title: Based on NetLogo Simulation for Credit Risk Management

abstract: With economic development, the credit card business has entered a stage of rapid development. Using quantitative methods of modeling to analyses and predicts the consumers use the credit card behaviors. It is one of hot project in the experimental economics, and it is important for avoiding credit risk. The essay based on a simple NetLogo simulation model to simulate individual consumers and economic activity, to observe the risk of credit card operation system. The experimental results is, in the different income levels, the cardholders’ consuming and withdrawal behaviors would have no limits, then the issuing bank would be set line of credit is not higher twice than their income. In this situation, the cardholders could pay back the money on time. Otherwise, it will have risks for issuing bank. This method is common, so it can be used in many study areas.


title: Mobile robot navigation based on lidar

abstract: A wheeled mobile robot based on Arduino was described in this article. Hardware selection, Simultaneous Localization and Mapping (SLAM), Path Planning and the principle of the algorithm applied in this process are introduced in detail. The algorithm used in ROS is further verified by experiments. In order to verify the feasibility of two different 2D SLAM algorithms, we use the wheeled robot built in the lab to do the experiment in the same indoor environment and compare their results.


title: Learning Low-shot facial representations via 2D warping

abstract: Face recognition has seen a significant improvement by using the deep convolutional neural networks. In this work, we mainly study the influence of the 2D warping module for one-shot face recognition. To achieve this, we first propose a 2D-Warping Layer to generate new features for the novel classes during the training, then fine-tuning the network by adding the recent proposed fisher loss to learn more discriminative features. We evaluate the proposed method on two popular databases for unconstrained face recognition, the Labeled Faces in the Wild (LFW) and the Youtube Faces (YTF) database. In both cases, the proposed method achieves competitive results with the accuracy of 99.25\% for LFW and 94.3\% for YTF, separately. Moreover, the experimental results on MS-Celeb-1M one-shot faces dataset show that with the proposed method, the model achieves comparable results of 77.92\% coverage rate at precision = 99\% for the novel classes while still keeps top-1 accuracy of 99.80\% for the normal classes.


title: NetGAN: Generating Graphs via Random Walks

abstract: We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over the input graph. The proposed model is based on a stochastic neural network that generates discrete output samples and is trained using the Wasserstein GAN objective. NetGAN is able to produce graphs that exhibit the well-known network patterns without explicitly specifying them in the model definition. At the same time, our model exhibits strong generalization properties, as highlighted by its competitive link prediction performance, despite not being trained specifically for this task. Being the first approach to combine both of these desirable properties, NetGAN opens exciting further avenues for research.


title: Cognitive vulnerability: A model of the etiology of fear

abstract: This paper attempts to fill the partial theoretical vacuum surrounding the understanding of fear acquisition. A review of recent and contemporary theories of the etiology of fear is presented, serving as a justification for further theorizing and allowing for greater understanding of those aspects of fear that remain to be adequately explained. A new model of the etiology of specific fears is subsequently put forward and the various aspects and implications of this model are discussed. How an individual perceives a stimulus is proposed as being critical in determining fear in relation to the stimulus. In particular, perceptions of the stimulus as uncontrollable, unpredictable, dangerous and disgusting create a schema of vulnerability. The Cognitive Vulnerability Model integrates much of the extensive body of research on fears and specific phobias into a unifying theory of the etiology of fear. The model offers parsimonious explanations for the various characteristics of specific fears and phobias.


title: Deep Continuous Clustering

abstract: Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoencoder is optimized as part of the clustering process. The resulting network produces clustered data. The presented approach does not rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear dimensionality reduction and clustering are formulated as optimization of a global continuous objective. We thus avoid discrete reconfigurations of the objective that characterize prior clustering algorithms. Experiments on datasets from multiple domains demonstrate that the presented algorithm outperforms state-of-the-art clustering schemes, including recent methods that use deep networks.


title: Active Sense Queue Management (ASQM)

abstract: Active Queue Management (AQM) algorithms have seen a lot of attention in recent academic literature as well as in the popular press. The problem with traditional AQM is that it is designed to operate on queues at the IP layer. However, the problem popularly called bufferbloat can move about among many queues some of which are resistant to traditional AQM such as Layer 2MAC protocols used in cable/DSL links. We call this problem bufferbloat displacement. Our contribution is a new class of AQM algorithm called Active Sense Queue Management (ASQM). ASQM is an IP layer AQM protocol that has the added benefit of managing Layer 2 link delay, i.e. bufferbloat displacement. We provide testbed experiments comparing ASQM's bandwidth delay characteristics with traditional AQM algorithms demonstrating ASQM's ability to manage bufferbloat displacement where traditional AQM algorithms cannot.


title: Robust Visual Tracking via Structured Multi-Task Sparse Learning

abstract: In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $$\ell _{p,q}$$ mixed norms $$(\text{ specifically} p\in \{2,\infty \}$$ and $$q=1),$$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $$L_1$$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259---2272, 2011) is a special case of our MTT formulation (denoted as the $$L_{11}$$ tracker) when $$p=q=1.$$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.


title: Introducing Connected Vehicles [Connected Vehicles]

abstract: The term connected vehicles refers to applications, services, and technologies that connect a vehicle to its surroundings. Adopting a definition similar to that of AUTO Connected Car News, a connected vehicle is basically the presence of devices in a vehicle that connect to other devices within the same vehicle and/or devices, networks, applications, and services outside the vehicle. Applications include everything from traffic safety and efficiency, infotainment, parking assistance, roadside assistance, remote diagnostics, and telematics to autonomous self-driving vehicles and global positioning systems (GPS). Typically, vehicles that include interactive advanced driver-assistance systems (ADASs) and cooperative intelligent transport systems (C-ITS) can be regarded as connected. Connected-vehicle safety applications are designed to increase situation awareness and mitigate traffic accidents through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. ADAS technology can be based on vision/camera systems, sensor technology, vehicle data networks, V2V, or V2I systems. Features may include adaptive cruise control, automate braking, incorporate GPS and traffic warnings, connect to smartphones, alert the driver to hazards, and keep the driver aware of what is in the blind spot. V2V communication technology could mitigate traffic collisions and improve traffic congestion by exchanging basic safety information such as location, speed, and direction between vehicles within range of each other. It can supplement active safety features, such as forward collision warning and blind-spot detection.


DoS Detection Method based on Artificial Neural Networks


title: TiFi: Taxonomy Induction for Fictional Domains [Extended version]

abstract: Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin.


title: 18th European Conference on Information Systems AIMING AT A MOVING TARGET: IT ALIGNMENT IN TOY COMPANIES

abstract: For global companies that compete in high-velocity industries, business strategies and initiatives change rapidly, and thus the CIO struggles to keep the IT organization aligned with a moving target. In this paper we report on research-in-progress that focuses on how the CIO attempts to meet this challenge. Specifically, we are conducting case studies to closely examine how toy industry CIOs develop their IT organizations’ assets, competencies, and dynamic capabilities in alignment with their companies’ evolving strategy and business priorities (which constitute the “moving target”). We have chosen to study toy industry CIOs, because their companies compete in a global, high-velocity environment, yet this industry has been largely overlooked by the information systems research community. Early findings reveal that four IT application areas are seen as holding strong promise: supply chain management, knowledge management, data mining, and eCommerce, and that toy CIO’s are attempting to both cope with and capitalize on the current financial crisis by more aggressively pursuing offshore outsourcing than heretofore. We conclude with a discussion of next steps as the study proceeds.


title: Minimum jitter adaptive decision feedback equalizer for 4PAM serial links

abstract: This paper presents a minimum jitter-based adaptive decision feedback equalizer (DFE) for 4PAM serial links. For each signal level, a dedicated sign-sign least-mean-square (SS-LMS) algorithm is employed to adjust corresponding DFE tap coefficient as per data rate and the characteristics of channels. The proposed adaptive DFE is embedded in a 2 Gbps serial link over a 12-inch FR4 channel implemented in an IBM 130 nm 1.2V CMOS technology. The link is analyzed using Spectre from Cadence Design Systems with BSIM4 device models. Simulation results demonstrate that the proposed adaptive DFE is capable of opening completely closed data eyes.


title: Robust Text Detection in Natural Scene Images

abstract: Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.


title: High Performance Packet Processing with FlexNIC

abstract: The recent surge of network I/O performance has put enormous pressure on memory and software I/O processing sub systems. We argue that the primary reason for high memory and processing overheads is the inefficient use of these resources by current commodity network interface cards (NICs). We propose FlexNIC, a flexible network DMA interface that can be used by operating systems and applications alike to reduce packet processing overheads. FlexNIC allows services to install packet processing rules into the NIC, which then executes simple operations on packets while exchanging them with host memory. Thus, our proposal moves some of the packet processing traditionally done in software to the NIC, where it can be done flexibly and at high speed. We quantify the potential benefits of FlexNIC by emulating the proposed FlexNIC functionality with existing hardware or in software. We show that significant gains in application performance are possible, in terms of both latency and throughput, for several widely used applications, including a key-value store, a stream processing system, and an intrusion detection system.


A Review on Internet of Things Based Garbage Bins Detection Systems


title: Shortcomings of the web of documents and data for managing personal knowledge and collaboration

abstract: As one of the key drivers of the Information Age, the World Wide Web (of documents) affords its users to disseminate their knowledge and opinions and to interrelate on a global scale. In a Knowledge Age, this wealth of resources could be far better utilized, if distinct items contained would relate to others and if the meanings of it would also be comprehensible to machines for deducing further knowledge. This is the aim of the Semantic Web or Web of Data. However, in fierce pursuit of these technical facets, alternative visionary ideas, cautioning forewarnings, and emerging burdens have been neglected. The aim of this paper is not just to critically report on this dilemma, but to take the points of contention and ideas and constructively utilize them for considering a novel personal knowledge management concept and prototype under development whose design and implications have been elaborated on in a series of prior publications.


Analysis of Anxious Word Usage on Online Health Forums.


title: A hybrid collaborative filtering model for social influence prediction in event-based social networks

abstract: Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Understanding users' social influences in social networks can benefit many applications, such as social recommendation and social marketing. In this paper, we focus on the problem of predicting users' social influences on upcoming events in EBSNs. We formulate this prediction problem as the estimation of unobserved entries of the constructed user-event social influence matrix, where each entry represents the influence value of a user on an event. In particular, we define a user's social influence on a given event as the proportion of the user's friends who are influenced by him/her to attend the event. To solve this problem, we present a hybrid collaborative filtering model, namely, Matrix Factorization with Event-User Neighborhood (MF-EUN) model, by incorporating both event-based and user-based neighborhood methods into matrix factorization. Due to the fact that the constructed social influence matrix is very sparse and the overlap values in the matrix are few, it is challenging to find reliable similar neighbors using the widely adopted similarity measures (e.g., Pearson correlation and Cosine similarity). To address this challenge, we propose an additional information based neighborhood discovery (AID) method by considering both event-specific and user-specific features in EBSNs. The parameters of our MF-EUN model are determined by minimizing the associated regularized squared error function through stochastic gradient descent. We conduct a comprehensive performance evaluation on real-world datasets collected from DoubanEvent. Experimental results show that our proposed hybrid collaborative filtering model is superior than several alternatives, which provides excellent performance with RMSE and MAE reaching 0.248 and 0.1266 respectively in the 90% training data of 10000 users dataset. HighlightsWe define and solve the event-level social influence prediction problem in event-based social networks (i.e., EBSNs).We present a novel hybrid collaborative filtering prediction model which incorporates neighborhood methods into matrix factorization for social influence prediction in EBSNs.We extract three event-specific features and three user-specific features in EBSNs.We propose an additional information based neighborhood discovery (AID) method to find reliable similar neighbors.The experimental results on real-world datasets show the superiority of our model.


title: Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation

abstract: Peer code review is a cost-effective software defect detection technique. Tool assisted code review is a form of peer code review, which can improve both quality and quantity of reviews. However, there is a significant amount of human effort involved even in tool based code reviews. Using static analysis tools, it is possible to reduce the human effort by automating the checks for coding standard violations and common defect patterns. Towards this goal, we propose a tool called Review Bot for the integration of automatic static analysis with the code review process. Review Bot uses output of multiple static analysis tools to publish reviews automatically. Through a user study, we show that integrating static analysis tools with code review process can improve the quality of code review. The developer feedback for a subset of comments from automatic reviews shows that the developers agree to fix 93% of all the automatically generated comments. There is only 14.71% of all the accepted comments which need improvements in terms of priority, comment message, etc. Another problem with tool assisted code review is the assignment of appropriate reviewers. Review Bot solves this problem by generating reviewer recommendations based on change history of source code lines. Our experimental results show that the recommendation accuracy is in the range of 60%-92%, which is significantly better than a comparable method based on file change history.


title: Resting high frequency heart rate variability is not associated with the recognition of emotional facial expressions in healthy human adults

abstract: This study explores whether the myelinated vagal connection between the heart and the brain is involved in emotion recognition. The Polyvagal theory postulates that the activity of the myelinated vagus nerve underlies socio-emotional skills. It has been proposed that the perception of emotions could be one of this skills dependent on heart-brain interactions. However, this assumption was differently supported by diverging results suggesting that it could be related to confounded factors. In the current study, we recorded the resting state vagal activity (reflected by High Frequency Heart Rate Variability, HF-HRV) of 77 (68 suitable for analysis) healthy human adults and measured their ability to identify dynamic emotional facial expressions. Results show that HF-HRV is not related to the recognition of emotional facial expressions in healthy human adults. We discuss this result in the frameworks of the polyvagal theory and the neurovisceral integration model.


title: Exploring the impact of transformational leadership on nurse innovation behaviour: a cross-sectional study.

abstract: AIM ::: This study explored the influences of transformational leadership on nurse innovation behaviour and the mediating role of organisational climate. ::: ::: ::: BACKGROUND ::: Recently, global nursing experts have been aggressively encouraging nurses to pursue innovation in nursing in order to improve nursing outcomes. Nursing innovation, in turn, is affected by nursing leadership. ::: ::: ::: METHOD ::: We employed a questionnaire survey to collect data, and selected a sample of nurses from hospitals in Taiwan. A total of 439 valid surveys were obtained. Hierarchical multiple regression model analysis was conducted to test the study hypothesis. ::: ::: ::: RESULT ::: The mean values of agreement of nurse innovation behaviour and transformational leadership were 3.40 and 3.78, respectively. Patient safety climate and innovation climate were found to have full mediating effects on the relationship between transformational leadership and innovation behaviour. ::: ::: ::: CONCLUSION ::: Organisational climate has a significant impact on innovation behaviour. Transformational leadership has indirect effects on innovation behaviour via the mediation of patient safety climate and innovation climate. ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: Hospitals should enhance transformational leadership by designing leadership training programmes and establishing transformational culture. In addition, nursing managers should foster nursing innovation through improvements in organisational climate.


title: Protecting browser state from web privacy attacks

abstract: Through a variety of means, including a range of browser cache methods and inspecting the color of a visited hyperlink, client-side browser state can be exploited to track users against their wishes. This tracking is possible because persistent, client-side browser state is not properly partitioned on per-site basis in current browsers. We address this problem by refining the general notion of a "same-origin" policy and implementing two browser extensions that enforce this policy on the browser cache and visited links.We also analyze various degrees of cooperation between sites to track users, and show that even if long-term browser state is properly partitioned, it is still possible for sites to use modern web features to bounce users between sites and invisibly engage in cross-domain tracking of their visitors. Cooperative privacy attacks are an unavoidable consequence of all persistent browser state that affects the behavior of the browser, and disabling or frequently expiring this state is the only way to achieve true privacy against colluding parties.


title: Randomized, controlled trial of cannabis-based medicine in central pain in multiple sclerosis

abstract: Background:Central pain in multiple sclerosis (MS) is common and often refractory to treatment. Methods:We conducted a single-center, 5-week (1-week run-in, 4-week treatment), randomized, double-blind, placebo-controlled, parallel-group trial in 66 patients with MS and central pain states (59 dysesthetic, seven painful spasms) of a whole-plant cannabis-based medicine (CBM), containing delta-9-tetrahydrocannabinol:cannabidiol (THC:CBD) delivered via an oromucosal spray, as adjunctive analgesic treatment. Each spray delivered 2.7 mg of THC and 2.5 of CBD, and patients could gradually self-titrate to a maximum of 48 sprays in 24 hours. Results:Sixty-four patients (97%) completed the trial, 34 received CBM. In week 4, the mean number of daily sprays taken of CBM (n = 32) was 9.6 (range 2 to 25, SD = 6.0) and of placebo (n = 31) was 19.1 (range 1 to 47, SD = 12.9). Pain and sleep disturbance were recorded daily on an 11-point numerical rating scale. CBM was superior to placebo in reducing the mean intensity of pain (CBM mean change −2.7, 95% CI: −3.4 to −2.0, placebo –1.4 95% CI: −2.0 to −0.8, comparison between groups, p = 0.005) and sleep disturbance (CBM mean change –2.5, 95% CI: −3.4 to −1.7, placebo –0.8, 95% CI: −1.5 to −0.1, comparison between groups, p = 0.003). CBM was generally well tolerated, although more patients on CBM than placebo reported dizziness, dry mouth, and somnolence. Cognitive side effects were limited to long-term memory storage. Conclusions:Cannabis-based medicine is effective in reducing pain and sleep disturbance in patients with multiple sclerosis related central neuropathic pain and is mostly well tolerated.


title: Structure and Context in Prostatic Gland Segmentation and Classification

abstract: A novel gland segmentation and classification scheme applied to an H&E histology image of the prostate tissue is proposed. For gland segmentation, we associate appropriate nuclei objects with each lumen object to create a gland segment. We further extract 22 features to describe the structural information and contextual information for each segment. These features are used to classify a gland segment into one of the three classes: artifact, normal gland and cancer gland. On a dataset of 48 images at 5x magnification (which includes 525 artifacts, 931 normal glands and 1,375 cancer glands), we achieved the following classification accuracies: 93% for artifacts v. true glands; 79% for normal v. cancer glands, and 77% for discriminating all three classes. The proposed method outperforms state of the art methods in terms of segmentation and classification accuracies and computational efficiency.


title: Distribution of the southern brown bandicoot (Isoodon obesulus) in the Portland region of south-western Victoria

abstract: The southern brown bandicoot (Isoodon obesulus) occurs across the periphery of southern and eastern Australia as a series of isolated regional populations. Historical records and recent surveys conducted for I. obesulus indicate that it has disappeared or decreased significantly from many parts of its former range. Vegetation clearance, habitat fragmentation, feral predators and fire have all been implicated in the decline of the species. This paper examines the distribution of I. obesulus in the Portland region of south-western Victoria. Historical records of I. obesulus were compiled from the specimen collection of Museum Victoria, the Atlas of Victorian Wildlife, Portland Field Naturalists’ Club records and anecdotal sources. Field surveys were conducted to determine the current distribution of I. obesulus in the study area based on evidence of its foraging activity. The historical records reveal limited information: most are clustered around centres of human activity, indicating observational bias. The field surveys demonstrate that I. obesulus occurs in the Portland region as a series of local populations. Each local population is associated with a patch of remnant native vegetation separated from neighbouring patches by dispersal barriers. Within these habitat remnants the occurrence of the species is sporadic. Approximately 69% of the potential habitat is managed by the Forests Service, 31% is managed by Parks Victoria, and less than 0.5% is held under other tenures. Spatial isolation of habitat remnants, fires and feral predators are the main threats to I. obesulus in the Portland region.


title: The relationship between Internet parenting styles and Internet usage of children and adolescents

abstract: Parenting styles, which are known to have a significant effect on children's development, also have a significant effect on children's Internet use. This study was designed to demonstrate how parenting styles in relation to Internet use are perceived by children and parents and how these styles affect children's Internet use. Both qualitative and quantitative data-collection techniques were used in the study. The quantitative data-collection process was carried out through the participation of 1289 students, and the qualitative data-collection process was carried out with 20 parents and 23 children. The result of the study showed that the Internet parenting styles could be categorized as laissez-faire, permissive, authoritative, and authoritarian. A significant relationship was shown between Internet parenting style and child's gender and mothers' education level. It was also found that as the students' age and grade level increased, Internet parenting styles that were interpreted as initially authoritative were later interpreted as laissez-faire. As the grade level increased, the Internet parenting style tended to lean toward laissez-faire. A laissez-faire Internet parenting style is the style most commonly preferred by parents.The relationship between child/parent characteristics and Internet parenting style is significant.Internet parenting styles significantly affect the child's Internet usage.Parent's Internet style and the child's view of the parent's style do not exactly coincide with each other.


title: Generalized Character-Level Spelling Error Correction

abstract: We present a generalized discriminative model for spelling error correction which targets character-level transformations. While operating at the character level, the model makes use of wordlevel and contextual information. In contrast to previous work, the proposed approach learns to correct a variety of error types without guidance of manuallyselected constraints or language-specific features. We apply the model to correct errors in Egyptian Arabic dialect text, achieving 65% reduction in word error rate over the input baseline, and improving over the earlier state-of-the-art system.


title: A UML profile for goal-oriented and use case-driven representation of NFRs and FRs

abstract: In order for a software system to be of value, it should meet both functional requirements (FRs) and non-functional requirements (NFRs). Concerning FRs, UML has been used as the de facto object-oriented analysis and design notation. Concerning NFRs, the NFR framework extends, and complements, UML, by treating NFRs as potentially conflicting or synergistic softgoals to be achieved, thereby allowing for the consideration of alternatives and analysis of trade-offs among the alternatives. Albeit the complementary nature of the two, UML and the NFR framework offer two different notations -syntactically, semantically and visually, which makes it difficult to produce requirements models that integrate both FRs and NFRs. In this paper, we propose an integrated modeling language by extending UML with the NFR framework using the standard extension mechanism called UML profile where we define a metamodel to represent the concepts in the NFR framework and identify the extension points for integrating the two notations. We also show how CASE tools may use this profile in building an integrated requirements model based on a model of the well known London ambulance case study.


title: Raman microscopy of bone.

abstract: Raman microscopy is a non-destructive technique requiring minimal sample preparation that can be used to measure the chemical properties of the mineral and collagen parts of bone simultaneously. Modern Raman instruments contain the necessary components and software to acquire the standard information required in most bone studies. The spatial resolution of the technique is about a micron. As it is non-destructive and small samples can be used, it forms a useful part of a bone characterisation toolbox.


title: Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling

abstract: The specific requirements of UAV-photogrammetry necessitate particular solutions for system development, which have mostly been ignored or not assessed adequately in recent studies. Accordingly, this paper presents the methodological and experimental aspects of correctly implementing a UAV-photogrammetry system. The hardware of the system consists of an electric-powered helicopter, a high-resolution digital camera and an inertial navigation system. The software of the system includes the in-house programs specifically designed for camera calibration, platform calibration, system integration, on-board data acquisition, flight planning and on-the-job self-calibration. The detailed features of the system are discussed, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The developed system is extensively tested for precise modeling of the challenging environment of an open-pit gravel mine. The accuracy of the results is evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy are assessed. The experiments demonstrated that 1.55 m horizontal and 3.16 m vertical absolute modeling accuracy could be achieved via direct geo-referencing, which was improved to 0.4 cm and 1.7 cm after indirect geo-referencing.


title: Deterministic Initialization of the K-Means Algorithm Using Hierarchical Clustering

abstract: K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. Many of these methods, however, have superlinear complexity in the number of data points, making them impractical for large data sets. On the other hand, linear methods are often random and/or order-sensitive, which renders their results unrepeatable. Recently, Su and Dy proposed two highly successful hierarchical initialization methods named Var-Part and PCA-Part that are not only linear, but also deterministic (nonrandom) and order-invariant. In this paper, we propose a discriminant analysis based approach that addresses a common deficiency of these two methods. Experiments on a large and diverse collection of data sets from the UCI machine learning repository demonstrate that Var-Part and PCA-Part are highly competitive with one of the best random initialization methods to date, i.e. k-means++, and that the proposed approach significantly improves the performance of both hierarchical methods.


Abstractive Document Summarization with a Graph-Based Attentional Neural Model


title: Design of dual-polarized series-fed microstrip arrays with low losses and high polarization purity

abstract: The design of a dual-polarized microstrip series-fed linear traveling-wave array is described in this paper. The array is composed of two identical subarrays formed by cascading an equal number of four-port aperture-coupled cross-patch elements and terminated on a two-port radiating matched load. By properly exciting the array, dual linear or circular polarization can be accomplished and by virtue of the symmetric arrangement of the antenna cross-polarization is annihilated. A straightforward design strategy is proposed for the synthesis of a desired current distribution along the array antenna. Some proof-of-concept linear arrays are developed and the corresponding numerical results, obtained through a full-wave approach based on the method of moments (MoM), are provided. As an independent validation, supplementary analyses by the finite integration technique (FIT) are also reported. Very close agreement is found between prescribed and synthesized array performance, which qualifies the proposed design approach as an accurate and effective tool for the synthesis of series-fed aperture-coupled patch arrays.


title: Characterization of semilumped CPW elements for Millimeter-wave filter design

abstract: In this paper, two accurate models for interdigital capacitors and shunt inductive stubs in coplanar-waveguide structures are presented and validated over the entire W-band frequency range. Using these models, a novel bandpass filter (BPF) and a miniaturized high-pass filter are designed and fabricated. By inserting interdigital capacitors in BPF resonators, an out-of-band transmission null is introduced, which improves rejection level up to 17 dB over standard designs of similar filters. A high-pass filter is also designed, using semilumped-element models in order to miniaturize the filter structure. It is shown that a fifth-order high-pass filter can be built with a maximum dimension of less than /spl lambda//sub g//3. Great agreement between simulated and measured responses of these filters is demonstrated.


title: An adaptive LU factorization algorithm for parallel circuit simulation

abstract: Sparse matrix solver has become the bottleneck in SPICE simulator. It is difficult to parallelize the solver because of the high data-dependency during the numerical LU factorization. This paper proposes a parallel LU factorization (with partial pivoting) algorithm on shared-memory computers with multi-core CPUs, to accelerate circuit simulation. Since not every matrix is suitable for parallel algorithm, a predictive method is proposed to decide whether a matrix should use parallel or sequential algorithm. The experimental results on 35 circuit matrices reveal that the developed algorithm achieves speedups of 2.11×∼8.38× (on geometric-average), compared with KLU, with 1∼8 threads, on the matrices which are suitable for parallel algorithm. Our solver can be downloaded from http://nicslu.weebly.com.


title: A mathematical analysis of the effects of control strategies on the transmission dynamics of malaria

abstract: We formulate a deterministic model with two latent periods in the non-constant host and vector populations, in order to theoretically assess the potential impact of personal protection, treatment and possible vaccination strategies on the transmission dynamics of malaria. The thresholds and equilibria for the model are determined. The model is analysed qualitatively to determine criteria for control of a malaria epidemic and is used to compute the threshold vaccination and treatment rates necessary for community-wide control of malaria. In addition to having a disease-free equilibrium, which is locally asymptotically stable when the basic reproductive number is less than unity, the model exhibits the phenomenon of backward bifurcation where a stable disease-free equilibrium coexists with a stable endemic equilibrium for a certain range of associated reproductive number less than one. From the analysis we deduce that personal protection has a positive impact on disease control but to eradicate the disease in the absence of any other control measures the efficacy and compliance should be very high. Our results show that vaccination and personal protection can suppress the transmission rates of the parasite from human to vector and vice-versa. If the treated populations are infectious then certain conditions should be satisfied for treatment to reduce the spread of malaria in a community. Among the interesting dynamical behaviours of the model, numerical simulations show a backward bifurcation which gives a challenge to the designing of effective control measures.


A literature Review on Android Permission System


title: Phoneme-based English-Amharic Statistical Machine Translation

abstract: This research considers the application of Statistical method to automatic Machine Translation (MT) from English to Amharic. The research focuses on improving the translation quality by applying phonemic transcription on the target side, which is Amharic. Accordingly, the BLEU score results for the phoneme-based EASMT system is 37.53% a gain of 2.21 BLEU point from another baseline phrase-based EASMT with a BLEU score result of 35.32%. This clearly shows that phoneme-based translation outperforms the baseline system.


title: Performance evaluation of advanced manufacturing technologies: A De novo approach

abstract: Abstract Modern manufacturing world is a super competitive place. To sustain in this environment, manufacturing organizations need a bit more of effectiveness, flexibility and innovation while manufacturing their products. This can be achieved by grasping the benefits extracted from the use of Advanced Manufacturing Technology (AMT). Selection and evaluation of AMTs have been receiving significant attention in today's manufacturing environment as this seems to be the only way out for sustainable development. The present paper focuses on the two almost unrevealed multi-criteria approaches namely preference ranking organization method for enrichment evaluations (PROMETHEE) and Dempster-Shafer Theory of evidence (DST) based on the basic idea of technique for order preference by similarity to ideal solution (TOPSIS). It also depicts a comparative study of the two for solving an AMT selection problem in a given organizational environment.


title: Design and implementation of a PID control system for a coaxial two-wheeled mobile robot

abstract: This paper presents the design of a control system for a coaxial two-wheeled inverted pendulum robot. The control strategy implemented was developed based on a dynamic model of the robot that takes into consideration its three degrees of freedom, and consists of two decoupled PID controllers, tuned by Ziegler-Nichols technique. Such strategy is responsible for controlling the robot's pitch motion and linear positioning regardless of its yaw motion, ensuring system stability, proper set-points tracking and disturbances rejection.


title: Video face replacement system using a modified Poisson blending technique

abstract: In this paper, we present a system for video face replacement that requires only two videos of a source actor and a target actor using only a single digital camera. Existing video face replacement techniques usually need special equipment or 3D models; the proposed system achieves a realistic replacement of faces without using 3D models or special equipment. There are many applications of the proposed system that are presented in this paper using only two footages of actors. We can replace a frontal face with another one; this gives the possibility to change the appearance of actors without makeup or any prior settings. We introduce a new technique for face blending that is based on a gradient domain method called Modified Poisson Blending (MPB) to reduce the bleeding problem of Poisson image editing, and achieve realistic results of face replacement. Experimental results demonstrate that the proposed system using MPB technique produces more realistic results than the results of other cloning techniques.


title: Efficient gather and scatter operations on graphics processors

abstract: Gather and scatter are two fundamental data-parallel operations, where a large number of data items are read (gathered) from or are written (scattered) to given locations. In this paper, we study these two operations on graphics processing units (GPUs). With superior computing power and high memory bandwidth, GPUs have become a commodity multiprocessor platform for general-purpose high-performance computing. However, due to the random access nature of gather and scatter, a naive implementation of the two operations suffers from a low utilization of the memory bandwidth and consequently a long, unhidden memory latency. Additionally, the architectural details of the GPUs, in particular, the memory hierarchy design, are unclear to the programmers. Therefore, we design multi-pass gather and scatter operations to improve their data access locality, and develop a performance model to help understand and optimize these two operations. We have evaluated our algorithms in sorting, hashing, and the sparse matrix-vector multiplication in comparison with their optimized CPU counterparts. Our results show that these optimizations yield 2--4X improvement on the GPU bandwidth utilization and 30--50% improvement on the response time. Overall, our optimized GPU implementations are 2--7X faster than their optimized CPU counterparts.


title: Lock Trace Reduction for Multithreaded Programs

abstract: Many happened-before-based detectors for debugging multithreaded programs implement vector clocks to incrementally track the casual relations among synchronization events produced by concurrent threads and generate trace logs. They update the vector clocks via vector-based comparison and content assignment in every case. We observe that many such tracking comparison and assignment operations are removable in part or in whole, which if identified and used properly, have the potential to reduce the log traces thus produced. This paper presents our analysis to identify such removable tracking operations and shows how they could be used to reduce log traces. We implement our analysis result as a technique entitled LOFT. We evaluate LOFT on the well-studied PARSEC benchmarking suite and five large-scale real-world applications. The main experimental result shows that on average, LOFT identifies 63.9 percent of all synchronization operations incurred by the existing approach as removable and does not compromise the efficiency of the latter.


title: Effects of cognitive-behavioral therapy on brain activation in specific phobia

abstract: Abstract Little is known about the effects of successful psychotherapy on brain function in subjects with anxiety disorders. The present study aimed to identify changes in brain activation following cognitive-behavioral therapy (CBT) in subjects suffering from specific phobia. Using functional magnetic resonance imaging (fMRI), brain activation to spider videos was measured in 28 spider phobic and 14 healthy control subjects. Phobics were randomly assigned to a therapy-group (TG) and a waiting-list control group (WG). Both groups of phobics were scanned twice. Between scanning sessions, CBT was given to the TG. Before therapy, brain activation did not differ between both groups of phobics. As compared to control subjects, phobics showed greater responses to spider vs. control videos in the insula and anterior cingulate cortex (ACC). CBT strongly reduced phobic symptoms in the TG while the WG remained behaviorally unchanged. In the second scanning session, a significant reduction of hyperactivity in the insula and ACC was found in the TG compared to the WG. These results propose that increased activation in the insula and ACC is associated with specific phobia, whereas an attenuation of these brain responses correlates with successful therapeutic intervention.


A Review of Capacitive EEG Sensor


title: Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs

abstract: We consider wireless LANs such as IEEE 802.11 operating in the unlicensed radio spectrum. While their nominal bit rates have increased considerably, the MAC layer remains practically unchanged despite much research effort spent on improving its performance. We observe that most proposals for tuning the access method focus on a single aspect and disregard others. Our objective is to define an access method optimized for throughput and fairness, able to dynamically adapt to physical channel conditions, to operate near optimum for a wide range of error rates, and to provide equal time shares when hosts use different bit rates.We propose a novel access method derived from 802.11 DCF [2] (Distributed Coordination Function) in which all hosts use similar values of the contention window CW to benefit from good short-term access fairness. We call our method Idle Sense, because each host observes the mean number of idle slots between transmission attempts to dynamically control its contention window. Unlike other proposals, Idle Sense enables each host to estimate its frame error rate, which can be used for switching to the right bit rate. We present simulations showing how the method leads to high throughput, low collision overhead, and low delay. The method also features fast reactivity and time-fair channel allocation.


The Conceptual Model on Smart Phone Addiction among Early Childhood


title: Speaker-independent phonetic classification in continuous English letters

abstract: A phonetic front-end for speaker-independent recognition of continuous letter strings is described. A feedforward neutral network is trained to classify 3 msec speech frames as one of the 30 phonemes in the English alphabet. Phonetic context is used in two ways: first, by providing spectral and waveform information before and after the frame to be classified, and second, by a second-pass network that uses both acoustic features and the phonetic outputs of the first-pass network. This use of context reduced the error rate by 50%. The effectiveness of the DFT and the more compact PLP (perceptual linear predictive) analysis is compared, and several other features, such as zero crossing rate, are investigated. A frame-based phonetic classification performance of 75.7% was achieved. >


title: Auxiliary Tasks in Multi-task Learning

abstract: Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expected to boost the performance of the ultimately desired main tasks. To study the proposed approach, we picked vision-based road scene understanding (RSU) as an exemplary application. Since multi-task learning requires specialized datasets, particularly when using extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU, called synMT. More than 2.5 $\cdot$ 10^5 synthetic images, annotated with 21 different labels, were acquired from the video game Grand Theft Auto V (GTA V). Our proposed deep multi-task CNN architecture was trained on various combination of tasks using synMT. The experiments confirmed that auxiliary tasks can indeed boost network performance, both in terms of final results and training time.


DialSQL: Dialogue Based Structured Query Generation


title: Two-Dimensional Optical Beam Steering With InP-Based Photonic Integrated Circuits

abstract: Two-dimensional optical beam steering using an InP photonic integrated circuit has been demonstrated. Lateral beam steering controlled by a 1-D phased array has been made easier through on-chip interferometer monitors. Longitudinal beam steering controlled by the input wavelength has demonstrated an efficiency of 0.14 °/nm. Very fast beam steering (>107 °/s) in both dimensions has been demonstrated as well. As the latest development, a widely tunable sampled-grating distributed Bragg reflector laser has been monolithically integrated and 2-D beam steering has been demonstrated with this on-chip tunable laser source.


Cannabis chemovar classification: terpenes hyper-classes and targeted genetic markers for accurate discrimination of flavours and effects


title: Multi-scale Orderless Pooling of Deep Convolutional Activation Features

abstract: Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets.


title: Graphene Terahertz Plasmon Oscillators

abstract: In this paper we propose and discuss coherent terahertz sources based on charge density wave (plasmon) amplification in two dimensional graphene. The coupling of the plasmons to interband electron-hole transitions in population inverted graphene layers can lead to plasmon amplification through stimulated emission. Plasmon gain values in graphene can be very large due to the small group velocity of the plasmons and the strong confinement of the plasmon field in the vicinity of the graphene layer. We present a transmission line model for plasmon propagation in graphene that includes plasmon dissipation and plasmon interband gain due to stimulated emission. Using this model, we discuss design for terahertz plasmon oscillators and derive the threshold condition for oscillation taking into account internal losses and also losses due to external coupling. The large gain values available at terahertz frequencies in graphene can lead to integrated oscillators that have dimensions in the 1-10 micron range.


title: Learning Structured Inference Neural Networks with Label Relations

abstract: Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with fine-grained labels that describe major components, coarse-grained labels that depict high level abstraction or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.


title: Theory and application of array coils in MR spectroscopy

abstract: The theory and application of array coils are reviewed in the context of phased array spectroscopy. The optimization of the signal-to-noise ratio from an array of coils is developed by considering the efficiency of a phased array transmit coil. This approach avoids the need to consider noise correlation, and should be useful in future considerations of transmit phased array coils for MR spectroscopy. Methods to characterize array coil performance, including fields and coupling are briefly summarized, along with methods to minimize the effects of mutual inductance. The signal-to-noise advantages of array coils over single coils are examined for both planar and cylindrical arrays. Numerical simulations of planar arrays of 2 x 2, 4 x 4 and 8 x 8 elements and constant overall dimension are compared to a single coil of the same size. The results demonstrate a significant improvement in sensitivity near the array coil. Although the benefits of the array decrease as a function of distance from the array, the array sensitivity never drops below that of a single coil with the same overall dimensions, or that of a single element of the array. Similar results are obtained for a sixteen element cylindrical array, which is compared to a standard quadrature birdcage coil using both computational methods and phantom measurements. The phased array techniques reviewed are demonstrated with proton spectroscopic images of the brain.


title: Investigating Contingency Awareness Using Atari 2600 Games

abstract: Contingency awareness is the recognition that some aspects of a future observation are under an agent's control while others are solely determined by the environment. This paper explores the idea of contingency awareness in reinforcement learning using the platform of Atari 2600 games. We introduce a technique for accurately identifying contingent regions and describe how to exploit this knowledge to generate improved features for value function approximation. We evaluate the performance of our techniques empirically, using 46 unseen, diverse, and challenging games for the Atari 2600 console. Our results suggest that contingency awareness is a generally useful concept for model-free reinforcement learning agents.


title: Learning Extended Finite State Machines

abstract: We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.


title: Non-linear matrix factorization with Gaussian processes

abstract: A popular approach to collaborative filtering is matrix factorization. In this paper we develop a non-linear probabilistic matrix factorization using Gaussian process latent variable models. We use stochastic gradient descent (SGD) to optimize the model. SGD allows us to apply Gaussian processes to data sets with millions of observations without approximate methods. We apply our approach to benchmark movie recommender data sets. The results show better than previous state-of-the-art performance.


title: Transformation-Grounded Image Generation Network for Novel 3D View Synthesis

abstract: We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Our approach first explicitly infers the parts of the geometry visible both in the input and novel views and then casts the remaining synthesis problem as image completion. Specifically, we both predict a flow to move the pixels from the input to the novel view along with a novel visibility map that helps deal with occulsion/disocculsion. Next, conditioned on those intermediate results, we hallucinate (infer) parts of the object invisible in the input image. In addition to the new network structure, training with a combination of adversarial and perceptual loss results in a reduction in common artifacts of novel view synthesis such as distortions and holes, while successfully generating high frequency details and preserving visual aspects of the input image. We evaluate our approach on a wide range of synthetic and real examples. Both qualitative and quantitative results show our method achieves significantly better results compared to existing methods.


title: Moral Emotions and Moral Behavior

abstract: AbstractMoral emotions represent a key element of our human moral apparatus, influencing the link between moral standards and moral behavior. This chapter reviews current theory and research on moral emotions. We first focus on a triad of negatively valenced “self-conscious” emotions—shame, guilt, and embarrassment. As in previous decades, much research remains focused on shame and guilt. We review current thinking on the distinction between shame and guilt, and the relative advantages and disadvantages of these two moral emotions. Several new areas of research are highlighted: research on the domain-specific phenomenon of body shame, styles of coping with shame, psychobiological aspects of shame, the link between childhood abuse and later proneness to shame, and the phenomena of vicarious or “collective” experiences of shame and guilt. In recent years, the concept of moral emotions has been expanded to include several positive emotions—elevation, gratitude, and the sometimes morally relevant experience o...


title: HIGH-GAIN LOW SIDE LOBE LEVEL FABRY PEROT CAVITY ANTENNA WITH FEED PATCH ARRAY

abstract: In this paper, a high gain, low side lobe level Fabry Perot Cavity antenna with feed patch array is proposed. The antenna structure consists of a microstrip antenna array, which is parasitically coupled with an array of square parasitic patches fabricated on a FR4 superstrate. The patches are fabricated at the bottom of superstrate and suspended in air with the help of dielectric rods at 0:5‚0 height. Constant high gain is obtained by resonating parasitic patches at near close frequencies in 5.725{5.875GHz ISM band. The structure with 9£9 square parasitic patches with 1:125‚0 spacing between feed elements is fabricated on 5‚0 £ 5‚0 square ground. The fabricated structure provides gain of 21.5dBi associated with side lobe level less than i25dB, cross polarization less than i26dB and front to back lobe ratio of more than 26dB. The measured gain variation is less than 1dB and VSWR is less than 2 over 5.725{5.875GHz ISM band. The proposed structures are good candidates for base station cellular systems, satellite systems, and point-to-point links.


title: Microkernels Meet Recursive Virtual Machines

abstract: This paper describes a novel approach to providingmodular and extensible operating system functionality and encapsulated environments based on a synthesis of microkernel and virtual machine concepts. We have developed a software-based virtualizable architecture called Fluke that allows recursive virtual machines (virtual machines running on other virtual machines) to be implemented efficiently by a microkernel running on generic hardware. A complete virtual machine interface is provided at each level; efficiency derives from needing to implement only new functionality at each level. This infrastructure allows common OS functionality, such as process management, demand paging, fault tolerance, and debugging support, to be provided by cleanly modularized, independent, stackable virtual machine monitors, implemented as user processes. It can also provide uncommon or unique OS features, including the above features specialized for particular applications’ needs, virtual machines transparently distributed cross-node, or security monitors that allow arbitrary untrusted binaries to be executed safely. Our prototype implementation of this model indicates that it is practical to modularize operating systems this way. Some types of virtual machine layers impose almost no overhead at all, while others impose some overhead (typically 0–35%), but only on certain classes of applications.


title: Document-Level Neural Machine Translation with Hierarchical Attention Networks

abstract: Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model's own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.


title: APPLICATIONS OF DATA MINING TECHNIQUES IN LIFE INSURANCE

abstract: Knowledge discovery in financial organization have been built and operated mainly to support decision making using knowledge as strategic factor. In this paper, we investigate the use of various data mining techniques for knowledge discovery in insurance business. Existing software are inefficient in showing such data characteristics. We introduce different exhibits for discovering knowledge in the form of association rules, clustering, classification and correlation suitable for data characteristics. Proposed data mining techniques, the decision- maker can define the expansion of insurance activities to empower the different forces in existing life insurance sector.


title: Wireless Sensor Network Based Coal Mine Wireless and Integrated Security Monitoring Information System

abstract: According to the actual landform of underground coal mine and the demand of security monitoring, in this paper a coal mine wireless and integrated security monitoring system which combine wireless sensor network and wire monitoring network is proposed. This paper emphasizes a wireless sensor network for coal mine monitoring. In the light of the actual circumstances of coal mine, we propose two-leveled network architecture for the wireless sensor network, put forward a corresponding topology generation algorithm, and discuss monitoring mechanism about the network in details.


title: ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

abstract: Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize~\emph{proxy} tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present \emph{ProxylessNAS} that can \emph{directly} learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08\% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6$\times$ fewer parameters. On ImageNet, our model achieves 3.1\% better top-1 accuracy than MobileNetV2, while being 1.2$\times$ faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.


title: Layout guidelines for optimized ESD protection diodes

abstract: In this work, various layout options for ESD diodes' PN junction geometry and metal routing are investigated. The current compression point (ICP) is introduced to define the maximum current handling capability of ESD protection devices. The figures-of-merit ICP/C and RON*C are used to compare the performance of the structures investigated herein.


title: 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest

abstract: In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds.


title: Compression of facial images using the K-SVD algorithm q

abstract: The use of sparse representations in signal and image processing is gradually increasing in the past several years. Obtaining an overcomplete dictionary from a set of signals allows us to represent them as a sparse linear combination of dictionary atoms. Pursuit algorithms are then used for signal decomposition. A recent work introduced the K-SVD algorithm, which is a novel method for training overcomplete dictionaries that lead to sparse signal representation. In this work we propose a new method for compressing facial images, based on the K-SVD algorithm. We train K-SVD dictionaries for predefined image patches, and compress each new image according to these dictionaries. The encoding is based on sparse coding of each image patch using the relevant trained dictionary, and the decoding is a simple reconstruction of the patches by linear combination of atoms. An essential pre-process stage for this method is an image alignment procedure, where several facial features are detected and geometrically warped into a canonical spatial location. We present this new method, analyze its results and compare it to several competing compression techniques.


title: Abstract Contract Synthesis and Verification in the Symbolic K Framework

abstract: This work has been partially supported by the EU (FEDER) and Spanish MINECO under ::: grant TIN2015-69175-C4-1-R, and and TIN2013-45732-C4-1-P, and by Generalitat Valenciana ::: PROMETEOII/2015/013. Daniel Pardo was supported by FPU-ME grant FPU14/01830.


title: Millimeter-Wave IC-Antenna Cointegration for Integrated Transmitters and Receivers

abstract: Advances in CMOS and SiGe technologies have made millimeter (mm)-wave multielement arrays with complex architectures, high yields, and good interelement matching feasible. However, the antenna-in-package approach is challenging with a larger number of array elements due to the increase in mm-wave I/O, the need for impedance-controlled via/routing symmetry. In this work, we describe an antenna-IC cointegration approach that relies on aperture coupling between on-chip feed/ground-plane slot and an antenna-on-substrate to simultaneously achieve wide bandwidth and high efficiency. The proposed approach is validated through a 60-GHz packaged prototype in which a 60-GHz quadrupler and aperture-coupled antenna feed fabricated in a 0.18-μm SiGe technology is cointegrated with a 60-GHz patch antenna on a 100-μm liquid crystal polymer substrate. Measured E-plane and H-plane antenna patterns show 0-dB gain at 61 GHz (corresponding to 40% efficiency) and cross-polarization ratio of ~10 dB, demonstrating the feasibility of the proposed approach.


title: Internet of Tangible Things (IoTT): Challenges and Opportunities for Tangible Interaction with IoT

abstract: In the Internet of Things era, an increasing number of everyday objects are able to offer innovative services to the user. However, most of these devices provide only smartphone or web user interfaces. As a result, the interaction is disconnected from the physical world, decreasing the user experience and increasing the risk of user alienation from the physical world. We argue that tangible interaction can counteract this trend and this article discusses the potential benefits and the still open challenges of tangible interaction applied to the Internet of Things. After an analysis of open challenges for Human-Computer Interaction in IoT, we summarize current trends in tangible interaction and extrapolate eight tangible interaction properties that could be exploited for designing novel interactions with IoT objects. Through a systematic review of tangible interaction applied to IoT, we show what has been already explored in the systems that pioneered the field and the future explorations that still have to be conducted. In order to guide future work in this field, we propose a design card set for supporting the generation of tangible interfaces for IoT objects. The card set has been evaluated during a workshop with 21 people and the results are discussed.


title: An improved indoor localization based on RSSI and feedback correction of anchor node for WSN

abstract: Received signal strength indicator (RSSI)-based localization attracting a great deal of research interest since it requires a relatively low configuration, battery power and easy control. However, the indoor localization algorithm based on RSSI is easy to be influenced by the channel interference and the environment. In this paper, a novel method is presented for improving the accuracy of indoor localization, which is based on anchor node feedback correction. First, the whole environment is divided before positioning, after the minimum location area is determined, the model parameters of the path loss in the region are estimated, then the distance between nodes is calculated accurately, and the feedback of neighbor anchor nodes is used to improve the accuracy further. The simulation results indicate that the method improves the accuracy of node localization and lower the delay and energy consumption of networks compared with conventional RSSI-based algorithm.


title: Markov Chain Monte Carlo with People

abstract: Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these distributions indirectly. We propose a method for directly determining the assumptions of human learners by sampling from subjective probability distributions. Using a correspondence between a model of human choice and Markov chain Monte Carlo (MCMC), we describe a method for sampling from the distributions over objects that people associate with different categories. In our task, subjects choose whether to accept or reject a proposed change to an object. The task is constructed so that these decisions follow an MCMC acceptance rule, defining a Markov chain for which the stationary distribution is the category distribution. We test this procedure for both artificial categories acquired in the laboratory, and natural categories acquired from experience.


title: What Does the Narcissistic Personality Inventory Really Measure

abstract: The Narcissistic Personality Inventory (NPI) is a widely used measure of narcissism. However, debates persist about its exact factor structure with researchers proposing solutions ranging from two to seven factors. The present research aimed to clarify the factor structure of the NPI and further illuminate its nomological network. Four studies provided support for a three-factor model consisting of the dimensions of Leadership/Authority, Grandiose Exhibitionism, and Entitlement/Exploitativeness. The Leadership/Authority dimension was generally linked to adaptive outcomes whereas the other two dimensions, particularly Entitlement/Exploitativeness, were generally linked to maladaptive outcomes. These results suggest that researchers interested in the psychological and behavioral outcomes associated with the NPI should examine correlates at the facet level. In light of the findings, we propose a hierarchical model for the structure of the NPI and provide researchers with a scoring scheme for this commonly us...


title: Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

abstract: The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.


title: Design Techniques and Applications of Cyberphysical Systems: A Survey

abstract: Cyberphysical systems (CPSs) are new class of engineered systems that offer close interaction between cyber and physical components. The field of CPS has been identified as a key area of research, and CPSs are expected to play a major role in the design and development of future systems. In this paper, we survey recent advancements made in the development and applications of CPSs. We classify the existing research work based on their characteristics and identify the future challenges. We also discuss the examples of prototypes of CPSs. The aim of this survey is to enable researchers and system designers to get insights into the working and applications of CPSs and motivate them to propose novel solutions for making wide-scale adoption of CPS a tangible reality.


title: An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks

abstract: Several studies have demonstrated the benefits of using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks (WSNs). However, these benefits are dependent on the path taken by the mobile sink, particularly in delay-sensitive applications, as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multihopping to the nearest RP. The fundamental problem then becomes computing a tour that visits all these RPs within a given delay bound. Identifying the optimal tour, however, is an NP-hard problem. To address this problem, a heuristic called weighted rendezvous planning (WRP) is proposed, whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP. WRP is validated via extensive computer simulation, and our results demonstrate that WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption by 22% and increases network lifetime by 44%, as compared with existing algorithms.


title: A proposal for statistical outlier detection in relational structures

abstract: This paper extends unsupervised statistical outlier detection to the case of relational data. For nonrelational data, where each individual is characterized by a feature vector, a common approach starts with learning a generative statistical model for the population. The model assigns a likelihood measure for the feature vector that characterizes the individual; the lower the feature vector likelihood, the more anomalous the individual. A difference between relational and nonrelational data is that an individual is characterized not only by a list of attributes, but also by its links and by attributes of the individuals linked to it. We refer to a relational structure that specifies this information for a specific individual as the individual's database. Our proposal is to use the likelihood assigned by a generative model to the individual's database as the anomaly score for the individual; the lower the model likelihood, the more anomalous the individual. As a novel validation method, we compare the model likelihood with metrics of individual success. An empirical evaluation reveals a surprising finding in soccer and movie data:We observe in the data a strong correlation between the likelihood and success metrics.


title: License Plate Recognition From Still Images and Video Sequences: A Survey

abstract: License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.


title: Automated Whitebox Fuzz Testing

abstract: Fuzz testing is an effective technique for finding security vulnerabilities in software. Traditionally, fuzz testing tools apply random mutations to well-formed inputs of a program and test the resulting values. We present an alternative whitebox fuzz testing approach inspired by recent advances in symbolic execution and dynamic test generation. Our approach records an actual run of the program under test on a well-formed input, symbolically evaluates the recorded trace, and gathers constraints on inputs capturing how the program uses these. The collected constraints are then negated one by one and solved with a constraint solver, producing new inputs that exercise different control paths in the program. This process is repeated with the help of a code-coverage maximizing heuristic designed to find defects as fast as possible. We have implemented this algorithm in SAGE (Scalable, Automated, Guided Execution), a new tool employing x86 instruction-level tracing and emulation for whitebox fuzzing of arbitrary file-reading Windows applications. We describe key optimizations needed to make dynamic test generation scale to large input files and long execution traces with hundreds of millions of instructions. We then present detailed experiments with several Windows applications. Notably, without any format-specific knowledge, SAGE detects the MS07-017 ANI vulnerability, which was missed by extensive blackbox fuzzing and static analysis tools. Furthermore, while still in an early stage of development, SAGE has already discovered 30+ new bugs in large shipped Windows applications including image processors, media players, and file decoders. Several of these bugs are potentially exploitable memory access violations.


title: The Probability of a Possibility: Adding Uncertainty to Default Rules

abstract: We present a semantics for adding uncertainty to conditional logics for default reasoning and belief revision. We are able to treat conditional sentences as statements of conditional probability, and express rules for revision such as "If A were believed, then B would be believed to degree p." This method of revision extends conditionalization by allowing meaningful revision by sentences whose probability is zero. This is achieved through the use of counterfactual probabilities. Thus, our system accounts for the best properties of qualitative methods of update (in particular, the AGM theory of revision) and probabilistic methods. We also show how our system can be viewed as a unification of probability theory and possibility theory, highlighting their orthogonality and providing a means for expressing the probability of a possibility. We also demonstrate the connection to Lewis's method of imaging.


title: Don't Worry, Be Happy: Endocannabinoids and Cannabis at the Intersection of Stress and Reward

abstract: Cannabis enables and enhances the subjective sense of well-being by stimulating the endocannabinoid system (ECS), which plays a key role in modulating the response to stress, reward, and their interactions. However, over time, repeated activation of the ECS by cannabis can trigger neuroadaptations that may impair the sensitivity to stress and reward. This effect, in vulnerable individuals, can lead to addiction and other adverse consequences. The recent shift toward legalization of medical or recreational cannabis has renewed interest in investigating the physiological role of the ECS as well as the potential health effects, both adverse and beneficial, of cannabis. Here we review our current understanding of the ECS and its complex physiological roles. We discuss the implications of this understanding vis-a-vis the ECS's modulation of stress and reward and its relevance to mental disorders in which these processes are disrupted (i.e., addiction, depression, posttraumatic stress disorder, schizophrenia), ...


title: Exploring the Impact of Social Media on Hotel Service Performance A Sentimental Analysis Approach

abstract: Online user-generated content in various social media websites, such as consumer experiences, user feedback, and product reviews, has increasingly become the primary information source for both consumers and businesses. In this study, we aim to look beyond the quantitative summary and unidimensional interpretation of online user reviews to provide a more comprehensive view of online user-generated content. Moreover, we would like to extend the current literature to the more customer-driven service industries, particularly the hotel industry. We obtain a unique and extensive dataset of online user reviews for hotels across various review sites and over long time periods. We use the sentiment analysis technique to decompose user reviews into different dimensions to measure hotel service quality and performance based on the SERVPERF model. Those dimensions are then incorporated into econometrics models to examine their effect in shaping users’ overall evaluation and content-generating behavior. The results s...


title: Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods

abstract: Objective: Diabetic nephropathy is damage to the kidney caused by diabetes mellitus. It is a common complication and a leading cause of death in people with diabetes. However, the decline in kidney function varies considerably between patients and the determinants of diabetic nephropathy have not been clearly identified. Therefore, it is very difficult to predict the onset of diabetic nephropathy accurately with simple statistical approaches such as t-test or @g^2-test. To accurately predict the onset of diabetic nephropathy, we applied various machine learning techniques to irregular and unbalanced diabetes dataset, such as support vector machine (SVM) classification and feature selection methods. Visualization of the risk factors was another important objective to give physicians intuitive information on each patient's clinical pattern. Methods and materials: We collected medical data from 292 patients with diabetes and performed preprocessing to extract 184 features from the irregular data. To predict the onset of diabetic nephropathy, we compared several classification methods such as logistic regression, SVM, and SVM with a cost sensitive learning method. We also applied several feature selection methods to remove redundant features and improve the classification performance. For risk factor analysis with SVM classifiers, we have developed a new visualization system which uses a nomogram approach. Results: Linear SVM classifiers combined with wrapper or embedded feature selection methods showed the best results. Among the 184 features, the classifiers selected the same 39 features and gave 0.969 of the area under the curve by receiver operating characteristics analysis. The visualization tool was able to present the effect of each feature on the decision via graphical output. Conclusions: Our proposed method can predict the onset of diabetic nephropathy about 2-3 months before the actual diagnosis with high prediction performance from an irregular and unbalanced dataset, which statistical methods such as t-test and logistic regression could not achieve. Additionally, the visualization system provides physicians with intuitive information for risk factor analysis. Therefore, physicians can benefit from the automatic early warning of each patient and visualize risk factors, which facilitate planning of effective and proper treatment strategies.


Chinese Preposition Selection for Grammatical Error Diagnosis


title: Minutiae-based fingerprint matching decomposition

abstract: Fingerprint recognition, and in particular minutiae-based matching methods, are ever more deeply implanted into many companies and institutions. As the size of their identification databases grows, there is a need of flexible, reliable structures for fingerprint recognition systems. In this paper, we propose a generic decomposition methodology for minutiae-based matching algorithms that splits the calculation of the matching scores into lower level steps that can be carried out in parallel in a flexible manner. The decomposition allows to adapt any minutiae-based algorithm to frameworks such as MapReduce or Apache Spark. General and specific guidelines to enhance the performance of the adapted matching algorithms are also described. The proposal is evaluated over two matching algorithms, two Big Data frameworks (Apache Hadoop and Apache Spark) and two large-scale fingerprint databases, with promising results concerning the identification time, in addition to the reliability, scalability, distribution and availability capabilities that are provided by such underlying frameworks.


title: Real-Time Direct Dense Matching on Fisheye Images Using Plane-Sweeping Stereo

abstract: In this paper, we propose an adaptation of camera projection models for fisheye cameras into the plane-sweeping stereo matching algorithm. This adaptation allows us to do plane-sweeping stereo directly on fisheye images. Our approach also works for other non-pinhole cameras such as omni directional and catadioptric cameras when using the unified projection model. Despite the simplicity of our proposed approach, we are able to obtain full, good quality and high resolution depth maps from the fisheye images. To verify our approach, we show experimental results based on depth maps generated by our approach, and dense models produced from these depth maps.


title: Self-Interference Cancellation with Nonlinear Distortion Suppression for Full-Duplex Systems

abstract: In full-duplex systems, due to the strong self-interference signal, system nonlinearities become a significant limiting factor that bounds the possible cancellable self-interference power. In this paper, a self-interference cancellation scheme for full-duplex orthogonal frequency division multiplexing systems is proposed. The proposed scheme increases the amount of cancellable self-interference power by suppressing the distortion caused by the transmitter and receiver nonlinearities. An iterative technique is used to jointly estimate the self-interference channel and the nonlinearity coefficients required to suppress the distortion signal. The performance is numerically investigated showing that the proposed scheme achieves a performance that is less than 0.5dB off the performance of a linear full-duplex system.


title: Artificial Magnetic Materials Using Fractal Hilbert Curves

abstract: Novel configurations based on Fractal Hilbert curves are proposed for realizing artificial magnetic materials. It is shown that the proposed configuration gives significant rise to miniaturization of artificial unit cells which in turn results in higher homogeneity in the material, and reduction in the profile of the artificial substrate. Analytical formulas are proposed for design and optimization of the presented structures, and are verified through full wave numerical characterization. The electromagnetic properties of the proposed structures are studied in detail and compared to square spiral from the point of view of size reduction, maximum value of the resultant permeability, magnetic loss, and frequency dispersion. To validate the analytical model and the numerical simulation results, an artificial substrate containing second-order Fractal Hilbert curve is fabricated and experimentally characterized using a microstrip-based characterization method.


title: Artificial Intelligence for Long-Term Robot Autonomy: A Survey

abstract: Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. weeks, months, or years) poses many challenges. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation&mapping, perception, knowledge representation&reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as 'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy.


title: Will Simulation Sickness Slow Down the Diffusion of Virtual Environment Technology?

abstract: The widespread diffusion of immersive virtual environments VE is threatened by persistent reports that some users experience simulation sickness, a form of motion sickness that accompanies extended use of the medium. Experience with the problem of simulation sickness is most extensive in the military where the illness has accompanied the use of various simulators since the 1950s. This article considers the obstacles presented by simulation sickness to the diffusion of VE systems, its physiological and technological causes, and, finally, the remedies that have been suggested to fix the problems. This issue is also considered in light of previous reports of purported illnesses that accompanied the diffusion of other communication technologies.