title: Topology-constrained surface reconstruction from cross-sections

abstract: In this work we detail the first algorithm that provides topological control during surface reconstruction from an input set of planar cross-sections. Our work has broad application in a number of fields including surface modeling and biomedical image analysis, where surfaces of known topology must be recovered. Given curves on arbitrarily oriented cross-sections, our method produces a manifold interpolating surface that exactly matches a user-specified genus. The key insight behind our approach is to formulate the topological search as a divide-and-conquer optimization process which scores local sets of topologies and combines them to satisfy the global topology constraint. We further extend our method to allow image data to guide the topological search, achieving even better results than relying on the curves alone. By simultaneously satisfying both geometric and topological constraints, we are able to produce accurate reconstructions with fewer input cross-sections, hence reducing the manual time needed to extract the desired shape.


title: Performance Evaluation of Channel Decoding with Deep Neural Networks

abstract: With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.


title: From Hand-Held to Body-Worn: Embodied Experiences of the Design and Use of a Wearable Movement-Based Interaction Concept

abstract: This paper argues that movement-based interaction should be designed from a non-technological, people-centered point of view in order to create embodied and engaging interaction experiences. Further, it discusses social and contextual aspects that need to be taken into account when designing for movement-based interaction. The paper presents the design process and user explorations of a wearable movement-based interaction concept that was created in order to explore full-body movement as interaction modality. The starting point was taken in people's own experiences of communication and interaction through bodily movements, inspired by methods and theories used within modern dance. As design guidelines for the prototyped interaction concept we used aspects on movement that were directly derived from field studies of physical expression. The user explorations of the concept show preliminary examples of how people engage in movement-based interaction and how they are affected by the social interaction context.


title: SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification

abstract: In this article we present SmartRoad, a crowd-sourced road sensing system that detects and identifies traffic regulators, traffic lights, and stop signs, in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification under realistic and practical settings, SmartRoad automatically adapts to different application requirements by (i) intelligently choosing the most appropriate information representation and transmission schemes, and (ii) dynamically evolving its core detection and identification engines to effectively take advantage of any external ground truth information or manual label opportunity. We implemented SmartRoad on a vehicular smartphone test bed, and deployed it on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively, and efficiently carry out the detection and identification tasks.


title: Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training

abstract: Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures. Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks. These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, providing a stronger link between modulated neuronal function and behavior.


title: Bobtail: A Proof-of-Work Target that Minimizes Blockchain Mining Variance (Draft)

abstract: Blockchain systems are designed to produce blocks at a constant average rate. The most popular systems currently employ a Proof of Work (PoW) algorithm as a means of creating these blocks. Bitcoin produces, on average, one block every 10 minutes. An unfortunate limitation of all deployed PoW blockchain systems is that the time between blocks has high variance. For example, 5% of the time, Bitcoin's inter-block time is at least 40 minutes. This variance impedes the consistent flow of validated transactions through the system. We propose an alternative process for PoW-based block discovery that results in an inter-block time with significantly lower variance. Our algorithm, called Bobtail, generalizes the current algorithm by comparing the mean of the k lowest order statistics to a target. We show that the variance of inter-block times decreases as k increases. If our approach were applied to Bitcoin, about 80% of blocks would be found within 7 to 12 minutes, and nearly every block would be found within 5 to 18 minutes; the average inter-block time would remain at 10 minutes. Further, we show that low-variance mining significantly thwarts doublespend and selfish mining attacks. For Bitcoin and Ethereum currently (k=1), an attacker with 40% of the mining power will succeed with 30% probability when the merchant sets up an embargo of 8 blocks; however, when k>=20, the probability of success falls to less than 1%. Similarly, for Bitcoin and Ethereum currently, a selfish miner with 40% of the mining power will claim about 66% of blocks; however, when k>=5, the same miner will find that selfish mining is less successful than honest mining. The cost of our approach is a larger block header.


title: Semantic Culturomics (vision paper)

abstract: Newspapers are testimonials of history. The same is increasingly true of social media such as online forums, online communities , and blogs. By looking at the sequence of articles over time, one can discover the birth and the development of trends that marked society and history – a field known as " Culturomics ". But Culturomics has so far been limited to statistics on keywords. In this vision paper, we argue that the advent of large knowledge bases (such as YAGO [37], NELL [5], DBpedia [3], and Freebase) will revolutionize the field. If their knowledge is combined with the news articles, it can breathe life into what is otherwise just a sequence of words for a machine. This will allow discovering trends in history and culture, explaining them through explicit logical rules, and making predictions about the events of the future. We predict that this could open up a new field of research , " Semantic Culturomics " , in which no longer human text helps machines build up knowledge bases, but knowledge bases help humans understand their society.


title: Cortisol and finfish welfare

abstract: Previous reviews of stress, and the stress hormone cortisol, in fish have focussed on physiology, due to interest in impacts on aquaculture production. Here, we discuss cortisol in relation to fish welfare. Cortisol is a readily measured component of the primary (neuroendocrine) stress response and is relevant to fish welfare as it affects physiological and brain functions and modifies behaviour. However, we argue that cortisol has little value if welfare is viewed purely from a functional (or behavioural) perspective—the cortisol response itself is a natural, adaptive response and is not predictive of coping as downstream impacts on function and behaviour are dose-, time- and context-dependent and not predictable. Nevertheless, we argue that welfare should be considered in terms of mental health and feelings, and that stress in relation to welfare should be viewed as psychological, rather than physiological. We contend that cortisol can be used (with caution) as a tractable indicator of how fish perceive (and feel about) their environment, psychological stress and feelings in fish. Cortisol responses are directly triggered by the brain and fish studies do indicate cortisol responses to psychological stressors, i.e., those with no direct physicochemical action. We discuss the practicalities of using cortisol to ask the fish themselves how they feel about husbandry practices and the culture environment. Single time point measurements of cortisol are of little value in assessing the stress level of fish as studies need to account for diurnal and seasonal variations, and environmental and genetic factors. Areas in need of greater clarity for the use of cortisol as an indicator of fish feelings are the separation of (physiological) stress from (psychological) distress, the separation of chronic stress from acclimation, and the interactions between feelings, cortisol, mood and behaviour.


title: Recursive Matrix Algorithms, Distributed Dynamic Control, Scaling, Stability

abstract: The report is devoted to the concept of creating block-recursive matrix algorithms for computing on a super-computer with distributed memory and dynamic decentralized control.


Ef fi ciency drivers in Brazilian insurance : A two-stage DEA meta frontier-data mining approach


title: Context-Aware Sequential Recommendation

abstract: Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.


title: Internet of nano-things healthcare applications: Requirements, opportunities, and challenges

abstract: Ubiquitous healthcare is becoming a reality thanks to the advances in sensing and communication technologies, which make it possible to provide monitoring and diagnosis services outside the premises of healthcare providers. The Internet of Things (IoT) is the main paradigm through which medical devices will be connected to the Internet, thereby empowering near-realtime health services and transforming a patient's physical space into a smart space. Recent developments in nanotechnology are giving rise to the Internet of Nano-Things, with a new set of finegrained and highly sophisticated healthcare applications that can be run inside the human body. In this paper, we outline a vision of the ubiquitous healthcare ecosystem and its architectural requirements in order to incorporate nanonetworks. We identify some of the envisioned IoNT healthcare applications and the IoNT requirements that are necessary to support the different application categories, as well as the underlying healthcare service opportunities. In order to understand the current status of implementation, we provide a brief analysis of the major efforts targeted at IoNT performance analysis and evaluation. We finally discuss the most pressing challenges that the IoNT paradigm poses for healthcare applications and services.


title: Spotting and Aggregating Salient Regions for Video Captioning

abstract: Towards an interpretable video captioning process, we target to locate salient regions of video objects along with the sequentially uttering words. This paper proposes a new framework to automatically spot salient regions in each video frame and simultaneously learn a discriminative spatio-temporal representation for video captioning. First, in a Spot Module, we automatically learn the saliency value of each location to separate salient regions from video content as the foreground and the rest as background by two operations of 'hard separation' and 'soft separation', respectively. Then, in an Aggregate Module, to aggregate the foreground/background descriptors into a discriminative spatio-temporal representation, we devise a trainable video VLAD process to learn the aggregation parameters. Finally, we utilize the attention mechanism to decode the spatio-temporal representations of different regions into video descriptions. Experiments on two benchmark datasets demonstrate our method outperforms most of the state-of-the-art methods in terms of Bleu@4, METEOR and CIDEr metrics for the task of video captioning. Also examples demonstrate our method can successfully utter words to sequentially salient regions of video objects.


title: Enhancing personalized search by mining and modeling task behavior

abstract: Personalized search systems tailor search results to the current user intent using historic search interactions. This relies on being able to find pertinent information in that user's search history, which can be challenging for unseen queries and for new search scenarios. Building richer models of users' current and historic search tasks can help improve the likelihood of finding relevant content and enhance the relevance and coverage of personalization methods. The task-based approach can be applied to the current user's search history, or as we focus on here, all users' search histories as so-called "groupization" (a variant of personalization whereby other users' profiles can be used to personalize the search experience). We describe a method whereby we mine historic search-engine logs to find other users performing similar tasks to the current user and leverage their on-task behavior to identify Web pages to promote in the current ranking. We investigate the effectiveness of this approach versus query-based matching and finding related historic activity from the current user (i.e., group versus individual). As part of our studies we also explore the use of the on-task behavior of particular user cohorts, such as people who are expert in the topic currently being searched, rather than all other users. Our approach yields promising gains in retrieval performance, and has direct implications for improving personalization in search systems.


title: The Nature, Importance, and Difficulty of Machine Ethics

abstract: The question of whether machine ethics exists or might exist in the future is difficult to answer if we can't agree on what counts as machine ethics. Some might argue that machine ethics obviously exists because humans are machines and humans have ethics. Others could argue that machine ethics obviously doesn't exist because ethics is simply emotional expression and machines can't have emotions. A wide range of positions on machine ethics are possible, and a discussion of the issue could rapidly propel us into deep and unsettled philosophical issues. Perhaps, understandably, few in the scientific arena pursue the issue of machine ethics. As we expand computers' decision-making roles in practical matters, such as computers driving cars, ethical considerations are inevitable. Computer scientists and engineers must examine the possibilities for machine ethics because, knowingly or not, they've already engaged in some form of it. Before we can discuss possible implementations of machine ethics, however, we need to be clear about what we're asserting or denying


title: Prior schemata transfer as an account for assessing the intuitive use of new technology

abstract: New devices are considered intuitive when they allow users to transfer prior knowledge. Drawing upon fundamental psychology experiments that distinguish prior knowledge transfer from new schema induction, a procedure was specified for assessing intuitive use. This procedure was tested with 31 participants who, prior to using an on-board computer prototype, studied its screenshots in reading vs. schema induction conditions. Distinct patterns of transfer or induction resulted for features of the prototype whose functions were familiar or unfamiliar, respectively. Though moderated by participants' cognitive style, these findings demonstrated a means for quantitatively assessing transfer of prior knowledge as the operation that underlies intuitive use. Implications for interface evaluation and design, as well as potential improvements to the procedure, are discussed.


title: Coupled Compound Poisson Factorization

abstract: We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model and, as examples of our framework, implement three different data-generating models---a mixture model, linear regression, and factor analysis---to robustly model non-random missing data in the context of clustering, prediction, and matrix factorization. In all three cases, we test our framework against models that ignore the missing-data mechanism on large scale studies with non-random missing data, and we show that explicitly modeling the missing-data mechanism substantially improves the quality of the results, as measured using data log likelihood on a held-out test set.


title: Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering

abstract: Cancer tissues in histopathology images exhibit abnormal patterns; it is of great clinical importance to label a histopathology image as having cancerous regions or not and perform the corresponding image segmentation. However, the detailed annotation of cancer cells is often an ambiguous and challenging task. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL), to classify, segment and cluster cancer cells in colon histopathology images. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), pixel-level segmentation (cancer vs. non-cancer tissue), and patch-level clustering (cancer subclasses). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to perform the above three tasks in an integrated framework. Experimental results demonstrate the efficiency and effectiveness of MCIL in analyzing colon cancers.


title: Assessing the Contribution of Twitter's Textual Information to Graph-based Recommendation

abstract: Graph-based recommendation approaches can model associations between users and items alongside additional contextual information. Recent studies demonstrated that representing features extracted from social media (SM) auxiliary data, like friendships, jointly with traditional users/items ratings in the graph, contribute to recommendation accuracy. In this work, we take a step further and propose an extended graph representation that includes socio-demographic and personal traits extracted from the content posted by the user on SM. Empirical results demonstrate that processing unstructured textual information collected from Twitter and representing it in structured form in the graph improves recommendation performance, especially in cold start conditions.


title: Visualizing Motion Data in Virtual Reality: Understanding the Roles of Animation, Interaction, and Static Presentation

abstract: We present a study of interactive virtual reality visualizations of scientific motions as found in biomechanics experiments. Our approach is threefold. First, we define a taxonomy of motion visualizations organized by the method (animation, interaction, or static presentation) used to depict both the spatial and temporal dimensions of the data. Second, we design and implement a set of eight example visualizations suggested by the taxonomy and evaluate their utility in a quantitative user study. Third, together with biomechanics collaborators, we conduct a qualitative evaluation of the eight example visualizations applied to a current study of human spinal kinematics. Results suggest that visualizations in this style that use interactive control for the time dimension of the data are preferable to others. Within this category, quantitative results support the utility of both animated and interactive depictions for space; however, qualitative feedback suggest that animated depictions for space should be avoided in biomechanics applications. © 2012 Wiley Periodicals, Inc.


title: Network Function Virtualization: Challenges and Directions for Reliability Assurance

abstract: Network Function Virtualization (NFV) is an emerging solution that aims at improving the flexibility, the efficiency and the manageability of networks, by leveraging virtualization and cloud computing technologies to run network appliances in software. Nevertheless, the "notarization" of network functions imposes software reliability concerns on future networks, which will be exposed to software issues arising from virtualization technologies. In this paper, we discuss the challenges for reliability in NFVIs, and present an industrial research project on their reliability assurance, which aims at developing novel fault injection technologies and systematic guidelines for this purpose.


title: Autochthonous dengue fever in Croatia, August–September 2010

abstract: After information about a dengue case in Germany acquired in Croatia, health professionals and the public in Croatia were alerted to assess the situation and to enhance mosquito control, resulting in the diagnosis of a second case of autochthonous dengue fever in the same area and the detection of 15 persons with evidence of recent dengue infection. Mosquito control measures were introduced. The circumstances of dengue virus introduction to Croatia remain unresolved.


title: SCAN: a structural clustering algorithm for networks

abstract: Network clustering (or graph partitioning) is an important task for the discovery of underlying structures in networks. Many algorithms find clusters by maximizing the number of intra-cluster edges. While such algorithms find useful and interesting structures, they tend to fail to identify and isolate two kinds of vertices that play special roles - vertices that bridge clusters (hubs) and vertices that are marginally connected to clusters (outliers). Identifying hubs is useful for applications such as viral marketing and epidemiology since hubs are responsible for spreading ideas or disease. In contrast, outliers have little or no influence, and may be isolated as noise in the data. In this paper, we proposed a novel algorithm called SCAN (Structural Clustering Algorithm for Networks), which detects clusters, hubs and outliers in networks. It clusters vertices based on a structural similarity measure. The algorithm is fast and efficient, visiting each vertex only once. An empirical evaluation of the method using both synthetic and real datasets demonstrates superior performance over other methods such as the modularity-based algorithms.


title: The involvement of primary motor cortex in mental rotation revealed by transcranial magnetic stimulation

abstract: We used single-pulse transcranial magnetic stimulation of the left primary hand motor cortex and motor evoked potentials of the contralateral right abductor pollicis brevis to probe motor cortex excitability during a standard mental rotation task. Based on previous findings we tested the following hypotheses. (i) Is the hand motor cortex activated more strongly during mental rotation than during reading aloud or reading silently? The latter tasks have been shown to increase motor cortex excitability substantially in recent studies. (ii) Is the recruitment of the motor cortex for mental rotation specific for the judgement of rotated but not for nonrotated Shepard & Metzler figures? Surprisingly, motor cortex activation was higher during mental rotation than during verbal tasks. Moreover, we found strong motor cortex excitability during the mental rotation task but significantly weaker excitability during judgements of nonrotated figures. Hence, this study shows that the primary hand motor area is generally involved in mental rotation processes. These findings are discussed in the context of current theories of mental rotation, and a likely mechanism for the global excitability increase in the primary motor cortex during mental rotation is proposed.


title: Children's moral emotions and moral cognition: Towards an integrative perspective

abstract: This chapter presents a brief introduction to the developmental and educational literature linking children’s moral emotions to cognitive moral development. A central premise of the chapter is that an integrative developmental perspective on moral emotions and moral cognition provides an important conceptual framework for understanding children’s emerging morality and designing developmentally sensitive moral intervention strategies. The subsequent chapters present promising conceptual approaches and empirical evidence linking children’s moral emotions to moral cognition. Examples of integrated educational interventions intended to enhance children’s moral development are presented and discussed. © Wiley Periodicals, Inc.


title: Real-time video summarization on mobile

abstract: This paper proposes a novel approach for real-time video summarization on mobile using Dictionary Learning, Global Camera Motion analysis and Colorfulness. A dictionary is represented as a distinct set of events that are described as spatio-temporal features. Uniqueness measure is predicted based on the correlation scores of the dictionary elements whereas the quality measure is estimated using Global Camera Motion analysis and Colorfulness. Our proposed technique combines the uniqueness measure and the quality measure to predict the interestingness. Experiments indicate that our method outperforms state-of-the-arts in terms of computation speed while retaining the similar subjective quality.


title: Optimal Collision-Free Robot Trajectory Generation Based on Time Series Prediction of Human Motion

abstract: In this letter, we propose that the joint motion of a human worker doing repetitive work could be predicted using a time series model. With a motion capture system, the elbow joint rotation data are collected and used to fit an autoregressive model. An online parameter adaptation algorithm is employed to update model parameters in real time. A safety index with a distance factor is defined to describe the level of safety during physical human–robot interaction. An optimization problem is formulated to generate a collision-free trajectory for the manipulator based on human motion prediction to make the generated trajectory smoother. Planar and three-dimensional simulations are conducted to validate the efficacy of the algorithm by comparing the trajectories with and without real-time human motion prediction. Experiments are conducted on a 6-DOF manipulator Universal Robots UR5 with a Microsoft Kinect sensor (ver. 1). The optimization problem can be solved within the sampling time of the system, which suggests this algorithm can be applied in real-time trajectory generation. The trajectories generated with different control horizons are compared, and the results show that the manipulator can achieve the goal faster via a smoother trajectory with the help of human motion prediction.


title: Certificate-based encryption and the certificate revocation problem

abstract: We introduce the notion of certificate-based encryption. In this model, a certificate - or, more generally, a signature - acts not only as a certificate but also as a decryption key. To decrypt a message, a keyholder needs both its secret key and an up-to-date certificate from its CA (or a signature from an authorizer). Certificate-based encryption combines the best aspects of identity-based encryption (implicit certification) and public key encryption (no escrow). We demonstrate how certificate-based encryption can be used to construct an efficient PKI requiring less infrastructure than previous proposals, including Micali's Novomodo, Naor-Nissim and Aiello-Lodha-Ostrovsky.


title: Choosing nodes in parametric curve interpolation

abstract: Abstract In parametric curve interpolation, the choice of the interpolating nodes makes a great deal of difference in the resulting curve. Uniform parametrization is generally unsatisfactory. It is often suggested that a good choice of nodes is the cumulative chord length parametrization. Examples presented here, however, show that this is not so. Heuristic reasoning based on a physical analogy leads to a third parametrization, (the ‘centripetal model’), which almost invariably results in better shapes than either the chord length or the uniform parametrization. As with the previous two methods, this method is ‘global’ and is ‘invariant’ under similarity transformations. (It turns out that, in some sense, the method has been anticipated in a paper by Hosaka and Kimura.)


An XXXX Sex Chromosome Complex in Two Mentally Defective Females


title: Using parse tree validation to prevent SQL injection attacks

abstract: An SQL injection attack targets interactive web applications that employ database services. Such applications accept user input, such as form fields, and then include this input in database requests, typically SQL statements. In SQL injection, the attacker provides user input that results in a different database request than was intended by the application programmer. That is, the interpretation of the user input as part of a larger SQL statement, results in an SQL statement of a different form than originally intended. We describe a technique to prevent this kind of manipulation and hence eliminate SQL injection vulnerabilities. The technique is based on comparing, at run time, the parse tree of the SQL statement before inclusion of user input with that resulting after inclusion of input. Our solution is efficient, adding about 3 ms overhead to database query costs. In addition, it is easily adopted by application programmers, having the same syntactic structure as current popular record set retrieval methods. For empirical analysis, we provide a case study of our solution in J2EE. We implement our solution in a simple static Java class, and show its effectiveness and scalability.


title: Going the Distance: Trust Work for Citizen Participation

abstract: Trust is a vital component of citizen participation-whether citizens decide to engage in opportunities for participation in local government can hinge entirely on the existence of trust between citizens and public officials. Understanding the role of trust in this space is vital for HCI and the growing area of Digital Civics which works to improve or create new modes of citizen participation. Currently, however, trust is understudied from the perspectives of public officials. This gap creates a critical blind spot as technical interventions may be mismatched to the ways trust is put into action by public officials working to support citizen participation. We begin to address this gap by presenting a broad qualitative study of how public officials in a large US city operationalize trust in citizen participation. We found trust is enacted through ongoing practices that man-age distance in relationships between public officials and city residents.


title: Time-Domain Broadband 60 GHz Channel Sounder for Vehicle-to-Vehicle Channel Measurement

abstract: The paper deals with a time varying vehicle-to-vehicle channel measurement in the 60 GHz millimeter wave (MMW) band using a unique time-domain channel sounder built from off-the-shelf components and standard measurement devices and employing Golay complementary sequences as the excitation signal. The aim of this work is to describe the sounder architecture, primary data processing technique, achievable system parameters, and preliminary measurement results. We measured the signal propagation between two passing vehicles and characterized the signal reflected by a car driving on a highway. The proper operation of the channel sounder is verified by a reference measurement performed with an MMW vector network analyzer in a rugged stationary office environment. The goal of the paper is to show the measurement capability of the sounder and its superior features like 8 GHz measuring bandwidth enabling high time resolution or good dynamic range allowing an analysis of weak multipath components.


title: Hub-Accelerator: Fast and Exact Shortest Path Computation in Large Social Networks

abstract: Shortest path computation is one of the most fundamental operations for managing and analyzing large social networks. Though existing techniques are quite effective for finding the shortest path on large but sparse road networks, social graphs have quite different characteristics: they are generally non-spatial, non-weighted, scale-free, and they exhibit small-world properties in addition to their massive size. In particular, the existence of hubs, those vertices with a large number of connections, explodes the search space, making the shortest path computation surprisingly challenging. In this paper, we introduce a set of novel techniques centered around hubs, collectively referred to as the Hub-Accelerator framework, to compute the k-degree shortest path (finding the shortest path between two vertices if their distance is within k). These techniques enable us to significantly reduce the search space by either greatly limiting the expansion scope of hubs (using the novel distance- preserving Hub-Network concept) or completely pruning away the hubs in the online search (using the Hub2-Labeling approach). The Hub-Accelerator approaches are more than two orders of magnitude faster than BFS and the state-of-the-art approximate shortest path method Sketch for the shortest path computation. The Hub- Network approach does not introduce additional index cost with light pre-computation cost; the index size and index construction cost of Hub2-Labeling are also moderate and better than or comparable to the approximation indexing Sketch method.


Towards a Distributed, Scalable and Real-Time RDF Stream Processing Engine


title: Gaussian Process Change Point Models

abstract: We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.


Ethanol determination by packed GLC: A quick method with small sample amount and high sensitivity.


Presenting the NapoNet: Developing Global Competencies through Communications Technology in the Peruvian Amazon


title: How Unique and Traceable are Usernames?

abstract: Usernames are ubiquitously used for identification and authentication purposes on web services and the Internet at large, ranging from the local-part of email addresses to identifiers in social networks. Usernames are generally alphanumerical strings chosen by the users and, by design, are unique within the scope of a single organization or web service. In this paper we investigate the feasibility of using usernames to trace or link multiple profiles across services that belong to the same individual. The intuition is that the probability that two usernames refer to the same physical person strongly depends on the "entropy" of the username string itself. Our experiments, based on usernames gathered from real web services, show that a significant portion of the users' profiles can be linked using their usernames. In collecting the data needed for our study, we also show that users tend to choose a small number of related usernames and use them across many services. To the best of our knowledge, this is the first time that usernames are considered as a source of information when profiling users on the Internet.


title: AskWorld: budget-sensitive query evaluation for knowledge-on-demand

abstract: Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, Ask World, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate Ask World's capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.


title: Extending MBI Model using ITIL and COBIT Processes

abstract: Most organizations today operate in a highly complex and competitive business environment and need to be able to react to rapidly changing market conditions. IT management frameworks are widely used to provide effective support for business objectives through aligning IT with business and optimizing the use of IT resources. In this paper we analyze three IT management frameworks (ITIL, COBIT and MBI) with the objective to identify the relationships between these frameworks, and mapping ITIL and COBIT processes to MBI tasks. As a result of this analysis we propose extensions to the MBI model to incorporate IT Performance Management and a Capability Maturity Model.


title: Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

abstract: Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance. If we see these implicit models as dynamical systems, some of these issues are caused by being unable to control their behavior in a meaningful way during the course of training. In this work, we propose a theoretically grounded method to guide the training trajectories of GANs by augmenting the GAN loss function with a kernel-based regularization term that controls local and global discrepancies between the model and true distributions. This control signal allows us to inject prior knowledge into the model. We provide theoretical guarantees on the stability of the resulting dynamical system and demonstrate different aspects of it via a wide range of experiments.


FAMILY THERAPY-A COMPARISON OF APPROACHES


title: On the Contribution of Discourse Structure on Text Complexity Assessment

abstract: This paper investigates the influence of discourse features on text complexity assessment. To do so, we created two data sets based on the Penn Discourse Treebank and the Simple English Wikipedia corpora and compared the influence of coherence, cohesion, surface, lexical and syntactic features to assess text complexity. ::: Results show that with both data sets coherence features are more correlated to text complexity than the other types of features. In addition, feature selection revealed that with both data sets the top most discriminating feature is a coherence feature.


title: Home automation using IoT and a chatbot using natural language processing

abstract: Home automation — controlling the fans, lights and other electrical appliances in a house using Internet of things is widely preferred in recent days. In this paper, we propose a web application using which the fans, lights and other electrical appliances can be controlled over the Internet. The important features of the web application is that firstly, we have a chatbot algorithm such that the user can text information to control the functioning of the electrical appliances at home. The messages sent using the chatbot is processed using Natural Language processing techniques. Secondly, any device connected to the local area network of the house can control the devices and other appliances in the house. Thirdly, the web application used to enable home automation also has a security feature that only enables certain users to access the application. And finally, it also has a functionality of sending an email alert when intruder is detected using motion sensors.


title: Forum Thread Recommendation for Massive Open Online Courses

abstract: Recently, Massive Open Online Courses (MOOCs) have garnered a high level of interest in the media. With larger and larger numbers of students participating in each course, finding useful and informative threads in increasingly crowded course discussion forums becomes a challenging issue for students. In this work, we address this thread overload problem by taking advantage of an adaptive feature-based matrix factorization framework to make thread recommendations. A key component of our approach is a feature space design that effectively characterizes student behaviors in the forum in order to match threads and users. This effort includes content level modeling, social peer connections, and other forum activities. The results from our experiment conducted on one MOOC course show promise that our thread recommendation method has potential to direct students to threads they might be interested in.


title: A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks

abstract: Large-scale, self-organizing wireless sensor and mesh network deployments are being driven by recent technological developments such as The Internet of Things (IoT), Smart Grids and Smart Environment applications. Efficient use of the limited energy resources of wireless sensor network (WSN) nodes is critically important to support these advances, and application of topology control methods will have a profound impact on energy efficiency and hence battery lifetime. In this survey, we focus on the energy efficiency issue and present a comprehensive study of topology control techniques for extending the lifetime of battery powered WSNs. First, we review the significant topology control algorithms to provide insights into how energy efficiency is achieved by design. Further, these algorithms are classified according to the energy conservation approach they adopt, and evaluated by the trade-offs they offer to aid designers in selecting a technique that best suits their applications. Since the concept of "network lifetime" is widely used for assessing the algorithms' performance, we highlight various definitions of the term and discuss their merits and drawbacks. Recently, there has been growing interest in algorithms for non-planar topologies such as deployments in underwater environments or multi-level buildings. For this reason, we also include a detailed discussion of topology control algorithms that work efficiently in three dimensions. Based on the outcomes of our review, we identify a number of open research issues for achieving energy efficiency through topology control.


title: A probabilistic interpretation of canonical correlation analysis

abstract: We give a probabilistic interpretation of canonical correlation (CCA) analysis as a latent variable model for two Gaussian random vectors. Our interpretation is similar to the probabilistic interpretation of principal component analysis (Tipping and Bishop, 1999, Roweis, 1998). In addition, we cast Fisher linear discriminant analysis (LDA) within the CCA framework.


title: One parser to rule them all

abstract: Despite the long history of research in parsing, constructing parsers for real programming languages remains a difficult and painful task. In the last decades, different parser generators emerged to allow the construction of parsers from a BNF-like specification. However, still today, many parsers are handwritten, or are only partly generated, and include various hacks to deal with different peculiarities in programming languages. The main problem is that current declarative syntax definition techniques are based on pure context-free grammars, while many constructs found in programming languages require context information. In this paper we propose a parsing framework that embraces context information in its core. Our framework is based on data-dependent grammars, which extend context-free grammars with arbitrary computation, variable binding and constraints. We present an implementation of our framework on top of the Generalized LL (GLL) parsing algorithm, and show how common idioms in syntax of programming languages such as (1) lexical disambiguation filters, (2) operator precedence, (3) indentation-sensitive rules, and (4) conditional preprocessor directives can be mapped to data-dependent grammars. We demonstrate the initial experience with our framework, by parsing more than 20000 Java, C#, Haskell, and OCaml source files.


title: Metamaterials Application in Sensing

abstract: Metamaterials are artificial media structured on a size scale smaller than wavelength of external stimuli, and they can exhibit a strong localization and enhancement of fields, which may provide novel tools to significantly enhance the sensitivity and resolution of sensors, and open new degrees of freedom in sensing design aspect. This paper mainly presents the recent progress concerning metamaterials-based sensing, and detailedly reviews the principle, detecting process and sensitivity of three distinct types of sensors based on metamaterials, as well as their challenges and prospects. Moreover, the design guidelines for each sensor and its performance are compared and summarized.


Lyrics-based Analysis and Classification of Music


title: REDISCOVERY OF TRADITIONAL ECOLOGICAL KNOWLEDGE AS ADAPTIVE MANAGEMENT

abstract: Indigenous groups offer alternative knowledge and perspectives based on their own locally developed practices of resource use. We surveyed the international literature to focus on the role of Traditional Ecological Knowledge in monitoring, responding to, and managing ecosystem processes and functions, with special attention to ecological resilience. Case studies revealed that there exists a diversity of local or traditional practices for ecosystem management. These include multiple species management, resource rotation, succession management, landscape patchiness management, and other ways of responding to and managing pulses and ecological surprises. Social mechanisms behind these traditional practices include a number of adaptations for the generation, accumulation, and transmission of knowledge; the use of local institutions to provide leaders/stewards and rules for social regulation; mechanisms for cultural internalization of traditional practices; and the development of appropriate world views and cultural values. Some traditional knowledge and management systems were characterized by the use of local ecological knowledge to interpret and respond to feedbacks from the environment to guide the direction of resource management. These traditional systems had certain similarities to adaptive management with its emphasis on feedback learning, and its treatment of uncertainty and unpredictability intrinsic to all ecosystems.


, Reorganization and Affects Decision-Making Chronic Stress Causes


title: Human Motion Modeling using DVGANs

abstract: We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization over actions, and generation of long diverse sequences. We evaluate our approach on Human 3.6M and CMU motion capture datasets using inception scores.


title: Syllable-based automatic Arabic speech recognition

abstract: In this paper, we concentrate on the automatic recognition of Egyptian Arabic speech using syllables. Arabic spoken digits were described by showing their constructing phonemes, triphones, syllables and words. Speaker-independent hidden markov models (HMMs)-based speech recognition system was designed using Hidden markov model toolkit (HTK). The database used for both training and testing consists from forty-four Egyptian speakers. Experiments show that the recognition rate using syllables outperformed the rate obtained using monophones, triphones and words by 2.68%, 1.19% and 1.79% respectively. A syllable unit spans a longer time frame, typically three phones, thereby offering a more parsimonious framework for modeling pronunciation variation in spontaneous speech. Moreover, syllable-based recognition has relatively smaller number of used units and runs faster than word-based recognition.


title: Energy expenditure during walking and jogging.

abstract: Background. The aim of this investigation was to compare the physiological and subjective responses during treadmill walking and jogging at several corresponding speeds in physically active young women. Methods. Experimental design: Maximal oxygen uptake was determined during a continuous treadmill test to exhaustion. The walking protocol consisted of treadmill walking for five min at each of the following speeds: 4.0, 5.6, 7.2, 8.0, 8.8, 9.6 and 10.4 km.hr -1 . The jogging protocol consisted of treadmill walking for five min at 4.0, and 5.6 km.hr -1 and treadmill jogging for five min at each of the following speeds: 7.2, 8.0, 8.8, 9.6 and 10.4 km.hr -1 . Setting: This research was performed at Washington University School of Medicine. Participants: Fifteen healthy women (mean±SE, age; 26.9±1.4 yrs, BMI; 22.5±0.70, VO 2max ; 41.9±1.9 ml.hr -1 .min -1 ) performed a maximal treadmill exercise test, a walking test and a jogging test. Results. The rate of oxygen consumption, calculated energy expenditure per distance (kJ.hr -1 .mile -1 ) and heart rates were significantly higher during walking compared to jogging at treadmill speeds ≥8.8 km.hr -1 , Plasma lactate concentration and respiratory exchange ratio were significantly higher at treadmill speeds ≥8.0 km.hr -1 during walking as compared to jogging. Subjects subjectively rated their exertion during walking as being significantly greater when compared to jogging across the range of overlapping treadmill speeds. Conclusions. These findings demonstrated that walking at speeds > 8.0 km.hr -1 resulted in rates of energy expenditure that were as high or higher than jogging at the same speeds. Also, the higher rates of energy expenditure during walking as compared to jogging at speeds greater than 8.0 km.hr -1 were associated with higher heart rates, RER, RPE and plasma lactate response.


title: Hierarchical Extreme Learning Machine for unsupervised representation learning

abstract: Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.


title: Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization

abstract: 6DOF camera relocalization is an important component of autonomous driving and navigation. Deep learning has recently emerged as a promising technique to tackle this problem. In this paper, we present a novel relative geometry-aware Siamese neural network to enhance the performance of deep learning-based methods through explicitly exploiting the relative geometry constraints between images. We perform multi-task learning and predict the absolute and relative poses simultaneously. We regularize the shared-weight twin networks in both the pose and feature domains to ensure that the estimated poses are globally as well as locally correct. We employ metric learning and design a novel adaptive metric distance loss to learn a feature that is capable of distinguishing poses of visually similar images from different locations. We evaluate the proposed method on public indoor and outdoor benchmarks and the experimental results demonstrate that our method can significantly improve localization performance. Furthermore, extensive ablation evaluations are conducted to demonstrate the effectiveness of different terms of the loss function.


title: An Improved Lexicographical Sort Algorithm of Copy-move Forgery Detection

abstract: As a technique for digital image tampering, copy-move forgery is frequently used. In this paper, an improved lexicographical sort algorithm based on discrete cosine transform (DCT) is developed to detect copy-move forgery. Firstly, the image is split into 8*8 blocks, and the image data undergoes a DCT. Then the DCT coefficients were grouped to reduce the dimension according to the frequency property. Finally, the distance of eigenvectors, instead of the DCT coefficients, was taken as the eigenvalue to fulfill the block matching. Experiments results showed that the false matching ratio of the proposed algorithm was reduced while the detecting ability was maintained.


title: Adaptive B-Greedy (ABG): A simple yet efficient scheduling algorithm

abstract: In order to improve processor utilizations on parallel systems, adaptive scheduling with parallelism feedback was recently proposed. A-Greedy, an existing adaptive scheduler, offers provably-good job execution time and processor utilization. Unfortunately, it suffers from unstable feedback and hence unnecessary processor reallocations even when the job has constant parallelism. This problem may cause difficulties in the management of system resources. We propose a new adaptive scheduler called ABG (for Adaptive B-Greedy), which ensures both performance and stability. In a direct comparison with A-Greedy using simulated data- parallel jobs, ABG shows an average 50% reduction in wasted processor cycles and an average 20% improvement in running time. For a set of jobs, ABG also outperforms A-Greedy by 10% to 15% on average in terms of both makespan and mean response time, provided the system is not heavily loaded. Our detailed analysis shows that ABG indeed offers improved transient and steady-state behaviors in terms of control-theoretic metrics. Using trim analysis, we show that ABG provides nearly linear speedup for individual jobs and good processor utilizations. Using competitive analysis, we also show that ABG offers good makespan and mean response time bounds.


title: Memristor for computing: Myth or reality?

abstract: CMOS technology and its sustainable scaling have been the enablers for the design and manufacturing of computer architectures that have been fuelling a wider range of applications. Today, however, both the technology and the computer architectures are suffering from serious challenges/ walls making them incapable to deliver the right computing power at pre-defined constraints. This motivates the need of exploring new architectures and new technologies; not only to maintain the economic benefit of scaling, but also to enable the solutions of emerging computer power and data storage hungry applications such as big-data and data-intensive applications. This paper discusses the emerging memristor device as complementary (or alternative) to CMOS device and shows how this device can enable new ways of computing that will at least solve the challenges of today's architectures for some applications. The paper shows not only the potential of memristor devices in enabling new memory technologies and new logic design styles, but also their potential in enabling memory intensive architectures as well as neuromorphic computing due to their unique properties such as the tight integration with CMOS and the ability to learn and adapt.


title: Combining Word Order and CNN-LSTM for Sentence Sentiment Classification

abstract: Neural network models have been demonstrated to be capable of achieving state-of-the-art performance in sentence sentiment classification. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two widely used neural network models for NLP. However, since sentences consist of the same words in different order may represent different meaning in sentiment, it cannot be neglected that the word embedding training model ignores the factor of word order in sentence to quicken the training process. In this work, we mainly consider that word order is important for sentence sentiment classification, designing an encode-decode model called CNN-LSTM combined the strength of CNN and LSTM to demonstrate that word order of sentence plays an important role in sentiment analysis based on the word embedding which is designed as an order_w2v model taking in word order during word2vec training process. We evaluate the CNN-LSTM and order_w2v in sentiment classification both on Chinese and English datasets. The experimental results verify that the model considering the word order can achieve better results in sentiment analysis.


title: Computational fact checking from knowledge networks

abstract: Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.


title: Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations

abstract: We propose and analyze estimators for statistical functionals of one or more distributions under nonparametric assumptions. Our estimators are based on the theory of influence functions, which appear in the semiparametric statistics literature. We show that estimators based either on data-splitting or a leave-one-out technique enjoy fast rates of convergence and other favorable theoretical properties. We apply this framework to derive estimators for several popular information theoretic quantities, and via empirical evaluation, show the advantage of this approach over existing estimators.


title: Active multiple kernel learning for interactive 3D object retrieval systems

abstract: An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. The proposed framework aims to efficiently identify an optimal combination of multiple kernels by asking the users to label the most informative 3D images. We evaluate the proposed techniques on a dataset of over 10,000 3D models collected from the World Wide Web. Our experimental results show that the proposed AMKL technique is significantly more effective for 3D object retrieval than the regular relevance feedback techniques widely used in interactive content-based image retrieval, and thus is promising for enhancing user's interaction in such interactive intelligent retrieval systems.


SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics


title: Long-Range Pipeline Monitoring by Distributed Fiber Optic Sensing

abstract: Distributed fiber optic sensing presents unique features that have no match in conventional sensing techniques. The ability to measure temperatures and strain at thousands of points along a single fiber is particularly interesting for the monitoring of elongated structures such as pipelines, flow lines, oil wells, and coiled tubing. Sensing systems based on Brillouin and Raman scattering are used, for example, to detect pipeline leakages, to verify pipeline operational parameters and to prevent failure of pipelines installed in landslide areas, to optimize oil production from wells, and to detect hot spots in high-power cables. Recent developments in distributed fiber sensing technology allow the monitoring of 60 km of pipeline from a single instrument and of up to 300 km with the use of optical amplifiers. New application opportunities have demonstrated that the design and production of sensing cables are a critical element for the success of any distributed sensing instrumentation project. Although some telecommunication cables can be effectively used for sensing ordinary temperatures, monitoring high and low temperatures or distributed strain presents unique challenges that require specific cable designs. This contribution presents advances in long-range distributed sensing and in novel sensing cable designs for distributed temperature and strain sensing. This paper also reports a number of significant field application examples of this technology, including leakage detection on brine and gas pipelines, strain monitoring on gas pipelines and combined strain and temperature monitoring on composite flow lines, and composite coiled tubing pipes.


title: Audio Chord Recognition with Recurrent Neural Networks

abstract: In this paper, we present an audio chord recognition system based on a recurrent neural network. The audio features are obtained from a deep neural network optimized with a combination of chromagram targets and chord information, and aggregated over different time scales. Contrarily to other existing approaches, our system incorporates acoustic and musicological models under a single training objective. We devise an efficient algorithm to search for the global mode of the output distribution while taking long-term dependencies into account. The resulting method is competitive with state-of-the-art approaches on the MIREX dataset in the major/minor prediction task.


title: A Thermal-Aware Device Design Considerations for Nanoscale SOI and Bulk FinFETs

abstract: Thermal performance characteristics of fin-shaped FETs (FinFETs) are studied and analyzed in this paper for sub-22-nm technologies using the well-calibrated TCAD simulations. In this paper, we show that bulk FinFETs have a relatively better thermal performance as compared with SOI FinFETs. In order to understand the isothermal characteristics of these devices because of thermal effects, we use pulse rise-time as well as ac conductance methods. We demonstrate that the ac conductance method fails to accurately capture thermal time constants for FinFETs, as self-heating and gate resistance regions are indistinguishable from each other. A pulse rise-time method gives isothermal characteristics of these devices. As verified from both these independent techniques, only at high frequencies (>1 GHz), FinFETs show a suppression of thermally induced degradation, which can be attributed to their higher surface-to-volume ratio. It is observed that bulk FinFETs will perform better than SOI FinFETs for small effective fin heights. However, as we show in this paper, increased body doping in bulk FinFETs will increase the self-heating effects. Channel length scaling in FinFETs, which shows a decrease in drain current degradation with heating, will be an important design parameter for sub-22-nm devices.


title: Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure

abstract: The wide adoption of mobile devices and social media platforms have dramatically increased the collection and sharing of personal information. More and more frequently, users are called to take decisions concerning the disclosure of their personal information. In this study, we investigate the factors affecting users' choices toward the disclosure of their personal data, including not only their demographic and self-reported individual characteristics, but also their social interactions and their mobility patterns inferred from months of mobile phone data activity. We report the findings of a field-study conducted with a community of 63 subjects provided with (i) a smart-phone and (ii) a Personal Data Store (PDS) enabling them to control the disclosure of their data. We monitor the sharing behavior of our participants through the PDS, and evaluate the contribution of different factors affecting their disclosing choices of location and social interaction data. Our analysis shows that social interaction inferred by mobile phones is an important factor revealing willingness to share, regardless of the data type. In addition, we provide further insights on the individual traits relevant to the prediction of sharing behavior.


Distributed intelligence: Overview of the field and its application in multi-robot systems


Information Technology for Knowledge Management


title: Fast CdTe and CdZnTe Semiconductor Detector Arrays for Spectroscopic X-Ray Imaging

abstract: A growing interest has recently been observed in high flux X-ray imaging detectors with energy discrimination or spectroscopy capabilities. In this study we evaluate the performances of energy sensitive CdTe and CdZnTe detectors for X-ray imaging at fluxes of up to 2 107 X/mm2/s in the 20-150 keV energy range. Linear array detectors made from 3 mm thick single CdTe or CdZnTe crystals have 16 pixels with an 800 in pitch. These detectors were coupled to an innovative custom-designed 16-channel fast spectroscopy front-end electronic circuit. For each channel, the signal is continuously digitized and a FPGA controls acquisition and reconstructs the energy spectra on 256 bins for each channel. The detector was tested under X-rays for fluxes in the 105 to 2 × 107 X/mm2/s range. The main problem encountered at such high fluxes is the multiplication of pile-up events that reduces count rate and degrades energy resolution. When a very short shaping time was used, dead time was lower than 50 ns, and an energy resolution of 11 keV full width at half maximum (FWHM) at 2 × 106 X/mm2 s and 20 keV (FWHM) at 107 X/mm2/ s was achieved. We also show imaging results that illustrate the interest of the spectral information provided by each pixel.


title: Wideband LDS antenna using two radiating elements

abstract: In this paper, an innovative 4G antenna solution operating in the 698–960 MHz band, is presented. It is composed of two radiating elements recombined in a single feed antenna-system using an integrated power divider/combiner, synthesized thanks to five lumped components. Simulations using realistic SMD (Surface Mount Devices) components allow reaching a matching better than −6 dB, confirmed by measurement and an average total efficiency of 30% over the full frequency band. These results are benchmarked with the performance of measured commercial handset antennas, demonstrating a key improvement of the achievable bandwidth and total efficiency. Proposed solution seems promising in order to address 4G wideband antenna design challenges.


title: An Image Encryption Algorithm Based on Knight's Tour and Slip Encryption-Filter

abstract: A new image encryption algorithm is proposed in the paper. Its essential idea is that, firstly, we produce a knight's tour matrix emended as slip encryption-filter template matrix; And then, we produce a knight's tour matrix again with the same size of the image waiting for encryption as a knight's tour slip matrix; Finally, we let the center of the slip encryption-filter template move along with the knight's tour slip matrix to do convolvsion operation, and achieve the cipher image. Experimental results show that the algorithm has perfert encryption effect, high encryption efficiency, simplity to achieve, huge secret key space and high security.


title: A Compact 24 GHz Sensor for Beam-Forming and Imaging

abstract: A compact 24 GHz sensor setup for experimental verifications of radar imaging and digital beam-forming has been designed and realized. The sensor consists of a FM-CW radar front-end and a remote radar electronic unit. The front-end consists of a switched antenna array of 25 linearly arranged elements fed by one transmitter and two parallel homodyne receivers connected to separate receive antenna elements. The remote radar electronic unit includes the base-band signal generation, analog inputs for data acquisition and programmable digital circuitry for measurement control, data storage and interface handling. The sensor can be operated from a notebook computer via standard PC interfaces, thus forming a compact imaging radar system well suited for field tests. The sensor addresses primarily short and medium range automotive applications, but its flexibility in signal generation, sampling and coherent signal processing makes it interesting for many other application fields. In this article a detailed description of the total system is given and exemplary signal processing methods for imaging and beam-forming are introduced and demonstrated with measurement results.


title: Optimal allocation of energy storage in a future congolese power system

abstract: The Democratic Republic of Congo is facing a dramatic electricity crisis. For the population, the access to electricity is 1% in rural areas, 30% for cities and 9% nationally. Energy supply based on renewable energy source is one of the promising solutions for now or in the future to deal with the limited fossil fuel resources as well as the emission of harmful waste. Out of various renewable resources the sun, wind and biomass associated with energy storage are considered to hold one of the most promising alternative to the electricity crisis in Democratic Republic of Congo (DRC). A large central power plant associated with many smaller power sources closer to customers can provide power to all provinces, cities, municipalities and villages of the DRC holding down the price. Using renewable energy generators at the location of high harvest potential, appropriate rating and location of storage and transmission system between different regions, a cost efficient system can be achieved. This study facilitates the best storage system associated with the integration of renewable energy technology into the multiple DRC power plant systems. The benefits of such systems will include high reliability, lower cost, and fewer blackouts. This research intends to present the solution that will produce electricity from renewable energies (Sun, Wind and Biomass) into the main grid at lower cost when using a suitable energy storage system


title: Modeling and design of a soft pneumatic finger for hand rehabilitation

abstract: In China, there are such a huge amount of the physical disabled demanding rehabilitation devices to assist their activities of daily living (ADL). Among all the parts of body, hand is the most dexterous and multifunctional so that its rehabilitation is complex and difficult. The soft finger detailed in this paper could help the disable hand bend and extend in a large range of safety. Its components (extending deformation and bending actuator) get analyzed from three aspects including theoretical derivation, finite element modeling and experimental verification. Then the structure of the soft finger is designed and proves to be validated. This work serves as a guideline for designing soft robot hand for specific application such as rehabilitation hand and industrial gripper for soft grasping.


Comparing Performances of Big Data Stream Processing Platforms with RAM3S


title: Learning to Optimize Neural Nets

abstract: Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture. More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.


title: A critical overview of latest challenges and solutions of Mobile Cloud Computing

abstract: The unabated plethora of research activities to augment multifarious mobile devices by leveraging cloud resources have created new research field called Mobile Cloud Computing (MCC). Recently, researchers have found Mobile Cloud Computing (MCC) to be a promising venture combining the advantage of both the cloud and mobility in giving users available resources on the cloud wherever they are using their mobile phone. Despite its benefits, many challenges still remain with MCC. This paper addressed recent challenges ranging from limited computational capacity, connectivity, security, latency, and heterogeneity. Solutions for these challenges are suggested including mobile processes offloading, using HTML5 technologies to compensate for connectivity loss, SDN centralized control features for data security, utilizing the cloud for time consuming applications, and using cross platform applications neutralizes heterogeneous technologies. Finally, the paper concludes with an extracted list of new research idea for scholars to work on to further enhance MCC.


title: Antimicrobial susceptibility and ESBL prevalence in Pseudomonas aeruginosa isolated from burn patients in the North West of Pakistan.

abstract: Pseudomonas aeruginosa is one of the most prevalent pathogen in burn infections. Infections with P. aeruginosa are associated with higher mortality rate and antibiotic costs in hospitalized patients. These bacteria also produce enzymes called Expanded Spectrum Beta-Lactamases (ESBL) which render penicillins and cephalosporins inactive. The aim of this study was to assess the antimicrobial susceptibility pattern and prevalence of ESBL in P. aeruginosa in Peshawar, North West of Pakistan. During 2005-2006, one hundred and six P. aeruginosa isolates were collected from burn patients at a tertiary care hospital. Antibiotic susceptibility testing and ESBL detection were carried out according to Clinical Laboratory and Standards Institute (CLSI) criteria. Eighteen antibiotics were tested in this study. A total of 38 (35.85%) isolates were found to be ESBL producers. Thirty one (29.24%) isolates were resistant to 3 or more antibiotics (multidrug resistance). Meropenem and imipenem showed high potency with 99% and 96% isolates being susceptible respectively. Susceptibility to amikacin was 70%; gentamicin 25%; ciprofloxacin 49%; enoxacin 47%; gatifloxacin 42%; doxycycline 21% and to co-trimoxazole only 16%. This study reveals that P. aeruginosa isolated from burns in this region are multidrug resistant and produce ESBL in large proportions.


title: Cloud manufacturing – a critical review of recent development and future trends

abstract: There is an ongoing paradigm shift in manufacturing, in which the modern manufacturing industry is changing towards global manufacturing networks and supply chains. This will lead to the flexible usage of different globally distributed, scalable and sustainable, service-oriented manufacturing systems and resources. Combining recently emerged technologies, such as Internet of Things, Cloud Computing, Semantic Web, service-oriented technologies, virtualisation and advanced high-performance computing technologies, with advanced manufacturing models and information technologies, Cloud Manufacturing is a new manufacturing paradigm built on resource sharing, supporting and driving this change. It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities and equipment, will then be made accessible to presumptive consumers on a worldwide basis. Cloud Manufacturing has been in focus for a great deal of research interest and suggested applications during recent years, by both industrial and academic communities. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with identified outstanding research issues, and future trends and directions within Cloud Manufacturing.


title: An interference-aware 5.8GHz wake-up radio for ETCS

abstract: Wake-up radios have been a popular transceiver architecture in recent years for battery-powered applications such as wireless body area networks (WBANs) [1], wireless sensor networks (WSNs) [2,3], and even electronic toll collection systems (ETCS) [4]. The most important consideration in implementing a wake-up receiver (WuRX) is low power dissipation while maximizing sensitivity. Because of this requirement of very low power, WuRX are usually designed by a simple RF envelope detector (RFED) consisting of Schottky diodes [1,3] or MOSFETs in the weak inversion region [2] without active filtering or amplification of the input signal. Therefore, the performance of the RFED itself is critical for attaining good sensitivity of the WuRX. Moreover, the poor filtering of the input signal renders the WuRX vulnerable to interferers from nearby terminals with high transmit power such as mobile phones and WiFi devices, and this can result in false wake-ups [1]. Although the RFED has very low power, a false wake-up will increase the power consumption of the wake-up radio as it will enable the power-hungry main transceiver.


title: The chemokine system in diverse forms of macrophage activation and polarization.

abstract: Plasticity and functional polarization are hallmarks of the mononuclear phagocyte system. Here we review emerging key properties of different forms of macrophage activation and polarization (M1, M2a, M2b, M2c), which represent extremes of a continuum. In particular, recent evidence suggests that differential modulation of the chemokine system integrates polarized macrophages in pathways of resistance to, or promotion of, microbial pathogens and tumors, or immunoregulation, tissue repair and remodeling.


title: A character-level convolutional neural network with dynamic input length for Thai text categorization

abstract: A Character-level Convolutional Neural Network (Char-CNN) is an efficient text categorization method. It can be used in categorization task without a word segmentation step, which is necessary by traditional method for Thai. Currently, the existing model of Char-CNN uses a fixed input length and requires cutting off exceeding characters, which may lead to a missing of important content. In this paper, we propose a new Char-CNN model with a capability to accept any length of input by employing k-max pooling before a fully connected layer. The result shows that our model outperforms a Char-CNN model with a fixed input length on Thai news categorization. Moreover, our proposed method gives a better accuracy than many word-level methods: Naive Bayes, Logistic Regression, Support Vector Machine except a word-level CNN.


title: Properties of Chemicals and Estimation Methodologies

abstract: As outlined in Chapter 1, risk assessment is based on data for fate parameters and effect parameters. Fate modelling requires data for a variety of parameters including information on sorption to soil and sediment, bioaccumulation in organisms, evaporation and transformation rates, etc. (see Chapters 3 and 4). It is clear, however, that because the required data for most chemicals are not available for either fate or effect concentrations, risk assessments can be carried out for only very few substances. As there are around 100,000 chemicals on the European Inventory of Existing Commercial Chemical Substances (EINECS), it is unlikely that the required data will become available in the near future. Even if sufficiently large testing capacities were available, the experimental assessment of all compounds would be a waste of money and effort, since many do not present any significant hazard potential. Therefore, for a relatively large number of existing chemicals, predictions will provide a good alternative to actual testing. Before giving the reader a too optimistical impression regarding the possibilities of using predictions instead of real experimental data, we must emphasize that models developed for forecasting always have limitations. It is extremely important to know these limitations. Only when those limitations are respected is the use of predictions legitimate. When using estimates in any risk assessment scheme, it is essential to evaluate carefully the reliability of a predicted parameter. It is also important to realize that in many computer programs (such as fate modelling programs), estimates and predictions are implicitly incorporated in the routines. A detailed knowledge of estimation methods and their limitations in particular, is essential.


title: (In-)Secure messaging with the Silent Circle instant messaging protocol

abstract: Silent Text, the instant messaging application by the company Silent Circle, provides its users with end-to-end encrypted communication on the Blackphone and other smartphones. The underlying protocol, SCimp, has received many extensions during the update to version 2, but has not been subjected to critical review from the cryptographic community. In this paper, we analyze both the design and implementation of SCimp by inspection of the documentation (to the extent it exists) and code. Many of the security properties of SCimp version 1 are found to be secure, however many of the extensions contain vulnerabilities and the implementation contains bugs that affect the overall security. These problems were fed back to the SCimp maintainers and some bugs were fixed in the code base. In September 2015, Silent Circle replaced SCimp with a new protocol based on the Signal Protocol.


title: A comprehensive study of the relationship between enterprise resource planning selection criteria and enterprise resource planning system success

abstract: We developed a conceptual framework for investigating how ERP selection criteria are linked to system quality and the service provided by suppliers and consultants, and thus how these influenced ERP implementation success. Through a cross-sectional survey of the top 5000 largest companies in Taiwan, using a balanced scorecard concept and path analysis, we showed that four system selection criteria (consultant's suggestion, a certified high-stability system, compatibility between the system and the business process, and the provision of best practices) were positively related to system quality. Three supplier selection criteria (international market position, training support by the supplier and supplier technical support and experience) had a significant influence on supplier service quality, and two consultant selection criteria (consultant's ERP implementation experience in a similar industry and consultant's support after going live) were related to consultant service quality. However, we found that most organizations did not consider all these criteria when implementing ERP systems. Our study also suggested that enhanced system quality and service quality could increase user perspective and ERP success.


title: Predicting player behavior in Tomb Raider: Underworld

abstract: This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other non-linear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.


title: Feldspar: A domain specific language for digital signal processing algorithms

abstract: A new language, Feldspar, is presented, enabling high-level and platform-independent description of digital signal processing (DSP) algorithms. Feldspar is a pure functional language embedded in Haskell. It offers a high-level dataflow style of programming, as well as a more mathematical style based on vector indices. The key to generating efficient code from such descriptions is a high-level optimization technique called vector fusion. Feldspar is based on a low-level, functional core language which has a relatively small semantic gap to machine-oriented languages like C. The core language serves as the interface to the back-end code generator, which produces C. For very small examples, the generated code performs comparably to hand-written C code when run on a DSP target. While initial results are promising, to achieve good performance on larger examples, issues related to memory access patterns and array copying will have to be addressed.


title: AppWatcher: unveiling the underground market of trading mobile app reviews

abstract: Driven by huge monetary reward, some mobile application (app) developers turn to the underground market to buy positive reviews instead of doing legal advertisements. These promotion reviews are either directly posted in app stores like iTunes and Google Play, or published on some popular websites that have many app users. Until now, a clear understanding of this app promotion underground market is still lacking. In this work, we focus on unveiling this underground market and statistically analyzing the promotion incentives, characteristics of promoted apps and suspicious reviewers. To collect promoted apps, we built an automatic data collection system, AppWatcher, which monitored 52 paid review service providers for four months and crawled all the app metadata from their corresponding app stores. Finally, AppWatcher exposes 645 apps promoted in app stores and 29, 680 apps promoted in some popular websites. The current underground market is then reported from various perspectives (e.g., service price, app volume). We identified some interesting features of both promoted apps and suspicious reviewers, which are significantly different from those of randomly chosen apps. Finally, we built a simple tracer to narrow down the suspect list of promoted apps in the underground market.


title: The SIMC Method for Smooth PID Controller Tuning

abstract: The SIMC method for PID controller tuning (Skogestad in J. Process. Control 13:291–309, 2003) has already found widespread industrial usage. This chapter gives an updated overview of the method, mainly from a user’s point of view. The basis for the SIMC method is a first-order plus time delay model, and we present a new effective method to obtain the model from a simple closed-loop experiment. An important advantage of the SIMC rule is that there is a single tuning parameter (τ c ) that gives a good balance between the PID parameters (K c ,τ I ,τ D ) and can be adjusted to get a desired trade-off between performance (“tight” control) and robustness (“smooth” control). Compared to the original paper of Skogestad (J. Process. Control 13:291–309, 2003), the choice of the tuning parameter τ c is discussed in more detail, and lower and upper limits are presented for tight and smooth tuning, respectively. Finally, the optimality of the SIMC PI rules is studied by comparing the performance (IAE) versus robustness (M s ) trade-off with the Pareto-optimal curve. The difference is small, which leads to the conclusion that the SIMC rules are close to optimal. The only exception is for pure time delay processes, so we introduce the “improved” SIMC rule to improve the performance for this case.


title: Psychometric properties of internet administered versions of Penn State Worry Questionnaire (PSWQ) and Depression, Anxiety, and Stress Scale (DASS)

abstract: A limited amount of research has been conducted on the psychometric properties of commonly used measures of anxious or depressive symptomatology for use on the internet, although such measures are seeing increasing use in internet administration for both clinical and research uses. A plethora of advantages exist for the use of internet administration of questionnaires, both in terms of assessment and the potential use in treatment monitoring as well as for research. The aim of this study was to examine the psychometric properties of two common clinical measures, the Penn State Worry Questionnaire and the. Depression, Anxiety, and Stress Scale, in an internet administered format (N=1138). Results suggest that these two measures may be used with confidence in an online format in terms of reliability and validity.


title: A Multi-Purpose Balanced Transformer for Railway Traction Applications

abstract: A multi-purpose balanced (MPB) transformer is proposed in this paper for electric railway applications. The MPB transformer is developed from impedance-matching (IM) transformers widely used in China's railway systems. The proposed MPB transformer has three main functions: (1) it provides a symmetrical two-phase voltage of 27.5 kV for railway overhead lines to power electric trains; (2) it supplies a three-phase balanced voltage of 10.5 kV for railway substations; and (3) it can accommodate a three-phase harmonic filter with improved power factor and reduced manufacturing cost. In this paper, the operating principles of the MPB transformer are elaborated, the design of the harmonic filters is discussed, and the results of computer simulations are provided. A 16-MVA MPB transformer was designed and commissioned, and real-time field measurements are provided.


title: Bidirectional positive buck-boost converter

abstract: In a positive buck-boost (PBB) converter, inductor current and capacitor voltage can be decoupled which may improve system stability. In fact for a specific level of capacitor voltage, the inductor current can be adjusted at different levels and can be utilized to increase the robustness of the converter against input voltage and load disturbances. But when demand is a fast response with respect to step change in reference voltage, this topology needs to be modified. In this paper, a family of topologies based on a positive buck boost converter are presented which have a fast response and bidirectional power flow capability. This feature leads to some applications in hybrid vehicle systems and telecommunications. Simulations have been carried out to validate fast response of the proposed converters.


False Data Injection Attacks in Control Systems


title: Autonomous stair climbing algorithm for a small four-tracked robot

abstract: In outdoor environments, mobility, adaptability and reliability of a robot are more important than its speed and precise trajectory. From a practical point of view, tracked robots have an advantage over wheeled robots in outdoor applications. The tracked robot is frequently operated by using the remote controller, but the remote operation is not effective for all cases. To overcome some complex obstacles such as rocks or stairs, the information related to the robot posture is required. However, the sensor information is not intuitive to the user to control the robot. In this research, a multi-active crawler robot (MACbot) was developed and the autonomous stair climbing algorithm was implemented to deal with those problems. Various experiments show that the MACbot can climb the stair autonomously.