title: Antecedents of Project Implementation Success in Crowdfunding

abstract: Crowdfunding is emerging as a booming financing channel for entrepreneurs to raise money for their projects. However, crowdfunding project implementation has been a major challenge which does not gain enough attention in the current literature. This paper developed a research model to investigate the antecedents of crowdfunding project success measured in three dimensions, i.e., award (product) delivery timeliness, the extent to which the award meets the specifications (meeting specifications), and sponsor overall satisfaction. We conducted a survey in Demohour, one of the famous crowdfunding platforms in China, to test the proposed model. The findings suggested that compared with delivery timeliness, meeting specifications is of more importance to increase overall satisfaction. The results also revealed that crowdfunding project difficulty significantly reduces the implementation success. In addition, team experience plays an important role in improving project success. Project planning is also a critical predictor for delivery timeliness. Both of theoretical research and crowdfunding industrial practice can draw some enlightenment from this study.


title: Cloud Elasticity: A Survey

abstract: Cloud elasticity is a unique feature of cloud environments, which allows for the on demand de-provisioning or reconfiguration of the resources of cloud deployments. The efficient handling of cloud elasticity is a challenge that attracts the interest of the research community. This work constitutes a survey of research efforts towards this direction. The main contribution of this work is an up-to-date review of the latest elasticity handling approaches and a detailed classification scheme, focusing on the elasticity decision making techniques. Finally, we discuss various research challenges and directions of further research, regarding all phases of cloud elasticity, which can be deemed as a special case of autonomic behavior of computing systems This research has been co-financed by the European Union European Social Fund - ESF and Greek national funds through the Operational Program "Education and Lifelong Learning of the National Strategic Reference Framework NSRF - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.".


title: Logic-Based Question Answering

abstract: Question answering systems aim to provide concise and correct responses to arbitrary questions, communicating with the user in a natural language. This way they help making the knowledge of large textual sources accessible in an intuitive manner which goes beyond the capabilities of conventional search engines. In the LogAnswer project the universities of Hagen and Koblenz cooperate to build a German language question answering system which combines computational linguistics and automated reasoning to deduce answers from a knowledge base derived from Wikipedia.


title: An efficient parallel solver for SDD linear systems

abstract: We present the first parallel algorithm for solving systems of linear equations in symmetric, diagonally dominant (SDD) matrices that runs in polylogarithmic time and nearly-linear work. The heart of our algorithm is a construction of a sparse approximate inverse chain for the input matrix: a sequence of sparse matrices whose product approximates its inverse. Whereas other fast algorithms for solving systems of equations in SDD matrices exploit low-stretch spanning trees, our algorithm only requires spectral graph sparsifiers.


title: Towards Seamless Hybrid Graphical–Textual Modelling for UML and Profiles

abstract: Domain-specific modelling languages, in particular those described in terms of UML profiles, use graphical notations to maximise human understanding and facilitate communication among stakeholders. Nevertheless, textual notations are preferred for specific purposes, due to the nature of a specific domain, or for personal preference. The mutually exclusive use of graphical or textual modelling is not sufficient for the development of complex systems developed by large heterogeneous teams. We envision a modern modelling framework supporting seamless hybrid graphical and textual modelling. Such a framework would provide several benefits, among which: flexible separation of concerns, multi-view modelling based on multiple notations, convenient text-based editing operations, and text-based model editing outside the modelling environment, and faster modelling activities.


title: Using institutional theory and dynamic simulation to understand complex e-Government phenomena

abstract: Abstract Governments around the world have developed e-Government programs expecting to obtain important benefits such as improved efficiency or greater transparency. However, many e-Government projects fail to deliver their promises in terms of specific outcomes. Some of such failures are the result of a lack of understanding about the relationships among technologies, information use, organizational factors, institutional arrangements, and socio-economic contexts involved in the selection, implementation, and use of information and communication technologies (ICT), producing mismatches and unintended consequences. This paper proposes the use of institutional theory and dynamic simulation, particularly system dynamics, as an integrated and comprehensive approach to understand e-Government phenomena. Combining a sound theory and a sophisticated analytical technique will help to improve our understanding about ICT in government settings. The paper draws on the case of the e-Mexico program, particularly on the strategy to create web-based content portals for citizens in the areas of education, health, economy, and government. Using the same technological infrastructure and under the leadership of the same Federal Ministry, four different networks of government and non-government organizations engaged in the creation of internet portals and their content. The results provide evidence to demonstrate important bidirectional relationships between formal processes (institutions), agency networks (organizational forms), and the resulting characteristics of the four thematic portals (enacted technology).


title: Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization

abstract: Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distinctions between similar subcategories. However, they generally have two limitations: 1) discriminative localization relies on region proposal methods to hypothesize the locations of discriminative regions, which are time-consuming and the bottleneck of improving classification speed and 2) the training of discriminative localization depends on object or part annotations which are heavily labor-consuming and the obstacle of marching toward practical application. It is highly challenging to address the two limitations simultaneously , while existing methods only focus on one of them. Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: 1) multi-level attention guided localization learning is proposed to localize discriminative regions with different focuses automatically, without using object and part annotations, avoiding the labor consumption. Different level attentions focus on different characteristics of the image, which are complementary and boost classification accuracy and 2) $n$ -pathway end-to-end discriminative localization network is proposed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by a region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation. Both are jointly employed to simultaneously improve classification speed and eliminate dependence on object and part annotations. Comparing with state-of-the-art methods on two widely used fine-grained image classification data sets, our WSDL approach achieves the best accuracy and the efficiency of classification.


GANerated Hands for Real-Time 3 D Hand Tracking from Monocular RGB — Extended


title: Variable stiffness actuators: The user’s point of view

abstract: Since their introduction in the early years of this century, variable stiffness actuators VSA witnessed a sustained growth of interest in the research community, as shown by the growing number of publications. While many consider VSA very interesting for applications, one of the factors hindering their further diffusion is the relatively new conceptual structure of this technology. When choosing a VSA for their application, educated practitioners, who are used to choosing robot actuators based on standardized procedures and uniformly presented data, would be confronted with an inhomogeneous and rather disorganized mass of information coming mostly from scientific publications. In this paper, the authors consider how the design procedures and data presentation of a generic VSA could be organized so as to minimize the engineer's effort in choosing the actuator type and size that would best fit the application needs. The reader is led through the list of the most important parameters that will determine the ultimate performance of their VSA robot, and influence both the mechanical design and the controller shape. This set of parameters extends the description of a traditional electric actuator with quantities describing the capability of the VSA to change its output stiffness. As an instrument for the end-user, the VSA datasheet is intended to be a compact, self-contained description of an actuator that summarizes all of the salient characteristics that the user must be aware of when choosing a device for their application. At the end some examples of compiled VSA datasheets are reported, as well as a few examples of actuator selection procedures.


title: Maxillary sinus elevation by lateral window approach: evolution of technology and technique.

abstract: CONTEXT ::: The maxillary sinus elevation procedure has become an important pre-prosthetic surgical procedure for the creation of bone volume in the edentulous posterior maxilla for the placement of dental implants. Research and clinical experience over the past 30 years has increased the predictability of this procedure as well as reduced patient morbidity. ::: ::: ::: EVIDENCE ACQUISITION ::: Data on grafting materials and implant survival rates comes from 10 published evidence-based reviews that include all relevant published data from 1980 to 2012. Supporting clinical material comes from the experience of the authors. ::: ::: ::: EVIDENCE SYNTHESIS ::: The evidence-based reviews report and compare the implant survival rates utilizing various grafting materials, implant surfaces, and the use or non-use of barrier membranes over the lateral window. Clinical studies report on complication rates utilizing piezoelectric surgery and compare them to complication rates with rotary instrumentation. ::: ::: ::: CONCLUSIONS ::: The conclusions of all the evidence-based reviews indicate that the utilization of bone replacement grafts, rough-surfaced implants, and barrier membranes result in the most positive outcomes when considering implant survival. Further, the utilization of piezoelectric surgery, rather than rotary diamond burs, for lateral window preparation and membrane separation leads to a dramatic reduction in the occurrence of the intraoperative complications of bleeding and membrane perforation.


title: I Know the Feeling: Learning to Converse with Empathy

abstract: Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling. One challenge for dialogue agents is being able to recognize feelings in the conversation partner and reply accordingly, a key communicative skill that is trivial for humans. Research in this area is made difficult by the paucity of large-scale publicly available datasets both for emotion and relevant dialogues. This work proposes a new task for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k conversations grounded in emotional contexts to facilitate training and evaluating dialogue systems. Our experiments indicate that models explicitly leveraging emotion predictions from previous utterances are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores).


Doing interdisciplinary research in strategic management—without a paradigm war


title: Sum-Rate Performance of Millimeter Wave MIMO Shared Spectrum Systems

abstract: This paper study the effect of interference management arising due to millimeter wave (mmWave) communication on the performance of multiple input multiple output (MIMO) based spectrum sharing cognitive systems. The highly directed mmWave signals have short wavelength property which enables to fabricate an increased number of antennas in a small confined space of the communication system. The large antenna elements are utilized to harvest high beamforming gains and to improve the signal to noise ratio (SNR) by analog processing at the cognitive base station (CBS) and secondary users. Analog processing at the CBS and at secondary user have hardware and computation restrictions. These restrictions are solved by designing radio frequency (RF) precoding and RF combining. In the digital domain, it is shown that interference between secondary users can be canceled by baseband block diagonalization technique. Towards this end, the underlay mode of operation is considered to control interference on the primary user and to derive the expression for optimal power allocation. The derived power allocation is a solution to the rate maximization problem of mmWave MIMO cognitive radio (CR) systems. Finally, numerical simulations are performed to yield various significant insights into for different mmWave CR system conditions and also validate the improvement in performance in comparison to the only digital communication systems.


title: Constructing Deep Neural Networks by Bayesian Network Structure Learning

abstract: We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth and inter-layer connectivity where conditional independencies in the input distribution are encoded hierarchically in the network structure. Thus, the depth of the network is determined inherently. The proposed method casts the problem of neural network structure learning as a problem of Bayesian network structure learning. Then, instead of directly learning the discriminative structure, it learns a generative graph, constructs its stochastic inverse, and then constructs a discriminative graph. We prove that conditional-dependency relations among the latent variables in the generative graph are preserved in the class-conditional discriminative graph. We demonstrate on image classification benchmarks that the deepest layers (convolutional and dense) of common networks can be replaced by significantly smaller learned structures, while maintaining classification accuracy---state-of-the-art on tested benchmarks. Our structure learning algorithm requires a small computational cost and runs efficiently on a standard desktop CPU.


Standardised Evaluation of English Noun Compound Interpretation


title: An integrated method of copy-move and splicing for image forgery detection

abstract: Splicing and copy-move are two well known methods of passive image forgery. In this paper, splicing and copy-move forgery detection are performed simultaneously on the same database CASIA v1.0 and CASIA v2.0. Initially, a suspicious image is taken and features are extracted through BDCT and enhanced threshold method. The proposed technique decides whether the given image is manipulated or not. If it is manipulated then support vector machine (SVM) classify that the given image is gone through splicing forgery or copy-move forgery. For copy-move detection, ZM-polar (Zernike Moment) is used to locate the duplicated regions in image. Experimental results depict the performance of the proposed method.


title: Evaluating Semantic Parsing against a Simple Web-based Question Answering Model

abstract: Semantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be answered from a single web document. In this paper, we propose to evaluate semantic parsing-based question answering models by comparing them to a question answering baseline that queries the web and extracts the answer only from web snippets, without access to the target knowledge-base. We investigate this approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional language, and find that our model obtains reasonable performance (35 F1 compared to 41 F1 of state-of-the-art). We find in our analysis that our model performs well on complex questions involving conjunctions, but struggles on questions that involve relation composition and superlatives.


title: Phrase-based Image Captioning

abstract: Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO.


title: A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires

abstract: This paper presents a cooperative perception system for multiple heterogeneous unmanned aerial vehicles (UAVs). It considers different kind of sensors: infrared and visual cameras and fire detectors. The system is based on a set of multipurpose low-level image-processing functions including segmentation, stabilization of sequences of images, and geo-referencing, and it also involves data fusion algorithms for cooperative perception. It has been tested in field experiments that pursued autonomous multi-UAV cooperative detection, monitoring, and measurement of forest fires. This paper presents the overall architecture of the perception system, describes some of the implemented cooperative perception techniques, and shows experimental results on automatic forest fire detection and localization with cooperating UAVs. © 2006 Wiley Periodicals, Inc.


title: Chloroplast transit peptide prediction: a peek inside the black box

abstract: Previous work in predicting protein localization to the chloroplast organelle in plants led to the development of an artificial neural network-based approach capable of remarkable accuracy in its prediction (ChloroP). A common criticism against such neural network models is that it is difficult to interpret the criteria that are used in making predictions. We address this concern with several new prediction methods that base predictions explicitly on the abundance of different amino acid types in the N-terminal region of the protein. Our successful prediction accuracy suggests that ChloroP uses little positional information in its decision-making; an unexpected result given the elaborate ChloroP input scheme. By removing positional information, our simpler methods allow us to identify those amino acids that are useful for successful prediction. The identification of important sequence features, such as amino acid content, is advantageous if one of the goals of localization predictors is to gain an understanding of the biological process of chloroplast localization. Our most accurate predictor combines principal component analysis and logistic regression. Web-based prediction using this method is available online at http://apicoplast.cis.upenn.edu/pclr/.


title: Analyzing Change over Time in Organizations' Publics with a Semantic Network Include List: An Illustration with Facebook

abstract: This research highlights a kind of semantic network analysis based on an include list. We analyze the networks only among words on the list as they appear in a series of text corpora word pairs for an organization. The example uses documents about Facebook over a 12-month period, dividing them into 12 time-based files. In each time slice we map networks among key publics and measure the centrality of each from one time period to the next. The network of publics becomes more complex across time. Publics fluctuate in centrality. We describe other kinds of semantic network analysis for business applications using include lists.


title: Millimeter wave radar sensor for automotive intelligent cruise control (ICC)

abstract: If automotive intelligent cruise-control (ICC) systems are to be successful in the marketplace, they must provide robust performance in a complex roadway environment. Inconveniences caused by reduced performance during inclement weather, interrupted performance due to dropped tracks, and annoying nuisance alarms will not be tolerated by the consumer, and would likely result in the rejection of this technology in the marketplace. An all-weather automotive millimeter-wave (MMW) radar sensor is described that uses a frequency-modulation coplanar-wave (FMCW) radar design capable of acquiring and tracking all obstacles in its field of view. Design tradeoffs are discussed and radar-sensor test results are presented along with the applicability of the radar to collision-warning systems.


Decision Trees: Theory and Algorithms.


Question Generation for Question Answering


title: Collaborative Inquiry Learning: Models, tools, and challenges

abstract: Collaborative inquiry learning is one of the most challenging and exciting ventures for today’s schools. It aims at bringing a new and promising culture of teaching and learning into the classroom where students in groups engage in self‐regulated learning activities supported by the teacher. It is expected that this way of learning fosters students’ motivation and interest in science, that they learn to perform steps of inquiry similar to scientists and that they gain knowledge on scientific processes. Starting from general pedagogical reflections and science standards, the article reviews some prominent models of inquiry learning. This comparison results in a set of inquiry processes being the basis for cooperation in the scientific network NetCoIL. Inquiry learning is conceived in several ways with emphasis on different processes. For an illustration of the spectrum, some main conceptions of inquiry and their focuses are described. In the next step, the article describes exemplary computer tools and env...


title: Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies

abstract: Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms. Current methods typically fail to find the appropriate level of generalization in a concept hierarchy for a given set of visual examples. Recent work in cognitive science on Bayesian models of generalization addresses this challenge, but prior results assumed that objects were perfectly recognized. We present an algorithm for learning visual concepts directly from images, using probabilistic predictions generated by visual classifiers as the input to a Bayesian generalization model. As no existing challenge data tests this paradigm, we collect and make available a new, large-scale dataset for visual concept learning using the ImageNet hierarchy as the source of possible concepts, with human annotators to provide ground truth labels as to whether a new image is an instance of each concept using a paradigm similar to that used in experiments studying word learning in children. We compare the performance of our system to several baseline algorithms, and show a significant advantage results from combining visual classifiers with the ability to identify an appropriate level of abstraction using Bayesian generalization.


title: A Web-scale system for scientific knowledge exploration

abstract: To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.


title: Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding

abstract: The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.


title: ROAR: A QoS-oriented modeling framework for automated cloud resource allocation and optimization

abstract: Abstract Cloud computing offers a fast, easy and cost-effective way to configure and allocate computing resources for web applications, such as consoles for smart grid applications, medical records systems, and security management platforms. Although a diverse collection of cloud resources (e.g., servers) is available, choosing the most optimized and cost-effective set of cloud resources for a given web application and set of quality of service (QoS) goals is not a straightforward task. Optimizing cloud resource allocation is a critical task for offering web applications using a software as a service model in the cloud, where minimizing operational cost while ensuring QoS goals are met is critical to meeting customer demands and maximizing profit. Manual load testing with different sets of cloud resources, followed by comparison of test results to QoS goals is tedious and inaccurate due to the limitations of the load testing tools, challenges characterizing resource utilization, significant manual test orchestration effort, and challenges identifying resource bottlenecks. This paper introduces our work using a modeling framework – ROAR (Resource Optimization, Allocation and Recommendation System) to simplify, optimize, and automate cloud resource allocation decisions to meet QoS goals for web applications, including complex multi-tier application distributed in different server groups. ROAR uses a domain-specific language to describe the configuration of the web application, the APIs to benchmark and the expected QoS requirements (e.g., throughput and latency), and the resource optimization engine uses model-based analysis and code generation to automatically deploy and load test the application in multiple resource configurations in order to derive a cost-optimal resource configuration that meets the QoS goals.


title: Temporal Connectives In A Discourse Context

abstract: We examine the role of temporal connectives in multi-sentence discourse. In certain contexts, sentences containing temporal connectives that are equivalent in temporal structure can fail to be equivalent in terms of discourse coherence. We account for this by offering a novel, formal mechanism for accommodating the presuppositions in temporal subordinate clauses. This mechanism encompasses both accommodation by discourse attachment and accomodation by temporal addition. As such, it offers a precise and systematic model of interactions between presupposed material, discourse context, and the reader's background knowledge. We show how the results of accommodation help to determine a discourse's coherence.


WebTP- A Transport-Layer Differentiated Services Architecture


title: UrbanSim: Modeling Urban Development for Land Use, Transportation and Environmental Planning

abstract: Abstract Metropolitan areas have come under intense pressure to respond to federal mandates to link planning of land use, transportation, and environmental quality; and from citizen concerns about managing the side effects of growth such as sprawl, congestion, housing affordability, and loss of open space. The planning models used by metropolitan planning organizations were generally not designed to address these issues, creating a gap in the ability of planners to systematically assess them. UrbanSim is a new model system that was developed to respond to these emerging requirements and has now been applied in three metropolitan areas. This article describes the model system and its application to Eugene-Springfield, Oregon.


title: The diagnostic value of basophil activation test in patients with an immediate hypersensitivity reaction to radiocontrast media.

abstract: BACKGROUND ::: No available test diagnoses allergic reactions to radiocontrast media (RCM). The basophil activation test (BAT) has been introduced for the diagnosis of both immunoglobulin (Ig) E and non-IgE-dependent mast cell degranulation, but its value to diagnose immediate RCM reactions is still unknown. ::: ::: ::: OBJECTIVE ::: This study aims to evaluate the diagnostic value of BAT in immediate RCM hypersensitivity. ::: ::: ::: METHODS ::: The BATs were performed in 26 patients with immediate RCM reactions and in 43 specimens from healthy volunteers. The sample's whole blood was incubated with the responsible RCM and % activated (CD63+/CCR3+) basophils were analyzed by flow cytometry. Receiver operating characteristics (ROC) curve analysis was performed to calculate the optimal cutoff value of activated basophils to diagnose patients with RCM hypersensitivity. ::: ::: ::: RESULTS ::: The incubation of blood with RCM yielded significantly higher activated basophil percentages in patients with a history of immediate RCM reactions than in normal controls with both 1:100 and 1:10 dilutions (13.11% vs. 2.71%, P value = .01; and 19.23% vs. 3.73%, P = .001, respectively). Both % activated basophils and stimulation index (SI) had acceptable discrimination powers to diagnose RCM hypersensitivity. The area under the curve was 0.79 (95% CI 0.67-0.91, P = .000) by using SI as the diagnostic criteria with 1:100 dilution of RCM. The specificity of the test ranged from 88.4% to 100%. ::: ::: ::: CONCLUSION ::: Our study demonstrated the potential of BAT as a diagnostic tool for an immediate RCM hypersensitivity, particularly as a confirmation test. Further studies are required to confirm the test accuracy and identify a patient's predisposing factors.


title: Brain correlates of music-evoked emotions

abstract: Music is a universal feature of human societies, partly owing to its power to evoke strong emotions and influence moods. During the past decade, the investigation of the neural correlates of music-evoked emotions has been invaluable for the understanding of human emotion. Functional neuroimaging studies on music and emotion show that music can modulate activity in brain structures that are known to be crucially involved in emotion, such as the amygdala, nucleus accumbens, hypothalamus, hippocampus, insula, cingulate cortex and orbitofrontal cortex. The potential of music to modulate activity in these structures has important implications for the use of music in the treatment of psychiatric and neurological disorders.


title: On the usability of business process modelling tools - a review and future research directions

abstract: The choice of a business process modelling (BPM) tool in combination with the selection of a modelling language is one of the crucial steps in BPM project preparation. Different aspects influence the decision: tool functionality, price, modelling language support, etc. In this paper we discuss the aspect of usability, which has already been recognized as an important topic in software engineering and web design. We conduct a literature review to find out the current state of research on the usability in the BPM field. The results of the literature review show, that although a number of research papers mention the importance of usability for BPM tools, real usability evaluation studies have rarely been undertaken. Based on the results of the literature analysis, the possible research directions in the field of usability of BPM tools are suggested.


title: A methodology based on Deep Learning for advert value calculation in CPM, CPC and CPA networks

abstract: In this research, we propose a methodology for advert value calculation in CPM, CPC and CPA networks. Accurately estimating this value increases the three previous networks' incomes by selecting the most profitable advert. By increasing income, publishers are better paid and improved services are afforded to advertisers. To develop this methodology, we propose a system based on traditional Machine Learning methods and Deep Learning methods. The system has two inputs and one output. The inputs are the user visit and the data about the advertiser. The output is the advert value expressed in dollars. Deep Learning predicts model behavior more precisely for many supervised problems. The three experiments carried out allow us to conclude that DL is a supervised method that is very efficient in the classification of spam adverts and in the estimation of the CTR. In the prediction of online sales, DLNN have shown, on average, worse performance than cubist and random forest methods, although better performance than model tree, model rules and linear regression methods.


title: A Transition-based Model for Joint Segmentation, POS-tagging and Normalization

abstract: We propose a transition-based model for joint word segmentation, POS tagging and text normalization. Different from previous methods, the model can be trained on standard text corpora, overcoming the lack of annotated microblog corpora. To evaluate our model, we develop an annotated corpus based on microblogs. Experimental results show that our joint model can help improve the performance of word segmentation on microblogs, giving an error reduction in segmentation accuracy of 12.02%, compared to the traditional approach.


title: Design considerations and strategies for high-reliable STT-MRAM

abstract: Abstract Benefiting from Spin Transfer Torque (STT) switching approach, second generation of Magnetic RAM (MRAM) promises low power, great miniaturization prospective (


title: Predicting the Human Curiosity from Users' Profiles on Facebook

abstract: Nowadays, social networks store a massive amount of data, which can say a lot about our personality. Given the ease to access the internet everywhere at any time, many people use social networks in daily life, reflecting in a certain way their behaviour from the "real world" to the "virtual world", by storing in social networks what they like, hate, feel, where they travel to, their relationships, opinions, etc. Once obtained the access to this data, some authors have tried to infer the personality of the individual without the use of long questionnaires, only working with data in an implicit way, that is, transparently to user. In this scenario, our work is focused on predicting just one of the human personality traits, the Curiosity. In this paper, we analyse the information that can be extracted from the users' profile on Facebook and the set of features that can be used to describe their degree of curiosity. Finally, we generate a prediction model in the form of a decision tree.


title: Effect of Solvent Evaporation on Fiber Morphology in Rotary Jet Spinning

abstract: The bulk production of polymeric nanofibers is important for fabricating high-performance, nanoscale materials. Rotary jet spinning (RJS) enables the mass production of nanostructured fibers by centrifugal forces but may result in inconsistent surface morphologies. Because nanofiber performance is dependent upon its surface features, we asked which parameters must be optimized during production to control fiber morphology. We developed and tested a mathematical model that describes how the competition between fluid instability and solvent removal in RJS regulates the degree of beading in fibers. Our data suggest that solvent evaporation during the spinning process causes an increase in jet viscosity and that these changes inhibit both bead formation and jet thinning. The RJS was used to vary experimental parameters, showing that fiber beading can be reduced by increasing solvent volatility, solution viscosity, and spinning velocity. Collectively, our results demonstrate that nanofiber morphology and diame...


title: Emotion Detection from Text via Ensemble Classification Using Word Embeddings

abstract: Emotion detection from text has become a popular task due to the key role of emotions in human-machine interaction. Current approaches represent text as a sparse bag-of-words vector. In this work, we propose a new approach that utilizes pre-trained, dense word embedding representations. We introduce an ensemble approach combining both sparse and dense representations. Our experiments include five datasets for emotion detection from different domains and show an average improvement of 11.6% in macro average F1-score.


title: A survey of commodity markets and structural models for electricity prices

abstract: The goal of this survey is to review the major idiosyncrasies of the commodity markets and the methods which have been proposed to handle them in spot and forward price models. We devote special attention to the most idiosyncratic of all: electricity markets. Following a discussion of traded instruments, market features, historical perspectives, recent developments and various modelling approaches, we focus on the important role of other energy prices and fundamental factors in setting the power price. In doing so, we present a detailed analysis of the structural approach for electricity, arguing for its merits over traditional reduced-form models. Building on several recent articles, we advocate a broad and flexible structural framework for spot prices, incorporating demand, capacity and fuel prices in several ways, while calculating closed-form forward prices throughout.


title: Fritzing: a tool for advancing electronic prototyping for designers

abstract: Today a growing community of DIY-practitioners, artists and designers are using microcontroller-based toolkits to express their concepts for digital artifacts by building them. However, as these prototypes are generally constructed using solder-free technologies, they are often fragile and unreliable. This means a huge burden of care and upkeep for these inventions when they are either exhibited or sold. We present a software application called Fritzing which allows artists, designers and DIY-tinkerers to prepare their hardware inventions for production. Through an interface metaphor based on the typical workflow of the target group, Fritzing has proven its ability to provide useful support in the steps following the invention of an interactive artifact. Fritzing serves also as a tool for documenting these interactive artifacts. As sharing of knowledge has been a driving force within this new DIY-movement, there is a need for a consistent and readable form of documentation which Fritzing can provide. Fritzing has also proven to be a useful tool in teaching electronics to people without an engineering background.


title: Phentolamine reverses NPY-induced inhibition of insulin secretion in isolated rat islets.

abstract: It has been shown that the rodent pancreas is innervated by neuropeptide Y (NPY) nerves, some of which are adrenergic, and that NPY inhibits glucose-induced insulin secretion in vivo in the mouse and that from isolated rat islets in vitro. We now investigated whether the alpha-adrenoceptor antagonist phentolamine affects the inhibitory action of NPY on insulin secretion from isolated rat islets. It was found that NPY dose dependently inhibited insulin secretion stimulated by glucose (11.1 mM). At a concentration of 10(-7) M, NPY totally abolished the insulin secretory response to glucose. It was also found that incubation with the alpha-adrenoceptor antagonist phentolamine (10(-6) M) itself enhanced the insulin secretion at 3.3 mM but not at 16.7 mM glucose. Moreover, phentolamine counteracted the inhibitory action of NPY. Thus, at 10(-8) M, NPY could no longer inhibit insulin secretion when phentolamine (10(-6) M) was present, whether 3.3 mM or 11.1 mM glucose was present. In contrast, somatostatin (10(-7) M) could inhibit insulin secretion, both in the presence and absence of phentolamine (10(-6) M); this showed that phentolamine does not reverse all types of inhibition. However, when the dose of phentolamine was decreased to 10(-7) M, the inhibitory action of NPY on glucose-induced insulin secretion was retained indicating that the reversal of the NPY effect by phentolamine is a competitive effect. It is concluded that NPY inhibits glucose-induced insulin secretion by a direct action on the islets, and that phentolamine reverses this inhibitory action of NPY in a competitive manner.


title: A spectral clustering approach to speaker diarization

abstract: In this paper, we present a spectral clustering approach to explore the possibility of discovering structure from audio data. To apply the Ng-Jordan-Weiss (NJW) spectral clustering algorithm to speaker diarization, we propose some domain specific solutions to the open issues of this algorithm: choice of metric; selection of scaling parameter; estimation of the number of clusters. Then, a postprocessing step ‐ “Cross EM refinement” ‐ is conducted to further improve the performance of spectral learning. In experiments, this approach has performance very similar to the traditional hierarchical clustering on the audio data of Japanese Parliament Panel Discussions, but it runs much faster than the latter. Index Terms: Speaker Diarization, Spectral Clustering, Cross EM refinement, BIC.


title: Arduino and GSM based smart energy meter for advanced metering and billing system

abstract: Every management system is trying to make automatic, portable and remote control. This work presents a novel smart energy meter for an automatic and superior metering and billing system. The integration of the Arduino and GSM Short Message Service (SMS) provide the meter reading system with some automatic functions that are predefined. Firstly, we have simulated the project in PROTEUS 8.0 then successfully implemented on the circuit board in laboratory. The proposed energy meter system can incorporate with embedded controller and GSM modem to transmit the data like consumed energy in kWh, generated bill, security services (line Cut/On) over GSM mobile network such as data can be then fed and integrated into existing energy management systems located at power companies or organizations to provide the services among the customers without man-power. Our implemented project is able to provide all required services remotely for metering and billing with high fidelity.


Leaf Recognition using Texture Features for Herbal Plant Identification


title: Cognos: crowdsourcing search for topic experts in microblogs

abstract: Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodology for discovering topic experts in the popular Twitter social network. Our methodology relies on the wisdom of the Twitter crowds -- it leverages Twitter Lists, which are often carefully curated by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provides valuable semantic cues to the experts' domain of expertise. We mined List information to build Cognos, a system for finding topic experts in Twitter. Detailed experimental evaluation based on a real-world deployment shows that: (a) Cognos infers a user's expertise more accurately and comprehensively than state-of-the-art systems that rely on the user's bio or tweet content, (b) Cognos scales well due to built-in mechanisms to efficiently update its experts' database with new users, and (c) Despite relying only on a single feature, namely crowdsourced Lists, Cognos yields results comparable to, if not better than, those given by the official Twitter experts search engine for a wide range of queries in user tests. Our study highlights Lists as a potentially valuable source of information for future content or expert search systems in Twitter.


title: Placental Drug Transport‐on‐a‐Chip: A Microengineered In Vitro Model of Transporter‐Mediated Drug Efflux in the Human Placental Barrier

abstract: The current lack of knowledge about the effect of maternally administered drugs on the developing fetus is a major public health concern worldwide. The first critical step toward predicting the safety of medications in pregnancy is to screen drug compounds for their ability to cross the placenta. However, this type of preclinical study has been hampered by the limited capacity of existing in vitro and ex vivo models to mimic physiological drug transport across the maternal-fetal interface in the human placenta. Here the proof-of-principle for utilizing a microengineered model of the human placental barrier to simulate and investigate drug transfer from the maternal to the fetal circulation is demonstrated. Using the gestational diabetes drug glyburide as a model compound, it is shown that the microphysiological system is capable of reconstituting efflux transporter-mediated active transport function of the human placental barrier to limit fetal exposure to maternally administered drugs. The data provide evidence that the placenta-on-a-chip may serve as a new screening platform to enable more accurate prediction of drug transport in the human placenta.


title: Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach

abstract: Background: Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating. There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentions to change, and actual health behaviors. Objective: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health app use in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of health apps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommended guidelines for fruit and vegetable intake and physical activity. Methods: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 Health Information National Trends Survey (HINTS), which was designed to provide nationally representative estimates for health information in the United States and is publicly available on the Internet. We used multivariable logistic regression models to assess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentions to change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss. Results: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+ years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or less than high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of having adopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted a mobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likely to report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P<.01) consumption, physical activity (83.0% vs 65.4%, P<.01), and weight loss (83.4% vs 71.8%, P<.01). Individuals with apps were also more likely to meet recommendations for physical activity compared with those without a device or health apps (56.2% with apps vs 47.8% without apps, P<.01). Conclusions: The main users of health apps were individuals who were younger, had more education, reported excellent health, and had a higher income. Although differences persist for gender, age, and educational attainment, many individual sociodemographic factors are becoming less potent in influencing engagement with mobile devices and health app use. App use was associated with intentions to change diet and physical activity and meeting physical activity recommendations. [J Med Internet Res 2017;19(4):e125]


title: Using Goffman's Frameworks to Explain Presence and Reality

abstract: This paper defines presence in terms of frames and involvement [1]. The value of this analysis of presence is demonstrated by applying it to several issues that have been raised about presence: residual awareness of nonmediation, imaginary presence, presence as categorical or continuum, and presence breaks. The paper goes on to explore the relationship between presence and reality. Goffman introduced frames to try to answer the question, “Under what circumstances do we think things real?” Under frame analysis there are three different conditions under which things are considered unreal, these are explained and related to the experience of presence. Frame analysis is used to show why virtual environments are not usually considered to be part of reality, although the virtual spaces of phone interaction are considered real. The analysis also yields practical suggestions for extending presence within virtual environments.


title: Real-Time subspace integration for St. Venant-Kirchhoff deformable models

abstract: In this paper, we present an approach for fast subspace integration of reduced-coordinate nonlinear deformable models that is suitable for interactive applications in computer graphics and haptics. Our approach exploits dimensional model reduction to build reduced-coordinate deformable models for objects with complex geometry. We exploit the fact that model reduction on large deformation models with linear materials (as commonly used in graphics) result in internal force models that are simply cubic polynomials in reduced coordinates. Coefficients of these polynomials can be precomputed, for efficient runtime evaluation. This allows simulation of nonlinear dynamics using fast implicit Newmark subspace integrators, with subspace integration costs independent of geometric complexity. We present two useful approaches for generating low-dimensional subspace bases: modal derivatives and an interactive sketching technique. Mass-scaled principal component analysis (mass-PCA) is suggested for dimensionality reduction. Finally, several examples are given from computer animation to illustrate high performance, including force-feedback haptic rendering of a complicated object undergoing large deformations.


title: Automated Behavioral Regression Testing

abstract: When a program is modified during software evolution, developers typically run the new version of the program against its existing test suite to validate that the changes made on the program did not introduce unintended side effects (i.e., regression faults). This kind of regression testing can be effective in identifying some regression faults, but it is limited by the quality of the existing test suite. Due to the cost of testing, developers build test suites by finding acceptable tradeoffs between cost and thoroughness of the tests. As a result, these test suites tend to exercise only a small subset of the program's functionality and may be inadequate for testing the changes in a program. To address this issue, we propose a novel approach called Behavioral Regression Testing (BERT). Given two versions of a program, BERT identifies behavioral differences between the two versions through dynamical analysis, in three steps. First, it generates a large number of test inputs that focus on the changed parts of the code. Second, it runs the generated test inputs on the old and new versions of the code and identifies differences in the tests' behavior. Third, it analyzes the identified differences and presents them to the developers. By focusing on a subset of the code and leveraging differential behavior, BERT can provide developers with more (and more detailed) information than traditional regression testing techniques. To evaluate BERT, we implemented it as a plug-in for Eclipse, a popular Integrated Development Environment, and used the plug-in to perform a preliminary study on two programs. The results of our study are promising, in that BERT was able to identify true regression faults in the programs.


title: Machine learning for mask/wafer hotspot detection and mask synthesis

abstract: Machine learning is a powerful computer science technique that can derive knowledge from big data and make predictions/decisions. Since nanometer integrated circuits (IC) and manufacturing have extremely high complexity and gigantic data, there is great opportunity to apply and adapt various machine learning techniques in IC physical design and verification. This paper will first give an introduction to machine learning, and then discuss several applications, including mask/wafer hotspot detection, and machine learning-based optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion. We will further discuss some challenges and research directions.


title: Rovables: Miniature On-Body Robots as Mobile Wearables

abstract: We introduce Rovables, a miniature robot that can move freely on unmodified clothing. The robots are held in place by magnetic wheels, and can climb vertically. The robots are untethered and have an onboard battery, microcontroller, and wireless communications. They also contain a low-power localization system that uses wheel encoders and IMU, allowing Rovables to perform limited autonomous navigation on the body. In the technical evaluations, we found that Rovables can operate continuously for 45 minutes and can carry up to 1.5N. We propose an interaction space for mobile on-body devices spanning sensing, actuation, and interfaces, and develop application scenarios in that space. Our applications include on-body sensing, modular displays, tactile feedback and interactive clothing and jewelry.


title: Jl. of Educational Multimedia and Hypermedia (2004) 13(1),93-106 Digital Literacy: A Conceptual Framework for Survival Skills in the Digital Era

abstract: Digital literacy involves more than the mere ability to use software or operate a digital device; it includes a large variety of complex cognitive, motor, sociological, and emotional skills, which users need in order to function effectively in digital environments. The tasks required in this context include, for example, “reading” instructions from graphical displays in user interfaces; using digital reproduction to create new, meaningful materials from existing ones; constructing knowledge from a nonlinear, hypertextual navigation; evaluating the quality and validity of information; and have a mature and realistic understanding of the “rules” that prevail in the cyberspace. This newly emerging concept of digital literacy may be used as a measure of the quality of learners’ work in digital environments, and provide scholars and developers with a more effective means of communication in designing better user-oriented environments. This article proposes a holistic, refined conceptual framework for digital literacy, which includes photo-visual literacy; reproduction literacy; branching literacy; information literacy; and socioemotional literacy.


title: Different Levels of Detail Display for Exposure Fusion Using Local Laplacian Filtering

abstract: Exposure fusion is an efficient method for directly fusing multi-exposure images into a high-quality low dynamic range image, without the h igh dynamic range (HDR) production and tone mapping process . The previous e xposure fusion methods only produce d a n image that contains a fixed amount of details , which can not satisfy further demands for more detail information. W e introduce Local Laplacian Filtering (LLF) for edge-aware image processing to multi-resolution Laplacian pyramid weighted blending method . Our method not only preserves details in the overexposed and underexposed areas, but also shows multi-level detail manipulation b y adjusting a simple parameter . Compared with p r evious methods, our interactive method has a greater flexibility on detail display for the needs of different users .


title: Learning to Cooperate via Policy Search

abstract: Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to value-based methods for partially observable environments. In this paper, we provide a gradient-based distributed policy-search method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain.


title: Reading senseless sentences: brain potentials reflect semantic incongruity

abstract: In a sentence reading task, words that occurred out of context were associated with specific types of event-related brain potentials. Words that were physically aberrant (larger than normal) elecited a late positive series of potentials, whereas semantically inappropriate words elicited a late negative wave (N400). The N400 wave may be an electrophysiological sign of the "reprocessing" of semantically anomalous information.


title: A Low-Profile Broadband 32-Slot Continuous Transverse Stub Array for Backhaul Applications in $E$ -Band

abstract: A high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched ( $S_{11} dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band.


title: Methodology for development and employment of ontology based knowledge management applications

abstract: In this article we illustrate a methodology for introducing and maintaining ontology based knowledge management applications into enterprises with a focus on Knowledge Processes and Knowledge Meta Processes. While the former process circles around the usage of ontologies, the latter process guides their initial set up. We illustrate our methodology by an example from a case study on skills management.


title: Design space extension for secure implementation of block ciphers

abstract: Security has been identified as a critical dimension in the design of embedded systems for almost a decade. A well-recognised critical threat against the security of embedded systems is represented by ‘side-channel attacks (SCAs)’, which mandate the application of specially tailored countermeasures. These countermeasures are significantly demanding in terms of computation effort, and have traditionally been applied by hand. The recent introduction of a methodology to gauge the security margins provided by software cipher implementations, allows the integration of the automated application of countermeasures into platform-based system-level design methodologies. The authors introduce in the design space of block cipher implementations a new metric concerning the resistance against SCAs, provide a systematic method for the selection of the most appropriate cipher given the security and performance trade-offs, and point out the performance requirements for the random number generator. Moreover, they discuss the implications of the design space extension on system runtime adaptivity. The experimental evaluation demonstrates that a single cipher does not cover optimally a range of convenient operating points and that ciphers like a Serpent, which are considered slow in non-protected implementations, can outperform primitives like the Advanced Encryption Standard when implementations with equal security guarantees against SCAs are considered.


title: Oriented Speckle Reducing Anisotropic Diffusion

abstract: Ultrasound imaging systems provide the clinician with noninvasive, low-cost, and real-time images that can help them in diagnosis, planning, and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The speckle reducing anisotropic diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2-D synthetic image with two different levels of multiplicative noise and on a 3-D synthetic image of a Y-junction, and the new filter is applied on a 3-D real ultrasound image of the liver


title: Semi-Supervised Generation with Cluster-aware Generative Models

abstract: Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster-aware Generative Model, that uses unlabelled information to infer a latent representation that models the natural clustering of the data, and additional labelled data points to refine this clustering. The generative performances of the model significantly improve when labelled information is exploited, obtaining a log-likelihood of -79.38 nats on permutation invariant MNIST, while also achieving competitive semi-supervised classification accuracies. The model can also be trained fully unsupervised, and still improve the log-likelihood performance with respect to related methods.


title: An empirical investigation into a large-scale Java open source code repository

abstract: Getting insight into different aspects of source code artifacts is increasingly important -- yet there is little empirical research using large bodies of source code, and subsequently there are not much statistically significant evidence of common patterns and facts of how programmers write source code. We pose 32 research questions, explain rationale behind them, and obtain facts from 2,080 randomly chosen Java applications from Sourceforge. Among these facts we find that most methods have one or zero arguments or they do not return any values, few methods are overridden, most inheritance hierarchies have the depth of one, close to 50% of classes are not explicitly inherited from any classes, and the number of methods is strongly correlated with the number of fields in a class.


title: ‘Wasteaware’ benchmark indicators for integrated sustainable waste management in cities

abstract: This paper addresses a major problem in international solid waste management, which is twofold: a lack of data, and a lack of consistent data to allow comparison between cities. The paper presents an indicator set for integrated sustainable waste management (ISWM) in cities both North and South, to allow benchmarking of a city’s performance, comparing cities and monitoring developments over time. It builds on pioneering work for UN-Habitat’s solid waste management in the World’s cities. The comprehensive analytical framework of a city’s solid waste management system is divided into two overlapping ‘triangles’ – one comprising the three physical components, i.e. collection, recycling, and disposal, and the other comprising three governance aspects, i.e. inclusivity; financial sustainability; and sound institutions and proactive policies. The indicator set includes essential quantitative indicators as well as qualitative composite indicators. This updated and revised ‘Wasteaware’ set of ISWM benchmark indicators is the cumulative result of testing various prototypes in more than 50 cities around the world. This experience confirms the utility of indicators in allowing comprehensive performance measurement and comparison of both ‘hard’ physical components and ‘soft’ governance aspects; and in prioritising ‘next steps’ in developing a city’s solid waste management system, by identifying both local strengths that can be built on and weak points to be addressed. The Wasteaware ISWM indicators are applicable to a broad range of cities with very different levels of income and solid waste management practices. Their wide application as a standard methodology will help to fill the historical data gap.


title: Bullet: high bandwidth data dissemination using an overlay mesh

abstract: In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes self-organize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burden on the underlying network. In this paper, we target high-bandwidth data distribution from a single source to a large number of receivers. Applications include large-file transfers and real-time multimedia streaming. For these applications, we argue that an overlay mesh, rather than a tree, can deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to self-organize into a high bandwidth overlay mesh. We construct Bullet around the insight that data should be distributed in a disjoint manner to strategic points in the network. Individual Bullet receivers are then responsible for locating and retrieving the data from multiple points in parallel.Key contributions of this work include: i) an algorithm that sends data to different points in the overlay such that any data object is equally likely to appear at any node, ii) a scalable and decentralized algorithm that allows nodes to locate and recover missing data items, and iii) a complete implementation and evaluation of Bullet running across the Internet and in a large-scale emulation environment reveals up to a factor two bandwidth improvements under a variety of circumstances. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing. In a tree, it is critical that a node's parent delivers a high rate of application data to each child. In Bullet however, nodes simultaneously receive data from multiple sources in parallel, making it less important to locate any single source capable of sustaining a high transmission rate.


title: Bartendr: a practical approach to energy-aware cellular data scheduling

abstract: Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost. In this paper, we make several contributions towards a practical system for energy-aware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energy-aware scheduling algorithms for different workloads - syncing and streaming - and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60%. Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S.


title: Quality-driven architecture development using architectural tactics

abstract: This paper presents a quality-driven approach to embodying non-functional requirements (NFRs) into software architecture using architectural tactics. Architectural tactics are reusable architectural building blocks, providing general architectural solutions for common issues pertaining to quality attributes. In this approach, architectural tactics are represented as feature models, and their semantics is defined using the Role-Based Metamodeling Language (RBML) which is a UML-based pattern specification notation. Given a set of NFRs, architectural tactics are selected and composed, and the composed tactic is used to instantiate an initial architecture for the application. The proposed approach addresses both the structural and behavioral aspects of architecture. We describe the approach using tactics for performance, availability and security to develop an architecture for a stock trading system. We demonstrate tool support for instantiating a composed tactic to generate an initial architecture of the stock trading system.


title: Visualizing Deep Neural Networks with Interaction of Super-pixels

abstract: An effective way to visualize the prediction of deep neural networks on an image is to decompose the prediction into the contribution of units (pixels or patches). In the existing works, these units are largely considered independently, thus limiting the performance of visualization. In this paper, we propose a new predication visualization method that uses super-pixel as a contribution unit. Moreover, our method takes into consideration of the interaction of adjacent super-pixels. We implement our technique and evaluate its performance with various images. Our results show its excellent performance.


title: Metrics for Quality Assurance of Web Based Applications

abstract: Web-Commerce applications are now an indispensable aspect of businesses around the world. More businesses are now migrating from outdated applications to a new type of combined e-business designs. With such large volumes of applications that need to be put online, there is now a dire need for measurable and quantifiable metrics that can help in gauging the quality of these websites.The development considerations for both domains may be deemed similar in their final purpose, that is to provide a service to its end-users, however, web-applications today face a myriad of constraints, with most businesses opting to go online, the crucial questions are; Is the Web info metrics are any different, or is it just an application of classical metrics (desktop metrics) to a new medium (web metrics).In our research, we propose to investigate these issues, and present the distinguishable metrics for the Quality Assurance(QA) processes involved in Web-Applications, as opposed to traditional desktop software application.


title: Semantic Co-segmentation in Videos

abstract: Discovering and segmenting objects in videos is a challenging task due to large variations of objects in appearances, deformed shapes and cluttered backgrounds. In this paper, we propose to segment objects and understand their visual semantics from a collection of videos that link to each other, which we refer to as semantic co-segmentation. Without any prior knowledge on videos, we first extract semantic objects and utilize a tracking-based approach to generate multiple object-like tracklets across the video. Each tracklet maintains temporally connected segments and is associated with a predicted category. To exploit rich information from other videos, we collect tracklets that are assigned to the same category from all videos, and co-select tracklets that belong to true objects by solving a submodular function. This function accounts for object properties such as appearances, shapes and motions, and hence facilitates the co-segmentation process. Experiments on three video object segmentation datasets show that the proposed algorithm performs favorably against the other state-of-the-art methods.


title: Case Based Reasoning: Case Representation Methodologies

abstract: Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the proper operation of the overall CBR system. In this paper, we survey and evaluate all of the existing case representation methodologies. Moreover, the case retrieval and future challenges for effective CBR are explained. Case representation methods are grouped in to knowledge-intensive approaches and traditional approaches. The first group overweight the second one. The first methods depend on ontology and enhance all CBR processes including case representation, retrieval, storage, and adaptation. By using a proposed set of qualitative metrics, the existing methods based on ontology for case representation are studied and evaluated in details. All these systems have limitations. No approach exceeds 53% of the specified metrics. The results of the survey explain the current limitations of CBR systems. It shows that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.


title: Automatically Documenting Unit Test Cases

abstract: Maintaining unit test cases is important during the maintenance and evolution of a software system. In particular, automatically documenting these unit test cases can ameliorate the burden on developers maintaining them. For instance, by relyingon up-to-date documentation, developers can more easily identify test cases that relate to some new or modified functionality of the system. We surveyed 212 developers (both industrial and open-source) to understand their perspective towards writing, maintaining, and documenting unit test cases. In addition, we mined change histories of C# software systems and empirically found that unit test methods seldom had preceding comments andinfrequently had inner comments, and both were rarely modified as those methods were modified. In order to support developers in maintaining unit test cases, we propose a novel approach -- UnitTestScribe -- that combines static analysis, natural language processing, backwardslicing, and code summarization techniques to automatically generate natural language documentation of unit test cases. We evaluated UnitTestScribe on four subject systems by means of an online survey with industrial developers and graduate students. In general, participants indicated that UnitTestScribe descriptions are complete, concise, and easy to read.


title: An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation

abstract: This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steady- state oscillation (to practically zero) once the maximum power point (MPP) is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The algorithm is simple and can be computed very rapidly; thus, its implementation using a low-cost microcontroller is possible. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging conditions, namely step changes in irradiance, step changes in load, and partial shading of the PV array. Its performance is compared with the conventional Hill Climbing (HC) method. Finally, an experimental rig that comprises of a buck-boost converter fed by a custom-designed solar array simulator is set up to emulate the simulation. The soft- ware development is carried out in the Dspace 1104 environment using a TMS320F240 digital signal processor. The superiority of the proposed method over the HC in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results.


title: Case Reports of Adipose-derived Stem Cell Therapy for Nasal Skin Necrosis after Filler Injection

abstract: With the gradual increase of cases using fillers, cases of patients treated by non-medical professionals or inexperienced physicians resulting in complications are also increasing. We herein report 2 patients who experienced acute complications after receiving filler injections and were successfully treated with adipose-derived stem cell (ADSCs) therapy. Case 1 was a 23-year-old female patient who received a filler (Restylane) injection in her forehead, glabella, and nose by a non-medical professional. The day after her injection, inflammation was observed with a 3×3 cm skin necrosis. Case 2 was a 30-year-old woman who received a filler injection of hyaluronic acid gel (Juvederm) on her nasal dorsum and tip at a private clinic. She developed erythema and swelling in the filler-injected area A solution containing ADSCs harvested from each patient's abdominal subcutaneous tissue was injected into the lesion at the subcutaneous and dermis levels. The wounds healed without additional treatment. With continuous follow-up, both patients experienced only fine linear scars 6 months postoperatively. By using adipose-derived stem cells, we successfully treated the acute complications of skin necrosis after the filler injection, resulting in much less scarring, and more satisfactory results were achieved not only in wound healing, but also in esthetics.


title: Diagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting

abstract: BACKGROUND ::: Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. ::: ::: ::: METHODS AND RESULTS ::: Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]). ::: ::: ::: CONCLUSIONS ::: The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF.


title: The hidden image of mobile apps: geographic, demographic, and cultural factors in mobile usage

abstract: While mobile apps have become an integral part of everyday life, little is known about the factors that govern their usage. Particularly the role of geographic and cultural factors has been understudied. This article contributes by carrying out a large-scale analysis of geographic, cultural, and demographic factors in mobile usage. We consider app usage gathered from 25,323 Android users from 44 countries and 54,776 apps in 55 categories, and demographics information collected through a user survey. Our analysis reveals significant differences in app category usage across countries and we show that these differences, to large degree, reflect geographic boundaries. We also demonstrate that country gives more information about application usage than any demographic, but that there also are geographic and socio-economic subgroups in the data. Finally, we demonstrate that app usage correlates with cultural values using the Value Survey Model of Hofstede as a reference of cross-cultural differences.


title: Codesign of an impulse generator and miniaturized antennas for IR-UWB

abstract: The codesign of an impulse generator and miniaturized antennas for ultra-wideband impulse radio is described. The impulse generator, discussed by Bragga in 2004, is designed with differential outputs that are fed to the antenna, producing an optimum match of the generator to the antenna, an improved magnitude response, and reduced ringing of the radiated pulse. The impulse generator is preceded by a programmable pulse-position modulator and consists of a triangular pulse generator and a cascade of complex first-order systems, which, in turn, are made up of differential pairs employing partial positive feedback to approximate a Gaussian monocycle waveform. The complete pulse generator is fabricated in IBM 0.18-/spl mu/m Bi-CMOS IC technology. Measurements show the correct operation of the circuit for supply voltages of 1.8 V and a power consumption of 45 mW. The output pulse approximates the Gaussian monocycle having a pulse duration of about 375 ps. Proper modulation of the pulse in time is confirmed. A number of antennas with differentially fed baluns and input impedances of 100 /spl Omega/ have been designed. From measurements, it can be seen that ringing is considerably smaller as compared to conventionally fed antennas.


title: The distance function effect on k-nearest neighbor classification for medical datasets

abstract: IntroductionK-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.Case descriptionSince the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.Discussion and evaluationThe experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.ConclusionsIn this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.


title: EdgeChain: Blockchain-based Multi-vendor Mobile Edge Application Placement

abstract: The state-of-the-art mobile edge applications are generating intense traffic and posing rigorous latency requirements to service providers. While resource sharing across multiple service providers can be a way to maximize the utilization of limited resources at the network edge, it requires a centralized repository maintained by all parties for service providers to share status. Moreover, service providers have to trust each other for resource allocation fairness, which is difficult because of potential conflicts of interest. We propose EdgeChain, a blockchain-based architecture to make mobile edge application placement decisions for multiple service providers. We first formulate a stochastic programming problem minimizing the placement cost for mobile edge application placement scenarios. Based on our model, we present a heuristic mobile edge application placement algorithm. As a decentralized public ledger, the blockchain then takes the logic of our algorithm as the smart contract, with the consideration of resources from all mobile edge hosts participating in the system. The algorithm is agreed by all parties and the results will only be accepted by majority of the mining nodes on the blockchain. When a placement decision is made, an edge host meeting the consumer's latency and budget requirements will be selected at the lowest cost. All placement transactions are stored on the blockchain and are traceable by every mobile edge service provider and application vendor who consumes resources at the mobile edge.


title: A Robust Braille Recognition System

abstract: Braille is the most effective means of written communication between visually-impaired and sighted people. This paper describes a new system that recognizes Braille characters in scanned Braille document pages. Unlike most other approaches, an inexpensive flatbed scanner is used and the system requires minimal interaction with the user. A unique feature of this system is the use of context at different levels (from the pre-processing of the image through to the post-processing of the recognition results) to enhance robustness and, consequently, recognition results. Braille dots composing characters are identified on both single and double-sided documents of average quality with over 99% accuracy, while Braille characters are also correctly recognised in over 99% of documents of average quality (in both single and double-sided documents).


title: One Size Does Not Fit All---A Contingency Approach to Data Governance

abstract: Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With data governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals from business and IT departments. This research aims at starting a scientific discussion on data governance by transferring concepts from IT governance and organizational theory to the previously largely ignored field of data governance. The article presents the first results of a community action research project on data governance comprising six international companies from various industries. It outlines a data governance model that consists of three components (data quality roles, decision areas, and responsibilities), which together form a responsibility assignment matrix. The data governance model documents data quality roles and their type of interaction with DQM activities. In addition, the article describes a data governance contingency model and demonstrates the influence of performance strategy, diversification breadth, organization structure, competitive strategy, degree of process harmonization, degree of market regulation, and decision-making style on data governance. Based on these findings, companies can structure their specific data governance model.


title: A Comparison of the Homomorphic Encryption Schemes FV and YASHE

abstract: We conduct a theoretical and practical comparison of two Ring-LWE-based, scale-invariant, leveled homomorphic encryption schemes – Fan and Vercauteren’s adaptation of BGV and the YASHE scheme proposed by Bos, Lauter, Loftus and Naehrig. In particular, we explain how to choose parameters to ensure correctness and security against lattice attacks. Our parameter selection improves the approach of van de Pol and Smart to choose parameters for schemes based on the Ring-LWE problem by using the BKZ-2.0 simulation algorithm.


title: Opportunity model for e-commerce recommendation: right product; right time

abstract: Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time. This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction/utility in push-based systems.


title: How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT

abstract: This article evaluates purported progress over the past years in RST discourse parsing. Several studies report a relative error reduction of 24 to 51% on all metrics that authors attribute to the introduction of distributed representations of discourse units. We replicate the standard evaluation of 9 parsers, 5 of which use distributed representations, from 8 studies published between 2013 and 2017, using their predictions on the test set of the RST-DT. Our main finding is that most recently reported increases in RST discourse parser performance are an artefact of differences in implementations of the evaluation procedure. We evaluate all these parsers with the standard Parseval procedure to provide a more accurate picture of the actual RST discourse parsers performance in standard evaluation settings. Under this more stringent procedure, the gains attributable to distributed representations represent at most a 16% relative error reduction on fully-labelled structures.


title: TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

abstract: We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time–ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.


title: Key Challenges for the Smart City: Turning Ambition into Reality

abstract: Smart city is a label internationally used by cities, researchers and technology providers with different meanings. As a popular concept it is widely used by city administrators and politicians to promote their efforts. It is hard enough to find a good definition for smart cities, but even harder to find a trustworthy description of what it takes to become a smart city and how a city administration is impacted. This paper sets out to investigate how a city, aspiring to become a 'smart city', can manage the organization to realize that ambition. Specifically, the paper describes the case of the City of Ghent, Belgium, and the key challenges it has been facing in its ongoing efforts to be a smart city. Based on in depth interviews with city representatives six key challenges for smart city realization were identified and tested with a panel of representatives from five European cities that are in the process of becoming a smart city. This way, the study contributes to a more professional pursuit of the smart city concept.


title: Darwin's “Natural Science of Babies”

abstract: In 1877, the newly founded British journal Mind published two papers on child development. The earlier, by Hippolyte Taine, prompted the second article: an account of his own son's development by the naturalist Charles Darwin. In its turn, Darwin's paper, “A Biographical Sketch of an Infant,” influenced others. Diary studies similar to Taine's and Darwin's appeared in Mind from 1878. In addition, the medical profession started to consider normal child language acquisition as a comparison for the abnormal. Shortly before his death in 1882, Darwin continued with his theme, setting out a series of proposals for a program of research on child development with suggested methodology and interpretations. Darwin, whose interest in infants and the developing mind predated his 1877 paper by at least 40 years, sought to take the subject out of the nursery and into the scientific domain. The empirical study of the young child's developing mental faculties was a source of evidence with important implications for his g...