title: ReFlex: A Flexible Smartphone with Active Haptic Feedback for Bend Input

abstract: ReFlex is a flexible smartphone with bend input and active haptic feedback. ReFlex's features allow the introduction of sensations such as friction or resistance. We report results from an experiment using ReFlex in a targeting task, as well as initial users' reactions to the prototype. We explore both absolute and relative tactile haptic feedback, paired with two types of bend input mappings: position-control and rate-control. We observed that position-controlled cursors paired well with relative bend feedback, while rate-controlled cursors paired well with absolute bend feedback to indicate targets. We also explored an eyes-free condition. Results suggest that while eyes-free, haptic feedback conditions were more error-prone than visual-only conditions, the size of the error was relatively small, and users were able to complete the task in all cases. We present two application scenarios that take advantage of the unique input and output modalities of ReFlex and discuss its potential for within document navigation.


title: DESIGN ENHANCEMENT OF COMBINATIONAL NEURAL NETWORKS USING HDL BASED FPGA FRAMEWORK FOR PATTERN RECOGNITION

abstract: The fast emerging highly-integrated multimedia devices require complex video/image processing tasks leading to a very challenging design process; as it demands more efficient and high processing systems. Neural networks are used in many of these imaging applications to represent the complex input-output relationships. Software implementation of these networks attain accuracy with tradeoffs between processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. The current trends involve conventional processor being replaced by the Field programmable gate array (FPGA) systems due to their high performance when processing huge amount of data. The goal is to design the Combinational Neural Networks (CNN) for pattern recognition using an FPGA based platform for accelerated performance. The enhancement in speed and computation from the hardware is being compared to the software (using MATLAB) model. The employment of HDL on the FPGA enables operations to be performed in parallel. Thus allowing the exploitation of the vast parallelism found in many real-world applications such as in robotics, controller free gaming and sign/gesture recognition. As a validation of the CNN hardware model a case study in pattern recognition is being explored and implemented on Xilinx Spartan 3E FPGA board. To measure the quality of learning in the trained network mean squared error is used. The processing performance of this non-linear stochastic tool is determined by comparing the HDL (parallel model) simulations with the MATLAB design (sequential model). The gain in training time and memory used for processing is also derived.


title: COCO: The Bi-objective Black Box Optimization Benchmarking (bbob-biobj) Test Suite

abstract: The bbob-biobj test suite contains 55 bi-objective functions in continuous domain which are derived from combining functions of the well-known single-objective noiseless bbob test suite. Besides giving the actual function definitions and presenting their (known) properties, this documentation also aims at giving the rationale behind our approach in terms of function groups, instances, and potential objective space normalization.


Problem solving in multiple-goal situations.


title: Similarity Preserving Representation Learning for Time Series Analysis

abstract: A considerable amount of machine learning algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with equal or unequal lengths to a matrix format. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation. The learned feature representation is particularly suitable to the class of learning problems that are sensitive to data similarities. Given a set of $n$ time series, we first construct an $n\times n$ partially observed similarity matrix by randomly sampling $O(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an extremely efficient algorithm that solves a highly non-convex and NP-hard problem to learn new features based on the partially observed similarity matrix. We use the learned features to conduct experiments on both data classification and clustering tasks. Our extensive experimental results demonstrate that the proposed framework is both effective and efficient.


title: Computation of total capacity for discrete memoryless multiple-access channels

abstract: The Arimoto-Blahut (1972) algorithm is generalized for computation of the total capacity of discrete memoryless multiple-access channels (MAC). In addition, a class of MAC is defined with the property that the uniform distribution achieves the total capacity. These results are based on the specialization of the Kuhn-Tucker condition for the total capacity of the MAC, and an extension of a known symmetry property for single-user channels.


title: A New Method of Learning Bayesian Networks Structures from Incomplete Data

abstract: Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data.


title: Towards a Benchmark for Fact Checking with Knowledge Bases

abstract: Fact checking is the task of determining if a given claim holds. Several algorithms have been developed to check facts with reference information in the form of knowledge bases. While individual algorithms have been experimentally evaluated, we provide a first publicly available benchmark evaluating fact checking implementations across a range of assumptions about the properties of the facts and the reference data. We used our benchmark to compare algorithms designed on different principles and assumptions, as well as algorithms that can solve similar tasks developed in closely related communities. Our evaluation provided us with a number of new insights concerning the factors that impact the performance of the different methods.


title: Word Embeddings for the Construction Domain

abstract: We introduce word vectors for the construction domain. Our vectors were obtained by running word2vec on an 11M-word corpus that we created from scratch by leveraging freely-accessible online sources of construction-related text. We first explore the embedding space and show that our vectors capture meaningful construction-specific concepts. We then evaluate the performance of our vectors against that of ones trained on a 100B-word corpus (Google News) within the framework of an injury report classification task. Without any parameter tuning, our embeddings give competitive results, and outperform the Google News vectors in many cases. Using a keyword-based compression of the reports also leads to a significant speed-up with only a limited loss in performance. We release our corpus and the data set we created for the classification task as publicly available, in the hope that they will be used by future studies for benchmarking and building on our work.


Supporting Information for “The Biosphere Under Potential Paris Outcomes”


title: Subgenome parallel selection is associated with morphotype diversification and convergent crop domestication in Brassica rapa and Brassica oleracea

abstract: Brassica species, including crops such as cabbage, turnip and oilseed, display enormous phenotypic variation. Brassica genomes have all undergone a whole-genome triplication (WGT) event with unknown effects on phenotype diversification. We resequenced 199 Brassica rapa and 119 Brassica oleracea accessions representing various morphotypes and identified signals of selection at the mesohexaploid subgenome level. For cabbage morphotypes with their typical leaf-heading trait, we identified four subgenome loci that show signs of parallel selection among subgenomes within B. rapa, as well as four such loci within B. oleracea. Fifteen subgenome loci are under selection and are shared by these two species. We also detected strong subgenome parallel selection linked to the domestication of the tuberous morphotypes, turnip (B. rapa) and kohlrabi (B. oleracea). Overall, we demonstrated that the mesohexaploidization of the two Brassica genomes contributed to their diversification into heading and tuber-forming morphotypes through convergent subgenome parallel selection of paralogous genes.


title: Employee turnover: a neural network solution

abstract: In today's working environment, a company's human resources are truly the only sustainable competitive advantage. Product innovations can be duplicated, but the synergy of a company's workforce cannot be replicated. It is for this reason that not only attracting talented employees but also retaining them is imperative for success. The study of employee turnover has attempted to explain why employees leave and how to prevent the drain of employee talent. This paper focuses on using a neural network (NN) to predict turnover. If turnover can be found to be predictable the identification of at-risk employees will allow us to focus on their specific needs or concerns in order to retain them in the workforce. Also, by using a Modified Genetic Algorithm to train the NN we can also identify relevant predictors or inputs, which can give us information about how we can improve the work environment as a whole. This research found that a NNSOA trained NN in a 10-fold cross validation experimental design can predict with a high degree of accuracy the turnover rate for a small mid-west manufacturing company.


title: Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

abstract: Background: Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources. Objective: The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS. Methods: We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers. Results: The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study. Conclusions: The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS. [JMIR Med Inform 2017;5(3):e18]


title: Corpus annotation with paraphrase types: new annotation scheme and inter-annotator agreement measures

abstract: Paraphrase corpora annotated with the types of paraphrases they contain constitute an essential resource for the understanding of the phenomenon of paraphrasing and the improvement of paraphrase-related systems in natural language processing. In this article, a new annotation scheme for paraphrase-type annotation is set out, together with newly created measures for the computation of inter-annotator agreement. Three corpora different in nature and in two languages have been annotated using this infrastructure. The annotation results and the inter-annotator agreement scores for these corpora are proof of the adequacy and robustness of our proposal.


title: Improved Plagiarism Detection Algorithm Based on Abstract Syntax Tree

abstract: Statements with conditionals are widely used in C, C++ and java, such as if and while statements and they are easy to plagiarize by adjusting the logical structure of the corresponding statements. However, the existing relative algorithms and tools cannot effectively detect code plagiarism of these statements. This paper puts forward an improved code plagiarism detection algorithm based on abstract syntax tree. The algorithm calculates the hash value for each node of the abstract syntax tree, and compares the hash values node by node. Based on this, it analyzes the if-statement plagiarism, as if-statements are representative in the statements with conditionals, and puts forward the corresponding detection schemes in order to detect plagiarism effectively. After that, with the results of many experiments, the algorithm is proved effective on detecting if-statement plagiarisms.


title: Color clustering and learning for image segmentation based on neural networks

abstract: An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L/sup */u/sup */v/sup */ color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.


title: GMDBSCAN: Multi-Density DBSCAN Cluster Based on Grid

abstract: DBSCAN is one of the most popular algorithms for cluster analysis. It can discover all clusters with arbitrary shape and separate noises. But this algorithm canpsilat choose parameter according to distributing of dataset. It simply uses the global MinPts parameter, so that the clustering result of multi-density database is inaccurate. In addition, when it is used to cluster large databases, it will cost too much time. For these problems, we propose GMDBSCAN algorithm which is based on spatial index and grid technique. An experimental evaluation shows that GMDBSCAN is effective and efficient.


title: Modeling and Adaptive Control of an Omni-Mecanum-Wheeled Robot

abstract: The complete dynamics model of a four-Mecanum-wheeled robot considering mass eccentricity and friction uncertainty is derived using the Lagrange’s equation. Then based on the dynamics model, a nonlinear stable adaptive control law is derived using the backstepping method via Lyapunov stability theory. In order to compensate for the model uncertainty, a nonlinear damping term is included in the control law, and the parameter update law with σ-modification is considered for the uncertainty estimation. Computer simulations are conducted to illustrate the suggested control approach.


title: The Constructive, Destructive, and Reconstructive Power of Social Norms

abstract: Despite a long tradition of effectiveness in laboratory tests, normative messages have had mixed success in changing behavior in field contexts, with some studies showing boomerang effects. To test a theoretical account of this inconsistency, we conducted a field exper- iment in which normative messages were used to promote householdenergyconservation.Aspredicted,adescriptive normative message detailing average neighborhood usage produced either desirable energy savings or the undesir- able boomerang effect, depending on whether households were already consuming at a low or high rate. Also as predicted, adding an injunctive message (conveying social approval or disapproval) eliminated the boomerang effect. The results offer an explanation for the mixed success of persuasive appeals based on social norms and suggest how such appeals should be properly crafted.


title: Apoptotic cell death in patients with sepsis, shock, and multiple organ dysfunction.

abstract: OBJECTIVES ::: The purpose of this study was to determine whether apoptosis is a major mechanism of cell death in patients with sepsis. The activities of caspase-3 and the antiapoptotic protein, BCL-2, were investigated also. ::: ::: ::: DESIGN ::: A prospective study of 20 patients who died of sepsis and multiple organ dysfunction was performed. The control group of 16 patients consisted of critically ill, nonseptic patients who were evaluated either prospectively (7) or retrospectively (9). In addition, normal colon sections from seven patients who had bowel resections were included. Apoptosis was evaluated in hematoxylin and eosin-stained specimens by deoxyuridine triphosphate nick end-labeling (TUNEL) and by DNA gel electrophoresis. ::: ::: ::: SETTING ::: Two academic medical centers. ::: ::: ::: PATIENTS ::: Critically ill patients. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: In septic patients, apoptosis was detected in diverse organs by all three methods with a predominance in lymphocytes and intestinal epithelial cells. Hematoxylin and eosin-stained specimens from septic patients demonstrated at least focal apoptosis in 56.3% of spleens, 47.1% of colons, and 27.7% of ileums. Indirect evidence of lymphocyte apoptosis in septic patients included extensive depletion of lymphocytes in white pulp and a marked lymphocytopenia in 15 of 19 patients. Hematoxylin and eosin from nonseptic patients' tissues revealed a low level of apoptosis in one patient only. The TUNEL method increased in positivity with a delay in tissue fixation and was highly positive in many tissues from both septic and nonseptic patients. Immunohistochemical staining for active caspase-3 showed a marked increase in septic vs. nonseptic patients (p < .01), with >25% to 50% of cells being positive focally in the splenic white pulp of six septic but in no nonseptic patients. ::: ::: ::: CONCLUSIONS ::: We conclude that caspase-3-mediated apoptosis causes extensive lymphocyte apoptosis in sepsis and may contribute to the impaired immune response that characterizes the disorder.


title: The productivity crisis in pharmaceutical R&D

abstract: We analyze the decline of R&D productivity in pharmaceuticals and its determinants. Since the molecular biology revolution, science has dramatically expanded the set of plausible therapeutic targets. However, innovation has become more difficult to achieve, and attrition rates of R&D projects have increased, especially in late-phase clinical trials. We show that the R&D productivity slowdown is associated with a higher concentration of R&D investments in high-risk domains, corresponding to unsolved therapeutic needs and unexploited biological mechanisms. We compare the strategies of European and US companies, finding differences in the composition of R&D portfolios, but no evidence of any productivity gap.


title: Security Issues for Cloud Computing

abstract: In this paper, the authors discuss security issues for cloud computing and present a layered framework for secure clouds and then focus on two of the layers, i.e., the storage layer and the data layer. In particular, the authors discuss a scheme for secure third party publications of documents in a cloud. Next, the paper will converse secure federated query processing with map Reduce and Hadoop, and discuss the use of secure co-processors for cloud computing. Finally, the authors discuss XACML implementation for Hadoop and discuss their beliefs that building trusted applications from untrusted components will be a major aspect of secure cloud computing.


title: Reinforcement Learning with A* and a Deep Heuristic

abstract: A* is a popular path-finding algorithm, but it can only be applied to those domains where a good heuristic function is known. Inspired by recent methods combining Deep Neural Networks (DNNs) and trees, this study demonstrates how to train a heuristic represented by a DNN and combine it with A*. This new algorithm which we call aleph-star can be used efficiently in domains where the input to the heuristic could be processed by a neural network. We compare aleph-star to N-Step Deep Q-Learning (DQN Mnih et al. 2013) in a driving simulation with pixel-based input, and demonstrate significantly better performance in this scenario.


title: Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations

abstract: Convolutional neural network (CNN) based approaches are the state of the art in various computer vision tasks including face recognition. Considerable research effort is currently being directed toward further improving CNNs by focusing on model architectures and training techniques. However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce. In this paper, we try to fill this gap and study the effects of different covariates on the verification performance of four recent CNN models using the Labelled Faces in the Wild dataset. Specifically, we investigate the influence of covariates related to image quality and model characteristics, and analyse their impact on the face verification performance of different deep CNN models. Based on comprehensive and rigorous experimentation, we identify the strengths and weaknesses of the deep learning models, and present key areas for potential future research. Our results indicate that high levels of noise, blur, missing pixels, and brightness have a detrimental effect on the verification performance of all models, whereas the impact of contrast changes and compression artefacts is limited. We find that the descriptor-computation strategy and colour information does not have a significant influence on performance.


title: Simple Personalized Search Based on Long-Term Behavioral Signals

abstract: Extensive research has shown that content-based Web result ranking can be significantly improved by considering personal behavioral signals (such as past queries) and global behavioral signals (such as global click frequencies). In this work we present a new approach to incorporating click behavior into document ranking, using ideas of click models as well as learning to rank. We show that by training a click model with pairwise loss, as is done in ranking problems, our approach achieves personalized reranking performance comparable to the state-of-the-art while eliminating much of the complexity required by previous models. This contrasts with other approaches that rely on complex feature engineering.


title: Static analysis of event-driven Node.js JavaScript applications

abstract: Many JavaScript programs are written in an event-driven style. In particular, in server-side Node.js applications, operations involving sockets, streams, and files are typically performed in an asynchronous manner, where the execution of listeners is triggered by events. Several types of programming errors are specific to such event-based programs (e.g., unhandled events, and listeners that are registered too late). We present the event-based call graph, a program representation that can be used to detect bugs related to event handling. We have designed and implemented three analyses for constructing event-based call graphs. Our results show that these analyses are capable of detecting problems reported on StackOverflow. Moreover, we show that the number of false positives reported by the analysis on a suite of small Node.js applications is manageable.


title: Empathy hurts: Compassion for another increases both sensory and affective components of pain perception

abstract: Abstract Recent studies demonstrate that some brain structures activated by pain are also engaged when an individual observes someone else in pain, and that these empathy-related responses are modulated as a function of the affective link between the empath and the individual in pain. In this study we test the hypothesis that empathy-evoked activation in the pain network leads to heightened pain perception. After inducing in half of our subjects a state of high empathy for an actor and in the other half a state of low empathy towards him, we measured the sensitivity to heat stimuli of various intensities in healthy participants while they watched the actor being exposed to similar stimuli. Participants in the “high-empathy” group rated painful (but not non-painful) stimuli applied to themselves as more intense and unpleasant than did those in the “low-empathy” group. Positive correlations between state empathy scores and pain ratings further suggest that this perceptual phenomenon depends on the magnitude of empathic response induced in the participants. The effects were observed when subjects watched the model receiving either neutral or painful stimuli, suggesting that it is empathy itself that alters pain perception, and not necessarily the observation of pain behaviors.


title: The limiting information capacity of a neuronal link

abstract: The maximum rate at which a synaptic link could theoretically transmit information depends on the type of coding used. In a binary modulation system it depends chiefly on the relaxation time, and the limiting capacity equals the maximum attainable impulse rate. In a system using pulse-interval modulation, temporal precision may be a more important limiting factor. It is shown that in a number of typical cases a system of the second type could transmit several times more information per second through a synaptic link than a binary system, and the relation between relative efficiency, relaxation-time, and temporal resolving power is generalized in graphical form. It is concluded, not that interval modulation rather than binary modulation “ought” to be the mode of action of the central nervous system, but that the contrary assumption is unsupported by considerations of efficiency.


title: Temporal Analytics on Big Data for Web Advertising

abstract: "Big Data" in map-reduce (M-R) clusters is often fundamentally temporal in nature, as are many analytics tasks over such data. For instance, display advertising uses Behavioral Targeting (BT) to select ads for users based on prior searches, page views, etc. Previous work on BT has focused on techniques that scale well for offline data using M-R. However, this approach has limitations for BT-style applications that deal with temporal data: (1) many queries are temporal and not easily expressible in M-R, and moreover, the set-oriented nature of M-R front-ends such as SCOPE is not suitable for temporal processing, (2) as commercial systems mature, they may need to also directly analyze and react to real-time data feeds since a high turnaround time can result in missed opportunities, but it is difficult for current solutions to naturally also operate over real-time streams. Our contributions are twofold. First, we propose a novel framework called TiMR (pronounced timer), that combines a time-oriented data processing system with a M-R framework. Users write and submit analysis algorithms as temporal queries - these queries are succinct, scale-out-agnostic, and easy to write. They scale well on large-scale offline data using TiMR, and can work unmodified over real-time streams. We also propose new cost-based query fragmentation and temporal partitioning schemes for improving efficiency with TiMR. Second, we show the feasibility of this approach for BT, with new temporal algorithms that exploit new targeting opportunities. Experiments using real data from a commercial ad platform show that TiMR is very efficient and incurs orders-of-magnitude lower development effort. Our BT solution is easy and succinct, and performs up to several times better than current schemes in terms of memory, learning time, and click-through-rate/coverage.


title: Gaussian Process Model Predictive Control of unmanned quadrotors

abstract: Two issues of quadrotor control without deterministic dynamical equations are addressed in this paper by using Gaussian Process (GP) based Model Predictive Control (MPC) algorithm. Firstly, the first issue of modelling unknown dynamical motions is solved by using GP models based on sampled data. In this way, the model uncertainty can be numerically evaluated during modelling and prediction process. This is not easy when using other data-driven methods, such as Artificial Neural Networks (ANN) and Fuzzy Models (FMs). Then a MPC scheme based on obtained GP models is proposed to address the second issue of designing appropriate quadrotor controllers. The proposed algorithm directly takes model uncertainty into account when planning MPC policies, and can be computationally efficiently implemented through using analytical gradients in the optimization process. The performance of quadrotor control using proposed approach is demonstrated by simulations on a trajectory tracking problem.


Towards Generalizable Sentence Embeddings


title: Sequentiality and prefetching in database systems

abstract: Sequentiality of access is an inherent characteristic of many database systems. We use this observation to develop an algorithm which selectively prefetches data blocks ahead of the point of reference. The number of blocks prefetched is chosen by using the empirical run length distribution and conditioning on the observed number of sequential block references immediately preceding reference to the current block. The optimal number of blocks to prefetch is estimated as a function of a number of “costs,” including the cost of accessing a block not resident in the buffer (a miss), the cost of fetching additional data blocks at fault times, and the cost of fetching blocks that are never referenced. We estimate this latter cost, described as memory pollution, in two ways. We consider the treatment (in the replacement algorithm) of prefetched blocks, whether they are treated as referenced or not, and find that it makes very little difference. Trace data taken from an operational IMS database system is analyzed and the results are presented. We show how to determine optimal block sizes. We find that anticipatory fetching of data can lead to significant improvements in system operation.


title: WOTS2E: A search engine for a Semantic Web of Things

abstract: A Semantic Web of Things (SWoT) brings together the Semantic Web and the Web of Things (WoT), associating semantically annotated information to web-enabled physical devices, services and their data, towards seamless data integration and better understanding of real-world information. A missing element in order to realize SWoT is a standardized, scalable and flexible way to globally discover in (near) real time web-connected embedded devices, as well as their semantic data. To address this gap, we propose WOT Semantic Search Engine (WOTS2E), which is a search engine for the SWoT, based on web crawling, being able to discover Linked Data endpoints and, through them, WoT-enabled devices and their services. In this paper, we describe the design, development and implementation of WOTS2E, as well as an evaluation procedure showing its operation and performance across the web.


title: Does walking explain associations between access to greenspace and lower mortality?

abstract: Despite emerging evidence that access to greenspace is associated with longer life expectancy, little is understood about what causal mechanisms may explain this relationship. Based on social-ecological theories of health, greenspace has multifaceted potential to influence mortality but the potential alternative mediating pathways have not been empirically tested. This study evaluates relationships between access to greenspace, walking and mortality. Firstly, we test for an association between access to greenspace and self-reported levels of walking using a survey of 165,424 adults across England collected during 2007 and 2008. Negative binomial regression multilevel models were used to examine associations between greenspace access and self reported number of days walked in the last month, in total and for recreational and health purposes, after controlling for relevant confounders. Secondly we use an area level analysis of 6781 middle super output areas across England to examine if recreational walking mediates relationships between greenspace access and reduced premature mortality from circulatory disease. Results show clear evidence of better greenspace access being associated with higher reported recreational walking. There were between 13% and 18% more days of recreational walking in the greenest quintile compared with the least green after adjustment for confounders. Tests for mediation found no evidence that recreational walking explain the associations between greenspace and mortality. Futhermore, whilst the relationship between greenspace access and walking was observed for all areas, the relationship between greenspace access and reduced mortality was only apparent in the most deprived areas. These findings indicate that the association between greenspace and mortality, if causal, may be explained by mediators other than walking, such as psychosocial factors. Future research should concentrate on understanding the causal mechanisms underlying observed associations.


title: Dempster-Shafer Theory for Move Prediction in Start Kicking of The Bicycle Kick of Sepak Takraw Game

abstract: This paper presents Dempster-Shafer theory for move prediction in start kicking of the bicycle kick of sepak takraw game. Sepak takraw is a highly complex net-barrier kicking sport that involves dazzling displays of quick reflexes, acrobatic twists, turns and swerves of the agile human body movement. A bicycle kick or scissor kick is a physical move made by throwing the body up into the air, making a shearing movement with the legs to get one leg in front of the other without holding on to the ground. Specifically, this paper considers bicycle kick of sepak takraw game in start kicking of the ball with uncertainty where player has different awareness regarding the contingencies. We have chosen Dempster-Shafer theory because the advantages of this theory which includes the ability to model information in a flexible way without requiring a probability to be assigned to each element in a set, providing a convenient and simple mechanism for combining two or more pieces of evidence under certain conditions, it can model ignorance explicitly, rejection of the law of additivity for belief in disjoint propositions.


title: Semantic lexicon induction from twitter with pattern relatedness and flexible term length

abstract: With the rise of social media, learning from informal text has become increasingly important. We present a novel semantic lexicon induction approach that is able to learn new vocabulary from social media. Our method is robust to the idiosyncrasies of informal and open-domain text corpora. Unlike previous work, it does not impose restrictions on the lexical features of candidate terms - e.g. by restricting entries to nouns or noun phrases - while still being able to accurately learn multiword phrases of variable length. Starting with a few seed terms for a semantic category, our method first explores the context around seed terms in a corpus, and identifies context patterns that are relevant to the category. These patterns are used to extract candidate terms - i.e. multiword segments that are further analyzed to ensure meaningful term boundary segmentation. We show that our approach is able to learn high quality semantic lexicons from informally written social media text of Twitter, and can achieve accuracy as high as 92% in the top 100 learned category members.


Foundations of Adversarial Machine Learning


title: Millimeter-wave CMOS design

abstract: This paper describes the design and modeling of CMOS transistors, integrated passives, and circuit blocks at millimeter-wave (mm-wave) frequencies. The effects of parasitics on the high-frequency performance of 130-nm CMOS transistors are investigated, and a peak f/sub max/ of 135 GHz has been achieved with optimal device layout. The inductive quality factor (Q/sub L/) is proposed as a more representative metric for transmission lines, and for a standard CMOS back-end process, coplanar waveguide (CPW) lines are determined to possess a higher Q/sub L/ than microstrip lines. Techniques for accurate modeling of active and passive components at mm-wave frequencies are presented. The proposed methodology was used to design two wideband mm-wave CMOS amplifiers operating at 40 GHz and 60 GHz. The 40-GHz amplifier achieves a peak |S/sub 21/| = 19 dB, output P/sub 1dB/ = -0.9 dBm, IIP3 = -7.4 dBm, and consumes 24 mA from a 1.5-V supply. The 60-GHz amplifier achieves a peak |S/sub 21/| = 12 dB, output P/sub 1dB/ = +2.0 dBm, NF = 8.8 dB, and consumes 36 mA from a 1.5-V supply. The amplifiers were fabricated in a standard 130-nm 6-metal layer bulk-CMOS process, demonstrating that complex mm-wave circuits are possible in today's mainstream CMOS technologies.


title: The DRAM Latency PUF: Quickly Evaluating Physical Unclonable Functions by Exploiting the Latency-Reliability Tradeoff in Modern Commodity DRAM Devices

abstract: Physically Unclonable Functions (PUFs) are commonly used in cryptography to identify devices based on the uniqueness of their physical microstructures. DRAM-based PUFs have numerous advantages over PUF designs that exploit alternative substrates: DRAM is a major component of many modern systems, and a DRAM-based PUF can generate many unique identiers. However, none of the prior DRAM PUF proposals provide implementations suitable for runtime-accessible PUF evaluation on commodity DRAM devices. Prior DRAM PUFs exhibit unacceptably high latencies, especially at low temperatures (e.g., >125.8s on average for a 64KiB memory segment below 55C), and they cause high system interference by keeping part of DRAM unavailable during PUF evaluation. In this paper, we introduce the DRAM latency PUF, a new class of fast, reliable DRAM PUFs. The key idea is to reduce DRAM read access latency below the reliable datasheet specications using software-only system calls. Doing so results in error patterns that reect the compound eects of manufacturing variations in various DRAM structures (e.g., capacitors, wires, sense ampli- ers). Based on a rigorous experimental characterization of 223 modern LPDDR4 DRAM chips, we demonstrate that these error patterns 1) satisfy runtime-accessible PUF requirements, and 2) are quickly generated (i.e., at 88.2ms) irrespective of operating temperature using a real system with no additional hardware modications. We show that, for a constant DRAM capacity overhead of 64KiB, our implementation of the DRAM latency PUF enables an average (minimum, maximum) PUF evaluation time speedup of 152x (109x, 181x) at 70C and 1426x (868x, 1783x) at 55C when compared to a DRAM retention PUF and achieves greater speedups at even lower temperatures.


title: Weakly Supervised Instance Segmentation Using Class Peak Response

abstract: Weakly supervised instance segmentation with image-level labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by exploiting class peak responses to enable a classification network for instance mask extraction. With image labels supervision only, CNN classifiers in a fully convolutional manner can produce class response maps, which specify classification confidence at each image location. We observed that local maximums, i.e., peaks, in a class response map typically correspond to strong visual cues residing inside each instance. Motivated by this, we first design a process to stimulate peaks to emerge from a class response map. The emerged peaks are then back-propagated and effectively mapped to highly informative regions of each object instance, such as instance boundaries. We refer to the above maps generated from class peak responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed instance-level representation, which allows instance masks to be extracted even with some off-the-shelf methods. To the best of our knowledge, we for the first time report results for the challenging image-level supervised instance segmentation task. Extensive experiments show that our method also boosts weakly supervised pointwise localization as well as semantic segmentation performance, and reports state-of-the-art results on popular benchmarks, including PASCAL VOC 2012 and MS COCO.


title: Time-Based PWM Controller for Fully Integrated High Speed Switching DC-DC Converters — An Alternative to Conventional Analog and Digital Controllers

abstract: This tutorial discusses design of a highly integrated, low quiescent current continuous time PWM controller using time-based signal processing. By virtue of the continuous-time digital nature of the time-based PWM controller, it is capable of achieving very high resolution and speed without using any error amplifier and large compensation capacitor or any high resolution A/D converter and digital PWM while preserving all the benefits of both analog and digital PWM controllers. Using time as the processing variable, the controller operates with CMOS-level digital-like signals but without adding any quantization error. A voltage/current controlled ring oscillator is used as an integrator in place of conventional opamp-RC or Gm-C integrator while a voltage/current controlled delay line is used to perform voltage-to-time conversion. Starting with trade-offs with high speed design of conventional analog and digital PWM controllers, the concept of time based proportional-integral-derivative (PID) controller with complete architecture of a time-based buck converter is presented. The technique was successfully demonstrated on silicon with implementation of 10-25MHz single phase and 30-70MHz 4-phase buck converters on 180nm and 65nm CMOS processes, respectively.


title: Graph Summarization Methods and Applications: A Survey

abstract: While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.


title: Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning

abstract: In autonomous drone racing, a drone is required to fly through the gates quickly without any collision. Therefore, it is important to detect the gates reliably using computer vision. However, due to the complications such as varying lighting conditions and gates seen overlapped, traditional image processing algorithms based on color and geometry of the gates tend to fail during the actual racing. In this letter, we introduce a convolutional neural network to estimate the center of a gate robustly. Using the detection results, we apply a line-of-sight guidance algorithm. The proposed algorithm is implemented using low cost, off-the-shelf hardware for validation. All vision processing is performed in real time on the onboard NVIDIA Jetson TX2 embedded computer. In a number of tests our proposed framework successfully exhibited fast and reliable detection and navigation performance in indoor environment.


title: Communication aid device for illness deaf-mute

abstract: This paper presents the developing of a simple and low cost communication aid device for the illness deaf-mute people. The device is used as the interpreter between the deaf-mute people and the doctor. The developing is based on the 32 bits microcontroller with the .NET Micro Framework on visual studio C# language. The result from the preliminary field test has shown that the normal people gain understandable the symptom detail of deaf-mute clearly.


title: Information Dropout: Learning Optimal Representations Through Noisy Computation

abstract: The cross-entropy loss commonly used in deep learning is closely related to the defining properties of optimal representations, but does not enforce some of the key properties. We show that this can be solved by adding a regularization term, which is in turn related to injecting multiplicative noise in the activations of a Deep Neural Network, a special case of which is the common practice of dropout. We show that our regularized loss function can be efficiently minimized using Information Dropout, a generalization of dropout rooted in information theoretic principles that automatically adapts to the data and can better exploit architectures of limited capacity. When the task is the reconstruction of the input, we show that our loss function yields a Variational Autoencoder as a special case, thus providing a link between representation learning, information theory and variational inference. Finally, we prove that we can promote the creation of optimal disentangled representations simply by enforcing a factorized prior, a fact that has been observed empirically in recent work. Our experiments validate the theoretical intuitions behind our method, and we find that Information Dropout achieves a comparable or better generalization performance than binary dropout, especially on smaller models, since it can automatically adapt the noise to the structure of the network, as well as to the test sample.


title: Infrastructuring in participatory design

abstract: This paper reviews literature and reflects on infrastructuring in Participatory Design (PD) with a conceptual interest. It starts with the notion of information infrastructure introduced to the PD community in the mid-1990s by Star and collaborators. It traces how the notion has been adapted, appended, and negotiated within a number of PD approaches known as "infrastructuring." Based on this review, the paper discusses a number of themes arising from these approaches that relate to salient information infrastructure characteristics and speak to the specificity of infrastructuring in PD. This paper takes stock of what has happened in conceptual terms with regard to information infrastructure and infrastructuring in the field of PD to inform continuing work.


A Feasibility Study into Tracking Wi-Fi Enabled Mobile Devices .


title: Efficient design and hardware implementation of the OpenFlow v1.3 Switch on the Virtex-6 FPGA ML605

abstract: Software-defined network (SDN) has had the evolution of the current network with the aim of removing its restrictions so that the data plane has been separated from its control plane. In the architecture of the SDN, the most controversial device is the OpenFlow Switch in that in the OpenFlow Switch, it is packets which are processed and investigated. Now, OpenFlow Switch versions 1.0 and 1.1 have been implemented on hardware platforms and support limited specifications of the OpenFlow. The present article is to design and implement the architecture of the OpenFlow v1.3 Switch on the \(\hbox {Virtex}{\textregistered }\)-6 FPGA ML605 board because the FPGA platform has high flexibility, processing speed and reprogrammability. Although little research investigated performance parameters of the OpenFlow Switch, in the present study, the OpenFlow system (switch and controller) is to be implemented on the FPGA via the VHDL on the one hand, and performance parameters of the OpenFlow Switch and its stimulation performance is to be investigated via the ISE design suite on the other hand. In addition to enjoying high flexibility, this architecture has a consumer hardware at the level of other start-ups. The main advantage of the proposed design is that it increases the speed of packet pipeline processing in flow tables switch. Besides, it supports the features of the OpenFlow v1.3. Its parser supports 40 packet headers in the network and provides the possibility of switch development for next versions of the OpenFlow as easily as possible.


title: Material recognition in the wild with the Materials in Context Database

abstract: Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild. MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolutional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. For patch-based classification on MINC we found that the best performing CNN architectures can achieve 85.2% mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1% mean class accuracy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation.


title: Monocular 3D Object Detection for Autonomous Driving

abstract: The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain highquality object detections. The focus of this paper is on proposal generation. In particular, we propose an energy minimization approach that places object candidates in 3D using the fact that objects should be on the ground-plane. We then score each candidate box projected to the image plane via several intuitive potentials encoding semantic segmentation, contextual information, size and location priors and typical object shape. Our experimental evaluation demonstrates that our object proposal generation approach significantly outperforms all monocular approaches, and achieves the best detection performance on the challenging KITTI benchmark, among published monocular competitors.


title: Antarctic Space Weather Data Managed by IPS Radio and Space Services of Australia

abstract: Ionospheric Prediction Services (IPS) has an extensive collection of data from Antarctic field instruments, the oldest being ionospheric recordings from the 1950s. Its sensor network (IPSNET) spans Australasia and Antarctica collecting information on space weather. In Antarctica, sensors include ionosondes, magnetometers, riometers, and cosmic ray detectors. The (mostly) real-time data from these sensors flow into the IPS World Data Centre at Sydney, where the majority are available online to clients worldwide. When combined with other IPSNET-station data, they provide the basis for Antarctic space weather reports. This paper summarizes the datasets collected from Antarctica and their data management within IPS.


title: Nonlinear Dimension Reduction and Activation Detection for fMRI Dataset

abstract: Functional magnetic resonance imaging (fMRI) has been established as a powerful method for brain mapping. Different physical phenomena contribute to the dynamical changes in the fMRI signal, the task-related hemodynamic responses, non-task-related physiological rhythms, machine and motion artifacts, etc. In this paper, we propose a new approach for fMRI data analysis. Each fMRI time series is viewed as a point in RT . We are interested in learning the organization of the points in high dimensions and extracting useful information for data classification. A nonlinear manifold learning technique is applied to obtain a low dimensional embedding of a dataset. The embedding differentiates time series with different temporal patterns. By assuming that the subset of activated time series forms a low dimensional structure, we partition the dataset and separate subsets of points with low dimensionality. The correspondence between low dimensional subsets and time series that contain task-related responses is verified and the activation maps are generated accordingly. The proposed approach is data-driven. It does not require a model for the hemodynamic response. We have conducted several experiments with synthetic and in-vivo datasets that demonstrate the performance of our approach.


title: Assessing Box Office Performance Using Movie Scripts: A Kernel-Based Approach

abstract: We develop a methodology to predict box office performance of a movie at the point of green-lighting, when only its script and estimated production budget are available. We extract three levels of textual features (genre and content, semantics, and bag-of-words) from scripts using screenwriting domain knowledge, human input, and natural language processing techniques. These textual variables define a distance metric across scripts, which is then used as an input for a kernel-based approach to assess box office performance. We show that our proposed methodology predicts box office revenues more accurately (29 percent lower mean squared error (MSE)) compared to benchmark methods.


title: Using a Grid-Enabled Wireless Sensor Network for Flood Management

abstract: Flooding is becoming an increasing problem. As a result there is a need to deploy more sophisticated sensor networks to detect and react to flooding. This paper outlines a demonstration that illustrates our proposed solution to this problem involving embedded wireless hardware, component based middleware and overlay networks.


title: Process Mining Based on Regions of Languages

abstract: In this paper we give an overview, how to apply region based methodsfor the synthesis of Petri nets from languages to process mining. ::: ::: The research domain of process mining aims at constructing a process modelfrom an event log, such that the process model can reproduce the log, and doesnot allow for much more behaviour than shown in the log. We here considerPetri nets to represent process models. Event logs can be interpreted as finite languages.Region based synthesis methods can be used to construct a Petri net froma language generating the minimal net behaviour including the given language.Therefore, it seems natural to apply such methods in the process mining domain.There are several different region based methods in literature yielding differentPetri nets. We adapt these methods to the process mining domain and comparethem concerning efficiency and usefulness of the resulting Petri net.


title: Physicians' use of electronic medical records: barriers and solutions.

abstract: The electronic medical record (EMR) is an enabling technology that allows physician practices to pursue more powerful quality improvement programs than is possible with paper-based records. However, achieving quality improvement through EMR use is neither low-cost nor easy. Based on a qualitative study of physician practices that had implemented an EMR, we found that quality improvement depends heavily on physicians' use of the EMR-and not paper-for most of their daily tasks. We identified key barriers to physicians' use of EMRs. We then suggest policy interventions to overcome these barriers, including providing work/practice support systems, improving electronic clinical data exchange, and providing financial rewards for quality improvement.


title: A comparison on multi-class classification methods based on least squares twin support vector machine

abstract: Least Squares Twin Support Vector Machine (LSTSVM) is a binary classifier and the extension of it to multiclass is still an ongoing research issue. In this paper, we extended the formulation of binary LSTSVM classifier to multi-class by using the concepts such as "One-versus-All", "One-versus-One", "All-versus-One" and Directed Acyclic Graph (DAG). This paper performs a comparative analysis of these multi-classifiers in terms of their advantages, disadvantages and computational complexity. The performance of all the four proposed classifiers has been validated on twelve benchmark datasets by using predictive accuracy and training-testing time. All the proposed multi-classifiers have shown better performance as compared to the typical multi-classifiers based on 'Support Vector Machine' and 'Twin Support Vector Machine'. Friedman's statistic and Nemenyi post hoc tests are also used to test significance of predictive accuracy differences between classifiers.


title: Paleoenvironmental Reconstruction of Dongodien, Lake Turkana, Kenya and OSL Dating of Site Occupation During Late Holocene Climate Change

abstract: Dongodien (GaJi4) is a sequence of sub-lacustrine, beach, and sub-aerial lake margin sediments of the Galana Boi Formation at Koobi Fora, Lake Turkana, Kenya. The sediments accumulated under a climate of increasing aridity in the latter African Humid Period. The section contains two archaeologically rich beds (Horizons B and A). Here, we present new optically stimulated luminescence (OSL) dates that are independently corroborated with C-14. The lower bed (Horizon B) has an OSL age of 4.14 ± 0.27 ka, supported by C-14 ages of 4.71 ± 0.13, 4.79 ± 0.08, and 4.70 ± 0.06 ka cal BP. The upper bed (Horizon A) has an OSL age of 2.34 ± 0.20 ka, consistent with that of site stratigraphy. In contrast to luminescence dating attempts elsewhere in the East African Rift System, quartz-OSL from this locality and sites FwJj5 and FwJj25 ~40 km NW has a dominant fast component and robust intrinsic characteristics. OSL confirms Dongodien recorded the first known appearance of pastoralism in East Africa; it lays within a tsetse-free corridor between northern and southern Africa. Interpretation of archaeologically rich horizons as beach deposits is consistent with published lake level curves and Holocene highstands. Archaeological material (obsidian microliths, Nderit pottery, wild and domestic mammal bones, fish bones) suggests mixtures of subsistence strategies (hunting-gathering, fishing, herding) as the climate became more arid. The site may have been chosen for reoccupation because of abundant fish associated with lacustrine upwelling near Koobi Fora, a cuspate foreland.


title: Using machine learning to predict hypertension from a clinical dataset

abstract: Hypertension is an illness that often leads to severe and life threatening diseases such as heart failure, thickening of the heart muscle, coronary artery disease, and other severe conditions if left untreated. An artificial neural network is a powerful machine learning technique that allows prediction of the presence of the disease in susceptible populations while removing the potential for human error. In this paper, we identify the important risk factors based on patients' current health conditions, medical records, and demographics. These factors are then used to predict the presence of hypertension in an individual. These risk factors are also indicative of the probability of a person developing hypertension in the future and can, therefore, be used as an early warning system. We present a neural network model for predicting hypertension with about 82% accuracy. This is good performance given our chosen risk factors as inputs and the large integrated data used for the study. Our network model utilizes very large sample sizes (185,371 patients and 193,656 controls) from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data set. Finally, we present a literature study to show the use of these risk factors in other works along with experimental results obtained from our model.


title: Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis

abstract: One of the most interesting applications of mobile sensing is monitoring of individual behavior, especially in the area of mental health care. Most existing systems require an interaction with the device, for example they may require the user to input his/her mood state at regular intervals. In this paper we seek to answer whether mobile phones can be used to unobtrusively monitor individuals affected by depressive mood disorders by analyzing only their mobility patterns from GPS traces. In order to get ground-truth measurements, we have developed a smartphone application that periodically collects the locations of the users and the answers to daily questionnaires that quantify their depressive mood. We demonstrate that there exists a significant correlation between mobility trace characteristics and the depressive moods. Finally, we present the design of models that are able to successfully predict changes in the depressive mood of individuals by analyzing their movements.


title: Method of risk evaluation of information security based on neural networks

abstract: Risk assessment of information security is an important assessment method and decision mechanism in the process of making information security system. The risk evaluation is a process of computing risk value by means of risk assessment, which is from assessment and evaluation of assets, threats, and vulnerabilities to the risk evaluation of information asset. Focused on the uncertainty and complexity of information, we integrate the artificial neural network to apply them in studying risk evaluation. Firstly, since the artificial neural network is suited for the quantity data processing, and poor to the qualitative analyze, and risk is uncertain , the risk factors are quantized by fuzzy evaluation method proposed in this dissertation so that the input of ANN are pre-treated. Secondly, wavelet analysis and the neural network is integrated, we apply them in studying risk evaluation. The shortcomings of the neural network can be overcome, such as a slower study velocity and lower fit accuracy. The simulation results show that risk level of the information system can be evaluated quantitatively by trained neural network, and the shortcomings of current evaluating methods can be overcome, such as more subjectivity and fuzzy conclusion.


title: The ElectroAcoustic Resource Site (EARS): philosophy, foundation and aspirations

abstract: This paper introduces the ElectroAcoustic Resource Site project (EARS), taking a tripartite approach: first outlining the project's philosophy, then reporting on work-to-date and finishing with a discussion of the project's ambitions and aims.The project's aim is the development of a dynamic, multi-lingual, international, publicly available Internet-based bibliographical resource designed to enhance the scholarly infrastructure of electroacoustic music studies, in particular, the musicology of electroacoustic music. Through the use of hypertext structures and linking systems the site will help to contextualise specific research within the broad field of electroacoustic music studies, as well as making helpful links between related areas/items of scholarship. The project aspires to assist access to current, past and evolving areas of scholarship and will attempt to redress certain imbalances in the ease of access to areas of research within the field. The project will strive to conceive of electroacoustic music in its widest possible sense, acknowledge the interdisciplinary nature of the field, and aspire to the greatest possible breadth and inclusiveness. The EARS project is coordinated by an international consortium, is directed by the authors and can be found at http://www.mti.dmu.ac.uk/EARS


title: Prediction of Human Behavior in Human--Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots

abstract: When people interact with communication robots in daily life, their attitudes and emotions toward the robots affect their behavior. From the perspective of robotics design, we need to investigate the influences of these attitudes and emotions on human-robot interaction. This paper reports our empirical study on the relationships between people's attitudes and emotions, and their behavior toward a robot. In particular, we focused on negative attitudes, anxiety, and communication avoidance behavior, which have important implications for robotics design. For this purpose, we used two psychological scales that we had developed: negative attitudes toward robots scale (NARS) and robot anxiety scale (RAS). In the experiment, subjects and a humanoid robot are engaged in simple interactions including scenes of meeting, greeting, self-disclosure, and physical contact. Experimental results indicated that there is a relationship between negative attitudes and emotions, and communication avoidance behavior. A gender effect was also suggested.


title: Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication

abstract: As a digital version of word-of-mouth, online review has become a major information source for consumers and has very important implications for a wide range of management activities. While some researchers focus their studies on the impact of online product review on sales, an important assumption remains unexamined, that is, can online product review reveal the true quality of the product? To test the validity of this key assumption, this paper first empirically tests the underlying distribution of online reviews with data from Amazon. The results show that 53% of the products have a bimodal and non-normal distribution. For these products, the average score does not necessarily reveal the product's true quality and may provide misleading recommendations. Then this paper derives an analytical model to explain when the mean can serve as a valid representation of a product's true quality, and discusses its implication on marketing practices.


title: Design and Implementation of a Simple Web Search Engine

abstract: We present a simple web search engine for indexing and searching html documents using python programming language. Because python is well known for its simple syntax and strong support for main operating systems, we hope it will be beneficial for learning information retrieval techniques, especially web search engine technology.


Bat algorithm for rough set attribute reduction


title: Animate Characters

abstract: The world of everyday interactions is filled with characters, real or fictitious, and human knowledge of how to make these interactions satisfying and productive relies upon an understanding of character. As agents become more intelligent and more ubiquitous, we may naturally ask how we can endow them with life and personality to make them easier and more gratifying to use. This paper offers a broad definition of “animate character,” and examines the technical and artistic issues involved both in the creation and the evaluation of such systems. We provide example interactions with several character types. The paper concludes with an annotated bibliographic survey of work done in this area.


title: Eye temperature distribution in drunk persons using thermal imagery

abstract: Temperature distribution on the eyes of sober and drunk persons is studied and preliminary results are given regarding temperature changes. It is observed using simple image processing algorithms and histogram modification techniques that the temperature difference between the sclera and the iris increases when somebody consumes alcohol. Iris becomes darker, which means that its temperature decreases compared to sclera temperature.


title: Software Implementations of Elliptic Curve Cryptography

abstract: Elliptic Curve Cryptography (ECC) is a promising alternative for public-key algorithms in resource-constrained systems because it provides a similar level of security with much shorter keys than conventional integer-based publickey algorithms. ECC over binary field is of special interest because the operations in binary field are thought more space and time efficient. However, the software implementations of ECC over binary field are still slow, especially on low-end processors used in small computing devices such as sensor nodes. In this paper, we studied software implementations of ECC. We first investigated whether some architectural parameters such as word size may affect the choice of algorithms when implementing ECC with software. We identified a set of algorithms for ECC implementation for low-end processors. We also examined several improvements to the instruction set architecture of an 8-bit processor and studied their impact on the performance of ECC.


title: Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision

abstract: People can refer to quantities in a visual scene by using either exact cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few, most, all). In humans, these two processes underlie fairly different cognitive and neural mechanisms. Inspired by this evidence, the present study proposes two models for learning the objective meaning of cardinals and quantifiers from visual scenes containing multiple objects. We show that a model capitalizing on a 'fuzzy' measure of similarity is effective for learning quantifiers, whereas the learning of exact cardinals is better accomplished when information about number is provided.


Learning Word Representations with Regularization from Prior Knowledge.


title: A peridynamic perspective on spring-mass fracture

abstract: The application of spring-mass systems to the animation of brittle fracture is revisited. The motivation arises from the recent popularity of peridynamics in the computational physics community. Peridynamic systems can be regarded as spring-mass systems with two specific properties. First, spring forces are based on a simple strain metric, thereby decoupling spring stiffness from spring length. Second, masses are connected using a distance-based criterion. The relatively large radius of influence typically leads to a few hundred springs for every mass point. Spring-mass systems with these properties are shown to be simple to implement, trivially parallelized, and well-suited to animating brittle fracture.


Perfectoid Spaces and their Applications


title: A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part II: Full Device Model and Circuit Performance Benchmarking

abstract: This paper presents a complete circuit-compatible compact model for single-walled carbon-nanotube field-effect transistors (CNFETs) as an extension to Part 1 of this two-part paper. For the first time, a universal circuit-compatible CNFET model including the practical device nonidealities is implemented with HSPICE. In addition to the nonidealities included in the companion paper, this paper includes the elastic scattering in the channel region, the resistive source/drain (S/D), the Schottky-barrier resistance, and the parasitic gate capacitances. More than one nanotube per device can be modeled. Compared to silicon technology, the CNFETs show much better device performance based on the intrinsic gate-delay metric (six times for nFET and 14 times for pFET) than the MOSFET device at the 32-nm node, even with device nonidealities. This large speed improvement is significantly degraded (by a factor of five to eight) by interconnect capacitance in a real circuit environment. We performed circuit-performance comparison with all the standard digital library cells between CMOS random logic and CNFET random logic with HSPICE simulation. Compared to CMOS circuits, the CNFET circuits with one to ten carbon nanotubes per device is about two to ten times faster, the energy consumption per cycle is about seven to two times lower, and the energy-delay product is about 15-20 times lower, considering the realistic layout pattern and the interconnect wiring capacitance.


title: Modular one-to-many clutchable actuator for a soft elbow exosuit

abstract: Exoskeletons have been developed for a wide range of applications, from the military to the medical field, with the aim of augmenting human performance or compensating for neuromuscular deficiencies. However, to empower the high number of degrees of freedom of the human body, they often employ a high number of motors, increasing the size, weight and power consumption of the system. We hereby present an actuation strategy to empower our elbow exosuit that adopts a single motor to drive multiple, independently actuated, degrees of freedom. This paradigm, known as One-to-many, is achieved using a modular design where each module comprises a clutchable mechanism that allows to convert a single directional motion from the prime mover to a selectable bidirectional output. Moreover, the mechanism has a locking feature that enables the wearer of the exoskeleton to hold a static load with a minimal power consumption. We present a simple controller for the clutchable unit based on a finite-state machine model, and evaluate its performance for varying input velocities. The system is shown to have a bandwidth of 1.5 Hz, sufficient for daily elbow movements, whilst retaining a compact design.


Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data


Politics, Position, and Power: From the Positive to the Regulatory State


title: An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

abstract: This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.


title: Using directional antennas for medium access control in ad hoc networks

abstract: Previous research in wireless ad hoc networks typically assumes the use of omnidirectional antennas at all nodes. With omnidirectional antennas, while two nodes are communicating using a given channel, MAC protocols such as IEEE 802.11 require all other nodes in the vicinity to stay silent. With directional antennas, two pairs of nodes located in each other's vicinity may potentially communicate simultaneously, depending on the directions of transmission. This can increase spatial reuse of the wireless channel. In addition, the higher gain of directional antennas allows a node to communicate with other nodes located far away, implying that messages could be delivered to the destination in fewer hops. In this paper, we propose a MAC protocol that exploits the characteristics of directional antennas. Our design focuses on using multi-hop RTSs to establish links between distant nodes, and then transmit CTS, DATA and ACK over a single hop. Results show that our directional MAC protocol can perform better than IEEE 802.11, although we find that the performance is dependent on the topology configuration and the flow patterns in the system.


title: A taxonomy of grid monitoring systems

abstract: Monitoring is the act of collecting information concerning the characteristics and status of resources of interest. Monitoring grid resources is a lively research area given the challenges and manifold applications. The aim of this paper is to advance the understanding of grid monitoring by introducing the involved concepts, requirements, phases, and related standardisation activities, including Global Grid Forum's Grid Monitoring Architecture. Based on a refinement of the latter, the paper proposes a taxonomy of grid monitoring systems, which is employed to classify a wide range of projects and frameworks. The value of the offered taxonomy lies in that it captures a given system's scope, scalability, generality and flexibility. The paper concludes with, among others, a discussion of the considered systems, as well as directions for future research.


title: Fusion Scheme for Semantic and Instance-level Segmentation

abstract: A powerful scene understanding can be achieved by combining the tasks of semantic segmentation and instance level recognition. Considering that these tasks are complementary, we propose a multi-objective fusion scheme which leverages the capabilities of each task: pixel level semantic segmentation performs well in background classification and delimiting foreground objects from background, while instance level segmentation excels in recognizing and classifying objects as a whole. We use a fully convolutional residual network together with a feature pyramid network in order to achieve both semantic segmentation and Mask R-CNN based instance level recognition. We introduce a novel heuristic fusion approach for panoptic segmentation. The instance and semantic segmentation output of the network is fused into a panoptic segmentation. This is achieved using object sub-category class and instance propagation guidance by object category class from semantic segmentation. The proposed solution achieves significant improvements in semantic object segmentation and object mask boundaries refinement at low computational costs.


title: Anytime K-Nearest Neighbor Search for Database Applications

abstract: Many contemporary database applications require similarity-based retrieval of complex objects where the only usable knowledge of its domain is determined by a metric distance function. In support of these applications, we explored a search strategy for k- nearest neighbor searches with MVP-trees that greedily identifies k answers and then improves the answer set monotonically. The algorithm returns an approximate solution when terminated early, as determined by a limiting radius or an internal measure of progress. Given unbounded time the algorithm terminates with an exact solution. Approximate solutions to k-nearest neighbor search provide much needed speed improvement to hard nearest-neighbor problems. Our anytime approximate formulation is well suited for interactive search applications as well as applications where the distance function itself is an approximation. We evaluate the algorithm over a suite of workloads, including image retrieval, biological data and high-dimensional vector data. Experimental results demonstrate the practical applicability of our approach.


title: Accessible smart cities?: Inspecting the accessibility of Brazilian municipalities' mobile applications

abstract: The use of interactive technologies to aid in the implementation of smart cities has a significant potential to support disabled users in performing their activities as citizens. In this study, we present an investigation of the accessibility of a sample of 10 mobile Android™ applications of Brazilian municipalities, two from each of the five big geographical regions of the country, focusing especially on users with visual disabilities. The results showed that many of the applications were not in accordance with accessibility guidelines, with an average of 57 instances of violations and an average of 11.6 different criteria violated per application. The main problems included issues like not addressing labelling of non-textual content, headings, identifying user location, colour contrast, enabling users to interact using screen reader gestures, focus visibility and lack of adaptation of text contained in image. Although the growth in mobile applications for has boosted the possibilities aligned with the principles of smart cities, there is a strong need for including accessibility in the design of such applications in order for disabled people to benefit from the potential they can have for their lives.


Deep CNNs for Diabetic Retinopathy Detection


CHAPTER 8 POSITIVE EMOTIONS IN EDUCATION


Private Nearest Neighbors Classification in Federated Databases.


title: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

abstract: Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.


title: What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text

abstract: Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the changing world states. To supply this knowledge, we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the preconditions and effects of actions, and use it along with commonsense knowledge of persistence to answer questions about change. Our evaluation shows that our system, ProComp, significantly outperforms two strong reading comprehension (RC) baselines. Our contributions are two-fold: the Semantic Lexicon rulebase itself, and a demonstration of how a simulation-based approach to machine reading can outperform RC methods that rely on surface cues alone. Since this work was performed, we have developed neural systems that outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the Semantic Lexicon remains a novel and potentially useful resource, and its integration with neural systems remains a currently unexplored opportunity for further improvements in machine reading about processes.


title: An in vivo model of functional and vascularized human brain organoids

abstract: Differentiation of human pluripotent stem cells to small brain-like structures known as brain organoids offers an unprecedented opportunity to model human brain development and disease. To provide a vascularized and functional in vivo model of brain organoids, we established a method for transplanting human brain organoids into the adult mouse brain. Organoid grafts showed progressive neuronal differentiation and maturation, gliogenesis, integration of microglia, and growth of axons to multiple regions of the host brain. In vivo two-photon imaging demonstrated functional neuronal networks and blood vessels in the grafts. Finally, in vivo extracellular recording combined with optogenetics revealed intragraft neuronal activity and suggested graft-to-host functional synaptic connectivity. This combination of human neural organoids and an in vivo physiological environment in the animal brain may facilitate disease modeling under physiological conditions.


title: BodyNet: Volumetric Inference of 3D Human Body Shapes

abstract: Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. BodyNet is an end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them results in performance improvement as demonstrated by our experiments. To evaluate the method, we fit the SMPL model to our network output and show state-of-the-art results on the SURREAL and Unite the People datasets, outperforming recent approaches. Besides achieving state-of-the-art performance, our method also enables volumetric body-part segmentation.


title: An Unsupervised Approach to Detect Spam Campaigns that Use Botnets on Twitter

abstract: In recent years, Twitter has seen a proliferation of automated accounts or bots that send spam, offer clickbait, compromise security using malware, and attempt to skew public opinion. Previous research estimates that around 9% to 17% of Twitter accounts are bots contributing to between 16% to 56% of tweets on the medium. This paper introduces an unsupervised approach to detect Twitter spam campaigns in real-time. The bot groups we detect tweet duplicate content with shortened embedded URLs over extended periods of time. Our experiments with the detection protocol reveal that bots consistently account for 10% to 50% of tweets generated from 7 popular URL shortening services on Twitter. More importantly, we discover that bots using shortened URLs are connected to large scale spam campaigns that control thousands of domains. There appear to be two distinct mechanisms used to control bot groups and we investigate both in this paper. Our detection system runs 24/7 and actively collects bots involved in spam campaigns and adds them to an evolving database of malicious bots. We make our database of detected bots available for query through a REST API so others can filter tweets from malicious bots to get high quality Twitter datasets for analysis.


title: Short-term Effects of High-Intensity Laser Therapy Versus Ultrasound Therapy in the Treatment of People With Subacromial Impingement Syndrome: A Randomized Clinical Trial

abstract: Background. Subacromial impingement syndrome (SAIS) is a painful condition resulting from the entrapment of anatomical structures between the anteroinferior corner of the acromion and the greater tuberosity of the humerus. Objective. The aim of this study was to evaluate the short-term effectiveness of high-intensity laser therapy (HILT) versus ultrasound (US) therapy in the treatment of SAIS. Design. The study was designed as a randomized clinical trial. Setting. The study was conducted in a university hospital. Patients. Seventy patients with SAIS were randomly assigned to a HILT group or a US therapy group. Intervention. Study participants received 10 treatment sessions of HILT or US therapy over a period of 2 consecutive weeks. Measurements. Outcome measures were the Constant-Murley Scale (CMS), a visual analog scale (VAS), and the Simple Shoulder Test (SST). Results. For the 70 study participants (42 women and 28 men; mean [SD] age54.1 years [9.0]; mean [SD] VAS score at baseline6.4 [1.7]), there were no between-group differences at baseline in VAS, CMS, and SST scores. At the end of the 2-week intervention, participants in the HILT group showed a significantly greater decrease in pain than participants in the US therapy group. Statistically significant differences in change in pain, articular movement, functionality, and muscle strength (force-generating capacity) (VAS, CMS, and SST scores) were observed after 10 treatment sessions from the baseline for participants in the HILT group compared with participants in the US therapy group. In particular, only the difference in change of VAS score between groups (1.65 points) surpassed the accepted minimal clinically important difference for this tool. Limitations. This study was limited by sample size, lack of a control or placebo group, and follow-up period. Conclusions. Participants diagnosed with SAIS showed greater reduction in pain and improvement in articular movement functionality and muscle strength of the affected shoulder after 10 treatment sessions of HILT than did participants receiving US therapy over a period of 2 consecutive weeks.


title: Instrument translation process: a methods review.

abstract: BACKGROUND ::: Cross-cultural and international collaborative studies are needed in nursing research. Therefore, it is necessary to translate research instruments into the language of the culture being studied. In this methods review, different processes of instrument translation and evaluation of translation adequacy in published nursing research are described and classified into a hierarchy. ::: ::: ::: METHODS ::: Studies including translation of quantitative research instruments were reviewed. Forty-seven studies were included. These were classified into six categories. ::: ::: ::: RESULTS ::: Studies were classified into categories as follows: forward-only translation (2), forward-only translation with testing (7), back-translation (13), back-translation with monolingual test (18), back-translation with bilingual test (3), and back-translation with both monolingual and bilingual test (4). Strengths and weaknesses are analysed. ::: ::: ::: CONCLUSION ::: The studies reviewed used diverse methods of varying quality. There is need for consensus among researchers in how to achieve quality of instrument translation in cross-cultural research. Researchers should carefully attend to achieving and reporting evidence of the accuracy and validity of instrument translation. When back-translation fails to achieve semantic equivalence, the instrument development process should be replicated in the target language.


title: Large-scale brain networks in cognition: emerging methods and principles

abstract: An understanding of how the human brain produces cognition ultimately depends on knowledge of large-scale brain organization. Although it has long been assumed that cognitive functions are attributable to the isolated operations of single brain areas, we demonstrate that the weight of evidence has now shifted in support of the view that cognition results from the dynamic interactions of distributed brain areas operating in large-scale networks. We review current research on structural and functional brain organization, and argue that the emerging science of large-scale brain networks provides a coherent framework for understanding of cognition. Critically, this framework allows a principled exploration of how cognitive functions emerge from, and are constrained by, core structural and functional networks of the brain.


Character Sequence-to-Sequence Model with Global Attention for Universal Morphological Reinflection


title: Automatic drift calibration for EOG-based gaze input interface

abstract: A drift calibration technique for DC-coupled EOG (electrooculogram) systems is proposed. It assumes a non-linear relationship between EOG and eye angle and estimates the absolute eye angle by the EOG differences during saccade. Drift is calibrated every saccade without user's explicit action, so it is especially suitable for long-term gaze input interfaces. An experiment confirms that it can estimate horizontal absolute eye angle with an error of about 5° in addition to accurate eye movement.


title: Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers

abstract: In this paper, we present a system that associates faces with voices in a video by fusing information from the audio and visual signals. The thesis underlying our work is that an extremely simple approach to generating (weak) speech clusters can be combined with visual signals to effectively associate faces and voices by aggregating statistics across a video. This approach does not need any training data specific to this task and leverages the natural coherence of information in the audio and visual streams. It is particularly applicable to tracking speakers in videos on the web where a priori information about the environment (e.g., number of speakers, spatial signals for beamforming) is not available. We performed experiments on a real-world dataset using this analysis framework to determine the speaker in a video. Given a ground truth labeling determined by human rater consensus, our approach had ~71% accuracy.


title: Robust De-anonymization of Large Sparse Datasets

abstract: We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.


title: Semantic Resource Management of Federated IoT Testbeds

abstract: Testbeds and experimental network facilities accelerate the expansion of disruptive Internet services and support their evolution. The integration of IoT technologies in the context of Unmanned Vehicles (UxVs) and their deployment in federated, real–world testbeds introduce various challenging research issues. This paper presents the Semantic Aggregate Manager (SAM) that exploits semantic technologies for modeling and managing resources of federated IoT Testbeds. SAM introduces new semantics–based features tailored to the needs of IoT enabled UxVs, but on the same time allows the compatibility with existing legacy, “de facto” standardised protocols, currently utilized by multiple federated testbed management systems. The proposed framework is currently being deployed in order to be evaluated in real–world testbeds across several sites in Europe.


title: ART-UP: A Novel Method for Generating Scanning-robust Aesthetic QR codes

abstract: QR codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this paper, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.


title: MOURNING AND MORAL PSYCHOLOGY

abstract: This article investigates what it would be to complete the project of ancient Greek ethics and provide a robust moral psychology. It considers Hans Loewald’s interpretation of the psychoanalytic conception of mourning; and argues that this provides a paradigm of what an adequate moral psychology might look like.