title: Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †

abstract: Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implemented during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome. A recently published international standard indicates that regions medially adjacent to the inner canthi provide accurate estimates of core body temperature and are preferred sites for fever screening. Therefore, rapid, automated identification of the canthi regions within facial IR images may greatly facilitate rapid fever screening of asymptomatic travelers. However, it is more difficult to accurately identify the canthi regions from IR images than from visible images that are rich with exploitable features. In this study, we developed and evaluated techniques for multi-modality image registration (MMIR) of simultaneously captured visible and IR facial images for fever screening. We used free form deformation (FFD) models based on edge maps to improve registration accuracy after an affine transformation. Two widely used FFD models in medical image registration based on the Demons and cubic B-spline algorithms were qualitatively compared. The results showed that the Demons algorithm outperformed the cubic B-spline algorithm, likely due to overfitting of outliers by the latter method. The quantitative measure of registration accuracy, obtained through selected control point correspondence, was within 2.8 ± 1.2 mm, which enables accurate and automatic localization of canthi regions in the IR images for temperature measurement.


title: Fusion of local and global features for effective image extraction

abstract: Image extraction methods rely on locating interest points and describing feature vectors for these key points. These interest points provide different levels of invariance to the descriptors. The image signature can be described well by the pixel regions that surround the interest points at the local and global levels. This contribution presents a feature descriptor that combines the benefits of local interest point detection with the feature extraction strengths of a fine-tuned sliding window in combination with texture pattern analysis. This process is accomplished with an improved Moravec method using the covariance matrix of the local directional derivatives. These directional derivatives are compared with a scoring factor to identify which features are corners, edges or noise. Located interest point candidates are fetched for the sliding window algorithm to extract robust features. These locally-pointed global features are combined with monotonic invariant uniform local binary patterns that are extracted a priory as part of the proposed method. Extensive experiments and comparisons are conducted on the benchmark ImageNet, Caltech-101, Caltech-256 and Corel-100 datasets and compared with sophisticated methods and state-of-the-art descriptors. The proposed method outperforms the other methods with most of the descriptors and many image categories.


title: Untangling cluster management with Helix

abstract: Distributed data systems systems are used in a variety of settings like online serving, offline analytics, data transport, and search, among other use cases. They let organizations scale out their workloads using cost-effective commodity hardware, while retaining key properties like fault tolerance and scalability. At LinkedIn we have built a number of such systems. A key pattern we observe is that even though they may serve different purposes, they tend to have a lot of common functionality, and tend to use common building blocks in their architectures. One such building block that is just beginning to receive attention is cluster management, which addresses the complexity of handling a dynamic, large-scale system with many servers. Such systems must handle software and hardware failures, setup tasks such as bootstrapping data, and operational issues such as data placement, load balancing, planned upgrades, and cluster expansion. All of this shared complexity, which we see in all of our systems, motivates us to build a cluster management framework, Helix, to solve these problems once in a general way. Helix provides an abstraction for a system developer to separate coordination and management tasks from component functional tasks of a distributed system. The developer defines the system behavior via a state model that enumerates the possible states of each component, the transitions between those states, and constraints that govern the system's valid settings. Helix does the heavy lifting of ensuring the system satisfies that state model in the distributed setting, while also meeting the system's goals on load balancing and throttling state changes. We detail several Helix-managed production distributed systems at LinkedIn and how Helix has helped them avoid building custom management components. We describe the Helix design and implementation and present an experimental study that demonstrates its performance and functionality.


Efficient Local Search Algorithms for the Vehicle Routing Problem with Time Windows


title: Creating a Gesture-Speech Dataset for Speech-Based Automatic Gesture Generation

abstract: In our research, we recorded 298 min (1049 sentences) of speech audio data and the motion capture data of the accompanying gestures from two 25-year-old male participants aiming for future usage in deep learning concerning gesture and speech. The data was recorded in form of an interview, the participant explaining a topic prepared in advance, using a headset microphone and the motion capture software Open image in new window . The speech audio was stored in mp3, and the motion data was stored in bvh, related as data from the same sentence. We aimed to mainly acquire metaphoric gestures and iconic, as categorized by McNiel. For the categories of the recorded gestures, metaphoric gestures appeared the most, 68.41% of all gestures, followed by 23.73% beat gestures, 4.76% iconic gestures, and 3.11% deictic gestures.


title: TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data

abstract: We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.


title: Evaluation of neck masses in adults.

abstract: Neck masses are often seen in clinical practice, and the family physician should be able to determine the etiology of a mass using organized, efficient diagnostic methods. The first goal is to determine if the mass is malignant or benign; malignancies are more common in adult smokers older than 40 years. Etiologies can be grouped according to whether the onset/duration is acute (e.g., infectious), subacute (e.g., squamous cell carcinoma), or chronic (e.g., thyroid), and further narrowed by patient demographics. If the history and physical examination do not find an obvious cause, imaging and surgical tools are helpful. Contrast-enhanced computed tomography is the initial diagnostic test of choice in adults. Computed tomography angiography is recommended over magnetic resonance angiography for the evaluation of pulsatile neck masses. If imaging rules out involvement of underlying vital structures, a fine-needle aspiration biopsy can be performed, providing diagnostic information via cytology, Gram stain, and bacterial and acid-fast bacilli cultures. The sensitivity and specificity of fine-needle aspiration biopsy in detecting a malignancy range from 77% to 97% and 93% to 100%, respectively.


title: Inverted index compression and query processing with optimized document ordering

abstract: Web search engines use highly optimized compression schemes to decrease inverted index size and improve query throughput, and many index compression techniques have been studied in the literature. One approach taken by several recent studies first performs a renumbering of the document IDs in the collection that groups similar documents together, and then applies standard compression techniques. It is known that this can significantly improve index compression compared to a random document ordering. We study index compression and query processing techniques for such reordered indexes. Previous work has focused on determining the best possible ordering of documents. In contrast, we assume that such an ordering is already given, and focus on how to optimize compression methods and query processing for this case. We perform an extensive study of compression techniques for document IDs and present new optimizations of existing techniques which can achieve significant improvement in both compression and decompression performances. We also propose and evaluate techniques for compressing frequency values for this case. Finally, we study the effect of this approach on query processing performance. Our experiments show very significant improvements in index size and query processing speed on the TREC GOV2 collection of 25.2 million web pages.


title: Five dimensions of information security awareness

abstract: Until the era of the information society, information security was a concern mainly for organizations whose line of business demanded a high degree of security. However, the growing use of information tedmology is affecting the status of information security so that it is gradually becoming an area that plays an important role in our everyday lives. As a result, information security issues should now be regarded on a par with other security issues. Using this asseliion as the point of departure, dfis paper outlines the dimensions of information security awareness, namely its organizational, gene~ public, socio-political, computer ethical and institutional education dimensions, along with the categories (or target groups) within each dimension.


title: An L1-based variational model for Retinex theory and its application to medical images

abstract: Human visual system (HVS) can perceive constant color under varying illumination conditions while digital images record information of both reflectance (physical color) of objects and illumination. Retinex theory, formulated by Edwin H. Land, aimed to simulate and explain this feature of HVS. However, to recover the reflectance from a given image is in general an ill-posed problem. In this paper, we establish an L 1 -based variational model for Retinex theory that can be solved by a fast computational approach based on Bregman iteration. Compared with previous works, our L 1 -Retinex method is more accurate for recovering the reflectance, which is illustrated by examples and statistics. In medical images such as magnetic resonance imaging (MRI), intensity inhomogeneity is often encountered due to bias fields. This is a similar formulation to Retinex theory while the MRI has some specific properties. We then modify the L 1 -Retinex method and develop a new algorithm for MRI data. We demonstrate the performance of our method by comparison with previous work on simulated and real data.


Music generation from statistical models


title: Will Your Project Get the Green Light? Predicting the Success of Crowdfunding Campaigns

abstract: Capital is always essential for a business project over times. After emerging in 2000, crowdfunding gradually becomes one of the most popular fundraising resources. However, the mechanism of crowdfunding significantly differs from traditional capital-collecting approaches. As long as the amount of pledged money reaches the goal in time, the project succeeds, its initiator receives the funds, the platform gains the revenue, and its backers acquire rewards. Reaching the goal by deadline becomes an important issue. The goal of our study is to develop an effective technique for predicting whether a crowdfunding campaign will succeed or fail. On the basis of a dataset collected from Kickstarter, our empirical evaluation results suggest that our proposed technique significantly outperforms the benchmark method. In addition, with the use of time-dependent factors, the prediction accuracy improves from 72.89% at day 0 to 87.13% at the first day and eventually to 89.62% at day 7.


title: The Internet and Social Life

abstract: The Internet is the latest in a series of technological breakthroughs in interpersonal communication, following the telegraph, telephone, radio, and television. It combines innovative features of its predecessors, such as bridging great distances and reaching a mass audience. However, the Internet has novel features as well, most critically the relative anonymity afforded to users and the provision of group venues in which to meet others with similar interests and values. We place the Internet in its historical context, and then examine the effects of Internet use on the user's psychological well-being, the formation and maintenance of personal relationships, group memberships and social identity, the workplace, and community involvement. The evidence suggests that while these effects are largely dependent on the particular goals that users bring to the interaction—such as self-expression, affiliation, or competition—they also interact in important ways with the unique qualities of the Internet communicat...


title: People needs in the urban landscape: Analysis of Landscape and Urban Planning contributions.

abstract: The articles published in Landscape and Urban Planning during the past 16 years provide valuable insights into how humans interact with outdoor urban environments. This review paper explores the wide spectrum of human dimensions and issues, or human needs, addressed by 90 of these studies. As a basis for analysis, the major themes tapped by the findings were classified into two overarching groups containing three categories each. The Nature needs, directly linked with the physical features of the environmental setting, were categorized in terms of contact with nature, aesthetic preference, and recreation and play. The role of the environment is less immediate in the Human-interaction group, which includes the issues of social interaction, citizen participation in the design process, and community identity. Most significantly, the publications offer strong support for the important role nearby natural environments play in human well-being. Urban settings that provide nature contact are valuable not only in their own right, but also for meeting other needs in a manner unique to these more natural settings. In addition, although addressed in different ways, remarkable similarities exist concerning these six people requirements across diverse cultures and political systems. Urban residents worldwide express a desire for contact with nature and each other, attractive environments, places in which to recreate and play, privacy, a more active role in the design of their community, and a sense of community identity. The studies reviewed here offer continued evidence that the design of urban landscapes strongly influences the well-being and behavior of users and nearby inhabitants.


title: Processing of Kridanta (Participle) in Marathi

abstract: In this paper, we propose and evaluate a Finite State Machine (FSM) approach for Krudanta processing for Marathi; an agglutinative and highly inflectional language belonging to the Indo-European family with semblances of Dravidian languages. Krudanta is the phenomenon of participial construction that needs adroit handling for high quality output in machine translation (MT). We contextualize this work in a transfer based Marathi to Hindi Machine Translation system to observe the importance of Krudanta processing. Results with and without Krudanta processing establish the need for this work.


title: Knowledge Base Completion via Coupled Path Ranking

abstract: Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. The path ranking algorithm (PRA) is one of the most promising approaches to this task. Previous work on PRA usually follows a single-task learning paradigm, building a prediction model for each relation independently with its own training data. It ignores meaningful associations among certain relations, and might not get enough training data for less frequent relations. This paper proposes a novel multi-task learning framework for PRA, referred to as coupled PRA (CPRA). It first devises an agglomerative clustering strategy to automatically discover relations that are highly correlated to each other, and then employs a multi-task learning strategy to effectively couple the prediction of such relations. As such, CPRA takes into account relation association and enables implicit data sharing among them. We empirically evaluate CPRA on benchmark data created from Freebase. Experimental results show that CPRA can effectively identify coherent clusters in which relations are highly correlated. By further coupling such relations, CPRA significantly outperforms PRA, in terms of both predictive accuracy and model interpretability.


title: Applying multi-agent technique in multi-section flexible manufacturing system

abstract: In the highly competitive market, cooperative multi-agent transaction and negotiation mechanism have become an important research topic. This paper uses multi-agent technology to construct a multi-section flexible manufacturing system (FMS) model, and utilizes simulation to build a manufacturing environment based on JADE framework for multi-agent to combine with dispatching rules, such as shortest imminent processing time (SIPT), first come first serve (FCFS) earliest due date (EDD), and Buffer Sequence. This paper finds that using multi-agent technique for multi-section FMS model can enhance the production efficiency in practice. Meanwhile, in this study, multi-agent systems combined with dynamic dispatching can be used to identify the best dispatching rules combination for achieving largest throughput, and thus it can provide the reference for production scheduling in advance.


title: Automated 3D Segmentation and Analysis of Cotton Plants

abstract: One of the main challenges in high-throughput plant data acquisition is the robust and automated analysis of the data. This includes a high-resolution 3D plant model reconstruction and an automated 3D segmentation. In this paper we present our top-down partitioning pipeline used to automatically segment high-resolution plant meshes. The proposed method produces a smart partition of the initial mesh that allows to identify the main stem, branches, and leaves of the plant. Extracted regions are then processed through the next stage of the automated analysis, which retrieves accurate plant information such as stem length, leaf width, length or area. Results involved applying our top-down approach on a prototype population of 6 cotton-plant meshes studied at 3 or 4 time points. Using our partitioning pipeline, we obtained accurate meshes segmentations for 20 plants out of the initial 22. Results validate the feasibility of an automated analysis of plant data. Future work will involve extending our approach to multiple plant varieties and using an atlas-based iterative feedback scheme to improve the 3D plant reconstruction.


title: Are click-through data adequate for learning web search rankings?

abstract: Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examples is to employ human experts to judge the relevance of documents. Unfortunately, it is difficult, time-consuming and costly. In this paper, we study the problem of exploiting click-through data for learning web search rankings that can be collected at much lower cost. We extract pairwise relevance preferences from a large-scale aggregated click-through dataset, compare these preferences with explicit human judgments, and use them as training examples to learn ranking functions. We find click-through data are useful and effective in learning ranking functions. A straightforward use of aggregated click-through data can outperform human judgments. We demonstrate that the strategies are only slightly affected by fraudulent clicks. We also reveal that the pairs which are very reliable, e.g., the pairs consisting of documents with large click frequency differences, are not sufficient for learning.


Xavier: A Robot Navigation Architecture Based on Partially Observable Markov Decision Process Models


title: More Electric Aircraft: Review, Challenges, and Opportunities for Commercial Transport Aircraft

abstract: Similar to the efforts to move toward electric vehicles, much research has focused on the idea of a more electric aircraft (MEA). The motivations for this research are similar to that for vehicles and include goals to reduce emissions and decrease fuel consumption. In traditional aircraft, multiple systems may use one type or a combination of types of energy, including electrical, hydraulic, mechanical, and pneumatic energy. However, all energy types have different drawbacks, including the sacrifice of total engine efficiency in the process of harvesting a particular energy, as with hydraulic and pneumatic systems. The goal for future aircraft is to replace most of the major systems currently utilizing nonelectric power, such as environmental controls and engine start, with new electrical systems to improve a variety of aircraft characteristics, such as efficiency, emissions, reliability, and maintenance costs. This paper provides an in-depth look into how the systems have—or will be—changed. Future aircraft capabilities such as electric taxi and gas–electric propulsion for aircraft are also included for discussion. Most recent commercial transport aircrafts are described as the current state-of-the-art electric aircraft system. Future goals, including those of NASA, are presented for future advances in MEA.


title: User Preferences for Hybrid Explanations

abstract: Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demonstrate improved accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining recommendations to users becomes more challenging. In this paper we describe a hybrid recommender system built on a probabilistic programming language, and discuss the benefits and challenges of explaining its recommendations to users. We perform a mixed model statistical analysis of user preferences for explanations in this system. Through an online user survey, we evaluate explanations for hybrid algorithms in a variety of text and visual, graph-based formats, that are either novel designs or derived from existing hybrid recommender systems.


title: Deep Context: End-to-end Contextual Speech Recognition

abstract: In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E) ASR sys- tem that utilizes such context. Our approach, which we re- fer to as Contextual Listen, Attend and Spell (CLAS) jointly- optimizes the ASR components along with embeddings of the context n-grams. During inference, the CLAS system can be presented with context phrases which might contain out-of- vocabulary (OOV) terms not seen during training. We com- pare our proposed system to a more traditional contextualiza- tion approach, which performs shallow-fusion between inde- pendently trained LAS and contextual n-gram models during beam search. Across a number of tasks, we find that the pro- posed CLAS system outperforms the baseline method by as much as 68% relative WER, indicating the advantage of joint optimization over individually trained components. Index Terms: speech recognition, sequence-to-sequence models, listen attend and spell, LAS, attention, embedded speech recognition.


title: Task Intelligence of Robots: Neural Model-Based Mechanism of Thought and Online Motion Planning

abstract: The crux of the realization of task intelligence for robots is to design the memory module for storing temporal event sequences of tasks, the mechanism of thought for reasoning, and motion planning methodology for execution, among others. In this paper, task intelligence is realized using episodic memory, neural model-based mechanism of thought, and an online motion planning algorithm. Robots are taught either by demonstration or symbolic description. A behavior appropriate to the current situation is selected by the developmental episodic memory-based mechanism of thought, while a proper task is retrieved from Deep adaptive resonance theory (ART). The behaviors are executed safely and quickly with the proposed motion planning algorithm. The effectiveness and applicability of task intelligence are demonstrated through experiments with the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST.


title: Antioxidant properties of traditional balsamic vinegar and boiled must model systems

abstract: Traditional balsamic vinegar (TBV) is a natural product prepared with cooked and concentrated locally grown grape must. It has been demonstrated that TBV contains phenols and shows antioxidant activity. In this study we investigated the antioxidant properties of TBV in relation to its content of phenolic compounds, polymeric tannins and Maillard reaction products (MRPs). Results show that TBV has a high antioxidant activity measured with both FRAP and ABTS assays, which is higher or equal to those obtained in some red wines. About 45% of the antioxidant activity of TBV is due to the total polyphenolic fraction. Among polyphenols, tannins contribute to about 50% of the antioxidant activity of the total polyphenolic fraction. The residual antioxidant activity of TBV is due to the melanoidins (about 45%) synthesized during the boiling of the must and the ageing of TBV and to other compounds such as low molecular weight MRPs. When we investigated the effect of heating on the browning and on the formation of antioxidant MRPs in must model systems, we observed a major formation of antioxidant MRPs for the model system containing both amino acids and sugars with respect to the model system containing only sugars. We also tested the effect of some representative phenolic compounds present in must. Only polyphenols were stable in the model solution; however, at our experimental conditions they did not influence the browning and the formation of MRPs. Independent of their bioavailability, dietary antioxidants play an important role in protecting the gastrointestinal tract from oxidative damage and also possibly against a buildup of peroxides and their assimilitation in the digestive tract.


title: A document query search using an extended centrality with the word2vec

abstract: While everyday document search is done by keyword-based queries to search engines, we have situations that need deep search of documents such as scrutinies of patents, legal documents, and so on. In such cases, using document queries, instead of keyword-based queries, can be more helpful because it exploits more information from the query document. This paper studies a scheme of document search based on document queries. In particular, it uses centrality vectors, instead of tf-idf vectors, to represent query documents, combined with the Word2vec method to capture the semantic similarity in contained words. This scheme improves the performance of document search and provides a way to find documents not only lexically, but semantically close to a query document.


title: Online Anomaly Detection in Wireless Body Area Networks for Reliable Healthcare Monitoring

abstract: In this paper, we propose a lightweight approach for online detection of faulty measurements by analyzing the data collected from medical wireless body area networks. The proposed framework performs sequential data analysis using a smart phone as a base station, and takes into account the constrained resources of the smart phone, such as processing power and storage capacity. The main objective is to raise alarms only when patients enter in an emergency situation, and to discard false alarms triggered by faulty measurements or ill-behaved sensors. The proposed approach is based on the Haar wavelet decomposition, nonseasonal Holt-Winters forecasting, and the Hampel filter for spatial analysis, and on for temporal analysis. Our objective is to reduce false alarms resulting from unreliable measurements and to reduce unnecessary healthcare intervention. We apply our proposed approach on real physiological dataset. Our experimental results prove the effectiveness of our approach in achieving good detection accuracy with a low false alarm rate. The simplicity and the processing speed of our proposed framework make it useful and efficient for real time diagnosis.


title: Cross-view image synthesis using geometry-guided conditional GANs

abstract: Abstract We address the problem of generating images across two drastically different views, namely ground (street) and aerial (overhead) views. Image synthesis by itself is a very challenging computer vision task and is even more so when generation is conditioned on an image in another view. Due to the difference in viewpoints, there is small overlapping field of view and little common content between these two views. Here, we try to preserve the pixel information between the views so that the generated image is a realistic representation of cross view input image. For this, we resort to homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image. We then use generative adversarial networks to inpaint the missing regions in the transformed image and add realism to it. Our exhaustive evaluation and model comparison demonstrate that utilizing geometry constraints adds fine details to the generated images and can be a better approach for cross view image synthesis than purely pixel based synthesis methods.


title: Omega-3 Fatty Acids: Evidence Basis for Treatment and Future Research in Psychiatry

abstract: Objective: To determine if the available data support the use of omega-3 essential fatty acids (EFA) for clinical use in the prevention and/or treatment of psychiatric disorders. Participants: The authors of this article were invited participants in the Omega-3 Fatty Acids Subcommittee, assembled by the Committee on Research on Psychiatric Treatments of the American Psychiatric Association (APA). Evidence: Published literature and data presented at scientific meetings were reviewed. Specific disorders reviewed included major depressive disorder, bipolar disorder, schizophrenia, dementia, borderline personality disorder and impulsivity, and attention-deficit/hyperactivity disorder. Meta-analyses were conducted in major depressive and bipolar disorders and schizophrenia, as sufficient data were available to conduct such analyses in these areas of interest. Consensus Process: The subcommittee prepared the manuscript, which was reviewed and approved by the following APA committees: the Committee on Research on Psychiatric Treatments, the Council on Research, and the Joint Reference Committee. Conclusions: The preponderance of epidemiologic and tissue compositional studies supports a protective effect of omega-3 EFA intake, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in mood disorders. Meta-analyses of randomized controlled trials demonstrate a statistically significant benefit in unipolar and bipolar depression (p = .02). The results were highly heterogeneous, indicating that it is important to examine the characteristics of each individual study to note the differences in design and execution. There is less evidence of benefit in schizophrenia. EPA and DHA appear to have negligible risks and some potential benefit in major depressive disorder and bipolar disorder, but results remain inconclusive in most areas of interest in psychiatry. Treatment recommendations and directions for future research are described. Health benefits of omega-3 EFA may be especially important in patients with psychiatric disorders, due to high prevalence rates of smoking and obesity and the metabolic side effects of some psychotropic medications.


title: DaLI: Deformation and Light Invariant Descriptor

abstract: Recent advances in 3D shape analysis and recognition have shown that heat diffusion theory can be effectively used to describe local features of deforming and scaling surfaces. In this paper, we show how this description can be used to characterize 2D image patches, and introduce DaLI, a novel feature point descriptor with high resilience to non-rigid image transformations and illumination changes. In order to build the descriptor, 2D image patches are initially treated as 3D surfaces. Patches are then described in terms of a heat kernel signature, which captures both local and global information, and shows a high degree of invariance to non-linear image warps. In addition, by further applying a logarithmic sampling and a Fourier transform, invariance to photometric changes is achieved. Finally, the descriptor is compacted by mapping it onto a low dimensional subspace computed using Principal Component Analysis, allowing for an efficient matching. A thorough experimental validation demonstrates that DaLI is significantly more discriminative and robust to illuminations changes and image transformations than state of the art descriptors, even those specifically designed to describe non-rigid deformations.


title: A MAV that flies like an airplane and hovers like a helicopter

abstract: Near-earth environments, such as forests, caves, tunnels, and urban structures make reconnaissance, surveillance and search-and-rescue missions difficult and dangerous to accomplish. Micro-air-vehicles (MAVs), equipped with wireless cameras, can assist in such missions by providing real-time situational awareness. This paper describes an additional flight modality enabling fixed-wing MAVs to supplement existing endurance superiority with hovering capabilities. This secondary flight mode can also be used to avoid imminent collisions by quickly transitioning from cruise to hover flight. A sensor suite which allows for autonomous hovering by regulating the aircraft's yaw. pitch and roll angles is also described


title: Attention, similarity, and the identification-Categorization Relationship

abstract: A unified quantitative approach to modeling subjects' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Sbepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and Categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigrns. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed. In their 1956 classic, A Study of Thinking, Bruner, Goodnow, and Austin marveled at the capacity of people to discriminate stimuli and to identify them as unique items. At the same time they stressed the importance of categorization, the process by which discriminably different things are classified into groups and are thereby rendered equivalent. In one sense the processes of identification and categorization seem diametrically opposed, the former dealing with the particular and the latter with the general. Yet similar principles may underlie subjects' identification and categorization of multidimensional stimuli, and performance in these tasks may be highly related. Indeed, the present research renews the issue explored previously by Shepard, Hovland, and Jenkins (1961) and Shepard and Chang (1963)namely, Do the principles of stimulus generalization underlying identification performance also underlie categorization performance? Furthermore, given knowledge of performance in an identification paradigm, can one predict performance in a categorization paradigm using the same set of stimuli?


title: Firms’ linkages with universities and public research institutes in Argentina: factors driving the selection of different channels

abstract: Knowledge flows between public research organisations (PROs) and firms may occur through various channels. Channel selection may have different drivers and effects. Although much research has been carried out on the drivers of firms and researchers to connect with each other, less attention has been paid to the determinants of the selection of different channels of interaction. This research analysis factors driving firms’ selection of different channels of interactions with public research organisations (PROs), both public research institutes (PRIs) and universities (UNIs). The paper estimates bi-variate probit models with sample selection using micro data for 2007 from a representative survey of Argentinean firms. The classification of channels is based on previous research for Latin America and includes four types according to the main goals that firms and public research organisations seek when interacting: traditional, service, commercial and bi-directional channels. We find that factors driving the selection of the bi-directional channel are different from those driving selection of the others. In particular, firms choosing this channel employ a more skilled workforce and generally interact with PRIs and UNIs in order to benefit their own innovative activities. Thus, this commitment to knowledge capabilities and innovation when firms use the bi-directional channel may enhance the potential of PRO--firm interactions to upgrade the national innovation system (NIS).


title: The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring

abstract: Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performed face detection, face recognition, facial action unit detection, head pose detection, facial expression recognition, posture recognition, actigraphy analysis, sound pressure and light level detection, and visitation frequency detection. We were able to detect patient's face (Mean average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their postures (F1=0.94). We also found that all facial expressions, 11 activity features, visitation frequency during the day, visitation frequency during the night, light levels, and sound pressure levels during the night were significantly different between delirious and non-delirious patients (p-value<0.05). In summary, we showed that granular and autonomous monitoring of critically ill patients and their environment is feasible and can be used for characterizing critical care conditions and related environment factors.


title: Listening to the Crowd: Automated Analysis of Events via Aggregated Twitter Sentiment

abstract: Individuals often express their opinions on social media platforms like Twitter and Facebook during public events such as the U.S. Presidential debate and the Oscar awards ceremony. Gleaning insights from these posts is of importance to analyzing the impact of the event. In this work, we consider the problem of identifying the segments and topics of an event that garnered praise or criticism, according to aggregated Twitter responses. We propose a flexible factorization framework, SOCSENT, to learn factors about segments, topics, and sentiments. To regulate the learning process, several constraints based on prior knowledge on sentiment lexicon, sentiment orientations (on a few tweets) as well as tweets alignments to the event are enforced. We implement our approach using simple update rules to get the optimal solution. We evaluate the proposed method both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.


title: Collecting and analyzing qualitative data for system dynamics: Methods and models

abstract: System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most inﬂuential authors. Data-gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This article describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate communication among modelers and clients, and set up a methodological framework to promote constructive discussion around the merits of qualitative versus quantitative modeling. Copyright © 2003 John Wiley & Sons, Ltd.


Secure names for bit-strings


title: SDN Multi-Domain Orchestration and Control: Challenges and innovative future directions

abstract: This paper proposes a hierarchical Software-Defined Networking (SDN) Multi-Domain Orchestration and Control architectural framework to address complex multi-tier governance domain deployments by Carriers, Service Providers (SP), Cloud Service Providers (CSP), large Enterprises, and Network Functions Virtualization (NFV) Infrastructure/Platform as a Service (NFVIaaS/NFVPaaS) providers. The proposed framework allows for simpler networking control and orchestration, more agile development processes, accelerated pace of innovation, and increased speed of deployment, while improving scalability and configuration automation as compared to existing frameworks.


title: Surgeons should not hesitate to perform episioproctotomy for rectovaginal fistula secondary to cryptoglandular or obstetrical origin.

abstract: BACKGROUND ::: Closure of rectoanovaginal fistula from a cryptoglandular or obstetrical origin can be difficult. Multiple techniques exist and none are perfect. Although episioproctotomy offers the advantage of a simultaneous repair of the sphincter complex, it is a more extensive procedure. A rectal-advancement flap appears less traumatic and divides no perineal tissue or sphincter. The aim of this study was to evaluate the results of episioproctotomy and rectal-advancement flap on healing, postoperative continence, and sexual function. ::: ::: ::: METHODS ::: Data were retrospectively collected regarding 87 women with cryptoglandular or obstetrical rectoanovaginal fistula treated from June 1997 to 2009, who underwent episioproctotomy or rectal-advancement flap at the discretion of the treating surgeon. Healing, use of seton or stoma, number of previous procedures, smoking, age, body mass index, dyspareunia, SF-12 health survey, the IBD Quality of Life, and the Fecal Incontinence Quality of Life, and the Female Sexual Function Index were obtained from our database and via telephone interviews. The Fisher exact probability and χ tests were used. ::: ::: ::: RESULTS ::: The mean age of these 87 women was 42.8 ± 10.5 years. Mean follow-up was 49.2 ± 39.2 months. Fifty (57.5%) patients underwent episioproctotomy and 37 (42.5%) underwent rectal-advancement flap. Thirty-nine (78%) patients healed after episioproctotomy vs 23 (62.2%) patients after rectal-advancement flap (P = .1). Episioproctotomy was associated with significantly better fecal (P < .001) and sexual (P = .04) function. There was no significant difference in other studied variables between the 2 techniques. ::: ::: ::: CONCLUSIONS ::: Despite episioproctotomy being a more extensive procedure, healing rates were comparable between episioproctotomy and rectal-advancement flaps. In this select population, episioproctotomy may provide better continence and may confer better sexual function compared with rectal-advancement flap. In appropriate patients surgeons should not hesitate to perform episioproctotomy on cryptoglandular or obstetrical-associated rectoanovaginal fistula.


Model-Driven Engineering: Automatic Code Generation and Beyond


title: Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation

abstract: In this paper, we assess the economic viability of innovation by producers relative to two increasingly important alternative models: innovations by single-user individuals or firms and open collaborative innovation. We analyze the design costs and architectures and communication costs associated with each model. We conclude that both innovation by individual users and open collaborative innovation increasingly compete with and may displace producer innovation in many parts of the economy. We explain why this represents a paradigm shift with respect to innovation research, policy making, and practice. We discuss important implications and offer suggestions for further research.


title: MS-QI: A Modulation Spectrum-Based ECG Quality Index for Telehealth Applications

abstract: As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.


title: StuffNet: Using ‘Stuff’ to Improve Object Detection

abstract: We propose a Convolutional Neural Network (CNN) based algorithm – StuffNet – for object detection. In addition to the standard convolutional features trained for region proposal and object detection [33], StuffNet uses convolutional features trained for segmentation of objects and 'stuff' (amorphous categories such as ground and water). Through experiments on Pascal VOC 2010, we show the importance of features learnt from stuff segmentation for improving object detection performance. StuffNet improves performance from 18.8% mAP to 23.9% mAP for small objects. We also devise a method to train StuffNet on datasets that do not have stuff segmentation labels. Through experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of this method and show that StuffNet also significantly improves object detection performance on such datasets.


On Close and Distant Reading in Digital Humanities: A Survey and Future Challenges


title: Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals

abstract: This systematic review of mixed methods studies focuses on factors that can facilitate or limit the implementation of information and communication technologies (ICTs) in clinical settings. Systematic searches of relevant bibliographic databases identified studies about interventions promoting ICT adoption by healthcare professionals. Content analysis was performed by two reviewers using a specific grid. One hundred and one (101) studies were included in the review. Perception of the benefits of the innovation (system usefulness) was the most common facilitating factor, followed by ease of use. Issues regarding design, technical concerns, familiarity with ICT, and time were the most frequent limiting factors identified. Our results suggest strategies that could effectively promote the successful adoption of ICT in healthcare professional practices.


title: Cross-domain Recommendation with Semantic Correlation in Tagging Systems

abstract: The tagging system provides users with a platform to express their preferences as they annotate terms or keywords to items. Tag information is a bridge between two domains for transferring knowledge and helping to alleviate the data sparsity problem, which is a crucial and challenging problem in most recommender systems. Existing methods incorporate correlations extracted from overlapping tags at a lexical level in cross-domain recommendation, but they neglect semantical relationships between different tags, which impairs prediction accuracy in the target domain. To solve this challenging problem, we propose a cross-domain recommendation method with semantic correlation in tagging systems. This method automatically captures the semantic relationships between non-identical tags and applies them to the recommendation. The word2vec technique is used to learn the latent representations of tags. Semantically equivalent tags are then grouped to form a joint embedding space comprised of tag clusters. This embedding space serves as the bridge between domains. By mapping users and items from both the source and target domains into the same embedding space, similar users or items across domains can be identified. Thus, the recommendation in a sparse target domain is improved by transferring knowledge through correlated users and items. Experimental results with three datasets on six cross-domain recommendation tasks demonstrate that the proposed method exploits the semantic links from tags in two domains and outperforms five benchmarks in prediction accuracy. The results indicate that transferring knowledge through tags semantics is feasible and effective.


title: WIDE BEAM TAPERED SLOT ANTENNA FOR WIDE ANGLE SCANNING PHASED ARRAY ANTENNA

abstract: Design and development of a low proflle, compact, wide beam and wide band printed double layered exponentially tapered slot antenna (DTSA) with a coplanar waveguide (CPW) feed meant for wide scan active phased array antenna in X-band has been presented. DTSA satisfles the requirements on the maximum re∞ection coe-cient of i • i10dB for §60 - and §45 - scan from broadside in H- and E- planes, respectively with a moderate gain of 4{7dBi. Realized antenna has shown a symmetric pattern together with moderately high gain, low cross-polarization and 3dB beam width better than §60 - and §45 - in H- and E- planes, respectively. The designed structure is expected to flnd applications in mounting platforms with limited RF real estate available to it like in military aircrafts, owing to its easy integration with the uni-planar monolithic millimeter-wave integrated circuits.


title: Novel Thin and Compact H-Plane SIW Horn Antenna

abstract: The substrate integrated waveguide (SIW) technology allows to construct several types of commonly used antennas in a planar way. However, some practical constraints limit their performances when frequencies below 20 GHz are considered. In the case of SIW horn antennas, the available substrates are much thinner than the wavelength yielding to poor matching and undesired back radiation. In this paper, an innovative structure to overcome these limitations is presented. It consists of a transition printed on the same SIW substrate, which improves both the radiation and the matching performances of conventional SIW horns. The horn shape is also further optimized by reducing its dimensions required for a given directivity. This is obtained by modifying the horn profile in order to effectively combine different TE modes. Guidelines are provided to design this type of thin and compact SIW horn antenna. They were applied to manufacture a prototype in the Ku-band with a substrate thinner than λ0/10. Measurement results validate the proposed concepts showing excellent performances.


title: Decentralized IoT Data Management Using BlockChain and Trusted Execution Environment

abstract: Due to the centralization of authority in the management of data generated by IoT devices, there is a lack of transparency in how user data is being shared among third party entities. With the popularity of adoption of blockchain technology, which provide decentralized management of assets such as currency as seen in Bitcoin, we propose a decentralized system of data management for IoT devices where all data access permission is en-forced using smart contracts and the audit trail of data access is stored in the blockchain. With smart contracts applications, multiple parties can specify rules to govern their interactions which is independently enforced in the blockchain without the need for a centralized system. We provide a framework that store the hash of the data in the blockchain and store the raw data in a secure storage platform using trusted execution environment (TEE). In particular, we consider Intel SGX as a part of TEE that ensure data security and privacy for sensitive part of the application (code and data).


title: 3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks

abstract: Standard 3D convolution operations usually require larger amounts of memory and computation cost than 2D convolution operations. The fact increases the difficulty of the development of deep neural nets in many 3D vision tasks. In this paper, we investigate the possibility of applying depthwise separable convolutions in 3D scenario and introduce the use of 3D depthwise convolution. A 3D depthwise convolution splits a single standard 3D convolution into two separate steps, which would drastically reduce the number of parameters in 3D convolutions with more than one order of magnitude. We experiment with 3D depthwise convolution on popular CNN architectures and also compare it with a similar structure called pseudo-3D convolution. The results demonstrate that, with 3D depthwise convolutions, 3D vision tasks like classification and reconstruction can be carried out with more light-weighted neural networks while still delivering comparable performances.


Full System Emulation : Achieving Successful Automated Dynamic Analysis of Evasive Malware !


abstract: The authors developed and validated a measure of teachers' motivation toward specific work tasks: The Work Tasks Motivation Scale for Teachers (WTMST). The WTMST is designed to assess five motivational constructs toward six work tasks (e.g., class preparation, teaching). The authors conducted a preliminary (n = 42) and a main study among elementary and high school teachers (n = 609) to develop and validate the scale. Overall, results from the main study reveal that the WTMST is composed of 30 reliable and valid factors reflecting five types of motivation among six work tasks carried out by teachers. Results based on an extension of the multitrait—multimethod approach provide very good support for assessing teachers' motivation toward various work tasks. Support for the invariance of the WTMST over gender and teaching levels was also obtained. Results are discussed in light of self-determination theory and the multitask perspective.


title: Active error detection and resolution for speech-to-speech translation.

abstract: 1 We describe a novel two-way speech-to-speech (S2S) translation system that actively detects a wide variety of common error types and resolves them through user-friendly dialog with the user(s). We present algorithms for detecting out-of-vocabulary (OOV) named entities and terms, sense ambiguities, homophones, idioms, ill-formed input, etc. and discuss novel, interactive strategies for recovering from such errors. We also describe our approach for prioritizing different error types and an extensible architecture for implementing these decisions. We demonstrate the efficacy of our system by presenting analysis on live interactions in the English-to-Iraqi Arabic direction that are designed to invoke different error types for spoken language translation. Our analysis shows that the system can successfully resolve 47% of the errors, resulting in a dramatic improvement in the transfer of problematic concepts.


title: High speed electric machines — Challenges and design considerations

abstract: High-speed electric machines (HSEMs) have been widely used in many of today’s applications and are gaining more attention in the automotive industry. However, many special design challenges exist for HSEMs. This paper aims to summarize and discuss major challenges and design considerations for HSEMs. Solutions to overcome these challenges are also offered in this paper. Four major loss components of HSEMs are discussed and the methods to reduce each loss component are further explained. Bearing options for HSEMs are also compared. In addition, the rotor dynamics and structural challenges are discussed in detail, followed by some design suggestions and considerations. Thermal and cooling challenges are presented as an overview. At the end, the challenges for power electronics circuitry are also demonstrated and are followed by some proposed solutions. Thus, this paper offers a guideline for proper technique selection and the design of HSEMs and associated accessories.


title: Quitty: using technology to persuade smokers to quit

abstract: Health is an important topic in HCI research with an increasing amount of health risks surrounding individuals and society at large. It is well known that smoking cigarettes can have serious health implications. The importance of this problem motivates investigation into the use of technology to encourage behavior change. Our study was designed to gather empirical knowledge about the role a "quitting app" can play in persuading people to quit smoking. Our purpose-built app Quitty introduces different content types from different content sources to study how they are perceived and motivate health behavior change. Findings from our field study show that tailored content and push-messages are considered the most important for persuading people to stop smoking. Based on our empirical findings, we propose six guidelines on how to design mobile applications to persuade smokers to quit.


title: Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

abstract: We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of K actions in response to the observed context, and observes the reward only for that action. Our method assumes access to an oracle for solving fully supervised cost-sensitive classification problems and achieves the statistically optimal regret guarantee with only O(√KT) oracle calls across all T rounds. By doing so, we obtain the most practical contextual bandit learning algorithm amongst approaches that work for general policy classes. We conduct a proof-of-concept experiment which demonstrates the excellent computational and statistical performance of (an online variant of) our algorithm relative to several strong baselines.


title: A General Computational Framework and Simulations of Branching Programs of Boolean Circuits Using Higher Order Logic (HOL) Software - An Insight into ECAD Tool Design Paradigm

abstract: Integrated optics and Optical computing are now a mature technology offering many types of devices and manufacturing techniques. Recent breakthroughs in the field of silicon photonics showed low-loss insulators, passive wave guide devices, high speed optical switches, detectors, silicon lasers, and silicon amplifiers, optical amplifiers etc. These devices have provided the possibility to construct CMOS compatible optical circuits with low power consumption, high bandwidth and low latencies. A critical component that we intend to focus is on considering Branching Programs as CAD tools for the design of future electronics. Hence, BPs as viable means to accomplish the task of propelling the R&D of Electronics, are considered and simulated using HOL software based on boolean theory. We believe our research is one of the remarkable pioneering efforts, into the promising aspects of Branching Programs as CAD Tools.


title: Mortality and Associated Risk Factors in Community-Dwelling Persons With Early Dementia

abstract: Objective: Examine mortality and associations with baseline characteristics among Veterans with early dementia. Methods: Participants included dyads of community-based Veterans with early dementia and their caregivers (N=143) enrolled in a previous longitudinal study. Department of Veterans Health Affairs’ electronic records were used to retrospectively collect Veteran mortality outcomes, over a 6-year period. Measures included baseline: demographics, dementia-related factors, other comorbid conditions, functioning, and medication use. Associations with baseline characteristics and mortality were examined with bivariate analyses and a series of Cox proportional hazard models. Results: Over 6 years of study follow-up, 53.1% of participants died. The mean time to death was 3.09 years, with a range of 54 days to 5.91 years. Female sex, better cognition, and higher scores on the Tinetti Gait and Balance scale were protective factors in the final multivariable model, adjusting for other characteristics. Conclusions: While newly diagnosed with early dementia, over half of our sample died in the 6-year follow-up period, with the average death occurring only 3 years after initial diagnosis. The finding of lower mortality associated with better performance on gait/balance testing indicates an important opportunity for focused interventions and early detection of gait and balance changes early during cognitive decline.


title: Advertisement removal of Android applications by reverse engineering

abstract: Millions of apps cover all aspects of our lives and make smartphones success. Advertisers financially support developers but also collect users' privacy. Several ad blocking apps help users to eliminate advertisement by filtering or redirection for certain targets whose list is maintained by a third party, and some of them require root permission. We develop an advertisement removal program with the technique of reverse engineering, which can effectively patch the advertising code, even obfuscated by other tools. The proposed method takes no overhead at runtime, and requires neither root permission nor list updating. This study also gives a different perspective on tailoring behavior of Android applications.


title: Information propagation in the Bitcoin network

abstract: Bitcoin is a digital currency that unlike traditional currencies does not rely on a centralized authority. Instead Bitcoin relies on a network of volunteers that collectively implement a replicated ledger and verify transactions. In this paper we analyze how Bitcoin uses a multi-hop broadcast to propagate transactions and blocks through the network to update the ledger replicas. We then use the gathered information to verify the conjecture that the propagation delay in the network is the primary cause for blockchain forks. Blockchain forks should be avoided as they are symptomatic for inconsistencies among the replicas in the network. We then show what can be achieved by pushing the current protocol to its limit with unilateral changes to the client's behavior.


title: Spherical robot with new type of two-pendulum driving mechanism

abstract: This paper introduces a spherical robot, called KisBot II, with a new type of two-pendulum driving mechanism. A cross-shape frame is located horizontally in the center of the robot. The main axis of the frame is connected to the outer shell, and each pendulum is connected to the end of the other axis of the frame respectively. The main axis and pendulums can rotate 360 degrees inside the sphere orthogonally without interfering with each other, also the two pendulums can rotate identically or independent of each other. Due to this driving mechanism, KisBot II has various motion generation abilities, including a fast steering ability, turning ability in place and during travelling, and four(forward / backward / left / right) directional driving ability on the ground. Experiments for several motions verify the driving efficiency of the proposed spherical robot.


title: Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion

abstract: The revolution of Internet-of-Things (IoT) is reshaping the modern food supply chains with promising business prospects. To be successful in practice, the IoT solutions should create "income-centric" values beyond the conventional "traceability-centric" values. To accomplish what we promised to users, sensor portfolios and information fusion must correspond to the new requirements introduced by this income-centric value creation. In this paper, we propose a value-centric business-technology joint design framework. Based on it the income-centric added-values including shelf life prediction, sales premium, precision agriculture, and reduction of assurance cost are identified and assessed. Then corresponding sensor portfolios are developed and implemented. Three-tier information fusion architecture is proposed as well as examples about acceleration data processing, self-learning shelf life prediction and real-time supply chain re-planning. The feasibilities of the proposed design framework and solution have been confirmed by the field trials and an implemented prototype system.


title: As-exact-as-possible repair of unprintable STL files

abstract: Purpose ::: ::: ::: ::: ::: The class of models that can be represented by STL files is larger than the class of models that can be printed using additive manufacturing technologies. Stated differently, there exist well-formed STL files that cannot be printed. This paper aims to formalize such a gap and describe a fully automatic procedure to turn any such file into a printable model. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Based on well-established concepts from combinatorial topology, this paper provide an unambiguous description of all the mathematical entities involved in the modeling-printing pipeline. Specifically, this paper formally defines the conditions that an STL file must satisfy to be printable, and, based on these, an as-exact-as-possible repairing algorithm is designed. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: It has been found that, to cope with all the possible triangle configurations, the algorithm must distinguish between triangles that bind solid parts and triangles that constitute zero-thickness sheets. Only the former set can be fixed without distortion. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: Owing to the specific approach used that tracks the so-called “outer hull,” models with inner cavities cannot be treated. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: Thanks to this new method, the shift from a 3D model to a printed prototype is faster, easier and more reliable. ::: ::: ::: ::: ::: Social implications ::: ::: ::: ::: ::: The availability of this easily accessible model preparation tool has the potential to foster a wider diffusion of home-made 3D printing in non-professional communities. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Previous methods that are guaranteed to fix all the possible configurations provide only approximate solutions with an unnecessary distortion. Conversely, this procedure is as exact as possible, meaning that no visible distortion is introduced unless it is strictly imposed by limitations of the printing device. Thanks to such unprecedented flexibility and accuracy, this algorithm is expected to significantly simplify the modeling-printing process, in particular within the continuously emerging non-professional “maker” communities.


title: IT alignment strategies for customer relationship management

abstract: Customer relationship management (CRM) is the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction. A CRM strategy involves the entire enterprise and is employed on an ongoing basis. Despite the fact that CRM projects incur huge expenditures, a large percentage fails to achieve the stated objectives. Failure in CRM initiatives could be avoided if a firm's CRM strategies are intelligently linked with its employees, customers, channels, and IT infrastructure. In this paper, we focus on those linkages, particularly on the linkages between an organization's CRM strategies and its IT infrastructure. Even though the relationships between IT and business strategies have been extensively explored in the IT alignment literature, prior research has not addressed how a firm's CRM strategies are aligned with its IT infrastructure. In this paper, we investigate the issues relating to CRM-IT alignment based on an in-depth case study of a large, well-known Internet travel agency.


title: Effect of periodic environmental fluctuations on the Pearl–Verhulst model ☆

abstract: Abstract We address the effect of periodic environmental fluctuations on the Pearl–Verhulst model in population dynamics and clarify several important issues very actively discussed in the recent papers by Lakshmi [Lakshmi BS. Oscillating population models. Chaos Solitons & Fractals 2003;16:183–6; Lakshmi BS. Population models with time dependent parameters. Chaos Solitons & Fractals 2005;26:719–21], Leach and Andriopoulos [Leach PGL, Andriopoulos K. An oscillatory population model. Chaos Solitons & Fractals 2004;22:1183–8], Swart and Murrell [Swart JH, Murrell HC. An oscillatory model revisited. Chaos Solitons & Fractals 2007;32:1325–7]. Firstly, we review general results regarding existence and properties of periodic solutions and examine existence of a unique positive asymptotically stable periodic solution of a non-autonomous logistic differential equation when r ( t ) > 0 . Proceeding to the case where r ( t ) is allowed to take on negative values, we consider a modified Pearl–Verhulst equation because, as emphasized by Hallam and Clark [Hallam TG, Clark CE. Non-autonomous logistic equations as models of populations in deteriorating environment. J Theor Biol 1981;93:303–11], use of the classic one leads to paradoxical biological conclusions. For a modified logistic equation with ω -periodic coefficients, we establish existence of a unique asymptotically stable positive periodic solution with the same period. Special attention is paid to important cases where time average of the intrinsic growth rate is non-positive. Results of computer simulation demonstrating advantages of a modified equation for modeling periodic environmental fluctuations are presented.


title: Multiple object identification with passive RFID tags

abstract: We investigate the applicability of passive RFID systems to the task of identifying multiple tagged objects simultaneously, assuming that the number of tags is not known in advance. We present a combinatorial model of the communication mechanism between the reader device and the tags, and use this model to derive the optimal parameter setting for the reading process, based on estimates for the number of tags. Some results on the performance of an implementation are presented.


title: Recent Advances on Neural Headline Generation

abstract: Recently, neural models have been proposed for headline generation by learning to map documents to headlines with recurrent neural network. In this work, we give a detailed introduction and comparison of existing work and recent improvements in neural headline generation, with particular attention on how encoders, decoders and neural model training strategies alter the overall performance of the headline generation system. Furthermore, we perform quantitative analysis of most existing neural headline generation systems and summarize several key factors that impact the performance of headline generation systems. Meanwhile, we carry on detailed error analysis to typical neural headline generation systems in order to gain more comprehension. Our results and conclusions are hoped to benefit future research studies.


title: Emerging Optical Wireless Communications-Advances and Challenges

abstract: New data services and applications are emerging continuously and enhancing the mobile broadband experience. The ability to cope with these varied and sophisticated services and applications will be a key success factor for the highly demanding future network infrastructure. One such technology that could help address the problem would be optical wireless communications (OWC), which presents a growing research interest in the last few years for indoor and outdoor applications. This paper is an overview of the OWC systems focusing on visible light communications, free space optics, transcutaneous OWC, underwater OWC, and optical scattering communications.


title: Effects of injected light and optical feedback on directly modulated MQW lasers and OTDM multigigabit-per-second system performance

abstract: Injection-locked directly current modulated semiconductor laser transmitters are theoretically investigated with respect to transmission performance. By large signal simulation of laser, standard single-mode fiber propagation and direct detection optically pre-amplified PIN receiver, transmission distances of 80-120 km at 10 Gb/s and 40-60 km at 15 Gb/s have been evaluated with a bit-error-rate < 10-9 with reasonable power penalty. Exploiting fiber nonlinearities with higher power launched into the fiber is demonstrated to increase the transmission distance by about 40%. Additionally the dynamics of the residual chirp of the laser is shown to act favorably on transmission performance. Guidelines for operation conditions of injection-locked lasers depending on detuning between laser and slave laser and injected power are given. Gain switching including optical feedback and the gain- levering effect have been investigated with respect to pulse production for optical time domain multiplexing. A new method for short pulse generation is presented. It is based on single frequency CW light injection into an unmodulated single mode laser under nonstable locking- conditions. Repetition frequencies larger than 150 GHz can be achieved. By soliton generation in a dispersion shifted fiber pulse widths of less than 3 ps FWHM with a squared hyperbolic cosecans shape can be generated.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images

abstract: Medical imaging is shifting from film to electronic images. The STARE (structured analysis of the retina) system is a sophisticated image management system that will automatically diagnose images, compare images, measure key features in images, annotate image contents, and search for images similar in content. The authors concentrate on automated diagnosis. The images are annotated by segmentation of objects of interest, classification of the extracted objects, and reasoning about the image contents. The inferencing is accomplished with Bayesian networks that learn from image examples of each disease. This effort at image understanding in fundus images anticipates the future use of medical images. As these capabilities mature, the authors expect that ophthalmologists and physicians in other fields that rely in images will use a system like STARE to reduce repetitive work, to provide assistance to physicians in difficult diagnoses or with unfamiliar diseases, and to manage images in large image databases.


title: 3D collage: expressive non-realistic modeling

abstract: The ability of computer graphics to represent images symbolically has so far been used mostly to render existing models with greater clarity or with greater visual appeal. In this work, we present a method aimed at harnessing this symbolic representation power to increase the expressiveness of the 3D models themselves. We achieve this through modification of the actual representation of 3D shapes rather than their images. In particular, we focus on 3D collage creation, namely, a generation of compound representations of objects. The ability of such representations to convey multiple meanings has been recognized for centuries. At the same time, it has also been acknowledged that for humans, the creation of compound 3D shapes is extremely taxing. Thus, this expressive but technically challenging artistic medium is a particularly good candidate to address using computer graphics methods. We present an algorithm for 3D collage generation that serves as an artistic tool performing the challenging 3D processing tasks, thus enabling the artist to focus on the creative side of the process.


title: Design and Implementation of Fractional Order Controller for CSTR Process

abstract: A Fractional Order (FO) ProportionalIntegralDerivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative and fractional integrator. FOPID is a PID controller whose derivative and integral orders are of fractional rather than integer. The extension of derivative and integral order from integer to fractional order provides more flexibility in design of the controller, thereby controlling wide range of dynamics of a system. Frequency domain specifications are used as the performance criteria to be optimizing the FOPID controller parameters: Proportional (Kp), Integral (Ki), Derivative (Kd) gains, integral order (?), and the derivative order (?).


title: A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy

abstract: The concern of the safety of fingerprint authentication system is rising with its widely using for it is easy to be attacked by spoof (fake) fingerprints. Fake fingerprints are usually made of Ploy-Doh, silicon or other artifacts. So most current approaches rely on fingerprint liveness detection as main anti-spoofing mechanisms. Recently, researchers propose to use local feature descriptor for fingerprint liveness detection, but the results are still not satisfying the real world application requirement. Inspired by the newly trend of application of Deep Convolution Neural Network (DCNN) in computer vision field and its outstanding performance in face detection and image classification, we propose a novel fingerprint liveness detection method based on DCNN and voting strategy, which performs better than handcraft feature and optimize the process of feature extraction and classifier training simultaneously. The experimental results on the datasets of LivDet2011 and LivDet2013 show that the proposed algorithm has great improvement compare to the former state-of-the-art algorithm, and keep highly real-time performance at the same time.


title: Life-Course-Persistent Antisocial Behavior

abstract: The article reviewed the status of the Life Course Persistent category of antisocial behavior some two decades plus from its original formulation as well as the finding from the landmark Dunedin longitudinal study of antisocial behavior that this category is comprised almost entirely of males. The importance of this category for forensic psychology is the robust and remarkable finding that the small group of individuals (5-10%) who tend to cluster in this category are responsible for over half of all crimes in the United States and other developed countries, and an even greater proportion of violent crimes.


Microscopy of honey


Review of Tourism Augmented Reality ( AR ) in Urban Heritage Tourism


title: Appreciation as Audience Response: Exploring Entertainment Gratifications Beyond Hedonism

abstract: This article elaborates upon the notion of media enjoyment in the context of film viewing by proposing a complementary type of gratification that we conceptualize as appreciation. Three studies were conducted to tap into the multidimensionality of viewers' entertainment gratifications with a special focus on the domain of more serious, poignant, and pensive media experiences typically associated with genres such as drama, history, documentary, or art films. These studies provide evidence of and measurement for gratifications related to fun and suspense, but also gratifications related to moving and thought-provoking entertainment experiences, with all three gratifications leading to perceptions of entertainment having a more long-lasting or enduring effect. The results are discussed with regard to the theoretical conceptualization of entertainment gratification.


title: A more realistic approach for airport ground movement optimisation with stand holding

abstract: In addition to having to handle constantly increasing numbers of aircraft, modern airports also have to address a wide range of environmental regulations and requirements. As airports work closer and closer to their maximal possible capacity, the operations problems that need to be solved become more and more complex. This increasing level of complexity leads to a situation where the introduction of advanced decision support systems becomes more and more attractive. Such systems have the potential to improve efficient airside operations and to mitigate against the environmental impact of those operations. This paper addresses the problem of moving aircraft from one location within an airport to another as efficiently as possible in terms of time and fuel spent. The problem is often called the ground movement problem and the movements are usually from gate/stands to a runway or vice-versa. We introduce a new sequential graph based algorithm to address this problem. This approach has several advantages over previous approaches. It increases the realism of the modelling and it draws upon a recent methodology to more accurately estimate taxi times. The algorithm aims to absorb as much waiting time for delay as possible at the stand (with engines off) rather than out on the taxiways (with engines running). The impact of successfully achieving this aim is to reduce the environmental pollution. This approach has been tested using data from a European hub airport and it has demonstrated very promising results. We compare the performance of the algorithm against a lower bound on the taxi time and the limits to the amount of waiting time that can be absorbed at stand.


title: Unsupervised biomedical named entity recognition

abstract: Display Omitted BM-NER is approached by an unsupervised stepwise method.Noun phrase chunking is a good approximation of boundary detection.Distributional semantics works well in classifying entities.The system performs well on clinical and biological texts. Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work.


title: T-share: A large-scale dynamic taxi ridesharing service

abstract: Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).


title: Vehicle Re-Identification Using Quadruple Directional Deep Learning Features

abstract: In order to resist the adverse effect of viewpoint variations, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images for improving vehicle re-identification performance. The quadruple directional deep learning networks are of similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture that is a shortly and densely connected convolutional neural network is utilized to extract the basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling layer, vertical average pooling layer, diagonal average pooling layer, and anti-diagonal average pooling layer, to compress the basic feature maps into horizontal, vertical, diagonal, and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. The extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.


title: A Compensation Technique for Smooth Transitions in a Noninverting Buck–Boost Converter

abstract: With the advent of battery-powered portable devices and mandatory adoption of power factor correction, noninverting buck-boost converters are garnering lots of attention. Conventional two-switch or four-switch noninverting buck-boost converters choose their operation modes by measuring input and output voltage magnitude. The criterion for the selection of the operation mode can cause higher output voltage transients in the neighborhood, where input and output are close to each other. For the mode selection, due to the voltage drops raised by the parasitic components, it is not enough just to compare the magnitude of input and output voltages. In addition, the difference in the minimum and maximum effective duty cycles between controller output and switching device yields discontinuity at the instant of mode change. Moreover, the different properties of output voltage versus a given duty cycle of buck and boost operating modes contribute to the output voltage transients. In this paper, the effect of the discontinuity due to the effective duty cycle derived from the device switching time at the mode change is analyzed. A technique to compensate the output voltage transient due to this discontinuity is proposed. In order to attain additional mitigation of output transients and a linear input/output voltage characteristic in buck and boost modes, the linearization of DC gain of the large-signal model in boost operation is analyzed as well. Analytical, simulation, and experimental results are presented to validate the proposed theory.


title: A Compact Monopole Antenna for Super Wideband Applications

abstract: A planar microstrip-fed super wideband monopole antenna is initially proposed. By embedding a semielliptically fractal-complementary slot into the asymmetrical ground plane, a 10-dB bandwidth of 172% (1.44-18.8 GHz) is achieved with ratio bandwidth >;12:1. Furthermore, the proposed antenna also demonstrated a wide 14-dB bandwidth from 5.4 to 12.5 GHz, which is suitable for UWB outdoor propagation. This proposed antenna is able to cover the DVB-H in L-band (for PMP), DCS, PCS, UMTS, Bluetooth, WiMAX2500, LTE2600, and UWB bands.


Industrial Policy and Industrialization


title: Dlib-ml: A Machine Learning Toolkit

abstract: There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library, targeted at both engineers and research scientists, which aims to provide a similarly rich environment for developing machine learning software in the C++ language. Towards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS support. It also houses implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy use of these tools, the entire library has been developed with contract programming, which provides complete and precise documentation as well as powerful debugging tools.


title: Morphologic response to changes in neuromuscular patterns experimentally induced by altered modes of respiration

abstract: Abstract The present experiment was designed to test whether specific recordable changes in the neuromuscular system could be associated with specific alterations in soft- and hard-tissue morphology in the craniofacial region. The effect of experimentally induced neuromuscular changes on the craniofacial skeleton and dentition of eight rhesus monkeys was studied. The neuromuscular changes were triggered by complete nasal airway obstruction and the need for an oral airway. Alterations were also triggered 2 years later by removal of the obstruction and the return to nasal breathing. Changes in neuromuscular recruitment patterns resulted in changed function and posture of the mandible, tongue, and upper lip. There was considerable variation among the animals. Statistically significant morphologic effects of the induced changes were documented in several of the measured variables after the 2-year experimental period. The anterior face height increased more in the experimental animals than in the control animals; the occlusal and mandibular plane angles measured to the sella-nasion line increased; and anterior crossbites and malposition of teeth occurred. During the postexperimental period some of these changes were reversed. Alterations in soft-tissue morphology were also observed during both experimental periods. There was considerable variation in morphologic response among the animals. It was concluded that the marked individual variations in skeletal morphology and dentition resulting from the procedures were due to the variation in nature and degree of neuromuscular and soft-tissue adaptations in response to the altered function. The recorded neuromuscular recruitment patterns could not be directly related to specific changes in morphology.


title: Damage to Association Fiber Tracts Impairs Recognition of the Facial Expression of Emotion

abstract: An array of cortical and subcortical structures have been implicated in the recognition of emotion from facial expressions. It remains unknown how these regions communicate as parts of a system to achieve recognition, but white matter tracts are likely critical to this process. We hypothesized that (1) damage to white matter tracts would be associated with recognition impairment and (2) the degree of disconnection of association fiber tracts [inferior longitudinal fasciculus (ILF) and/or inferior fronto-occipital fasciculus (IFOF)] connecting the visual cortex with emotion-related regions would negatively correlate with recognition performance. One hundred three patients with focal, stable brain lesions mapped onto a reference brain were tested on their recognition of six basic emotional facial expressions. Association fiber tracts from a probabilistic atlas were coregistered to the reference brain. Parameters estimating disconnection were entered in a general linear model to predict emotion recognition impairments, accounting for lesion size and cortical damage. Damage associated with the right IFOF significantly predicted an overall facial emotion recognition impairment and specific impairments for sadness, anger, and fear. One subject had a pure white matter lesion in the location of the right IFOF and ILF. He presented specific, unequivocal emotion recognition impairments. Additional analysis suggested that impairment in fear recognition can result from damage to the IFOF and not the amygdala. Our findings demonstrate the key role of white matter association tracts in the recognition of the facial expression of emotion and identify specific tracts that may be most critical.