title: Comparing Automatic And Human Evaluation Of NLG Systems

abstract: We consider the evaluation problem in Natural Language Generation (NLG) and present results for evaluating several NLG systems with similar functionality, including a knowledge-based generator and several statistical systems. We compare evaluation results for these systems by human domain experts, human non-experts, and several automatic evaluation metrics, including NI ST, B LEU, and ROUGE. We find that NI ST scores correlate best (>0.8) with human judgments, but that all automatic metrics we examined are biased in favour of generators that select on the basis of frequency alone. We conclude that automatic evaluation of NLG systems has considerable potential, in particular where high-quality reference texts and only a small number of human evaluators are available. However, in general it is probably best for automatic evaluations to be supported by human based evaluations, or at least by studies that demonstrate that a particular metric correlates well with human judgments in a given domain.


title: An evidence-based approach to scoping reviews

abstract: Objective ::: Scoping reviews are used to assess the extent of a body of literature on a particular topic, and often to ensure that further research in that area is a beneficial addition to world knowledge. The aim of this paper reports upon the development of a methodology for scoping reviews based upon the Arksey and O'Malley framework, the Levac, Colquhoun, and O'Brien, and the Joanna Briggs Institute methods of evidence synthesis. ::: ::: Methods ::: A working group consisting of members of the Joanna Briggs collaborating organizations met to discuss the proposed framework for the methodology and develop a draft for the scoping review methodology based on the Arksey and O'Malley framework and the work of Levac et al. This was followed by a workshop attended by other members of the organizations consisting of 30 international researchers to discuss the proposed methodology. Further refinement of the methodology was undertaken as a result of the feedback received from the workshop. ::: ::: Results ::: The development of the methodology focused on five stages of the protocol and review development. These were identifying the research question by clarifying and linking the purpose and research question, identifying the relevant studies using a three-step literature search in order to balance feasibility with breadth and comprehensiveness, careful selection of the studies to using a team approach, charting the data and collating the results to identify the implications of the study findings for policy, practice, or research. ::: ::: Linking Evidence to Action ::: The current methodology recommends including both quantitative and qualitative research, as well as evidence from economic and expert opinion sources to answer questions of effectiveness, appropriateness, meaningfulness and feasibility of health practices and delivery methods. The proposed framework has the potential to provide options when faced with complex concepts or broad research questions.


title: Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study

abstract: Structural information in web text provides natural annotations for NLP problems such as word segmentation and parsing. In this paper we propose a discriminative learning algorithm to take advantage of the linguistic knowledge in large amounts of natural annotations on the Internet. It utilizes the Internet as an external corpus with massive (although slight and sparse) natural annotations, and enables a classifier to evolve on the large-scaled and real-time updated web text. With Chinese word segmentation as a case study, experiments show that the segmenter enhanced with the Chinese wikipedia achieves significant improvement on a series of testing sets from different domains, even with a single classifier and local features.


title: Cloud Migration: A Case Study of Migrating an Enterprise IT System to IaaS

abstract: This case study illustrates the potential benefits and risks associated with the migration of an IT system in the oil & gas industry from an in-house data center to Amazon EC2 from a broad variety of stakeholder perspectives across the enterprise, thus transcending the typical, yet narrow, financial and technical analysis offered by providers. Our results show that the system infrastructure in the case study would have cost 37% less over 5 years on EC2, and using cloud computing could have potentially eliminated 21% of the support calls for this system. These findings seem significant enough to call for a migration of the system to the cloud but our stakeholder impact analysis revealed that there are significant risks associated with this. Whilst the benefits of using the cloud are attractive, we argue that it is important that enterprise decision-makers consider the overall organizational implications of the changes brought about with cloud computing to avoid implementing local optimizations at the cost of organization-wide performance.


title: Euler Spiral for Shape Completion

abstract: In this paper we address the curve completion problem, e.g., the geometric continuation of boundaries of objects which are temporarily interrupted by occlusion. Also known as the gap completion or shape completion problem, this problem is a significant element of perceptual grouping of edge elements and has been approached by using cubic splines or biarcs which minimize total curvature squared (elastica), as motivated by a physical analogy. Our approach is motivated by railroad design methods of the early 1900's which connect two rail segments by “transition curves”, and by the work of Knuth on mathematical typography. We propose that in using an energy minimizing solution completion curves should not penalize curvature as in elastica but curvature variation. The minimization of total curvature variation leads to an Euler Spiral solution, a curve whose curvature varies linearly with arclength. The construction of this curve from a pair of points and tangents at these points is reduced to a nonlinear system of equations involving Fresnel Integrals, whose solution relies on optimization from a suitable initial condition constrained to satisfy given boundary conditions. Since the choice of an appropriate initial curve is critical in this optimization, we analytically derive an optimal solution in the class of biarc curves, which is then used as the initial curve. The resulting interpolations yield intuitive interpolation across gaps and occlusions, and are extensible, in contrast to scale invariant elastica. In addition, Euler Spiral segments can be used in other applications of curve completions, e.g., modeling boundary segments between curvature extrema or skeletal branch geometry.


title: Unsupervised soccer video abstraction based on pitch, dominant color and camera motion analysis

abstract: We present a soccer video abstraction method based on the analysis of the audio and video streams. This method could be applied to other sports as rugby or american football. The main contribution of this paper is the design of an unsupervised summarization method, and more specifically, the introduction of an efficient detector of excited speech segments. An excited commentary is supposed to correspond to an interesting moment of the game. It is simultaneously characterized by an increase of the pitch (or fundamental frequency) within the voiced segments and an increase of the energy supported by the harmonics of the pitch. The pitch is estimated from the autocorrelation function and its local increases are detected from a multiresolution technique. We introduce a specific energy measure for the voiced segments. A statistical analysis of the energy measures is performed to detect the most excited parts of the speech. A deterministic combination of excited speech detection, dominant color identification and camera motion analysis is then performed in order to discriminate between excited speech sequences of the game and excited speech sequences in commercials or in studio shots included in the processed TV programs. The method presented here does not need any learning stage. It has been tested on seven soccer videos for a total duration of almost 20 hours.


title: The evolving role of standardisation in technological innovation: the case of photovoltaics

abstract: Despite the increasing awareness on the importance of standards in supporting technological innovation, there are limited understanding and empirical evidence on various roles of standards in supporting innovation activities and how they evolve over time, due to the complexity involved in innovation and standardisation. The current research carefully explores the evolving role of standardisation, through the historical study of standardisation in photovoltaics technology. Analysing archival documents and expert interviews both quantitatively and qualitatively, it is observed that various types of standards played different roles and functions to support different technological activities across its innovation journey. The study is expected to broaden the evidence base to inform standards organisations and policymakers in developing standardisation strategies to support technological innovation more effectively.


An Empirical Study on Context Awareness Integrated Mobile Assisted Instruction and the Factors


title: Achievable Downlink Rates of MRC and ZF Precoders in Massive MIMO With Uplink and Downlink Pilot Contamination

abstract: Massive multi-input multi-output (MIMO) systems require channel state information (CSI) at base stations. In multicell environments, adjacent cells using the same spectrum cause a pilot contamination problem and degrade CSI quality. Under such practical environments, it is important to assess achievable rate of the system for a proper system design. There exist achievable rate bounds of uplink (UL) systems in the literature, but general closed-form rate bounds for downlink (DL) systems are not available yet. This paper studies a practical massive MIMO DL system with or without pilot-aided coherent detection under the scenario of pilot contamination, and derives closed-form approximate achievable DL rate expressions for both maximum ratio combining (MRC) and zero forcing (ZF) precoders. For the case with DL pilot, a corresponding DL effective channel estimator is also developed. Numerical simulation results corroborate accuracy of the closed-form rate expressions and they also show performance characteristics of pilot-contaminated massive MIMO DL systems with MRC and ZF precoders.


Latent Topic Embedding


title: A Bayesian game approach for intrusion detection in wireless ad hoc networks

abstract: In wireless ad hoc networks, although defense strategies such as intrusion detection systems (IDSs) can be deployed at each mobile node, significant constraints are imposed in terms of the energy expenditure of such systems. In this paper, we propose a game theoretic framework to analyze the interactions between pairs of attacking/defending nodes using a Bayesian formulation. We study the achievable Nash equilibrium for the attacker/defender game in both static and dynamic scenarios. The dynamic Bayesian game is a more realistic model, since it allows the defender to consistently update his belief on his opponent's maliciousness as the game evolves. A new Bayesian hybrid detection approach is suggested for the defender, in which a lightweight monitoring system is used to estimate his opponent's actions, and a heavyweight monitoring system acts as a last resort of defense. We show that the dynamic game produces energy-efficient monitoring strategies for the defender, while improving the overall hybrid detection power.


title: An Overview of BioCreative II.5

abstract: We present the results of the BioCreative II.5 evaluation in association with the FEBS Letters experiment, where authors created Structured Digital Abstracts to capture information about protein-protein interactions. The BioCreative II.5 challenge evaluated automatic annotations from 15 text mining teams based on a gold standard created by reconciling annotations from curators, authors, and automated systems. The tasks were to rank articles for curation based on curatable protein-protein interactions; to identify the interacting proteins (using UniProt identifiers) in the positive articles (61); and to identify interacting protein pairs. There were 595 full-text articles in the evaluation test set, including those both with and without curatable protein interactions. The principal evaluation metrics were the interpolated area under the precision/recall curve (AUC iP/R), and (balanced) F-measure. For article classification, the best AUC iP/R was 0.70; for interacting proteins, the best system achieved good macroaveraged recall (0.73) and interpolated area under the precision/recall curve (0.58), after filtering incorrect species and mapping homonymous orthologs; for interacting protein pairs, the top (filtered, mapped) recall was 0.42 and AUC iP/R was 0.29. Ensemble systems improved performance for the interacting protein task.


title: Longitudinal and Lateral Control for Automated Yielding Maneuvers

abstract: Automated driving is predicted to enhance traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually realize fully automated driving, the intelligent vehicle system must have the ability to plan different maneuvers while adapting to the surrounding traffic environment. This paper presents an algorithm for longitudinal and lateral trajectory planning for automated driving maneuvers where the vehicle does not have right of way, i.e., yielding maneuvers. Such maneuvers include, e.g., lane change, roundabout entry, and intersection crossing. In the proposed approach, the traffic environment which the vehicle must traverse is incorporated as constraints on its longitudinal and lateral positions. The trajectory planning problem can thereby be formulated as two loosely coupled low-complexity model predictive control problems for longitudinal and lateral motion. Simulation results demonstrate the ability of the proposed trajectory planning algorithm to generate smooth collision-free maneuvers which are appropriate for various traffic situations.


title: BTM: Topic Modeling over Short Texts

abstract: Short texts are popular on today’s web, especially with the emergence of social media. Inferring topics from large scale short texts becomes a critical but challenging task for many content analysis tasks. Conventional topic models such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) learn topics from document-level word co-occurrences by modeling each document as a mixture of topics, whose inference suffers from the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a novel way for short text topic modeling, referred as biterm topic model (BTM) . BTM learns topics by directly modeling the generation of word co-occurrence patterns (i.e., biterms) in the corpus, making the inference effective with the rich corpus-level information. To cope with large scale short text data, we further introduce two online algorithms for BTM for efficient topic learning. Experiments on real-word short text collections show that BTM can discover more prominent and coherent topics, and significantly outperform the state-of-the-art baselines. We also demonstrate the appealing performance of the two online BTM algorithms on both time efficiency and topic learning.


title: Understand Short Texts by Harvesting and Analyzing Semantic Knowledge

abstract: Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. We argue that semantic knowledge is required in order to better understand short texts. In this work, we build a prototype system for short text understanding which exploits semantic knowledge provided by a well-known knowledgebase and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.


title: Parallel clustering method for non-disjoint partitioning of large-scale data based on spark framework

abstract: Clustering large scale data has become an important challenge which motivates several recent works. While the emphasis has been on the organization of massive data into disjoint groups, this work considers the identification of non-disjoint groups rather than the disjoint ones. In this setting, it is possible for data object to belong simultaneously to several groups since many real-world applications of clustering require non-disjoint partitioning to fit data structures. For this purpose, we propose the Parallel Overlapping k-means method (POKM) which is able to perform parallel clustering processes leading to non-disjoint partitioning of data. The proposed method is implemented within Spark framework to ensure the distribution of works over the different computation nodes. Experiments which we have performed on simulated and real-world multi-labeled datasets shows both faster execution times and high quality of clustering compared to existing methods.


title: Efficient and Flexible Sampling with Blue Noise Properties of Triangular Meshes

abstract: This paper deals with the problem of taking random samples over the surface of a 3D mesh describing and evaluating efficient algorithms for generating different distributions. We discuss first the problem of generating a Monte Carlo distribution in an efficient and practical way avoiding common pitfalls. Then, we propose Constrained Poisson-disk sampling, a new Poisson-disk sampling scheme for polygonal meshes which can be easily tweaked in order to generate customized set of points such as importance sampling or distributions with generic geometric constraints. In particular, two algorithms based on this approach are presented. An in-depth analysis of the frequency characterization and performance of the proposed algorithms are also presented and discussed.


title: Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

abstract: As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.


title: Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

abstract: The problem of high energy consumption is becoming more and more serious due to the construction of large-scale cloud data centers. In order to reduce the energy consumption and SLA violation, a new virtual machine VM placement algorithm named ATEA adaptive three-threshold energy-aware algorithm, which takes good use of the historical data from resource usage by VMs, is presented. In ATEA, according to the load handled, data center hosts are divided into four classes: hosts with little load, hosts with light load, hosts with moderate load, and hosts with heavy load. ATEA migrates VMs on heavily loaded or little-loaded hosts to lightly loaded hosts, while the VMs on lightly loaded and moderately loaded hosts remain unchanged. Then, on the basis of ATEA, two kinds of adaptive three-threshold algorithm and three kinds of VMs selection policies are proposed. Finally, we verify the effectiveness of the proposed algorithms by CloudSim toolkit utilizing real-world workload. The experimental results show that the proposed algorithms efficiently reduce energy consumption and SLA violation.


title: A Chatbot-based Interactive Question Answering System

abstract: Interactive question answering (QA) systems, where a dialogue interface enables followup and clarification questions, are a recent field of research. We report our experience on the design, implementation and evaluation of a chatbot-based dialogue interface for our open-domain QA system, showing that chatbots can be effective in supporting interactive QA.


title: Machine Learning in Fetal Cardiology: What to Expect.

abstract: In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.


Predicting Latent Narrative Mood Using Audio and Physiologic Data.


title: Fundamental Design of a Consequent-Pole Transverse-Flux Motor for Direct-Drive Systems

abstract: Transverse-flux motors are suitable for large-torque generation because multipole devices are easy to be designed. However, these motors employ a surface permanent magnet rotor that consists of many magnets and mainly depend not on reluctance torque, but on magnetic torque, which is generated by the interaction between the magnetic fluxes produced by the magnets and stator-winding excitation. This paper proposes a consequent-pole transverse-flux motor that generates almost the same torque as conventional transverse-flux motors but uses half the number of magnets by generating both magnetic torque and reluctance torque.


title: Co-simulation of a marine electrical power system using PowerFactory and MATLAB/Simulink

abstract: In this work, an approach to co-simulate a marine electrical power system is set forth using PowerFactory and MATLAB/Simulink. Although MATLAB/Simulink is one of the well-known simulation tools to design and simulate marine power modules, it lacks the features for comprehensive power system analysis, which are offered by PowerFactory. In this paper, a methodology is proposed to map the MATLAB/Simulink power system module model as an object in PowerFactory environment. This approach offers the benefits of exploiting the features of both platforms as well as the potential reduction in development time and costs by recycling available models.


title: A girl with mosaicism for a dicentric X chromosome (45,X/46,X,dic(X) (Xqter→p22::p22→qter))

abstract: A 12-year-old girl was examined for growth retardation and a few very discrete dysmorphologic stigmata of Turner's syndrome; the genitalia were infantile yet both ovaries possessed functioning follicles. R- and C-banding techniques and Brdu treatment demonstrated a 45,X formula in 95% of lymphocytes, with 5% presenting a 46,X,dic(X) formula. Cytogenetic and clinical problems raised by this observation are discussed in relation to data from the literature.


title: Cryptoraptor: High throughput reconfigurable cryptographic processor

abstract: This paper describes a high performance, low power, and highly flexible cryptographic processor, Cryptoraptor, which is designed to support both today's and tomorrow's symmetric-key cryptography algorithms and standards. To the best of our knowledge, the proposed cryptographic processor supports the widest range of cryptographic algorithms compared to other solutions in the literature and is the only crypto-specific processor targeting future standards as well. Our 1GHz design achieves a peak throughput of 128Gbps for AES-128 which is competitive with ASIC designs and has 25X and 160X higher throughputs per area than CPU and GPU solutions, respectively.


title: Prevention of cardiovascular disease in high-risk individuals in low-income and middle-income countries: health effects and costs

abstract: In 2005, a global goal of reducing chronic disease death rates by an additional 2% per year was established. Scaling up coverage of evidence-based interventions to prevent cardiovascular disease in high-risk individuals in low-income and middle-income countries could play a major part in reaching this goal. We aimed to estimate the number of deaths that could be averted and the financial cost of scaling up, above current coverage levels, a multidrug regimen for prevention of cardiovascular disease (a statin, aspirin, and two blood-pressure-lowering medicines) in 23 such countries. Identification of individuals was limited to those already accessing health services, and treatment eligibility was based on the presence of existing cardiovascular disease or absolute risk of cardiovascular disease by use of easily measurable risk factors. Over a 10-year period, scaling up this multidrug regimen could avert 17.9 million deaths from cardiovascular disease (95% uncertainty interval 7.4 million-25.7 million). 56% of deaths averted would be in those younger than 70 years, with more deaths averted in women than in men owing to larger absolute numbers of women at older ages. The 10-year financial cost would be US$47 billion ($33 billion-$61 billion) or an average yearly cost per head of $1.08 ($0.75-1.40), ranging from $0.43 to $0.90 across low-income countries and from $0.54 to $2.93 across middle-income countries. This package could effectively meet three-quarters of the proposed global goal with a moderate increase in health expenditure.


title: How to empower employees: using training to enhance work units’ collective empowerment

abstract: Purpose - – The purpose of this paper is to examine, theoretically and empirically, whether an employee training program can enhance the collective perception of empowerment of work units within an organization. The authors hypothesized that training participation relates to empowerment by enhancing the potency, meaningfulness, impact, and autonomy of the employees. Design/methodology/approach - – The authors collected data at two time points, before and after the training intervention. Over the two periods, the sample consisted of an average of 2,383 employees nested in 36 work units of a large multinational company. Findings - – The results indicated a positive relationship between training participation and increased levels of collective psychological empowerment, with differential effects on the dimensions of empowerment. Practical implications - – This study provides evidence of the positive relationship between training and empowerment, suggesting training effects across levels of analysis. The results indicated dimensions of empowerment that are more and such that are less prone to training. Such knowledge may help to inform organizations in developing training strategies. The authors provide recommendations for a respective training program. Originality/value - – This is one of the first studies to investigate the relationship between training participation of individual employees and shared empowerment perceptions within their work units, adding an important antecedent to the research on empowerment. In addition, the authors propose ways of how individual employees can affect shared perceptions among work-unit members. The study offers insights into the development of empowered work units, the vertical transfer of training across levels of analysis and implications for training programs.


title: A label-free fluorescence sensor for probing the interaction of oligonucleotides with target molecules.

abstract: This paper describes a universal, label-free fluorescence sensor that gauges the interaction of oligonucleotides with their targets. The sensor is based on supramolecular assemblies formed by oligonucleotides (polyanions) and small molecule cation surfactant in aqueous solution. The environmentally dependent dye pyrene, encapsulated in the apolar interiors of the assemblies via hydrophobic interactions works as the fluorescence probe. Target binding causes the conformational change of the oligonucleotides, which results in disorganization of supramolecular assemblies, release of pyrene into the aqueous solution and subsequent quenching of its fluorescence. The kinetic processes (including the formation of supramolecular assemblies and the release of pyrenes after adding the targets) were investigated. The fluorescence decreases of pyrenes are proportional to the concentrations of targets within the linear ranges. This label-free fluorescence system is simple, convenient, low cost, and can serve as an alternative tool for interaction studies of oligonucleotides with their targets, especially with small molecular targets.


title: Imaging cellular network dynamics in three dimensions using fast 3D laser scanning

abstract: Spatiotemporal activity patterns in three-dimensionally organized cellular networks are fundamental to the function of the nervous system. Despite advances in functional imaging of cell populations, a method to resolve local network activity in three dimensions has been lacking. Here we introduce a three-dimensional (3D) line-scan technology for two-photon microscopy that permits fast fluorescence measurements from several hundred cells distributed in 3D space. We combined sinusoidal vibration of the microscope objective at 10 Hz with 'smart' movements of galvanometric x-y scanners to repeatedly scan the laser focus along a closed 3D trajectory. More than 90% of cell somata were sampled by the scan line within volumes of 250 microm side length. Using bulk-loading of calcium indicator, we applied this method to reveal spatiotemporal activity patterns in neuronal and astrocytic networks in the rat neocortex in vivo. Two-photon population imaging using 3D scanning opens the field for comprehensive studies of local network dynamics in intact tissue.


Data, Schema, Ontology and Logic Integration


title: Nonlinear Inverse Reinforcement Learning with Gaussian Processes

abstract: We present a probabilistic algorithm for nonlinear inverse reinforcement learning. The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse reinforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the reward as a nonlinear function, while also determining the relevance of each feature to the expert's policy. Our probabilistic algorithm allows complex behaviors to be captured from suboptimal stochastic demonstrations, while automatically balancing the simplicity of the learned reward structure against its consistency with the observed actions.


title: Representing shape with a spatial pyramid kernel

abstract: The objective of this paper is classifying images by the object categories they contain, for example motorbikes or dolphins. There are three areas of novelty. First, we introduce a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel. These are designed so that the shape correspondence between two images can be measured by the distance between their descriptors using the kernel. Second, we generalize the spatial pyramid kernel, and learn its level weighting parameters (on a validation set). This significantly improves classification performance. Third, we show that shape and appearance kernels may be combined (again by learning parameters on a validation set). Results are reported for classification on Caltech-101 and retrieval on the TRECVID 2006 data sets. For Caltech-101 it is shown that the class specific optimization that we introduce exceeds the state of the art performance by more than 10%.


title: Modeling Uncertainty and Inconsistency in Ontology-Based Context-Awareness Computing

abstract: The ubiquitous city is an urban application based on ubiquitous and context-aware computing. Therefore, context- aware systems for the ubiquitous city need to construct multiple ontologies which conceptualize complicate structure of heterogeneous contexts of the city. In this paper, we focus on uncertainty and inconsistency caused by integration of two or more ontologies for interpreting sensor data, inferring contexts, and applying appropriate rules. We define concepts of uncertainty and inconsistency in context-awareness computing and provide the method for detecting and resolving uncertainty and inconsistency through building an example context, flood context ontology.


title: Dynamic weighting ensemble classifiers based on cross-validation

abstract: Dynamic ensemble learning methods explore the use of different classifiers for different samples, therefore, may get better generalization ability than static ensemble learning methods. However, for most of dynamic approaches based on KNN rule, it needs to take out additional part of training samples to estimate “local classification performance” of each base classifier. When the number of training samples is not sufficient enough, it would lead to the unreliableness of estimating local performances of base classifiers, so further hurt the integrated performance. This paper presents a new dynamic ensemble model which introduces cross-validation technique in the process of evaluation to dynamically assign a weight to each component classifier. The introduction of cross-validation reduced the risk brought by the shortage of training samples. Experiments on 6 UCI data sets show that when the size of training set is not large enough, the proposed method can achieve better performance compared with some dynamic ensemble methods as well as some classical static ensemble approaches.


title: Distributed REScala: an update algorithm for distributed reactive programming

abstract: Reactive programming improves the design of reactive applications by relocating the logic for managing dependencies between dependent values away from the application logic to the language implementation. Many distributed applications are reactive. Yet, existing change propagation algorithms are not suitable in a distributed setting. We propose Distributed REScala, a reactive language with a change propagation algorithm that works without centralized knowledge about the topology of the dependency structure among reactive values and avoids unnecessary propagation of changes, while retaining safety guarantees (glitch freedom). Distributed REScala enables distributed reactive programming, bringing the benefits of reactive programming to distributed applications. We demonstrate the enabled design improvements by a case study. We also empirically evaluate the performance of our algorithm in comparison to other algorithms in a simulated distributed setting.


title: A Large Subcategorization Lexicon for Natural Language Processing Applications

abstract: We introduce a large computational subcategorization lexicon which includes subcategorization frame (SCF) and frequency information for 6,397 English verbs. This extensive lexicon was acquired automatically from five corpora and the Web using the current version of the comprehensive subcategorization acquisition system of Briscoe and Carroll (1997). The lexicon is provided freely for research use, along with a script which can be used to filter and build sub-lexicons suited for different natural language processing (NLP) purposes. Documentation is also provided which explains each sub-lexicon option and evaluates its accuracy.


title: EEG-based brain-computer interaction: Improved accuracy by automatic single-trial error detection

abstract: Brain-computer interfaces (BCIs), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the EEG recorded right after the occurrence of an error. Six healthy volunteer subjects with no prior BCI experience participated in a new human-robot interaction experiment where they were asked to mentally move a cursor towards a target that can be reached within a few steps using motor imagination. This experiment confirms the previously reported presence of a new kind of ErrP. These "Interaction ErrP" exhibit a first sharp negative peak followed by a positive peak and a second broader negative peak (~290, ~350 and ~470 ms after the feedback, respectively). But in order to exploit these ErrP we need to detect them in each single trial using a short window following the feedback associated to the response of the classifier embedded in the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 81.8% and 76.2%, respectively. Furthermore, we have achieved an average recognition rate of the subject's intent while trying to mentally drive the cursor of 73.1%. These results show that it's possible to simultaneously extract useful information for mental control to operate a brain-actuated device as well as cognitive states such as error potentials to improve the quality of the brain-computer interaction. Finally, using a well-known inverse model (sLORETA), we show that the main focus of activity at the occurrence of the ErrP are, as expected, in the pre-supplementary motor area and in the anterior cingulate cortex.


title: Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context

abstract: This paper describes a new method to segment printed mathematical documents precisely and extract formulas automatically from their images. Unlike classical methods, it is more directed towards segmentation rather than recognition, isolating mathematical formulas outside and inside text-lines. Our ultimate goal is to delimit parts of text that could disturb OCR applications, not yet trained for formula recognition and restructuring. The method is based on a global and a local segmentation. The global segmentation separates isolated formulas from the text lines using a primary labeling. The local segmentation propagates the context around the mathematical operators met to discard embedded formulas from plain text. The primary labeling identifies some mathematical symbols by models created at a learning step using fuzzy logic. The secondary labeling reinforces the results of the primary labeling and locates the subscripts and the superscripts inside the text. A heuristic has been defined that guides this automatic process. In this paper, the different modules making up the automated segmentation of mathematical document system are presented with examples of results. Experiments carried out on some commonly seen mathematical documents show that our proposed method can achieve quite satisfactory rates, making mathematical formula extraction more feasible for real-world applications. The average rate of primary labeling of mathematical operators is about 95.3% and their secondary labeling can improve the rate by about 4%. The formula extraction rate, evaluated with 300 formulas and 100 mathematical documents having variable complexity, is close to 93%


title: Towards a Neural Statistician

abstract: An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a method for computing representations, or statistics, of datasets in an unsupervised fashion. The network is trained to produce statistics that encapsulate a generative model for each dataset. Hence the network enables efficient learning from new datasets for both unsupervised and supervised tasks. We show that we are able to learn statistics that can be used for: clustering datasets, transferring generative models to new datasets, selecting representative samples of datasets and classifying previously unseen classes.


title: Latent fingerprint indexing: Fusion of level 1 and level 2 features

abstract: Fingerprints have been widely used as a biometric trait for person recognition. Due to the wide acceptance and deployment of fingerprint matching systems, there is a steady increase in the size of fingerprint databases in law enforcement and national ID agencies. Thus, it is of great interest to develop methods that, for a given query fingerprint (rolled or latent), can efficiently filter out a large portion of the reference or background database based on a coarse matching (or indexing) strategy. In this work, we propose an indexing technique, primarily for latents, that combines multiple level 1 and level 2 features to filter out a large portion of the background database while maintaining the latent matching accuracy. Our approach consists of combining minutiae, singular points, orientation field and frequency information. Experimental results carried out on 258 latents in NIST SD27 against a large background database (267K rolled prints) show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. At a penetration rate of 20%, our approach can reach a hit rate of 90.3%, with a five-fold reduction in the latent search (indexing + matching) time, while maintaining the latent matching accuracy.


title: International Affective Picture System (IAPS) in Chile: A cross-cultural adaptation and validation study

abstract: The International Affective Picture System (IAPS) (Lang, Ohman, & Vaitl, 1988) is a standardized set of photographs to induce emotions in the context of experimental investigations. The aim of this study was determine the psychometric behavior of the IAPS for the preparation of standards in the Chilean population and the subsequent validation of the instrument for its use in Chile. 208 university students participated (40% men) and had an affective experience induced using the presentation of visual stimuli extracted from sets 7 and 14 of the IAPS. In order to evaluate valence, arousal and dominance, the self-assessment manikin (SAM) was used, made up of pictographic scales for each dimension and a scale of continuous variation of emotional reactions. The IAPS showed psychometric behavior comparable to the original, with the same boomerang-shaped distribution of scores being found in the valence/arousal relationship. The IAPS is a reliable tool for inducing emotions in studies related to affectivity.


title: AI, Native Supercomputing and The Revival of Moore's Law

abstract: Based on Alan Turing's proposition on AI and computing machinery, which shaped Computing as we know it today, the new AI computing machinery should comprise a universal computer and a universal learning machine. The later should understand linear algebra natively to overcome the slowdown of Moore's law. In such a universal learnig machine, a computing unit does not need to keep the legacy of a universal computing core. The data can be distributed to the computing units, and the results can be collected from them through Collective Streaming, reminiscent of Collective Communication in Supercomputing. It is not necessary to use a GPU-like deep memory hierarchy, nor a TPU-like fine-grain mesh.


title: Constrained Bayesian Optimization with Noisy Experiments

abstract: Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multiple continuous parameters, but existing approaches degrade in performance when the noise level is high, limiting its applicability to many randomized experiments. We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized. Simulations with synthetic functions show that optimization performance on noisy, constrained problems outperforms existing methods. We further demonstrate the effectiveness of the method with two real-world experiments conducted at Facebook: optimizing a ranking system, and optimizing server compiler flags.


title: Skin ligaments: regional distribution and variation in morphology.

abstract: Skin ligaments (SL) (L. retinacula cutis) are present extensively in the face, hands, feet, and in breast tissue, but have seldom been reported elsewhere in the body. The traditional histological view of the subcutaneous region is that it comprises a matrix of loose connective tissue devoid of fibrous specializations. The purpose of this study was to determine the structure and distribution of skin ligaments. Eight embalmed cadavers (3 males, 5 females, 69-90 years of age) were used in this study. Tissue was prepared using the E12 plastination technique. Macroscopic and microscopic examination demonstrated the widespread presence in the limbs and most of the rest of the body of fibrous strands linking the base of the dermis and the superficial fibers of the underlying deep fascia. The morphology and distribution of these skin ligaments were similar in the individuals examined. Variations in the structure of the skin ligaments depended on the presence of underlying muscle, neurovascular bundles, intermuscular septa and adipose tissue. We conclude that skin ligaments are complex fibrous structures that are present over most of the body. They form an extensive peripheral network in the subcutaneous fat. These 'ligaments' seem to provide an anchorage of skin to deep fascia that is flexible and yet resistant to mechanical loading from multi-directional forces. The use of the E12 plastination technique coupled with fluorescent confocal microscopy has been of benefit in visualizing and delineating SLs from other soft tissue structures in three planes.


Cognitive and Ocular Factors Jointly Determine Pupil Responses under Equiluminance


title: Learning Beam Search Policies via Imitation Learning

abstract: Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do not explicitly learn how to use the beam. We develop an unifying meta-algorithm for learning beam search policies using imitation learning. In our setting, the beam is part of the model, and not just an artifact of approximate decoding. Our meta-algorithm captures existing learning algorithms and suggests new ones. It also lets us show novel no-regret guarantees for learning beam search policies.


title: Developmental Dyspraxia by Any Other Name: Are They All Just Clumsy Children?

abstract: The recent introduction of the diagnostic category developmental coordination disorder (DCD) (American Psychiatric Association [APA], 1987, 1994), has generated confusion among researchers and clinicians in many fields, including occupational therapy. Although the diagnostic criteria appear to be similar to those used to define clumsy children, children with developmental dyspraxia, or children with sensory integrative dysfunction, we are left with the question: Are children who receive the diagnosis of DCD the same as those who receive the other diagnoses, a subgroup, or an entirely distinct group of children? This article will examine the theoretical and empirical literature and use the results to support the thesis that these terms are not interchangeable and yet are not being used in the literature in a way that clearly defines each subgroup of children. Clear definitions and characteristic features need to be identified and associated with each term to guide occupational therapy assessment and intervention and clinical research.


title: BLDC-control of forces in passive magnetic bearings in blood pump

abstract: Artificial heart needs blood pump which satisfies not only mechanical and electrical properties, but also very important biomedical properties, particularly frictionless, with compact design and efficiency. The only one solution to meet the requirements. It is blood pump integrated with BLDC motor with rotor suspended using passive magnetic bearings. Passive magnetic bearings are solution with all required features, they are frictionless, compact and lossless. The one disadvantage is instability of passive bearing system. A new solution for stabilization of magnetic bearings is proposed in this paper. It consists in application of proper control of BLDC drive to obtain forces that together with the forces generated by permanent magnets make overall system stable. Because the only one direction (one degree of freedom) in passive bearings is unstable, it can be stabilized using magnetic forces that come from the motor. An idea of passive bearings stabilized by BLDC drive is presented in the paper. There are also selected results of FEM modelling, magnetic field and force distribution.


title: Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm

abstract: This paper proposes a path planning strategy for an Autonomous Ground Vehicle (AGV) navigating in a partially known environment. Global path planning is performed by first using a spatial database of the region to be traversed containing selected attributes such as height data and soil information from a suitable spatial database. The database is processed using a biomimetic swarm algorithm that is inspired by the nest building strategies followed by termites. Local path planning is performed online utilizing information regarding contingencies that affect the safe navigation of the AGV from various sensors. The simulation discussed has been implemented on the open source Player-Stage-Gazebo platform.


title: Security Requirements Analysis for the IoT

abstract: Due to the rapid growth of network infrastructure and sensor, the age of the IoT (internet of things) that can be implemented into the smart car, smart home, smart building, and smart city is coming. IoT is a very useful ecosystem that provides various services (e.g., amazon echo); however, at the same time, risk can be huge too. Collecting information to help people could lead serious information leakage, and if IoT is combined with critical control system (e.g., train control system), security attack would cause loss of lives. Furthermore, research on IoT security requirements is insufficient now. Therefore, this paper focuses on IoT security, and its requirements. First, we propose basic security requirements of IoT by analyzing three basic characteristics (i.e., heterogeneity, resource constraint, dynamic environment). Then, we suggest six key elements of IoT (i.e., IoT network, cloud, user, attacker, service, platform) and analyze their security issues for overall security requirements. In addition, we evaluate several IoT security requirement researches.


title: An improved LoRaWAN protocol based on adaptive duty cycle

abstract: In the low power wide area network, the LoRaWAN protocol adopts ALOHA type to access channel without duty cycle limited. The energy-efficient of the node and the robustness for varying traffic load are not considered in the situation which may lead to unfairness of node. To balance the energy consumption and network performance, an adaptive duty cycle medium access control (ADC-MAC) protocol is proposed. The basic idea of the protocol is that each sensor node maintains three indicators including the residual energy, the node load and the network congestion rate. The sensor node selects a different duty cycle to control the minimum interval of the uplinks. The asynchronous scheme is easier to implement as time synchronization is not necessary. The results, acquired by OPNET, demonstrate that the proposed ADC-MAC protocol have obvious advantages in extending the battery lifetime and improving packet delivery rate.


title: Wisteria: Nurturing Scalable Data Cleaning Infrastructure

abstract: Analysts report spending upwards of 80% of their time on problems in data cleaning. The data cleaning process is inherently iterative, with evolving cleaning workflows that start with basic exploratory data analysis on small samples of dirty data, then refine analysis with more sophisticated/expensive cleaning operators (e.g., crowdsourcing), and finally apply the insights to a full dataset. While an analyst often knows at a logical level what operations need to be done, they often have to manage a large search space of physical operators and parameters. We present Wisteria, a system designed to support the iterative development and optimization of data cleaning workflows, especially ones that utilize the crowd. Wisteria separates logical operations from physical implementations, and driven by analyst feedback, suggests optimizations and/or replacements to the analyst's choice of physical implementation. We highlight research challenges in sampling, in-flight operator replacement, and crowdsourcing. We overview the system architecture and these techniques, then provide a demonstration designed to showcase how Wisteria can improve iterative data analysis and cleaning. The code is available at: http://www.sampleclean.org.


title: Optimisation Of Boids Swarm Model Based On Genetic Algorithm And Particle Swarm Optimisation Algorithm (Comparative Study).

abstract: In this paper, we present two optimisation methods for a generic boids swarm model which is derived from the original Reynolds’ boids model to simulate the aggregate moving of a fish school. The aggregate motion is the result of the interaction of the relatively simple behaviours of the individual simulated boids. The aggregate moving vector is a linear combination of every simple behaviour rule vector. The moving vector coefficients should be identified and optimised to have a realistic flocking moving behaviour. We proposed two methods to optimise these coefficients, by using genetic algorithm (GA) and particle swarm optimisation algorithm (PSO). Both GA and PSO are population based heuristic search techniques which can be used to solve the optimisation problems. The experimental results show that optimisation of boids model by using PSO is faster and gives better convergence than using GA.


title: Optimal greedy diversity for recommendation

abstract: The need for diversification manifests in various recommendation use cases. In this work, we propose a novel approach to diversifying a list of recommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be viewed as maximization of a modular function on the polytope of a submodular function, which can be solved optimally by a greedy method. We evaluate our approach in an offline analysis, which incorporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods.


title: A framework for structural risk minimisation

abstract: The paper introduces a framework for studying structural risk minimisation. The model views structural risk minimisation in a PAC context. It then considers the more general case when the hierarchy of classes is chosen in response to the data. This theoretically explains the impressive performance of the maximal margin hyperplane algorithm of Vapnik. It may also provide a general technique for exploitingserendipitous simplicity in observed data to obtain better prediction accuracy from small training sets.


title: Bayesian approach for neural networks -- review and case studies. Neural Networks

abstract: We give a short review on the Bayesian approach for neural network learning and demonstrate the advantages of the approach in three real applications. We discuss the Bayesian approach with emphasis on the role of prior knowledge in Bayesian models and in classical error minimization approaches. The generalization capability of a statistical model, classical or Bayesian, is ultimately based on the prior assumptions. The Bayesian approach permits propagation of uncertainty in quantities which are unknown to other assumptions in the model, which may be more generally valid or easier to guess in the problem. The case problem studied in this paper include a regression, a classification, and an inverse problem. In the most thoroughly analyzed regression problem, the best models were those with less restrictive priors. This emphasizes the major advantage of the Bayesian approach, that we are not forced to guess attributes that are unknown, such as the number of degrees of freedom in the model, non-linearity of the model with respect to each input variable, or the exact form for the distribution of the model residuals.


Header for SPIE use Content-Based Image Retrieval in Medical Applications: A Novel Multi-Step Approach


title: Working memory deficits can be overcome: Impacts of training and medication on working memory in children with ADHD

abstract: This study evaluated the impact of two interventions—a training program and stimulant medication—on working memory (WM) function in children with attention deficit hyperactivity disorder (ADHD). Twenty-five children aged between 8 and 11 years participated in training that taxed WM skills to the limit for a minimum of 20 days, and completed other assessments of WM and IQ before and after training, and with and without prescribed drug treatment. While medication significantly improved visuo-spatial memory performance, training led to substantial gains in all components of WM across untrained tasks. Training gains associated with the central executive persisted over a 6-month period. IQ scores were unaffected by either intervention. These findings indicate that the WM impairments in children with ADHD can be differentially ameliorated by training and by stimulant medication.


title: Learning from concept drifting data streams with unlabeled data

abstract: Most existing work on classification of data streams assumes that all streaming data are labeled and the class labels are immediately available. However, in real-world applications, such as credit fraud and intrusion detection, this assumption is not always valid. Thus, it is a challenge to learn from concept drifting data streams with unlabeled data. With this motivation, we propose a Semi-supervised classification algorithm for data streams with concept drifts and UNlabeled data (SUN) in this paper. In SUN, a clustering algorithm is developed from k-Modes and implemented to produce concept clusters at leaves in an incremental decision tree. In terms of deviations between history concept clusters and new ones, potential concept drifts are distinguished from noise. Extensive studies on both synthetic and real-world data demonstrate that SUN performs well compared to several state-of-the-art online supervised and semi-supervised algorithms, even when there are more than 90% unlabeled data. A conclusion is hence drawn that SUN provides a promising framework for tackling concept drifting data streams with unlabeled data.


title: An ethical basis for relationship marketing: a virtue ethics perspective

abstract: Purpose – The purpose of this paper is to provide an ethical foundation for relationship marketing using a virtue ethics approach.Design/methodology/approach – The approach is a conceptual one providing a background on relationship marketing from both American and European perspectives. Earlier studies published in EJM on relationship marketing are featured in a table.Findings – The proposed ethical relationship marketing approach has three stages (establishing, sustaining and reinforcing) that are paired with specific virtues (trust, commitment and diligence). These and other facilitating virtues are shown in a figure.Researchlimitations/implications – The model and its components have yet to be tested empirically. Some strategies for undertaking such research are discussed.Practical implications – Several European and American companies that currently practice ethical relationship marketing are discussed.Originality/value – Although relationship marketing has been studied for a number of years by many s...


title: Towards a conceptual framework for visual analytics of time and time-oriented data

abstract: Time is an important data dimension with distinct characteristics that is common across many application domains. This demands specialized methods in order to support proper analysis and visualization to explore trends, patterns, and relationships in different kinds of time-oriented data. The human perceptual system is highly sophisticated and specifically suited to spot visual patterns. For this reason, visualization is successfully applied in aiding these tasks. But facing the huge volumes of data to be analyzed today, applying purely visual techniques is often not sufficient. Visual analytics systems aim to bridge this gap by combining both, interactive visualization and computational analysis. In this paper, we introduce a concept for designing visual analytics frameworks and tailored visual analytics systems for time and time-oriented data. We present a number of relevant design choices and illustrate our concept by example.


title: Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features

abstract: Due to the fact that it is prohibitively expensive to completely annotate visual relationships, i.e., the (obj1, rel, obj2) triplets, relationship models are inevitably biased to object classes of limited pairwise patterns, leading to poor generalization to rare or unseen object combinations. Therefore, we are interested in learning object-agnostic visual features for more generalizable relationship models. By"agnostic", we mean that the feature is less likely biased to the classes of paired objects. To alleviate the bias, we propose a novel \texttt{Shuffle-Then-Assemble} pre-training strategy. First, we discard all the triplet relationship annotations in an image, leaving two unpaired object domains without obj1-obj2 alignment. Then, our feature learning is to recover possible obj1-obj2 pairs. In particular, we design a cycle of residual transformations between the two domains, to capture shared but not object-specific visual patterns. Extensive experiments on two visual relationship benchmarks show that by using our pre-trained features, naive relationship models can be consistently improved and even outperform other state-of-the-art relationship models. Code has been made available at: \url{https://github.com/yangxuntu/vrd}.


title: Challenging Gender Stereotypes: Resistance and Exclusion

abstract: Very early in life, children begin conforming to gender stereotypes and interacting in gender-segregated social groups. Children’s knowledge of gender stereotypes, including choice of toys and activities, emerges during the preschool period (Martin et al., 2012), and research indicates that, even as early as preschool, interaction with teachers and peers can influence gender segregation in play and activities (Glassman, 2000; Goble, Martin, Hanish, & Fabes, 2012). Extensive evidence demonstrates that gender stereotypes guide children’s preferences for activities, occupations, and career goals (Liben & Bigler, 2002; Ruble, Martin, & Berenbaum, 2006), even though young children often view adherence to gender norms to be a matter of personal choice (Conry-Murray, 2013) or a convention (Smetana et al., 2012).


title: Short Segment Fixation for Thoracolumbar Burst Fracture Accompanying Osteopenia : A Comparative Study

abstract: Objective ::: The purpose of this study was to compare the results of three types of short segment screw fixation for thoracolumbar burst fracture accompanying osteopenia.


title: Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities

abstract: Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high efficiency Legal Information Retrieval (IR) and Question Answering (QA) methods. The IR task in particular has a set of unique challenges that invite the use of semantic motivated NLP techniques. In this work, a two-stage method for Legal Information Retrieval is proposed, combining lexical statistics and distributional sentence representations in the context of Competition on Legal Information Extraction/Entailment (COLIEE). The combination is done with the use of disambiguation rules, applied over the rankings obtained through n-gram statistics. After the ranking is done, its results are evaluated for ambiguity, and disambiguation is done if a result is decided to be unreliable for a given query. Competition and experimental results indicate small gains in overall retrieval performance using the proposed approach. Additionally, an analysis of error and improvement cases is presented for a better understanding of the contributions.


Evaluating EcoLexiCAT: a Terminology-Enhanced CAT Tool.


title: Image Co-segmentation via Multi-scale Local Shape Transfer

abstract: Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category. In real-world cases, however, common foreground objects often vary greatly in appearance, making their global shapes highly inconsistent across images and difficult to be segmented. To address this problem, this paper proposes a novel co-segmentation approach that transfers patch-level local object shapes which appear more consistent across different images. In our framework, a multi-scale patch neighbourhood system is first generated using proposal flow on arbitrary image-pair, which is further refined by Locally Linear Embedding. Based on the patch relationships, we propose an efficient algorithm to jointly segment the objects in each image while transferring their local shapes across different images. Extensive experiments demonstrate that the proposed method can robustly and effectively segment common objects from an image set. On iCoseg, MSRC and Coseg-Rep dataset, the proposed approach performs comparable or better than the state-of-thearts, while on a more challenging benchmark Fashionista dataset, our method achieves significant improvements.


title: Continuous vs. intermittent neurofeedback to regulate auditory cortex activity of tinnitus patients using real-time fMRI - A pilot study

abstract: The emerging technique of real-time fMRI neurofeedback trains individuals to regulate their own brain activity via feedback from an fMRI measure of neural activity. Optimum feedback presentation has yet to be determined, particularly when working with clinical populations. To this end, we compared continuous against intermittent feedback in subjects with tinnitus. Fourteen participants with tinnitus completed the whole experiment consisting of nine runs (3 runs × 3 days). Prior to the neurofeedback, the target region was localized within the auditory cortex using auditory stimulation (1 kHz tone pulsating at 6 Hz) in an ON-OFF block design. During neurofeedback runs, participants received either continuous (n = 7, age 46.84 ± 12.01, Tinnitus Functional Index (TFI) 49.43 ± 15.70) or intermittent feedback (only after the regulation block) (n = 7, age 47.42 ± 12.39, TFI 49.82 ± 20.28). Participants were asked to decrease auditory cortex activity that was presented to them by a moving bar. In the first and the last session, participants also underwent arterial spin labeling (ASL) and resting-state fMRI imaging. We assessed tinnitus severity using the TFI questionnaire before all sessions, directly after all sessions and six weeks after all sessions. We then compared neuroimaging results from neurofeedback using a general linear model (GLM) and region-of-interest analysis as well as behavior measures employing a repeated-measures ANOVA. In addition, we looked at the seed-based connectivity of the auditory cortex using resting-state data and the cerebral blood flow using ASL data. GLM group analysis revealed that a considerable part of the target region within the auditory cortex was significantly deactivated during neurofeedback. When comparing continuous and intermittent feedback groups, the continuous group showed a stronger deactivation of parts of the target region, specifically the secondary auditory cortex. This result was confirmed in the region-of-interest analysis that showed a significant down-regulation effect for the continuous but not the intermittent group. Additionally, continuous feedback led to a slightly stronger effect over time while intermittent feedback showed best results in the first session. Behaviorally, there was no significant effect on the total TFI score, though on a descriptive level TFI scores tended to decrease after all sessions and in the six weeks follow up in the continuous group. Seed-based connectivity with a fixed-effects analysis revealed that functional connectivity increased over sessions in the posterior cingulate cortex, premotor area and part of the insula when looking at all patients while cerebral blood flow did not change significantly over time. Overall, these results show that continuous feedback is suitable for long-term neurofeedback experiments while intermittent feedback presentation promises good results for single session experiments when using the auditory cortex as a target region. In particular, the down-regulation effect is more pronounced in the secondary auditory cortex, which might be more susceptible to voluntary modulation in comparison to a primary sensory region.


title: Stereo camera upgraded to equal baseline multiple camera set (EBMCS)

abstract: The paper presents the results of using a set of five cameras called Equal Baseline Multiple Camera Set (EBMCS) for making 3D images, disparity maps and depth maps. Cameras in the set are located in the vicinity of each other and therefore the set can be used for the purpose of stereoscopy similarly as a stereo camera. EBMCS provides disparity maps and depth maps which have a better quality than these maps obtained with the use of a stereo camera. Moreover, EBMCS has many advantages over other kinds of equipment for making 3D images such as a Time-of-flight camera (TOF), Light Detection and Ranging (LIDAR), a structured-light 3D scanner, a camera array and a camera matrix. These advantages are described in the paper. The paper also compares the performance of EBMCS to the performance of stereo cameras.


title: A Physical Layer Security Scheme for Mobile Health Cyber-Physical Systems

abstract: Mobile health (m-Health) is one potential application of cyber-physical systems, where biomedical sensors and mobile devices interact tightly together to transmit medical data to an m-Health server. In this paper, we consider a three-tier hierarchical m-Health system: 1) the sensor network tier capturing vital signals; 2) the mobile computing network tier processing and routing the sensed data to a fixed remote location; and 3) the back-end network tier processing and analyzing the sensed medical data along with patient’s medical history. Based on this architecture, a physical layer security scheme for the second tier is developed and network performance of the introduced scheme is analyzed under different metrics using stochastic geometry. To be specific, secure transmission range and average end-to-end delay are analyzed for two different strategies: the mobile device transmits: 1) to the nearest neighbor and 2) to the furthest neighbor. Furthermore, we consider the cases of full knowledge on eavesdroppers’ locations and when such information is unavailable. Results show that transmitting to the nearest neighbor achieves the highest secure transmission distance with the lowest mean delay when full information on eavesdroppers is available.


title: Analysis of importance of pre-processing in prediction of hypertension

abstract: Hypertension is a leading risk factor contributing to cardiovascular diseases and cardiovascular mortality and morbidity across the world. Developing good risk prediction models to arbitrate individuals risk predictions, pay off high value by reducing mortality and morbidity. To build such a good prediction model a reliable BP data is needed, but real-life data is often noisy, inconsistent and incomplete. Hence, it is important to pre-process data before building prediction model. This paper aims to pre-process (impute data) the data using unsupervised neural network, Adaptive Resonance Theory 2 (ART2) clusters and build prediction model using decision trees, Naive Bayes, random forest after handling imbalanced class of dataset from one of the Health and Demographic Surveillance System (HDSS) site Vadu, India. Its a part of International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) network in developing countries, aiming to help developing countries to set health priorities based on longitudinal evidence. The experimental results of the proposed technique shows the importance of pre-processing in enhancing the performance of the prediction models.


title: Forecasting model of a movie's profitability

abstract: This study intends to estimate the profit of a movie through the construction of a predictive model that uses several Data Mining techniques, namely neural networks, regression and decision trees. The model will allow obtaining the prediction of box office revenue. Three different dependent variable approaches were used (interval, categorical and binary) aiming to study the difference and predictive influence that each one has on the results. Two metrics were used to determine the most accurate predictions: the misclassification error for the categorical models and the average squared error for the continuous one. In this study, the best predictive results were obtained through the use of multi-layer perceptron. Regarding the representative distinction between the dependent variable, the multiclass model presents a much higher error rate comparing to the remaining ones, which is explained with the increase of the number of classes to predict.


title: Investigating Topic Influence in Authorship Attribution

abstract: The aim of this paper is to explore text topic influence in authorship attribution. Specifically, we test the widely accepted belief that stylometric variables commonly used in authorship attribution are topic-neutral and can be used in multi-topic corpora. In order to investigate this hypothesis, we created a special corpus, which was controlled for topic and author simultaneously. The corpus consists of 200 Modern Greek newswire articles written by two authors in two different topics. Many commonly used stylometric variables were calculated and for each one we performed a two-way ANOVA test, in order to estimate the main effects of author, topic and the interaction between them. The results showed that most of the variables exhibit considerable correlation with the text topic and their exploitation in authorship analysis should be done with caution.


title: An investigation of a possible relationship between olfactory identification deficits at first episode and four-year outcomes in patients with psychosis

abstract: Abstract Objective Olfactory identification deficits are found in a significant proportion of patients with schizophrenia spectrum psychotic disorders and appear to be predictive of incomplete remission of negative and cognitive symptoms. In the current study, we examined whether patients with first episode psychosis who have olfactory identification deficits (microsmic) have poorer functional outcome than those whose olfactory status is normal (normosmic). Method Sixty-six (66) first episode psychosis patients (46 M and 20 F) were assessed with the University of Pennsylvania Smell Identification Test (UPSIT) at baseline. UPSIT scores served to classify patients into subgroups. The patients' psychiatrists completed the Social and Occupational Functioning Assessment Scale (SOFAS) and the Levels of Functioning Scale (LOFS) after at least 6 months of treatment. The Premorbid Assessment Scale (PAS) was rated by a parent at baseline. Results Thirty-eight percent (38%) of the sample was identified as ‘microsmic’. LOFS and SOFAS scores were significantly lower in the microsmic group than in the normosmic group. Symptoms were significantly worse in the microsmic group in comparison to the normosmic group. PAS scores did not differ between groups. Conclusions First episode patients identified as microsmic at baseline assessment went on to demonstrate poorer functional outcome compared to normosmic patients despite no differences in premorbid adjustment. Olfactory identification deficits at first episode may provide a marker for poorer outcome. Testing olfaction is simple and inexpensive, and could provide clinically valuable information at first episode to identify those patients who might benefit from more intensive interventions promoting functional recovery.


title: A.: Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships

abstract: The use of context is critical for scene understanding in computer vision, where the recognition of an object is driven by both local appearance and the object's relationship to other elements of the scene (context). Most current approaches rely on modeling the relationships between object categories as a source of context. In this paper we seek to move beyond categories to provide a richer appearance-based model of context. We present an exemplar-based model of objects and their relationships, the Visual Memex, that encodes both local appearance and 2D spatial context between object instances. We evaluate our model on Torralba's proposed Context Challenge against a baseline category-based system. Our experiments suggest that moving beyond categories for context modeling appears to be quite beneficial, and may be the critical missing ingredient in scene understanding systems.


title: Review of Recent Inkjet-Printed Capacitive Tactile Sensors

abstract: Inkjet printing is an advanced printing technology that has been used to develop conducting layers, interconnects and other features on a variety of substrates. It is an additive manufacturing process that offers cost-effective, lightweight designs and simplifies the fabrication process with little effort. There is hardly sufficient research on tactile sensors and inkjet printing. Advancements in materials science and inkjet printing greatly facilitate the realization of sophisticated tactile sensors. Starting from the concept of capacitive sensing, a brief comparison of printing techniques, the essential requirements of inkjet-printing and the attractive features of state-of-the art inkjet-printed tactile sensors developed on diverse substrates (paper, polymer, glass and textile) are presented in this comprehensive review. Recent trends in inkjet-printed wearable/flexible and foldable tactile sensors are evaluated, paving the way for future research.


title: Blazing Angels or Resident Evil? Can Violent Video Games Be a Force for Good?

abstract: Violent video games have been a source of controversy in the United States and elsewhere for several decades. Considerable concern has been raised in the public and scientific communities about the alleged deleterious effects of violent games. These concerns may coincide with periodic moral panics about media's influence, particularly on youth. This paper argues that the negative effects of violent games have been exaggerated by some elements of the scientific community, fitting with past cycles of media-focused moral panics. By contrast, potential positive effects of violent video game play have been ignored in the debate on violent games. The current paper considers research in several areas, including aggression, but also the nascent research fields of visuospatial cognition, social networking, and use as educational tools. It is argued that the debate on video game violence should be broadened to include both potential negative and positive effects.


title: Spanner: Becoming a SQL System

abstract: Spanner is a globally-distributed data management system that backs hundreds of mission-critical services at Google. Spanner is built on ideas from both the systems and database communities. The first Spanner paper published at OSDI'12 focused on the systems aspects such as scalability, automatic sharding, fault tolerance, consistent replication, external consistency, and wide-area distribution. This paper highlights the database DNA of Spanner. We describe distributed query execution in the presence of resharding, query restarts upon transient failures, range extraction that drives query routing and index seeks, and the improved blockwise-columnar storage format. We touch upon migrating Spanner to the common SQL dialect shared with other systems at Google.


title: A Real-time EMG-based Assistive Computer Interface for the Upper Limb Disabled

abstract: This paper presents the design of an assistive real-time system for the upper limb disabled to access a computer via residual muscle activities without standard computer interfaces (e.g. a mouse and a keyboard). For this purpose, electromyogram (EMG) signals from muscles in the lower arm were extracted and filtered using signal statistics (mean and variance). In order to control movement and clicking of a cursor from the obtained signals, six patterns were classified, applying a supervised multi-layer neural network trained by a backpropagation algorithm. In addition, an on-screen keyboard was developed, making it possible to enter Roman and Korean letters on the computer. Using this computer interface, the user can browse the Internet and read/send e-mail. The developed computer interface provides an alternative means for individuals with motor disabilities to access computers. A possible extension of our interface methodology can be incorporated in controlling bionic robot systems for the limb disabled (e.g. exoskeletons, limb prostheses).


title: Caudal duplication syndrome.

abstract: OBJECTIVE ::: To present the clinical and roentgenographic features of caudal duplication syndrome. ::: ::: ::: DESIGN ::: Retrospective review of the medical records and all available imaging studies. ::: ::: ::: SETTING ::: Two university-affiliated teaching hospitals. ::: ::: ::: PARTICIPANTS ::: Six children with multiple anomalies and duplications of distal organs derived from the hindgut, neural tube, and adjacent mesoderm. ::: ::: ::: INTERVENTIONS ::: None. ::: ::: ::: RESULTS ::: Spinal anomalies (myelomeningocele in two patients, sacral duplication in three, diplomyelia in two, and hemivertebrae in one) were present in all our patients. Duplications or anomalies of the external genitalia and/or the lower urinary and reproductive structures were also seen in all our patients. Ventral herniation (in one patient), intestinal obstructions (in one patient), and bowel duplications (in two patients) were the most common gastrointestinal abnormalities. ::: ::: ::: CONCLUSIONS ::: We believe that the above constellation of abnormalities resulted from an insult to the caudal cell mass and hindgut at approximately the 23rd through the 25th day of gestation. We propose the term caudal duplication syndrome to describe the association between gastrointestinal, genitourinary, and distal neural tube malformations.


title: iDice: problem identification for emerging issues

abstract: One challenge for maintaining a large-scale software system, especially an online service system, is to quickly respond to customer issues. The issue reports typically have many categorical attributes that reflect the characteristics of the issues. For a commercial system, most of the time the volume of reported issues is relatively constant. Sometimes, there are emerging issues that lead to significant volume increase. It is important for support engineers to efficiently and effectively identify and resolve such emerging issues, since they have impacted a large number of customers. Currently, problem identification for an emerging issue is a tedious and error-prone process, because it requires support engineers to manually identify a particular attribute combination that characterizes the emerging issue among a large number of attribute combinations. We call such an attribute combination effective combination, which is important for issue isolation and diagnosis. In this paper, we propose iDice, an approach that can identify the effective combination for an emerging issue with high quality and performance. We evaluate the effectiveness and efficiency of iDice through experiments. We have also successfully applied iDice to several Microsoft online service systems in production. The results confirm that iDice can help identify emerging issues and reduce maintenance effort.


title: Kootenaia burkei, a new genus and species of slug from northern Idaho, United States (Gastropoda: Pulmonata: Arionidae)

abstract: A new genus and species of arionid slug, Kootenaia burkei n. gen. et n. sp., are formally described from northern Idaho, United States. This taxonomic decision is based on comparative anatomical and molecular data involving representatives of a total of ten species and three additional genera (Hemphillia, Prophysaon, and Zacoleus) of the family Arionidae. The anatomical analyses show that the new genus is characterized by a major autapomorphy, the complete absence of an epiphallus, which is found in all other arionids. The molecular analyses using two mitochondrial genes and the anatomical data produce congruent topologies. Overall, there is a high degree of concordance between the anatomical and molecular datasets.


title: ASCOS++: An Asymmetric Similarity Measure for Weighted Networks to Address the Problem of SimRank

abstract: In this article, we explore the relationships among digital objects in terms of their similarity based on vertex similarity measures. We argue that SimRank—a famous similarity measure—and its families, such as P-Rank and SimRank++, fail to capture similar node pairs in certain conditions, especially when two nodes can only reach each other through paths of odd lengths. We present new similarity measures ASCOS and ASCOS++ to address the problem. ASCOS outputs a more complete similarity score than SimRank and SimRank’s families. ASCOS++ enriches ASCOS to include edge weight into the measure, giving all edges and network weights an opportunity to make their contribution. We show that both ASCOS++ and ASCOS can be reformulated and applied on a distributed environment for parallel contribution. Experimental results show that ASCOS++ reports a better score than SimRank and several famous similarity measures. Finally, we re-examine previous use cases of SimRank, and explain appropriate and inappropriate use cases. We suggest future SimRank users following the rules proposed here before naively applying it. We also discuss the relationship between ASCOS++ and PageRank.


title: Theory and methods for sampling in fractional domains

abstract: The fractional Fourier transform (FRFT) and linear canonical transform (LCT) have been shown to be one of powerful tools in signal processing community. While sampling is a fundamental and core topic in the field of signal processing. The research of sampling theory and methods in FRFT and LCT domains received much interests in recent years. In this paper, we review the developments and research trends of sampling theories and methods associate with FRFT and LCT.


title: Heroin epidemics, treatment and ODE modelling

abstract: Abstract The UN [United Nations Office on Drugs and Crime (UNODC): World Drug Report, 2005, vol. 1: Analysis. UNODC, 2005.] , EU [ European Monitoring Centre for Drugs and Drug Addiction (EMCDDA): Annual Report, 2005. http://annualreport.emcdda.eu.int/en/home-en.html .] and WHO [World Health Organisation (WHO): Biregional Strategy for Harm Reduction, 2005–2009. HIV and Injecting Drug Use. WHO, 2005.] have consistently highlighted in recent years the ongoing and persistent nature of opiate and particularly heroin use on a global scale. While this is a global phenomenon, authors have emphasised the significant impact such an epidemic has on individual lives and on society. National prevalence studies have indicated the scale of the problem, but the drug-using career, typically consisting of initiation, habitual use, a treatment-relapse cycle and eventual recovery, is not well understood. This paper presents one of the first ODE models of opiate addiction, based on the principles of mathematical epidemiology. The aim of this model is to identify parameters of interest for further study, with a view to informing and assisting policy-makers in targeting prevention and treatment resources for maximum effectiveness. An epidemic threshold value, R 0 , is proposed for the drug-using career. Sensitivity analysis is performed on R 0 and it is then used to examine the stability of the system. A condition under which a backward bifurcation may exist is found, as are conditions that permit the existence of one or more endemic equilibria. A key result arising from this model is that prevention is indeed better than cure.


title: How synchronization properties among second-order sensory neurons can mediate stimulus salience

abstract: Spatial patterns of glomerular activity in the vertebrate olfactory bulb and arthropod antennal lobe reflect an important component of first-order olfactory representation and contribute to odorant identification. Higher concentration odor stimuli evoke broader glomerular activation patterns, resulting in greater spatial overlap among different odor representations. However, behavioral studies demonstrate results contrary to what these data might suggest: Honeybees are more, not less, able to discriminate among odorants applied at higher concentrations. Using a computational model of the honeybee antennal lobe, the authors show that changes in synchronization patterns among antennal lobe projection neurons, as observed electrophysiologically, could parsimoniously underlie these observations. The results suggest that stimulus salience, as defined behaviorally, is directly correlated with the degree of synchronization among second-order olfactory neurons.


title: Multibeam SIW Slotted Waveguide Antenna System Fed by a Compact Dual-Layer Rotman Lens

abstract: A multibeam antenna system fed by a compact Rotman lens is presented in multilayer substrate integrated waveguide (SIW) technology. The lens is implemented in two layers using a new transition based on several star-shaped coupling slots and an SIW integrated reflector. Compared to standard rectangular coupling slots, this transition is broadband and maximizes power transfer between the two layers of the lens regardless the position of the beam port along the focal arc of the Rotman lens. The radiating part is an array of 15 slotted waveguides. These waveguides are centrally fed to enhance their bandwidth and reduce the “long line effect.” To validate the proposed concepts, an antenna system has been prototyped at 24.15 GHz. The designed transition leads to a physical size reduction of about 50% for the Rotman lens, and more than 33% for the antenna footprint compared to standard single-layer implementations. The experimental results demonstrate a scanning range of $\pm {33}^\circ $ and a 4% bandwidth for $\text{VSWR} ; the isolation between input ports is better than $- {10}\;\text{dB}$ .


title: Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women

abstract: ABSTRACTObjectiveBrain-gut-microbiota interactions may play an important role in human health and behavior. Although rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive, and social behaviors, there is little translational human evidence to date. In this study, we


title: Evaluating socio-economic state of a country analyzing airtime credit and mobile phone datasets

abstract: Reliable statistical information is important to make political decisions on a sound basis and to help measure the impact of policies. Unfortunately, statistics offices in developing countries have scarce resources and statistical censuses are therefore conducted sporadically. Based on mobile phone communications and history of airtime credit purchases, we estimate the relative income of individuals, the diversity and inequality of income, and an indicator for socioeconomic segregation for fine-grained regions of an African country. Our study shows how to use mobile phone datasets as a starting point to understand the socio-economic state of a country, which can be especially useful in countries with few resources to conduct large surveys.


title: The Economics of Bitcoin Mining, or Bitcoin in the Presence of Adversaries

abstract: The Bitcoin digital currency depends for its correctness and stability on a combination of cryptography, distributed algorithms, and incentivedriven behavior. We examine Bitcoin as a consensus game and determine that it relies on separate consensus about the rules and about game state. An important aspect of Bitcoin’s design is the mining mechanism, in which participants expend resources on solving computational puzzles in order to collect rewards. This mechanism purportedly protects Bitcoin against certain technical problems such as inconsistencies in the system’s distributed log data structure. We consider the economics of Bitcoin mining, and whether the Bitcoin protocol can survive attacks, assuming that participants behave according to their incentives. We show that there is a Nash equilibrium in which all players behave consistently with Bitcoin’s reference implementation, along with innitely many equilibria in which they behave otherwise. We also show how a motivated adversary might be able to disrupt the Bitcoin system and \crash" the currency. Finally, we argue that Bitcoin will require the emergence of governance structures, contrary to the commonly held view in the Bitcoin community that the currency is ungovernable.


title: Enhancing the educational value of video games

abstract: Lowering the barrier between education and real entertainment is an important challenge in order to better exploit the potential of computers and reach a demographic that is traditionally averse to learning. To this end, it is important to investigate how to exploit the appeal of video games (VGs) to also favor and induce learning via playing video games. Achieving this goal is not only a matter of content, since simply “superimposed” educational content risks being perceived as boring. Hence we believe that the game should feature mechanisms for acquiring knowledge and skill that are smoothly embedded in a meaningful, homogeneous, and compelling whole. Thus, there is a need to compartmentalize components of a game engine so that it becomes easy and efficient to integrate the graphics/interface—which has already been done very well by state-of-the-art successful video games and the educational aspect which is typically poor in those same games. Hence we have defined a general set of mechanisms and modules that can be inserted in state-of-the-art VG environments and are aimed at promoting various kinds of knowledge and procedural skill acquisition. In order to investigate and validate this concept, we have built an educational game, SeaGame, using a state-of-the-art commercial game development approach, and enriched the environment with instances of developed educational modules. Analyzing user test results, we conclude that SeaGame is perceived quite similarly to commercial VGs, which suggests that the proposed mechanisms do not compromise the overall enjoyability of the game, which is key to attracting a wide demographic that is not currently involved in educational activities during their leisure time. The results of this research can be generalized, since the standards of commercial games and the proposed educational enhancements can be instantiated in a variety of educational contexts and applied to different types of content.


Role of Big Data Analytics in Rural Health Care-A Step Towards Svasth Bharath