title: Attentional Factors in Conceptual Congruency

abstract: Conceptual congruency effects are biases induced by an irrelevant conceptual dimension of a task (e.g., location in vertical space) on the processing of another, relevant dimension (e.g., judging words’ emotional evaluation). Such effects are a central empirical pillar for recent views about how the mind/brain represents concepts. In the present paper, we show how attentional cueing (both exogenous and endogenous) to each conceptual dimension succeeds in modifying both the manifestation and the symmetry of the effect. The theoretical implications of this finding are discussed.


title: Integrating tags in a semantic content-based recommender

abstract: Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution and the advent of user generated content have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of user generated content that has drawn more attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. In this paper, we investigate whether folksonomies might be a valuable source of information about user interests. The main contribution is a strategy that enables a content-based recommender to infer user interests by applying machine learning techniques both on the "official" item descriptions provided by a publisher, and on tags which users adopt to freely annotate relevant items. Static content and tags are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests often hidden behind keywords. The proposed approach has been evaluated in the context of cultural heritage personalization. Preliminary experiments involving 30 real users show an improvement in the predictive accuracy of the tag-augmented recommender compared to the pure content-based one.


title: NATURAL IMAGE STATISTICS AND NEURAL REPRESENTATION

abstract: ▪ Abstract It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954), Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.


title: Modelling Behaviour for Distributed Additive Manufacturing

abstract: Distributed forms of construction in the biological world are characterized by the ability to generate complex adaptable large-scale structures with tunable properties. In contrast, state-of-the-art digital construction platforms in design lack such abilities. This is mainly due to limitations associated with fixed and inflexible gantry sizes as well as challenges associated with achieving additively manufacturing constructs that are at once structurally sound and materially tunable. To tackle these challenges we propose a multi-nodal distributed construction approach that can enable design and construction of larger-than-gantry-size structures. The system can generate and respond to integrated real-time feedback for parameters such as material curing duration and position awareness. We demonstrate this approach through a software environment designed to control multiple robots operating collaboratively to additively manufacture large-scale structures. We present and report on a novel computational workflow as well as work-in-progress of a digital fabrication environment. The environment combines a centralized system designed to manage top-down design intent given by environmental variables, with a decentralized system designed to compute, in a bottom up manner, parameters such as multi-node rule-based collision, asynchronous motion, multi-nodal construction sequence and variable material deposition properties. The paper reports on a successful first deployment of the system and demonstrates novel features characteristic of fabrication-information modelling such as multi-nodal cooperation, material-based flow and deposition, and environmentally informed digital construction.


title: Identifying localized changes in large systems: Change-point detection for biomolecular simulations

abstract: Research on change-point detection, the classical problem of detecting abrupt changes in sequential data, has focused predominantly on datasets with a single observable. A growing number of time series datasets, however, involve many observables, often with the property that a given change typically affects only a few of the observables. We introduce a general statistical method that, given many noisy observables, detects points in time at which various subsets of the observables exhibit simultaneous changes in data distribution and explicitly identifies those subsets. Our work is motivated by the problem of identifying the nature and timing of biologically interesting conformational changes that occur during atomic-level simulations of biomolecules such as proteins. This problem has proved challenging both because each such conformational change might involve only a small region of the molecule and because these changes are often subtle relative to the ever-present background of faster structural fluctuations. We show that our method is effective in detecting biologically interesting conformational changes in molecular dynamics simulations of both folded and unfolded proteins, even in cases where these changes are difficult to detect using alternative techniques. This method may also facilitate the detection of change points in other types of sequential data involving large numbers of observables—a problem likely to become increasingly important as such data continue to proliferate in a variety of application domains.


title: Study on high rate long range wireless communications in the 71–76 and 81–86 GHz bands

abstract: Performance of high data rate wireless line-of-sight communications in the E-band is analysed using an example of the spectrally efficient multi-gigabit system reported earlier. This paper discussed available technologies, potentials of a further increase of the communication range and challenges in development of the future multi-gigabit wireless networks.


title: A Comparative Study of Recent Wireless Sensor Network Simulators

abstract: Over recent years, the continuous interest in wireless sensor networks (WSNs) has led to the appearance of new modeling methods and simulation environments for WSN applications. A broad variety of different simulation tools have been designed to explore and validate WSN systems before actual implementation and real-world deployment. These tools address different design aspects and offer various simulation abstractions to represent and model real-world behavior. In this article, we present a comprehensive comparative study of mainstream open-source simulation tools for WSNs. Two benchmark applications are designed to evaluate the frameworks with respect to the simulation runtime performance, network throughput, communication medium modeling, packet reception rate, network latency, and power consumption estimation accuracy. Such metrics are also evaluated against measurements on physical prototypes. Our experiments show that the tools produce equivalent results from a functional point of view and capacity to model communication phenomena, while the ability to model details of the execution platform significantly impacts the runtime simulation performance and the power estimation accuracy. The benchmark applications are also made available in the public domain for further studies.


title: Dynamic capabilities, marketing and innovation capabilities and their impact on competitive advantage and firm performance

abstract: The objective of this paper is to examine the impact of dynamic capabilities (DCs) on competitiveness and performance of companies, considering the mediating role played by marketing capabilities (MCs) and innovation capabilities (ICs). The investigation of these effects is performed considering the moderating role of ambidexterity on the proposed relationships. This investigation advances a theoretical model tested using structural equation modelling (SEM). A 90-item questionnaire exploring the relationships between DCs and marketing and innovation was developed, and a total of 387 valid questionnaires were collected from a sample of Portuguese enterprises. The results show the existence of a positive direct and indirect influence of DCs on competitive advantage and performance variables as well as a direct impact on MCs and ICs. This study contributes to filling the gap in the existing research on the direct impact of DC's variables on competitive advantage and performance by considering mediating role of MCs and ICs.


High power RF and MF systems for sputter applications in flat panel display production


LightNet: A Lightweight 3D Convolutional Neural Network for Real-Time 3D Object Recognition


title: Exact algorithms for NP-hard problems: A survey

abstract: We discuss fast exponential time solutions for NP-complete problems. We survey known results and approaches, we provide pointers to the literature, and we discuss several open problems in this area. The list of discussed NP-complete problems includes the travelling salesman problem, scheduling under precedence constraints, satisfiability, knapsack, graph coloring, independent sets in graphs, bandwidth of a graph, and many more.


title: Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases

abstract: Summary Sugar-sweetened beverages (SSBs) are the single largest source of added sugar and the top source of energy intake in the U.S. diet. In this review, we evaluate whether there is sufficient scientific evidence that decreasing SSB consumption will reduce the prevalence of obesity and its related diseases. Because prospective cohort studies address dietary determinants of long-term weight gain and chronic diseases, whereas randomized clinical trials (RCTs) typically evaluate short-term effects of specific interventions on weight change, both types of evidence are critical in evaluating causality. Findings from well-powered prospective cohorts have consistently shown a significant association, established temporality and demonstrated a direct dose‐response relationship between SSB consumption and long-term weight gain and risk of type 2 diabetes (T2D). A recently published meta-analysis of RCTs commissioned by the World Health Organization found that decreased intake of added sugars significantly reduced body weight (0.80 kg, 95% confidence interval [CI] 0.39‐1.21; P < 0.001), whereas increased sugar intake led to a comparable weight increase (0.75 kg, 0.30‐1.19; P = 0.001). A parallel meta-analysis of cohort studies also found that higher intake of SSBs among children was associated with 55% (95% CI 32‐82%) higher risk of being overweight or obese compared with those with lower intake. Another metaanalysis of eight prospective cohort studies found that one to two servings per day of SSB intake was associated with a 26% (95% CI 12‐41%) greater risk of developing T2D compared with occasional intake (less than one serving per month). Recently, two large RCTs with a high degree of compliance provided convincing data that reducing consumption of SSBs significantly decreases weight gain and adiposity in children and adolescents. Taken together, the evidence that decreasing SSBs will decrease the risk of obesity and related diseases such as T2D is compelling. Several additional issues warrant further discussion. First, prevention of long-term weight gain through dietary changes such as limiting consumption of SSBs is more important than short-term weight loss in reducing the prevalence of obesity in the population. This is due to the fact that once an individual becomes obese, it is difficult to lose weight and keep it off. Second, we should consider the totality of evidence rather than selective pieces of evidence (e.g. from short-term RCTs only). Finally, while recognizing that the evidence of harm on health against SSBs is strong, we should avoid the trap of waiting for absolute proof before allowing public health action to be taken.


title: A Kernel-based predictive model of EV capacity for distributed voltage control and demand response

abstract: Energy storage and reactive power supplied by electric vehicles (EV) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power control should be robust to variations and offer a certain level of optimality by combining real-time control with an hours-ahead scheduling scheme. This paper introduces an optimization and control framework that can be used for charging batteries and managing available storage while using the remaining capacity of the chargers to generate reactive power and cooperatively perform voltage control. Stochastic distributed optimization of reactive power is realized by integrating a robust distributed sub-gradient method with conditional ensemble predictions of V2G capacity. Hence, the proposed solutions can meet system operational requirements for the upcoming hours by enabling instantaneous cooperation among distributed EVs.


title: A Frequency Domain Neural Network for Fast Image Super-resolution

abstract: In this paper, we present a frequency domain neural network for image super-resolution. The network employs the convolution theorem so as to cast convolutions in the spatial domain as products in the frequency domain. Moreover, the non-linearity in deep nets, often achieved by a rectifier unit, is here cast as a convolution in the frequency domain. This not only yields a network which is very computationally efficient at testing but also one whose parameters can all be learnt accordingly. The network can be trained using back propagation and is devoid of complex numbers due to the use of the Hartley transform as an alternative to the Fourier transform. Moreover, the network is potentially applicable to other problems elsewhere in computer vision and image processing which are often cast in the frequency domain. We show results on super-resolution and compare against alternatives elsewhere in the literature. In our experiments, our network is one to two orders of magnitude faster than the alternatives with an imperceptible loss of performance.


title: Belief Propagation Based Multi--User Detection

abstract: We apply belief propagation (BP) to multi--user detection in a spread spectrum system, under the assumption of Gaussian symbols. We prove that BP is both convergent and allows to estimate the correct conditional expectation of the input symbols. It is therefore an optimal --minimum mean square error-- detection algorithm. This suggests the possibility of designing BP detection algorithms for more general systems. As a byproduct we rederive the Tse-Hanly formula for minimum mean square error without any recourse to random matrix theory.


title: A computer scientist's introductory guide to business process management (BPM)

abstract: Computers play an integral part in designing, modelling, optimising and managing business processes within and across companies. While Business Process Management (BPM), Workflow Management (WfM) and Business Process Reengineering (BPR) have been IT-related disciplines with a history of about three decades, there is still a lack of publications clarifying definitions and scope of basic BPM terminologies like business process, BPM versus WfM, workflow, BPR, etc. Such a myriad of similar-sounding terminologies can be overwhelming for computer scientists and computer science students who may wish to venture into this area of research. This guide aims to address this gap by providing a high level overview of the key concepts, rationale, features and the developments of BPM.


title: An integrated approach to assess the impacts of tourism on community development and sustainable livelihoods

abstract: A key challenge in sustainable tourism is to develop economically viable enterprises that provide livelihood benefits to local communities while protecting indigenous cultures and environments. Such ventures are difficult to assess due to a general lack of effective assessment and monitoring methods, a lack of consensus about methodology and the inability of some monitoring systems to incorporate all elements of tourism impacts. This article presents a structured integrated assessment approach to assess the impacts of initiatives that purport to deliver net livelihood benefits to communities living adjacent to or within the tourism destination. The approach has been developed to enable generic use in different geographical contexts taking into account a variety of aspects including ownership structure, levels of employment, infrastructure, governance, and sustainable livelihoods (SL). The article examines two pilot case studies conducted in Maputaland, South Africa, to reflect on the implementation and theoretical underpinnings of the protocol.


title: A new taxonomy of web attacks suitable for efficient encoding

abstract: Web attacks, i.e. attacks exclusively using the HTTP/HTTPS protocol, are rapidly becoming one of the fundamental threats for information systems connected to the Internet. When the attacks suffered by Web servers through the years are analyzed, it is observed that most of them are very similar, using a reduced number of attacking techniques. It is generally agreed that classification can help designers and programmers to better understand attacks and build more secure applications. As an effort in this direction, a new taxonomy of Web attacks is proposed in this paper, with the objective of obtaining a useful reference framework for security applications. The use of the taxonomy is illustrated by means of multiplatform real world Web attack examples. Along with this taxonomy, important features of each attack category are discussed. A semantic-dependent Web attack encoding scheme is also defined that, together with the taxonomy, can be used to process the attack information with low time and memory consumption. Applications of the taxonomy and the encoding scheme are described, such as intrusion detection systems and application firewalls.


A multi-range architecture for collision-free off-road robot navigation


Cuisine Classification and Recipe Generation


The assumptions of ordinariness as a denial mechanism: Innovation and conflict in a coal mine


title: Imputation of Missing Data Using R Package

abstract: Missing data are quite common in practical applications of statistical methods. Im- putation is general statistical method for the analysis of incomplete data sets. The goal of the paper is to review selected imputation techniques. Special attention is paid to methods implemented in some packages working in the R environment. An example is presented to show how to handle missing values using a few procedures of single and multiple imputation implemented in R.


title: Magnetic Hamiltonian Monte Carlo

abstract: Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits \textit{non-canonical} Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3 dimensions a subset of the dynamics map onto the mechanics of a charged particle coupled to a magnetic field. We establish a theoretical basis for the use of non-canonical Hamiltonian dynamics in MCMC, and construct a symplectic, leapfrog-like integrator allowing for the implementation of magnetic HMC. Finally, we exhibit several examples where these non-canonical dynamics can lead to improved mixing of magnetic HMC relative to ordinary HMC.


title: A Hypergraph-Based Blockchain Model and Application in Internet of Things-Enabled Smart Homes

abstract: With the fast development and expansion of the Internet of Things (IoT), billions of smart devices are being continuously connected, and smart homes, as a typical IoT application, are providing people with various convenient applications, but face security and privacy issues. The idea of Blockchain (BC) theory has brought about a potential solution to the IoT security problem. The emergence of blockchain technology has brought about a change of decentralized management, providing an effective solution for the protection of network security and privacy. On the other hand, the smart devices in IoT are always lightweight and have less energy and memory. This makes the application of blockchain difficult. Against this background, this paper proposes a blockchain model based on hypergraphs. The aims of this model are to reduce the storage consumption and to solve the additional security issues. In the model, we use the hyperedge as the organization of storage nodes and convert the entire networked data storage into part network storage. We discuss the design of the model and security strategy in detail, introducing some use cases in a smart home network and evaluating the storage performance of the model through simulation experiments and an evaluation of the network.


title: Surveying Aviation Professionals on the Security of the Air Traffic Control System

abstract: In this paper, we report findings from an exploratory study concerning the security of 15 different wireless technologies used in aviation. 242 aviation professionals and experts from 24 different countries completed an on-line questionnaire about their use and perceptions of each of these technologies. We examine the respondents’ familiarity with and reliance on each technology, with particular regard to their security. Furthermore, we analyse respondents’ perceptions of the possible impact of a wireless attack on the air traffic control system, from both a safety and a business point of view. We deepen these insights with statistical analysis comparing five different stakeholder groups: pilots, air traffic controllers, aviation authorities, aviation engineers, and private pilots.


title: WiC: 10,000 Example Pairs for Evaluating Context-Sensitive Representations

abstract: By design, word embeddings are unable to model the dynamic nature of words' semantics, i.e., the property of words to correspond to potentially different meanings depending on the context in which they appear. To address this limitation, dozens of specialized word embedding techniques have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling for the standard de facto dataset, i.e., the Stanford Contextual Word Similarity. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive word embeddings. WiC is released in this https URL.


title: A Dual Process Evaluability Framework for decision anomalies

abstract: Alternative explanations have been offered to explain consumers’ inconsistent preferences in decision problems. We present a Dual Process Evaluability Framework (DPEF) which suggests that the characteristics of the decision problem, including response mode, presentation mode, and choice-set structure, are critical to predicting preference reversals related to decisions under risk and uncertainty, over time, and between product assortments, as well as presentation mode reversals involving joint versus separate evaluations, and response mode reversals involving a combination of choice tasks, monetary value tasks, and attractiveness ratings. Our framework, grounded in evaluability theory and dual process models, predicts how these decision problem characteristics directly affect the ease of evaluation of alternatives which subsequently affects the relative dominance of feeling versus calculation in these tasks. Application of DPEF to previously documented preference reversals, complemented by three studies which test new predictions of DPEF, reveals that DPEF provides a parsimonious explanation for a variety of decision anomalies.


title: Using Mentoring and Storytelling to Transfer Knowledge in the Workplace

abstract: The core capabilities of an organization include critical skills of employees, management systems, and norms and values. Core capabilities may be transferred formally and explicitly. However, much knowledge, particularly knowledge with rich tacit dimensions, is transferred informally through processes of socialization and internalization. We focus on two transfer mechanisms - mentoring and storytelling - that can leverage the knowledge of an organization, particularly its tacit knowledge, to build core capabilities. We draw on relevant research in learning and cognitive psychology to clarify the conditions under which mentoring and storytelling can be most effective as carriers of knowledge. Finally, we present recommendations for specific managerial practices that follow from our analysis.


title: Low-Profile Dual-Band Filtering Patch Antenna and Its Application to LTE MIMO System

abstract: This paper presents a low-profile dual-band filtering antenna element and its application to long-term evolution (LTE) multiple-input multiple-output (MIMO) system for wireless customer premise equipments (CPEs). The proposed element consists of two separate U-shaped patches operating at different frequencies and a multistub microstrip feed line. For size miniaturization, the smaller U-shaped patch is embedded in the larger one. In addition, the multistub feed line can generate two controllable resonant modes as well as two nulls in realized gain at the boresight direction. Since the modes of the patches and multistub feed line can be controlled individually, the two operating bands can be tuned to desired frequencies. Also, the radiation nulls in boresight gain can be controlled, high roll-off rate and out-of-band radiation rejection levels are thus obtained. For demonstration, a low profile ( $0.009\lambda _{0})$ dual-band filtering antenna element operating at 1.9 and 2.6 GHz for TD-LTE applications (B39- and B38-bands) is implemented. Dual-band bandpass responses and four radiation nulls are observed in the experiment. Measured in-band gains are 6.7 and 7.3 dBi, whereas out-of-band gains are less than −10 dBi. Based on this element, a four-element MIMO antenna is further designed for LTE CPEs, where low profile and high integration of multiple components are required. A low mutual coupling of less than −19.2 dB and the low envelope correlation coefficients of better than 0.2 are obtained with a small edge-to-edge spacing of $0.15\lambda _{0}$ .


An Easy Method to Secure Knots in Tissues Under Tension


title: Formulation Strategies to Improve the Bioavailability of Poorly Absorbed Drugs with Special Emphasis on Self-Emulsifying Systems

abstract: Poorly water-soluble drug candidates are becoming more prevalent. It has been estimated that approximately 60-70% of the drug molecules are insufficiently soluble in aqueous media and/or have very low permeability to allow for their adequate and reproducible absorption from the gastrointestinal tract (GIT) following oral administration. Formulation scientists have to adopt various strategies to enhance their absorption. Lipidic formulations are found to be a promising approach to combat the challenges. In this review article, potential advantages and drawbacks of various conventional techniques and the newer approaches specifically the self-emulsifying systems are discussed. Various components of the self-emulsifying systems and their selection criteria are critically reviewed. The attempts of various scientists to transform the liquid self-emulsifying drug delivery systems (SEDDS) to solid-SEDDS by adsorption, spray drying, lyophilization, melt granulation, extrusion, and so forth to formulate various dosage forms like self emulsifying capsules, tablets, controlled release pellets, beads, microspheres, nanoparticles, suppositories, implants, and so forth have also been included. Formulation of SEDDS is a potential strategy to deliver new drug molecules with enhanced bioavailability mostly exhibiting poor aqueous solubility. The self-emulsifying system offers various advantages over other drug delivery systems having potential to solve various problems associated with drugs of all the classes of biopharmaceutical classification system (BCS).


title: A Survey of Blue-Noise Sampling and Its Applications

abstract: In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.


title: EtherCAT-based platform for distributed control in high-performance industrial applications

abstract: Industrial Ethernet is gradually becoming pervasive in the automation field. EtherCAT is one such technology, combining high real-time performance with a rich functionality set. Besides that, EtherCAT offers short cycle times, accurate time synchronization, redundancy and high data throughput. Most EtherCAT applications are in the 500-1000μs domain. There are only few EtherCAT master implementations in the sub-100 or sub-50μs range for high-performance applications. This paper presents EtherCAT based distributed controller which is capable of less than 30μs cycle times and was implemented on P2020 processor. Total HW and SW overhead per cycle is 9.6μs including jitter of 5μs. Achieved preciseness of the distributed time is below 20ns. The stack was designed to be modular and also reused on different platforms.


title: CMCP: a novel page replacement policy for system level hierarchical memory management on many-cores

abstract: The increasing prevalence of co-processors such as the Intel Xeon Phi, has been reshaping the high performance computing (HPC) landscape. The Xeon Phi comes with a large number of power efficient CPU cores, but at the same time, it's a highly memory constraint environment leaving the task of memory management entirely up to application developers. To reduce programming complexity, we are focusing on application transparent, operating system (OS) level hierarchical memory management. In particular, we first show that state of the art page replacement policies, such as approximations of the least recently used (LRU) policy, are not good candidates for massive many-cores due to their inherent cost of remote translation lookaside buffer (TLB) invalidations, which are inevitable for collecting page usage statistics. The price of concurrent remote TLB invalidations grows rapidly with the number of CPU cores in many-core systems and outpace the benefits of the page replacement algorithm itself. Building upon our previous proposal, per-core Partially Separated Page Tables (PSPT), in this paper we propose Core-Map Count based Priority (CMCP) page replacement policy, which exploits the auxiliary knowledge of the number of mapping CPU cores of each page and prioritizes them accordingly. In turn, it can avoid TLB invalidations for page usage statistic purposes altogether. Additionally, we describe and provide an implementation of the experimental 64kB page support of the Intel Xeon Phi and reveal some intriguing insights regarding its performance. We evaluate our proposal on various applications and find that CMCP can outperform state of the art page replacement policies by up to 38%. We also show that the choice of appropriate page size depends primarily on the degree of memory constraint in the system.


title: Technologies for Printing Sensors and Electronics Over Large Flexible Substrates: A Review

abstract: Printing sensors and electronics over flexible substrates are an area of significant interest due to low-cost fabrication and possibility of obtaining multifunctional electronics over large areas. Over the years, a number of printing technologies have been developed to pattern a wide range of electronic materials on diverse substrates. As further expansion of printed technologies is expected in future for sensors and electronics, it is opportune to review the common features, the complementarities, and the challenges associated with various printing technologies. This paper presents a comprehensive review of various printing technologies, commonly used substrates and electronic materials. Various solution/dry printing and contact/noncontact printing technologies have been assessed on the basis of technological, materials, and process-related developments in the field. Critical challenges in various printing techniques and potential research directions have been highlighted. Possibilities of merging various printing methodologies have been explored to extend the lab developed standalone systems to high-speed roll-to-roll production lines for system level integration.


Gender as a Variable in Natural-Language Processing: Ethical Considerations


title: Document clustering of scientific texts using citation contexts

abstract: Document clustering has many important applications in the area of data mining and information retrieval. Many existing document clustering techniques use the "bag-of-words" model to represent the content of a document. However, this representation is only effective for grouping related documents when these documents share a large proportion of lexically equivalent terms. In other words, instances of synonymy between related documents are ignored, which can reduce the effectiveness of applications using a standard full-text document representation. To address this problem, we present a new approach for clustering scientific documents, based on the utilization of citation contexts. A citation context is essentially the text surrounding the reference markers used to refer to other scientific works. We hypothesize that citation contexts will provide relevant synonymous and related vocabulary which will help increase the effectiveness of the bag-of-words representation. In this paper, we investigate the power of these citation-specific word features, and compare them with the original document's textual representation in a document clustering task on two collections of labeled scientific journal papers from two distinct domains: High Energy Physics and Genomics. We also compare these text-based clustering techniques with a link-based clustering algorithm which determines the similarity between documents based on the number of co-citations, that is in-links represented by citing documents and out-links represented by cited documents. Our experimental results indicate that the use of citation contexts, when combined with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered by journal articles. More specifically, this document representation strategy when used by the clustering algorithm investigated in this paper, outperforms both the full-text clustering approach and the link-based clustering technique on both scientific journal datasets.


title: Real-time texture error detection on textured surfaces with compressed sensing

abstract: We present a real-time approach to detect and localise defects in grey-scale textures within a Compressed Sensing framework. Inspired by recent results in texture classification, we use compressed local grey-scale patches for texture description. In a first step, a Gaussian Mixture model is trained with the features extracted from a handful of defect-free texture samples. In a second step, the novelty detection of texture samples is performed by comparing each pixel to the likelihood obtained in the training process. The inspection stage is embedded into a multi-scale framework to enable real-time defect detection and localisation. The performance of compressed grey-scale patches for texture error detection is evaluated on two independent datasets. The proposed method is able to outperform the performance of non-compressed grey-scale patches in terms of accuracy and speed.


title: Thread clustering: sharing-aware scheduling on SMP-CMP-SMT multiprocessors

abstract: The major chip manufacturers have all introduced chip multiprocessing (CMP) and simultaneous multithreading (SMT) technology into their processing units. As a result, even low-end computing systems and game consoles have become shared memory multiprocessors with L1 and L2 cache sharing within a chip. Mid- and large-scale systems will have multiple processing chips and hence consist of an SMP-CMP-SMT configuration with non-uniform data sharing overheads. Current operating system schedulers are not aware of these new cache organizations, and as a result, distribute threads across processors in a way that causes many unnecessary, long-latency cross-chip cache accesses. In this paper we describe the design and implementation of a scheme to schedule threads based on sharing patterns detected online using features of standard performance monitoring units (PMUs) available in today's processing units. The primary advantage of using the PMU infrastructure is that it is fine-grained (down to the cache line) and has relatively low overhead. We have implemented our scheme in Linux running on an 8-way Power5 SMP-CMP-SMT multi-processor. For commercial multithreaded server workloads (VolanoMark, SPECjbb, and RUBiS), we are able to demonstrate reductions in cross-chip cache accesses of up to 70%. These reductions lead to application-reported performance improvements of up to 7%.


title: New efficient and practical verifiable multi-secret sharing schemes

abstract: In 2006, Zhao et al. proposed a practical verifiable multi-secret sharing based on Yang et al.'s and Feldman's schemes. In this paper we propose two efficient, computationally secure (t,n), and verifiable multi-secret sharing schemes based on homogeneous linear recursion. The first scheme has the advantage of better performance, a new simple construction and various techniques for the reconstruction phase. The second scheme requires fewer public values with respect to Zhao et al.'s and Shao and Cao schemes. These schemes are easy to implement and provide great capabilities for many applications.


title: Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference

abstract: Monocular depth estimation is a challenging task in complex compositions depicting multiple objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional neural networks (CNNs), the state-of-the-art monocular depth estimation methods still fall short to handle such real-world challenging scenarios. In this paper, we propose a deep end-to-end learning framework to tackle these challenges, which learns the direct mapping from a color image to the corresponding depth map. First, we represent monocular depth estimation as a multi-category dense labeling task by contrast to the regression based formulation. In this way, we could build upon the recent progress in dense labeling such as semantic segmentation. Second, we fuse different side-outputs from our front-end dilated convolutional neural network in a hierarchical way to exploit the multi-scale depth cues for depth estimation, which is critical to achieve scale-aware depth estimation. Third, we propose to utilize soft-weighted-sum inference instead of the hard-max inference, transforming the discretized depth score to continuous depth value. Thus, we reduce the influence of quantization error and improve the robustness of our method. Extensive experiments on the NYU Depth V2 and KITTI datasets show the superiority of our method compared with current state-of-the-art methods. Furthermore, experiments on the NYU V2 dataset reveal that our model is able to learn the probability distribution of depth.


title: Anatomy and Disorders of the Oral Cavity of Chinchillas and Degus.

abstract: Dental disease is among the most common causes for chinchillas and degus to present to veterinarians. Most animals with dental disease present with weight loss, reduced food intake/anorexia, and drooling. Degus commonly present with dyspnea. Dental disease has been primarily referred to as elongation and malocclusion of the cheek teeth. Periodontal disease, caries, and tooth resorption are common diseases in chinchillas, but are missed frequently during routine intraoral examination, even performed under general anesthesia. A diagnostic evaluation, including endoscopy-guided intraoral examination and diagnostic imaging of the skull, is necessary to detect oral disorders and to perform the appropriate therapy.


title: Advances in embedded traces for 1.5µm RDL on 2.5D glass interposers

abstract: This paper presents the first demonstration of 1.5µm ultra-fine copper trace re-distribution layers (RDL) with embedded trace processes for 2.5D glass interposers. Two approaches described in this paper include: (1) Copper Trace Transfer (CTT) and (2) Photo Trench Embedding (PTE), both of which can be fabricated using existing double-side package substrate process tools on large panels. 2.5D Interposers, interconnecting logic and memory devices at high-bandwidth, require ultra-fine I/O pitch below 40µm. In order to escape route bumps at less than 40µm pitch, the interposer requires RDLs with less than 5µm ultra-fine copper wiring. Organic package substrates, using semi-additive processes (SAP), face a number of challenges in achieving less than 5µm line and space. The two embedded trace process methods in this paper are targeted at extending RDL feature sizes beyond these SAP limits. In the CTT approach, each RDL metal layer is pre-fabricated on a removable carrier and then transferred to a polymer dielectric layer laminated on an interposer core substrate. 1.5 to 5µm Copper traces were plated and successfully transferred onto a polymer laminated glass core. In the PTE approach, a high-resolution photosensitive dry film (TMMF-2014, 14µm thick) and liquid photosensitive dielectric (PN-0371D) films were explored for the RDLs. The initial feasibility of fine line photolithography and trench copper filling processes was investigated. Results showed that the film has a 2µm resolution, with an aspect ratio of seven. The feasibility of resolving 1.2µm line and space patterns was demonstrated in 4.2µm thick liquid photosensitive dielectric material. RDL traces down to 3µm using dry film and 1.5µm using liquid-based film were plated with sputtered Ti-Cu seed layers and trench fill electroplating processes. Both embedded trace approaches demonstrate the potential to scale 1.5–3µm RDL wiring using double-side, large-panel processing for low-cost, high-density interposers.


title: Successful Reuse of Software Components: A Report from the Open Source Perspective

abstract: A promising way of software reuse is Component-Based Software Development (CBSD). There is an increasing number of OSS products available that can be freely used in product development. However, OSS communities themselves have not yet taken full advantage of the “reuse mechanism”. Many OSS projects duplicate effort and code, even when sharing the same application domain and topic. One successful counter-example is the FFMpeg multimedia project, since several of its components are widely and consistently reused into other OSS projects. This paper documents the history of the libavcodec library of components from the FFMpeg project, which at present is reused in more than 140 OSS projects. Most of the recipients use it as a black-box component, although a number of OSS projects keep a copy of it in their repositories, and modify it as such. In both cases, we argue that libavcodec is a successful example of reusable OSS library of components.


title: Multi-Document Summarization Using Graph-Based Iterative Ranking Algorithms and Information Theoretical Distortion Measures.

abstract: Text summarization is an important field in the area of natural language processing and text mining. This paper proposes an extraction-based model which uses graph-based and information theoretic concepts for multi-document summarization. Our method constructs a directed weighted graph from the original text by adding a vertex for each sentence, and compute a weighted edge between sentences which is based on distortion measures. In this paper we proposed a combination of these two models by representing the input as a graph, using distortion measures as the weight function and a ranking algorithm. Finally, a ranking algorithm is applied to identify the most important sentences to be included in the summary. By defining a proper distortion measure and ranking algorithm, this model gains promising results on the DUC2002 which is a well known real world data set. The results and ROUGE-1 scores of our model is fairly close to other successful models.


title: Area-Efficient and Low-Leakage Diode String for On-Chip ESD Protection

abstract: Diode string was used as the effective on-chip electrostatic discharge (ESD) protection device. To reduce the leakage current and the layout area, an area-efficient and low-leakage diode string is proposed in this paper. The standard steps of P− implantation and silicide blocking in CMOS process are used in this design to realize the proposed diode string with stacked P−/N+ diodes. The test devices of the proposed design have successfully been verified in the silicon chip. With the high ESD robustness, low leakage current, and small layout area, the proposed diode string can be a better solution for on-chip ESD protection applications.


title: Evaluation of 6.5-kV SiC p-i-n Diodes in a Medium-Voltage, High-Power 3L-NPC Converter

abstract: This study is focused on evaluating the advantages of 6.5-kV SiC p-i-n diodes in a 4.16-kV, 8-MVA neutral point clamped (NPC) voltage source converter. Electrothermal models of the power semiconductors are elaborated based on measurement data of a SiC diode module prototype, as well as of commercial Si devices. These models are integrated into a converter simulation, where the junction temperatures of each device are used to determine the maximum power output under different device configurations. For the analysis, a parallel connection of two 6.5-kV insulated-gate bipolar transistors (IGBTs) as well as a series connection of two 3.3-kV IGBTs are considered as switches. The influence of the current change rate during the IGBT turn-on is also studied. SiC p-i-n diodes are used as a replacement for the NPC diodes of the converter and as antiparallel diodes in IGBT modules. The results obtained indicate that an increase in the output power of 17% can be achieved using SiC NPC diodes for the studied current change rates. Alternatively, for a constant output power, the switching frequency of the converter can be increased by 69%, reducing the converter size and system costs by the use of smaller filters.


title: A Stabilizing Controller for Regulation of UAV With Manipulator

abstract: Stability is not trivial in aerial manipulation tasks because of the dynamical coupling between the aerial vehicle and the manipulator. This is especially true when the manipulator becomes heavy, so that its dynamics can be significant. In this letter, a stabilizing controller for the regulation of overall system will be designed without using any assumptions on the dynamic model. In addition, thorough discussion with simulation validation is presented.


title: Aggregating Deep Convolutional Features for Image Retrieval

abstract: Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregation approaches developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptors. ::: In this paper we investigate possible ways to aggregate local deep features to produce compact global descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides arguably the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.


title: Octave Bandwidth Compact Turnstile-Based Orthomode Transducer

abstract: A 64% instantaneous bandwidth scalable turnstile-based orthomode transducer to be used in the so-called extended C-band satellite link is presented. The proposed structure overcomes the current practical bandwidth limitations by adding a single-step widening at the junction of the four output rectangular waveguides. This judicious modification, together with the use of reduced-height waveguides and E-plane bends and power combiners, enables to approach the theoretical structure bandwidth limit with a simple, scalable and compact design. The presented orthomode transducer architecture exhibits a return loss better than 25 dB, an isolation between rectangular ports better than 50 dB and a transmission loss less than 0.04 dB in the 3.6-7 GHz range, which represents state-of-the-art achievement in terms of bandwidth.


title: Kernel Penalized K-means: A feature selection method based on Kernel K-means

abstract: Novel embedded feature selection approach for Kernel K-means.Optimization procedure based on scaling factors and a concave penalization.Best performance in terms of clustering quality and dimensionality reduction. We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously minimizing the violation of the initial cluster structure and penalizing the use of features via scaling factors. As the base method we use Kernel K-means which works similarly to K-means, one of the most popular clustering algorithms, but it provides more flexibility due to the use of kernel functions for distance calculation, thus allowing the detection of more complex cluster structures. We present an algorithm to solve the respective minimization problem iteratively, and perform experiments with several data sets demonstrating the superior performance of the proposed method compared to alternative approaches.


title: Mobile application for Indonesian medicinal plants identification using Fuzzy Local Binary Pattern and Fuzzy Color Histogram

abstract: This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.


title: Bayesian Inverse Reinforcement Learning

abstract: Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) and by the task of apprenticeship learning (learning policies from an expert). In this paper we show how to combine prior knowledge and evidence from the expert's actions to derive a probability distribution over the space of reward functions. We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions. Experimental results show strong improvement for our methods over previous heuristic-based approaches.


EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis


From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems.


title: Mechanical energetic contributions from individual muscles and elastic prosthetic feet during symmetric unilateral transtibial amputee walking: a theoretical study.

abstract: Energy storage and return (ESAR) foot-ankle prostheses have been developed in an effort to improve gait performance in lower-limb amputees. However, little is known about their effectiveness in providing the body segment mechanical energetics normally provided by the ankle muscles. The objective of this theoretical study was to use muscle-actuated forward dynamics simulations of unilateral transtibial amputee and non-amputee walking to identify the contributions of ESAR prostheses to trunk support, forward propulsion and leg swing initiation and how individual muscles must compensate in order to produce a normal, symmetric gait pattern. The simulation analysis revealed the ESAR prosthesis provided the necessary trunk support, but it could not provide the net trunk forward propulsion normally provided by the plantar flexors and leg swing initiation normally provided by the biarticular gastrocnemius. To compensate, the residual leg gluteus maximus and rectus femoris delivered increased energy to the trunk for forward propulsion in early stance and late stance into pre-swing, respectively, while the residual iliopsoas delivered increased energy to the leg in pre- and early swing to help initiate swing. In the intact leg, the soleus, gluteus maximus and rectus femoris delivered increased energy to the trunk for forward propulsion in the first half of stance, while the iliopsoas increased the leg energy it delivered in pre- and early swing. Thus, the energy stored and released by the ESAR prosthesis combined with these muscle compensations was able to produce a normal, symmetric gait pattern, although various neuromuscular and musculoskeletal constraints may make such a pattern non-optimal.


title: From blockchain consensus back to Byzantine consensus

abstract: Abstract Consensus is a fundamental problem of distributed computing. While this problem has been known to be unsolvable since 1985, existing protocols were designed these past three decades to solve consensus under various assumptions. Today, with the recent advent of blockchains, various consensus implementations were proposed to make replicas reach an agreement on the order of transactions updating what is often referred to as a distributed ledger. Very little work has however been devoted to explore its theoretical ramifications. As a result existing proposals are sometimes misunderstood and it is often unclear whether the problems arising during their executions are due to implementation bugs or more fundamental design issues. In this paper, we discuss the mainstream blockchain consensus algorithms and how the classic Byzantine consensus can be revisited for the blockchain context. In particular, we discuss proof-of-work consensus and illustrate the differences between the Bitcoin and the Ethereum proof-of-work consensus algorithms. Based on these definitions, we warn about the dangers of using these blockchains without understanding precisely the guarantees their consensus algorithm offers. In particular, we survey attacks against the Bitcoin and the Ethereum consensus algorithms. We finally discuss the advantage of the recent Blockchain Byzantine consensus definition over previous definitions, and the promises offered by emerging consistent blockchains.


title: Pain Assessment Tool in the Critically Ill Post–Open Heart Surgery Patient Population

abstract: Abstract Critical-care patients are at higher risk for untreated pain, because they are often unable to communicate owing to altered mental status, mechanical ventilation, and sedation. Pain that is persistent and untreated affects most body systems and results in development of complications chronic pain, and increased length of stay. This descriptive repeated-measures study compared three pain assessment tools in nonverbal critically ill patients in a cardiac postanesthesia care unit (n=24). Tools included the Critical-Care Pain Observation Tool (CPOT), adult Nonverbal Pain Scale (NVPS), and the Faces, Legs, Activity, Cry, and Consolability scale (FLACC). Two painful events, suctioning and repositioning, were studied. Data were collected immediately before the event, 1minute after, and 20minutes after. Both the CPOT and the NVPS demonstrated high reliability (Cronbach alpha coefficients 0.89). The NVPS and the CPOT were highly correlated for both raters (r>0.80, p =.00) (11 out of 12 times). Correlations between the two raters was generally moderate to high, but higher with the CPOT. There was more disagreement between raters in overall pain scores for the NVPS. When raters disagreed, it was most often in rating the face component on both scales. Disagreement was highest during the event. Both scales adequately capture pain in the nonverbal sedated critically ill patient based on assessment of patients' face, body movements, muscle tension, and respirations, with the NVPS also considering vital signs. Pictures depicting facial expressions for scoring purposes are helpful. Adequate education and understanding of use of the scales is critical for accurate assessment and subsequent interventions.


title: 6–18GHz 13W reactive matched GaN power amplifier MMIC

abstract: The design and performance of a compact 6–18GHz power amplifier MMIC utilizing 0.2 μm gallium nitride (GaN) high electron mobility transistor (HEMT) technology is presented. An output power of 41.4 dBm to 43.3 dBm (average 42.3dBm) with over 16 dB power gain and a power added efficiency (PAE) of 21% to 35% over the band of 6–18 GHz under a drain voltage of 28 V in CW mode have been achieved. The chip size is compact with the size of 2.6×3.6 mm2 and it delivers an average output power density 1.83 W/mm2 over the chip area. The thermal resistance is 1.55 °C/W measured in CW mode.


title: Attempting to change sex role attitudes in adolescents: explorations of reverse effects.

abstract: Previous interventions designed to reduce sex role stereotypes have been successful with women, but frequently have had no effects, or reverse effects, with men. These interventions have generally used presentations emphasizing women's roles. In the present study, three experiments were conducted using media presentations focusing on men. Two of these produced reverse effects in women. Despite the brevity of the interventions, the effects persisted three or four weeks later, and were intensified, suggesting a sleeper effect. These male-focused presentations produced positive effects in the male subjects in all three experiments, though these did not reach statistical significance. The results strongly suggest that current sex differences in sex role attitudes are partially due to situational determinants (as well as developmental factors). Implications of reverse effects for counselors and educators are discussed.


title: Flight Dynamics Modeling of Dual-Spin Guided Projectiles

abstract: This paper presents a complete nonlinear parameter-dependent mathematical model, as well as a procedure for computing the quasi -linear parameter-varying ( q -LPV) model of a class of spin-stabilized canard-controlled guided projectiles. The proposed projectile concept possesses a so-called dual-spin configuration: the forward section contains the necessary control and guidance software and hardware, whereas the aft roll-decoupled and rapidly spinning section contains the payload. Wind-axes instead of body-axes variables, as is the case in the existing literature, are preferred for the modeling of the highly coupled pitch/yaw airframe nonlinear dynamics, since they are better suited to control synthesis. A q -LPV model, approximating these nonlinear dynamics around the system equilibrium manifold and capturing their dependence on diverse varying flight parameters, is analytically obtained. In addition, a detailed stability analysis specific to this kind of weapons is performed throughout their large flight envelope using the aforementioned q -LPV model. Furthermore, a study is conducted in order to quantify the influence of reducing the dimension of the flight parameter vector on the exactness of the q -LPV model. Finally, the critical influence on the pitch/yaw-dynamics of the nose-embedded sensor position, and of uncertainty on the various static and dynamic aerodynamic coefficients as well as the aerodynamic angles, is shown.


title: Analytic ray curve tracing for outdoor sound propagation

abstract: Abstract Outdoor sound propagation, which propagates sound through inhomogeneous, moving media with complex obstacles, presents challenging scenarios for computational simulation. In this paper, we present a ray-tracing method that uses analytic ray curves as tracing primitives in order to improve the efficiency of outdoor sound propagation in fully general settings. This ray-curve tracer inherits the efficiency and flexibility of rectilinear ray tracers in handling boundary surfaces, and it overcomes the performance limitations imposed by approximating the curved propagation paths in inhomogeneous media with rectilinear rays. Adaptive media traversal, as well as acceleration structures for surfaces intersections, lead to further savings in computation. Our method’s speedup over existing ray models, at least an order of magnitude for simple 2D scenarios and up to two orders of magnitude for 3D complex scenes, is demonstrated on outdoor benchmark scenes.


title: Prior Knowledge, Level Set Representations & Visual Grouping

abstract: Abstract ::: In this paper, we propose a level set method for shape-driven object extraction. We introduce a voxel-wise probabilistic level set formulation to account for prior knowledge. To this end, objects are represented in an implicit form. Constraints on the segmentation process are imposed by seeking a projection to the image plane of the prior model modulo a similarity transformation. The optimization of a statistical metric between the evolving contour and the model leads to motion equations that evolve the contour toward the desired image properties while recovering the pose of the object in the new image. Upon convergence, a solution that is similarity invariant with respect to the model and the corresponding transformation are recovered. Promising experimental results demonstrate the potential of such an approach.


title: Dictionary Learning for Fast Classification Based on Soft-thresholding

abstract: Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in scenarios where computational power is restricted. We consider in this paper a simple yet efficient alternative to sparse coding for feature extraction. We study a classification scheme that applies the soft-thresholding nonlinear mapping in a dictionary, followed by a linear classifier. A novel supervised dictionary learning algorithm tailored for this low complexity classification architecture is proposed. The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver. We conduct experiments on several datasets, and show that our learning algorithm that leverages the structure of the classification problem outperforms generic learning procedures. Our simple classifier based on soft-thresholding also competes with the recent sparse coding classifiers, when the dictionary is learned appropriately. The adopted classification scheme further requires less computational time at the testing stage, compared to other classifiers. The proposed scheme shows the potential of the adequately trained soft-thresholding mapping for classification and paves the way towards the development of very efficient classification methods for vision problems.


title: Virtual Adversarial Training for Semi-Supervised Text Classification

abstract: Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself. The proposed method achieves state of the art results on multiple benchmark semi-supervised and purely supervised tasks. We provide visualizations and analysis showing that the learned word embeddings have improved in quality and that while training, the model is less prone to overfitting.


title: Recognizing human activity in smart home using deep learning algorithm

abstract: There is a topic of interest for many researchers about human activities recognition in recent years. In this paper, we propose the research to recognize human activities by deep learning (DL) algorithm. We collected the data from participants performing activities in order to evaluate the human activities recognition results. After pre-training the deep network, the fine-tuning process begins. Compared with Hidden Markov Model (HMM) and naive Bayes Classifier (NBC), the experiment results show that the proposed deep learning algorithm is an effective way for recognizing human activities in smart home.


title: Gramophone Noise Detection and Reconstruction Using Time Delay Artificial Neural Networks

abstract: Gramophone records were the main recording medium for more than seven decades and regained widespread popularity over the past several years. Being an analog storage medium, gramophone records are subject to distortions caused by scratches, dust particles, degradation, and other means of improper handling. The observed noise often leads to an unpleasant listening experience and requires a filtering process to remove the unwanted disruptions and improve the audio quality. This paper proposes a novel approach that employs various feed forward time delay artificial neural networks to detect and reconstruct noise in musical sound waves. A set of 800 songs from eight different genres were used to validate the performance of the neural networks. The performance was analyzed according to the outlier detection and interpolation accuracy, the computational time and the tradeoff between the accuracy and the time. The empirical results of both detection and reconstruction neural networks were compared to a number of other algorithms, including various statistical measurements, duplication approaches, trigonometric processes, polynomials, and time series models. It was found that the neural networks’ outlier detection accuracy was slightly lower than some of the other noise identification algorithms, but achieved a more efficient tradeoff by detecting most of the noise in real time. The reconstruction process favored neural networks with an increase in the interpolation accuracy compared to other widely used time series models. It was also found that certain genres such as classical, country, and jazz music were interpolated more accurately. Volatile signals, such as electronic, metal, and pop music were more challenging to reconstruct and were substantially better interpolated using neural networks than the other examined algorithms.


title: The State of the Art of Application Restrictions and Sandboxes: A Survey of Application-oriented Access Controls and their Shortfalls

abstract: Under most widely-used security mechanisms the programs users run possess more authority than is strictly necessary, with each process typically capable of utilising all of the user's privileges. Consequently such security mechanisms often fail to protect against contemporary threats, such as previously unknown ('zero-day') malware and software vulnerabilities, as processes can misuse a user's privileges to behave maliciously. Application restrictions and sandboxes can mitigate threats that traditional approaches to access control fail to prevent by limiting the authority granted to each process. This developing field has become an active area of research, and a variety of solutions have been proposed. However, despite the seriousness of the problem and the security advantages these schemes provide, practical obstacles have restricted their adoption.This paper describes the motivation for application restrictions and sandboxes, presenting an in-depth review of the literature covering existing systems. This is the most comprehensive review of the field to date. The paper outlines the broad categories of existing application-oriented access control schemes, such as isolation and rule-based schemes, and discusses their limitations. Adoption of these schemes has arguably been impeded by workflow, policy complexity, and usability issues. The paper concludes with a discussion on areas for future work, and points a way forward within this developing field of research with recommendations for usability and abstraction to be considered to a further extent when designing application-oriented access controls.


title: Tracking system — A proposed model on literature review

abstract: This review evaluates various tracking systems which monitors children, adult or aged people in an individual basis. This type of tracking is done due to children who helpless at the time of lost from school to home or home to school, adults also face issues like kidnapping, harassment by gangsters or others, even elders face issues like memory loss or autism. These problems mainly state us to study about the remedy measures to overcome the need of these people while at the time of risk. To avoid these problems among various peoples, this system alert the receiver end that the people in front end are under danger through messages or calling them automatically. These activities are done by GSM used for mobile communication & GPS latitude and longitude positions through direct satellite connection.


title: Label Informed Attributed Network Embedding

abstract: Attributed network embedding aims to seek low-dimensional vector representations for nodes in a network, such that original network topological structure and node attribute proximity can be preserved in the vectors. These learned representations have been demonstrated to be helpful in many learning tasks such as network clustering and link prediction. While existing algorithms follow an unsupervised manner, nodes in many real-world attributed networks are often associated with abundant label information, which is potentially valuable in seeking more effective joint vector representations. In this paper, we investigate how labels can be modeled and incorporated to improve attributed network embedding. This is a challenging task since label information could be noisy and incomplete. In addition, labels are completely distinct with the geometrical structure and node attributes. The bewildering combination of heterogeneous information makes the joint vector representation learning more difficult. To address these issues, we propose a novel Label informed Attributed Network Embedding (LANE) framework. It can smoothly incorporate label information into the attributed network embedding while preserving their correlations. Experiments on real-world datasets demonstrate that the proposed framework achieves significantly better performance compared with the state-of-the-art embedding algorithms.


title: Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks

abstract: One of the key ingredients for successful optimization of modern CNNs is identifying a suitable objective. To date, the objective is fixed a-priori at training time, and any variation to it requires re-training a new network. In this paper we present a first attempt at alleviating the need for re-training. Rather than fixing the network at training time, we train a ``Dynamic-Net'' that can be modified at inference time. Our approach considers an ``objective-space'' as the space of all linear combinations of two objectives, and the Dynamic-Net is emulating the traversing of this objective-space at test-time, without any further training. We show that this upgrades pre-trained networks by providing an out-of-learning extension, while maintaining the performance quality. The solution we propose is fast and allows a user to interactively modify the network, in real-time, in order to obtain the result he/she desires. We show the benefits of such an approach via several different applications.


title: Towards Good Practices for Very Deep Two-Stream ConvNets

abstract: Deep convolutional networks have achieved great success for object recognition in still images. However, for action recognition in videos, the improvement of deep convolutional networks is not so evident. We argue that there are two reasons that could probably explain this result. First the current network architectures (e.g. Two-stream ConvNets) are relatively shallow compared with those very deep models in image domain (e.g. VGGNet, GoogLeNet), and therefore their modeling capacity is constrained by their depth. Second, probably more importantly, the training dataset of action recognition is extremely small compared with the ImageNet dataset, and thus it will be easy to over-fit on the training dataset. ::: To address these issues, this report presents very deep two-stream ConvNets for action recognition, by adapting recent very deep architectures into video domain. However, this extension is not easy as the size of action recognition is quite small. We design several good practices for the training of very deep two-stream ConvNets, namely (i) pre-training for both spatial and temporal nets, (ii) smaller learning rates, (iii) more data augmentation techniques, (iv) high drop out ratio. Meanwhile, we extend the Caffe toolbox into Multi-GPU implementation with high computational efficiency and low memory consumption. We verify the performance of very deep two-stream ConvNets on the dataset of UCF101 and it achieves the recognition accuracy of $91.4\%$.


title: A One-Pass Algorithm for Accurately Estimating Quantiles for Disk-Resident Data

abstract: The cpquantile of an ordered sequence of data values is the element with rank ‘pn, where n is the total number of values. Accurate estimates of quantiles are required for the solution of many practical problems. In this paper, we present a new algorithm for estimating the quantile values for disk-resident data. Our algorithm has the following characteristics: (1) It requires only one pass over the data; (2) It is deterministic; (3) It produces good lower and upper bounds of the true values of the quantiles; (4) It requires no a priori knowledge of the distribution of the data set; (5) It has a scalable parallel formulation; (6) Extra time and memory for computing additional quantiles (beyond the first one) are constant per quantile. We present experimental results on the IBM SP-2. The experimental results show that the algorithm is indeed robust and does not depend on the distribution of the data sets.


title: Polymorph selectivity of superconducting CuSe₂ through kinetic control of solid-state metathesis.

abstract: Rational preparation of materials by design is a major goal of inorganic, solid-state, and materials chemists alike. Oftentimes, the use of nonmetallurgical reactions (e.g., chalcogenide fluxes, hydrothermal syntheses, and in this case solid-state metathesis) alters the thermodynamic driving force of the reaction and allows new, refractory, or otherwise energetically unfavorable materials to form under softer conditions. Taking this a step further, alteration of a metathesis reaction pathway can result in either the formation of the equilibrium marcasite polymorph (by stringent exclusion of air) or the kinetically controlled formation of the high-pressure pyrite polymorph of CuSe2 (by exposure to air). From analysis of the reaction coordinate with in situ synchrotron X-ray diffraction and pair distribution function analysis as well as differential scanning calorimetry, it is clear that the air-exposed reaction proceeds via slight, endothermic rearrangements of crystalline intermediates to form pyrite, which is attributed to partial solvation of the reaction from atmospheric humidity. In contrast, the air-free reaction proceeds via a significant exothermic process to form marcasite. Decoupling the formation of NaCl from the formation of CuSe2 enables kinetic control to be exercised over the resulting polymorph of these superconducting metal dichalcogenides.


Enhanced Reality Fieldwork: the Context Aware Archaeological Assistant


The Seventh PASCAL Recognizing Textual Entailment Challenge


title: DropoutNet: Addressing Cold Start in Recommender Systems

abstract: Latent models have become the default choice for recommender systems due to their performance and scalability. However, research in this area has primarily focused on modeling user-item interactions, and few latent models have been developed for cold start. Deep learning has recently achieved remarkable success showing excellent results for diverse input types. Inspired by these results we propose a neural network based latent model called DropoutNet to address the cold start problem in recommender systems. Unlike existing approaches that incorporate additional content-based objective terms, we instead focus on the optimization and show that neural network models can be explicitly trained for cold start through dropout. Our model can be applied on top of any existing latent model effectively providing cold start capabilities, and full power of deep architectures. Empirically we demonstrate state-of-the-art accuracy on publicly available benchmarks. Code is available at https://github.com/layer6ai-labs/DropoutNet.


title: Market mechanisms for managing datacenters with heterogeneous microarchitectures

abstract: Specialization of datacenter resources brings performance and energy improvements in response to the growing scale and diversity of cloud applications. Yet heterogeneous hardware adds complexity and volatility to latency-sensitive applications. A resource allocation mechanism that leverages architectural principles can overcome both of these obstacles. We integrate research in heterogeneous architectures with recent advances in multi-agent systems. Embedding architectural insight into proxies that bid on behalf of applications, a market effectively allocates hardware to applications with diverse preferences and valuations. Exploring a space of heterogeneous datacenter configurations, which mix server-class Xeon and mobile-class Atom processors, we find an optimal heterogeneous balance that improves both welfare and energy-efficiency. We further design and evaluate twelve design points along the Xeon-to-Atom spectrum, and find that a mix of three processor architectures achieves a 12× reduction in response time violations relative to equal-power homogeneous systems.


title: Robot ethics: a view from the philosophy of science

abstract: Robot ethics is a branch of applied ethics which endeavours to isolate and analyse ethical issues arising in connection with present and prospective uses of robots. These issues span human autonomy protection and promotion, moral responsibility and liability, privacy, fair access to technological resources, social and cultural discrimination, in addition to the ethical dimensions of personhood and agentivity. This chapter examines the distinctive role that epistemological and methodological reflections on robotics play in ethical inquiries on robotics. In particular, reflections of this sort on models of robot-environment interactions and on models of learning robotic systems are brought to bear on the analysis of autonomy and responsibility issues in robot ethics.


Humanoid Robot NAO as HRI Mediator to Teach Emotions using Game-centered Approach for Children with Autism


title: Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

abstract: Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks. These models appear promising for applications such as language modeling and machine translation. However, they scale poorly in both space and time as the amount of memory grows — limiting their applicability to real-world domains. Here, we present an end-to-end differentiable memory access scheme, which we call Sparse Access Memory (SAM), that retains the representational power of the original approaches whilst training efficiently with very large memories. We show that SAM achieves asymptotic lower bounds in space and time complexity, and find that an implementation runs 1,000 x faster and with 3,000x less physical memory than non-sparse models. SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring 100,000s of time steps and memories. As well, we show how our approach can be adapted for models that maintain temporal associations between memories, as with the recently introduced Differentiable Neural Computer.


title: Instagram Spam Detection

abstract: In recent years, Instagram has become one of top 15 online social networks. However, popularity of Instagram also causes advertisement and spam posts flooding. Therefore, it is necessary to build a spam detection model to decrease number of spam posts in Instagram. We present a scheme applying feature-based method and supervised learning technique to detect spam posts from Instagram. We use K-fold cross validation to find best pair of supervised learning model and parameters of the model and accuracy of our best model is 96.27%.


title: Process Mining for Clinical Processes: A Comparative Analysis of Four Australian Hospitals

abstract: Business process analysis and process mining, particularly within the health care domain, remain under-utilized. Applied research that employs such techniques to routinely collected health care data enables stakeholders to empirically investigate care as it is delivered by different health providers. However, cross-organizational mining and the comparative analysis of processes present a set of unique challenges in terms of ensuring population and activity comparability, visualizing the mined models, and interpreting the results. Without addressing these issues, health providers will find it difficult to use process mining insights, and the potential benefits of evidence-based process improvement within health will remain unrealized. In this article, we present a brief introduction on the nature of health care processes, a review of process mining in health literature, and a case study conducted to explore and learn how health care data and cross-organizational comparisons with process-mining techniques may be approached. The case study applies process-mining techniques to administrative and clinical data for patients who present with chest pain symptoms at one of four public hospitals in South Australia. We demonstrate an approach that provides detailed insights into clinical (quality of patient health) and fiscal (hospital budget) pressures in the delivery of health care. We conclude by discussing the key lessons learned from our experience in conducting business process analysis and process mining based on the data from four different hospitals.


title: Automatic Color Image Segmentation Using a Square Elemental Region-Based Seeded Region Growing and Merging Method

abstract: This paper presents an efficient automatic color image segmentation method using a seeded region growing and merging method based on square elemental regions. Our segmentation method consists of the three steps: generating seed regions, merging the regions, and applying a pixel-wise boundary determination algorithm to the resultant polygonal regions. The major features of our method are as follows: the use of square elemental regions instead of pixels as the processing unit, a seed generation method based on enhanced gradient values, a seed region growing method exploiting local gradient values, a region merging method using a similarity measure including a homogeneity distance based on Tsallis entropy, and a termination condition of region merging using an estimated desired number of regions. Using square regions as the processing unit substantially reduces the time complexity of the algorithm and makes the performance stable. The experimental results show that our method exhibits stable performance for a variety of natural images, including heavily textured areas, and produces good segmentation results using the same parameter values. The results of our method are fairly comparable to, and in some respects better than, those of existing algorithms.


title: Uninformative Photos Can Increase People's Perceived Knowledge of Complicated Processes

abstract: To what extent can photos influence people's evaluations of their own knowledge? For example, can photos affect how well people think they understand processes? To answer this question, in six experiments we asked people to indicate how well they understood various processes (such as how rainbows form). Sometimes the processes that were described appeared after a related photo (such as a photo of a rainbow) whereas other times the processes appeared alone. People tended to report that they understood processes that appeared with photos better than processes that appeared alone. This pattern fits with the idea that photos make it easier to generate relevant thoughts and images—an experience people tend to interpret as evidence that they know or understand related information.


title: To Copy or Not to Copy: Making In-Memory Databases Fast on Modern NICs

abstract: When databases resided primarily on disks, the problem of data layout was focused on structures that enabled efficient reads and writes from that medium, as well the as effective use of main memory as a cache. With in-memory databases, this part of the I/O problem is largely gone, but that does not mean that I/O considerations can be completely ignored. We argue for the importance of considering the “other” side of the I/O equation—network communication with clients—when designing in-memory databases.


title: LIDAR-based autonomous wheelchair

abstract: Electric wheelchairs restore mobility and independence to those with injuries limiting their movement. However, individuals suffering from arthritis or motor neuron degenerative diseases e.g. amyotrophic lateral sclerosis (ALS) lack the fine motor control to fluently control electric wheelchairs. These individuals are left dependent on others, drastically reducing their independence and quality of life. This study seeks to mitigate these problems by presenting a wheelchair capable of autonomous navigation, with minimal directive from the user. The wheelchair uses the robot operating system (ROS), microcontrollers, rotary encoders, and a LIDAR unit to navigate. The LI-DAR unit provides vital measurements of the environment which are used to construct a map of the surroundings. ROS receives input from the LIDAR sensor along with the rotary encoders to determine a navigable path to a user-defined destination. ROS sends movement commands to a microcontroller to move the wheelchair along the designated path. The navigation system is designed to adapt to changes in the environment and reroute the wheelchair if new obstacles emerge. During the autonomous system testing, involving ten trials, the wheelchair successfully navigated to the expected destination in 100% of the trials. Based on these preliminary results, the autonomous wheelchair has the potential to restore independent indoor mobility. Further development could provide autonomous navigation capabilities in outdoor environments.


title: First-Person Animal Activity Recognition from Egocentric Videos

abstract: This paper introduces the concept of first-person animal activity recognition, the problem of recognizing activities from a view-point of an animal (e.g., a dog). Similar to first-person activity recognition scenarios where humans wear cameras, our approach estimates activities performed by an animal wearing a camera. This enables monitoring and understanding of natural animal behaviors even when there are no people around them. Its applications include automated logging of animal behaviors for medical/biology experiments, monitoring of pets, and investigation of wildlife patterns. In this paper, we construct a new dataset composed of first-person animal videos obtained by mounting a camera on each of the four pet dogs. Our new dataset consists of 10 activities containing a heavy/fair amount of ego-motion. We implemented multiple baseline approaches to recognize activities from such videos while utilizing multiple types of global/local motion features. Animal ego-actions as well as human-animal interactions are recognized with the baseline approaches, and we discuss experimental results.


title: Google Drive: Forensic analysis of data remnants

abstract: Cloud storage is an emerging challenge to digital forensic examiners. The services are increasingly used by consumers, business, and government, and can potentially store large amounts of data. The retrieval of digital evidence from cloud storage services (particularly from offshore providers) can be a challenge in a digital forensic investigation, due to virtualisation, lack of knowledge on location of digital evidence, privacy issues, and legal or jurisdictional boundaries. Google Drive is a popular service, providing users a cost-effective, and in some cases free, ability to access, store, collaborate, and disseminate data. Using Google Drive as a case study, artefacts were identified that are likely to remain after the use of cloud storage, in the context of the experiments, on a computer hard drive and Apple iPhone3G, and the potential access point(s) for digital forensics examiners to secure evidence. Digital evidence can be stored in cloud storage services, such as Google Drive.Identification of potential data storage is a challenge to forensic examiners.Google Drive was examined in relation to data remnants on a PC and an iPhone.Investigation points include directory listings, prefetch, link and registry files.


title: The anatomy of choice: active inference and agency

abstract: This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behaviour. In particular, we consider prior beliefs that action minimises the Kullback-Leibler divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimises a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimising free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action – constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualises optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimisation, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution – that minimises free energy. This sensitivity corresponds to the precision of beliefs about behaviour, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behaviour entails a representation of confidence about outcomes that are under an agent's control.


title: BetterAuth: web authentication revisited

abstract: This paper presents "BetterAuth", an authentication protocol for Web applications. Its design is based on the experiences of two decades with the Web. BetterAuth addresses existing attacks on Web authentication, ranging from network attacks to Cross-site Request Forgery up to Phishing. Furthermore, the protocol can be realized completely in standard JavaScript. This allows Web applications an early adoption, even in a situation with limited browser support.


title: Places: An Image Database for Deep Scene Understanding

abstract: The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world. Using state of the art Convolutional Neural Networks, we provide impressive baseline performances at scene classification. With its high-coverage and high-diversity of exemplars, the Places Database offers an ecosystem to guide future progress on currently intractable visual recognition problems.


title: Scene classification with semantic Fisher vectors

abstract: With the help of a convolutional neural network (CNN) trained to recognize objects, a scene image is represented as a bag of semantics (BoS). This involves classifying image patches using the network and considering the class posterior probability vectors as locally extracted semantic descriptors. The image BoS is summarized using a Fisher vector (FV) embedding that exploits the properties of the space of these descriptors. The resulting representation is referred to as a semantic Fisher vector. Two implementations of a semantic FV are investigated. First involves modeling the BoS with a Dirichlet Mixture and computing the Fisher gradients for this model. Due to the difficulty of mixture modeling on a non-Euclidean probability simplex, this approach is shown to be unsuccessful. A second implementation is derived using the interpretation of semantic descriptors as parameters of a multinomial distribution. Like the parameters of any exponential family, these can be projected into their natural parameter space. For a CNN, this is shown equivalent to using inputs of its soft-max layer as patch descriptors. A semantic FV is then computed as a Gaussian Mixture FV in the space of these natural parameters. This representation is shown to outperform other alternatives such as FVs of features from the intermediate CNN layers or a classifier obtained by adapting (fine-tuning) the CNN. The proposed FV represents an embedding for object classification probabilities. As an image representation, therefore, it is complementary to the features obtained from a scene classification CNN. A combination of the two representations is shown to achieve state-of-the-art results on MIT Indoor scenes and SUN datasets.


title: Detecting phishing e-mails using text and data mining

abstract: This paper presents text and data mining in tandem to detect the phishing email. The study employs Multilayer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), Group Method of Data Handling (GMDH), Probabilistic Neural Net (PNN), Genetic Programming (GP) and Logistic Regression (LR) for classification. A dataset of 2500 phishing and non phishing emails is analyzed after extracting 23 keywords from the email bodies using text mining from the original dataset. Further, we selected 12 most important features using t-statistic based feature selection. Here, we did not find statistically significant difference in sensitivity as indicated by t-test at 1% level of significance, both with and without feature selection across all techniques except PNN. Since, the GP and DT are not statistically significantly different either with or without feature selection at 1% level of significance, DT should be preferred because it yields ‘if-then’ rules, thereby increasing the comprehensibility of the system.