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The prototype O system seamlessly integrates Web searching and browsing on touch-enabled tablets using two-handed gesture interaction, much like handling and marking content on a piece of paper. The system also leverages contextual information to infer user intent and significantly improve search results over traditional keyword-based approaches, enabling users to go beyond websites' "walled garden"' and more fully explore information objects. [ABSTRACT FROM PUBLISHER]

We present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. We find that gradient tree boosting performs by far the best, achieving an F1 of 0.865 in a cross-validation study. [ABSTRACT FROM AUTHOR]

The paper provides a high-level review of the current state of techno-pedagogical design in Augmented Reality Learning Experiences (ARLEs). The review is based on a rubric constructed from the Meaningful Learning with ICT framework and the Orchestration Load reduction framework, providing, respectively, a view of primarily student- and primarily teacher-focused dimensions in ARLEs. Fifty-nine ARLEs are reviewed based on an extensive search for papers based on both previous work in the field as well as academic databases. Exemplars for specific situations are provided as well as a correlation analysis between dimensions and an overall view of the maturity of the field in terms of relevant techno-pedagogical considerations, giving ARLE practitioners practical information on the current state of the field as well as assistance in better positioning their future ARLE design efforts. [ABSTRACT FROM PUBLISHER]

In this paper, the effects of motion blur on 2-D digital image correlation (DIC) measurements are discussed, with a particular focus on the description of displacement measurement uncertainty. The research started with the simulation of motion blur with a suitable literature-based algorithm. Then the state of the art of uncertainty analysis in the context of DIC was compared with simulation results, and it was found that the effects of motion blur on uncertainty are not predicted correctly by the literature models. This phenomenon is explained by the fact that the literature models do not consider motion blur. Consequently, a simple monodimensional case is analyzed. In this way, it has been possible to evaluate the sensitivity of DIC correlation function [evaluated as the sum of squared differences (SSD)] to motion blur. Using these results, it is possible to extend the analysis to the case of a generic image with the help of a power-law model. Eventually, the model proposed in this paper is able to represent the effects of motion blur correctly. [ABSTRACT FROM AUTHOR]

The work by Harrison, Ruzzo, and Ullman (the HRU paper) on safety in the context of the access matrix model is widely considered to be foundational work in access control. In this paper, we address two errors we have discovered in the HRU paper. To our knowledge, these errors have not been previously reported in the literature. The first error regards a proof that shows that safety analysis for mono-operational HRU systems is in (\bf NP). The error stems from a faulty assumption that such systems are monotonic for the purpose of safety analysis. We present a corrected proof in this paper. The second error regards a mapping from one version of the safety problem to another that is presented in the HRU paper. We demonstrate that the mapping is not a reduction, and present a reduction that enables us to infer that the second version of safety introduced in the HRU paper is also undecidable for the HRU scheme. These errors lead us to ask whether the notion of safety as defined in the HRU paper is meaningful. We introduce other notions of safety that we argue have more intuitive appeal, and present the corresponding safety analysis results for the HRU scheme. [ABSTRACT FROM AUTHOR]

In a relational database, tuples are called “duplicate” if they describe the same real-world entity. If such duplicate tuples are observed, it is recommended to remove them and to replace them with one tuple that represents the joint information of the duplicate tuples to a maximal extent. This remove-and-replace operation is called a fusion operation. Within the setting of a relational database management system, the removal of the original duplicate tuples can breach referential integrity. In this paper, a strategy is proposed to maintain referential integrity in a semantically correct manner, thereby optimizing the quality of relationships in the database. An algorithm is proposed that is able to propagate a fusion operation through the entire database. The algorithm is based on a framework of first and second order fusion functions on the one hand, and conflict resolution strategies on the other hand. It is shown how classical strategies for maintaining referential integrity, such as delete cascading, are highly specialized cases of the proposed framework. Experimental results are reported that (i) show the efficiency of the proposed algorithm and (ii) show the differences in quality between several second order fusion functions. It is shown that some strategies easily outperform delete cascading. [ABSTRACT FROM PUBLISHER]

The demand for multitasking GPUs increases whenever the GPU may be shared by multiple applications, either spatially or temporally. This requires that GPUs can be preempted and switch context to a new application while already executing one. Unlike CPUs, context switching in GPUs is prohibitively expensive due to the large context states to swap out. There have been a number of efforts on reducing the overhead of preemption, through reducing the context sizes or overlapping context switching with execution. All those techniques are reactive approaches, meaning that context switching occurs when the preemption request arrives. In this paper, we propose a dynamic and proactive mechanism to reduce the latency of preemption. We observe that kernel execution is almost always preceded by known commands in both CUDA and OpenCL implementations. Hence, a preemption can be anticipated before the actual request arrives. We study such lead time and develop a prediction scheme to perform an early state saving. When the actual preemption is invoked, an incremental update relative to the previous saved state is performed, much like the conventional checkpointing mechanism. Our design can also choose to drain or checkpointing dynamically and accurately according to the feature of kernels in the runtime. This design effectively reduces the stall time of the preempting kernel due to context switching by 58.6%. Moreover, through careful handling of the saved state, we can also reduce the overall size of saved state by an average of 23.3%, compared with a full context switching. [ABSTRACT FROM AUTHOR]

We address the problem of merging probabilistic knowledge in a centralized (nonflat) multiple participant system. Each participant independently reports a known distribution of a common quantity of interest to an extra participant, called the deliberator. The deliberator’s task is the optimal design of a compromise distribution, which seeks to minimize the expected loss imposed on each of the participants. We formulate this task as one of Bayesian decision-making, and solve it using fully probabilistic design. Solutions are currently available for discrete distributions only. In this paper, we provide two novel contributions. First, the deliberator’s unknown distribution is parametric on a continuous support. Second, the scheme adopts a hierarchical model for the deliberator, optimizing the deliberator’s parameter prior. This allows quantification of the deliberator’s uncertainty in their model choice. We formulate conditions under which a conjugate design is achieved, and provide an example where the design is analytically tractable. Extensive simulation-based evidence is reported, pointing to the significantly enhanced performance of our framework over logarithmic pools. [ABSTRACT FROM PUBLISHER]

Even though digital learning games have become common in education, relatively little is known about the usefulness of game-based assessment. This paper aims to explore if a game-based math test can provide added value to math education with respect to cognitive and affective outcomes. We used in-game measures, embedded in the game called Semideus Exam, focusing on conceptual fraction knowledge. In order to validate the game-based assessment approach, we compared the cognitive outcomes of 51 Finnish sixth graders, who completed both paper-based and game-based math tests in a randomized order. In addition, the students’ test anxiety and flow experience were measured to evaluate the affective outcomes. The results indicate that the game-based test scores correlated significantly with the paper-based test scores suggesting that the game-based assessment was successfully implemented and the game provided comparable data with the paper-based test approach. More importantly, the results revealed that game-based assessment lowered test anxiety and increased engagement which is likely to decrease assessment bias caused by test anxiety. In addition, the results show that earlier playing experience and gender did not influence the game-based test score suggesting fairness of the game-based assessment approach. Although we identified several benefits of the game-based assessment approach, more evidence is needed on the usefulness and fairness of game-based assessments. [ABSTRACT FROM AUTHOR]

Recently, a measure of “total uncertainty” (TU) in Dempster–Shafer theory, based on the pignistic distribution called ambiguity measure (AM), have been modified. The resulting new measure has been simply referred as modified AM (MAM). In the literature, it has been shown that AM, in addition to showing some undesirable behaviors, has important drawbacks related to two essential properties for such measures: 1) subadditivity and 2) monotonicity. The MAM measure has been developed to solve the AM subadditivity problem, but this paper demonstrates that MAM suffers the same drawback as AM with respect to monotonicity. A measure of uncertainty that cannot meet the monotonicity requirement has an important drawback for its exploitation in operational contexts such as in analytics, information fusion, and decision support. This paper aims at identifying and discussing drawbacks of this type of measures (AM, MAM). Our main motivation is to insist upon the important requirement of monotonicity that a TU measure should possess to improve its potential of being used and trusted in applications. This discussion is due time since the monotonicity problem needs first to be solved to avoid building too high expectations for usefulness and potential exploitation of such measures in operational communities. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to monitor complex process dynamics manifest in multivariate (multidimensional) time series data using a spectral (algebraic) graph theoretic approach. We test the hypothesis that the spectral graph-based topological invariants detect incipient process drifts earlier [lower average run length (ARL1)] and with higher fidelity (consistency of detection) when compared with the conventional statistics-based approaches. The presented approach maps a multidimensional sensor data stream \mathcal X^N\times d (visualize $N$ as time and d$ as the number of sensors) as an unweighted and undirected network graph G(V,E)$ , indexed by its vertices V$ and edges E . The rationale is that the graph-based topological invariants are surrogate representatives of the system state. We compare the monitoring performance of spectral graph theoretic invariants with conventional statistical features in an exponentially weighted moving average control chart setting. The practical utility of the approach is substantiated in the context of process monitoring in two advanced manufacturing scenarios, namely, ultraprecision machining (UPM) and semiconductor chemical mechanical planarization. These studies corroborate the hypothesis that graph theoretic invariants, when used as monitoring statistics, lead to lower ARL1 and more consistent detections in contrast to conventional statistical features. For instance, in the UPM case, the fault detection delay using graph theoretic invariants is less than 160 ms, compared with over 8 s of delay with statistical features.

Note to Practitioners—This paper addresses the critical problem of capturing process drifts from multidimensional (multisensor) data. The novelty of this paper is the development of a graph theoretic approach that combines signals from multiple in situ sensors for detecting abnormal process drifts. We show that this approach, which invokes graph-based topological invariants instead of statistical feature mining, is capable of capturing process drifts at an earlier stage (in terms of detection delay or lower average run length) and higher consistency of detection than conventional statistical features. As a practical consequence of this research, the operator can track the status of a complex process in a tractable control chart setting with only two graph theoretic topological invariants, as opposed to complex black-box models involving several features. [ABSTRACT FROM AUTHOR]

This paper develops a merging procedure for functional test sequences that achieves test compaction for a pool of functional test sequences by reducing the number of sequences in the pool. The procedure has the following new features: 1) in contrast to existing selection procedures, the merging procedure described in this paper increases the fault coverage of test sequences in the pool, thus enhancing the ability to reduce the number of sequences and 2) in contrast to existing procedures that concatenate or merge test sequences, the procedure described in this paper does not increase the lengths of the sequences it merges. The procedure is based on the concept of restoration of test vectors. In the context of test sequence merging, restoration consists of copying test vectors from a test sequence Tj into a test sequence Ti in order to allow Ti to detect faults that Tj detects. The merging procedure focuses on the removal of one test sequence Tj at a time by restoring test vectors from Tj into other sequences, allowing them to detect the faults that Tj detects. Experimental results for benchmark circuits demonstrate that the procedure reduces the number of sequences in a pool significantly. [ABSTRACT FROM PUBLISHER]

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses the behavior of a classical multiantenna GLRT test that allows to detect the presence of a known signal corrupted by a multipath propagation channel and by an additive temporally white Gaussian noise with unknown spatial covariance matrix. The paper is focused on the case where the number of sensors M is large, and of the same order of magnitude as the sample size N, a context which is modeled by the large system asymptotic regime M \rightarrow +\infty , N \rightarrow +\infty in such a way that M/N \rightarrow c for c \in (0,+\infty ). The purpose of this paper is to study the behaviour of a GLRT statistics in this regime, and to show that the corresponding theoretical analysis allows to accurately predict the performance of the test when M and N are of the same order of magnitude. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-aware recommendation algorithms that extend collaborative filtering (CF) approaches and utilize structured and unstructured latent contextual information. In particular, the first algorithm utilizes structured latent contexts and the second one combines the structured and the unstructured latent contextual representations. By using latent contextual information in a recommendation model, we capture and represent both the structure of the latent context in the form of a hierarchy and the values of contextual variables in the form of an unstructured vector. We tested the two proposed methods with two CF-based methods on several context-rich datasets under different experimental settings. We show that using hierarchical latent contextual representations leads to significantly better recommendations than the baselines for the datasets having high- and medium-dimensional contexts. Although this is not the case for the low-dimensional contextual data, the hybrid approach, combining structured and unstructured latent contextual information, significantly outperforms other baselines across all the experimental settings and dimensions of contextual data. [ABSTRACT FROM AUTHOR]

The linear model, in which a set of observations is assumed to be given by a linear combination of columns of a matrix (often termed a dictionary), has long been the mainstay of the statistics and signal processing literature. One particular challenge for inference under linear models is understanding the conditions on the dictionary under which reliable inference is possible. This challenge has attracted renewed attention in recent years, since many modern inference problems (e.g., high-dimensional statistics and compressed sensing) deal with the underdetermined setting, in which the number of observations is much smaller than the number of columns in the dictionary. This paper makes several contributions for this setting when the set of observations is given by a linear combination of a small number of groups of columns of the dictionary, termed the block-sparse case. First, it specifies conditions on the dictionary under which most block submatrices of the dictionary (often termed block subdictionaries) are well conditioned. This result is fundamentally different from prior work on block-sparse inference because: 1) it provides conditions that can be explicitly computed in polynomial time; 2) the given conditions translate into near-optimal scaling of the number of columns of the block subdictionaries as a function of the number of observations for a large class of dictionaries; and 3) it suggests that the spectral norm, rather than the column/block coherences of the dictionary, fundamentally limits the scaling of dimensions of the well-conditioned block subdictionaries. Second, in order to help understand the significance of this result in the context of block-sparse inference, this paper investigates the problems of block-sparse recovery and block-sparse regression in underdetermined settings. In both of these problems, this paper utilizes its result concerning conditioning of block subdictionaries and establishes that near-optimal block-sparse recovery and block-sparse regression is possible for a large class of dictionaries as long as the dictionary satisfies easily computable conditions and the coefficients describing the linear combination of groups of columns can be modeled through a mild statistical prior. Third, the paper reports extensive numerical experiments that highlight the effects of different measures of the dictionary in block-sparse inference problems. [ABSTRACT FROM PUBLISHER]

In order to tackle the incomplete and inaccurate of the samples in most tracking-by-detection algorithms, this paper presents an object tracking algorithm, termed as multi-scale spatio-temporal context (MSTC) learning tracking. MSTC collaboratively explores three different types of spatio-temporal contexts, named the long-term historical targets, the medium-term stable scene (i.e., a short continuous and stable video sequence), and the short-term overall samples to improve the tracking efficiency and reduce the drift phenomenon. Different from conventional multi-timescale tracking paradigm that chooses samples in a fixed manner, MSTC formulates a low-dimensional representation named fast perceptual hash algorithm to update long-term historical targets and the medium-term stable scene dynamically with image similarity. MSTC also differs from most tracking-by-detection algorithms that label samples as positive or negative, it investigates a fusion salient sample detection to fuse weights of the samples not only by the distance information, but also by the visual spatial attention, such as color, intensity, and texture. Numerous experimental evaluations with most state-of-the-art algorithms on the standard 50 video benchmark demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper explains various security issues in the existing home automation systems and proposes the use of logic-based security algorithms to improve home security. This paper classifies natural access points to a home as primary and secondary access points depending on their use. Logic-based sensing is implemented by identifying normal user behavior at these access points and requesting user verification when necessary. User position is also considered when various access points changed states. Moreover, the algorithm also verifies the legitimacy of a fire alarm by measuring the change in temperature, humidity, and carbon monoxide levels, thus defending against manipulative attackers. The experiment conducted in this paper used a combination of sensors, microcontrollers, Raspberry Pi and ZigBee communication to identify user behavior at various access points and implement the logical sensing algorithm. In the experiment, the proposed logical sensing algorithm was successfully implemented for a month in a studio apartment. During the course of the experiment, the algorithm was able to detect all the state changes of the primary and secondary access points and also successfully verified user identity 55 times generating 14 warnings and 5 alarms. [ABSTRACT FROM PUBLISHER]

The capacity of the AWGN broadcast channel is achieved by superposition coding, but the superposition of individual coded modulations expands the modulation alphabet and distorts its configuration. Hierarchical modulations, which appear in the literature mostly in the context of unequal error protection, can approach only a few isolated points on the boundary of the broadcast capacity region. This paper studies multilevel coding (MLC) for constellation-constrained broadcast-coded modulation. The conditions under which multilevel codes can achieve the constellation-constrained capacity of the AWGN broadcast channel are derived. We propose a pragmatic multilevel design technique with near-constellation-constrained-capacity performance where the coupling of the superposition inner and outer codes is localized to each bit-level. It is shown that this can be further relaxed to a code coupling on only one bit-level, with a little or no penalty under natural labeling. The rate allocation problem between the bit levels of the two users is studied and a pragmatic method is proposed, again with near-capacity performance. In further pursuit of lower complexity, a hybrid MLC-bit-interleaved-coded modulation is proposed, whose performance is shown to be very close to the boundary of the constellation-constrained capacity region. Simulation results show that good point-to-point LDPC codes produce excellent performance in the proposed coded modulation framework. [ABSTRACT FROM AUTHOR]

High-level synthesis (HLS) promises to increase designer productivity in the face of increasing field-programmable gate array sizes, and broaden the market of use, allowing software designers to reap the benefits of hardware implementation. One roadblock to HLS adoption is the lack of an in-system debugging infrastructure. Although designers can run their software code on a workstation, or simulate the register-transfer level, neither can reliably capture the behaviors, and therefore bugs, that may be present in the final system. Debugging hardware circuits in-system requires using signal-tracing to record circuit behavior for later offline analysis. In this paper, we present a debugging architecture, which automatically records key hardware signals, and relates them back to the original software source code. This architecture allows designers to debug HLS circuits in-system, in the context of the original source code. We present several signal-tracing techniques, tailored to HLS circuits, which allow a much longer execution trace to be captured. These techniques include signal compression, dynamically changing which signals are recorded cycle-by-cycle, and offline signal restoration. Compared to using an embedded logic analyzer to perform signal-tracing, our architecture increases the length of execution trace that can be recorded by 127X. For each 100 Kb of trace buffer memory, our architecture can record 15 369 executed lines of C code. [ABSTRACT FROM AUTHOR]

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1 $\beta$ products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems. [ABSTRACT FROM PUBLISHER]

This paper examines the close interplay between cooperation and adaptation for distributed detection schemes over fully decentralized networks. The combined attributes of cooperation and adaptation are necessary to enable networks of detectors to continually learn from streaming data and to continually track drifts in the state of nature when deciding in favor of one hypothesis or another. The results in this paper establish a fundamental scaling law for the steady-state probabilities of miss detection and false alarm in the slow adaptation regime, when the agents interact with each other according to distributed strategies that employ small constant step-sizes. The latter are critical to enable continuous adaptation and learning. This paper establishes three key results. First, it is shown that the output of the collaborative process at each agent has a steady-state distribution. Second, it is shown that this distribution is asymptotically Gaussian in the slow adaptation regime of small step-sizes. Third, by carrying out a detailed large deviations analysis, closed-form expressions are derived for the decaying rates of the false-alarm and miss-detection probabilities. Interesting insights are gained from these expressions. In particular, it is verified that as the step-size $\mu $ decreases, the error probabilities are driven to zero exponentially fast as functions of $1/\mu $ , and that the exponents governing the decay increase linearly in the number of agents. It is also verified that the scaling laws governing the errors of detection and the errors of estimation over the network behave very differently, with the former having exponential decay proportional to $1/\mu $ , while the latter scales linearly with decay proportional to $\mu $ . Moreover, and interestingly, it is shown that the cooperative strategy allows each agent to reach the same detection performance, in terms of detection error exponents, of a centralized stochastic-gradient solution. The results of this paper are illustrated by applying them to canonical distributed detection problems. [ABSTRACT FROM PUBLISHER]

As agent-based models (ABMs) are applied to various domains, the efficiency of model development has become an important issue in its applications. The current practice is that many models are developed from scratch, while they could have been built by reusing existing models. Moreover, when models need reconfiguration, they often need to be rebuilt significantly. These problems reduce the development efficiency and ultimately damage the efficacy of ABM. This paper partially resolves the challenges of model reusability from the systems engineering approach. Specifically, we propose a formalism-based ABM development and demonstrate its potential to promote model reuses. Our formalism, named large-scale, dynamic, extensible, and flexible (LDEF) formalism, encourages the building of a larger model by the composition of modularly developed components. Also, LDEF is tailored to the ABM contexts to represent the agent’s action procedure and support the dynamic changes of their interactions. This paper shows that LDEF improves the model reusability in ABM development through its practical examples and theoretical discussions. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of cooperative localization in mobile networks. In static networks, node locations can be obtained from pairwise distance measurements using the classical multidimensional scaling (MDS) approach. This paper introduces a modified MDS framework that also incorporates relative velocity measurements if available in mobile networks. The proposed cost function is minimized via a low complexity majorization algorithm. A distributed variant of the proposed algorithm is also outlined for use in large resource-constrained multi-hop networks. The proposed algorithm incurs low computational and communication cost, and handles practical constraints such as missing measurements and variable node velocities. Simulation results corroborate the performance gains obtained by the proposed algorithm over state-of-the-art localization algorithms. [ABSTRACT FROM AUTHOR]

This work deals with the satisfaction of the quality of experience (QoE) requirements in the perspective of the emerging future Internet framework. The evolution of the Internet is pointing out its limitations, which are likely to hinder its potential. In this respect, this paper introduces an innovative approach to cope with some key limitations of the present communication networks. In particular, the need of efficiently utilizing the available network resources and of guaranteeing the user expectations in terms of QoE requires a full cognitive approach, which is realized by the introduction of a novel architecture design, the so-called future Internet core platform. The future Internet core platform aims at bringing together the applications world with the network world, hence introducing a further cognitive level while enabling a new generation of applications: network-aware applications. This paper is concerned with an important aspect of the intelligent connectivity between applications and network: the service class association, which, if performed with a cognitive approach, can yield some important improvements and advantages in the emerging information era. The key idea presented in this paper is a real-time dynamic control procedure for the selection of the optimal service class. The approach is based on theoretical considerations validated by a proof-of-concept simulation. [ABSTRACT FROM PUBLISHER]

Information retrieval in associative memories was studied in a recent paper by Yaakobi and Bruck (2012). Associations between memory entries give us the t -neighbourhood of an entry. In their model, an information unit is retrieved from the memory with the aid of input clues, which are chosen from a reference set. In this paper, we consider the situation where the information unit is found unambiguously using the associated t -neighbourhoods of the input clues. A varying number of input clues are allowed, but a limit mu on the maximum number of them is imposed. Of course, we would like mu to be as small as possible. We consider the problem over the binary Hamming space \mathbb F^n and focus on the minimum of mu , denoted by $\nu (n;t)$ . Using linear reference sets, we show that $\nu (n;2) \leq 5$ for any $n \geq 9$ . We also give infinite families of reference sets, which provide good bounds on $\nu (n;t)$ for $t=3$ . In addition, efficient methods are given to obtain bounds on $\nu (n;t)$ for any $t$ from known reference sets. We also discuss the applications of this model to the Levenshtein’s sequence reconstruction problem and the sensor network monitoring. [ABSTRACT FROM AUTHOR]

This paper elucidates the underlying structures of \ell p -regularized least squares problems in the nonconvex case of 0

The problem of optimal placement of sensors for monitoring a spatial network (e.g., a road network with moving ground targets or intruders) is considered in this paper. In particular, the optimization of locations of a set of sensors (that can each obtain measurements in a local region around the sensor location) is considered so as to maximize an overall sensor coverage metric defined over the spatial network. The sensor coverage optimality metric for spatial network coverage is based on a novel formulation of a sensor influence wave based on a spatiotemporal model of the measurement reach of a set of sensors given a spatial network topology, a probabilistic model of target movements on the network, and spatial weight maps that model the relative importance/utility of different locations in the spatial region. The sensor placement optimization is based on an iterative genetic algorithm for the optimization of a scalar metric computed from the spatial integration of the sensor influence wave. The efficacy of the proposed approach is demonstrated through simulation studies for several road network geometries.

Note to Practitioners—This paper considers the problem of finding optimal locations for sensors for monitoring a spatial network for moving targets (e.g., a road network with moving ground targets or intruders). Sensor-based monitoring of a spatial region is relevant in a variety of applications (including, in general, such diverse application areas as traffic monitoring in transportation applications, intruder monitoring, surveillance, power systems monitoring, structural health monitoring, etc). This paper offers two primary novel aspects: an optimality metric formulation for target monitoring effectiveness on spatial networks and an iterative genetic algorithm-based method for optimization of the sensor placement configuration. The proposed approach enables addressing of multiple spatial criteria within a unified framework, including the probabilistic characterizations of spatial target movements over the network and the models of relative importance/utility of different locations/areas in the spatial region. [ABSTRACT FROM AUTHOR]

Representing, manipulating, and inferring trust from the user point of view certainly is a grand challenge in virtual worlds, including online games. When someone meets an unknown individual, the question is “Can I trust him/her or not?” This requires the user to have access to a representation of trust about others, as well as a set of operators to undertake inference about the trustability of other users/players. In this paper, we employ a trust representation generated from in-world data in order to feed individual trust decisions. To achieve that purpose, we assume that such a representation of trust already exists; in fact, it was proposed in another paper of ours. Thus, the focus here is on the trust mechanisms required to infer trustability of other users/players. More specifically, we use an individual trust representation deployed as a trust network as base to the inference mechanism that employs two subjective logic operators (consensus and discount) to automatically derive trust decisions. The proposed trust inference system has been validated through OpenSimulator scenarios, which has led to a 5% increase on trustability of avatars in relation to the reference scenario (without trust). [ABSTRACT FROM PUBLISHER]

Robust design optimization aims to find solutions that are competent and reliable under given uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this paper focuses on problems where uncertainties emanate from design variables. In commercial designs, being reliable is often of more practical value than being globally best (but unreliable). Robust optimization poses three key challenges: 1) appropriate formulation of the problem ; 2) accurate estimation of the “robustness” measure; and 3) efficient means to identify the set of tradeoff robust solutions with an affordable computational cost. In this paper, four different problem formulations for robust optimization are presented and analyzed. The proposed formulations offer a set of tradeoff solutions with robustness from two perspectives—feasibility robustness, i.e., robustness against failure and performance robustness, i.e., robustness assuring good performance. The approach also provides means to identify critical constraints or performance functions that affect the overall robustness. The problem is posed as a many-objective optimization problem and a decomposition-based evolutionary approach is used for solving it. The performance of the proposed approach and the consequences of using different formulations are illustrated using two numerical examples and four engineering problems. [ABSTRACT FROM PUBLISHER]

The capability to extract human activity context in a room environment can be used as meaningful feedback for various wireless indoor application systems. Being able to do so with easily installable resource-limited sensing components can even further increase the system’s applicability for various purposes. This paper introduces our efforts to design a system consisting of heterogeneous low-cost, resource-limited, wireless sensing platforms for accurately extracting the human activity context from an indoor environment. Specifically, we introduce Resource Limited Sensor-based activity Context Extraction (ReLiSCE), a system consisting of microphone array, passive infra-red (PIR), and illumination sensors that effectively detect the activities that occur in an office (meeting room) environment. The signal processing schemes used in ReLiSCE are designed so that their size and complexity is suitable for the resource limitations that many embedded computing platforms introduce. Using empirical evaluations with a prototype system, we show that despite the simplicity of its data processing schemes, ReLiSCE successfully classifies human activity states in various meeting scenarios. Furthermore, we show that high accuracy is achieved by combining results from heterogeneous sensors. We foresee this paper as a sub-system that interconnects with various application systems for autonomously configuring people’s everyday living environments in a more comfortable and energy-efficient manner. [ABSTRACT FROM AUTHOR]

This paper studies the newsvendor problem in the presence of consumer behavior, specifically, social interaction. We show that deterministic consumer valuation on products derived from social interaction can be an advantage for firms. This paper examines the implications of random consumer product valuation and a lower threshold number of subscribers to the proposed deal. Several implications have been yielded. [ABSTRACT FROM PUBLISHER]