title: Operating conditions-induced changes in product yield and characteristics during thermal-conversion of peanut shell to biochar in relation to economic analysis

abstract: Abstract The influences of pyrolysis conditions on the products yield distribution and physico-chemical characteristics of biochar derived from peanut shell in a fixed-bed reactor were investigated in this study. The pyrolysis conditions included the pyrolysis temperature (300–700 °C), retention time (15–90 min), heating rate (1–10 °C min−1), gas flow rate (20–200 mL min−1) and feedstock particle size (


A Genomic Score Prognostic of Outcome in Trauma Patients


title: Lux0R: Detection of Malicious PDF-embedded JavaScript code through Discriminant Analysis of API References

abstract: JavaScript is a dynamic programming language adopted in a variety of applications, including web pages, PDF Readers, widget engines, network platforms, office suites. Given its widespread presence throughout different software platforms, JavaScript is a primary tool for the development of novel -rapidly evolving- malicious exploits. If the classical signature- and heuristic-based detection approaches are clearly inadequate to cope with this kind of threat, machine learning solutions proposed so far suffer from high false-alarm rates or require special instrumentation that make them not suitable for protecting end-user systems. In this paper we present Lux0R "Lux 0n discriminant References", a novel, lightweight approach to the detection of malicious JavaScript code. Our method is based on the characterization of JavaScript code through its API references, i.e., functions, constants, objects, methods, keywords as well as attributes natively recognized by a JavaScript Application Programming Interface (API). We exploit machine learning techniques to select a subset of API references that characterize malicious code, and then use them to detect JavaScript malware. The selection algorithm has been thought to be "secure by design" against evasion by mimicry attacks. In this investigation, we focus on a relevant application domain, i.e., the detection of malicious JavaScript code within PDF documents. We show that our technique is able to achieve excellent malware detection accuracy, even on samples exploiting never-before-seen vulnerabilities, i.e., for which there are no examples in training data. Finally, we experimentally assess the robustness of Lux0R against mimicry attacks based on feature addition.


title: Rumor Detection with Hierarchical Social Attention Network

abstract: Microblogs have become one of the most popular platforms for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated on social networks, which could cause huge panic and threat during its propagation. In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts. Specifically, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM). We first build a hierarchical bidirectional long short-term memory model for representation learning. Then, the social contexts are incorporated into the network via attention mechanism, such that important semantic information is introduced to the framework for more robust rumor detection. Experimental results on two real world datasets demonstrate that the proposed method outperforms several state-of-the-arts in both rumor detection and early detection scenarios.


title: "Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data

abstract: The success of applications that process data critically depends on the quality of the ingested data. Completeness of a data source is essential in many cases. Yet, most missing value imputation approaches suffer from severe limitations. They are almost exclusively restricted to numerical data, and they either offer only simple imputation methods or are difficult to scale and maintain in production. Here we present a robust and scalable approach to imputation that extends to tables with non-numerical values, including unstructured text data in diverse languages. Experiments on public data sets as well as data sets sampled from a large product catalog in different languages (English and Japanese) demonstrate that the proposed approach is both scalable and yields more accurate imputations than previous approaches. Training on data sets with several million rows is a matter of minutes on a single machine. With a median imputation F1 score of 0.93 across a broad selection of data sets our approach achieves on average a 23-fold improvement compared to mode imputation. While our system allows users to apply state-of-the-art deep learning models if needed, we find that often simple linear n-gram models perform on par with deep learning methods at a much lower operational cost. The proposed method learns all parameters of the entire imputation pipeline automatically in an end-to-end fashion, rendering it attractive as a generic plugin both for engineers in charge of data pipelines where data completeness is relevant, as well as for practitioners without expertise in machine learning who need to impute missing values in tables with non-numerical data.


title: Video-to-Video Translation with Global Temporal Consistency

abstract: Although image-to-image translation has been widely studied, the video-to-video translation is rarely mentioned. In this paper, we propose an unified video-to-video translation framework to accom- plish different tasks, like video super-resolution, video colouriza- tion, and video segmentation, etc. A consequent question within video-to-video translation lies in the flickering appearance along with the varying frames. To overcome this issue, a usual method is to incorporate the temporal loss between adjacent frames in the optimization, which is a kind of local frame-wise temporal con- sistency. We instead present a residual error based mechanism to ensure the video-level consistency of the same location in different frames (called (lobal temporal consistency). The global and local consistency are simultaneously integrated into our video-to-video framework to achieve more stable videos. Our method is based on the GAN framework, where we present a two-channel discrimina- tor. One channel is to encode the video RGB space, and another is to encode the residual error of the video as a whole to meet the global consistency. Extensive experiments conducted on different video- to-video translation tasks verify the effectiveness and flexibleness of the proposed method.


title: Millimeter-wave automotive radar with PLL-generated ramps modelling by using SystemVue and MATLAB

abstract: Millimeter wave automotive radar is one of the most important components in the advanced driver assistance system (ADAS), which detects objects around the ego vehicle with moderate cost. Because automotive radar is composed of a vast variety of different components including RF, analog and digital circuits and radar signal processing software, modelling the whole radar system is challenging. This paper presents modelling a whole 24GHz automotive radar by using SystemVue and MATLAB. The ramps of the radar are generated by a phase locked loop. The radar radio frequency frontend and the radar target echo signal are modelled in SystemVue and the radar signal processing is taken place in MATLAB. Influences of the real RF signal source on the radar performance of object detection can be analyzed.


title: Adaptive relevance feedback in information retrieval

abstract: Relevance Feedback has proven very effective for improving retrieval accuracy. A difficult yet important problem in all relevance feedback methods is how to optimally balance the original query and feedback information. In the current feedback methods, the balance parameter is usually set to a fixed value across all the queries and collections. However, due to the difference in queries and feedback documents, this balance parameter should be optimized for each query and each set of feedback documents. In this paper, we present a learning approach to adaptively predict the optimal balance coefficient for each query and each collection. We propose three heuristics to characterize the balance between query and feedback information. Taking these three heuristics as a road map, we explore a number of features and combine them using a regression approach to predict the balance coefficient. Our experiments show that the proposed adaptive relevance feedback is more robust and effective than the regular fixed-coefficient feedback.


title: Clothing and People - A Social Signal Processing Perspective

abstract: In our society and century, clothing is not anymore used only as a means for body protection. Our paper builds upon the evidence, studied within the social sciences, that clothing brings a clear communicative message in terms of social signals, influencing the impression and behaviour of others towards a person. In fact, clothing correlates with personality traits, both in terms of self-assessment and assessments that unacquainted people give to an individual. The consequences of these facts are important: the influence of clothing on the decision making of individuals has been investigated in the literature, showing that it represents a discriminative factor to differentiate among diverse groups of people. Unfortunately, this has been observed after cumbersome and expensive manual annotations, on very restricted populations, limiting the scope of the resulting claims. With this position paper, we want to sketch the main steps of the very first systematic analysis, driven by social signal processing techniques, of the relationship between clothing and social signals, both sent and perceived. Thanks to human parsing technologies, which exhibit high robustness owing to deep learning architectures, we are now capable to isolate visual patterns characterising a large types of garments. These algorithms will be used to capture statistical relations on a large corpus of evidence to confirm the sociological findings and to go beyond the state of the art.


title: Evaluating effect, composite, and causal indicators in structural equation models

abstract: Although the literature on alternatives to effect indicators is growing, there has been little attention given to evaluating causal and composite (formative) indicators. This paper provides an overview of this topic by contrasting ways of assessing the validity of effect and causal indicators in structural equation models (SEMs). It also draws a distinction between composite (formative) indicators and causal indicators and argues that validity is most relevant to the latter. Sound validity assessment of indicators is dependent on having an adequate overall model fit and on the relative stability of the parameter estimates for the latent variable and indicators as they appear in different models. If the overall fit and stability of estimates are adequate, then a researcher can assess validity using the unstandardized and standardized validity coefficients and the unique validity variance estimate. With multiple causal indicators or with effect indicators influenced by multiple latent variables, collinearity diagnostics are useful. These results are illustrated with a number of correctly and incorrectly specified hypothetical models.


title: Comparing anomaly-detection algorithms for keystroke dynamics

abstract: Keystroke dynamics-the analysis of typing rhythms to discriminate among users-has been proposed for detecting impostors (i.e., both insiders and external attackers). Since many anomaly-detection algorithms have been proposed for this task, it is natural to ask which are the top performers (e.g., to identify promising research directions). Unfortunately, we cannot conduct a sound comparison of detectors using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. We collected data from 51 subjects typing 400 passwords each, and we implemented and evaluated 14 detectors from the keystroke-dynamics and pattern-recognition literature. The three top-performing detectors achieve equal-error rates between 9.6% and 10.2%. The results-along with the shared data and evaluation methodology-constitute a benchmark for comparing detectors and measuring progress.


title: A Chaotic Levy Flight Bat Algorithm for Parameter Estimation in Nonlinear Dynamic Biological Systems

abstract: We propose a synergistic approach to meta-heuristic search optimization algorithm. The fine balance between intensification (exploitation) and diversification (exploration) is very important to the overall efficiency and performance of a meta-heuristic search algorithm. Too little exploration and too much exploitation could cause the system to be trapped in local optima, which makes it very difficult or even impossible to find the global optimum. The diversification via randomization provides a good way to move away from local search to the search on the global scale and avoids the solutions being trapped at local optima, while increases the diversity of the solutions. The good combination of these two major components will usually ensure that the global optimality is achievable. It is worth pointing that the use of a uniform distribution is not the only way to achieve randomization. In fact, random walks such as Levy flights on a global scale are more efficient. The track of chaotic variable can travel ergodically over the whole search space. In general, the chaotic variable has special characters, i.e., ergodicity, pseudo-randomness and irregularity. To enrich the searching behavior and to avoid being trapped into local optimum, chaotic sequence and a chaotic Levy flight are incorporated in the metaheuristic search for efficiently generating new solutions. We presented synergistic strategies for meta-heuristic optimization learning, with an emphasis on the balance between intensification and diversification. In this paper, we apply the proposed search optimization algorithm and describe a general methodology to adaptively select the values of the model parameters for the reconstruction of biological system dynamics. We illustrate the application of the method by jointly estimating the parameter vector of the dynamics of endocytosis.


title: Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups

abstract: This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.


title: Multi-Task Transfer Methods to Improve One-Shot Learning for Multimedia Event Detection.

abstract: This paper proposes a new multi-task learning method with implicit intertask relevance estimation, and applies it to complex Internet video event detection, which is a challenging and important problem in practice, yet seldom has been addressed. In this paper, “detection” means to detect videos corresponding to the event of interest from a (large) video dataset, not to localize the event spatially or temporally in a video. In the problem definition, one positive and plenty of negative samples of one event are given as training data, and the goal is to return the videos of the same event from a large video dataset. In addition, we assume samples of other events are available. Fig. 1 shows an overview of the proposed methods. The widths of the lines between the one-exemplar event and others represent the inter-event relevance, which is unknown a priori in our problem settings. However, the proposed method can implicity infer the relevance and utilize the most relevant event(s) more in multi-task learning, where the shared information from the relevant events helps to build a better model from the one exemplar. The proposed method does not assume the relevance between other events, as indicated by the red line. Although the learning algorithm outputs models of all input events, only that of the one-exemplar event is applied to detect videos of the event of interest from the video set. Our method builds on the approach of graph-guided multi-task learning [1], which is described first. The training set {(xti,yti)∈R×{−1,+1}, t = 1,2, . . . ,T, i = 1,2, . . . ,Nt} is grouped into T related tasks, which are further organized as a graph G = . The tasks correspond to the elements in the vertex set V , and the pairwise relevance between Task t and k are represented by the weight rtk on edges etk ∈ E. The more relevant the two tasks are, the larger the edge weight is. The graph guided multi-task learning algorithm learns the corresponding T models jointly, by solving the optimization problem


Correspondence Measures for MT Evaluation


title: OBSERVE: Occupancy-based system for efficient reduction of HVAC energy

abstract: Heating, cooling and ventilation accounts for 35% energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This paper shows how real time occupancy data from a wireless sensor network can be used to create occupancy models which in turn can be integrated into building conditioning system for usage based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42% annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning (ASHRAE) comfort standards.


title: Systems Chronotherapeutics

abstract: Chronotherapeutics aim at treating illnesses according to the endogenous biologic rhythms, which moderate xenobiotic metabolism and cellular drug response. The molecular clocks present in individual cells involve approximately fifteen clock genes interconnected in regulatory feedback loops. They are coordinated by the suprachiasmatic nuclei, a hypothalamic pacemaker, which also adjusts the circadian rhythms to environmental cycles. As a result, many mechanisms of diseases and drug effects are controlled by the circadian timing system. Thus, the tolerability of nearly 500 medications varies by up to fivefold according to circadian scheduling, both in experimental models and/or patients. Moreover, treatment itself disrupted, maintained, or improved the circadian timing system as a function of drug timing. Improved patient outcomes on circadian-based treatments (chronotherapy) have been demonstrated in randomized clinical trials, especially for cancer and inflammatory diseases. However, recent technological advances have highlighted large interpatient differences in circadian functions resulting in significant variability in chronotherapy response. Such findings advocate for the advancement of personalized chronotherapeutics through interdisciplinary systems approaches. Thus, the combination of mathematical, statistical, technological, experimental, and clinical expertise is now shaping the development of dedicated devices and diagnostic and delivery algorithms enabling treatment individualization. In particular, multiscale systems chronopharmacology approaches currently combine mathematical modeling based on cellular and whole-body physiology to preclinical and clinical investigations toward the design of patient-tailored chronotherapies. We review recent systems research works aiming to the individualization of disease treatment, with emphasis on both cancer management and circadian timing system-resetting strategies for improving chronic disease control and patient outcomes.


title: Multi-Perspective Context Matching for Machine Comprehension

abstract: Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models. Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage. Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question. Then, we encode the question and weighted passage by using bi-directional LSTMs. For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector. Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points. Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.


title: [The Spanish version of the Newcastle Asthma Knowledge Questionnaire for parents of children with asthma (NAKQ). Transcultural adaptation and reliability analysis].

abstract: INTRODUCTION ::: The Newcastle Asthma Knowledge Questionnaire for parents of children with asthma (NAKQ) has proven to be a valid instrument for asthma knowledge assessment of patients and their parents. The widespread use of the SRS-22 in non-English-speaking countries requires its transcultural adaptation. Our objective was to obtain a version of the NAKQ questionnaire adapted to Spanish and to analyze its validity and reliability. ::: ::: ::: MATERIAL AND METHODS ::: The Spanish version was obtained by using the forward/back-translation method with expert, bilingual translators. The questionnaire was administered to 157 parents with high knowledge (n = 78) and low knowledge (n = 79) of asthma. The differences in the number of answers between both groups were analyzed with the chi(2) test. Internal consistency was determined with Cronbach's alpha coefficient and test-retest reliability with the tau-b of Kendall correlation coefficient, and kappa statistics. ::: ::: ::: RESULTS ::: At the end of the process, 23 of the 31 items were classified as of total equivalence and eight ase of moderate equivalence. The mean score of parents high knowledge was 23 +/- 2.94 and the mean score of parents low knowledge was 16.84 +/- 2.56. The difference between the two groups was statistically significant (p < 0.001). The overall Cronbach's alpha coefficient of the questionnaire was 0.72, the Kendall's tau-b 0,8573 was significant (p = 0.01) and the kappa index showed good or very good consistency in more than half of the items. ::: ::: ::: CONCLUSIONS ::: The Spanish version of the NAKQ has proven to be acceptable and culturally equivalent to the original version and it has a good degree of consistency, validity and reliability.


title: Comparing images using color coherence vectors

abstract: Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very different appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture with a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. We classify each pixel in a given color bucket as either coherent or incoherent, based on whether or not it is part of a large similarly-colored region. A color coherence vector (CCV) stores the number of coherent versus incoherent pixels with each color. By separating coherent pixels from incoherent pixels, CCV’s provide finer distinctions than color histograms. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV’s in under 2 seconds. We show that CCV’s can give superior results to color his∗To whom correspondence should be addressed tograms for image retrieval.


title: A wideband millimeter-wave frequency doubler-tripler in 0.13-µm CMOS

abstract: A combined frequency doubler and tripler is proposed for wideband millimeter wave frequency generation in CMOS. The circuit consists of a push-push FET frequency doubler along with a single-balanced mixer based frequency tripler. The frequency doubler-tripler can generate frequencies in the range of 23–48 GHz with more than −20dBm output power into 50Ω. The conversion gains of the doubler and tripler are measured to be −2.6dB and −12.3 dB, respectively, with a 0dBm input at 14.4GHz. Fabricated in 0.13-µm CMOS, the circuit has an active area of 600×440 µm2. The frequency multipliers consume 12.6mW dc power from 1.2V supply, while the output buffers consume 11.9mW.


title: Architecture evaluation for power-efficient FPGAs

abstract: This paper presents a flexible FPGA architecture evaluation framework, named fpgaEVA-LP, for power efficiency analysis of LUT-based FPGA architectures. Our work has several contributions: (i) We develop a mixed-level FPGA power model that combines switch-level models for interconnects and macromodels for LUTs; (ii) We develop a tool that automatically generates a back-annotated gate-level netlist with post-layout extracted capacitances and delays; (iii) We develop a cycle-accurate power simulator based on our power model. It carries out gate-level simulation under real delay model and is able to capture glitch power; (iv) Using the framework fpgaEVA-LP, we study the power efficiency of FPGAs, in 0.10um technology, under various settings of architecture parameters such as LUT sizes, cluster sizes and wire segmentation schemes and reach several important conclusions. We also present the detailed power consumption distribution among different FPGA components and shed light on the potential opportunities of power optimization for future FPGA designs (e.g., ≤: 0.10um technology).


title: Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text

abstract: We present the time-dependent topic-cluster model, a hierarchical approach for combining Latent Dirichlet Allocation and clustering via the Recurrent Chinese Restaurant Process. It inherits the advantages of both of its constituents, namely interpretability and concise representation. We show how it can be applied to streaming collections of objects such as real world feeds in a news portal. We provide details of a parallel Sequential Monte Carlo algorithm to perform inference in the resulting graphical model which scales to hundred of thousands of documents.


title: The physics of spreading processes in multilayer networks

abstract: The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of complex systems, which often include different types of relationships (i.e.,"multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and be a major obstacle towards attempts to understand complex systems. The recent"multilayer"approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remain hidden when using ordinary graphs, the traditional network representation. Here we survey progress towards attaining a deeper understanding of spreading processes on multilayer networks, and we highlight some of the physical phenomena related to spreading processes that emerge from multilayer structure.


title: A Comprehensive Survey on Bengali Phoneme Recognition

abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed. Comparison among these methods is discussed.


title: A Deep Network with Visual Text Composition Behavior

abstract: While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.


title: Dynamic Pricing on the Internet: Importance and Implications for Consumer Behavior

abstract: The pricing of products and services sold over the Internet channel is becoming more dynamic. In part this is due to the increasing use of auction models in business and consumer markets to sell commodities, excess inventories, used merchandise, rare items collectibles, and other items. Marketers are resorting to dynamic prices even for goods and services sold at posted prices, spurred partly by the lower menu cost of changing prices on the Internet and partly as a response to consumer use of price-comparison bots. This paper explains the relevance of dynamic pricing in the digital economy by comparing the physical value chain with the virtual-information-based value chain. It explores the implications of certain aspects of dynamic pricing in consumer markets (e.g., dynamic pricing of posted prices, reverse auction pricing of goods and services as used by Priceline) from the perspective of consumer price expectations, the role of information and consumer learning, and their impact on consumer responses to prices across different product categories. Several propositions are developed, and issues for research are identified.


title: Variations in normal color vision. VI. Factors underlying individual differences in hue scaling and their implications for models of color appearance

abstract: Observers with normal color vision vary widely in their judgments of color appearance, such as the specific spectral stimuli they perceive as pure or unique hues. We examined the basis of these individual differences by using factor analysis to examine the variations in hue-scaling functions from both new and previously published data. Observers reported the perceived proportion of red, green, blue or yellow in chromatic stimuli sampling angles at fixed intervals within the LM and S cone-opponent plane. These proportions were converted to hue angles in a perceptual-opponent space defined by red vs. green and blue vs. yellow axes. Factors were then extracted from the correlation matrix using PCA and Varimax rotation. These analyses revealed that inter-observer differences depend on seven or more narrowly-tuned factors. Moreover, although the task required observers to decompose the stimuli into four primary colors, there was no evidence for factors corresponding to these four primaries, or for opponent relationships between primaries. Perceptions of “redness” in orange, red, and purple, for instance, involved separate factors rather than one shared process for red. This pattern was compared to factor analyses of Monte Carlo simulations of the individual differences in scaling predicted by variations in standard opponent mechanisms, such as their spectral tuning or relative sensitivity. The observed factor pattern is inconsistent with these models and thus with conventional accounts of color appearance based on the Hering primaries. Instead, our analysis points to a perceptual representation of color in terms of multiple mechanisms or decision rules that each influence the perception of only a relatively narrow range of hues, potentially consistent with a population code for color suggested by cortical physiology.


title: Secure Crowdsourced Indoor Positioning Systems

abstract: Indoor positioning systems (IPSes) can enable many location-based services in large indoor environments where GPS is not available or reliable. Mobile crowdsourcing is widely advocated as an effective way to construct IPS maps. This paper presents the first systematic study of security issues in crowd-sourced WiFi-based IPSes to promote security considerations in designing and deploying crowdsourced IPSes. We identify three attacks on crowdsourced WiFi-based IPSes and propose the corresponding countermeasures. The efficacy of the attacks and also our countermeasures are experimentally validated on a prototype system. The attacks and countermeasures can be easily extended to other crowdsourced IPSes.


title: Robust model predictive control: A survey

abstract: This paper gives an overview of robustness in Model Predictive Control (MPC). After reviewing the basic concepts of MPC, we survey the uncertainty descriptions considered in the MPC literature, and the techniques proposed for robust constraint handling, stability, and performance. The key concept of “closedloop prediction” is discussed at length. The paper concludes with some comments on future research directions.


title: Measuring and evaluating large-scale CDNs

abstract: CDNs play a critical and central part of today’s Internet infrastructure. In this paper we conduct extensive and thorough measurements that accurately characterize the performance of two large-scale commercial CDNs: Akamai and Limelight. Our measurements include charting the CDNs (locating all their content and DNS servers), assessing their server availability, and quantifying their world-wide delay performance. Our measurement techniques can be adopted by CDN customers to independently evaluate the performance of CDN vendors. It can also be used by a new CDN entrant to choose an appropriate CDN design and to locate its servers. Based on the measurements, we shed light on two radically different design philosophies for CDNs: the Akamai design, whichenters deep into ISPs; and the Limelight design, which brings ISPs to home. We compare these two CDNs with regards to the numbers of their content servers, their internal DNS designs, the geographic locations of their data centers, and their DNS and content server delays. Furthermore, we study where Limelight can locate additional servers to reap the greatest delay performance gains. As a byproduct, we also evaluate Limelight’s use of IP anycast, and gain insight into a large-scale IP anycast production system.


Phenomenographic methods: A case illustration


title: Detecting video spammers in YouTube social media

abstract: Social media is any site that provides a network of people with a place to make connections.An example of the media is YouTube that connects people through video sharing.Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-promote their videos or broadcast unrelated content. Even though the detection of malicious users is based on various features such as content details, social activity, social network analyzing, or hybrid, the detection rate is still considered low (i.e. 46%).This study proposes a new set of features by constructing features based on the Edge Rank algorithm.Experiments were performed using nine classifiers of different learning; decision tree, function-based and Bayesian. The results showed that the proposed ::: video spammers detection feature set is beneficial as the highest accuracy (i.e average) is as high as 98% and the lowest was 74%.The proposed work ::: would benefit YouTube users as malicious users who are sharing non relevant content can be automatically detected.This is because system resources can be optimized as YouTube users are presented with the required ::: content only.


title: Comparative analysis of blockchain technologies and TOR network: Two faces of the same reality?

abstract: Blockchain technology is being considered as one of the ultimate revolutions that will be able to disrupt several pillars of our society. It is a public and distributed ledger built for security and interoperability. Blockchain provides all parties a secure and synchronized record of immutable transactions assembled together and permanently stored with a fingerprint, creating therefore an irreversible chain. In order to operate, this technology does not rely on any central authority. All transactions are sent over the network and the consensus is achieved by the mutual calculation and agreement. In this paper, we evaluate the blockchain technology and its evolution. Then, we characterize some essential features of the distributed ledger technologies (DLT) focusing on the three main blockchains actors: Bitcoin, Ethereum and Hyperledger. Besides, we present their security challenges and explore their drawbacks that can lead to use the blockchain network in order to conduct several attack scenarios. Finally, we describe their relationship with the onion router network (Tor) that beyond the malicious uses of the blockchain via Tor, these two networks share many common points.


title: On orchestrating virtual network functions

abstract: Middleboxes or network appliances like firewalls, proxies, and WAN optimizers have become an integral part of today's ISP and enterprise networks. Middlebox functionalities are usually deployed on expensive and proprietary hardware that require trained personnel for deployment and maintenance. Middleboxes contribute significantly to a network's capital and operational costs. In addition, organizations often require their traffic to pass through a specific sequence of middleboxes for compliance with security and performance policies. This makes the middlebox deployment and maintenance tasks even more complicated. Network Function Virtualization (NFV) is an emerging and promising technology that is envisioned to overcome these challenges. It proposes to move packet processing from dedicated hardware middleboxes to software running on commodity servers. In NFV terminology, software middleboxes are referred to as Virtual Network Functions (VNFs). It is a challenging problem to determine the required number and placement of VNFs that optimize network operational costs and utilization, without violating service level agreements. We call this the VNF Orchestration Problem (VNF-OP) and provide an Integer Linear Programming (ILP) formulation with implementation in CPLEX. We also provide a dynamic programming based heuristic to solve larger instances of VNF-OP. Trace driven simulations on real-world network topologies demonstrate that the heuristic can provide solutions that are within 1.3 times of the optimal solution. Our experiments suggest that a VNF based approach can provide more than 4 χ reduction in the operational cost of a network.


title: Biosynthesis of nanoparticles and silver nanoparticles

abstract: In this century, the development of nanotechnology is projected to be the establishment of a technological evolutionary of this modern era. Recently, nanotechnology is one of the most active subjects of substantial research in modern material sciences and hence metal nanoparticles have a great scientific interest because of their unique optoelectronic and physicochemical properties with applications in diverse areas such as electronics, catalysis, drug delivery, or sensing. Nanotechnology provides an understanding on fundamental properties of objects at the atomic, molecular, and supramolecular levels. Besides, nanotechnology also leads an alternative technological pathway for the exploration and revolution of biological entities, whereas biology provides role models and biosynthetic constituents to nanotechnology. The findings of this review are important to provide an alternative for the green synthesis of silver nanoparticles. It showed more cost-effective and environmental friendly application as well as easier for large production, with relation to the properties of silver nanoparticles as antimicrobial, can be served well as an alternative antiseptic agent in various fields. Typically, silver nanoparticles are smaller than 100 nm and consist of about 20–15,000 silver atoms. Due to the attractive physical and chemical properties of silver at the nanoscale, the development of silver nanoparticles is expanding in recent years and is nowadays significant for consumer and medical products.


title: Smart Health Monitoring Systems: An Overview of Design and Modeling

abstract: Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.


title: From big smartphone data to worldwide research: The Mobile Data Challenge

abstract: This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.


title: Event Forecasting with Pattern Markov Chains

abstract: We present a system for online probabilistic event forecasting. We assume that a user is interested in detecting and forecasting event patterns, given in the form of regular expressions. Our system can consume streams of events and forecast when the pattern is expected to be fully matched. As more events are consumed, the system revises its forecasts to reflect possible changes in the state of the pattern. The framework of Pattern Markov Chains is used in order to learn a probabilistic model for the pattern, with which forecasts with guaranteed precision may be produced, in the form of intervals within which a full match is expected. Experimental results from real-world datasets are shown and the quality of the produced forecasts is explored, using both precision scores and two other metrics: spread, which refers to the "focusing resolution" of a forecast (interval length), and distance, which captures how early a forecast is reported.


title: Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images

abstract: Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%–2% and help to eliminate “salt and pepper” phenomena and block effects.


title: Mindfully going omni-channel: An economic decision model for evaluating omni-channel strategies

abstract: Abstract In the digital age, customers want to define on their own how to interact with organizations during their customer journeys. Thus, many organizations struggle to implement an omni-channel strategy (OCS) that meets their customers' channel preferences and can be operated efficiently. Despite this high practical need, research on omni-channel management predominantly takes a descriptive perspective. What is missing is prescriptive knowledge that guides organizations in the valuation and selection of an appropriate OCS. Most existing studies investigate single facets of omni-channel management in detail while neglecting the big picture. They also require customer journeys to follow sequential and organization-defined purchase decision processes. To address this research gap, we propose an economic decision model that considers online and offline channels, the opening and closing of channels, non-sequential customer journeys, and customers' channel preferences. Drawing from the principles of value-based management, the decision model recommends choosing the OCS with the highest contribution to an organization's long-term firm value. We applied and validated the decision model based on real-world data from a German bank.


title: Improving Entity Linking by Modeling Latent Relations between Mentions

abstract: Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base. Entity linking systems often exploit relations between textual mentions in a document (e.g., coreference) to decide if the linking decisions are compatible. Unlike previous approaches, which relied on supervised systems or heuristics to predict these relations, we treat relations as latent variables in our neural entity-linking model. We induce the relations without any supervision while optimizing the entity-linking system in an end-to-end fashion. Our multi-relational model achieves the best reported scores on the standard benchmark (AIDA-CoNLL) and substantially outperforms its relation-agnostic version. Its training also converges much faster, suggesting that the injected structural bias helps to explain regularities in the training data.


title: A federated capability-based access control mechanism for internet of things (IoTs)

abstract: The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide intelligent services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird and Smart Cities, IoT also incurs more concerns on privacy and security. Among the top security challenges that IoTs face is that access authorization is critical in resource and information protection over IoTs. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanisms to meet requirement of IoT systems. The extraordinary large number of nodes, heterogeneity as well as dynamicity, necessitate more fine-grained, lightweight mechanisms for IoT devices. In this paper, a federated capability-based access control (FedCAC) framework is proposed to enable an effective access control processes to devices, services and information in large scale IoT systems. An identity-based capability token management strategy is presented, which involves registering, propagation and revocation of the access authorization. Through delegating centralized authorization decision-making policy to local domain delegator, the access authorization process is locally conducted on the service provider that integrates situational awareness (SAW) and customized contextual conditions. Implemented and tested on both resources-constrained devices, like smart sensors and Raspberry PI, and non-resource-constrained devices, like laptops and smart phones, our experimental results demonstrate the feasibility of the proposed FedCAC approach to offer a scalable, lightweight and fine-grained access control solution to IoT systems connected to a system network.


title: Nonlinear phase portrait modeling of fingerprint orientation

abstract: Fingerprint orientation is crucial for automatic fingerprint identification. However, recovery of orientation is still difficult especially in noisy region. A way to aid recovery of the orientation is to provide an orientation model. In this paper, an orientation model for the entire fingerprint orientation using high order phase portrait is suggested. Proper analysis of the orientation pattern at the singular point regions is provided. Then a low-order phase portrait near each of the singular point is added as constraint to the high-order phase portrait to provide accurate orientation modeling for the entire fingerprint image. The main advantage of the proposed approach is that the nonlinear model itself is able to model all type of fingerprint orientations completely. Experiments and visual analysis show the effectiveness of the proposed model.


title: Challenges in Combining SysML and MARTE for Model-Based Design of Embedded Systems

abstract: Using model-based approaches for designing embedded systems helps abstract away unnecessary details in a manner that increases the potential for easy validation and verification, and facilitates reuse and evolution. A common practice is to use UML as the base language, possibly specialized by the so-called profiles. Despite the ever increasing number of profiles being built in many domains, there is still insufficient focus on discussing the issue of combining multiple profiles. Indeed, a single profile may not be adequate to cover all aspects required in the multidisciplinary domain of embedded systems. In this paper, we assess possible strategies for combining the SysML and MARTE profiles in a common modelling framework, while avoiding specification conflicts. We show that, despite some semantic and syntactical overlapping, the two are highly complementary for specifying embedded systems at different abstraction levels. We conclude, however, that a convergence agenda is highly desirable to align some key language features.


Sport Marheting Quarterly, 2004,13,158-166, © 2004 West Virginia University Current Issues and Conceptualizations of Service Quality in the Recreation Sport Industry


title: Feature selection for nonlinear models with extreme learning machines

abstract: In the context of feature selection, there is a trade-off between the number of selected features and the generalisation error. Two plots may help to summarise feature selection: the feature selection path and the sparsity-error trade-off curve. The feature selection path shows the best feature subset for each subset size, whereas the sparsity-error trade-off curve shows the corresponding generalisation errors. These graphical tools may help experts to choose suitable feature subsets and extract useful domain knowledge. In order to obtain these tools, extreme learning machines are used here, since they are fast to train and an estimate of their generalisation error can easily be obtained using the PRESS statistics. An algorithm is introduced, which adds an additional layer to standard extreme learning machines in order to optimise the subset of selected features. Experimental results illustrate the quality of the presented method.


title: A simple and efficient estimator for hyperbolic location

abstract: An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives an explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, spherical-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than spherical-interpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamforming and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method. >


title: Energy-Autonomous, Flexible, and Transparent Tactile Skin

abstract: Tactile or electronic skin is needed to provide critical haptic perception to robots and amputees, as well as in wearable electronics for health monitoring and wellness applications. Energy autonomy of skin is a critical feature that would enable better portability and longer operation times. This study shows a novel structure, consisting of a transparent tactile sensitive layer based on single-layer graphene, and a photovoltaic cell underneath as a building block for energy-autonomous, flexible, and tactile skin. Transparency of the touch sensitive layer is considered a key feature to allow the photovoltaic cell to effectively harvest light. Moreover, ultralow power consumed by the sensitive layer (20 nW cm−2) further reduces the photovoltaic area required to drive the tactile skin. In addition to its energy autonomy, the fabricated skin is sensitive to touch, mainly because a transparent polymeric protective layer, spin-coated on the sensor's active area, makes the coplanar capacitor sensitive to touch, detecting minimum pressures of 0.11 kPa with a uniform sensitivity of 4.3 Pa−1 along a broad pressure range. Finally, the tactile skin patches are integrated on a prosthetic hand, and the responses of the sensors for static and dynamic stimuli are evaluated by performing tasks, ranging from simple touching to grabbing of soft objects.


title: User Requirements of Mobile Technology: A Summary of Research Results

abstract: As advanced mobile technology becomes more widespread, the impacts on professional environments and on the personal lives of individual users continue to increase. Devices, such as smart cell phones, personal digital assistants (PDAs), and laptop computers can free their owners of the need to remain close to a wired information system infrastructure that is provided in a stationary office environment, and provide the opportunity to perform tasks in a wide variety of use contexts. With changes in use context, however, come changes in requirements, such as the need to limit weight and size of a device. In order to achieve success in the form of adoption, use, and positive impacts on user performance, a thorough understanding is needed about the functional and non-functional technology requirements of mobile professionals. In this paper, we summarize the results of a series of research studies that we conducted to explore the technology requirements of mobile professionals. The research studies included a content analysis of online user reviews, two empirical surveys, and a series of user interviews. Our research findings indicate that (1) user-perceived technology maturity is a critical factor to explain and predict the use of mobile technology by mobile professionals; (2) mobile technology needs to be available in a broad variety of use-situations; (3) users require basic communication and productivity-related functionality, in particular to support non-routine and supervisory task profiles; and (4) mobile technology can have considerable impacts on the job performance and on the personal lives of its users. Our findings have implications for the design, management, and research of mobile information systems.


title: Emotions evoked by the sound of music: Characterization, classification, and measurement.

abstract: One reason for the universal appeal of music lies in the emotional rewards that music offers to its listeners. But what makes these rewards so special? The authors addressed this question by progressively characterizing music-induced emotions in 4 interrelated studies. Studies 1 and 2 (n 354) were conducted to compile a list of music-relevant emotion terms and to study the frequency of both felt and perceived emotions across 5 groups of listeners with distinct music preferences. Emotional responses varied greatly according to musical genre and type of response (felt vs. perceived). Study 3 (n 801)—a field study carried out during a music festival—examined the structure of music-induced emotions via confirmatory factor analysis of emotion ratings, resulting in a 9-factorial model of music-induced emotions. Study 4 (n 238) replicated this model and found that it accounted for music-elicited emotions better than the basic emotion and dimensional emotion models. A domain-specific device to measure musically induced emotions is introduced—the Geneva Emotional Music Scale.


title: Software-defined wireless mesh networks: architecture and traffic orchestration

abstract: SDN has been envisioned as the next generation network paradigm by decoupling control plane and data plane, such that network management and optimization can be conducted in a centralized manner using global network information. In this article, we study how to apply SDN concept to a wireless mesh network that has been widely adopted by various applications. We first propose a novel architecture of SD-WMNs, and identify several critical challenges. Since wireless spectrum is a scarce resource that is shared by both data and control traffic in SD-WMNs, we propose three spectrum allocation and scheduling algorithms, namely FB-NS, NFB-NS, and NFB-S that orchestrate both control and data traffic. Finally, performance is evaluated via extensive simulations.


title: Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health

abstract: Technology has a long track record of enabling and accelerating scientific discovery and innovation. Two prime examples are the telescope and the microscope; although these predate the industrial revolution, both can be found in today’s laboratories throughout the world and they remain at the center of our scientific endeavors. In a similar way, as scientists depend on these instruments for observing the natural world, most of us have come to depend on our smartphones as tools for organizing and navigating our daily lives. Our use of these devices generates, as a byproduct, a surprisingly rich tapestry of social and behavioral fingerprints. Given that these digital fingerprints reflect the lived experiences of people in their natural environments, with granular temporal resolution, it might be possible to leverage them to develop precise and temporally dynamic disease phenotypes and markers to diagnose and treat psychiatric and other illnesses.


title: Visualizing the non-visual: spatial analysis and interaction with information from text documents

abstract: The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.


title: A new look at habits and the habit–goal interface

abstract: The present model outlines the mechanisms underlying habitual control of responding and the ways in which habits interface with goals. Habits emerge from the gradual learning of associations between responses and the features of performance contexts that have historically covaried with them (e.g., physical settings, preceding actions). Once a habit is formed, perception of contexts triggers the associated response without a mediating goal. Nonetheless, habits interface with goals. Constraining this interface, habit associations accrue slowly and do not shift appreciably with current goal states or infrequent counterhabitual responses. Given these constraints, goals can (a) direct habits by motivating repetition that leads to habit formation and by promoting exposure to cues that trigger habits, (b) be inferred from habits, and (c) interact with habits in ways that preserve the learned habit associations. Finally, the authors outline the implications of the model for habit change, especially for the selfregulation of habit cuing.


title: A Versatile Resonant Tank Identification Methodology for Induction Heating Systems

abstract: Induction heating has become the most advance heating process due to benefits, such as efficiency, performance, cleanness, and safety. In this process, the electromagnetic coupling between the induction coil and the induction target is a key, determining the heating performance as well as the resonant power converter operation. This letter proposes an accurate and cost-effective resonant tank identification method applied to induction heating systems. It is based on monitoring the resonant capacitor voltage in order to calculate the resonant tank quality factor. The proposed methodology has been tested using a versatile power electronics test bench applied to domestic induction heating, proving the feasibility of this proposal.


title: Considerations in dynamic time warping algorithms for discrete word recognition

abstract: The technique of dynamic time warping for time registration of a reference and test utterance has found widespread use in the area of discrete word recognition. As originally proposed, the algorithm placed strong constraints on the possible set of dynamic paths—namely, it was assumed that the initial and final frames of both the test and reference utterances were in exact time synchrony. Because of inherent practical difficulties with satisfying the assumptions under which the above constraints are valid, we have considered some modifications to the dynamic time‐warping algorithm. In particular an algorithm in which an uncertainty exists in the registration both for initial and final frames was studied. Another modification constrains the dynamic path to follow (within a given range) the path which is locally minimum at each frame. This modification tends to work well when the location of the final frame of the test utterance is significantly in error due to breath noise, etc. To test the different time warping algorithms a set of 10 isolated words spoken by 100 speakers was used. Several different comparisons between the algorithms were made and will be described in the talk.The technique of dynamic time warping for time registration of a reference and test utterance has found widespread use in the area of discrete word recognition. As originally proposed, the algorithm placed strong constraints on the possible set of dynamic paths—namely, it was assumed that the initial and final frames of both the test and reference utterances were in exact time synchrony. Because of inherent practical difficulties with satisfying the assumptions under which the above constraints are valid, we have considered some modifications to the dynamic time‐warping algorithm. In particular an algorithm in which an uncertainty exists in the registration both for initial and final frames was studied. Another modification constrains the dynamic path to follow (within a given range) the path which is locally minimum at each frame. This modification tends to work well when the location of the final frame of the test utterance is significantly in error due to breath noise, etc. To test the different time w...


title: Building lightweight intrusion detection system using wrapper-based feature selection mechanisms

abstract: Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and network infrastructures. How to build a lightweight IDS is a hot topic in network security. Moreover, feature selection is a classic research topic in data mining and it has attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we effectively introduced feature selection methods to intrusion detection domain. We propose a wrapper-based feature selection algorithm aiming at building lightweight intrusion detection system by using modified random mutation hill climbing (RMHC) as search strategy to specify a candidate subset for evaluation, as well as using modified linear Support Vector Machines (SVMs) iterative procedure as wrapper approach to obtain the optimum feature subset. We verify the effectiveness and the feasibility of our feature selection algorithm by several experiments on KDD Cup 1999 intrusion detection dataset. The experimental results strongly show that our approach is not only able to speed up the process of selecting important features but also to yield high detection rates. Furthermore, our experimental results indicate that intrusion detection system with feature selection algorithm has better performance than that without feature selection algorithm both in detection performance and computational cost.


title: Mobile Virtual Network Admission Control and Resource Allocation for Wireless Network Virtualization: A Robust Optimization Approach

abstract: Wireless network virtualization is a promising technology in next generation wireless networks. In this paper, motivated by the experience of user equipment (UE) admission control in traditional wireless networks, we propose a novel concept of mobile virtual network (MVN) admission control for wireless virtualization. By limiting the number of MVNs embedded in the physical network, MVN admission control can effectively guarantee quality of service (QoS) experienced by users of MVNs and maximize the utilization of the physical networks at the same time. Specifically, we propose a two-stage MVN embedding mechanism that can decouple short-term physical resource allocation from long-term admission control and resource leasing. With recent advances in robust optimization, we formulate the MVN admission control problem as a robust optimization problem. Both the long-term admission control and short- term resource allocation problems are transformed to convex problems, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.


title: Reference Sequence Construction for Relative Compression of Genomes

abstract: Relative compression, where a set of similar strings are compressed with respect to a reference string, is an effective method of compressing DNA datasets containing multiple similar sequences. Moreover, it supports rapid random access to the underlying data. The main difficulty of relative compression is in selecting an appropriate reference sequence. In this paper, we explore using the dictionary of repeats generated by COMRAD, RE-PAIR and DNA-X algorithms as reference sequences for relative compression. We show that this technique allows for better compression, and allows more general repetitive datasets to be compressed using relative compression.


title: The effect of creating a quiet side on annoyance and sleep disturbances due to road traffic noise

abstract: There is growing evidence that having access to a quiet side of a dwelling reduces the harmful effects of road traffic noise on health and wellbeing. One measure to create a quiet side in existing noise-exposed residential areas is to erect shielding buildings that fill existing gaps through which road traffic noise penetrates. Within the EU-financed project QSIDE, we investigated the effect of this type of measure on the acoustical conditions and resident’s noise responses in a socio-acoustic intervention study. Results on sound levels, road traffic noise induced annoyance and sleep disturbances, as well as the perceived sound environment before and after the creation of a quiet side are presented in relation to results from previous similar studies. The implication for guidelines and sustainable goals related to public health, urban noise policy and urban development plans are discussed.


title: On the regularization of Wasserstein GANs

abstract: Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. It was proposed that training can be improved by instead augmenting the loss by a regularization term that penalizes the deviation of the gradient of the critic (as a function of the network's input) from one. We present theoretical arguments why using a weaker regularization term enforcing the Lipschitz constraint is preferable. These arguments are supported by experimental results on toy data sets.


title: Audio-based multimedia event detection using deep recurrent neural networks

abstract: Multimedia event detection (MED) is the task of detecting given events (e.g. birthday party, making a sandwich) in a large collection of video clips. While visual features and automatic speech recognition typically provide the best features for this task, nonspeech audio can also contribute useful information, such as crowds cheering, engine noises, or animal sounds. MED is typically formulated as a two-stage process: the first stage generates clip-level feature representations, often by aggregating frame-level features; the second stage performs binary or multi-class classification to decide whether a given event occurs in a video clip. Both stages are usually performed "statically", i.e. using only local temporal information, or bag-of-words models. In this paper, we introduce longer-range temporal information with deep recurrent neural networks (RNNs) for both stages. We classify each audio frame among a set of semantic units called "noisemes" the sequence of frame-level confidence distributions is used as a variable-length clip-level representation. Such confidence vector sequences are then fed into long short-term memory (LSTM) networks for clip-level classification. We observe improvements in both frame-level and clip-level performance compared to SVM and feed-forward neural network baselines.


Multiple Choice Question Generation Utilizing An Ontology


title: Increasing performance in byzantine fault-tolerant systems with on-demand replica consistency

abstract: Traditional agreement-based Byzantine fault-tolerant (BFT) systems process all requests on all replicas to ensure consistency. In addition to the overhead for BFT protocol and state-machine replication, this practice degrades performance and prevents throughput scalability. In this paper, we propose an extension to existing BFT architectures that increases performance for the default number of replicas by optimizing the resource utilization of their execution stages. Our approach executes a request on only a selected subset of replicas, using a selector component co-located with each replica. As this leads to divergent replica states, a selector on-demand updates outdated objects on the local replica prior to processing a request. Our evaluation shows that with each replica executing only a part of all requests, the overall performance of a Byzantine fault-tolerant NFS can be almost doubled; our prototype even outperforms unreplicated NFS.


title: In-frame deletion in a novel centrosomal/ciliary protein CEP290/NPHP6 perturbs its interaction with RPGR and results in early-onset retinal degeneration in the rd16 mouse

abstract: Centrosome- and cilia-associated proteins play crucial roles in establishing polarity and regulating intracellular transport in post-mitotic cells. Using genetic mapping and positional candidate strategy, we have identified an in-frame deletion in a novel centrosomal protein CEP290 (also called NPHP6), leading to early-onset retinal degeneration in a newly identified mouse mutant, rd16. We demonstrate that CEP290 localizes primarily to centrosomes of dividing cells and to the connecting cilium of retinal photoreceptors. We show that, in the retina, CEP290 associates with several microtubule-based transport proteins including RPGR, which is mutated in approximately 15% of patients with retinitis pigmentosa. A truncated CEP290 protein (DeltaCEP290) is detected in the rd16 retina, but in considerably reduced amounts; however, the mutant protein exhibits stronger association with specific RPGR isoform(s). Immunogold labeling studies demonstrate the redistribution of RPGR and of phototransduction proteins in the photoreceptors of rd16 retina. Our findings suggest a critical function for CEP290 in ciliary transport and provide insights into the mechanism of early-onset photoreceptor degeneration.


title: Robust Reconstruction of Building Facades for Large Areas Using Spaceborne TomoSAR Point Clouds

abstract: With data provided by modern meter-resolution synthetic aperture radar (SAR) sensors and advanced multipass interferometric techniques such as tomographic SAR inversion (TomoSAR), it is now possible to reconstruct the shape and monitor the undergoing motion of urban infrastructures on the scale of centimeters or even millimeters from space in very high level of details. The retrieval of rich information allows us to take a step further toward generation of 4-D (or even higher dimensional) dynamic city models, i.e., city models that can incorporate temporal (motion) behavior along with the 3-D information. Motivated by these opportunities, the authors proposed an approach that first attempts to reconstruct facades from this class of data. The approach works well for small areas containing only a couple of buildings. However, towards automatic reconstruction for the whole city area, a more robust and fully automatic approach is needed. In this paper, we present a complete extended approach for automatic (parametric) reconstruction of building facades from 4-D TomoSAR point cloud data and put particular focus on robust reconstruction of large areas. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from a stack of TerraSAR-X high-resolution spotlight images from ascending orbit covering an approximately 2- km ::: 2 ::: high-rise area in the city of Las Vegas.


title: Modeling Social Perception of Faces [Social Sciences]

abstract: The face is our primary source of visual information for identifying people and reading their emotional and mental states. With the exception of prosopagnosics (who are unable to recognize faces) and those suffering from such disorders of social cognition as autism, people are extremely adept at these two tasks. However, our cognitive powers in this regard come at the price of reading too much into the human face. The face is often treated as a window into a person's true nature. Given the agreement in social perception of faces, this paper discusses that it should be possible to model this perception.


title: Mental Health Literacy: Empowering the Community to Take Action for Better Mental Health.

abstract: For major physical diseases, it is widely accepted that members of the public will benefit by knowing what actions they can take for prevention, early intervention, and treatment. However, this type of public knowledge about mental disorders (mental health literacy) has received much less attention. There is evidence from surveys in several countries for deficiencies in (a) the public's knowledge of how to prevent mental disorders, (b) recognition of when a disorder is developing, (c) knowledge of help-seeking options and treatments available, (d) knowledge of effective self-help strategies for milder problems, and (e) first aid skills to support others affected by mental health problems. Nevertheless, there is evidence that a range of interventions can improve mental health literacy, including whole-of-community campaigns, interventions in educational settings, Mental Health First Aid training, and information websites. There is also evidence for historical improvements in mental health literacy in some countries. Increasing the community's mental health literacy needs to be a focus for national policy and population monitoring so that the whole community is empowered to take action for better mental health.


title: Cloud Workload Prediction and Generation Models

abstract: Cloud computing allows for elasticity as users can dynamically benefit from new virtual resources when their workload increases. Such a feature requires highly reactive resource provisioning mechanisms. In this paper, we propose two new workload prediction models, based on constraint programming and neural networks, that can be used for dynamic resource provisioning in Cloud environments. We also present two workload trace generators that can help to extend an experimental dataset in order to test more widely resource optimization heuristics. Our models are validated using real traces from a small Cloud provider. Both approaches are shown to be complimentary as neural networks give better prediction results, while constraint programming is more suitable for trace generation.


title: 4G/Multiband handheld device ground antennas

abstract: Promising internal handheld device ground antennas are presented. Using the ground antenna concept, the antenna element functions mainly as an exciter and can have a very small size to achieve 4G/multiband operation. In this paper, ground antenna structures for handheld devices including the use of a loop strip, a half-loop strip, a simple shorted strip, or an inverted-F strip with the aid of a capacitive element to achieve decreased resonant length and the use of a small-size strip or patch with an inductive element to combine with the device ground plane to form as an asymmetric dipole structure are discussed. By aided with proper matching circuits, the ground antennas are promising to provide wideband operations. A small corner strip antenna with its resonant length 29 mm only (less than 0.08λ at 750 MHz) is also demonstrated for the LTE/WWAN tablet computer application. The ground antennas are also very promising to form high-isolation antenna systems for MIMO and diversity operation in the slim handheld devices.


Response to Lieberman on “Monkey vocal tracts are speech-ready”


title: Decentralizing Privacy Enforcement for Internet of Things Smart Objects

abstract: Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where high- speed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their privacy preferences. Compliance check of user individual privacy preferences is performed directly by smart objects. Moreover, acknowledging that embedding the enforcement mechanism into smart objects implies some overhead, we have extensively tested the proposed framework on different scenarios, and the obtained results show the feasibility of our approach.


title: Assessment of Knowledge Regarding Oral Hygiene among Parents of Pre-School Children Attending Pediatric Out Patient Department in Dhulikhel Hospital

abstract: Background Level of knowledge regarding oral hygiene among the parents of pre-school children plays an important role on maintaining the good oral hygiene of their children. In Nepal, sufficient research has not been carried out on this area. Objective Objective of this study is to assess the level of knowledge on oral hygiene of pre-school children’s parents attending pediatric outpatient department in Dhulikhel Hospital. Method A descriptive study was conducted from November 2012 to January 2013 among one hundred parents of preschool children visiting pediatrics outpatient department of Dhulikhel Hospital. Paper and pencil based semi structured questionnaire was used for collecting data. Questions related to demographic information and knowledge were asked. Thirty questions were used for assessing knowledge level. Knowledge score was calculated by allocating one point for each correct answer and zero point for each wrong answer. Analyzed data were presented in terms of numbers and percentages. Total knowledge scores were categorized based on percentage. Knowledge score was categorized on four group - exclusive intervals - namely-poor (0-40%), moderate (40-60%), good (60-80%) and excellent (80-100%). Mann-Whitney U test and Kruskal-Wallis test were applied to check significance difference and chi-square test was used to check association among different background characteristic. Result It was found that 81% had moderate knowledge, 15% had poor knowledge and 4% had good knowledge about oral hygiene. Median knowledge score was found to be 15 with range 10 to 21. Following variables were found to be significant difference on knowledge category: Education status (p<0.001), education level (p= 0.041), past experience about oral health problem (p = 0.008), Further significant association was found between knowledge category and educational status (p < 0.001) and between knowledge category and past experience (p < 0.001). Conclusion Knowledge regarding oral hygiene was found satisfactory among the parents of preschool children visiting pediatric OPD of Dhulikhel Hospital. Kathmandu University Medical Journal Vol.13(1) 2015; 38-43


title: Relationship contracting in Australia

abstract: Trust is the principle foundation of partnering and relationship management. Successful partnering can only be achieved when a cooperative relationship is built between partners. In order to build either a long or short-term relationship, trust is a major component because of the increase in openness it engenders between parties. This paper discusses how procurement methods in the construction industry are changing and moving towards a more relationship-based contracting approach. This paper details the role of trust in facilitating relationship-based contracting from a general perspective to its implications for a construction project. The authors indicate how changes in procurement culture towards sustainable business relationships benefit different parties in the industry and have the potential to achieve empowerment and regional development. This paper is based on research undertaken in the public sector in Queensland, Australia.


Facebook © Adoption as Computer-Mediated Communication for University Students


SHREC ’ 16 : Partial Matching of Deformable Shapes


title: AN EXPLORATORY STUDY OF SOCIAL MEDIA IN BUSINESS-TO-BUSINESS SELLING: SALESPERSON CHARACTERISTICS, ACTIVITIES AND PERFORMANCE

abstract: Despite the evolution of social media and its increasingly significant impact on the marketing environment, research examining how social media relates to the critical area of sales is lacking. To create an improved framework of the strategic usage of social media, this paper presents a profile of B2B sales professionals who employ social media. Importantly, the results reveal salespeople who use social media typically have key differences from sales professionals who don’t utilize this tool. Users of social media have discovered social media impacts overall sales performance. While the study found users of social media are typically from younger generations, this age dynamic creates interesting implications for managers desiring to integrate this tool into their strategic marketing mix. This study therefore advances the sales literature in the understanding of social media as a strategic sales tool that influences sales performance.


title: Massively-Parallel Lossless Data Decompression

abstract: Today's exponentially increasing data volumes and the high cost of storage make compression essential for the Big Data industry. Although research has concentrated on efficient compression, fast decompression is critical for analytics queries that repeatedly read compressed data. While decompression can be parallelized somewhat by assigning each data block to a different process, break-through speed-ups require exploiting the massive parallelism of modern multi-core processors and GPUs for data decompression within a block. We propose two new techniques to increase the degree of parallelism during decompression. The first technique exploits the massive parallelism of GPU and SIMD architectures. The second sacrifices some compression efficiency to eliminate data dependencies that limit parallelism during decompression. We evaluate these techniques on the decompressor of the DEFLATE scheme, called Inflate, which is based on LZ77 compression and Huffman encoding. We achieve a 2X speed-up in a head-to-head comparison with several multi-core CPU-based libraries, while achieving a 17% energy saving with comparable compression ratios.


title: SUPERVISORY CONTROL OF PARALLEL HYBRID ELECTRIC VEHICLES FOR FUEL AND EMISSIONS REDUCTION

abstract: Past research on Hybrid Electric Vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the light- off of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the Dynamic Programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy.The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solution over the entire state space via the comprehensive extraction method. The DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up with minimal cold-start engine-out emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and achieves near- optimal fuel economy and emissions performance.


Implementation of AES Algorithm Using FPGA & Its Performance Analysis


title: Circuit-ABE from LWE: Unbounded Attributes and Semi-adaptive Security

abstract: We construct an LWE-based key-policy attribute-based encryption ABE scheme that supports attributes of unbounded polynomial length. Namely, the size of the public parameters is a fixed polynomial in the security parameter and a depth bound, and with these fixed length parameters, one can encrypt attributes of arbitrary length. Similarly, any polynomial size circuit that adheres to the depth bound can be used as the policy circuit regardless of its input length recall that a depth d circuit can have as many as $$2^d$$ inputs. This is in contrast to previous LWE-based schemes where the length of the public parameters has to grow linearly with the maximal attribute length. ::: ::: We prove that our scheme is semi-adaptively secure, namely, the adversary can choose the challenge attribute after seeing the public parameters but before any decryption keys. Previous LWE-based constructions were only able to achieve selective security. We stress that the "complexity leveraging" technique is not applicable for unbounded attributes. ::: ::: We believe that our techniques are of interest at least as much as our end result. Fundamentally, selective security and bounded attributes are both shortcomings that arise out of the current LWE proof techniques that program the challenge attributes into the public parameters. The LWE toolbox we develop in this work allows us to delay this programming. In a nutshell, the new tools include a way to generate an a-priori unbounded sequence of LWE matrices, and have fine-grained control over which trapdoor is embedded in each and every one of them, all with succinct representation.


title: Autoencoder Trees

abstract: We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. The encoder tree takes the input and generates a lower dimensional representation in the leaves and the decoder tree takes this and reconstructs the original input. Exploiting the continuity of the trees, autoencoder trees are trained with stochastic gradient descent. On handwritten digit and news data, we see that the autoencoder trees yield good reconstruction error compared to traditional autoencoder perceptrons. We also see that the autoencoder tree captures hierarchical representations at different granularities of the data on its different levels and the leaves capture the localities in the input space.


title: Model Driven Architecture with OOHDM.

abstract: A model-driven architecture (MDA) transforms a platformindependent model (PIM) into a platform-specific model (PSM) before it transforms the PSM into code. OOHDM together with a recently proposed business process extension allows to model hypermedia-based and advanced Web applications by an object model which is a platformindependent. But an OOHDM model cannot directly be used as PIM since it is not an executable model. A behavioral semantics definition of the OOHDM core features and business process makes an OOHDM model executable on a platform-independent Web Application virtual Machine (WAM). As a consequence, an OOHDM behavioral semantics model can serve as a PIM for a MDA. We present two different aspects of the PIM, and give two concrete examples for platform-specific models (PSM) into which the PIM is transformed.


title: Registration of stereo and temporal images of the retina

abstract: The registration of retinal images is required to facilitate the study of the optic nerve head and the retina. The method we propose combines the use of mutual information as the similarity measure and simulated annealing as the search technique. It is robust toward large transformations between the images and significant changes in light intensity. By using a pyramid sampling approach combined with simulated reannealing we find that registration can be achieved to predetermined precision, subject to choice of interpolation and the constraint of time. The algorithm was tested on 49 pairs of stereo images and 48 pairs of temporal images with success.


title: INFLUENCE OF CUSTOMER SATISFACTION ON LOYALTY: A STUDY ON MOBILE TELECOMMUNICATION INDUSTRY

abstract: The mobile telecommunication companies uphold their service quality and change their marketing core strategies to retain their existing customers by enhancing and optimizing the customer loyalty. Therefore, it becomes significant for telecom companies to identify factors of services that influence customer loyalty and in turn creates loyal customers by satisfying them. This present study aims to explore the influence of customer satisfaction on customer loyalty in the context of Bangladesh. This study focused on six factors such as communication, price structure, value-added service, convenience, sales-promotions and customer service. Both primary and secondary information were collected to test the pre-set hypotheses. Descriptive statistics and simple linear regression were employed to analyze the data. Result shows that five factors: communication, price structure, value-added services, convenience and customer service/care have positive correlations with customer loyalty.


Modelling and Stability Control of Two-Wheeled Robots in Low-Traction Environments


title: Sequential Event Prediction with Association Rules

abstract: We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules, and provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an \adjusted condence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis.


title: Thermal analysis of a 3D die-stacked high-performance microprocessor

abstract: 3-dimensional integrated circuit (3D IC) technology places circuit blocks in the vertical dimension in addition to the conventional horizontal plane. Compared to conventional planar ICs, 3D ICs have shorter latencies as well as lower power consumption, due to shorter wires. The benefits of 3D ICs increase as we stack more die, due to successive reductions in wire lengths. However, as we stack more die, the power density increases due to increasing proximity of active (heat generating) devices, thus causing the temperatures to increase. Also, the topmost die on the 3D stack are located further from the heat sink and experience a longer heat dissipation path. Prior research has already identified thermal management as a critical issue in 3D technology. In this paper, we evaluate the thermal impact of building high-performance microprocessors in 3D. We estimate the temperatures of a planar IC based on the Alpha 21364 processor as well as 2-die and 4-die 3D implementations of the same. We show that, compared to the planar IC, the 2-die implementation and 4-die implementation increase the maximum temperature by 17 Kelvin and 33 Kelvin, respectively.


An integrated text mining system based on network analysis for knowledge discovery of human gene-disease associations ( GenDisFinder )