On the Capacity Comparison Between MIMO-NOMA and MIMO-OMA

Non-orthogonal multiple access (NOMA) has been shown in the literature to have a better performance than OMA in terms of sum channel capacity; however, the capacity superiority of NOMA over OMA has been only proved for single antenna systems, and the proof for the capacity superiority of multiple-input multiple-output NOMA (MIMO-NOMA) over conventional MIMO-OMA has not been available yet. In this paper, we will provide our proof to demonstrate that the MIMO-NOMA is strictly better than MIMO-OMA in terms of sum channel capacity (except for the case where only one user is being communicated to), i.e., for any rate pair achieved by MIMO-OMA, there is a power split for which MIMO-NOMA can achieve rate pairs that are strictly larger. Based on this result, we prove that the MIMO-NOMA can also achieve a larger sum ergodic capacity than MIMO-OMA. Our analytical results are verified by simulations.


Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs

Knowledge graphs are large, useful, but incomplete knowledge repositories. ey encode knowledge through entities and relations which dene each other through the connective structure of the graph. is has inspired methods for the joint embedding of entities and relations in continuous low-dimensional vector spaces, that can be used to induce new edges in the graph, i.e., link prediction in knowledge graphs. Learning these representations relies on contrasting positive instances with negative ones. Knowledge graphs include only positive relation instances, leaving the door open for a variety of methods for selecting negative examples. We present an empirical study on the impact of negative sampling on the learned embeddings, assessed through the task of link prediction. We use state-of-the-art knowledge graph embedding methods – Rescal , TransE, DistMult and ComplEX – and evaluate on benchmark datasets – FB15k and WN18. We compare well known methods for negative sampling and propose two new embedding based sampling methods. We note a marked dierence in the impact of these sampling methods on the two datasets, with the ”traditional” corrupting positives method leading to best results on WN18, while embedding based methods benet FB15k.


BRIDE - A toolchain for framework-independent development of industrial service robot applications

Software integration is still a challenging and time consuming task and therefore a major part of the development of industrial and domestic service robot applications. The presented toolchain BRIDE is able to streamline this process by the separation of user roles and the separation of developer concerns of software components to ensure a frame-work independent implementation. The impact of the BRIDE toolchain in the development process is demonstrated in a case study on the SyncMM mobile manipulation control framework.


The String B-tree: A New Data Structure for String Search in External Memory and Its Applications

We introduce a new text-indexing data structure, the String B-Tree, that can be seen as a link between some traditional external-memory and string-matching data structures. In a short phrase, it is a combination of B-trees and Patricia tries for internal-node indices that is made more effective by adding extra pointers to speed up search and update operations. Consequently, the String B-Tree overcomes the theoretical limitations of inverted files, B-trees, prefix B-trees, suffix arrays, compacted tries and suffix trees. String B-trees have the same worst-case performance as B-trees but they manage unbounded-length strings and perform much more powerful search operations such as the ones supported by suffix trees. String B-trees are also effective in main memory (RAM model) because they improve the online suffix tree search on a dynamic set of strings. They also can be successfully applied to database indexing and software duplication.


Risk indicators and psychopathology in traumatised children and adolescents with a history of sexual abuse

Childhood sexual abuse (CSA) is widespread amongst South African (SA) children, yet data on risk factors and psychiatric consequences are limited and mixed. Traumatised children and adolescents referred to our Youth Stress Clinic were interviewed to obtain demographic, sexual abuse, lifetime trauma and psychiatric histories. Data for 94 participants (59 female, 35 male; mean age 14.25 [8.25–19] years) exposed to at least one lifetime trauma were analysed. Sexual abuse was reported in 53% of participants (42.56% females, 10.63% males) with 64% of violations committed by perpetrators known to them. Multinomial logistic regression analysis revealed female gender (P = 0.002) and single-parent families (P = 0.01) to be significant predictors of CSA (62.5%). CSA did not predict exposure to other traumas. Sexually abused children had significantly higher physical and emotional abuse subscale scores and total CTQ scores than non-abused children. Depression (33%, X 2 = 10.89, P = 0.001) and PTSD (63.8%, X 2 = 4.79, P = 0.034) were the most prevalent psychological consequences of trauma and both were significantly associated with CSA. High rates of CSA predicted high rates of PTSD in this traumatised sample. Associations we found appear consistent with international studies of CSA and, should be used to focus future social awareness, prevention and treatment strategies in developing countries.


Estimating software based on use case points

It is well documented that software product cost estimates are notoriously inaccurate across the software industry. Creating accurate cost estimates for software product development projects early in the product development lifecycle has always been a challenge for the industry. This article describes how a large multi-team software engineering organization (over 450 engineers) estimates project cost accurately and early in the software development lifecycle using Use Case Points, and the process of evaluating metrics to ensure the accuracy of the model.The engineering teams of Agilis Solutions in partnership with FPT Software, provide our customers with accurate estimates for software product projects early in the product lifecycle. The bases for these estimates are initial definitions of Use Cases, given point factors and modified for technical and environmental factors according to the Use Case Point method defined within the Rational Unified Process. After applying the process across hundreds of sizable (60 man-months average) software projects, we have demonstrated metrics that prove an estimating accuracy of less than 9% deviation from actual to estimated cost on 95% of our projects. Our process and this success factor is documented over a period of five years, and across more than 200 projects.


Fast Change Point Detection on Dynamic Social Networks

A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.


Thin skin elastodynamics

We present a novel approach for simulating thin hyperelastic skin. Real human skin is only a few millimeters thick. It can stretch and slide over underlying body structures such as muscles, bones, and tendons, revealing rich details of a moving character. Simulating such skin is challenging because it is in close contact with the body and shares its geometry. Despite major advances in simulating elastodynamics of cloth and soft bodies for computer graphics, such methods are difficult to use for simulating thin skin due to the need to deal with non-conforming meshes, collision detection, and contact response. We propose a novel Eulerian representation of skin that avoids all the difficulties of constraining the skin to lie on the body surface by working directly on the surface itself. Skin is modeled as a 2D hyperelastic membrane with arbitrary topology, which makes it easy to cover an entire character or object. Unlike most Eulerian simulations, we do not require a regular grid and can use triangular meshes to model body and skin geometry. The method is easy to implement, and can use low resolution meshes to animate high-resolution details stored in texture-like maps. Skin movement is driven by the animation of body shape prescribed by an artist or by another simulation, and so it can be easily added as a post-processing stage to an existing animation pipeline. We provide several examples simulating human and animal skin, and skin-tight clothes.


Boosting Alzheimer Disease Diagnosis Using PET Images

Alzheimer's disease (AD) is one of the most frequent type of dementia. Currently there is no cure for AD and early diagnosis is crucial to the development of treatments that can delay the disease progression. Brain imaging can be a biomarker for Alzheimer's disease. This has been shown in several works with MR Images, but in the case of functional imaging such as PET, further investigation is still needed to determine their ability to diagnose AD, especially at the early stage of Mild Cognitive Impairment (MCI). In this paper we study the use of PET images of the ADNI database for the diagnosis of AD and MCI. We adopt a Boosting classification method, a technique based on a mixture of simple classifiers, which performs feature selection concurrently with the segmentation thus is well suited to high dimensional problems. The Boosting classifier achieved an accuracy of 90.97% in the detection of AD and 79.63% in the detection of MCI.


The BOBYQA algorithm for bound constrained optimization without derivatives

BOBYQA is an iterative algorithm for finding a minimum of a function F (x), x∈R, subject to bounds a≤x≤b on the variables, F being specified by a “black box” that returns the value F (x) for any feasible x. Each iteration employs a quadratic approximation Q to F that satisfies Q(y j ) = F (y j ), j = 1, 2, . . . , m, the interpolation points y j being chosen and adjusted automatically, but m is a prescribed constant, the value m= 2n+1 being typical. These conditions leave much freedom in Q, taken up when the model is updated by the highly successful technique of minimizing the Frobenius norm of the change to the second derivative matrix of Q. Thus no first derivatives of F are required explicitly. Most changes to the variables are an approximate solution to a trust region subproblem, using the current quadratic model, with a lower bound on the trust region radius that is reduced cautiously, in order to keep the interpolation points well separated until late in the calculation, which lessens damage from computer rounding errors. Some other changes to the variables are designed to improve the model without reducing F . These techniques are described. Other topics include the starting procedure that is given an initial vector of variables, the value of m and the initial trust region radius. There is also a new device called RESCUE that tries to restore normality if severe loss of accuracy occurs in the matrix calculations of the updating of the model. Numerical results are reported and discussed for two test problems, the numbers of variables being between 10 and 320. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, England.


Stigma and its public health implications


Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This paper proposes a novel approach that uses a convolutional neural network (CNN) to extract hidden state-time features to mitigate the curse of partial observability. More specific, a CNN is used as a function approximator to estimate the state-action value function or <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-function in the supervised learning step of fitted <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-iteration. The approach is evaluated in a qualitative simulation, comprising a cluster of thermostatically controlled loads that only share their air temperature, while their envelope temperature remains hidden. The simulation results show that the presented approach is able to capture the underlying hidden features and able to successfully reduce the electricity cost the cluster.


Forgetting Is Regulated through Rac Activity in Drosophila

Initially acquired memory dissipates rapidly if not consolidated. Such memory decay is thought to result either from the inherently labile nature of newly acquired memories or from interference by subsequently attained information. Here we report that a small G protein Rac-dependent forgetting mechanism contributes to both passive memory decay and interference-induced forgetting in Drosophila. Inhibition of Rac activity leads to slower decay of early memory, extending it from a few hours to more than one day, and to blockade of interference-induced forgetting. Conversely, elevated Rac activity in mushroom body neurons accelerates memory decay. This forgetting mechanism does not affect memory acquisition and is independent of Rutabaga adenylyl cyclase-mediated memory formation mechanisms. Endogenous Rac activation is evoked on different time scales during gradual memory loss in passive decay and during acute memory removal in reversal learning. We suggest that Rac's role in actin cytoskeleton remodeling may contribute to memory erasure.


Automatic Labelling of Tabla Signals

Most of the recent developments in the field of music indexing and music information retrieval are focused on western music. In this paper, we present an automatic music transcription system dedicated to Tabla a North Indian percussion instrument. Our approach is based on three main steps: firstly, the audio signal is segmented in adjacent segments where each segment represents a single stroke. Secondly, rhythmic information such as relative durations are calculated using beat detection techniques. Finally, the transcription (recognition of the strokes) is performed by means of a statistical model based on Hidden Markov Model (HMM). The structure of this model is designed in order to represent the time dependencies between successives strokes and to take into account the specificities of the tabla score notation (transcription symbols may be context dependent). Realtime transcription of Tabla soli (or performances) with an error rate of 6.5% is made possible with this transcriber. The transcription system, along with some additional features such as sound synthesis or phrase correction, are integrated in a user-friendly environment called Tablascope.


Active Appearance Models Revisited

Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instance; i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed (“projected out”) using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.


Virtual try-on of eyeglasses using 3D model of the head

This work presents a system for virtual try-on of eyeglasses using a 3D model of user's face and head. The 3D head model is reconstructed using only one image of the user. The 3D glasses model are then fitted onto this head model, and the user's head movement is tracked in real-time to rotate the 3D head model with glasses accordingly.


A mixed reality system for virtual glasses try-on

In this paper we present an augmented reality system for automatic try-on of 3D virtual eyeglasses. The user can select from various virtual models of eyeglasses for trying-on and the system will automatically fit the selected virtual glasses on the user's face. The user can see his/her face as in a mirror with the 3D virtual glasses fitted on it. We also propose a method for handling the occlusion problem, to display only those parts of the glasses that are not occluded by the face. This system can be used for online shopping, or short listing a large set of available models to a few before physical try-on at a retailer's site.


Linear Object Classes and Image Synthesis From a Single Example Image

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and°object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced [11, 6, 5] techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter [12]. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.


Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.


Temporal Pattern Attention for Multivariate Time Series Forecasting

Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in all of these with a single exception. Our source code is available at https://github.com/gantheory/TPA-LSTM. * indicates equal contribution. This work was financially supported by the Ministry of Science and Technology of Taiwan. Shun-Yao Shih National Taiwan University E-mail: shunyaoshih@gmail.com Fan-Keng Sun National Taiwan University E-mail: b03901056@ntu.edu.tw Hung-yi Lee National Taiwan University E-mail: hungyilee@ntu.edu.tw ar X iv :1 80 9. 04 20 6v 2 [ cs .L G ] 2 7 N ov 2 01 8 2 Shun-Yao Shih* et al. Fig. 1 Historical prices of crude oil, gasoline, and lumber. Units are omitted and scales are normalized for simplicity.


Seven element wideband planar log-periodic antenna for TVWS base station

This paper presents design and simulation of a wide band antenna for base station in the TV White Space (TVWS) spectrum band operating between 470 MHz-700 MHz. Concept of printed Log Periodic Dipole Array (LPDA), which provides wide bandwidth, has been used to realize wide band antenna. The antenna elements are printed on low cost FR4 substrate with εr = 4.4 and tan δ = 0.02. These elements are printed alternatively on both side of the substrate. Total volume of the antenna is 303 × 162.3 × 1.6 mm3. To reduce the size, scaling factor (τ) for this design is considered as 0.89 and the relative spacing (σ) is chosen as 0.054. The antenna is fed at the base of smallest element. The antenna shows an impedance bandwidth for VSWR ≤ 2 in the frequency range of 470 MHz–700 MHz. The gain of this antenna is between 5.3 dB to 6.5 dB in the entire band of operation. The radiation pattern shows end fire behavior with uniform radiation pattern in both E and H-planes with maximum front to back lobe ratio (F/B) of 30 dB.


An ontology mindset for system engineering

This paper presents an approach to support model-based system engineering (MBSE) which combines a standard metamodel such as UML, SysML, or NAF, with dedicated project ontologies that ease the understanding and creation of models. The approach is formalized using category theory to determine the conditions for use of project ontologies while ensuring the resulting models comply with the selected metamodel. The paper exposes the advantages of the proposed approach over a direct metamodel-based approach or an ontology-based approach using a domain specific language (DSL).


Moving towards a new vision: implementation of a public health policy intervention

BACKGROUND
Public health systems in Canada have undergone significant policy renewal over the last decade in response to threats to the public's health, such as severe acute respiratory syndrome. There is limited research on how public health policies have been implemented or what has influenced their implementation. This paper explores policy implementation in two exemplar public health programs -chronic disease prevention and sexually-transmitted infection prevention - in Ontario, Canada. It examines public health service providers', managers' and senior managements' perspectives on the process of implementation of the Ontario Public Health Standards 2008 and factors influencing implementation.

METHODS
Public health staff from six health units representing rural, remote, large and small urban settings were included. We conducted 21 focus groups and 18 interviews between 2010 (manager and staff focus groups) and 2011 (senior management interviews) involving 133 participants. Research assistants coded transcripts and researchers reviewed these; the research team discussed and resolved discrepancies. To facilitate a breadth of perspectives, several team members helped interpret the findings. An integrated knowledge translation approach was used, reflected by the inclusion of academics as well as decision-makers on the team and as co-authors.

RESULTS
Front line service providers often were unaware of the new policies but managers and senior management incorporated them in operational and program planning. Some participants were involved in policy development or provided feedback prior to their launch. Implementation was influenced by many factors that aligned with Greenhalgh and colleagues' empirically-based Diffusion of Innovations in Service Organizations Framework. Factors and related components that were most clearly linked to the OPHS policy implementation were: attributes of the innovation itself; adoption by individuals; diffusion and dissemination; the outer context - interorganizational networks and collaboration; the inner setting - implementation processes and routinization; and, linkage at the design and implementation stage.

CONCLUSIONS
Multiple factors influenced public health policy implementation. Results provide empirical support for components of Greenhalgh et al's framework and suggest two additional components - the role of external organizational collaborations and partnerships as well as planning processes in influencing implementation. These are important to consider by government and public health organizations when promoting new or revised public health policies as they evolve over time. A successful policy implementation process in Ontario has helped to move public health towards the new vision.


ICDAR 2013 Chinese Handwriting Recognition Competition

This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27 systems for five tasks: classification on extracted features, online/offline isolated character recognition, online/offline handwritten text recognition. The best results (correct rates) are 93.89% for classification on extracted features, 94.77% for offline character recognition, 97.39% for online character recognition, 88.76% for offline text recognition, and 95.03% for online text recognition, respectively. In addition to the test results, we also provide short descriptions of the recognition methods and brief discussions on the results.


Hunting for Spammers: Detecting Evolved Spammers on Twitter

Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.


Real-Time Keyword Extraction from Conversations

We introduce a novel, fully unsupervised method to extract keywords from meeting speech in real-time. Our approach represents text as a word co-occurrence network and leverages the k-core graph decomposition algorithm and properties of submodular functions. We outperform multiple baselines in a real-time scenario emulated from the AMI and ICSI meeting corpora. Evaluation is conducted against both extractive and abstractive gold standard using two standard performance metrics and a newer one based on word embeddings.


Building the Next Generation of Cyber Security Professionals

Cyber security is an area of strategic and policy interest to governments and enterprises globally, which results in an increase in the demand for cyber security professionals. However, there is a lack of education based on sound theories, standards and practices. In this paper, we adapted the Situational Crime Prevention Theory and the NICE National Cybersecurity Workforce Framework in the design and delivery of our courses, particularly in the Cyber Security Exercise (CSE) which forms an integral part of the courses. The CSE is an attack/defence environment where students are grouped and given a virtual machine with which to host a number of services (e.g. HTTP(S), FTP and SSH) for access by other groups. The CSE is designed to mirror real-world environments where the students’ skills will be applied. An overview of the CSE architecture was also provided for readers interested in replicating the exercise in their institutions. Based on student assessment and feedback, we found that our approach was useful in transferring theoretical knowledge to practical skills suitable for the cyber security workforce.


Contextual Consent: Ethical Mining of Social Media for Health Research

Social media are a rich source of insight for data mining and u sercentred research, but the question of consent arises when st udying such data without the express knowledge of the creator. Case studies that mine social data from users of online services s uch as Facebook and Twitter are becoming increasingly common. Thi s has led to calls for an open discussion into how researchers c an best use these vast resources to make innovative findings whi le still respecting fundamental ethical principles. In this positi n paper we highlight some key considerations for this topic and argu e that the conditions of informed consent are often not being met, a nd that using social media data that some deem free to access and analyse may result in undesirable consequences, particula ly within the domain of health research and other sensitive topics. We posit that successful exploitation of online personal data, part icularly for health and other sensitive research, requires new and us able methods of obtaining consent from the user.


Engineering Fast Route Planning Algorithms

Algorithms for route planning in transportation networks have recently undergone a rapid development, leading to methods that are up to one million times faster than Dijkstra’s algorithm. We outline ideas, algorithms, implementations, and experimental methods behind this development. We also explain why the story is not over yet because dynamically changing networks, flexible objective functions, and new applications pose a lot of interesting challenges.


Privacy awareness about information leakage: who knows what about me?

The task of protecting users' privacy is made more difficult by their attitudes towards information disclosure without full awareness and the economics of the tracking and advertising industry. Even after numerous press reports and widespread disclosure of leakages on the Web and on popular Online Social Networks, many users appear not be fully aware of the fact that their information may be collected, aggregated and linked with ambient information for a variety of purposes. Past attempts at alleviating this problem have addressed individual aspects of the user's data collection. In this paper we move towards a comprehensive and efficient client-side tool that maximizes users' awareness of the extent of their information leakage. We show that such a customizable tool can help users to make informed decisions on controlling their privacy footprint.


An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems

In the last two decades, we have witnessed that Interval Type-2 Fuzzy Logic Systems (IT2-FLSs) have been successfully implemented in various engineering areas. In this paper, we will introduce a free open source Mat lab/Simulink toolbox for the development of IT2-FLSs for a wider accessibility to users beyond the type-2 fuzzy logic community. The presented IT2-FLS toolbox allows intuitive implementation of IT2-FLSs where it is capable to cover all the phases of its design. In order to allow users to easily construct IT2-FISs, a GUI is developed which is similar to that of Matlab® Fuzzy Logic Toolbox. We have embedded various Type Reduction (TR) methods in the toolbox since the TR method is the most important operation that must be taken into consideration. This gives the opportunity to the user to examine the performance of his/her IT2-FLS design with respect to the TR methods. We have also developed an IT2-FLS Simulink library so that the designer can perform various simulation analyses and can also investigate the effect of TR method on the performance of the IT2-FLS. Moreover, the developed IT2-FLS Mat lab/Simulink toolbox contains an automatic connection between Matlab and Simulink environments. Once the user has finished the design of the IT2-FLS via the GUI, it is possible to export his/her design directly to Simulink via an automatic Simulink file generation. We believe that the availability of the developed free and open-source IT2-FLS Mat lab/Simulink toolbox will be an important step for a wider deployment and development of IT2-FLSs.


Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms

Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner’s point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today’s big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.


Swarm Intelligence Approaches for Grid Load Balancing

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.


Routing Around Congestion: Defeating DDoS Attacks and Adverse Network Conditions via Reactive BGP Routing

In this paper, we present Nyx, the first system to both effectively mitigate modern Distributed Denial of Service (DDoS) attacks regardless of the amount of traffic under adversarial control and function without outside cooperation or an Internet redesign. Nyx approaches the problem of DDoS mitigation as a routing problem rather than a filtering problem. This conceptual shift allows Nyx to avoid many of the common shortcomings of existing academic and commercial DDoS mitigation systems. By leveraging how Autonomous Systems (ASes) handle route advertisement in the existing Border Gateway Protocol (BGP), Nyx allows the deploying AS to achieve isolation of traffic from a critical upstream AS off of attacked links and onto alternative, uncongested, paths. This isolation removes the need for filtering or de-prioritizing attack traffic. Nyx controls outbound paths through normal BGP path selection, while return paths from critical ASes are controlled through the use of specific techniques we developed using existing traffic engineering principles and require no outside coordination. Using our own realistic Internet-scale simulator, we find that in more than 98% of cases our system can successfully route critical traf?c around network segments under transit-link DDoS attacks; a new form of DDoS attack where the attack traf?c never reaches the victim AS, thus invaliding defensive filtering, throttling, or prioritization strategies. More significantly, in over 95% of those cases, the alternate path provides complete congestion relief from transit-link DDoS. Nyx additionally provides complete congestion relief in over 75% of cases when the deployer is being directly attacked.


Machine Learning for High-Throughput Stress Phenotyping in Plants.

Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.


Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photorealistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the imageto-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors’ own photos), our model can generate high-quality facial expression videos of which about 50% are labeled as real by Amazon Mechanical Turk workers. Please see our project page1 for more demonstrations.


A deep learning based stock trading model with 2-D CNN trend detection

The success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account.


Detecting product review spammers using rating behaviors

This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.


Combating Web Spam with TrustRank

Web spam pages use various techniques to achieve higher-than-deserved rankings in a search engine’s results. While human experts can identify spam, it is too expensive to manually evaluate a large number of pages. Instead, we propose techniques to semiautomatically separate reputable, good pages from spam. We first select a small set of seed pages to be evaluated by an expert. Once we manually identify the reputable seed pages, we use the link structure of the web to discover other pages that are likely to be good. In this paper we discuss possible ways to implement the seed selection and the discovery of good pages. We present results of experiments run on the World Wide Web indexed by AltaVista and evaluate the performance of our techniques. Our results show that we can effectively filter out spam from a significant fraction of the web, based on a good seed set of less than 200 sites.


Towards a General Rule for Identifying Deceptive Opinion Spam

Consumers’ purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam— fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews. Our approach tries to capture the general difference of language usage between deceptive and truthful reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their sites.1


What Yelp Fake Review Filter Might Be Doing?

Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp’s algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp’s filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90% accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.


Finding unusual review patterns using unexpected rules

In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.


A fast garment fitting algorithm using skeleton-based error metric

Funding information National Natural Science Foundation of China, Grant/Award Number: 61732015; National Key R&D Program of China, Grant/Award Number: 2017YFB1002600; Key Research and Development Program of Zhejiang Province, Grant/Award Number: 2018C01090; National Natural Science Foundation of China, Grant/Award Number: 61472355 Abstract We present a fast and automatic method to fit a given 3D garment onto a human model with various shapes and poses, without using a reference human model. Our approach uses a novel skeleton-based error metric to find the pose that best fits the input garment. Specifically, we first generate the skeleton of the given human model and its corresponding skinning weights. Then, we iteratively rotate each bone to find its best position to fit the garment. After that, we rig the surface of the human model according to the transformations of the skeleton. Potential penetrations are resolved using collision handling and physically based simulation. Finally, we restore the human model back to the original pose in order to obtain the desired fitting result. Our experiment results show that besides its efficiency and automation, our method is about two orders of magnitudes faster than existing approaches, and it can handle various garments, including jacket, trousers, skirt, a suit of clothing, and even multilayered clothing.


Exploring Context with Deep Structured Models for Semantic Segmentation

We propose an approach for exploiting contextual information in semantic image segmentation, and particularly investigate the use of patch-patch context and patch-background context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets.


Two-Switch Voltage Equalizer Using an LLC Resonant Inverter and Voltage Multiplier for Partially Shaded Series-Connected Photovoltaic Modules

Various kinds of differential power processing converters and voltage equalizers have been proposed for series-connected photovoltaic (PV) modules to prevent negative influences of partial shading, such as significant reduction in power generation and the occurrence of multiple maximum power points (MPPs), including local and global MPPs, that hinders and confuses MPP tracking algorithms to operate properly. However, since conventional topologies are based on multiple individual dc-dc converters, the required switch count increases proportionally to the number of modules connected in series, increasing the complexity. A two-switch voltage equalizer using an LLC resonant inverter and voltage multiplier is proposed in this paper. The circuitry can be dramatically simplified compared with conventional topologies due to the two-switch configuration. Detailed operation analyses for the LLC resonant inverter and voltage multiplier are separately performed. Experimental equalization tests emulating partially shaded conditions were performed for four PV modules connected in series. With the proposed voltage equalizer, local MPPs successfully disappeared, and extractable maximum power increased compared with those without equalization, demonstrating the effectiveness and performance of the proposed voltage equalizer.


A Low-Cost Real-Time Embedded Stereo Vision System for Accurate Disparity Estimation Based on Guided Image Filtering

Stereo matching, a key element towards extracting depth information from stereo images, is widely used in several embedded consumer electronic and multimedia systems. Such systems demand high processing performance and accurate depth perception, while their deployment in embedded and mobile environments implies that cost, energy and memory overheads need to be minimized. Hardware acceleration has been demonstrated in efficient embedded stereo vision systems. To this end, this paper presents the design and implementation of a hardware-based stereo matching system able to provide high accuracy and concurrently high performance for embedded vision devices, which are associated with limited hardware and power budget. We first implemented a compact and efficient design of the guided image filter, an edge-preserving filter, which reduces the hardware complexity of the implemented stereo algorithm, while at the same time maintains high-quality results. The guided filter design is used in two parts of the stereo matching pipeline, showing that it can simplify the hardware complexity of the Adaptive Support Weight aggregation step, and efficiently enable a powerful disparity refinement unit, which improves matching accuracy, even though cost aggregation is based on simple, fixed support strategies. We implemented several variants of our design on a Kintex-7 FPGA board, which was able to process HD video (1,280 × 720) in real-time (60 fps), using ~57.5k and ~71k of the FPGA's logic (CLB) and register resources, respectively. Additionally, the proposed stereo matching design delivers leading accuracy when compared to state-of-the-art hardware implementations based on the Middlebury evaluation metrics (at least 1.5 percent less bad matching pixels).


Generalization Properties and Implicit Regularization for Multiple Passes SGM

We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical findings.


Trajectory Outlier Detection: A Partition-and-Detect Framework

Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.


Decision support for diffuse pollution management

The effort to manage diffuse pollution at the catchment scale is an ongoing challenge that needs to take into account trade-offs between environmental and economic objectives. Best Management Practices (BMPs) are gaining ground as a means to address the problem, but their application (and impact) is highly dependant on the characteristics of the crops and of the land in which they are to be applied. In this paper, we demonstrate a new methodology and associated decision support tool that suggests the optimal location for placing BMPs to minimise diffuse surface water pollution at the catchment scale, by determining the trade-off among economic and multiple environmental objectives. The decision support tool consists of a non-point source (NPS) pollution estimator, the SWAT (Soil and Water Assessment Tool) model, a genetic algorithm (GA), which serves as the optimisation engine for the selection and placement of BMPs across the agricultural land of the catchment, and of an empirical economic function for the estimation of the mean annual cost of BMP implementation. In the proposed decision support tool, SWAT was run a number of times equal to the number of tested BMPs, to predict nitrates nitrogen (N-NO3) and total phosphorus (TP) losses from all the agricultural Hydrologic Response Units (HRUs) and possible BMPs implemented on them. The results were then saved in a database which was subsequently used for the optimisation process. Fifty different BMPs, including sole or combined changes in livestock, crop, soil and nutrient application management in alfalfa, corn and pastureland fields, were evaluated in the reported application of the tool in a catchment in Greece, by solving a three-objective optimisation process (cost, TP and N-NO3). The relevant two-dimensional trade-off curves of cost-TP, cost-N-NO3 and N-NO3eTP are presented and discussed. The strictest environmental target, expressed as a 45% reduction of TP at the catchment outlet, which also resulted in a 25% reduction of the annual N-NO3 yield was met at an affordable annual cost of 25 V/person by establishing an optimal combination of BMPs. The methodology could be used to assist in a more cost-effective implementation of environmental


100-Mb/s NRZ Visible Light Communications Using a Postequalized White LED

This letter describes a high-speed visible light communications link that uses a white-light light-emitting diode (LED). Such devices have bandwidths of few megahertz, severely limiting the data rates of any communication system. Here, we demonstrate that by detecting only the blue component of the LED, and using a simple first-order analogue equalizer, a data rate of 100 Mb/s can be achieved using on-off keying nonreturn-to-zero modulation.


Systematic literature studies: Database searches vs. backward snowballing

Systematic studies of the literature can be done in different ways. In particular, different guidelines propose different first steps in their recommendations, e.g. start with search strings in different databases or start with the reference lists of a starting set of papers.
 In software engineering, the main recommended first step is using search strings in a number of databases, while in information systems, snowballing has been recommended as the first step. This paper compares the two different search approaches for conducting literature review studies.
 The comparison is conducted by searching for articles addressing "Agile practices in global software engineering". The focus of the paper is on evaluating the two different search approaches.
 Despite the differences in the included papers, the conclusions and the patterns found in both studies are quite similar. The strengths and weaknesses of each first step are discussed separately and in comparison with each other.
 It is concluded that none of the first steps is outperforming the other, and the choice of guideline to follow, and hence the first step, may be context-specific, i.e. depending on the area of study.


Chapter 7 Towards Automatically-Tuned Deep Neural Networks

Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any human intervention. The first version, Auto-Net 1.0, builds upon ideas from the competition-winning system Auto-sklearn by using the Bayesian Optimization method SMAC and uses Theano as the underlying deep learning (DL) framework. The more recent Auto-Net 2.0 builds upon a recent combination of Bayesian Optimization and HyperBand, called BOHB, and uses PyTorch as DL framework. To the best of our knowledge, Auto-Net 1.0 was the first automatically-tuned neural network to win competition datasets against human experts (as part of the first AutoML challenge). Further empirical results show that ensembling Auto-Net 1.0 with Auto-sklearn can perform better than either approach alone, and that Auto-Net 2.0 can perform better yet.


A Model Based on Aras-G and AHP Methods for Multiple Criteria Prioritizing of Heritage Value

The paper discusses the meaning and nature of urban cultural heritage, and the available methods for its valuation in the perspective of sustainable city development. From this perspective, decision-making problems of renovation often involve a complex decision-making process in which multiple requirements and conditions have to be taken into consideration simultaneously. In project development it is hardly possible to get exhaustive and accurate information. As a result, the situations occur, the consequences of which can be very damaging to the project. Sometimes the loss is related to symbolic values that the public perceive as disregarded by the project, despite the overall improved conditions. This paper presents the multiple criteria assessment of alternatives of the cultural heritage renovation projects in Vilnius city. The model consists of the following elements: determining attributes set a®ecting built and human environment renovation; information collection and analysis, decision modeling and solution selection. The main purpose of the model is to improve the condition of the built and human environment through e±cient decision making in renovation supported by multiple attribute evaluation. Delphi, AHP and ARAS-G methods, considering di®erent environment factors as well as stakeholders' needs, are applied to solve problem.


Illuminating the Effects of Dynamic Lighting on Student Learning

Light is universally understood as essential to the human condition. Yet light quality varies substantially both in nature and in controlled environments leading to questions of which artificial light characteristics facilitate maximum learning. Recent research has examined lighting variables of color temperature, and illuminance for impacting sleep, mood, focus, motivation, concentration and work and school performance. This has resulted in artificial light systems intended to support human beings in their actualization through dynamic lighting technology allowing for different lighting conditions per task. Eighty-four third graders were exposed to either focus (6000K-100fc average maintained) or normal lighting. Focus lighting led to a higher percentage increase in oral reading fluency performance (36%) than did control lighting (17%). No lighting effects were found for motivation or concentration, possibly attributable to the younger age level of respondents as compared to European studies. These findings illuminate the need for further research on artificial light and learning. The Effects of Lighting on Humans in General The human evolution is shaped by light. In the course of evolution, human beings have adapted and developed an internal clock that under natural light conditions is synchronized to the earth’s 24-hour light-dark rotational cycle (Czeisler et al., 1999). Research reveals the mechanism for how light is essential for human functioning (Boyce, Hunter, & Howlett, 2003). Light is a strong enabler for visual performance (Grangaard, 1995), regulates a large variety of bodily processes such as sleep and alertness (Dijk et al., 1997; Wright et al., 2006; Takasu et al., 2006; Viola et al., 2008), is essential for cognition and mood (Veitch & McColl, 2001; Goven et al., 2011; Taras, 2005), enables production of important hormones such as melatonin and cortisol (Berson, 2002; dijk et al., 1997; Leproult et al., 2001), and is essential for a healthy rest-activity pattern (Wurtman, 1975). Lights of different wavelengths also affect blood pressure, pulse, respiration rates, brain activity, and biorhythms. The role of lighting in our daily lives is essential in order to operate ideally in every environment. Thus, lighting directly influences every dimension of human existence. Tanner reiterated: “Light is the most important environmental input, after food and water, in controlling bodily functions (as cited in Wurtman, 1975). Since the industrial revolution, people spend more and more time indoors while artificial lighting has shown the power to at least partially compensate for the processes that stabilize the body, mind, and emotions (Knez, 1995; Tanner, 2008, van Someren et al.,2005; Takasu et al., 2006; Mishima et al., 2001; Viola et al., 2008). In the following we elaborate a bit more on the proven effects artificial light has on human functioning. Circadian rhythm Sleep is one of the most basic physical requirements for human functioning. Amount and quality of lighting invariably affects the degree and quality of sleep in humans and regulates our biological clocks. In 2002, Berson et al. (2002) identified a new non-image forming photo-pigment residing within a cell type in the retina of the eye. It is referred to as melanopsin and regulates the biological effects of light. When ocular light (light perceived by the eyes) reaches these cells, a complex chemical reaction occurs, producing electrical impulses that are sent via separate nerve pathways to our biological clock, the suprachiasmatic nuclei (SCN). The SCN in turn regulates the circadian (daily) and circannual (seasonal) rhythms of a large variety of bodily processes, such as sleep, and some important hormones, such as melatonin and cortisol, essential for a healthy rest-activity pattern. The system that generates the circadian rhythmicity of biological processes is denoted as the circadian system. The melanopsin pigment is most sensitive to blue light with a peak sensitivity at 480 nm (Brainard et al., 2001; Thapan et al., 2001; Hankins et al., 2008). Sleep consolidation is optimal when sleep timing coincides with the period of melatonin secretion (Dijk et al., 1997). People that sleep during their melatonin peak (as in normal, i.e., well-synchronized, people), are reported to have a longer total sleep time and less wakefulness after sleep onset as compared with people that schedule their wakefulness during the melatonin peak (non-synchronized people) (Wright et al., 2006). Moreover, the same study indicates that cognitive performance (i.e., learning) was better in a synchronized group of people, whereas learning was impaired in a non-synchronized group of people. This indicates that proper alignment between sleep-wakefulness and biological (internal circadian) time is crucial, not only for sleep quality, but also for enhancement of cognitive performance. Vision The most obvious effect of light on humans is to enable vision and performance of visual tasks through the eyes. The eye contains “photoreceptor cells” called rods and cones. These photoreceptor cells regulate the visual effects. When light reaches these cells, a complex chemical reaction occurs. The chemical that is formed creates electrical impulses in the nerve that connects the photoreceptor cells to the visual cortex at the back of the brain. In the visual cortex the electrical impulses are interpreted as “vision”. Rod cells “operate in low-level light situations and do not permit color vision.” Cone cells on the other hand, operate best in “normal daytime lighting conditions,” and are “responsible for sharpness, detail, and color vision.” Studies show that the nature of the task – as well as the amount, spectrum, and distribution of the light – determines the level of performance that is achieved. Mood and Cognition Lighting plays an important role in evoking emotions. Lighting can be used to make an architectural space more aesthetically pleasing or it can create an atmosphere in that space; both affect people’s emotions. In addition, the user’s well-being can be directly influenced by light. Brightness, color, direction, contrast and time are parameters used to create lighting conditions that address this. Nevertheless, concerning the relationship between lighting and mood/cognition, research has not shown consistent results. In a study by Knez (1995), two experiments were performed in order to analyze the effects of color temperature and illuminance levels on mood and cognitive performance tasks including long-term recall, free recall, and performance appraisal between males and females. After each experiment, a test to measure each subject’s mood was administered. The results showed that females performed better in warm white lighting environments, whereas males performed better on cognitive tasks in cool white lighting. Both males and females perceived and responded differently in evaluating the illuminance levels and color index of the lighting and therefore each gender’s mood was affected differently. Positive mood measures showed no increase in mood in both genders; however, the cooler lighting had a more negative effect on females’ moods. Thus, females’ performance on cognitive tasks decreased under cooler lighting. Because physiological changes occur when humans are exposed to light, mood and cognition can be affected indirectly and variably. According to Veitch and McColl (2001), lighting’s cognitive and mood-related effects on people have noteworthy implications: 1) better performance on cognitive related tasks in the workplace or academic environment and 2) overall improved quality of life and well-being. Both visual perception strength and adequate sleep could have a considerable impact on cognitive abilities such as concentration and memory. Mood may also determine the sharpness of these cognitive abilities. Mood can be influenced by the quality and amount of lighting (Partonen et al., 2002; Veitch & McColl, 2001; inter alia Beauchemin & Hays, 1996; Benedetti et al., 2001; Goven, 2011). For instance, light therapy has proven a successful treatment for those with Seasonal Affective Disorder (SAD) and other non-seasonal mood related disorders such as depression and eating disorders (Spiegel et al., 2004; Van Cauter et al., 2000; McColl & Veitch, 2001). Effects for Lighting and Learning Because lighting profoundly impacts numerous levels of human functioning such as vision, circadian rhythms, mood, and cognition, its implicit effects on learning and classroom achievement cannot be dismissed. Several studies have addressed how the quality and color of lighting can either impair or enhance students’ visual skills and thus, academic performance. Visual impairments alone can induce behavioral problems in students as well as level of concentration and motivation in the classroom. Cheatum and Hammond (2000) estimated that around 20% of children that enter the school encounter visual problems (e.g., problems with focusing, eye tracking, training, lazy eye, and trabismus). Among elementary school children, 41% have experienced trouble with tracking, 6% have refractive errors and 4% have strabismus (Koslowe, 1995, as cited in Cheatum & Hammond, 2000). The same study suggests that “the inability of visual tracking is also thought to be the cause of behavioral problems and being illiterate.” Winterbottom (2009) suggested that certain features of lighting can cause discomfort and impair visual and cognitive performance. These features include “imperceptible 100 Hz flicker from fluorescent lighting and glare induced by 1) daylight and fluorescent lighting, 2) interactive whiteboards (IWBs) and dry-erase whiteboards (DWBs)” (2009). The purpose of his study was to determine the degree and magnitude to which students are subjected to the above stated lighting inefficiencies in the classroom. The 100 Hz flicker from fluorescent lighting was displayed in 80% of the 90 UK classrooms u


Computer graphics and geometric ornamental design

Computer Graphics and Geometric Ornamental Design


Does this sentence need citation? In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. The task is to detect whether a sentence in a scientific article needs to be cited or not. It can be incorporated into citation recommendation systems to help automate the citation process by marking sentences where needed. It may also be useful for publishers to regularize the citation process. We construct a dataset using the ACLAnthology Reference Corpus; consisting of over 1.1M “not_cite” and 85K “cite” sentences. We study the performance of a set of state-of-the-art sentence classifiers for the citation worthiness task and show the practical challenges. We also explore sectionwise difficulty of the task and analyze the performance of our best model on a published article. ACM Reference Format: Hamed Bonab, Hamed Zamani, Erik Learned-Miller, and James Allan. 2018. Citation Worthiness of Sentences in Scientific Reports. In SIGIR ’18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8–12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3209978.3210162


Witnessing spouse abuse and experiencing physical abuse: A “double whammy”?

Child witnesses to parental violence, both abused (N =40) and nonabused (N =44), who were temporarily residing in a battered women's shelter were compared with children from a similar economic background (N =66) on measures of anxiety, depression, and behavior problems. Mothers of the three groups of children (comparison, witness, abused/witness) across the age range from 4 to 12 years completed a behavior problem inventory; the youngsters responded to paper-and-pencil self-report measures. Results indicated that the abused/witness children were manifesting significantly more distress on the behavior problem measure than the comparison youngsters, with the witness children showing a moderate amount and the comparison children the least. However, these patterns were mediated by the age of the child. Implications of these differential findings are discussed.


Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children.

BACKGROUND
We examined the relationship between overweight in preschool children and three environmental factors--the proximity of the children's residences to playgrounds and to fast food restaurants and the safety of the children's neighborhoods. We hypothesized that children who lived farther from playgrounds, closer to fast food restaurants, and in unsafe neighborhoods were more likely to be overweight.

METHODS
This was a cross-sectional study of 7,020 low-income children, 36 through 59 months of age living in Cincinnati, OH. Overweight was defined as a measured body mass index > or =95th percentile. The distance between each child's residence and the nearest public playground and fast food restaurant was determined with geographic information systems. Neighborhood safety was defined by the number of police-reported crimes per 1,000 residents per year in each of 46 city neighborhoods.

RESULTS
Overall, 9.2% of the children were overweight, 76% black, and 23% white. The mean (+/- SD) distances from a child's home to the nearest playground and fast food restaurant were 0.31 (+/- 0.22) and 0.70 (+/- 0.38) miles, respectively. There was no association between child overweight and proximity to playgrounds, proximity to fast food restaurants, or level of neighborhood crime. The association between child overweight and playground proximity did not differ by neighborhood crime level.

CONCLUSIONS
Within a population of urban low-income preschoolers, overweight was not associated with proximity to playgrounds and fast food restaurants or with the level of neighborhood crime.


Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

The success of opinion mining for automatically processing vast amounts of opinionated content available on the Internet has been demonstrated as a less expensive and lower latency solution for gathering public opinion. In this paper, we investigate whether it is possible to predict variations in vote intention based on sentiment time series extracted from news comments, using three Brazilian elections as case study. The contributions of this case study are: a) the comparison of two approaches for opinion mining in user-generated content in Brazilian Portuguese, b) the proposition of two types of features to represent sentiment behavior towards political candidates that can be used for prediction, c) an approach to predict polls vote intention variations that is adequate for scenarios of sparse data. We developed experiments to assess the influence on the forecasting accuracy of the proposed features, and their respective preparation. Our results display an accuracy of 70% in predicting positive and negative variations. These are important contributions towards a more general framework that is able to blend opinions from several different sources to find representativeness of the target population, and make more reliable predictions.


Innocent by association: early recognition of legitimate users

This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.


A Double-Tail Latch-Type Voltage Sense Amplifier with 18ps Setup+Hold Time

A latch-type voltage sense amplifier in 90nm CMOS is designed with a separated input and cross-coupled stage. This separation enables fast operation over a wide common-mode and supply voltage range. With a 1-sigma offset of 8mV, the circuit consumes 92fJ/decision with a 1.2V supply. It has an input equivalent noise of 1.5mV and requires 18ps setup-plus-hold time


MobiMine: Monitoring the Stock Market from a PDA

This paper describes an experimental mobile data mining system that allows intelligent monitoring of time-critical financial data from a hand-held PDA. It presents the overall system architecture and the philosophy behind the design. It explores one particular aspect of the system---automated construction of personalized focus area that calls for user's attention. This module works using data mining techniques. The paper describes the data mining component of the system that employs a novel Fourier analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks. The paper also discusses a quadratic programming-based personalization module that runs on the PDAs and the multi-media based user-interfaces. It reports experimental results using an ad hoc peer-to-peer IEEE 802.11 wireless network.


A Study on Evaluation of E-Government Service Quality

Service quality is the highest requirement by users, especially for the service in electronic government. During the past decades, it has become a major area of academic investigation. Considering this issue, there are a lot of researches that evaluated the dimensions and e-service contexts. This study also identified the dimensions of service quality, but focuses on a new concept and provides a new methodology in developing measurement scales of e-service quality such as information quality, service quality and organization quality. Finally, this study will suggest a key factor to evaluate e-government service quality better. Keywords—E-government, e-service, e-service quality, dimensionality.


Holistic Multi-modal Memory Network for Movie Question Answering

Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial interactions among data sources in one attention hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop. In addition, it takes answer choices into consideration during the context retrieval stage. Therefore, the proposed framework effectively integrates multi-modal context, question, and answer information, which leads to more informative context retrieved for question answering. Our HMMN framework achieves state-of-the-art accuracy on MovieQA dataset. Extensive ablation studies show the importance of holistic reasoning and contributions of different attention strategies.


Graphical Models for Game Theory

We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in (one-stage) games represented by trees or sparse graphs.


A 64 GHz 2 Gbps transmit/receive phased-array communication link in SiGe with 300 meter coverage

This paper presents a 64 GHz transmit/receive communication link between two 32-element SiGe-based phased arrays. The antenna element is a series-fed patch array, which provides directivity in the elevation plane. The transmit array results in an EIRP of 42 dBm, while the receive array provides an electronic gain of 33 dB and a system NF < 8 dB including the T/R switch and antenna losses. The arrays can be scanned to +/−50° in the azimuth using a 5-bit phase shifter on the SiGe chip, while keeping very low sidelobes and a near-ideal pattern. The communication link uses one array on the transmit side and another array on the receive side, together with external mixers and IF amplifiers. A Keysight M8195A arbitrary waveform generator is used to generate the modulated waveforms on the transmit side and a Keysight DSO804A oscilloscope is used to demodulate the received IF signal. The link performance was measured for different scan angles and modulation formats. Data rates of 1 Gbps using 16-QAM and 2 Gbps using QPSK are demonstrated at 300 m. The system also results in > 4 Gbps data rate at 100 meters, and ∼ 500 Mbps data rate at 800 meters.


Fabrication and measurement of a large, monolithic, PCB-based AESA

This paper discusses a fabrication approach and experimental validation of a very large, planar active electronically scanned array (AESA). The planar AESA architecture employs a monolithic printed circuit board (PCB) with 768 active antenna elements at X-Band. Manufacturing physically large arrays with high element counts is discussed in relation to construction, assembly and yield considerations. Measured active array patterns of the ESA are also presented.


A Millimeter-Wave (40–45 GHz) 16-Element Phased-Array Transmitter in 0.18-$\mu$ m SiGe BiCMOS Technology

This paper demonstrates a 16-element phased-array transmitter in a standard 0.18-mum SiGe BiCMOS technology for Q-band satellite applications. The transmitter array is based on the all-RF architecture with 4-bit RF phase shifters and a corporate-feed network. A 1:2 active divider and two 1:8 passive tee-junction dividers constitute the corporate-feed network, and three-dimensional shielded transmission-lines are used for the passive divider to minimize area. All signals are processed differentially inside the chip except for the input and output interfaces. The phased-array transmitter results in a 12.5 dB of average power gain per channel at 42.5 GHz with a 3-dB gain bandwidth of 39.9-45.6 GHz. The RMS gain variation is < 1.3 dB and the RMS phase variation is < for all 4-bit phase states at 35-50 GHz. The measured input and output return losses are < -10 dB at 36.6-50 GHz, and <-10 dB at 37.6-50 GHz, respectively. The measured peak-to-peak group delay variation is plusmn 20 ps at 40-45 GHz. The output P-1dB is -5plusmn1.5 dBm and the maximum saturated output power is - 2.5plusmn1.5 dBm per channel at 42.5 GHz. The transmitter shows <1.8 dB of RMS gain mismatch and < 7deg of RMS phase mismatch between the 16 different channels over all phase states. A - 30 dB worst-case port-to-port coupling is measured between adjacent channels at 30-50 GHz, and the measured RMS gain and phase disturbances due to the inter-channel coupling are < 0.15 dB and < 1deg, respectively, at 35-50 GHz. All measurements are obtained without any on-chip calibration. The chip consumes 720 mA from a 5 V supply voltage and the chip size is 2.6times3.2 mm2.


A 28GHz Bulk-CMOS dual-polarization phased-array transceiver with 24 channels for 5G user and basestation equipment

Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.


7.2 A 28GHz 32-element phased-array transceiver IC with concurrent dual polarized beams and 1.4 degree beam-steering resolution for 5G communication

Next-generation mobile technology (5G) aims to provide an improved experience through higher data-rates, lower latency, and improved link robustness. Millimeter-wave phased arrays offer a path to support multiple users at high data-rates using high-bandwidth directional links between the base station and mobile devices. To realize this vision, a phased-array-based pico-cell must support a large number of precisely controlled beams, yet be compact and power efficient. These system goals have significant mm-wave radio interface implications, including scalability of the RFIC+antenna-array solution, increase in the number of concurrent beams by supporting dual polarization, precise beam steering, and high output power without sacrificing TX power efficiency. Packaged Si-based phased arrays [1–3] with nonconcurrent dual-polarized TX and RX operation [2,3], concurrent dual-polarized RX operation [3] and multi-IC scaling [3,4] have been demonstrated. However, support for concurrent dual-polarized operation in both RX and TX remains unaddressed, and high output power comes at the cost of power consumption, cooling complexity and increased size. The RFIC reported here addresses these challenges. It supports concurrent and independent dual-polarized operation in TX and RX modes, and is compatible with a volume-efficient, scaled, antenna-in-package array. A new TX/RX switch at the shared antenna interface enables high output power without sacrificing TX efficiency, and a t-line-based phase shifter achieves <1° RMS error and <5° phase steps for precise beam control.


A novel activation function for multilayer feed-forward neural networks

Traditional activation functions such as hyperbolic tangent and logistic sigmoid have seen frequent use historically in artificial neural networks. However, nowadays, in practice, they have fallen out of favor, undoubtedly due to the gap in performance observed in recognition and classification tasks when compared to their well-known counterparts such as rectified linear or maxout. In this paper, we introduce a simple, new type of activation function for multilayer feed-forward architectures. Unlike other approaches where new activation functions have been designed by discarding many of the mainstays of traditional activation function design, our proposed function relies on them and therefore shares most of the properties found in traditional activation functions. Nevertheless, our activation function differs from traditional activation functions on two major points: its asymptote and global extremum. Defining a function which enjoys the property of having a global maximum and minimum, turned out to be critical during our design-process since we believe it is one of the main reasons behind the gap observed in performance between traditional activation functions and their recently introduced counterparts. We evaluate the effectiveness of the proposed activation function on four commonly used datasets, namely, MNIST, CIFAR-10, CIFAR-100, and the Pang and Lee’s movie review. Experimental results demonstrate that the proposed function can effectively be applied across various datasets where our accuracy, given the same network topology, is competitive with the state-of-the-art. In particular, the proposed activation function outperforms the state-of-the-art methods on the MNIST dataset.


A Survey on Anomalies Detection Techniques and Measurement Methods

Dynamic research area has been applied and researched on anomaly detection in various domains. And various techniques have been proposed to identify unexpected items or events in datasets which differ from the norm. This review tries to provide a basic and structured overview of the anomaly detection techniques. Also, this review discusses major anomaly detection techniques using statistical based and machine learning based techniques. The outcome of this review may facilitate a better understanding of the different techniques in which research has been done on this topic by comparing the pros and cons of the identified techniques. In addition, this review also discusses on the measurement methods used by other researchers in validating their anomalies detection techniques.


Comparison of EEG signal features and ensemble learning methods for motor imagery classification

Classifying electroencephalogram (EEG) signal in Brain Computer Interface (BCI) is a useful methods to analysis different organs of human body and it can be used for communicate with the outside world and controlling external device. Accuracy classification of extracted features from EEG signals is a problem which many researcher try to improve it. Although many methods for extracting feature and classifying EEG signal have been proposed and developed, many of them suffer from extracting less accurate data from EEG signals. In this work, four signal feature extraction and three ensemble learning method have been reviewed and performances of classification techniques are compared for motor imagery task.


Linking GloVe with word2vec

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. [3] is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) [2] implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.


Expanded Hemodialysis: A New Therapy for a New Class of Membranes.

A wide spectrum of molecules is retained in end-stage kidney disease, normally defined as uremic toxins. These solutes have different molecular weights and radii. Current dialysis membranes and techniques only remove solutes in the range of 50-15,000 Da, with limited or no capability to remove solutes in the middle to high molecular weight range (up to 50,000 Da). Improved removal has been obtained with high cut-off (HCO) membranes, with albumin loss representing a limitation to their practical application. Hemodiafiltration (HDF) at high volumes (>23 L/session) has produced some results on middle molecules and clinical outcomes, although complex hardware and high blood flows are required. A new class of membrane has been recently developed with a cut off (MWCO) close to the molecular weight of albumin. While presenting negligible albumin loss, these membranes have a very high retention onset (MWRO), allowing high clearances of solutes in a wide spectrum of molecular weights. These membranes originally defined (medium cut off) are probably better classified as high retention onset. The introduction of such membranes in the clinical routine has allowed the development of a new concept therapy called "expanded hemodialysis" (HDx). The new therapy is based on a special hollow fiber and dialyzer design. Its simple set-up and application offer the possibility to use it even in patients with suboptimal vascular access or even with an indwelling catheter. The system does not require a particular hardware or unusual nursing skill. The quality of dialysis fluid is, however, mandatory to ensure a safe conduction of the dialysis session. This new therapy is likely to modify the outcome of end-stage kidney disease patients, thanks to the enhanced removal of molecules traditionally retained by current dialysis techniques.


Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing field


The effects of caffeine on heart rate variability in newborns with apnea of prematurity

Objective:Apnea of prematurity is a common complication in premature newborns and caffeine is a widespread medication used to treat this complication. Caffeine may have adverse effects on the cardiovascular and central nervous system, yet its effects on the autonomic nervous system modulation of heart rate have not been studied in premature newborns, which was the objective of our study.Study Design:We prospectively studied 21 premature newborns who were treated with caffeine. We analyzed heart rate variability by power spectral density and by dynamic nonlinear analyses methods.Result:There were no changes in heart rate, blood pressure or the autonomic nervous system tone following administration of caffeine, nor were the nonlinear dynamical properties of the system altered by caffeine.Conclusion:Caffeine does not have detrimental effects on heart rate variablility, heart rate or blood pressure in conventional doses given to premature newborns.


The end of the end of ideology.

The "end of ideology" was declared by social scientists in the aftermath of World War II. They argued that (a) ordinary citizens' political attitudes lack the kind of stability, consistency, and constraint that ideology requires; (b) ideological constructs such as liberalism and conservatism lack motivational potency and behavioral significance; (c) there are no major differences in content (or substance) between liberal and conservative points of view; and (d) there are few important differences in psychological processes (or styles) that underlie liberal versus conservative orientations. The end-of-ideologists were so influential that researchers ignored the topic of ideology for many years. However, current political realities, recent data from the American National Election Studies, and results from an emerging psychological paradigm provide strong grounds for returning to the study of ideology. Studies reveal that there are indeed meaningful political and psychological differences that covary with ideological self-placement. Situational variables--including system threat and mortality salience--and dispositional variables--including openness and conscientiousness--affect the degree to which an individual is drawn to liberal versus conservative leaders, parties, and opinions. A psychological analysis is also useful for understanding the political divide between "red states" and "blue states."


DeltaIoT: A Self-Adaptive Internet of Things Exemplar

Internet of Things (IoT) consists of networked tiny embedded computers (motes) that are capable of monitoring and controlling the physical world. Examples range from building security monitoring to smart factories. A central problem of IoT is minimising the energy consumption of the motes, while guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance or inefficiencies. Enhancing the IoT network with self-adaptation can automate these tasks. This paper presents DeltaIoT, an exemplar that enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in IoT. DeltaIoT is the first exemplar for research on self-adaptation that provides both a simulator for offline experimentation and a physical setup that can be accessed remotely for real-world experimentation.