title: Frequency or Probability? A Qualitative Study of Risk Communication Formats Used in Health Care

abstract: Background. The communication of probabilistic outcomes is an essential aspect of shared medical decision making.Methods. The authors conducted a qualitative study using focus groups to evaluate the response of women to various formats used in the communication of breast cancer risk.Findings. Graphic discrete frequency formats using highlighted human figures had greater salience than continuous probability formats using bar graphs. Potential biases in the estimation of risk magnitude were associated with the use of highlighted human figures versus bar graphs and the denominator size in graphics using highlighted human figures. The presentation of uncertainty associated with risk estimates caused some to loose trust in the information, whereas others were accepting of uncertainty in scientific data.Conclusion. The qualitative study identified new constructs with regard to how patients process probabilistic information. Further research in the clinical setting is needed to provide a theoretical justificatio...


title: Self-adaptive particle swarm optimization: a review and analysis of convergence

abstract: Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not time-dependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge.


title: A critical discourse analysis of women’s portrayal in news reporting of sexual violence

abstract: This study explores and compares the portrayal of women in the news reporting of crimes of sexual violence against women between two newspapers from different cultures, the Jakarta Post and the Guardian. The Jakarta Post is an English quality newspaper published in Indonesia, and the Guardian is a quality broadsheet from Great Britain. To explore the representation of women, this study accounts the portrayal of men as well since the two entities are strongly inter-related. The analytical tool used in this study is naming analysis of social actors, which is a part of critical discourse analysis. This analysis is aimed at probing the representation through the choice of lexical items in representing the main news actors. The findings of the analysis indicate that the choices of the naming categories used by both newspapers are different. The Jakarta Post mostly functionalises both the victims and the perpetrators in terms of their legal status in the criminal cases. This suggests that the broadsheet tends to view them as part of the legal processes instead of as people. The Guardian typically classifies the victims in terms of their age and gender and refers to the perpetrators with their surnames instead of as parts of the criminal cases. The Guardian’s tendency to represent both perpetrators and victims as people instead of parts of legal processes indicates that the paper is attempting to focus the reports more on the crimes themselves rather than the participants involved in the cases.


title: Regulation of the vitamin B12 metabolism and transport in bacteria by a conserved RNA structural element.

abstract: Cobalamin in the form of adenosylcobalamin (Ado-CBL) is known to repress expression of genes for vitamin B(12) biosynthesis and be transported by a posttranscriptional regulatory mechanism, which involves direct binding of Ado-CBL to 5'untranslated gene regions (5'UTR). Using comparative analysis of genes and regulatory regions, we identified a highly conserved RNA structure, the B12-element, which is widely distributed in 5'UTRs of vitamin B(12)-related genes in eubacteria. Multiple alignment of approximately 200 B12-elements from 66 bacterial genomes reveals their common secondary structure and several extended regions of sequence conservation, including the previously known B12-box motif. In analogy to the model of regulation of the riboflavin and thiamin biosynthesis, we suggest Ado-CBL-mediated regulation based on formation of alternative RNA structures including the B12-element. In Gram-negative proteobacteria, as well as in cyanobacteria, actinobacteria, and the CFB group, the cobalamin biosynthesis and vitamin B(12) transport genes are predicted to be regulated by inhibition of translation initiation, whereas in the Bacillus/Clostridium group of Gram-positive bacteria, these genes seem to be regulated by transcriptional antitermination. Phylogenetic analysis of the B12-elements reveals a large number of likely duplications of B12-elements in several bacterial genomes. These lineage-specific duplications of RNA regulatory elements seem to be a major evolutionary mechanism for expansion of the vitamin B(12) regulon.


Speech Recognition Using Deep Learning Algorithms


title: Development of PCR-based CNP marker of rice Waxy gene with confronting two-pair primers

abstract: Rice amylose content (AC) is a key determinant for grain end-use quality attributes. The base substitution ((G → T) at +1 loci of the first intron (In1) of Waxy (Wx), a major gene controlling AC in rice, results in decreased AC. A new SNP typing method of Wx In1 based on polymerase chain reaction with confronting two-pair primers (PCR-CTPP) was reported here: first its practicability was confirmed by 23 varieties with known SNP and AC; and then the segregation ratio at target SNP loci were checked and it fitted well for 1: 2: 1 single gene segregation; at last SNP typing and AC assay with 150 mini core collections (MCC) in China showed that average AC of 53 G type varieties (22.5%) was significantly higher than that of 97 T type varieties (13.7%) (p < 0.01) and the target SNP loci explained 77.8% AC variation. So this method could be used to estimate AC of rice variety roughly or in marker-assisted breeding, that is, using variety with known and desired AC as Wx allele donor parent and aided with crossbreed, backcross and marker-assisted selection (MAS) reported here, rice breeders could improve AC of varieties with comprehensively excellent performance to meet special end-products.


title: The Stock Sonar — Sentiment Analysis of Stocks Based on a Hybrid Approach

abstract: The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier.


title: Compression Molding Encapsulants for Wafer-Level Embedded Active Devices: Wafer Warpage Control by Epoxy Molding Compounds

abstract: Fan-out wafer level packaging technology (FOWLP) is one of the advanced packages in semiconductor industry. Recently, its interest has been raised due to the benefits such as thin package, board fan-out capability, high I/O, substrate-less process, integration of passives into structure, good thermal resistance, and electrical performance. Despite these many advantages, FOWLP is reluctant to be applied packaging industries due to the difficulty of controlling wafer warpage. Its broad molding area causes warpage control of FOWLP to be more difficult than other packages. The relationship between molding area and warpage have trade-offs, which have to be optimized beforehand in order to ensure successful subsequent processing. In order to control both factors, it is necessary to study the properties of Young’s modulus, coefficient of thermal expansion (CTE), glass transition temperature (Tg), and flow-ability for wafer level mold compounds of interest. In this paper, our group introduces an evaluation tool based on all above parameters and explains how to optimize warpage of FOWLP based on this tool.


title: Corrugated waveguide slow-wave structure for THz travelling wave tube

abstract: THz applications require sources and amplifiers compact, lightweight and powerful. Vacuum electron devices are the candidate solution. Among others, the Corrugated Waveguide Slow-Wave Structure seems particularly suitable for Traveling Wave Tubes in the THz region. THz vacuum electron devices require high precision technological processes with high aspect ratio such as SU-8 process. However, fabrication tolerances could highly affect the overall performances. Therefore a statistical analysis is fundamental for a reliable design. In this summary it is proposed a method based on an analytical model of the corrugated waveguide together with the Pierce theory, to fastly compute the gain of corrugated waveguide vacuum traveling wave tubes. The method is validated by three-dimensional electromagnetic softwares, both for cold and hot parameters. The proved accuracy and fast computation time make the model suitable for performing the sensitivity analysis of the Corrugated waveguide Vacuum tube to be realized by SU-8 technology process.


title: Software Validation using Power Profiles

abstract: The validation of modern software systems incorporates both functional and quality requirements. This paper proposes a validation approach for software quality requirement - its power consumption. This approach validates whether the software produces the desired results with a minimum expenditure of energy. We present energy requirements and an approach for their validation using a power consumption model, test-case specification, software traces, and power measurements. Three different approaches for power data gathering are described. The power consumption of mobile phone applications is obtained and matched against the power consumption model.


title: Managerial information overload

abstract: Overwhelmed by the organizational imperative to collect every kind of information available, and finding technical solutions generally miss the point, knowledge workers need to improve their personal capacity for inquiry.


title: A widely tunable active RF filter topology

abstract: An active inductor based bandpass radio-frequency (RF) filter circuit topology is presented that achieves a wide tuning range. Based on the proposed structure, a prototype 2/sup th/-order filter is designed, simulated, and laid out in a 0.18/spl mu/m CMOS process. The center frequency of the filter can be tuned from 880MHz to 3.72GHz. The quality factor of the filter can also be tuned. The filter, including the input and output buffers, consumes between 12 to 26mW from a 1.8V supply and occupies a chip area of 115/spl mu/m /spl times/ 70/spl mu/m.


Understanding Models in Earth and Space Science


title: The role of MRI and ultrasound imaging in Morton’s neuroma and the effect of size of lesion on symptoms

abstract: We investigated 29 cases, diagnosed clinically as having Morton’s neuroma, who had undergone MRI and ultrasound before a neurectomy. The accuracy with which pre-operative clinical assessment, ultrasound and MRI had correctly diagnosed the presence of a neuroma were compared with one another based on the histology and the clinical outcome. Clinical assessment was the most sensitive and specific modality. The accuracy of the ultrasound and MRI was similar and dependent on size. Ultrasound was especially inaccurate for small lesions. There was no correlation between the size of the lesion and either the pre-operative pain score or the change in pain score following surgery. Reliance on single modality imaging would have led to inaccurate diagnosis in 18 cases and would have only benefited one patient. Even imaging with both modalities failed to meet the predictive values attained by clinical assessment. There is no requirement for ultrasound or MRI in patients who are thought to have a Morton’s neuroma. Small lesions,


title: A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics

abstract: Due to green legislations, industries track the used products through reverse logistics contractors. A reverse logistics programme offers significant cost savings in procurement, transportation, disposal and inventory carrying. Since reverse logistics operations and the supply chains they support are considerably more complex than traditional manufacturing supply chains, it can be offered to third party contractors. But availability of more number of contractors make evaluating and selecting the most efficient Reverse Logistics Contractor (RLC) a challenging task and treated as a multi-criteria decision making problem. In this paper, a hybrid method using Analytical Hierarchy Process (AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is proposed. AHP is used to obtain the initial weights and Fuzzy TOPSIS is used to get the final ranking. A case study demonstrates the application of the proposed method. Finally sensitivity analysis is carried out to confirm the robustness.


title: An MCMC-based Particle Filter for Tracking Multiple Interacting Targets

abstract: We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.


title: Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data

abstract: Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC.


title: A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes

abstract: Recent multi-voxel pattern classification (MVPC) studies have shown that in early visual cortex patterns of brain activity generated during mental imagery are similar to patterns of activity generated during perception. This finding implies that low-level visual features (e.g., space, spatial frequency, and orientation) are encoded during mental imagery. However, the specific hypothesis that low-level visual features are encoded during mental imagery is difficult to directly test using MVPC. The difficulty is especially acute when considering the representation of complex, multi-object scenes that can evoke multiple sources of variation that are distinct from low-level visual features. Therefore, we used a voxel-wise modeling and decoding approach to directly test the hypothesis that low-level visual features are encoded in activity generated during mental imagery of complex scenes. Using fMRI measurements of cortical activity evoked by viewing photographs, we constructed voxel-wise encoding models of tuning to low-level visual features. We also measured activity as subjects imagined previously memorized works of art. We then used the encoding models to determine if putative low-level visual features encoded in this activity could pick out the imagined artwork from among thousands of other randomly selected images. We show that mental images can be accurately identified in this way; moreover, mental image identification accuracy depends upon the degree of tuning to low-level visual features in the voxels selected for decoding. These results directly confirm the hypothesis that low-level visual features are encoded during mental imagery of complex scenes. Our work also points to novel forms of brain-machine interaction: we provide a proof-of-concept demonstration of an internet image search guided by mental imagery.


title: Hippocampal volume reduction in major depression.

abstract: OBJECTIVE: Elevated levels of glucocorticoids in depression have been hypothesized to be associated with damage to the hippocampus, a brain area involved in learning and memory. The purpose of this study was to measure hippocampal volume in patients with depression. METHOD: Magnetic resonance imaging was used to measure the volume of the hippocampus in 16 patients with major depression in remission and 16 case-matched nondepressed comparison subjects. RESULTS: Patients with depression had a statistically significant 19% smaller left hippocampal volume than comparison subjects, without smaller volumes of comparison regions (amygdala, caudate, frontal lobe, and temporal lobe) or whole brain volume. The findings were significant after brain size, alcohol exposure, age, and education were controlled for. CONCLUSIONS: These findings are consistent with smaller left hippocampal volume in depression.


title: An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data

abstract: Display Omitted We augmented an autism ontology with SWRL rules to infer phenotypes from ADI-R items.We represented DSM diagnostic criteria for autism spectrum disorder in OWL.We developed a custom Protege plugin for enumerating combinatorial OWL axioms.OWL reasoner thus infers autism-related phenotypes from ADI-R questionnaire results.We evaluated the classification results with data from Simons Foundation. ObjectiveOur goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data. Materials and methodsKnowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protege plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data. ResultsWe extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94. DiscussionThe ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy. ConclusionThe ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology.


title: Optimal Space-time Tradeoffs for Inverted Indexes

abstract: Inverted indexes are usually represented by dividing posting lists into constant-sized blocks and representing them with an encoder for sequences of integers. Different encoders yield a different point in the space-time trade-off curve, with the fastest being several times larger than the most space-efficient. An important design decision for an index is thus the choice of the fastest encoding method such that the index fits in the available memory. However, a better usage of the space budget could be obtained by using faster encoders for frequently accessed blocks, and more space-efficient ones those that are rarely accessed. To perform this choice optimally, we introduce a linear time algorithm that, given a query distribution and a set of encoders, selects the best encoder for each index block to obtain the lowest expected query processing time respecting a given space constraint. To demonstrate the effectiveness of this approach we perform an extensive experimental analysis, which shows that our algorithm produces indexes which are significantly faster than single-encoder indexes under several query processing strategies, while respecting the same space constraints.


title: Unsupervised detection of non-iris occlusions

abstract: Accurate iris defect detection.Unconstrained mobile devices high resolution color iris images.Multispectral Markovian spatial texture model.Ranked first from the 97+1 recent Noisy Iris Challenge Evaluation contest alternative methods.Promising performance on the very challenging, high resolution, and highly variable Mobile Iris Challenge Evaluation data. Display Omitted This paper presents a fast precise unsupervised iris defects detection method based on the underlying multispectral spatial probabilistic iris textural model and adaptive thresholding applied to demanding high resolution mobile device measurements. The accurate detection of iris eyelids and reflections is the prerequisite for the accurate iris recognition, both in near-infrared or visible spectrum measurements. The model adaptively learns its parameters on the iris texture part and subsequently checks for iris reflections using the recursive prediction analysis. The method is developed for color eye images from unconstrained mobile devices but it was also successfully tested on the UBIRIS v2 eye database. Our method ranked first from the 97+1 recent Noisy Iris Challenge Evaluation contest alternative methods on this large color iris database using the exact contest data and methodology.


title: Discovering Overlapping Groups in Social Media

abstract: The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, book marking in Delicious, twittering in Twitter, etc. are reshaping people’s social life and redefining their social roles. People with shared interests tend to form their groups in social media, and users within the same community likely exhibit similar social behavior (e.g., going for the same movies, having similar political viewpoints), which in turn reinforces the community structure. The multiple interactions in social activities entail that the community structures are often overlapping, i.e., one person is involved in several communities. We propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. In our method, users are connected via tags and tags are connected to users. This explicit representation of users and tags is useful for understanding group evolution by looking at who is interested in what. The efficacy of our method is supported by empirical evaluation in both synthetic and online social networking data.


title: Information Theoretic Subspace Clustering

abstract: This paper addresses the problem of grouping the data points sampled from a union of multiple subspaces in the presence of outliers. Information theoretic objective functions are proposed to combine structured low-rank representations (LRRs) to capture the global structure of data and information theoretic measures to handle outliers. In theoretical part, we point out that group sparsity-induced measures ( $\ell _{2,1}$ -norm, $\ell _{\alpha }$ -norm, and correntropy) can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates both convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify HQ-based group sparsity methods into a common framework. In algorithmic part, we develop information theoretic subspace clustering methods via correntropy. With the help of Parzen window estimation, correntropy is used to handle either outliers under any distributions or sample-specific errors in data. Pairwise link constraints are further treated as a prior structure of LRRs. Based on the HQ framework, iterative algorithms are developed to solve the nonconvex information theoretic loss functions. Experimental results on three benchmark databases show that our methods can further improve the robustness of LRR subspace clustering and outperform other state-of-the-art subspace clustering methods.


Empathy in Psychoanalysis and Medical Education - what can we learn from each other?


The causes of corruption: A cross-national study


title: TIGA-CUB – manualised psychoanalytic child psychotherapy versus treatment as usual for children aged 5–11 years with treatment-resistant conduct disorders and their primary carers: study protocol for a randomised controlled feasibility trial

abstract: The National Institute for Health and Care Excellence (NICE) recommends evidence-based parenting programmes as a first-line intervention for conduct disorders (CD) in children aged 5–11 years. As these are not effective in 25–33% of cases, NICE has requested research into second-line interventions. Child and Adolescent Psychotherapists (CAPTs) address highly complex problems where first-line treatments have failed and there have been small-scale studies of Psychoanalytic Child Psychotherapy (PCP) for CD. A feasibility trial is needed to determine whether a confirmatory trial of manualised PCP (mPCP) versus Treatment as Usual (TaU) for CD is practicable or needs refinement. The aim of this paper is to publish the abridged protocol of this feasibility trial. TIGA-CUB (Trial on improving Inter-Generational Attachment for Children Undergoing Behaviour problems) is a two-arm, pragmatic, parallel-group, multicentre, individually randomised (1:1) controlled feasibility trial (target n = 60) with blinded outcome assessment (at 4 and 8 months), which aims to develop an optimum practicable protocol for a confirmatory, pragmatic, randomised controlled trial (RCT) (primary outcome: child’s behaviour; secondary outcomes: parental reflective functioning and mental health, child and parent quality of life), comparing mPCP and TaU as second-line treatments for children aged 5–11 years with treatment-resistant CD and inter-generational attachment difficulties, and for their primary carers. Child-primary carer dyads will be recruited following a referral to, or re-referral within, National Health Service (NHS) Child and Adolescent Mental Health Services (CAMHS) after an unsuccessful first-line parenting intervention. PCP will be delivered by qualified CAPTs working in routine NHS clinical practice, using a trial-specific PCP manual (a brief version of established PCP clinical practice). Outcomes are: (1) feasibility of recruitment methods, (2) uptake and follow-up rates, (3) therapeutic delivery, treatment retention and attendance, intervention adherence rates, (4) follow-up data collection, and (5) statistical, health economics, process evaluation, and qualitative outcomes. TIGA-CUB will provide important information on the feasibility and potential challenges of undertaking a confirmatory RCT to evaluate the effectiveness and cost-effectiveness of mPCP. Current Controlled Trials, ID: ISRCTN86725795 ::: . Registered on 31 May 2016.


title: Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

abstract: The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model. We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories. We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.


title: Influence of dynamic versus static core exercises on performance in field based fitness tests

abstract: Summary Minimal evidence supports the claim that core stability training transfers into improved performance and the most effective training method to perform core exercises is still unknown. The purpose of the study was to compare the effects of a 6 week unstable static versus unstable dynamic core training program, on field based fitness tests. A static ( n = 6) and dynamic ( n = 6) training group performed two 45 min sessions per week for six weeks. Seven performance tests, consisting of three core (plank; double leg lowering; back extensions), one static (standing stork) and three dynamic (overhead medicine ball throw; vertical jump; 20 m sprint), were administered pre- and post training. Between group differences were assessed using a repeated measures MANOVA ( P P P > 0.05). Findings indicate that both types of training improved specific measures of core stability but did not transfer into any sport-related skill.


title: Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives

abstract: In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.


title: Interactive Visualization of Small World Graphs

abstract: Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example


title: Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)

abstract: What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document? We present a preliminary study that makes a first step towards answering this question. Given a query, we train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all.


title: A study on virtual machine deployment for application outsourcing in mobile cloud computing

abstract: In mobile cloud computing, application offloading is implemented as a software level solution for augmenting computing potentials of smart mobile devices. VM is one of the prominent approaches for offloading computational load to cloud server nodes. A challenging aspect of such frameworks is the additional computing resources utilization in the deployment and management of VM on Smartphone. The deployment of Virtual Machine (VM) requires computing resources for VM creation and configuration. The management of VM includes computing resources utilization in the monitoring of VM in entire lifecycle and physical resources management for VM on Smartphone. The objective of this work is to ensure that VM deployment and management requires additional computing resources on mobile device for application offloading. This paper analyzes the impact of VM deployment and management on the execution time of application in different experiments. We investigate VM deployment and management for application processing in simulation environment by using CloudSim, which is a simulation toolkit that provides an extensible simulation framework to model the simulation of VM deployment and management for application processing in cloud-computing infrastructure. VM deployment and management in application processing is evaluated by analyzing VM deployment, the execution time of applications and total execution time of the simulation. The analysis concludes that VM deployment and management require additional resources on the computing host. Therefore, VM deployment is a heavyweight approach for process offloading on smart mobile devices.


title: Ultra High Voltage Transmission in China: Developments, Current Status and Future Prospects

abstract: The developments and current status of ultra high voltage (UHV) alternating current (AC) and direct current (DC) transmission in China were reviewed in this paper. The UHV transmission historical developments in the past twenty years; the demand of development UHV transmission; the research results of the UHV key technologies including electromagnetic environment, over-voltage and insulation coordination, light- ning performance, live-line working and equipment manufac- ture recent years in China; the design parameters and construction of UHV AC, UHV DC test and demonstration trans- mission lines, UHV AC, DC test bases and state grid simulation center were introduced. The suggestions at the aspects of UHV transmission future design, construction, commercial opera- tion and maintenance were simply presented and discussed. The review and discussion are important for the safe and reliable operation of UHV grid in China and can be a reference for other countries that want to develop UHV AC and DC transmission.


Chapter 11. Organizational Culture 1


Magic Quadrant for Business Intelligence Platforms


Kinect Sensor based Human Fall Detection System Using Skeleton Detection Algorithm


title: XYZ Indoor Navigation through Augmented Reality: A Research in Progress

abstract: We present an overall framework of services for indoor navigation, which includes Indoor Mapping, Indoor Positioning, Path Planning, and En-route Assistance. Within such framework we focus on an augmented reality (AR) solution for en-route assistance. AR assists the user walking in a multi-floor building by displaying a directional arrow under a camera view, thus freeing the user from knowing his/her position. Our AR solution relies on geomagnetic positioning and north-oriented space coordinates transformation. Therefore, it can work without infrastructure and without relying on GPS. The AR visual interface and the integration with magnetic positioning is the main novelty of our solution, which has been validated by experiments and shows a good performance.


title: A 0.1°/HR bias drift electronically matched tuning fork microgyroscope

abstract: This paper presents an architecture that utilizes the often ignored residual quadrature error in a gyroscope to achieve and maintain perfect mode-matching, i.e., ~ 0 Hz split between the drive and sense mode frequencies, as well as electronically control the sensor bandwidth. A 6 mW, 3 V CMOS ASIC and control algorithm have been interfaced with a 60 mum thick mode-matched tuning fork gyroscope (M2-TFG) to implement an angular rate sensor with bias drift as low as 0.15deg/radichr and angle random walk (ARW) of 0.003deg/radichr - the lowest recorded to date for a silicon MEMS gyro. The maximum scale factor of the gyroscope is 88 m V/deg/s and the microsystem bandwidth can be configured between 1 to 10 Hz.


title: BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency

abstract: In this paper we present gradient boosted decision tables (BDTs). A d-dimensional decision table is essentially a mapping from a sequence of d boolean tests to a real value in {R}. We propose novel algorithms to fit decision tables. Our thorough empirical study suggests that decision tables are better weak learners in the gradient boosting framework and can improve the accuracy of the boosted ensemble. In addition, we develop an efficient data structure to represent decision tables and propose a novel fast algorithm to improve the scoring efficiency for boosted ensemble of decision tables. Experiments on public classification and regression datasets demonstrate that our method is able to achieve 1.5x to 6x speedups over the boosted regression trees baseline. We complement our experimental evaluation with a bias-variance analysis that explains how different weak models influence the predictive power of the boosted ensemble. Our experiments suggest gradient boosting with randomly backfitted decision tables distinguishes itself as the most accurate method on a number of classification and regression problems. We have deployed a BDT model to LinkedIn news feed system and achieved significant lift on key metrics.


title: Prejudice and schizophrenia: a review of the ‘mental illness is an illness like any other’ approach

abstract: Objective: Many anti-stigma programmes use the ‘mental illness is an illness like any other’ approach. This review evaluates the effectiveness of this approach in relation to schizophrenia. ::: ::: Method: The academic literature was searched, via PsycINFO and MEDLINE, to identify peer-reviewed studies addressing whether public espousal of a biogenetic paradigm has increased over time, and whether biogenetic causal beliefs and diagnostic labelling are associated with less negative attitudes. ::: ::: Results: The public, internationally, continues to prefer psychosocial to biogenetic explanations and treatments for schizophrenia. Biogenetic causal theories and diagnostic labelling as ‘illness’, are both positively related to perceptions of dangerousness and unpredictability, and to fear and desire for social distance. ::: ::: Conclusion: An evidence-based approach to reducing discrimination would seek a range of alternatives to the ‘mental illness is an illness like any other’ approach, based on enhanced understanding, from multi-disciplinary research, of the causes of prejudice.


title: Inclusion of Socio-economic Indicators in the Cost Modeling of a WAN Network

abstract: The most important stages in designing a computer network in a wider geographical area include: definition of requirements, topological description, identification and calculation of relevant parameters (i.e. traffic matrix), determining the shortest paths between nodes, quantification of the effect of various levels of technical and technological development of urban areas involved, the cost of technology, and the cost of services. These parameters differ for WAN networks in different regions -- their calculation depends directly on the data "on the ground": number of inhabitants, distance between populated areas, density of network traffic, as well as available bandwidth. However, behavioral interactions of individuals and groups play a significant role and have to be taken into consideration by employing either simple or composite indicators of socioeconomic status. In this paper, we propose the inclusion of a composite socioeconomic indicator in the cost-modeling of a WAN network, as a linear sum of products of weights and several measures of labor status and/or education.


title: Redemption: Real-Time Protection Against Ransomware at End-Hosts

abstract: Ransomware is a form of extortion-based attack that locks the victim’s digital resources and requests money to release them. The recent resurgence of high-profile ransomware attacks, particularly in critical sectors such as the health care industry, has highlighted the pressing need for effective defenses. While users are always advised to have a reliable backup strategy, the growing number of paying victims in recent years suggests that an endpoint defense that is able to stop and recover from ransomware’s destructive behavior is needed.


Successful Adaptive Management:The Integration of Research and Management


title: Sub-Channel Assignment, Power Allocation, and User Scheduling for Non-Orthogonal Multiple Access Networks

abstract: In this paper, we study the resource allocation and user scheduling problem for a downlink non-orthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm, which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.


title: Learning to Ask: Neural Question Generation for Reading Comprehension

abstract: We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence-to-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e., grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).


title: L2P2: Location-aware location privacy protection for location-based services

abstract: Location privacy has been a serious concern for mobile users who use location-based services provided by the third-party provider via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may not be true in real-life scenarios. In this paper, we introduce a new location privacy problem: Location-aware Location Privacy Protection (L2P2) problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through multiple simulations on a large data set of trajectories for one thousand mobile users, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.


title: Automatic gain control of ultra-low leakage synaptic scaling homeostatic plasticity circuits

abstract: Homeostatic plasticity is a stabilizing mechanism that allows neural systems to maintain their activity around a functional operating point. This is an extremely useful mechanism for neuromorphic computing systems, as it can be used to compensate for chronic shifts, for example due to changes in the network structure. However, it is important that this plasticity mechanism operates on time scales that are much longer than conventional synaptic plasticity ones, in order to not interfere with the learning process. In this paper we present a novel ultra-low leakage cell and an automatic gain control scheme that can adapt the gain of analog log-domain synapse circuits over extremely long time scales. To validate the proposed scheme, we implemented the ultra-low leakage cell in a standard 180 nm Complementary Metal-Oxide-Semiconductor (CMOS) process, and integrated it in an array of dynamic synapses connected to an adaptive integrate and fire neuron. We describe the circuit and demonstrate how it can be configured to scale the gain of all synapses afferent to the silicon neuron in a way to keep the neuron's average firing rate constant around a set operating point. The circuit occupies a silicon area of 84 μm× 22 μm and consumes approximately 10.8 nW with a 1.8 V supply voltage. It exhibits time constants of up to 25 kilo-seconds, thanks to a controllable leakage current that can be scaled down to 1.2 atto-Amps (7.5 electrons/s).


title: Classification of Images Using Support Vector Machines

abstract: Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusions that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.


title: Comparing word, character, and phoneme n-grams for subjective utterance recognition

abstract: In this paper, we compare the performance of classifiers trained using word n-grams, character n-grams, and phoneme n-grams for recognizing subjective utterances in multiparty conversation. We show that there is value in using very shallow linguistic representations, such as character n-grams, for recognizing subjective utterances, in particular, gains in the recall of subjective utterances. Copyright © 2008 ISCA.


ISPRS Commission V Symposium 'Image Engineering and Vision Metrology' CLASSIFICATION AND SEGMENTATION OF TERRESTRIAL LASER SCANNER POINT CLOUDS USING LOCAL VARIANCE INFORMATION


title: Solving Geometry Problems: Combining Text and Diagram Interpretation

abstract: This paper introduces GEOS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding geometry questions as submodular optimization, and identify a formal problem description likely to be compatible with both the question text and diagram. GEOS then feeds the description to a geometric solver that attempts to determine the correct answer. In our experiments, GEOS achieves a 49% score on official SAT questions, and a score of 61% on practice questions. 1 Finally, we show that by integrating textual and visual information, GEOS boosts the accuracy of dependency and semantic parsing of the question text.


title: A Group Recommender System for Tourist Activities

abstract: This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK) , a domain-independent taxonomy-driven search engine that manages the group recommendation.


title: Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

abstract: Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.


Motion sickness in automated vehicles


title: A Hybrid Approach for Web Document Clustering Using K-means and Artificial Bee Colony Algorithm

abstract: Nowadays data growth is directly proportional to time and it is a major challenge to store the data in an organised fashion. Document clustering is the solution for organising relevant documents together. In this paper, a web clustering algorithm namely WDC-KABC is proposed to cluster the web documents effectively. The proposed algorithm uses the features of both K-means and Artificial Bee Colony (ABC) clustering algorithm. In this paper, ABC algorithm is employed as the global search optimizer and K-means is used for refining the solutions. Thus, the quality of the cluster is improved. The performance of WDC-KABC is analysed with four different datasets (webkb, wap, rec0 and 7sectors). The proposed algorithm is compared with existing algorithms such as K-means, Particle Swarm Optimization, Hybrid of Particle Swarm Optimization and K-means and Ant Colony Optimization. The experimental results of WDC-KABC are satisfactory, in terms of precision, recall, f-measure, accuracy and error rate.


title: Management of trust in the e-marketplace: the role of the buyer's experience in building trust

abstract: The e-marketplace, or broker-managed online market, is one of today's most profitable e-business models. In these marketplaces, buyers routinely engage with businesses and individual sellers with whom they have had little or no prior interaction, making trust one of the most important issues in the e-marketplaces. Therefore, a clear understanding of the trust building process in the e-marketplace is important for the success in the market. In this study, we analyze the process by which e-marketplace customers develop trust in both a market-maker and sellers. Additionally, this analysis provides a theoretical framework to identify the antecedents of trust. In the framework, we suggest that the impact of trust on transaction intention is moderated by the customer's previous experiences in the e-marketplace. The theoretical model presented here was tested on survey and transaction data collected from 692 respondents. The results indicate that customers value the importance of trust in the market-maker and sellers differentially by the level of transaction experience. It is also shown that market-maker's characteristics (reputation, website's usability and security) and seller's characteristics (expertise) play an important role in the formation and development of trust toward a market-maker and sellers, respectively.


title: Road anomaly estimation: Model based pothole detection

abstract: This paper describes a model-based pothole detection algorithm that exploits a multi-phase dynamic model. The responses of hitting potholes are empirically broken down into three phases governed by three simpler dynamic system sub-models. Each sub-model is based on a rigid-ring tire and quarter-car suspension model. The model is validated by comparing simulation results over various scenarios with FTire, a commercial simulation software for tire-road interaction. Based on the developed model, a pothole detection algorithm with Unscented Kalman Filter (UKF) and Bayesian estimation is developed and demonstrated.


title: Muscle Forces and Their Contributions to Vertical and Horizontal Acceleration of the Center of Mass During Sit-to-Stand Transfer in Young, Healthy Adults

abstract: Sit-to-stand transfer is a common task that is challenging for older adults and others with musculoskeletal impairments. Associated joint torques and muscle activations have been analyzed two-dimensionally, neglecting possible three-dimensional (3D) compensatory movements in those who struggle with sit-to-stand transfer. Furthermore, how muscles accelerate an individual up and off the chair remains unclear; such knowledge could inform rehabilitation strategies. We examined muscle forces, muscleinduced accelerations, and interlimb muscle force differences during sit-to-stand transfer in young, healthy adults. Dynamic simulations were created using a custom 3D musculoskeletal model; static optimization and induced acceleration analysis were used to determine muscle forces and their induced accelerations, respectively. The gluteus maximus generated the largest force (2009.07 ± 277.31 N) and was a main contributor to forward acceleration of the center of mass (COM) (0.62 ± 0.18 m/s2), while the quadriceps opp...


title: Time-Aware Latent Hierarchical Model for Predicting House Prices

abstract: It is widely acknowledged that the value of a house is the mixture of a large number of characteristics. House price prediction thus presents a unique set of challenges in practice. While a large body of works are dedicated to this task, their performance and applications have been limited by the shortage of long time span of transaction data, the absence of real-world settings and the insufficiency of housing features. To this end, a time-aware latent hierarchical model is introduced to capture underlying spatiotemporal interactions behind the evolution of house prices. The hierarchical perspective obviates the need for historical transaction data of exactly same houses when temporal effects are considered. The proposed framework is examined on a large-scale dataset of the property transaction in Beijing. The whole experimental procedure strictly complies with the real-world scenario. The empirical evaluation results demonstrate the outperformance of our approach over alternative competitive methods.


Spectral Clustering with Two Views


Visual overviews for discovering key papers and influences across research fronts


title: Malicious Data Attacks on the Smart Grid

abstract: Malicious attacks against power systems are investigated, in which an adversary controls a set of meters and is able to alter the measurements from those meters. Two regimes of attacks are considered. The strong attack regime is where the adversary attacks a sufficient number of meters so that the network state becomes unobservable by the control center. For attacks in this regime, the smallest set of attacked meters capable of causing network unobservability is characterized using a graph theoretic approach. By casting the problem as one of minimizing a supermodular graph functional, the problem of identifying the smallest set of vulnerable meters is shown to have polynomial complexity. For the weak attack regime where the adversary controls only a small number of meters, the problem is examined from a decision theoretic perspective for both the control center and the adversary. For the control center, a generalized likelihood ratio detector is proposed that incorporates historical data. For the adversary, the trade-off between maximizing estimation error at the control center and minimizing detection probability of the launched attack is examined. An optimal attack based on minimum energy leakage is proposed.


European Heart Rhythm Association Practical Guide on the use of non-vitamin K antagonist anticoagulants in patients with non-valvular atrial fibrillation


title: Distribution and Inhibitory Activity on Pancreatic Lipase of Hyperin in Acanthopanax senticosus

abstract: The content of hyperin in root,shoot,new branch,skin of shoot,phloem of shoot,leaf,flower and fruit was determined with HPLC and the activity of pancreatic lipase on hyperin was studied.The results showed that the content of hyperin was the highest in flower,2.4%.At the concentration of 1 mg/mL,hyperin inhibited the activity of panvreatic lipase by 24.852%.


title: Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network

abstract: If the one-step forecasting of a time series is already a challenging task, performing multi-step ahead forecasting is more difficult. Several approaches that deal with this complex problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, combination of both the recursive and direct strategies, called DirREC, the Multi-Input Multi-Output (MIMO) strategy, and the last strategy, called DirMO which aims to preserve the advantageous aspects of both the Direct and MIMO strategies. This paper aims to review existing strategies for multi-step ahead forecasting using neural networks and compare their performances empirically. To attain such an objective, we performed several experiments of these different strategies on three datasets: NN3 competition dataset, the Vietnam composite stock price index (VNINDEX) and the closing prices of the FPT stock. The most consistent findings are that the DirREC strategy is better than all the other strategies for multi-step ahead forecasting using neural network.


title: Succinic acid production by Anaerobiospirillum succiniciproducens: effects of the H2/CO2 supply and glucose concentration

abstract: Several environmental factors affecting the succinic acid production by Anaerobiospirillum succiniciproducens were studied. External CO2 supply had a negative effect on cell growth at both pH 6.2 and pH 6.5, while it had a somewhat positive effect on succinic acid production. The effect of CO2 on succinic acid yield was higher at pH 6.5 than pH 6.2. External supply of H2 enhanced the cell growth rate and succinic acid yield. The optimum ratio of H2/CO2 was 5:95% (v/v) when 20 g l−1 glucose was used. Under this condition, succinic acid yield and productivity were 91% and 1.8 g l−1 h−1, respectively. The formation of the byproduct was not much influenced by the external supply of H2. The distribution of the fermentation products was not affected by varying the initial glucose concentration in a reasonable range, but the succinic acid yield and productivity were higher at 40 g l−1 glucose. It was concluded that the external supply of CO2 and H2 at the optimum ratio makes it possible to produce succinic acid with a high yield and productivity.


title: A Mammalian Organelle Map by Protein Correlation Profiling

abstract: Protein localization to membrane-enclosed organelles is a central feature of cellular organization. Using protein correlation profiling, we have mapped 1,404 proteins to ten subcellular locations in mouse liver, and these correspond with enzymatic assays, marker protein profiles, and confocal microscopy. These localizations allowed assessment of the specificity in published organellar proteomic inventories and demonstrate multiple locations for 39% of all organellar proteins. Integration of proteomic and genomic data enabled us to identify networks of coexpressed genes, cis-regulatory motifs, and putative transcriptional regulators involved in organelle biogenesis. Our analysis ties biochemistry, cell biology, and genomics into a common framework for organelle analysis.


title: GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

abstract: We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.


title: Compact Planar Loop–Dipole Composite Antenna With Director for Bandwidth Enhancement and Back Radiation Suppression

abstract: A wideband and unidirectional loop–dipole composite antenna with a planar structure is proposed. The antenna includes a half-wavelength dipole and a one-wavelength loop that are printed on different sides of a substrate. To control the radiation characteristics of each element separately, the dipole is fed using a coaxial cable, whereas the loop is fed using offset broad-side coupling. To overcome the low front-to-back ratio (FBR) at lower and upper frequencies of the band, where the loop–dipole mode is not dominant, while maintaining a compact size, a parasitic strip is located in the vicinity of the loop. The strip acts as a director for the dipole at the low frequencies and for the loop at the high frequencies and thus significantly improves the FBR at those frequencies. The antenna achieves a wide fractional operating bandwidth of 49% at 0.7–1.15 GHz, a size reduction of more than 50% compared with recent similar designs, stable gain with a peak value of 4.8 dBi, and a minimum FBR of 11 dB across the operating bandwidth. Owing to its compact and planar structure, lightweight, simple fabrication, and stable radiation characteristic, it can be utilized in various wireless applications operating at the ultrahigh-frequency band.


title: Analytical magnetic circuit design optimization of electrical machines with air gap winding using a Halbach array

abstract: This paper aims to analyze the influence of a Halbach array by using a semi analytical design optimization approach on a novel electrical machine design with slotless air gap winding. The useable magnetic flux density caused by the Halbach array magnetization is studied and compared to conventional radial magnetization systems. First, several discrete magnetic flux densities are analyzed for an infinitesimal wire size in an air gap range from 0.1 mm to 5 mm by the finite element method in Ansys Maxwell. Fourier analysis is used to approximate continuous functions for each magnetic flux density characteristic for each air gap height. Then, using a six-step commutation control, the magnetic flux acting on a certain phase geometry is considered for a parametric machine model. The design optimization approach utilizes the design freedom of the magnetic flux density shape in air gap as well as the heights and depths of all magnetic circuit components, which are stator and rotor cores, permanent magnets, air gap, and air gap winding. Use of a nonlinear optimization formulation, allows for fast and precise analytical calculation of objective function. In this way the influence of both magnetizations on Pareto optimal machine design sets, when mass and efficiency are weighted, are compared. Other design requirements, such as torque, current, air gap and wire height, are considered via constraints on this optimization. Finally, an optimal motor design study for the Halbach array magnetization pattern is compared to the conventional radial magnetization. As a reference design, an existing 15-inch rim wheel-hub motor with air gap winding is used.


title: PDFS: Partially Dedupped File System for Primary Workloads

abstract: Primary storage dedup is difficult to be accomplished because of challenges to achieve low IO latency and high throughput while eliminating data redundancy effectively in the critical IO Path. In this paper, we design and implement the PDFS, a partially dedupped file system for primary workloads, which is built on a generalized framework using partial data lookup for efficient searching of redundant data in quickly chosen data subsets instead of the whole data. PDFS improves IO latency and throughput systematically by techniques including write path optimization, data dedup parallelization and write order preserving. Such design choices bring dedup to the masses for general primary workloads. Experimental results show that PDFS achieves 74-99 percent of the theoretical maximum dedup ratio with very small or even negative performance degradations compared with main stream file systems without dedup support. Discussions about varied configuring experiences of PDFS are also carried out.


title: What is Computational Intelligence and where is it going ?

abstract: What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with “computational intelligence” in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed.


title: A highly integrated dual-band tri-mode transceiver chipset for CDMA TIA/EIA-95 and AMPS applications

abstract: In this paper, a two device chip-set integrating the RF transceiver front-end function for the dual-band, dual-mode CDMA/AMPS cellular telephone standard TIA/EIA-98 is described. Fabricated in a double polysilicon, 25 GHz f/sub T/, silicon bipolar process, the transceiver achieves a total power dissipation of less than 480 mW at 3 V with 9 dBm transmitter power.


title: Modeling multi-step relevance propagation for expert finding

abstract: An expert finding system allows a user to type a simple text query and retrieve names and contact information of individuals that possess the expertise expressed in the query. This paper proposes a novel approach to expert finding in large enterprises or intranets by modeling candidate experts (persons), web documents and various relations among them with so-called expertise graphs. As distinct from the state of-the-art approaches estimating personal expertise through one-step propagation of relevance probability from documents to the related candidates, our methods are based on the principle of multi-step relevance propagation in topic specific expertise graphs. We model the process of expert finding by probabilistic random walks of three kinds: finite, infinite and absorbing. Experiments on TREC Enterprise Track data originating from two large organizations show that our methods using multi-step relevance propagation improve over the baseline one-step propagation based method in almost all cases.


title: Bridge between Black Box and White Box – Gray Box Testing Technique

abstract: Software testing is a highly complex and time consu ming activity- It is even difficult to say when tes ting is complete. The effective combination of black box (external) a nd white box (internal) testing is known as Gray-bo x testing. Gray box testing is a powerful idea if one knows something about how the product works on the inside; one can test it b etter, even from the outside. Gray box testing is not black box testing, because the tester does know some of the internal workings of the software under test. It is not to be confused with white box testing, testi ng approach that attempts to cover the internals of the product in detail. Gray box testing is a test strategy based partly on internal s. This paper will present all the three methodolog y Black-box, White-box, Gray- box and how this method has been applied to validat e critical software systems.


title: Improvements to keystroke-based authentication by adding linguistic context

abstract: Traditional keystroke-based authentication methods rely on timing of and between individual keystrokes, oblivious to the context in which the typing is taking place. By incorporating linguistic context into a keystroke-based user authentication system, we are able to improve performance, as measured by EER. Taking advantage of patterns in keystroke dynamics, we show that typists employ unique behavior relative to syntactic and lexical constructs, which can be used to help identify the typist.


title: Optimization of perturb and observe maximum power point tracking method

abstract: Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point (MPP) which depends on panels temperature and on irradiance conditions. The issue of MPPT has been addressed in different ways in the literature but, especially for low-cost implementations, the perturb and observe (PO moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper it is shown that, in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such parameters is also carried out. Results of experimental measurements are in agreement with the predictions of theoretical analysis.


title: Witness encryption and its applications

abstract: We put forth the concept of witness encryption. A witness encryption scheme is defined for an NP language L (with corresponding witness relation R). In such a scheme, a user can encrypt a message M to a particular problem instance x to produce a ciphertext. A recipient of a ciphertext is able to decrypt the message if x is in the language and the recipient knows a witness w where R(x,w) holds. However, if x is not in the language, then no polynomial-time attacker can distinguish between encryptions of any two equal length messages. We emphasize that the encrypter himself may have no idea whether $x$ is actually in the language. Our contributions in this paper are threefold. First, we introduce and formally define witness encryption. Second, we show how to build several cryptographic primitives from witness encryption. Finally, we give a candidate construction based on the NP-complete Exact Cover problem and Garg, Gentry, and Halevi's recent construction of "approximate" multilinear maps. Our method for witness encryption also yields the first candidate construction for an open problem posed by Rudich in 1989: constructing computational secret sharing schemes for an NP-complete access structure.


title: Learning to Rank Personalized Search Results in Professional Networks

abstract: LinkedIn search is deeply personalized - for the same queries, different searchers expect completely different results. This paper presents our approach to achieving this by mining various data sources available in LinkedIn to infer searchers' intents (such as hiring, job seeking, etc.), as well as extending the concept of homophily to capture the searcher-result similarities on many aspects. Then, learning-to-rank is applied to combine these signals with standard search features.


title: The Impact of Healthcare Workers Job Environment on Their Mental-emotional Health. Coping Strategies: The Case of a Local General Hospital

abstract: Workplace stress can influence healthcare professionals’ physical and emotional well-being by curbing their efficiency and having a negative impact on their overall quality of life. The aim of the present study was to investigate the impact that work environment in a local public general hospital can have on the health workers’ mental-emotional health and find strategies in order to cope with negative consequences. The study took place from July 2010 to October 2010. Our sample consisted of 200 healthcare professionals aged 21-58 years working in a 240-bed general hospital and the response rate was 91.36%). Our research protocol was first approved by the hospital’s review board. A standardized questionnaire that investigates strategies for coping with stressful conditions was used. A standardized questionnaire was used in the present study Coping Strategies for Stressful Events, evaluating the strategies that persons employ in order to overcome a stressful situation or event. The questionnaire was first tested for validity and reliability which were found satisfactory (Cronbach’s α=0.862). Strict anonymity of the participants was guaranteed. The SPSS 16.0 software was used for the statistical analysis. Regression analysis showed that health professionals’ emotional health can be influenced by strategies for dealing with stressful events, since positive re-assessment, quitting and seeking social support are predisposing factors regarding the three first quality of life factors of the World Health Organization Quality of Life -BREF. More specifically, for the physical health factor, positive re-assessment (t=3.370, P=0.001) and quitting (t=−2.564, P=0.011) are predisposing factors. For the ‘mental health and spirituality’ regression model, positive re-assessment (t=5.528, P=0.000) and seeking social support (t=−1.991, P=0.048) are also predisposing factors, while regarding social relationships positive re-assessment (t=4.289, P=0.000) is a predisposing factor. According to our findings, there was a notable lack of workplace stress management strategies, which the participants usually perceive as a lack of interest on behalf of the management regarding their emotional state. Some significant factors for lowering workplace stress were found to be the need to encourage and morally reward the staff and also to provide them with opportunities for further or continuous education.


title: Do firms rebalance their capital structures

abstract: We empirically examine the trade-off theory of capital structure, allowing for costly adjustment. After confirming that financing behavior is consistent with the presence of adjustment costs, we use a dynamic duration model to show that firms behave as though adhering to a dynamic trade-off policy in which they actively rebalance their leverage to stay within an optimal range. We find that firms respond to changes in their equity value, due to price shocks or equity issuances, by adjusting their leverage over the two to four years following the change. The presence of adjustment costs, however, often prevents this response from occurring immediately, resulting in shocks to leverage that have a persistent effect.We then show that this persistence in leverage is positively correlated with adjustment costs. Our evidence suggests that this persistence is more likely a result of optimizing behavior in the presence of adjustment costs, as opposed to indifference with regard to capital structure.


title: Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

abstract: Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.


title: Robots Tutoring Children: Longitudinal Evaluation of Social Engagement in Child-Robot Interaction

abstract: This paper explores children's social engagement to a robotic tutor by analyzing their behavioral reactions to socially significant events initiated by the robot. Specific questions addressed in this paper are whether children express signs of social engagement as a reaction to such events, and if so, in what way. The second question is whether these reactions differ between different types of social events, and finally, whether such reactions disappear or change over time. Our analysis indicates that children indeed show behaviors that indicate social engagement using a range of communicative channels. While gaze towards the robot's face is the most common indication for all types of social events, verbal expressions and nods are especially common for questions, and smiles are most common after positive feedback. Although social responses in general decrease slightly over time, they are still observable after three sessions with the robot.


title: Analysis and Design of an Integrated Notch Filter for the Rejection of Interference in UWB Systems

abstract: A 0.13-mu m CMOS fourth-order notch filter for the rejection of the 5-6 GHz interference in UWB front-ends is reported. The filter is integrated into an analog front-end for Mode #1 UWB. A thorough analysis based on a simplified model of the filter is carried out. An algorithm for the automatic tuning and calibration of the filter is also discussed and demonstrated. Two versions of the circuit are designed and fabricated: the first comprises a low-noise amplifier and the filter, and the second expands it to a complete front-end. In the latter version the filter was also redesigned. The filter provides more than 35 dB of attenuation and has a tuning range of 900 MHz, adding less than 30% power consumption to the LNA. The out-of-band IIP3 (higher than -13.2 dBm with the filter off) takes a 9-dB advantage from the filter and the compression of the gain due to the out-of-band blocker is reduced by at least 6 dB in the complete front-end. The conversion gain of the front-end is 25 dB per channel, its average noise figure is lower than 6.2 dB, and its in-band 1-dB compression point is higher than - 30 dBm at a power consumption of 32 mW.


title: Architectural framework of smart water meter reading system in IoT environment

abstract: Internet of Things (IoT) has provided promising opportunities to create powerful industrial and domestic applications. One of its main applications is smart metering. Water is the most precious resource that must be used responsibly. Information about the usage of water can reduce the water wastage and will help in water management. In this paper we propose the architectural framework for IoT based water meter. Water flow and heat measurements are taken by water meter (STUF-280T) which is based on ultrasonic flow measurement technology. We have described the Mediatek Cloud Sandbox which we are using as a cloud platform. For water meter to communicate with the cloud we have used LinkIt ONE development board which is an open source platform for prototyping IoT devices. As data will be stored and analyzed on the cloud, this system will be very economical. Also user can access, analyze and share data anytime, from anywhere.


title: Anti-social Behaviour and Urban Configuration Using Space Syntax to Understand Spatial Patterns of Socio-environmental Disorder

abstract: This paper explores spatial properties of urban environments in relation to the occurrence of antisocial ::: behaviour (ASB). This is a new approach depart from previous research which considers ASB ::: predominantly from the socio-economic perspective. ASB, in contrast to crime, is a phenomenon ::: which lacks clear formal definition - its borders to 'normal' or acceptable behaviour on the one end ::: of the spectrum and criminal behaviour on its other end are blurred. What is perceived as 'antisocial' ::: by the individual is typically highly subjective and depends on the observer's personal ::: background (age, cultural background, gender etc). Sensitivity to disorder is also dependent on the ::: backdrop of the urban scene - certain behaviour appears acceptable in certain environments ::: whereas in others it is not. Any data set of reported ASB incidents is therefore inherently affected not ::: only by lack of definition / definability of ASB and the reporting and recording practices, but also ::: biased by the type of urban environment in which it takes place. Our findings suggests that, ::: controlling for social differences, that patterns of ASB incidents can be correlated to physical ::: properties of the environment, if we consider the environment in terms of its syntactical properties. ::: We find generic patterns of incidents in different types of spatial layouts. We suggest that these ::: patterns can be explained in the light of co-presence for pedestrians on the street and surveillance ::: from residential entrances on the streets, with ASB withdrawing from both. This links our work to ::: findings in the research on crime, and also to findings in the field of the social sciences: our ::: findings might contribute to an understanding how communities organise in space and become ::: 'socially effective' against disorder and crime.


title: Infrared and visible image fusion for face recognition

abstract: Considerable progress has been made in face recognition research ::: over the last decade especially with the development of powerful ::: models of face appearance (i.e., eigenfaces). Despite the variety ::: of approaches and tools studied, however, face recognition is not ::: accurate or robust enough to be deployed in uncontrolled ::: environments. Recently, a number of studies have shown that ::: infrared (IR) imagery offers a promising alternative to visible ::: imagery due to its relative insensitive to illumination changes. ::: However, IR has other limitations including that it is opaque to ::: glass. As a result, IR imagery is very sensitive to facial ::: occlusion caused by eyeglasses. In this paper, we propose fusing ::: IR with visible images, exploiting the relatively lower ::: sensitivity of visible imagery to occlusions caused by eyeglasses. ::: Two different fusion schemes have been investigated in this study: ::: (1) image-based fusion performed in the wavelet domain and, (2) ::: feature-based fusion performed in the eigenspace domain. In both ::: cases, we employ Genetic Algorithms (GAs) to find an optimum ::: strategy to perform the fusion. To evaluate and compare the ::: proposed fusion schemes, we have performed extensive recognition ::: experiments using the Equinox face dataset and the popular method ::: of eigenfaces. Our results show substantial improvements in ::: recognition performance overall, suggesting that the idea of ::: fusing IR with visible images for face recognition deserves ::: further consideration.


title: Human fingernail segmentation

abstract: In this paper we present a method for segmentation of fingernail patterns and differentiate them as distinct nail parts; fingernail plate with lunula and distal free edge of nail plate. In the research work, focus is on fixed area of the fingernail plate plus lunula, as it remains unchanged in structure, where as the distal nail edge extends and changes in structure over a period of time. In order to segment fingernail parts, we have devised an algorithm that automatically separates unchanging region of fingernail plate from free distal edge of nail structure. The fingernail plate that includes lunula within (may or may not be prominently present in fingernails), is used as biometric in our advance study. Theory suggests, every fingernail within finger formation comprises of the brightest regions amongst the captured finger data set (in our system). Proposed method is of two stages. In first stage, color image is converted to gray scale and contrast enhancement is applied using adaptive histogram equalization. In second stage, we perform segmentation using watershed method that exercises maxima and minima properties of marker controlled watershed principles. In order to verify the results of the algorithm, we have constructed a confusion matrix where evaluation has been done with ground truth. Additionally, the segmented object's from both the methods is considered for quality metrics assessment. Similarity accuracy between the ground truth and watershed result is 84.0% correctness for fingernail plate. Initial fingernail segmentation results are promising, supporting its use for biometric application.


title: Decision support with text-based emotion recognition: Deep learning for affective computing

abstract: Emotions widely affect the decision-making of humans and, hence, affective computing takes emotional states into account with the goal of tailoring decision support to individuals. However, the accurate recognition of emotions within narrative materials presents a challenging undertaking due to the complexity and ambiguity of language. Even though deep learning has evolved as the state-of-the-art in various tasks from text mining, its benefits with regard to affective computing are not yet understood. We thus propose the following innovations: (1) we adapt recurrent neural networks from the field of deep learning to affective computing. (2) We extend these networks for predicting the score of different affective dimensions. (3) We implement transfer learning for pre-training word embeddings. Analyzing the results, we find that deep learning consistently outperforms traditional machine learning with improvements of up to 21% in F1-score when labeling emotions and 6% in forecast errors when rating the intensity of emotions. Altogether, the findings have considerable implications for the use of affective computing in providing decision support.


title: Application of concentrated windings in interior permanent magnet machine

abstract: The use of concentrated windings in the interior permanent magnet (IPM) machine reduces its axial length and increases high-speed performance by enhancing flux- weakening capability although at the expense of higher cogging torque, decrease in winding factor and saliency ratio. In order to take full advantage of the concentrated winding in the IPM machine, optimization is required to minimize cogging torque and to maximize winding factor and saliency ratio. This paper is part of a detail investigation in which authors will attempt to optimize the concentrated winding structure for a high performance IPM machine. The full review of the existing optimization technologies for the concentrated winding applied to the permanent magnet machine is the first step toward developing new methods for the IPM machine. The paper presents the study of the various optimization techniques of the concentrated winding and their suitability in context with the IPM machine.


title: Design of a Passively-Powered, Programmable Sensing Platform for UHF RFID Systems

abstract: This paper presents a wireless, battery-free, platform for sensing and computation that is powered and read by a standards compliant ultra-high frequency (UHF) radio frequency identification (RFID) reader. The WISP (wireless identification and sensing platform) includes a fully-programmable 16 bit microcontroller with analog-to-digital converter. The microcontroller firmware implements portions of the electronic product code (EPC) class 1 generation 1 protocol. When queried, the platform communicates arbitrary sensor data by emulating an EPC tag whose ID encodes the desired sensor data; the required 16-bit CRC is computed dynamically by the microcontroller. The RFID reader reports the received tag ID to application software which can interpret the information contained in the tag ID. The programmability of the WISP along with its implementation as a PCB allows for flexible integration of arbitrary low-power sensors. Furthermore, sensors are also exclusively powered from the RFID reader resulting in a completely battery free device. Sensors integrated into the WISP platform so far include light, temperature, and rectified voltage, and are shown experimentally to have an operating range of up to 4.5m. To the authors' knowledge, WISP is the first fully programmable computing platform that can operate using power transmitted from a long-range (UHF) RFID reader and communicate arbitrary, multi-bit data in a single response packet.


title: PROJECT MANAGEMENT PROCESS MATURITY (PM)2 MODEL

abstract: This paper presents the project management process maturity (PM) 2 model that determines and positions an organization's relative project management level with other organizations. The comprehensive model follows a systematic approach to establish an organization's current project management level. Each maturity level consists of major project management characteristics, factors, and processes. The model evolves from functionally driven organizational practices to project driven organization that incorporates continuous project learning. The (PM) 2 model provides an orderly, disciplined process to achieve higher levels of project management maturity.


Title On the psychology of self-prediction : Consideration ofsituational barriers to intended actions


title: Relational HOG feature with wild-card for object detection

abstract: This paper proposes Relational HOG (R-HOG) features for object detection, and binary selection by using a wild-card “*” with Real AdaBoost. HOG features are effective for object detection, but their focus on local regions makes them high-dimensional features. To reduce the memory required for the HOG features, this paper proposes a new feature, R-HOG, which creates binary patterns from the HOG features extracted from two local regions. This approach enables the created binary patterns to reflect the relationships between local regions. Furthermore, we extend the R-HOG features by shifting the gradient orientations. These shifted Relational HOG (SR-HOG) features make it possible to clarify the size relationships of the HOG features. However, since R-HOG and SR-HOG features contain binary values not needed for classification, we have added a process to the Real AdaBoost learning algorithm in which “*” permits either of the two binary values “0” and “1”, and so valid binary values can be selected. Evaluation experiment demonstrated that the SR-HOG features introducing “*” offers better detection performance than the conventional method (HOG feature) despite the reduced memory requirements.


title: A Modified Particle Swarm Optimizer Algorithm

abstract: This paper presented a modified particle swarm optimizer algorithm (MPSO). The aggregation degree of the particle swarm was introduced. The particles' diversity was improved through periodically monitoring aggregation degree of the particle swarm. On the later development of the PSO algorithm, it has been taken strategy of the Gaussian mutation to the best particle's position, which enhanced the particles' capacity to jump out of local minima. Several typical benchmark functions with different dimensions have been used for testing. The simulation results show that the proposed method improves the convergence precision and speed of PSO algorithm effectively.


title: The ‘who’ and ‘what’ of #diabetes on Twitter

abstract: Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all of the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions. (1) What themes arise in these tweets? (2) Who are the most influential users? (3) Which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction and c...


Homosexuality and Change: Results of a NARTH Survey