title: The psychological impacts of global climate change.

abstract: An appreciation of the psychological impacts of global climate change entails recognizing the complexity and multiple meanings associated with climate change; situating impacts within other social, technological, and ecological transitions; and recognizing mediators and moderators of impacts. This article describes three classes of psychological impacts: direct (e.g., acute or traumatic effects of extreme weather events and a changed environment); indirect (e.g., threats to emotional well-being based on observation of impacts and concern or uncertainty about future risks); and psychosocial (e.g., chronic social and community effects of heat, drought, migrations, and climate-related conflicts, and postdisaster adjustment). Responses include providing psychological interventions in the wake of acute impacts and reducing the vulnerabilities contributing to their severity; promoting emotional resiliency and empowerment in the context of indirect impacts; and acting at systems and policy levels to address broad psychosocial impacts. The challenge of climate change calls for increased ecological literacy, a widened ethical responsibility, investigations into a range of psychological and social adaptations, and an allocation of resources and training to improve psychologists' competency in addressing climate change-related impacts.


title: Robust non-rigid point set registration method based on asymmetric Gaussian and structural feature

abstract: Point set registration is a fundamental problem in many domains of computer vision. In previous work on the registration, the point sets are often represented using Gaussian mixture models and the registration process is represented as a form of a probabilistic solution. For non-rigid point set registration, however, the asymmetric Gaussian (AG) model can capture spatially asymmetric distributions compared with symmetric Gaussian, and the structural feature of the point sets reserve relatively complete and has important significance in registration. In this work, the authors designed a new shape context (SC) descriptor which combines the local and global structures of the point set. Meanwhile, they proposed a non-rigid point set registration algorithm which formulates a registration process as the mixture probability density estimation of the AG mixture model, and the method introduce the structural feature by the new SC. Extensive experiments show that the proposed algorithm has a clear improvement over the state-of-the-art methods.


title: Human skin as data transmission medium for improved privacy and usability in wearable electronics

abstract: Wearable electronics have multiple benefits in medical applications, such as feedback, monitoring and prevention. The number of these devices in circulation is rapidly growing, emphasizing increasing problems in their use caused by one of two standard communication types - wireless communications and wired communications. These problems include obtrusiveness and inconvenient usage, limited bandwidth, privacy and security issues and relatively high energy consumption. Wireless communications are less secure, with a more limited bandwidth but more unobtrusive and convenient than wired ones. On the other hand, wired communications don’t need so many batteries, thereby devices can be charged more rarely and provide more privacy. A third, emerging technology for wearable sensor network communication using the body itself as communication medium tries to solve these problems. The body coupled communications benefit from the communication privacy of the wired networks, while not requiring obtrusive wiring. Unfortunately this type of communication requires complex signal processing for reliable communications as the signal changes depending on the body parts connected. To solve this problem and allow more rapid adoption of body coupled communications in wearable electronics we develop a transfer function for calculating the parameters of human skin as data transmission medium, allowing more usable and privacy oriented communications between the wearable nodes. Additionally this has the potential of reducing the energy consumption of wearable devices making them more practical to use for a prolonged period of time as well as opening new applications as secure personal data exchange using touch.


title: Facial Attribute Editing by Latent Space Adversarial Variational Autoencoders

abstract: This work focuses on the problem of editing facial images by manipulating specified attributes of interest. To learn latent representations disentangled with respect to specified face attribute, a novel attribute-disentangled generative model is proposed by combining variational autoencoders (VAEs) and generative adversarial networks (GANs). In the proposed model, only two deep mappings are included: an encoder and a decoder, similarly as the counterparts in the context of VAEs. Latent space mapped by the encoder is split into two parts: style space and attribute space. The former represents attribute-irrelevant factors, such as identity, position, illumination and background, etc. The latter represents the attributes, such as hair color, gender, with or without glasses, etc, of which each dimension represents one single attribute. By regarding constraints on the output of the encoder as discriminative objectives, the encoder can act not only as a discriminator that is expected to discriminate a sample is a real or a generated one, but also as an attribute classifier that can discriminate whether a sample has the specified attributes or not. Combining reconstruction and Kullback-Leibler (KL) divergence regularization losses like in VAEs, the adversarial training loss defined for the style and the attribute in the latent space is introduced, which drives the proposed model to generate images whose distribution are close to the real data distribution in the latent space. Finally, the model was evaluated on the CelebA dataset and experimental results showed its effectiveness in disentangling face attributes and generating high-quality face images.


title: High speed, Low power Approximate Multipliers

abstract: Adders and multipliers form the significant components in all kind of digital applications and has always been a thought-provoking topic for the researchers. Multiplication process will initially find out the set of partial products and then groups the partial products according to their respective bit positions and takes the summation. Among the phases of multiplication, partial product reduction tree consumes the maximum power and area as it is done using several adders. In this paper, a High Speed, Low Power Approximate Multiplier is proposed in which a new 4–2 compressor is being projected and is made use while performing vector merge addition after transforming the partial product terms to lead varying probability terms. Simulation results shows that the proposed Approximate Multiplier achieved 3.19% less Area, 53.74% less Delay, 3.79% less Power compared to Approximate Multiplier [6] and 31% less Area, 81.35% less Delay and 59.83% less Power compared to that of an Exact Multiplier. When we make use of Approximate Multipliers, there are likelihoods of error occurring in some cases. Hence approximate multipliers are suggested only for Error Resilient applications. Video compression, Digital Image processing and Test Data compression can be categorized as such applications. The proposed approximate multiplier takes less Power, Area and Time and also can be paralleled to the existing approximate multiplier algorithms. Hence for Error resilient application, it is always sagacious to use less power consuming constituents since power is a crucial one to save in the current scenario.


title: CHOPPER: Optimizing Data Partitioning for In-memory Data Analytics Frameworks

abstract: The performance of in-memory based data analytic frameworks such as Spark is significantly affected by how data is partitioned. This is because the partitioning effectively determines task granularity and parallelism. Moreover, different phases of a workload execution can have different optimal partitions. However, in the current implementations, the tuning knobs controlling the partitioning are either configured statically or involve a cumbersome programmatic process for affecting changes at runtime. In this paper, we propose CHOPPER, a system for automatically determining the optimal number of partitions for each phase of a workload and dynamically changing the partition scheme during workload execution. CHOPPER monitors the task execution and DAG scheduling information to determine the optimal level of parallelism. CHOPPER repartitions data as needed to ensure efficient task granularity, avoids data skew, and reduces shuffle traffic. Thus, CHOPPER allows users to write applications without having to hand-tune for optimal parallelism. Experimental results show that CHOPPER effectively improves workload performance by up to 35.2% compared to standard Spark setup.


title: Modality specific assessment of video game player’s experience using the Emotiv

abstract: Abstract A growing body of literature has emerged that focuses upon cognitive assessment of video game player experience. Given the growing popularity of video gaming and the increasing literature on cognitive aspects of video gamers, there is a growing need for novel approaches to assessment of the cognitive processes that occur while persons are immersed in video games. In this study, we assessed various stimulus modalities and gaming events using an off-the-shelf EEG devise. A significant difference was found among different stimulus modalities with increasingly difficult cognitive demands. Specifically, beta and gamma power were significantly increased during high intensity events when compared to low intensity gaming events. Our findings suggest that the Emotiv EEG can be used to differentiate between varying stimulus modalities and accompanying cognitive processes.


title: Fast infant pain detection method

abstract: within this paper, pain detection is exposed and reviewed for detecting facial changes of patient in a hospital in Neonatal Intensive Care Unit (NICU). The system propesed three stage. The first stage implements Haar Cascade detection to detect the infant face. Secondly, PCA was employed for feature extraction. The third module extracts the PCA features of faces by measuring certain dimensions of pain and no pain regions with Support Vector Machine classifier. From 300 samples of face images, it is found that the identification rate of reaches 93.18%.


title: Towards total scene understanding: Classification, annotation and segmentation in an automatic framework

abstract: Given an image, we propose a hierarchical generative model that classifies the overall scene, recognizes and segments each object component, as well as annotates the image with a list of tags. To our knowledge, this is the first model that performs all three tasks in one coherent framework. For instance, a scene of a `polo game' consists of several visual objects such as `human', `horse', `grass', etc. In addition, it can be further annotated with a list of more abstract (e.g. `dusk') or visually less salient (e.g. `saddle') tags. Our generative model jointly explains images through a visual model and a textual model. Visually relevant objects are represented by regions and patches, while visually irrelevant textual annotations are influenced directly by the overall scene class. We propose a fully automatic learning framework that is able to learn robust scene models from noisy Web data such as images and user tags from Flickr.com. We demonstrate the effectiveness of our framework by automatically classifying, annotating and segmenting images from eight classes depicting sport scenes. In all three tasks, our model significantly outperforms state-of-the-art algorithms.


title: Enhancing GPS With Lane-Level Navigation to Facilitate Highway Driving

abstract: Lane-level navigation has received a lot of attention in recent years. It has played a great role in assisting route planning, as well as navigating automated vehicles. Aside from sticking to the planned route, abnormal traffic situations which result in blocking lanes could impact lane switching decisions. Unfortunately, there is currently no navigation system that can sense and track a vehicle's lane position and to advise the driver of lane switching decisions. Google Maps stores a priori the number of lanes and their directions at each highway exit and provides this information to drivers when navigating. However, even with this information, some drivers may not be able to make an informed decision regarding when and where to make a correct lane switch. This motivated us to develop a mechanism for the detection and tracking of real-time lane changes. In this paper, we propose a GPS-aiding system that can sense and track a vehicle's lane position. The system leverages smart phones’ computing capability, rear cameras, and inertial motion sensors. With little extra computational overhead, the system applies computer vision techniques to achieve lane-level positioning. We also design a machine learning-based algorithm to detect and track lane switching. We conduct a series of experiments, analyze our system in real-world environments, and achieve very promising results. We believe our system can be a great asset to current smart phone navigation systems.


title: Revenue management under a general discrete choice model of consumer behavior. Working paper, Universitat Pompeu Fabra

abstract: Customer choice behavior, such as buy-up and buy-down, is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon - or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg reserve management problem in which the buyers' choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy for this problem has a quite simple form. Namely, it consists of identifying an ordered family of "efficient" subsets S 1 ,..., S m , and at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S 1 ? S 2 ?... ? S m . Moreover, we give a characterization of when nesting by fare order is optimal. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.


title: Reinforcement learning in multidimensional continuous action spaces

abstract: The majority of learning algorithms available today focus on approximating the state (V ) or state-action (Q) value function and efficient action selection comes as an afterthought. On the other hand, real-world problems tend to have large action spaces, where evaluating every possible action becomes impractical. This mismatch presents a major obstacle in successfully applying reinforcement learning to real-world problems. In this paper we present an effective approach to learning and acting in domains with multidimensional and/or continuous control variables where efficient action selection is embedded in the learning process. Instead of learning and representing the state or state-action value function of the MDP, we learn a value function over an implied augmented MDP, where states represent collections of actions in the original MDP and transitions represent choices eliminating parts of the action space at each step. Action selection in the original MDP is reduced to a binary search by the agent in the transformed MDP, with computational complexity logarithmic in the number of actions, or equivalently linear in the number of action dimensions. Our method can be combined with any discrete-action reinforcement learning algorithm for learning multidimensional continuous-action policies using a state value approximator in the transformed MDP. Our preliminary results with two well-known reinforcement learning algorithms (Least-Squares Policy Iteration and Fitted Q-Iteration) on two continuous action domains (1-dimensional inverted pendulum regulator, 2-dimensional bicycle balancing) demonstrate the viability and the potential of the proposed approach.


title: How do lncRNAs regulate transcription?

abstract: It has recently become apparent that RNA, itself the product of transcription, is a major regulator of the transcriptional process. In particular, long noncoding RNAs (lncRNAs), which are so numerous in eukaryotes, function in many cases as transcriptional regulators. These RNAs function through binding to histone-modifying complexes, to DNA binding proteins (including transcription factors), and even to RNA polymerase II. In other cases, it is the act of lncRNA transcription rather than the lncRNA product that appears to be regulatory. We review recent progress in elucidating the molecular mechanisms by which lncRNAs modulate gene expression and future opportunities in this research field.


title: Deliberate Practice and Performance in Music, Games, Sports, Education, and Professions A Meta-Analysis

abstract: More than 20 years ago, researchers proposed that individual differences in performance in such domains as music, sports, and games largely reflect individual differences in amount of deliberate practice, which was defined as engagement in structured activities created specifically to improve performance in a domain. This view is a frequent topic of popularscience writing—but is it supported by empirical evidence? To answer this question, we conducted a meta-analysis covering all major domains in which deliberate practice has been investigated. We found that deliberate practice explained 26% of the variance in performance for games, 21% for music, 18% for sports, 4% for education, and less than 1% for professions. We conclude that deliberate practice is important, but not as important as has been argued.


title: Gamifying Education and Research on ICS Security: Design, Implementation and Results of S3

abstract: In this work, we consider challenges relating to security for Industrial Control Systems (ICS) in the context of ICS security education and research targeted both to academia and industry. We propose to address those challenges through gamified attack training and countermeasure evaluation. We tested our proposed ICS security gamification idea in the context of the (to the best of our knowledge) first Capture-The-Flag (CTF) event targeted to ICS security called SWaT Security Showdown (S3). Six teams acted as attackers in a security competition leveraging an ICS testbed, with several academic defense systems attempting to detect the ongoing attacks. The event was conducted in two phases. The online phase (a jeopardy-style CTF) served as a training session. The live phase was structured as an attack-defense CTF. We acted as judges and we assigned points to the attacker teams according to a scoring system that we developed internally based on multiple factors, including realistic attacker models. We conclude the paper with an evaluation and discussion of the S3, including statistics derived from the data collected in each phase of S3.


title: A fast Iterative Shrinkage-Thresholding Algorithm with application to wavelet-based image deblurring

abstract: We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) which preserves the computational simplicity of ISTA, but with a global rate of convergence which is proven to be significantly better, both theoretically and practically. Initial promising numerical results for wavelet-based image deblurring demonstrate the capabilities of FISTA.


title: Robust Hyperspectral Classification Using Relevance Vector Machine

abstract: The curse of dimensionality is the main reason for the computational complexity and the Hughes phenomenon in supervised hyperspectral classification. Previous studies seldom consider in a simultaneous fashion the real situation of insufficiency of available training samples, particularly for small land covers that often contain the key information of the scene, and the problem of complexity. In this paper, the capabilities of a feature reduction technique used for discrimination are combined with the advantages of a Bayesian learning-based probabilistic sparse kernel model, the relevance vector machine (RVM), to develop a new supervised classification method. In the proposed method, the hyperdimensional data are first transformed to a lower dimensionality feature space using the feature reduction technique to maximize separability between classes. The transformed data are then processed by a multiclass RVM classifier based on the parallel architecture and one-against-one strategy. To verify the effectiveness of the method, experiments were carried out on real hyperspectral data. The results are compared with the most efficient supervised classification techniques such as the support vector machine using appropriate performance indicators. The results show that the proposed method performs better than the other approaches particularly for small and scattered landcover classes which are harder to be precisely classified. In addition, this method has the advantages of low computational complexity and robustness to the Hughes phenomenon.


title: Digital development: a database of cell lineage differentiation in C. elegans with lineage phenotypes, cell-specific gene functions and a multiscale model

abstract: Developmental systems biology is poised to exploit large-scale data from two approaches: genomics and live imaging. The combination of the two offers the opportunity to map gene functions and gene networks in vivo at single-cell resolution using cell tracking and quantification of cellular phenotypes. Here we present Digital Development (http://www.digital-development.org), a database of cell lineage differentiation with curated phenotypes, cell-specific gene functions and a multiscale model. The database stores data from recent systematic studies of cell lineage differentiation in the C. elegans embryo containing ∼ 200 conserved genes, 1400 perturbed cell lineages and 600,000 digitized single cells. Users can conveniently browse, search and download four categories of phenotypic and functional information from an intuitive web interface. This information includes lineage differentiation phenotypes, cell-specific gene functions, differentiation landscapes and fate choices, and a multiscale model of lineage differentiation. Digital Development provides a comprehensive, curated, multidimensional database for developmental biology. The scale, resolution and richness of biological information presented here facilitate exploration of gene-specific and systems-level mechanisms of lineage differentiation in Metazoans.


title: Using CART to generate partially synthetic, public use microdata

abstract: To limit disclosure risks, one approach is to release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple imputations. This article presents and evaluates the use of classification and regression trees to generate partially synthetic data. Two potential applications of CART are studied via simulation: (i) generate synthetic data for sensitive variables; and, (ii) generate synthetic data for variables that are key identifiers.


title: Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey

abstract: The use of distributed energy resources is increasingly being pursued as a supplement and an alternative to large conventional central power stations. The specification of a power-electronic interface is subject to requirements related not only to the renewable energy source itself but also to its effects on the power-system operation, especially where the intermittent energy source constitutes a significant part of the total system capacity. In this paper, new trends in power electronics for the integration of wind and photovoltaic (PV) power generators are presented. A review of the appropriate storage-system technology used for the integration of intermittent renewable energy sources is also introduced. Discussions about common and future trends in renewable energy systems based on reliability and maturity of each technology are presented


title: Detecting DNS Tunnel through Binary-Classification Based on Behavior Features

abstract: DNS tunnel is a typical Internet covert channel used by attackers or bots to evade the malicious activities detection. The stolen information is encoded and encapsulated into the DNS packets to transfer. Since DNS traffic is common, most of the firewalls directly allow it to pass and IDS does not trigger an alarm with it. The popular signature-based detection methods and threshold-based methods are not flexible and make high false alarms. The approaches based on characters distribution features also do not perform well, because attackers can modify the encoding method to disturb the characters distributions.In this paper, we propose an effective and applicable DNS tunnel detection mechanism. The prototype system is deployed at the Recursive DNS for tunnel identification. We use four kinds of features including time-interval features, request packet size features, record type features and subdomain entropy features. We evaluate the performance of our proposal with Support Vector Machine, Decision Tree and Logistical Regression. The experiments show that the method can achieve high detection accuracy of 99.96%.


title: Knowledge representation learning with entities, attributes and relations

abstract: Distributed knowledge representation (KR) encodes both entities and relations in a low-dimensional semantic space, which has significantly promoted the performance of relation extraction and knowledge reasoning. In many knowledge graphs (KG), some relations indicate attributes of entities (attributes) and others indicate relations between entities (relations). Existing KR models regard all relations equally, and usually suffer from poor accuracies when modeling one-to-many and many-to-one relations, mostly composed of attribute. In this paper, we distinguish existing KG-relations into attributes and relations, and propose a new KR model with entities, attributes and relations (KR-EAR). The experiment results show that, by special modeling of attribute, KR-EAR can significantly outperform state-of-the-art KR models in prediction of entities, attributes and relations. The source code of this paper can be obtained from https://github.com/thunlp/KR-EAR.


title: Equivalent complex permeability and conductivity of Litz wire in wireless power transfer systems

abstract: In this paper the skin and proximity effect losses for Litz wire winding used in wireless power transfer system is calculated using exact 2-D method. Based on those losses the equivalent complex permeability and conductivity are calculated for each strand in the bundle. Due to specific properties of Litz wire, the whole bundle is homogenized using isotropic complex permeability and conductivity and is simulated numerically in Finite Element (FE) verifying the analytical and experimental results.


title: Evolutionary games on graphs

abstract: Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.


title: ChronoStream: Elastic stateful stream computation in the cloud

abstract: We introduce ChronoStream, a distributed system specifically designed for elastic stateful stream computation in the cloud. ChronoStream treats internal state as a first-class citizen and aims at providing flexible elastic support in both vertical and horizontal dimensions to cope with workload fluctuation and dynamic resource reclamation. With a clear separation between application-level computation parallelism and OS-level execution concurrency, ChronoStream enables transparent dynamic scaling and failure recovery by eliminating any network I/O and state-synchronization overhead. Our evaluation on dozens of computing nodes shows that ChronoStream can scale linearly and achieve transparent elasticity and high availability without sacrificing system performance or affecting collocated tenants.


title: Temporal Convolutional Neural Networks for Diagnosis from Lab Tests

abstract: Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K individuals over 8 years, 18 common lab measurements, and 171 diseases show that the temporal signatures learned via convolution are significantly more predictive than baselines commonly used for early disease diagnosis.


title: Metrics for Requirements Engineering and Automated Requirements Tools

abstract: Software requirements are the foundations from which quality is measured. Measurement enables to improve the software process; assist in planning, tracking and controlling the software project and assess the quality of the software thus produced. Quality issues such as accuracy, security and performance are often crucial to the success of a software system. Quality should be maintained from starting phase of software development. Requirements management, play an important role in maintaining quality of software. A project can deliver the right solution on time and within budget with proper requirements management. Software quality can be maintained by checking quality attributes in requirements document. Requirements metrics such as volatility, traceability, size and completeness are used to measure requirements engineering phase of software development lifecycle. Manual measurement is expensive, time consuming and prone to error therefore automated tools should be used. Automated requirements tools are helpful in measuring requirements metrics. The aim of this paper is to study, analyze requirements metrics and automated requirements tools, which will help in choosing right metrics to measure software development based on the evaluation of Automated Requirements Tools


title: A Survey of Automated Visual Inspection

abstract: In this paper, we survey the automated visual inspection systems and techniques that have been reported in the literature from 1988 to 1993. Several earlier systems are also discussed. In the survey, a taxonomy of inspection systems based on their sensory input is presented. The general benefits and feasibility of automated visual inspection are also discussed. We present common approaches to visual inspection and also consider the specification and analysis of dimensional tolerances and their influence on the inspection task(s). One of the recent developments in automated visual inspection, namely the expanded role of computer-aided design (CAD) data in many systems, is examined in detail.


title: Corporation Architecture for Multiple Cloud Service Providers in JointCloud Computing

abstract: Nowadays, cloud computing is hard to effectively sustain the implementation of the commercial model of Internet Service globalization. There is a growing trend to build an environment of cloud service, with the capacity to serve anytime and anywhere, by mutual cooperation between cloud service providers around the world. However, this tendency will raise a key issue which is how to provide a benign environment, that allows self-collaboration and fair competition, for different cloud service providers with diverse stakeholder. Inspired by the concept and structure of Service-Oriented Architecture (SOA) service, this paper proposes a structure named JointCloud Corporation Environment (JCCE), which offers a mutual benefit and win-win JointCloud environment for global cloud service providers. JCCE contains three core services, which are Distributed Cloud Transaction, Distributed Cloud Community and Distributed Cloud Supervision. Also, facing with different cloud service participants, JCCE offers three main service modes for their consumption, supply and coordination. This study plays a significant role in supporting the sharing and self-collaboration of multiple cloud entities, and promoting the development of cloud service market healthy and orderly.


title: Privacy-preserving decision trees over vertically partitioned data

abstract: Privacy and security concerns can prevent sharing of data, derailing data mining projects.Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.


title: Digital Forensics in the Age of Big Data: Challenges, Approaches, and Opportunities

abstract: The age of big data opens new opportunities in various fields. While the availability of a big dataset can be helpful in some scenarios, it introduces new challenges in digital forensics investigations. The existing tools and infrastructures cannot meet the expected response time when we investigate on a big dataset. Forensics investigators will face challenges while identifying necessary pieces of evidence from a big dataset, and collecting and analyzing those evidence. In this article, we propose the first working definition of big data forensics and systematically analyze the big data forensics domain to explore the challenges and issues in this forensics paradigm. We propose a conceptual model for supporting big data forensics investigations and present several use cases, where big data forensics can provide new insights to determine facts about criminal incidents.


title: Design and evaluation of a compiler algorithm for prefetching

abstract: Software-controlled data prefetching is a promising technique for improving the performance of the memory subsystem to match today’s high-performance processors. While prefctching is useful in hiding the latency, issuing prefetches incurs an instruction overhead and can increase the load on the memory subsystem. As a resu 1~ care must be taken to ensure that such overheads do not exceed the benefits. This paper proposes a compiler algorithm to insert prefetch instructions into code that operates on dense matrices. Our algorithm identiEes those references that are likely to be cache misses, and issues prefetches only for them. We have implemented our algorithm in the SUfF (Stanford University Intermediate Form) optimizing compiler. By generating fully functional code, we have been able to measure not only the improvements in cache miss rates, but also the oversdl performance of a simulated system. We show that our algorithm significantly improves the execution speed of our benchmark programs-some of the programs improve by as much as a factor of two. When compared to an algorithm that indiscriminately prefetches alf array accesses, our algorithm can eliminate many of the unnecessary prefetches without any significant decrease in the coverage of the cache misses.


title: Substrate integrated waveguide (SIW) based on novel double-sided-complementary spiral resonators (DS-CSRs)

abstract: In this paper, a novel substrate integrated waveguide (SIW) loaded with double-sided complementary spiral resonators (DS-CSRs) is investigated and its applications to the miniaturized bandpass filters will be presented. The proposed structures allow implementation of a forward-wave passband propagating below the characteristic cutoff frequency of the waveguide. Due to extra coupling effect between CSRs of the top and bottom layer, a much lower resonance frequency has been achieved compared to conventional single CSR, which is etched on the surface layer of SIW. Then the method of designing bandpass filters is illustrated. As a practical case, the application of this novel waveguide and DS-CSR combination technique to design the miniaturized bandpass filer with two-order is designed and investigated soon afterwards. All of these resonators and filters are simulated by CST MWS software, which is based on finite different time domain. Good agreement is obtained between the analysis and the simulation results.


title: The Neural Circuitry of a Broken Promise

abstract: Promises are one of the oldest human-specific psychological mechanisms fostering cooperation and trust. Here, we study the neural underpinnings of promise keeping and promise breaking. Subjects first make a promise decision (promise stage), then they anticipate whether the promise affects the interaction partner's decision (anticipation stage) and are subsequently free to keep or break the promise (decision stage). Findings revealed that the breaking of the promise is associated with increased activation in the DLPFC, ACC, and amygdala, suggesting that the dishonest act involves an emotional conflict due to the suppression of the honest response. Moreover, the breach of the promise can be predicted by a perfidious brain activity pattern (anterior insula, ACC, inferior frontal gyrus) during the promise and anticipation stage, indicating that brain measurements may reveal malevolent intentions before dishonest or deceitful acts are actually committed.


title: A modular DC-DC converter with collapsible input voltage of series connected modules without additional bypass switch

abstract: Modular DC-DC converters have been used in high power applications to increase both the maximum power and voltage ratings of DC-DC converters. In parallel-connected modular DC-DC converters, light-load efficiency has been reported to improve by shutting down unnecessary modules. In this paper, low voltage efficiency of series-connected converter is improved by shutting down modules to shift the input voltage of the remaining modules to higher values for higher efficiency. Moreover, minimum input voltage of the converter is also reduced and input voltage range is therefore increased. The collapse of input voltage is required during this process and energy stored in the input bus capacitors has to be discharged. The focus of this paper is to use available switches in the modules as bypass switches. A method to control these switches for operation within their safe operating area (SOA) during this process is discussed. Finally, a modular DC-DC convert prototype with input series and output parallel (ISOP) connections is built to verify this concept.


title: Solving job-shop scheduling problems by genetic algorithm

abstract: Job-shop scheduling problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used to solve the problem is the branch-and-bound method, which takes considerable computing time when the size of problem is large. We propose a new method for solving JSP using genetic algorithm (GA) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of GA are how to represent the schedules as an individuals and to design the genetic operators for the representation in order to produce better results. >


title: Global Precedence, Spatial Frequency Channels, and the Statistics of Natural Images

abstract: A great deal of evidence suggests that early in processing, retinal images are filtered by parallel, spatial frequency selective channels. We attempt to incorporate this view of early vision with the principle of global precedence, which holds that Gestalt-like processes sensitive to global image configurations tend to dominate local feature processing in human pattern perception. Global precedence is inferred from the pattern of reaction times observed when visual patterns contain multiple cues at different levels of spatial scale. Specifically, it is frequently observed that global processing times are largely unaffected by conflicting local cues, but local processing times are substantially lengthened by conflicting global cues. The asymmetry of these effects suggests the dominant role of global configurations. Since global spatial information is effectively represented by low spatial frequencies, global precedence potentially implies a low frequency dominance. The thesis is that low spatial frequencies tend to be available before information carried by higher frequency bands, producing a coarse-to-fine temporal order in visual spatial perception. It is suggested that a variety of factors contribute to the “prior entry” of low frequency information, including the high contrast gain of the magnocellular pathway, the amplitude spectra typical of natural images, and inhibitory interactions between the parallel frequency-tuned channels. Evidence suggesting a close relationship between global precedence and spatial frequency channels is provided by observations that the essential features of the global precedence effect are obtained using patterns consisting of low and high frequency sinusoids. The hypothesis that these asymmetric interference effects are due to interactions between parallel spatial channels is supported by an analysis of reaction times (RTs), which shows that RTs to redundant low and high frequency cues produce less facilitation than predictions that assume the channels are independent. In view of previous work showing that global precedence depends upon the low frequency content of the stimuli, we suggest that low spatial frequencies represent the sine qua non for the dominance of configurational cues in human pattern perception, and that this configurational dominance reflects the microgenesis of visual pattern perception. This general view of the temporal dynamics of visual pattern recognition is discussed, is considered from an evolutionary perspective, and is related to certain statistical regularities in natural scenes. Potential adaptive advantages of an interactive parallel architecture that confers an initial processing advantage to low resolution information are explored.


title: Creating Algorithmic Traders with Hierarchical Reinforcement Learning

abstract: There has recently been a considerable amount of research into algorithmic traders that learn [7, 27, 21, 19]. A variety of machine learning techniques have been used, including reinforcement learning [20, 11, 19, 5, 21]. We propose a reinforcement learning agent that can adapt to underlying market regimes by observing the market through signals generated at short and long timescales, and by using the CHQ algorithm [23], a hierarchical method which allows the agent to change its strategies after observing certain signals. We hypothesise that reinforcement learning agents using hierarchical reinforcement learning are superior to standard reinforcement learning agents in markets with regime change. This was tested through a market simulation based on data from the Russell 2000 index [4]. A significant difference was only found in the trivial case, and we concluded that a difference does not exist for our agent design. It was also observed and empirically verified that our standard agent learns different strategies depending on how much information it is given and whether it is charged a commission cost for trading. We therefore provide a novel example of an adaptive algorithmic trader.


title: Hierarchical Neural Network Generative Models for Movie Dialogues.

abstract: We consider the task of generative dialogue modeling for movie scripts. To this end, we extend the recently proposed hierarchical recurrent encoder decoder neural network and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We show that its performance can be improved considerably by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.


University of Groningen Will my auxiliary tagging task help ? Estimating Auxiliary Tasks Effectivity in MultiTask Learning


The History of Artificial Intelligence


title: CrowdStory: Fine-Grained Event Storyline Generation by Fusion of Multi-Modal Crowdsourced Data

abstract: Event summarization based on crowdsourced microblog data is a promising research area, and several researchers have recently focused on this field. However, these previous works fail to characterize the fine-grained evolution of an event and the rich correlations among posts. The semantic associations among the multi-modal data in posts are also not investigated as a means to enhance the summarization performance. To address these issues, this study presents CrowdStory, which aims to characterize an event as a fine-grained, evolutionary, and correlation-rich storyline. A crowd-powered event model and a generic event storyline generation framework are first proposed, based on which a multi-clue--based approach to fine-grained event summarization is presented. The implicit human intelligence (HI) extracted from visual contents and community interactions is then used to identify inter-clue associations. Finally, a cross-media mining approach to selective visual story presentation is proposed. The experiment results indicate that, compared with the state-of-the-art methods, CrowdStory enables fine-grained event summarization (e.g., dynamic evolution) and correctly identifies up to 60% strong correlations (e.g., causality) of clues. The cross-media approach shows diversity and relevancy in visual data selection.


title: QuestionBank: Creating A Corpus Of Parse-Annotated Questions

abstract: This paper describes the development of QuestionBank, a corpus of 4000 parse-annotated questions for (i) use in training parsers employed in QA, and (ii) evaluation of question parsing. We present a series of experiments to investigate the effectiveness of QuestionBank as both an exclusive and supplementary training resource for a state-of-the-art parser in parsing both question and non-question test sets. We introduce a new method for recovering empty nodes and their antecedents (capturing long distance dependencies) from parser output in CFG trees using LFG f-structure reentrancies. Our main findings are (i) using QuestionBank training data improves parser performance to 89.75% labelled bracketing f-score, an increase of almost 11% over the baseline; (ii) back-testing experiments on non-question data (Penn-II WSJ Section 23) shows that the retrained parser does not suffer a performance drop on non-question material; (iii) ablation experiments show that the size of training material provided by QuestionBank is sufficient to achieve optimal results; (iv) our method for recovering empty nodes captures long distance dependencies in questions from the ATIS corpus with high precision (96.82%) and low recall (39.38%). In summary, QuestionBank provides a useful new resource in parser-based QA research.


title: Robotic SLAM: a Review from Fog Computing and Mobile Edge Computing Perspective

abstract: Offloading computationally expensive Simultaneous Localization and Mapping (SLAM) task for mobile robots have attracted significant attention during the last few years. Lack of powerful on-board compute capability in these energy constrained mobile robots and rapid advancement in compute cloud access technologies laid the foundation for development of several Cloud Robotics platforms that enabled parallel execution of computationally expensive robotic algorithms, especially involving multiple robots. In this work the Cloud Robotics concept is extended to include the current emphasis of computing at the network edge nodes along with the Cloud. The requirements and advantages of using edge nodes for computation offloading over remote cloud or local robot clusters are discussed with reference to the ETSI 'Mobile-Edge Computing' initiative and OpenFog Consortium's 'OpenFog Architecture'. A Particle Filter algorithm for SLAM is modified and implemented for offloading in a multi-tier edge+cloud setup. Additionally a model is proposed for offloading decision in such a setup with experiments and results demonstrating the efficacy of the proposed dynamic offloading scheme over static offloading strategies.


The Frankfurt airport attack: A case study on the radicalization of a lone-actor terrorist.


title: 3D-printed low-cost, low-loss microwave components up to 40 GHz

abstract: This contribution investigates the manufacturability and the performance of waveguide components fabricated using a consumer-grade 3D printer. This low-cost process enables a fast fabrication of microwave circuits almost directly from a 3D simulation model to a ready-to-use component. Two different components, a simple waveguide and a more complex array with integrated power combiner, are manufactured and characterized to validate the approach.


title: Perceived absorptive capacity of individual users in performance of Enterprise Resource Planning (ERP) usage: The case for Korean firms

abstract: We examined the effect of absorptive capacity of users on their use of ERP in a Korean context. The three components considered were understanding, assimilating, and applying ERP knowledge. We found that the capacities of users to assimilate and apply the knowledge had both direct and indirect effects on its value. The users' ability to understand ERP knowledge was found to influence its performance by their assimilating and applying the knowledge. We also found that organizational support moderated the relationship between their absorptive capacity and performance.


title: A Novel Radio Frame Structure for 5G Dense Outdoor Radio Access Networks

abstract: This paper proposes a novel frame structure for the radio access interface of the next generation of mobile networks. The proposed frame structure has been designed to support multiuser spatial multiplexing, short latencies on the radio access interface, as well as mobility and small packet transmissions. The focus is on ultra dense small cell networks deployed in outdoor environments. This paper also highlights the various prospects and constraints of the proposed dense outdoor system in comparison with alternative system designs. Numerical results are included and a comparison to the Long Term Evolution (LTE) system is provided. Results show that the proposed radio frame structure leads to an improvement of the area spectral efficiency by a factor of ~2.4 as well as a reduction of the average air interface latency by a factor of 5, thus remaining shorter than 1 millisecond.


title: An introduction to quantum computing for non-physicists

abstract: Richard Feynman's observation that certain quantum mechanical effects cannot be simulated efficiently on a computer led to speculation that computation in general could be done more efficiently if it used these quantum effects. This speculation proved justified when Peter Shor described a polynomial time quantum algorithm for factoring intergers. In quantum systems, the computational space increases exponentially with the size of the system, which enables exponential parallelism. This parallelism could lead to exponentially faster quantum algorithms than possible classically. The catch is that accessing the results, which requires measurement, proves tricky and requires new nontraditional programming techniques. The aim of this paper is to guide computer scientists through the barriers that separate quantum computing from conventional computing. We introduce basic principles of quantum mechanics to explain where the power of quantum computers comes from and why it is difficult to harness. We describe quantum cryptography, teleportation, and dense coding. Various approaches to exploiting the power of quantum parallelism are explained. We conclude with a discussion of quantum error correction.


Privacy Preserving Group Nearest Neighbor Search


title: Minimap2: fast pairwise alignment for long nucleotide sequences

abstract: Summary: Minimap2 is a general-purpose mapper to align long noisy DNA or mRNA sequences against a large reference database. It targets query sequences of 1kb--100Mb in length with per-base divergence typically below 25%. For DNA sequence reads, minimap2 is ~30 times faster than many mainstream long-read aligners and achieves higher accuracy on simulated data. It also employs concave gap cost and rescues inversions for improved alignment around potential structural variations. For real long RNA-seq reads, minimap2 is ~40 times faster than peers and produces alignment more consistent with existing gene annotations. Availability and implementation: https://github.com/lh3/minimap2 Contact: hengli@broadinstitute.org


title: A real time GSM/GPS based tracking system based on GSM mobile phone

abstract: A GPS based tracking system is proposed which keeps track of the location of a vehicle and its speed based on a mobile phone text messaging system. The system is able to provide real-time text alerts for speed and location. Particularly, the present location can be locked and the system will alert the owner if the vehicle is moved from the present locked location. In addition, the speed can be locked and an alert texted if this speed is exceeded.


title: Composite right/left-handed transmission line based compact resonant antennas for RF module integration

abstract: Several electrically small resonant antennas employing the composite right/left-handed transmission line (CRLH-TL) are presented for integration with portable RF modules. The proposed antenna designs are based on the unique property of anti-parallel phase and group velocity of the CRLH-TL at its fundamental mode. In this mode, the propagation constant increases as the frequency decreases, therefore, a small guided wavelength can be obtained at a lower frequency to provide the small lambdag/2 resonant length used to realize a compact antenna design. Furthermore, the physical size and the operational frequency of the antenna depend on the unit cell size and the equivalent transmission line model parameters of the CRLH-TL, including series inductance, series capacitance, shunt inductance and shunt capacitance. Optimization of these parameters as well as miniaturization techniques of the physical size of unit cell is investigated. A four unit-cell resonant antenna is designed and tested at 1.06 GHz. The length, width and height of the proposed antenna are 1/19lambda0, 1/23lambda0 and 1/83lambda0, respectively. In addition, a compact antenna using a 2-D three by three mushroom like unit cell arrangement is developed at 1.17 GHz, showing that an increased gain of 0.6 dB and higher radiation efficiency can be achieved over the first prototype antenna. The same design is applied in the development of a circularly polarized antenna operating at 2.46 GHz. A 116deg beamwidth with axial ratio better than 3 dB is observed. The physical size of the proposed mushroom type small antenna and the circularly polarized antenna is 1/14lambda0 by 1/14lambda0 by 1/39lambda0 and 1/10lambda0 by 1/10lambda 0 by 1/36lambda0, respectively


title: Expecting to teach enhances learning and organization of knowledge in free recall of text passages

abstract: The present research assessed the potential effects of expecting to teach on learning. In two experiments, participants studied passages either in preparation for a later test or in preparation for teaching the passage to another student who would then be tested. In reality, all participants were tested, and no one actually engaged in teaching. Participants expecting to teach produced more complete and better organized free recall of the passage (Experiment 1) and, in general, correctly answered more questions about the passage than did participants expecting a test (Experiment 1), particularly questions covering main points (Experiment 2), consistent with their having engaged in more effective learning strategies. Instilling an expectation to teach thus seems to be a simple, inexpensive intervention with the potential to increase learning efficiency at home and in the classroom.


title: Elascale: Autoscaling and Monitoring as a Service

abstract: Auto-scalability has become an evident feature for cloud software systems including but not limited to big data and IoT applications. Cloud application providers now are in full control over their applications' microservices and macroservices; virtual machines and containers can be provisioned or deprovisioned on demand at runtime. Elascale strives to adjust both micro/macro resources with respect to workload and changes in the internal state of the whole application stack. Elascale leverages Elasticsearch stack for collection, analysis and storage of performance metrics. Elascale then uses its default scaling engine to elastically adapt the managed application. Extendibility is guaranteed through provider, schema, plug-in and policy elements in the Elascale by which flexible scalability algorithms, including both reactive and proactive techniques, can be designed and implemented for various technologies, infrastructures and software stacks. In this paper, we present the architecture and initial implementation of Elascale; an instance will be leveraged to add auto-scalability to a generic IoT application. Due to zero dependency to the target software system, Elascale can be leveraged to provide auto-scalability and monitoring as-a-service for any type of cloud software system.


title: A Multigrasp Hand Prosthesis for Providing Precision and Conformal Grasps

abstract: This paper presents the design of an anthropomorphic prosthetic hand that incorporates four motor units in a unique configuration to explicitly provide both precision and conformal grasp capability. The paper describes the design of the hand prosthesis, and additionally describes the design of an embedded control system located in the palm of the hand that enables self-contained control of hand movement. Following the design description, the paper provides experimental characterizations of hand performance, including digit force capability, bandwidth of digit movement, physical properties such as size and mass, and electrical power measurements during activities of daily living.


An Intelligent Model for Pairs Trading Using Genetic Algorithms


title: Transfer Learning for Reinforcement Learning Domains: A Survey

abstract: The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.


title: Recognizing Text-Based Traffic Signs

abstract: We propose a novel system for the automatic detection and recognition of text in traffic signs. Scene structure is used to define search regions within the image, in which traffic sign candidates are then found. Maximally stable extremal regions (MSERs) and hue, saturation, and value color thresholding are used to locate a large number of candidates, which are then reduced by applying constraints based on temporal and structural information. A recognition stage interprets the text contained within detected candidate regions. Individual text characters are detected as MSERs and are grouped into lines, before being interpreted using optical character recognition (OCR). Recognition accuracy is vastly improved through the temporal fusion of text results across consecutive frames. The method is comparatively evaluated and achieves an overall $F_{\rm measure}$ of 0.87.


title: Parasite removal by natural wastewater treatment systems: performance of waste stabilisation ponds and constructed wetlands

abstract: Parasite removal and low cost systems for wastewater treatment have become increasingly important requirements in developed and developing countries to safeguard public health from wastewater-associated intestinal diseases. Pilot and field-scale ponds and wetlands in Brazil and Egypt have been investigated for the fate and removal of eggs of human intestinal parasites from domestic wastewater. In northeast Brazil, parasite removal was investigated for a series of five waste stabilisation ponds treating raw wastewater. In Egypt, parasite removal was studied for Gravel Bed Hydroponic constructed wetlands treating partially treated wastewater. Influents to ponds and wetlands contained a variety of parasite helminth eggs (e.g. Ascaris, hookworm, Trichuris, and Hymenolepis spp.). The ponds consistently removed parasite eggs though rate of removal by individual ponds may have been related to influent egg numbers and extent of short-circuiting. Parasite eggs were reduced on average by 94% and 99.9% in the anaerobic and facultative ponds respectively. No eggs were found in effluent from the second maturation pond. In the wetland system, parasite removal varied with reedbed length. The majority of parasite eggs were retained within the first 25 m. Parasite eggs were reduced on average by 98% after treatment in 50 m beds and completely removed after treatment in 100 m beds.


title: Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent

abstract: We propose a generic method for iteratively approximating various second-order gradient steps - Newton, Gauss-Newton, Levenberg-Marquardt, and natural gradient - in linear time per iteration, using special curvature matrix-vector products that can be computed in O(n). Two recent acceleration techniques for on-line learning, matrix momentum and stochastic meta-descent (SMD), implement this approach. Since both were originally derived by very different routes, this offers fresh insight into their operation, resulting in further improvements to SMD.


title: Hierarchical attribute-based encryption and scalable user revocation for sharing data in cloud servers

abstract: With rapid development of cloud computing, more and more enterprises will outsource their sensitive data for sharing in a cloud. To keep the shared data confidential against untrusted cloud service providers (CSPs), a natural way is to store only the encrypted data in a cloud. The key problems of this approach include establishing access control for the encrypted data, and revoking the access rights from users when they are no longer authorized to access the encrypted data. This paper aims to solve both problems. First, we propose a hierarchical attribute-based encryption scheme (HABE) by combining a hierarchical identity-based encryption (HIBE) system and a ciphertext-policy attribute-based encryption (CP-ABE) system, so as to provide not only fine-grained access control, but also full delegation and high performance. Then, we propose a scalable revocation scheme by applying proxy re-encryption (PRE) and lazy re-encryption (LRE) to the HABE scheme, so as to efficiently revoke access rights from users.


title: The cortical language circuit: from auditory perception to sentence comprehension

abstract: Over the years, a large body of work on the brain basis of language comprehension has accumulated, paving the way for the formulation of a comprehensive model. The model proposed here describes the functional neuroanatomy of the different processing steps from auditory perception to comprehension as located in different gray matter brain regions. It also specifies the information flow between these regions, taking into account white matter fiber tract connections. Bottom-up, input-driven processes proceeding from the auditory cortex to the anterior superior temporal cortex and from there to the prefrontal cortex, as well as top-down, controlled and predictive processes from the prefrontal cortex back to the temporal cortex are proposed to constitute the cortical language circuit.


title: The Microsoft academic search dataset and KDD Cup 2013

abstract: KDD Cup 2013 challenged participants to tackle the problem of author name ambiguity in a digital library of scientific publications. The competition consisted of two tracks, which were based on large-scale datasets from a snapshot of Microsoft Academic Search, taken in January 2013 and including 250K authors and 2.5M papers. Participants were asked to determine which papers in an author profile are truly written by a given author (track 1), as well as to identify duplicate author profiles (track 2). Track 1 and track 2 were launched respectively on April 18 and April 20, 2013, with a common final submission deadline on June 12, 2013. For track 1 a training dataset with correct labels was diclosed at the start of the competition. This track was the most popular one, attracting submissions of 561 different teams. Track 2, which was formulated as an unsupervised learning task, received submissions from 241 participants. This paper presents details about the problem definitions, the datasets, the evaluation metrics and the results.


title: Wideband diplexed feed chains for FSS + BSS applications

abstract: Three different RF chain architectures operating in the FSS (Fixed Satellite Services) + BSS (Broadcast Satellite) spectrum are presented and discussed. The RF chains are based on a common wideband corrugated feed horn, but differ on the approach used for bands and polarizations separation. A breadboard of a novel self-diplexed configuration has been designed, manufactured and tested. It proves to be the preferred candidate for bandwidth, losses and power handling. Very good correlation of the RF performance to the theoretical design is found.


title: Psychosocial and cultural factors affecting the perceived risk of genetically modified food: an overview of the literature

abstract: The rapid globalization of the world economy has increased the need for an astute understanding of cultural differences in perceptions, values, and ways of thinking about new food technologies. In this paper, we describe how socio-psychological and cultural factors may affect public perceptions of the risk of genetically modified (GM) food. We present psychological, sociological, and anthropological research on risk perception as a framework for understanding cross-national differences in reactions to GM food. Differences in the cultural values and circumstances of people in the US, European countries, and the developing world are examined. The implications of cultural theory for risk communication and decision making about GM food are discussed and directions for future research highlighted.


Web Content Credibility


title: A proof procedure for normal default theories

abstract: Recent research by Delgrande [6] and Geffner and Pearl [10] suggests two different semantic interpretations for normal defaults with one single representation as conditional sentences. However, they both need additional formal mechanisms for handling irrelevant information when their approaches are applied to formalising default reasoning. Delgrande in [5, 6] suggests two meta-strategies which he considers to be adequately strong to handle the orderings of defaults, and he claims they are equivalent. Furthermore, each of Delgrande's strategies is defined in terms of all sentences of the object language. In this paper, we shall prove that Delgrande's claim that his meta-strategies are equivalent is incorrect and that one of his meta-strategies can be reformulated within the framework of First Order Predicate Calculus (FOPC) and without having to consider every sentence of the object language. One advantage of such a reformalisation is its computational simplicity: to give an extension of a default theory there is only a need to consider those sentences which occur in the default theory under consideration rather than every sentence in the object language; furthermore, to provide a proof procedure for Delgrande's system as based on the meta-strategy we have formalised, one need only employ a FOPC proof procedure, rather than a conditional one.


title: Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units

abstract: Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it, has been a challenging problem in the industry for many years. With the evolution of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose two independent methods for this very task. The first method uses autoencoders to construct a unique representation of each emotion, while the second method is an 8-layer convolutional neural network (CNN). These methods were trained on the posed-emotion dataset (JAFFE), and to test their robustness, both the models were also tested on 100 random images from the Labeled Faces in the Wild (LFW) dataset, which consists of images that are candid than posed. The results show that with more fine-tuning and depth, our CNN model can outperform the state-of-the-art methods for emotion recognition. We also propose some exciting ideas for expanding the concept of representational autoencoders to improve their performance.


title: 3D object pose estimation using stereo vision for object manipulation system

abstract: Object pose estimation is one of the crucial parts in vision-based object manipulation system using standard industrial robot manipulator, particularly in pose estimation of the end effector of the robot arm to grasp the object targeted. This paper presents the utilization of stereo vision system to estimate the 3D (3 dimensional) object position and orientation to pick up and place the object targeted in an arbitrary location within the workspace. In order to accomplish this task, a calibrated stereo camera in the eye to hand configuration is used to capture the images of the object on the left and right camera. Then, the specific object feature is extracted and the 3D position and orientation of the object are calculated using image processing algorithm. Finally, the end effector of robot arm equipped with gripper will pick up the object targeted according to the object pose estimation output, and then place it to the desired location. The experimental results using 6 DOF robot arm are demonstrated and show the effectiveness of the proposed approach with good performance.


DC Motor Speed Control using PID Controllers


title: Towards a mobile airbag system using MEMS sensors and embedded intelligence

abstract: This paper introduces the development of a mobile human airbag system that is designed for fall protection for the elderly. A Micro Inertial Measurement Unit (mulMU) that is 66 mm x 20 mm x 20 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, and a Bluetooth module. It records human motion information, and, with a Support Vector Machine (SVM) training process, we can classify falls and other normal motions successfully with an SVM filter. Based on the SVM filter, an embedded DSP (Digital Signal Processing) system is developed for real-time fall detection. Also, a smart mechanical airbag deployment system is finalized. This system weighs 253.5 grams (including a 42.5 g compressed CO2 cylinder) and is 190 mm long, 57 mm wide, and 30 mm thick. Its size will be reduced in future to allow it to fit onto a belt. The response time of the trigger is 0.133 seconds, which allows more time for compressed air release before a person falls down to the ground. The integrated system is tested, and the feasibility of the airbag system for real-time fall protection is demonstrated.


title: Learning A Translation Lexicon From Monolingual Corpora

abstract: This paper presents work on the task of constructing a word-level translation lexicon purely from unrelated monolingual corpora. We combine various clues such as cognates, similar context, preservation of word similarity, and word frequency. Experimental results for the construction of a German-English noun lexicon are reported. Noun translation accuracy of 39% scored against a parallel test corpus could be achieved.


title: Second step algorithms in the Burrows-Wheeler compression algorithm

abstract: In this paper we fix our attention on the second step algorithms of the Burrows‐Wheeler compression algorithm, which in the original version is the Move To Front transform. We discuss many of its replacements presented so far, and compare compression results obtained using them. Then we propose a new algorithm that yields a better compression ratio than the previous ones.


title: Social translucence: an approach to designing systems that support social processes

abstract: We are interested in desiging systems that support communication and collaboration among large groups of people over computing networks. We begin by asking what properties of the physical world support graceful human-human communication in face-to-face situations, and argue that it is possible to design digital systems that support coherent behavior by making participants and their activites visible to one another. We call such systems “socially translucent systems” and suggest that they have three characteristics—visbility, awareness, and accountability—which enable people to draw upon their experience and expertise to structure their interactions with one another. To motivate and focus our ideas we develop a vision of knowledge communities, conversationally based systems that support the creation, management and reuse of knowledge in a social context. We describe our experience in designing and deploying one layer of functionality for knowledge communities, embodied in a working system called “Barbie” and discuss research issues raised by a socially translucent approach to design.


title: Anticipated versus inferred politeness

abstract: A number of researchers have recently argued that politeness is not always inferred in the form of an implicature as claimed by Brown and Levinson (1987), but rather can be anticipated by addressees when it involves expected behaviour. The distinction between anticipated and inferred politeness is thus an important area for further development of politeness theory. In this paper, the way in which the notion of 'expectations' is related to politeness is first considered, before outlining the distinction between anticipated and inferred politeness in some detail. It is then argued that discourse politeness theory (Usami, 1998, 2001a, b, 2002) shows greater promise for deepening our understanding of this distinction than the proposals made thus far by relevance theorists. It is concluded that any investigation of the distinction between anticipating and inferring politeness must ultimately be grounded in empirical studies of politeness phenomena.


title: Adaptive Model Rules from Data Streams

abstract: Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms.


Supply Chain Optimization within Aviation MRO


title: Open Extraction of Fine-Grained Political Statements

abstract: Text data has recently been used as evidence in estimating the political ideologies of individuals, including political elites and social media users. While inferences about people are often the intrinsic quantity of interest, we draw inspiration from open information extraction to identify a new task: inferring the political import of propositions like OBAMA IS A SOCIALIST. We present several models that exploit the structure that exists between people and the assertions they make to learn latent positions of people and propositions at the same time, and we evaluate them on a novel dataset of propositions judged on a political spectrum.


title: Semi-Supervised Web Wrapper Repair via Recursive Tree Matching.

abstract: Continuous data extraction pipelines using wrappers have become common and integral parts of businesses dealing with stock, flight, or product information. Extracting data from websites that use HTML templates is difficult because available wrapper methods are not designed to deal with websites that change over time (the inclusion or removal of HTML elements). We are the first to perform large scale empirical analyses of the causes of shift and propose the concept of domain entropy to quantify it. We draw from this analysis to propose a new semi-supervised search approach called XTPath. XTPath combines the existing XPath with carefully designed annotation extraction and informed search strategies. XTPath is the first method to store contextual node information from the training DOM and utilize it in a supervised manner. We utilize this data with our proposed recursive tree matching method which locates nodes most similar in context. The search is based on a heuristic function that takes into account the similarity of a tree compared to the structure that was present in the training data. We systematically evaluate XTPath using 117,422 pages from 75 diverse websites in 8 vertical markets that covers vastly different topics. Our XTPath method consistently outperforms XPath and a current commercial system in terms of successful extractions in a blackbox test. We are the first supervised wrapper extraction method to make our code and datasets available (online here: this http URL).


title: Availability of caregiver‐friendly workplace policies (CFWPs): an international scoping review

abstract: Little research has been done to summarise: what is currently available to caregiver-employees (CEs), what types of employers are offering caregiver-friendly workplace policies (CFWPs), and the characteristics of employers offering CFWPs. The purpose of this scoping review was to explore the availability of CFWPs within workplaces on an international scale while being observant of how gender is implicated in care-giving. This paper followed the Arksey & O’Malley (2005) methodology for conducting scoping reviews. The authors applied an iterative method of determining study search strings, study inclusion and data extraction, and qualitative thematic analysis of the search results. Searches were performed in both the academic and grey literature, published between 1994 and 2014. A total of 701 articles were found. Seventy (n = 70) articles met all inclusion criteria and were included in this review. Four main qualitative themes were identified: (i) Diversity and Inclusiveness, (ii) Motivation, (iii) Accessibility, and (iv) Workplace Culture. Policy recommendations are discussed. This scoping review narrows the gap in the literature with respect to determining: (i) the workplaces which offer CFWPs, (ii) the sectors of the labour force shown to be supportive and (iii) the most frequently offered CFWPs.


title: Profetching and memory system behavior of the SPEC95 benchmark suite

abstract: This paper presents instruction and data cache miss rates for the SPEC95™ benchmark suite. We have simulated the instruction and data traffic resulting from 500 million instructions of each of the 18 programs. Simulation results show that only a few of the applications place more than modest demands on the memory system. This was noticed for instruction caches, where only a few workloads required more than a 32Kb cache to achieve miss rates of less than one miss every 1000 instructions. We also analyze two prefetching algorithms using the SPEC95 workload: next-sequential prefetching and shadow-directory prefetching. Each prefetching algorithm is evaluated using three performance metrics: coverage, accuracy, and traffic. Variations in each prefetching algorithm involve the use of a confirmation mechanism that receives feedback information about the quality of each prefetch. With confirmation, the prefetching algorithm is able to enhance the accuracy of prefetching decisions. The results show that shadow-directory prefetching averages miss coverage about ten percent higher than next-sequential prefetching when used in prefetching instructions (about 60 percent coverage for next-sequential prefetching versus 70 percent for shadow-directory prefetching). The prefetching accuracy for both algorithms is more than 90 percent when a confirmation mechanism is used. In general, data prefetching is shown to be less accurate and to provide less coverage than instruction prefetching. Shadow-directory prefetching averaged about a 40 percent miss coverage versus a 25 percent miss coverage for next-sequential prefetching. Prefetching accuracy is over 70 percent when confirmation is applied.


title: Security Analysis of salt||password Hashes

abstract: Protection of passwords used to authenticate computer systems and networks is one of the most important application of cryptographic hash functions. Due to the application of precomputed memory look up attacks such as birthday and dictionary attacks on the hash values of passwords to find passwords, it is usually recommended to apply hash function to the combination of both the salt and password, denoted salt||password, to prevent these attacks. In this paper, we present the first security analysis of salt||password hashing application. We show that when hash functions based on the compression functions with easily found fixed points are used to compute the salt||password hashes, these hashes are susceptible to precomputed offline birthday attacks. For example, this attack is applicable to the salt||password hashes computed using the standard hash functions such as MD5, SHA-1, SHA-256 and SHA-512 that are based on the popular Davies-Meyer compression function. This attack exposes a subtle property of this application that although the provision of salt prevents an attacker from finding passwords, salts prefixed to the passwords do not prevent an attacker from doing a precomputed birthday attack to forge an unknown password. In this forgery attack, we demonstrate the possibility of building multiple passwords for an unknown password for the same hash value and salt. Interestingly, password||salt (i.e. salts suffixed to the passwords) hashes computed using Davies-Meyer hash functions are not susceptible to this attack, showing the first security gap between the prefix-salt and suffix-salt methods of hashing passwords.


THE STRUCTURE OF CREDIT RISK


title: Blockchain based hybrid network architecture for the smart city

abstract: Abstract Recently, the concept of “Smart Cities” has developed considerably with the rise and development of the Internet of Things as new form of sustainable development. Smart cities are based on autonomous and distributed infrastructure that includes intelligent information processing and control systems heterogeneous network infrastructure, and ubiquitous sensing involving millions of information sources Due to the continued growth of data volume and number of connected IoT devices, however, issues such as high latency, bandwidth bottlenecks, security and privacy, and scalability arise in the current smart city network architecture. Designing an efficient, secure, and scalable distributed architecture by bringing computational and storage resources closer to endpoints is needed to address the limitations of today’s smart city network. In this paper, we propose a novel hybrid network architecture for the smart city by leveraging the strength of emerging Software Defined Networking and blockchain technologies. To achieve efficiency and address the current limitations, our architecture is divided into two parts: core network and edge network. Through the design of a hybrid architecture, our proposed architecture inherits the strength of both centralized and distributed network architectures. We also propose a Proof-of-Work scheme in our model to ensure security and privacy. To evaluate the feasibility and performance of our proposed model, we simulate our model and evaluate it based on various performance metrics. The result of the evaluation shows the effectiveness of our proposed model.


title: The Discrete Shearlet Transform: A New Directional Transform and Compactly Supported Shearlet Frames

abstract: It is now widely acknowledged that analyzing the intrinsic geometrical features of the underlying image is essential in many applications including image processing. In order to achieve this, several directional image representation schemes have been proposed. In this paper, we develop the discrete shearlet transform (DST) which provides efficient multiscale directional representation and show that the implementation of the transform is built in the discrete framework based on a multiresolution analysis (MRA). We assess the performance of the DST in image denoising and approximation applications. In image approximations, our approximation scheme using the DST outperforms the discrete wavelet transform (DWT) while the computational cost of our scheme is comparable to the DWT. Also, in image denoising, the DST compares favorably with other existing transforms in the literature.


title: Optimization of anemia treatment in hemodialysis patients via reinforcement learning

abstract: Abstract Objective Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. Methods RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. Results The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. Conclusion Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.


title: Can a personal website be useful as an information source to assess individual scientists? The case of European highly cited researchers

abstract: The web is not only the main scholarly communication tool but also an important source of additional information about the individual researchers, their scientific and academic activities and their formally and informally published results. The aim of this study is to investigate whether successful scientists use their personal websites to disseminate their work and career details and to know which specific contents are provided on those sites, in order to check if they could be used in research evaluation. The presence of the highly cited researchers working at European institutions were analysed, a group clearly biased towards senior male researchers working in large countries (United Kingdom and Germany). Results show that about two thirds of them have a personal website, specially the scientists from Denmark, Israel and the United Kingdom. The most frequent disciplines in those websites are economics, mathematics, computer sciences and space sciences, which probably reflect the success of open access subject repositories like RepEc, Arxiv or CiteSeerX. Other pieces of information analysed from the websites include personal and contact data, past experience and description of expertise, current activities and lists of the author's scientific papers. Indicators derived from most of these items can be used for developing a portfolio with evaluation purposes, but the overall availability of them in the population analysed is not representative enough by now for achieving that objective. Reasons for that insufficient coverage and suggestions for improvement are discussed.


title: IT Consumerization and its Effects on IT Business Value, IT Capabilities, and the IT Function

abstract: IT consumerization is both a major opportunity and significant challenge for organizations. However, IS research has hardly discussed the implications for IT management so far. In this paper we address this topic by empirically identifying organizational themes for IT consumerization and conceptually exploring the direct and indirect effects on the business value of IT, IT capabilities, and the IT function. More specifically, based on two case studies, we identify eight organizational themes: consumer IT strategy, policy development and responsibilities, consideration of private life of employees, user involvement into IT-related processes, individualization, updated IT infrastructure, end user support, and data and system security. The contributions of this paper are: ::: ::: (1) the identification of organizational themes for IT consumerization; ::: ::: (2) the proposed effects on the business value of IT, IT capabilities and the IT function, and; ::: ::: (3) combining empirical insights into IT consumerization with managerial theories in the IS discipline.


title: Identification and Characterization of Cell Type–Specific and Ubiquitous Chromatin Regulatory Structures in the Human Genome

abstract: The identification of regulatory elements from different cell types is necessary for understanding the mechanisms controlling cell type–specific and housekeeping gene expression. Mapping DNaseI hypersensitive (HS) sites is an accurate method for identifying the location of functional regulatory elements. We used a high throughput method called DNase-chip to identify 3,904 DNaseI HS sites from six cell types across 1% of the human genome. A significant number (22%) of DNaseI HS sites from each cell type are ubiquitously present among all cell types studied. Surprisingly, nearly all of these ubiquitous DNaseI HS sites correspond to either promoters or insulator elements: 86% of them are located near annotated transcription start sites and 10% are bound by CTCF, a protein with known enhancer-blocking insulator activity. We also identified a large number of DNaseI HS sites that are cell type specific (only present in one cell type); these regions are enriched for enhancer elements and correlate with cell type–specific gene expression as well as cell type–specific histone modifications. Finally, we found that approximately 8% of the genome overlaps a DNaseI HS site in at least one the six cell lines studied, indicating that a significant percentage of the genome is potentially functional.


title: Trichogramma egg parasitism of Helicoverpa armigera on pigeonpea and sorghum in southern India

abstract: Trichogrammaspp. (Hymenoptera: Trichogrammatidae) only rarely parasitize eggs of Helicoverpa armigera(Hubner) (Lepidoptera: Noctuidae) on pigeonpea (Cajanus cajan (L.) Millspaugh), while on other plants including sorghum (Sorghum bicolor(L.)) high parasitism levels of this host are found. In this study two strategies designed to increase the parasitoid activity on pigeonpea were tested in the field: intercropping pigeonpea with sorghum and mass-releasing T. chilonis Ishii. Neither strategy led to an increase in parasitism. On pigeonpea, H. armigera oviposited >74.8% of its eggs on calyxes and pods. Parasitism levels in host eggs collected from different plant structures varied significantly with 3.6, 0.3, and 40.7% of eggs on calyxes, pods, and leaves parasitized. Earlier studies have shown that calyxes and pods possess long glandular and non-glandular trichomes, and are covered by sticky trichome exudates which inhibit parasitoid searching behaviour. Parasitism levels between 27.9 and 100% were recorded from host eggs on the intercropped sorghum. Trichogramma chiloniswas the dominant parasitoid species. The mean clutch size was 2.03, but up to six parasitoids emerged per egg. Progeny sex ratio (% females) decreased with clutch size, from 63.1% at a clutch size of one to 46.0% at a clutch size of five. Sticky trap catches showed that while the parasitoid population in sorghum increased when H. armigera started ovipositing, the population within pigeonpea did not benefit from either a high parasitoid population in sorghum or a high host egg density on pigeonpea. During one of five seasons studied, however, high parasitism levels (up to 73%) were recorded on pigeonpea. During this season, H. armigera oviposited on pigeonpea plants in the vegetative growth stage and a high proportion of eggs were collected from leaves. Parasitism levels were positively correlated with the percentage of eggs collected from leaves. This study shows that the parasitization efficiency of Trichogrammaspp. on pigeonpea depends mainly on the location of the host eggs. This explains why parasitism levels of H. armigera eggs on pigeonpea did not increase when intercropped with sorghum or after mass-releasing T. chilonis.


title: Face detection speed improvement using bitmap-based Histogram of Oriented gradien

abstract: Since the Viola-Jones seminal work, the boosted cascade with simple features has become the most popular and effective approach for practical face detection. More improved face detectors that can handle uncontrolled face detection scenarios have achieved by applying more advanced features such as Histogram of oriented Gradients (HoG). The great improvement in accuracy delivered by these methods has been accompanied by a large increase in the computational burden, which limited adoption in embedded solutions particularly. The improved bitmap-based HoG approaches resolved this problem by limitation of HoG window to non-rectangular irregular pattern of the object and its boundary avoid processing of extra background and (partially) foreground pixels respectively. In this paper, bHoG and bbHoG along with three different bitmap patterns are applied to the face detection problem to not only benefits from the robustness of HoG, but also to amend its high computational cost significantly. Experimental results show an decrease of 92.5% in the workload associated with HoG/SVM classifiers compared to the state-of-the-art, along with approximately the same performance as standard HoG and an average decrease about 5% in recall and precision in comparison for the smaller cell sizes.