifying the orientation (or viewpoint) of a vehicle in a given image. Current equipping sensors in self-driving car is able to produce bounding box of vehicles in the proximity, but it does not recognize the viewpoint of them. Analyzing surrounding cars' direction in very complex environment has a significant role for autonomous driving. Utilizing nothing but a captured image, the purpose of this research is to classify viewpoint of vehicle: (1) front; (2) rear; (3) side; (4) front-side; and (5) rear-side. Deep convolutional neural network is used as the tool in performing classification task. The approach involves examining different CNN architectures using a large scale car dataset. In addition to that, the goal of the model is to be small and fast enough for limited hardware resource. We are able to achieve 95% accuracy, 57ms inference time on Nvidia GRID K520 GPU, and 1.6 MB Caffe model size.


title: Multi-keyframe abstraction from videos

abstract: This paper presents a method for abstracting multi-keyframe from video datasets. Existing video abstraction methods focused on simple view videos, and the results will be unacceptable if applied to overlapping views directly due to limitations like unavoidable redundancy and complicated inner correlations. We propose a correlation map to naturally model the correlations with various attributes among multi-keyframe, keyframe importance and weighted correlations are then computed to construct the map. The weighted correlations, unlike the unweighted ones, not only model probabilistic relationship among keyframes but also address the temporal and visual similarity. We facilitate the abstraction process via SVM classification and keyframes reduction using rough set. The multi-keyframe correlation map, which serially assembles event-centered keyframes in temporal order, is presented for displaying the abstraction, which shows the correlations and improves the browsability of video datasets.


title: EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data

abstract: Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogenous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.


Time Series Prediction by Using a Connectionist Network with Internal Delay Lines


Yingzhen Li Topics in Approximate Inference 0 Front Matter


title: Obesity and Student Performance at School

abstract: To review the state of research on the association between obesity among school-aged children and academic outcomes, the authors reviewed published studies investigating obesity, school performance, and rates of student absenteeism. A table with brief descriptions of each study's research methodology and outcomes is included. Research demonstrates that overweight and obesity are associated with poorer levels of academic achievement. Data on the association of child overweight or obesity with levels of attendance are too sparse to draw conclusions. (J Sch Health. 2005;75(8):291-295) A n understanding of the impact of student health on educational outcomes has major implications. Among them are ramifications for how schools address health concerns. The National Coordinating Committee on School Health and Safety (NCCSHS), comprising repre- sentatives of federal departments and national nongovern- mental organizations, encourages school districts to respond to evolving challenges by developing coordinated school health programs. To enhance awareness of existing evidence linking health and school performance and to identify gaps in knowledge, the NCCSHS has begun a pro- ject designed to ascertain the status of research in these areas. The project involves a literature search of peer- reviewed, published research reporting on the relationship between students' health and their performance in school. Compilations of research articles that explore the associa- tion between academic performance and health include various chronic conditions. This article summarizes what is known on the association of obesity with academic out- comes among school-aged children.


title: Behavior Explanation as Intention Signaling in Human-Robot Teaming

abstract: Facilitating a shared team understanding is an important task in human-robot teaming. In order to achieve efficient collaboration between the human and robot, it requires not only the robot to understand what the human is doing, but also the robot's behavior be understood by (a.k.a. explainable to) the human. While most prior work has focused on the first aspect, the latter has also begun to draw significant attention. We propose an approach to explaining robot behavior as intention signaling using natural language sentences. In contrast to recent approaches to generating explicable and legible plans, intention signaling does not require the robot to deviate from its optimal plan; neither does it require humans to update their knowledge as generally required for explanation generation. The key questions to be answered here for intention signaling are the what (content of signaling) and when (timing). Based on our prior work, we formulate human interpreting robot actions as a labeling process to be learned. To capture the dependencies between the interpretation of robot actions that are far apart, skip-chain Conditional Random Fields (CRFs) are used. The answers to the when and what can then be converted to an inference problem in the skip-chain CRFs. Potential timings and content of signaling are explored by fixing the labels of certain actions in the CRF model; the configuration that maximizes the underlying probability of being able to associate a label with the remaining actions, which reflects the human's understanding of the robot's plan, is returned for signaling. For evaluation, we construct a synthetic domain to verify that intention signaling can help achieve better teaming by reducing criticism on robot behavior that may appear undesirable but is otherwise required, e.g., due to information asymmetry that results in misinterpretation. We use Amazon Mechanical Turk (MTurk) to assess robot behavior with two settings (i.e., with and without signaling). Results show that our approach achieves the desired effect of creating more explainable robot behavior.


title: Low-Transition-Temperature Mixtures (LTTMs): A New Generation of Designer Solvents

abstract: A new generation of designer solvents emerged in the last decade as promising green media for multiple applications, including separation processes: the low-transition-temperature mixtures (LTTMs). They can be prepared by mixing natural high-melting-point starting materials, which form a liquid by hydrogen-bond interactions. Among them, deep-eutectic solvents (DESs) were presented as promising alternatives to conventional ionic liquids (ILs). Some limitations of ILs are overcome by LTTMs, which are cheap and easy to prepare from natural and readily available starting materials, biodegradable, and renewable.


title: Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving Smoothing Regularization

abstract: Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the degradation of the observed image, which would lead to a poor SR performance. In this paper, we propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse representation. An EPS regularization term is designed based on gradient-domain-guided filtering to preserve image edges and reduce noise in the reconstructed image. Furthermore, a smoothing-aware factor adaptively determined by the estimation of the noise level of LR images without manual interference is presented to obtain an optimal balance between the data fidelity term and the proposed EPS regularization term. An iterative shrinkage algorithm is used to obtain the SR image results for LR images. The proposed adaptive smoothing-aware scheme makes our method robust to different levels of noise. Experimental results indicate that the proposed method can preserve image edges and reduce noise and outperforms the current state-of-the-art methods for noisy images.


title: Investigation of a new GaN AC/DC topology for battery charging application

abstract: Switching losses are reduced by gallium nitride (GaN) technology, which allows faster device switching speeds. At the same time, the weight and size of converters utilizing these devices are greatly reduced in several applications by using high frequency (HF) transformers. A new, low voltage high current, GaN-based AC/DC topology for battery charging applications is simulated, modeled, and investigated in this paper. The proposed topology is designed to operate at 1 MHz for 120Vac to 48Vdc/60Vdc conversion for 1.4 kW load. The integration of GaN totem-pole power factor correction rectifier (TP-PFC), high-frequency series-resonant converter (SRC), and current doubler rectifier (CDR) presents an opportunity to further increase the efficiency and compactness of this GaN battery charging system. In this work, a new GaN topology to combine the advantages of TP-PFC and SRC with CDR in a battery charging converter is investigated, simulated, and modelled. Loss analysis of the proposed topology is addressed.


Compressed Volume Rendering using Deep Learning


title: Evidence for a Common Representation of Decision Values for Dissimilar Goods in Human Ventromedial Prefrontal Cortex

abstract: To make economic choices between goods, the brain needs to compute representations of their values. A great deal of research has been performed to determine the neural correlates of value representations in the human brain. However, it is still unknown whether there exists a region of the brain that commonly encodes decision values for different types of goods, or if, in contrast, the values of different types of goods are represented in distinct brain regions. We addressed this question by scanning subjects with functional magnetic resonance imaging while they made real purchasing decisions among different categories of goods (food, nonfood consumables, and monetary gambles). We found activity in a key brain region previously implicated in encoding goal-values: the ventromedial prefrontal cortex (vmPFC) was correlated with the subjects' value for each category of good. Moreover, we found a single area in vmPFC to be correlated with the subjects' valuations for all categories of goods. Our results provide evidence that the brain encodes a "common currency" that allows for a shared valuation for different categories of goods.


title: Growth performance and structure of a mangrove afforestation project on a former seagrass bed, Mindanao Island, Philippines

abstract: The Philippines has lost nearly 70% of its natural mangrove cover since the early 1900s. As a result, large investments have been made to restore mangrove forests and the many ecosystem services that they provide. Most of these restoration efforts have been through outplanting of Rhizophora sp. seedlings, many of which have failed because the proper hydrological and ecological conditions were not properly assessed. Other afforestation projects involved planting seedlings in inappropriate places (e.g., seagrass beds, mudflats) that resulted in replacing one valuable ecosystem with another. The aim of this research was to investigate the growth of 3-, 9-, and 21-year-old afforested stands of Rhizophora spp. mangrove forest. We also investigated the impact of these plantations on local seagrass beds. The total aboveground biomass was 42.6, 74.4, and 111.7 Mg ha−1 for the 3-, 9-, and 21-year-old mangrove stands, respectively. Seagrass bed cover decreased under the closed canopy of the mangrove due to reduced photosynthetically active radiation and competition for growing space. This study shows that mangroves can grow to some extent on seagrass beds, though mangrove planting in these areas could eventually lead to seagrass loss. Thus, mangroves should not be planted in areas that are naturally occupied by other ecologically important ecosystems. The purpose of mangrove restoration should be clear and efforts should be focused on formerly deforested or degraded areas. Additional studies are needed from different locations to understand how mangrove planting in seagrass beds impacts growth performance and ecological functions of the latter ecosystem.


title: TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling

abstract: We present a system for taxonomy construction that reached the first place in all subtasks of the SemEval 2016 challenge on Taxonomy Extraction Evaluation. Our simple yet effective approach harvests hypernyms with substring inclusion and Hearst-style lexicosyntactic patterns from domain-specific texts obtained via language model based focused crawling. Extracted taxonomies are evaluated on English, Dutch, French and Italian for three domains each (Food, Environment and Science). Evaluations against a gold standard and by human judgment show that our method outperforms more complex and knowledge-rich approaches on most domains and languages. Furthermore, to adapt the method to a new domain or language, only a small amount of manual labour is needed.


title: Chromosome inversions, local adaptation and speciation.

abstract: We study the evolution of inversions that capture locally adapted alleles when two populations are exchanging migrants or hybridizing. By suppressing recombination between the loci, a new inversion can spread. Neither drift nor coadaptation between the alleles (epistasis) is needed, so this local adaptation mechanism may apply to a broader range of genetic and demographic situations than alternative hypotheses that have been widely discussed. The mechanism can explain many features observed in inversion systems. It will drive an inversion to high frequency if there is no countervailing force, which could explain fixed differences observed between populations and species. An inversion can be stabilized at an intermediate frequency if it also happens to capture one or more deleterious recessive mutations, which could explain polymorphisms that are common in some species. This polymorphism can cycle in frequency with the changing selective advantage of the locally favored alleles. The mechanism can establish underdominant inversions that decrease heterokaryotype fitness by several percent if the cause of fitness loss is structural, while if the cause is genic there is no limit to the strength of underdominance that can result. The mechanism is expected to cause loci responsible for adaptive species-specific differences to map to inversions, as seen in recent QTL studies. We discuss data that support the hypothesis, review other mechanisms for inversion evolution, and suggest possible tests.


title: Engaging with practices: design case studies as a research framework in CSCW

abstract: Information and communications technology (ICT) pervades most aspects of our lives and changes everyday's practices in work and leisure time. When designing innovative ICTs, we need to engage with given practices, institutional arrangements, and technological infrastructures. We describe the research framework used at the University of Siegen. It is based on a collection of design case studies in particular fields of practice and identifies cross-cutting issues to compare and aggregate insights between these cases. To illustrate this framework, we describe our research activities and discuss three themes which became important in different design case studies.


title: Protestant Relational Ideology: The cognitive underpinnings and organizational implications of an American anomaly.

abstract: Abstract Cross-national comparisons of relational work styles suggest that the United States is an anomaly in its low relational focus. This article describes Protestant Relational Ideology (PRI), a cultural construct that explains the origins and nature of this anomaly. This construct refers to a deep-seated belief that affective and relational concerns are considered inappropriate in work settings and, therefore, are to be given less attention than in social, non-work settings. Akin to an institutional imprinting perspective, a review of sociological and historical research links PRI to the beliefs and practices of the founding communities of American society. A social cognition perspective is used to explain the mechanisms through which PRI influences American relational workways. The article also describes a program of research that uses PRI to address a wider set of organizational behavior issues that include: antecedents of prejudice and discrimination in diverse organizations; sources of intercultural miscommunication; beliefs about team conflict; mental models of “professionalism” and its effect on organizational recruitment and selection.


title: VGRIS: Virtualized GPU Resource Isolation and Scheduling in Cloud Gaming

abstract: Fueled by the maturity of virtualization technology for Graphics Processing Unit (GPU), there is an increasing number of data centers dedicated to GPU-related computation tasks in cloud gaming. However, GPU resource sharing in these applications is usually poor. This stems from the fact that the typical cloud gaming service providers often allocate one GPU exclusively for one game. To achieve the efficiency of computational resource management, there is a demand for cloud computing to employ the multi-task scheduling technologies to improve the utilization of GPU. In this paper, we propose VGRIS, a resource management framework for Virtualized GPU Resource Isolation and Scheduling in cloud gaming. By leveraging the mature GPU paravirtualization architecture, VGRIS resides in the host through library API interception, while the guest OS and the GPU computing applications remain unmodified. In the proposed framework, we implemented three scheduling algorithms in VGRIS for different objectives, i.e., Service Level Agreement (SLA)-aware scheduling, proportional-share scheduling, and hybrid scheduling that mixes the former two. By designing such a scheduling framework, it is possible to handle different kinds of GPU computation tasks for different purposes in cloud gaming. Our experimental results show that each scheduling algorithm can achieve its goals under various workloads.


title: A Conceptual Integrated Model for Measuring the Success of eLearning in Developing Countries: Literature Review

abstract: The increase in funding for e-learning is a good move that requires monitoring, as e-learning, like any other information systems, is a long-term investment with uncertainty for returns. This necessitates the need for e-learning evaluation to determine post adoption success. The evaluation of e-learning is a complex process, that requires comprehensive tools for measuring post adoption success. Subsequently, leading the study to investigate approaches utilised for performing post adoption e-learning success. Thereafter, proposing an adapted IS success integrated model for measuring post adoption e-learning success in developing country context, specifically, South Africa. A systematic literature review method was adopted to achieve an inductive study. The study objectives influenced the key words employed for searching relevant literature. They also drove the decision to follow thematic analysis method. Through a systematic review the study discovered that the Delone & Mclean IS success model is the most adopted and applied for measuring post adoption e-learning success. The model is also received as an effective tool to comprehensively gauge post adoption e-learning success. Additionally, the model is flexible and modifiable to suite different contexts. The study suggests further investigation and use of the adapted IS success model within HEIs to comprehensively assess post adoption e-learning success in South Africa. Future studies should be focused on conducting in-depth literature review on approaches utilised for assessing e-learning success post adoption. They should also do testing to determine the suitable process flow and constructs of an evaluation model for developing country contexts.


title: HIGH VOLTAGE IMPULSE GENERATOR USING HV-IGBTs

abstract: We are reporting on a High-Voltage Impulse Generator, which consists of a step-up transformer, which is driven by new HV-IGBTs (High-Voltage Isolated Gate Bipolar Transistors). The new HV-IGBTs are individually packaged silicon-dies intended for Pulsed-Power Applications. The silicon dies are normally packaged in large modules for locomotive motor drives and similar traction applications. In our work we used the Powerex QIS4506001 discrete IGBT and the QRS4506001 discrete diode, both with a nominal rating of 4500V/60A, derived from continuous- duty applications. Our experiments have shown that the devices are capable of handling currents in excess of 1 kA during pulsed operation.


title: Flow based botnet detection through semi-supervised active learning

abstract: In a variety of Network-based Intrusion Detection System (NIDS) applications, one desires to detect groups of unknown attack (e.g., botnet) packet-flows, with a group potentially manifesting its atypicality (relative to a known reference “normal”/null model) on a low-dimensional subset of the full measured set of features used by the IDS. What makes this anomaly detection problem quite challenging is that it is a priori unknown which (possibly sparse) subset of features jointly characterizes a particular application, especially one that has not been seen before, which thus represents an unknown behavioral class (zero-day threat). Moreover, nowadays botnets have become evasive, evolving their behavior to avoid signature-based IDSes. In this work, we apply a novel active learning (AL) framework for botnet detection, facilitating detection of unknown botnets (assuming no ground truth examples of same). We propose a new anomaly-based feature set that captures the informative features and exploits the sequence of packet directions in a given flow. Experiments on real world network traffic data, including several common Zeus botnet instances, demonstrate the advantage of our proposed features and AL system.


title: VibViz: Organizing, visualizing and navigating vibration libraries

abstract: With haptics now common in consumer devices, diversity in tactile perception and aesthetic preferences confound haptic designers. End-user customization out of example sets is an obvious solution, but haptic collections are notoriously difficult to explore. This work addresses the provision of easy and highly navigable access to large, diverse sets of vibrotactile stimuli, on the premise that multiple access pathways facilitate discovery and engagement. We propose and examine five disparate organization schemes (taxonomies), describe how we created a 120-item library with diverse functional and affective characteristics, and present VibViz, an interactive tool for end-user library navigation and our own investigation of how different taxonomies can assist navigation. An exploratory user study with and of VibViz suggests that most users gravitate towards an organization based on sensory and emotional terms, but also exposes rich variations in their navigation patterns and insights into the basis of effective haptic library navigation.


title: Augmenting service recommender systems by incorporating contextual opinions from user reviews

abstract: Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location, time). However, most existing context-aware techniques only use contextual information at the item level when modeling users' preferences, i.e., contextual information that correlates with users' overall evaluations of items such as ratings. Few studies have attempted to detect more fine-grained contextual preferences at the level of item aspects (e.g., a hotel's "location", "food quality", and "service"). In this study, we use contextual weighting strategies to derive users' aspect-level context-dependent preferences from user-generated textual reviews. The inferred context-dependent preferences are then combined with users' context-independent preferences that are also inferred from reviews to reflect their stable requirements over time. To automatically incorporate both types of user preferences into the recommendation process, we propose a linear-regression-based algorithm that uses a stochastic gradient descent learning procedure. We tested the proposed recommendation algorithm with two real-life service datasets (one with hotel review data and the other with restaurant review data) and compared its contribution with three previously suggested approaches: one that does not consider contextual information; one that uses contextual information to pre-filter rating data before applying the recommendation algorithm; and one that generates recommendations according to users' aspect-level contextual preferences. The experiment results demonstrate that our approach outperforms the others in terms of recommendation accuracy.


title: Procedural Content Generation in Games

abstract: This book presents the most up-to-date coverage of procedural content generation (PCG) for games, specifically the procedural generation of levels, landscapes, items, rules, quests, or other types of content. Each chapter explains an algorithm type or domain, including fractal methods, grammar-based methods, search-based and evolutionary methods, constraint-based methods, and narrative, terrain, and dungeon generation. The authors are active academic researchers and game developers, and the book is appropriate for undergraduate and graduate students of courses on games and creativity; game developers who want to learn new methods for content generation; and researchers in related areas of artificial intelligence and computational intelligence.


title: A Semantic-Based Method for Visualizing Large Image Collections

abstract: Interactive visualization of large image collections is important and useful in many applications, such as personal album management and user profiling on images. However, most prior studies focus on using low-level visual features of images, such as texture and color histogram, to create visualizations without considering the more important semantic information embedded in images. This paper proposes a novel visual analytic system to analyze images in a semantic-aware manner. The system mainly comprises two components: a semantic information extractor and a visual layout generator. The semantic information extractor employs an image captioning technique based on convolutional neural network (CNN) to produce descriptive captions for images, which can be transformed into semantic keywords. The layout generator employs a novel co-embedding model to project images and the associated semantic keywords to the same 2D space. Inspired by the galaxy metaphor, we further turn the projected 2D space to a galaxy visualization of images, in which semantic keywords and images are visually encoded as stars and planets. Our system naturally supports multi-scale visualization and navigation, in which users can immediately see a semantic overview of an image collection and drill down for detailed inspection of a certain group of images. Users can iteratively refine the visual layout by integrating their domain knowledge into the co-embedding process. Two task-based evaluations are conducted to demonstrate the effectiveness of our system.


title: Who has the worst attitudes toward sexual minorities? Comparison of transphobia and homophobia levels in gender dysphoric individuals, the general population and health care providers

abstract: To date, few studies have addressed attitudes toward transgender individuals. In addition, little is known about health care providers’ (HCP) attitudes toward sexual minorities. The aim of the present study is to compare attitudes toward homosexual and transgender individuals between gender dysphoric individuals (GDs), general population controls (C) and HCP. A total of 310 subjects were considered, including 122 GDs (63 transwomen and 59 transmen), 53 heterosexual HCP (26 males and 27 females) and 135 C. Participants completed the Modern Homophobia Scale (MHS) and the Attitudes Toward Transgendered Individuals Scale (ATTI) in order to assess attitudes toward gay men and lesbian women and toward transgender individuals, respectively. In addition, GDs completed the Gender Identity/Gender Dysphoria Questionnaire (GIDYQ-AA) and ATTI to measure, respectively, gender dysphoria levels and internalized transphobia. Religious attitudes were evaluated by means of the Religious Fundamentalism Scale (RFS), and Discrimination and Stigma Scale (DISC-12) was used to measure perceived discrimination. (1) Men showed significantly higher levels of homophobia and transphobia when compared to women (p < 0.001); (2) perceived discrimination was higher in lesbian women compared to gay men and in transwomen compared to transmen (p < 0.001 and p < 0.05, respectively); and (3) religious fundamentalism was associated with both homophobia and transphobia (both p < 0.001). Our results underline the need to promote awareness and acceptance of the sexual minorities, who are more at risk of discriminatory attitudes, which are strongly dependent on religious precepts and dogma.


title: Learning a Combined Model of Visual Saliency for Fixation Prediction

abstract: A large number of saliency models, each based on a different hypothesis, have been proposed over the past 20 years. In practice, while subscribing to one hypothesis or computational principle makes a model that performs well on some types of images, it hinders the general performance of a model on arbitrary images and large-scale data sets. One natural approach to improve overall saliency detection accuracy would then be fusing different types of models. In this paper, inspired by the success of late-fusion strategies in semantic analysis and multi-modal biometrics, we propose to fuse the state-of-the-art saliency models at the score level in a para-boosting learning fashion. First, saliency maps generated by several models are used as confidence scores. Then, these scores are fed into our para-boosting learner (i.e., support vector machine, adaptive boosting, or probability density estimator) to generate the final saliency map. In order to explore the strength of para-boosting learners, traditional transformation-based fusion strategies, such as Sum , Min , and Max , are also explored and compared in this paper. To further reduce the computation cost of fusing too many models, only a few of them are considered in the next step. Experimental results show that score-level fusion outperforms each individual model and can further reduce the performance gap between the current models and the human inter-observer model.


title: A low-profile broadband phased array antenna

abstract: A fundamentally different approach to broadband array design is introduced and validated with measured data. This design approach relies on mutual coupling to increase array bandwidth as opposed to the conventional approach of attempting to minimize coupling between array elements designed in isolation. The design leverages from multi-layer FSS design, resulting in a broadband, low profile, highly efficient, conformal array.


title: Facial Expression Emotion Detection for Real-Time Embedded Systems

abstract: Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM — stereo camera module. The model was built on the Atlys™ Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant’s video in real-time and two-dimensional predict labels of the emotion at the same time.


title: Detection of Corona Enhancement of Hypervascular Hepatocellular Carcinoma by C-Arm Dual-Phase Cone-Beam CT During Hepatic Arteriography

abstract: The purpose of this study was to evaluate the detectability of corona enhancement around the hypervascular hepatocellular carcinoma (HCC) by dual-phase cone-beam computed tomography during hepatic arteriography (CBCTHA). Dual-phase CBCTHA was performed for 71 HCC lesions (mean ± SD 1.7 ± 0.9 cm), including seven presenting a nodule-in-nodule appearance and nine hypervascular pseudolesions. The first scan was performed during injection of 30–40 ml half-diluted contrast material at a rate of 1.5–2 ml/s through the hepatic artery. Scanning was initiated 7 s after the beginning of contrast material injection. The second scan was started 30 s after the end of the first scan. Detectability of corona enhancement on second-phase CBCTHA was evaluated. Thickness of corona enhancement was also analyzed as thin (≤2 mm) or thick (>2 mm). Corona enhancement was detected in 63 (88.7%) of 71 tumors (1.8 ± 0.9 cm), but it was not detected in eight tumors (1.0 ± 0.2 cm). Thin corona enhancement was seen in 18 tumors (1.2 ± 0.5 cm), and thick corona enhancement was seen in 45 tumors (2.0 ± 0.9 cm). There was a significant difference in tumor diameter between tumors with and those without corona enhancement (P = 0.0157) and between thin and thick corona enhancement (P = 0.001). In all seven early-stage tumors, corona enhancement was demonstrated around the hypervascular focus within the hypovascular tumor portion. None of the nine pseudolesions showed any corona enhancement. Dual-phase CBCTHA depicted corona enhancement in 88.7% of hypervascular HCC lesions. This technique may improve the diagnostic accuracy of HCC.


title: SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?

abstract: We introduce SceneNet RGB-D, a dataset providing pixel-perfect ground truth for scene understanding problems such as semantic segmentation, instance segmentation, and object detection. It also provides perfect camera poses and depth data, allowing investigation into geometric computer vision problems such as optical flow, camera pose estimation, and 3D scene labelling tasks. Random sampling permits virtually unlimited scene configurations, and here we provide 5M rendered RGB-D images from 16K randomly generated 3D trajectories in synthetic layouts, with random but physically simulated object configurations. We compare the semantic segmentation performance of network weights produced from pretraining on RGB images from our dataset against generic VGG-16 ImageNet weights. After fine-tuning on the SUN RGB-D and NYUv2 real-world datasets we find in both cases that the synthetically pre-trained network outperforms the VGG-16 weights. When synthetic pre-training includes a depth channel (something ImageNet cannot natively provide) the performance is greater still. This suggests that large-scale high-quality synthetic RGB datasets with task-specific labels can be more useful for pretraining than real-world generic pre-training such as ImageNet. We host the dataset at http://robotvault. bitbucket.io/scenenet-rgbd.html.


title: Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka

abstract: Scientific applications require large computing power, traditionally exceeding the amount that is available within the premises of a single institution. Therefore, clouds can be used to provide extra resources whenever required. For this vision to be achieved, however, requires both policies defining when and how cloud resources are allocated to applications and a platform implementing not only these policies but also the whole software stack supporting management of applications and resources. Aneka is a cloud application platform capable of provisioning resources obtained from a variety of sources, including private and public clouds, clusters, grids, and desktops grids. In this paper, we present Aneka's deadline-driven provisioning mechanism, which is responsible for supporting quality of service (QoS)-aware execution of scientific applications in hybrid clouds composed of resources obtained from a variety of sources. Experimental results evaluating such a mechanism show that Aneka is able to efficiently allocate resources from different sources in order to reduce application execution times.


title: Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

abstract: In subset ranking, the goal is to learn a ranking function that approximates a gold standard partial ordering of a set of objects (in our case, a set of documents retrieved for the same query). The partial ordering is given by relevance labels representing the relevance of documents with respect to the query on an absolute scale. Our approach consists of three simple steps. First, we train standard multi-class classifiers (AdaBoost.MH and multi-class SVM) to discriminate between the relevance labels. Second, the posteriors of multi-class classifiers are calibrated using probabilistic and regression losses in order to estimate the Bayes-scoring function which optimizes the Normalized Discounted Cumulative Gain (NDCG). In the third step, instead of selecting the best multi-class hyperparameters and the best calibration, we mix all the learned models in a simple ensemble scheme.Our extensive experimental study is itself a substantial contribution. We compare most of the existing learning-to-rank techniques on all of the available large-scale benchmark data sets using a standardized implementation of the NDCG score. We show that our approach is competitive with conceptually more complex listwise and pairwise methods, and clearly outperforms them as the data size grows. As a technical contribution, we clarify some of the confusing results related to the ambiguities of the evaluation tools, and propose guidelines for future studies.


A Tri-Space Visualization Interface for Analyzing Time-Varying Multivariate Volume Data


title: Radiofrequency facial rejuvenation: evidence-based effect.

abstract: BACKGROUND ::: Multiple therapies involving ablative and nonablative techniques have been developed for rejuvenation of photodamaged skin. Monopolar radiofrequency (RF) is emerging as a gentler, nonablative skin-tightening device that delivers uniform heat to the dermis at a controlled depth. ::: ::: ::: OBJECTIVE ::: We evaluated the clinical effects and objectively quantified the histologic changes of the nonablative RF device in the treatment of photoaging. ::: ::: ::: METHODS ::: Six individuals of Fitzpatrick skin type III to IV and Glogau class I to II wrinkles were subjected to 3 months of treatment (6 sessions at 2-week intervals). Standard photographs and skin biopsy specimens were obtained at baseline, and at 3 and 6 months after the start of treatment. We performed quantitative evaluation of total elastin, collagen types I and III, and newly synthesized collagen using computerized histometric and immunohistochemical techniques. Blinded photographs were independently scored for wrinkle improvement. ::: ::: ::: RESULTS ::: RF produced noticeable clinical results, with high satisfaction and corresponding facial skin improvement. Compared with the baseline, there was a statistically significant increase in the mean of collagen types I and III, and newly synthesized collagen, while the mean of total elastin was significantly decreased, at the end of treatment and 3 months posttreatment. ::: ::: ::: LIMITATIONS ::: A limitation of this study is the small number of patients, yet the results show a significant improvement. ::: ::: ::: CONCLUSIONS ::: Although the results may not be as impressive as those obtained by ablative treatments, RF is a promising treatment option for photoaging with fewer side effects and downtime.


title: Rethinking disability and what to do about it: disability studies and its implications for occupational therapy.

abstract: Disability studies seeks to reframe rehabilitation’s understanding and responses to disability. Disability scholars point out that rehabilitation’s perspectives and practices are not objective, but instead reflect particular historical and ideological forces. By demonstrating how rehabilitation practitioners can unintentionally do things that are unhelpful or even harmful, disability scholars challenge the profession of occupational therapy to reconsider aspects of practice, education, and research. In order to provide a context for a special issue devoted to disability studies, this paper examines disability studies’ major critiques of rehabilitation and considers their implications for occupational therapy. The paper identifies ways that occupational therapy can continue to respond reflectively to the themes of disability studies. It also identifies a number of questions raised by disability studies that will need to be addressed in practice, education, and research.


title: A Formal and Pragmatic Approach to Engineering Safety-critical Rail Vehicle Control Software.

abstract: The engineering processes for safety-critical systems, for instance in the health care or transportation domains, are regulated by law. For software in the railroad industry in Europe the certification procedures have to obey the norm EN50128. This paper presents the method that was introduced and employed for the development and the successful certification of the software for the vehicle control unit (VCU) of the Vossloh Locomotives’ G6 shunting locomotives. The primary goal in the development of the software was conformity to EN50128, the secondary goal is a cost-efficient process without sacrificing safety. To achieve these goals our method is based on formal techniques, but also designed to be easily applicable in our context (pragmatics). Central to our method are functional trees as a design specification mechanism. The outcome of employing this method was the successful certification of the locomotive G6 without any software-related problems.


title: Customer segmentation using centroid based and density based clustering algorithms

abstract: In recent years, customer segmentation has become one of the most significant and useful tools for e-commerce. It plays a vital role in online product recommendation system and also helps to understand local and global wholesale or retail market. Customer segmentation refers to grouping customers into different categories based on shared characteristics such as age, location, spending habit and so on. Similarly, clustering means putting things together in such a way that similar type of things remain in the same group. Due to having similarities between these two terms, it is possible to apply clustering algorithms for ensuring satisfactory and automatic customer segmentation. Among different types of clustering algorithms, centroid based and density based are the most popular. This paper illustrates the idea of applying density based algorithms for customer segmentation beside using centroid based algorithms like k-means. Applying DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm as one of the density based algorithms results in a meaningful customer segmentation.


title: Coherent line drawing

abstract: This paper presents a non-photorealistic rendering technique that automatically generates a line drawing from a photograph. We aim at extracting a set of coherent, smooth, and stylistic lines that effectively capture and convey important shapes in the image. We first develop a novel method for constructing a smooth direction field that preserves the flow of the salient image features. We then introduce the notion of flow-guided anisotropic filtering for detecting highly coherent lines while suppressing noise. Our method is simple and easy to implement. A variety of experimental results are presented to show the effectiveness of our method in producing self-contained, high-quality line illustrations.


title: The prioritization of voice fundamental frequency or formants in listeners’ assessments of speaker size, masculinity, and attractiveness

abstract: Key features of the voice--fundamental frequency (F(0)) and formant frequencies (Fn)--can vary extensively among individuals. Some of this variation might cue fitness-related, biosocial dimensions of speakers. Three experiments tested the independent, joint and relative effects of F(0) and Fn on listeners' assessments of the body size, masculinity (or femininity), and attractiveness of male and female speakers. Experiment 1 replicated previous findings concerning the joint and independent effects of F(0) and Fn on these assessments. Experiment 2 established frequency discrimination thresholds (or just-noticeable differences, JND's) for both vocal features to use in subsequent tests of their relative salience. JND's for F(0) and Fn were consistent in the range of 5%-6% for each sex. Experiment 3 put the two voice features in conflict by equally discriminable amounts and found that listeners consistently tracked Fn over F(0) in rating all three dimensions. Several non-exclusive possibilities for this outcome are considered, including that voice Fn provides more reliable cues to one or more dimensions and that listeners' assessments of the different dimensions are partially interdependent. Results highlight the value of first establishing JND's for discrimination of specific features of natural voices in future work examining their effects on voice-based social judgments.


title: Crime Analysis using K-Means Clustering

abstract: today's world security is an aspect which is given higher priority by all political and government worldwide and aiming to reduce crime incidence. As data mining is the appropriate field to apply on high volume crime dataset and knowledge gained from data mining approaches will be useful and support police force. So In this paper crime analysis is done by performing k-means clustering on crime dataset using rapid miner tool.


title: A 0.35 μm 700 V BCD technology with self-isolated and non-isolated ultra-low specific on-resistance DB-nLDMOS

abstract: Integrated in a 0.35 μm 700 V BCD process platform, ultra-low Ron, sp 700 V self-ISO (isolated) and NISO (non-isolated) DB-nLDMOS (dual P-buried-layer nLDMOS) are proposed in this paper. 800 V and 780 V are achieved for NISO and ISO DB-nLDMOS, of which Ron, sp are 11.5 Ω·mm2 and 11.2 Ω·mm2, respectively. Utra-low Ron, sp benefits from optimized device size and strict limitations for annealing temperature and time after P-bury-layer implantation. For ISO DB-nLDMOS, by separately implanting NWELLs, NWELL drift region of low doping concentration under gate poly is achieved and then premature avalanche breakdown around bird's beak is avoided. Moreover, a 600 V DB-nJFET (dual P-buried-layer nJFET) with innovative 3D pinch-off structure is also presented.


title: Bobtail: Avoiding Long Tails in the Cloud

abstract: Highly modular data center applications such as Bing, Facebook, and Amazon's retail platform are known to be susceptible to long tails in response times. Services such as Amazon's EC2 have proven attractive platforms for building similar applications. Unfortunately, virtualization used in such platforms exacerbates the long tail problem by factors of two to four. Surprisingly, we find that poor response times in EC2 are a property of nodes rather than the network, and that this property of nodes is both pervasive throughout EC2 and persistent over time. The root cause of this problem is co-scheduling of CPU-bound and latency-sensitive tasks. We leverage these observations in Bobtail, a system that proactively detects and avoids these bad neighboring VMs without significantly penalizing node instantiation. With Bobtail, common communication patterns benefit from reductions of up to 40% in 99.9th percentile response times.


title: Comparing clusterings---an information based distance

abstract: This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set. The criterion, called variation of information (VI), measures the amount of information lost and gained in changing from clustering C to clustering C^'. The basic properties of VI are presented and discussed. We focus on two kinds of properties: (1) those that help one build intuition about the new criterion (in particular, it is shown the VI is a true metric on the space of clusterings), and (2) those that pertain to the comparability of VI values over different experimental conditions. As the latter properties have rarely been discussed explicitly before, other existing comparison criteria are also examined in their light. Finally we present the VI from an axiomatic point of view, showing that it is the only ''sensible'' criterion for comparing partitions that is both aligned to the lattice and convexely additive. As a consequence, we prove an impossibility result for comparing partitions: there is no criterion for comparing partitions that simultaneously satisfies the above two desirable properties and is bounded.


title: Ontology-Based Information and Event Extraction for Business Intelligence

abstract: We would like to introduce BEECON, an information and event extraction system for business intelligence. This is the first ontology-based system for business documents analysis that is able to detect 41 different types of business events from unstructured sources of information. The described system is intended to enhance business intelligence efficiency by automatically extracting relevant content such as business entities and events. In order to achieve it, we use natural language processing techniques, pattern recognition algorithms and hand-written detection rules. In our test set consisting of 190 documents with 550 events, the system achieved 95% precision and 67% recall in detecting all supported business event types from newspaper texts.


A Multi-channel MAC Protocol with Power Control for Multi-hop Mobile Ad Hoc Networks


title: Analysis of Metastability Errors in Conventional, LSB-First, and Asynchronous SAR ADCs

abstract: A practical model for characterizing comparator metastability errors in SAR ADCs is presented, and is used to analyze not only the conventional SAR but also LSB-first and asynchronous versions. This work makes three main contributions: first, it is shown that for characterizing metastability it is more reasonable to use input signals with normal or Laplace distributions. Previous work used uniformly-distributed signals in the interest of making derivations easier, but this simplifying assumption overestimated SMR by as much as 18 dB compared to the more reasonable analysis presented here. Second, this work shows that LSB-first SAR ADCs achieve SMR performance equal to or better than conventional SARs with the same metastability window, depending on bandwidth. Finally, the analysis is used to develop a framework for calculating the maximum effective sample rate for asynchronous SAR ADCs, and in doing so demonstrates that proximity detectors are not effective solutions to improving metastability performance.


title: DENCLUE 2 . 0 : Fast Clustering based on Kernel Density Estimation

abstract: The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginning and never converges exactly to the maximum, it just comes close. ::: ::: We introduce a new hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra costs. We prove that the procedure converges exactly towards a local maximum by reducing it to a special case of the expectation maximization algorithm. We show experimentally that the new procedure needs much less iterations and can be accelerated by sampling based methods with sacrificing only a small amount of accuracy.


title: QR code detection using convolutional neural networks

abstract: Barcodes have been long used for data storage. Detecting and locating barcodes in images of complex background is an essential yet challenging step in the process of automatic barcode reading. This work proposed an algorithm that localizes and segments two-dimensional quick response (QR) barcodes. The localization involved a convolutional neural network that could detect partial QR barcodes. Majority voting was then applied to determine barcode locations. Then image processing algorithms were implemented to segment barcodes from the background. Experimental results shows that the proposed approach was robust to detect QR barcodes with rotation and deformation.


title: Compensation and Calibration Techniques for Current-Steering DACs

abstract: Digital-to-analog converters (DACs) are pervasive, critical components for radios and various signal processing systems. Therefore, a myriad of research efforts, covering architectural, circuit, and technological aspects have been made towards improving the performance of DACs. However, the quest to achieve stringent dynamic linearity requirements (g70 dBc SFDR ) over many gigahertz of bandwidth presents grand challenges to designers and high-yield manufacturers. In light of these challenges, various calibration and compensation techniques have evolved over the past two decades to overcome design and process technology variations and limitations.


title: Forecasting socioeconomic trends with cell phone records

abstract: National Statistical Institutes typically hire large numbers of enumerators to carry out periodic surveys regarding the socioeconomic status of a society. Such approach suffers from two drawbacks:(i) the survey process is expensive, especially for emerging countries that struggle with their budgets and (ii) the socioeconomic indicators are computed ex-post i.e., after socioeconomic changes have already happened. We propose the use of human behavioral patterns computed from calling records to predict future values of socioeconomic indicators. Our objective is to help institutions be able to forecast socioeconomic changes before they happen while reducing the number of surveys they need to compute. For that purpose, we explore a battery of different predictive approaches for time series and show that multivariate time-series models yield R-square values of up to 0.65 for certain socioeconomic indicators.


title: Code generation via higher-order rewrite systems

abstract: We present the meta-theory behind the code generation facilities of Isabelle/HOL. To bridge the gap between the source (higher-order logic with type classes) and the many possible targets (functional programming languages), we introduce an intermediate language, Mini-Haskell. To relate the source and the intermediate language, both are given a semantics in terms of higher-order rewrite systems (HRSs). In a second step, type classes are removed from Mini-Haskell programs by means of a dictionary translation; we prove the correctness of this step. Building on equational logic also directly supports a simple but powerful algorithm and data refinement concept.


title: Probabilistic Neural Architecture Search

abstract: In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task. Despite the success of recent approaches, most existing methods cannot be directly applied to large scale problems because of their prohibitive computational complexity or high memory usage. In this work, we propose a Probabilistic approach to neural ARchitecture SEarCh (PARSEC) that drastically reduces memory requirements while maintaining state-of-the-art computational complexity, making it possible to directly search over more complex architectures and larger datasets. Our approach only requires as much memory as is needed to train a single architecture from our search space. This is due to a memory-efficient sampling procedure wherein we learn a probability distribution over high-performing neural network architectures. Importantly, this framework enables us to transfer the distribution of architectures learnt on smaller problems to larger ones, further reducing the computational cost. We showcase the advantages of our approach in applications to CIFAR-10 and ImageNet, where our approach outperforms methods with double its computational cost and matches the performance of methods with costs that are three orders of magnitude larger.


Implementing Pushback : Router-Based Defense Against DDoS Attacks


title: The ballbot: An omnidirectional balancing mobile robot

abstract: The ballbot is a human-sized dynamically stable mobile robot that balances on a single ball. Unlike statically stable mobile robots, the ballbot is tall and narrow with a high center of gravity and a small footprint. Moreover, its dynamic stability enables it to be physically interactive. These characteristics make it better suited to navigate and interact in cluttered human environments. This paper presents the evolved hardware design of the ballbot with a four-wheel inverse mouse-ball drive to actuate the ball, and a yaw drive mechanism that enables unlimited rotation about its vertical axis. The ballbot also has a triad of legs that provide static stability when powered down. This paper presents a detailed description of the ballbot's control architecture, and it presents several experimental results that demonstrate its balancing and locomotion capabilities. This paper also presents a trajectory planning algorithm that plans for body lean motions, which, when tracked, result in the desired rest-to-rest motions of the robot. Finally, the paper illustrates some interesting human-robot physical interaction behaviors that can be achieved as a result of the ballbot's dynamic stability.


title: Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

abstract: Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.


title: Service-Oriented Business Intelligence Reference Architecture in Face of Advanced BI Concepts.

abstract: Business Intelligence promises improved decision making. To meet the changing business requirements, BI has to reduce latency between data acquisition and decision. As a result of growing needs, concepts like Real Time, Active, Operational, Business Performance Management or embedded BI are introduced in the literature. Nevertheless, the technical implementation prevents the realization of these concepts. Thus, overloaded infrastructures and heterogeneous system landscapes avoid further evolution. To prevent this lack of system integration and performance, we address the changing BI paradigms through a service-oriented approach. Hence, the novel BI concepts and Service-oriented Business Intelligence (SoBI) theories are illustrated and combined in a uniform artifact. The construct is developed by using reference modeling. As a result, a SoBI reference architecture accrues, which becomes validated successfully and provides a basis for future implementations. This paper offers a flexible approach that reduces the latency in order of dynamic market environments.


Are retinal findings in abused children different from those who suffer accidental head trauma? Results of a systematic review


title: Extracting and Ranking Product Features in Opinion Documents

abstract: An important task of opinion mining is to extract people's opinions on features of an entity. For example, the sentence, "I love the GPS function of Motorola Droid" expresses a positive opinion on the "GPS function" of the Motorola phone. "GPS function" is the feature. This paper focuses on mining features. Double propagation is a state-of-the-art technique for solving the problem. It works well for medium-size corpora. However, for large and small corpora, it can result in low precision and low recall. To deal with these two problems, two improvements based on part-whole and "no" patterns are introduced to increase the recall. Then feature ranking is applied to the extracted feature candidates to improve the precision of the top-ranked candidates. We rank feature candidates by feature importance which is determined by two factors: feature relevance and feature frequency. The problem is formulated as a bipartite graph and the well-known web page ranking algorithm HITS is used to find important features and rank them high. Experiments on diverse real-life datasets show promising results.


title: Approach-Withdrawal and Cerebral Asymmetry: Emotional Expression and Brain Physiology I

abstract: In this experiment, we combined the measurement of observable facial behavior with simultaneous measures of brain electrical activity to assess patterns of hemispheric activation in different regions during the experience of happiness and disgust. Disgust was found to be associated with right-sided activation in the frontal and anterior temporal regions compared with the happy condition. Happiness was accompanied by left-sided activation in the anterior temporal region compared with disgust. No differences in asymmetry were found between emotions in the central and parietal regions. When data aggregated across positive films were compared to aggregate negative film data, no reliable differences in brain activity were found. These findings illustrate the utility of using facial behavior to verify the presence of emotion, are consistent with the notion of emotion-specific physiological patterning, and underscore the importance of anterior cerebral asymmetries for emotions associated with approach and withdrawal. This and the accompanying report (Ekman, Davidson, & Friesen, 1990) represent the convergence of two independent yet overlapping lines of research. One is theory and evidence from Ekman's laboratory, which, from an evolutionary perspective, takes the position that each emotion is characterized by unique patterns of expressive behavior and physiological activity (e.g., Ekman, 1977, 1984). The second is research from Davidson's laboratory on cerebral asymmetry and emotion, which indicates the differential involvement of the two cerebral hemispheres in the control of certain positive and negative emotions (e.g., Davidson, 1984a, 1984b, 1987; Davidson &Tomarken, 1989).


title: Neural Circuit Mechanisms of Social Behavior

abstract: We live in a world that is largely socially constructed, and we are constantly involved in and fundamentally influenced by a broad array of complex social interactions. Social behaviors among conspecifics, either conflictive or cooperative, are exhibited by all sexually reproducing animal species and are essential for the health, survival, and reproduction of animals. Conversely, impairment in social function is a prominent feature of several neuropsychiatric disorders, such as autism spectrum disorders and schizophrenia. Despite the importance of social behaviors, many fundamental questions remain unanswered. How is social sensory information processed and integrated in the nervous system? How are different social behavioral decisions selected and modulated in brain circuits? Here we discuss conceptual issues and recent advances in our understanding of brain regions and neural circuit mechanisms underlying the regulation of social behaviors.


title: Novel Criteria to Measure Performance of Time Series Segmentation Techniques

abstract: An important task in signal processing and temporal data mining is time series segmentation. In order to perform tasks such as time series classification, anomaly detection in time series, motif detec- tion, or time series forecasting, segmentation is often a pre-requisite. However, there has not been much research on evaluation of time se- ries segmentation techniques. The quality of segmentation techniques is mostly measured indirectly using the least-squares error that an approx- imation algorithm makes when reconstructing the segments of a time series given by segmentation. In this article, we propose a novel evalua- tion paradigm, measuring the occurrence of segmentation points directly. The measures we introduce help to determine and compare the quality of segmentation algorithms better, especially in areas such as finding perceptually important points (PIP) and other user-specified points.


Explicit Retrofitting of Distributional Word Vectors


Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph


title: KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems

abstract: To develop a knowledge-aware recommender system, a key issue is how to obtain rich and structured knowledge base (KB) information for recommender system (RS) items. Existing data sets or methods ei...


title: Fully Automatic Lexicon Expansion For Domain-Oriented Sentiment Analysis

abstract: This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to appear successively in contexts. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94% on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.


Training Student Networks for Acceleration with Conditional Adversarial Networks


title: Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition

abstract: Automatic emotion recognition is a challenging task. In this paper, we present our effort for the audio-video based sub-challenge of the Emotion Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to assign a single emotion label to the video clip from the six universal emotions (Anger, Disgust, Fear, Happiness, Sad and Surprise) and Neutral. The proposed multimodal emotion recognition system takes audio, video and text information into account. Except for handcraft features, we also extract bottleneck features from deep neutral networks (DNNs) via transfer learning. Both temporal classifiers and non-temporal classifiers are evaluated to obtain the best unimodal emotion classification result. Then possibilities are extracted and passed into the Beam Search Fusion (BS-Fusion). We test our method in the EmotiW 2018 challenge and we gain promising results. Compared with the baseline system, there is a significant improvement. We achieve 60.34% accuracy on the testing dataset, which is only 1.5% lower than the winner. It shows that our method is very competitive.


title: CLASSIFICATION OF IMBALANCED DATA: A REVIEW

abstract: Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.


title: An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy

abstract: Efficient scheduling is of great significance to rationally make use of scarce satellite resources. Task clustering has been demonstrated to realize an effective strategy to improve the efficiency of satellite scheduling. However, the previous task clustering strategy is static. That is, it is integrated into the scheduling in a two-phase manner rather than in a dynamic fashion, without expressing its full potential in improving the satellite scheduling performance. In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs). First, we develop a formal model for the scheduling of Earth observing satellites. Second, we analyze the related constraints involved in the observation task clustering process. Thirdly, we detail an implementation of the dynamic task clustering strategy and the adaptive Simulated Annealing algorithm. The adaptive Simulated Annealing algorithm is efficient, with the endowment of some sophisticated mechanisms, i.e. adaptive temperature control, tabu-list based revisiting avoidance mechanism, and intelligent combination of neighborhood structures. Finally, we report on experimental simulation studies to demonstrate the competitive performance of ASA-DTC. Moreover, we show that ASA-DTC is especially effective when SOSPs contain a large number of targets or these targets are densely distributed in a certain area.


title: Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties

abstract: PURPOSE ::: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. ::: ::: ::: METHODS ::: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum--midband fit, slope, and 0-MHz-intercept--were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. ::: ::: ::: RESULTS ::: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004). ::: ::: ::: CONCLUSIONS ::: Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes.


title: Deep Reinforcement Learning for Dynamic Urban Transportation Problems

abstract: We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an optimization problem - such as optimal control problems in intelligent transportation systems and long term urban planning. Often transportation models used to represent dynamics of a transportation system involve large data sets with complex input-output interactions and are difficult to use in the context of optimization. Use of deep learning metamodels can produce a lower dimensional representation of those relations and allow to implement optimization and reinforcement learning algorithms in an efficient manner. In particular, we develop deep learning models for calibrating transportation simulators and for reinforcement learning to solve the problem of optimal scheduling of travelers on the network.


title: SPNets: Differentiable Fluid Dynamics for Deep Neural Networks

abstract: In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these lay- ers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mented as a neural network, the resulting fluid dynamics are fully differentiable. We then show how this can be successfully used to learn fluid parameters from data, perform liquid control tasks, and learn policies to manipulate liquids.


title: Visual saliency landmark detection algorithm based on DNN feature extraction

abstract: The integrated navigation pattern that is formed by the scene matching navigation and the inertial navigation or the satellite navigation is an important development direction of UAV autonomous navigation technology. As for the error identification problem of matching area in the process of scene matching landmark detection, the algorithm of visual saliency landmark detection for scene matching based on deep neural network for feature extraction is proposed. The experiments illustrated that the algorithm can effectively solve the problem of landmark detection in different environments and improve the robustness and effectiveness of landmark detection.