title: Teacher value beliefs associated with using technology: Addressing professional and student needs

abstract: Studies have indicated that when teachers believe technology uses are valuable, they are more likely to incorporate those uses into their practices. This hermeneutical phenomenology study investigated the value beliefs that underlie teachers' uses of technology. To measure value beliefs, teachers' uses (and reasons for those uses) of technology for teaching and learning were examined. Data were collected from eight award-winning teachers through an interview, observation, and electronic portfolio. Findings indicated that teachers used technology to address professional (e.g., creating customized classroom materials, improving classroom management by engaging students) and student needs (e.g., enhancing student comprehension, equipping students with technology skills), all of which related to the underlying value belief of promoting student learning. Based on these findings, professional development activities should emphasize the potential impact of specific technology uses on student learning.


dapting the Body Appreciation Scale-2 for Children : A psychometric nalysis of the BAS-2 C mma


title: Poster: IoTURVA: Securing Device-to-Device Communications for IoT

abstract: In this poster we present IoTurva, a platform for securing Device-to-Device (D2D) communication in IoT. Our solution takes a blackbox approach to secure IoT edge-networks. We combine user and device-centric context-information together with network data to classify network communication as normal or malicious. We have designed a dual-layer traffic classification scheme based on fuzzy logic, where the classification model is trained remotely. The remotely trained model is then used by the edge gateway to classify the network traffic. We have implemented a proof-of-concept prototype and evaluate its performance in a real world environment. Theevaluation shows that IoTurva causes very small overhead while it works with minimal hardware, and that our model training and classification approach can improve system efficiency and privacy.


title: Artificial bee colony algorithm with variable search strategy for continuous optimization

abstract: The artificial bee colony (ABC) algorithm is a swarm-based optimization technique proposed for solving continuous optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments.


title: Two-Stream Convolutional Networks for Dynamic Texture Synthesis

abstract: We introduce a two-stream model for dynamic texture synthesis. Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction. Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics. To generate a novel texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture. Inspired by recent work on image style transfer and enabled by the two-stream model, we also apply the synthesis approach to combine the texture appearance from one texture with the dynamics of another to generate entirely novel dynamic textures. We show that our approach generates novel, high quality samples that match both the framewise appearance and temporal evolution of input texture. Finally, we quantitatively evaluate our texture synthesis approach with a thorough user study.


title: Accounting for the phenomenology and varieties of auditory verbal hallucination within a predictive processing framework

abstract: Two challenges that face popular self-monitoring theories (SMTs) of auditory verbal hallucination (AVH) are that they cannot account for the auditory phenomenology of AVHs and that they cannot account for their variety. In this paper I show that both challenges can be met by adopting a predictive processing framework (PPF), and by viewing AVHs as arising from abnormalities in predictive processing. I show how, within the PPF, both the auditory phenomenology of AVHs, and three subtypes of AVH, can be accounted for.


title: The impact of a nursing transitions programme on retention and cost savings

abstract: AIM ::: To identify the benefits and essential elements of a new graduate residency programme. ::: ::: ::: BACKGROUND ::: Retention of nurses is a global nursing concern. New graduate nurses have the lowest retention rates and, therefore, the present study focused on the unique needs of this group. ::: ::: ::: EVALUATION ::: Valid and reliable tools were utilized to evaluate work satisfaction, clinical decision making, organizational commitment and skill development during and after each residency. We also compared resident retention and associated cost savings. ::: ::: ::: KEY ISSUES ::: The adoption and implementation of the residency programme represented a change in culture. Hiring and education practices for new nurses changed dramatically. ::: ::: ::: CONCLUSIONS ::: Before the development of a new graduate transition programme, our 1-year retention rate was as low as 50%. Five years after programme adoption, retention increased to 72.5%, resulting in major cost savings to the organization. ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: Nursing Management must be creative and transformational in their thinking in order to address nursing retention. Implications for nurse managers who are considering residency programmes include the potential for significant cost savings for the hospital, increased resident, nursing and unit satisfaction and a demand for nurses who desire to work at their hospitals.


title: Coattention based BiLSTM for answer selection

abstract: Attention based recurrent neural networks have achieved great success in answer selection, which is an important subtask of question answering (QA). However, previous work used fixed representation of question to compute the attention information for answers, which fails to extract the influence that answers make on question. In our work, we propose a coattention based bidirectional LSTM for answer selection. Distinct from previous attention based work, we resort to a coattention encoder to capture the interactions between the question and the answers, which generates different question representations according to the answers. We carry out large-scale experiments on WikiQA dataset. Our proposed model achieves good MAP (0.7148) and MRR (0.7289) on the test set and outperforms many strong baselines.


title: Describing Data Processing Pipelines in Scientific Publications for Big Data Injection

abstract: The rise of Big Data analytics has been a disruptive game changer for many application domains, allowing the integration into domain-specific applications and systems of insights and knowledge extracted from external big data sets. The effective "injection" of external Big Data demands an understanding of the properties of available data sets, and expertise on the available and most suitable methods for data collection, enrichment and analysis. A prominent knowledge source is scientific literature, where data processing pipelines are described, discussed, and evaluated. Such knowledge is however not readily accessible, due to its distributed and unstructured nature. In this paper, we propose a novel ontology aimed at modeling properties of data processing pipelines, and their related artifacts, as described in scientific publications. The ontology is the result of a requirement analysis that involved experts from both academia and industry. We showcase the effectiveness of our ontology by manually applying it to a collection of publications describing data processing methods.


Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention


title: Action of a femtosecond laser generated micro-cavity passing through a circular EHL contact

abstract: The increasingly severe operating conditions of lubricated contacts cause a decrease of the ratio of lubricant film thickness to surface roughness. In order to avoid a reduction of their durability, laser surface texturing is a promising technology allowing to optimize the surface micro-topography. The present work studies the phenomena generated by the passage of a single micro-dimple in an EHL contact. Thus, circular micro-cavities with various diameters and depths have been created on the contacting surfaces of an EHL tribometer by a femtosecond pulse laser, and measured using white-light interferometry. The lubrication phenomena induced by the passage of the micro-cavities were studied experimentally using the EHL ball-on-disk apparatus. The results obtained highlight significant modifications of the film thickness distribution due to the micro-dimples passing through the contact, acting as lubricant micro-reservoirs.


title: Capability Maturity Framework for eGovernment: A Multi-dimensional Model and Assessing Tool

abstract: This article describes an IT-based, eGov-centered and capability-driven model for assessing e-government capabilities and maturity of public agencies. It is the result of an initiative of the Chilean government to reinforce its e-government strategy. The proposed model, called eGov-MM (e-Government Maturity Model), has three dimensions (a cube) supporting business processes: information criteria, IT resources, and leverage domains. Changing the traditional and exclusive focus on IT, four Leverage Domains are defined: e-Strategy, IT Governance, Process Management, and People and Organization Capabilities. The Leverage Domains generate a hierarchical structure with a second level named Key Domain Areas. These areas should be measurable and controllable, so they are related to a third hierarchical level, called Critical Variables, allowing the model's elements to be assessed qualitatively and quantitatively. The capability and maturity of these variables associated with the intersection with the other two axes of the cube establish five levels of capability. The proposed model is strongly supported by the international experience and best practices for IT management and has already been field tested.


title: Ego lane estimation using vehicle observations and map information

abstract: An ego vehicle localization algorithm must be able to estimate where the vehicle is on the road. This is typically performed with a positioning filter that operates in global coordinates. Herein, we take a different approach, by splitting the localization problem into two parts: in-lane localization and ego lane estimation. The paper addresses the latter problem. For this, we have developed theory and algorithms which, based on information about the positions of surrounding vehicles, give the probability of being in each of the current number of lanes. The object positions are provided by one or several low-cost on-board perception sensors. The derived Bayesian filter is evaluated on real data from a prototype self-driving car. Preliminary results show that when other vehicles are present, the proposed method is able to estimate the lane of travel with high probability.


title: Attachment theory as a framework for explaining engagement with Facebook

abstract: Abstract Research on the relation between personality and styles of engagement with social media is surprisingly limited and has generated mixed results. The present research applied attachment theory to illuminate individual differences in styles of Facebook engagement. Two studies ( N = 583) supported a mediational model explaining various forms of active Facebook use as stemming from attachment anxiety, which predisposes individuals to sensitivity about social feedback, thereby leading them to engage in attention-seeking social media behavior. These results held while controlling for extraversion, neuroticism, and self-esteem. Attachment avoidance predicted restrained Facebook use, primarily due to its association with (low) extraversion. These findings resolve inconsistencies in previous research and demonstrate that attachment theory is a particularly useful framework through which to study the influence of personality on social-media behavior.


title: Graph-based text classification: learn from your neighbors

abstract: Automatic classification of data items, based on training samples, can be boosted by considering the neighborhood of data items in a graph structure (e.g., neighboring documents in a hyperlink environment or co-authors and their publications for bibliographic data entries). This paper presents a new method for graph-based classification, with particular emphasis on hyperlinked text documents but broader applicability. Our approach is based on iterative relaxation labeling and can be combined with either Bayesian or SVM classifiers on the feature spaces of the given data items. The graph neighborhood is taken into consideration to exploit locality patterns while at the same time avoiding overfitting. In contrast to prior work along these lines, our approach employs a number of novel techniques: dynamically inferring the link/class pattern in the graph in the run of the iterative relaxation labeling, judicious pruning of edges from the neighborhood graph based on node dissimilarities and node degrees, weighting the influence of edges based on a distance metric between the classification labels of interest and weighting edges by content similarity measures. Our techniques considerably improve the robustness and accuracy of the classification outcome, as shown in systematic experimental comparisons with previously published methods on three different real-world datasets.


IT 4 BI Master Thesis Representing ETL Flows with BPMN 2 . 0


title: Human cervical ripening, an inflammatory process mediated by cytokines

abstract: non-pregnant to term-pregnant (P < 0.01), and a further 10-fold to postpartum state (P < 0.0001). Concentrations of IL-6 and G-CSF were similarly increased. Specific IL-8 immunostaining was identified in the epithelia of pregnant cervical tissue (n 7) and was most pronounced in the epithelia and stroma of postpartum tissue (n 4). In conclusion, IL-8, IL-6 and G-CSF increase in the human cervix during the ripening process, indicating their important role in the cervical remodelling. These data demonstrate that cervical ripening is similar to an inflammatory process.


title: Diagnosing Latency in Multi-Tier Black-Box Services

abstract: As multi-tier cloud applications become pervasive, we need better tools for understanding their performance. This paper presents a system that analyzes observed or desired changes to end-to-end latency prole in a large distributed application, and identies their underlying causes. It recognizes changes to system conguration, workload, or performance of individual services that lead to the observed or desired outcome. Experiments on an industrial datacenter demonstrate the utility of the system.


title: A Formal Graph Model for RDF and Its Implementation

abstract: Formalizing an RDF abstract graph model to be compatible with the RDF formal semantics has remained one of the foundational problems in the Semantic Web. In this paper, we propose a new formal graph model for RDF datasets. This model allows us to express the current model-theoretic semantics in the form of a graph. We also propose the concepts of resource path and triple path as well as an algorithm for traversing the new graph. We demonstrate the feasibility of this graph model through two implementations: one is a new graph engine called GraphKE, and the other is extended from RDF-3X to show that existing systems can also benefit from this model. In order to evaluate the empirical aspect of our graph model, we choose the shortest path algorithm and implement it in the GraphKE and the RDF-3X. Our experiments on both engines for finding the shortest paths in the YAGO2S-SP dataset give decent performance in terms of execution time. The empirical results show that our graph model with well-defined semantics can be effectively implemented.


title: Image melding: combining inconsistent images using patch-based synthesis

abstract: Current methods for combining two different images produce visible artifacts when the sources have very different textures and structures. We present a new method for synthesizing a transition region between two source images, such that inconsistent color, texture, and structural properties all change gradually from one source to the other. We call this process image melding. Our method builds upon a patch-based optimization foundation with three key generalizations: First, we enrich the patch search space with additional geometric and photometric transformations. Second, we integrate image gradients into the patch representation and replace the usual color averaging with a screened Poisson equation solver. And third, we propose a new energy based on mixed L2/L0 norms for colors and gradients that produces a gradual transition between sources without sacrificing texture sharpness. Together, all three generalizations enable patch-based solutions to a broad class of image melding problems involving inconsistent sources: object cloning, stitching challenging panoramas, hole filling from multiple photos, and image harmonization. In several cases, our unified method outperforms previous state-of-the-art methods specifically designed for those applications.


title: Overlapping community detection using a community optimized graph swarm

abstract: Detection of communities within social networks is a nontrivial problem. Allowing communities to overlap—i.e. nodes can belong to more than one community simultaneously—further complicates the problem. Nevertheless, people do belong to multiple social groups simultaneously and being able to detect overlapping communities is an important step into being able to understand and analyze social networks. A common practice in community detection (clustering) is to view the network (graph) as a whole and have a central control process determine how nodes are clustered. That central control, we believe, is a limitation to performance. In our previous work, we showed that the individual’s view of his or hers social groups could be aggregated to produce communities. In this paper, we propose a unique approach to community detection that combines the individual’s view of a community, not having the view the graph as a whole, with swarm intelligence as a means of removing the central control mechanism. Our approach offers a community detection solution that finds overlapping communities while running in O(n log 2 n) time.


title: [Hyaluronic acid rheology: Basics and clinical applications in facial rejuvenation].

abstract: Hyaluronic acid (HA) is the most widely used dermal filler to treat facial volume deficits and winkles specially for facial rejuvenation. Depending on various areas of the face, filler is exposed to two different forces (shear deformation and compression/stretching forces) resulting from intrinsec and external mechanical stress. The purpose of this technical note is to explain how rheology, which is the study of the flow and deformation of matter under strains, can be used in our clinical practice of facial volumization with fillers. Indeed, comprehension of the rheological properties of HA has become essential in selection of dermal filler targeted to the area of the face. Viscosity, elasticity and cohesivity are the main three properties to be taken into consideration in this selection. Aesthetic physicians and surgeons have to familiarize with those basics in order to select the HA with the right rheological properties to achieve a natural-looking and long-lasting outcome.


title: They see us as less than human: Metadehumanization predicts intergroup conflict via reciprocal dehumanization.

abstract: Although the act of dehumanizing an outgroup is a pervasive and potent intergroup process that drives discrimination and conflict, no formal research has examined the consequences of being dehumanized by an outgroup-that is, "metadehumanization." Across 10 studies (N = 3,440) involving several real-world conflicts spanning 3 continents, we provide the first empirical evidence that metadehumanization (a) plays a central role in outgroup aggression that is (b) mediated by outgroup dehumanization, and (c) distinct from metaprejudice. Studies 1a and 1b demonstrate experimentally that Americans who learn that Arabs (Study 1a) or Muslims (Study 1b) blatantly dehumanize Americans are more likely to dehumanize that outgroup in return; by contrast, experimentally increasing outgroup dehumanization did not increase metadehumanization (Study 1c). Using correlational data, Study 2 documents indirect effects of metadehumanization on Americans' support for aggressive policies toward Arabs (e.g., torture) via Arab dehumanization. In the context of Hungarians and ethnic minority Roma, Study 3 shows that the pathway for Hungarians from metadehumanization to aggression through outgroup dehumanization holds controlling for outgroup prejudice. Study 4 examines Israelis' metaperceptions with respect to Palestinians, showing that: (a) feeling dehumanized (i.e., metadehumanization) is distinct from feeling disliked (i.e., metaprejudice), and (b) metadehumanization uniquely influences aggression through outgroup dehumanization, controlling for metaprejudice. Studies 5a and 5b explore Americans' metaperceptions regarding ISIS and Iran. We document a dehumanization-specific pathway from metadehumanization to aggressive attitudes and behavior that is distinct from the path from metaprejudice through prejudice to aggression. In Study 6, American participants learning that Muslims humanize Americans (i.e., metahumanization) humanize Muslims in turn. Finally, Study 7 experimentally contrasts metadehumanization and metahumanization primes, and shows that resulting differences in outgroup dehumanization are mediated by (a) perceived identity threat, and (b) a general desire to reciprocate the outgroup's perceptions of the ingroup. In summary, our research outlines how and why metadehumanization contributes to cycles of ongoing violence and animosity, providing direction for future research and policy. (PsycINFO Database Record(c) 2016 APA, all rights reserved). Language: en


title: Automated Early Detection of Diabetic Retinopathy

abstract: Purpose To compare the performance of automated diabetic retinopathy (DR) detection, using the algorithm that won the 2009 Retinopathy Online Challenge Competition in 2009, the Challenge2009, against that of the one currently used in EyeCheck, a large computer-aided early DR detection project. Design Evaluation of diagnostic test or technology. Participants Fundus photographic sets, consisting of 2 fundus images from each eye, were evaluated from 16670 patient visits of 16670 people with diabetes who had not previously been diagnosed with DR. Methods The fundus photographic set from each visit was analyzed by a single retinal expert; 793 of the 16670 sets were classified as containing more than minimal DR (threshold for referral). The outcomes of the 2 algorithmic detectors were applied separately to the dataset and were compared by standard statistical measures. Main Outcome Measures The area under the receiver operating characteristic curve (AUC), a measure of the sensitivity and specificity of DR detection. Results Agreement was high, and examination results indicating more than minimal DR were detected with an AUC of 0.839 by the EyeCheck algorithm and an AUC of 0.821 for the Challenge2009 algorithm, a statistically nonsignificant difference (z-score, 1.91). If either of the algorithms detected DR in combination, the AUC for detection was 0.86, the same as the theoretically expected maximum. At 90% sensitivity, the specificity of the EyeCheck algorithm was 47.7% and that of the Challenge2009 algorithm was 43.6%. Conclusions Diabetic retinopathy detection algorithms seem to be maturing, and further improvements in detection performance cannot be differentiated from best clinical practices, because the performance of competitive algorithm development now has reached the human intrareader variability limit. Additional validation studies on larger, well-defined, but more diverse populations of patients with diabetes are needed urgently, anticipating cost-effective early detection of DR in millions of people with diabetes to triage those patients who need further care at a time when they have early rather than advanced DR. Financial Disclosure(s) Proprietary or commercial disclosure may be found after the references.


Multi-class Support Vector Machines


title: City-Scale Localization for Cameras with Known Vertical Direction

abstract: We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle this problem, we use recent theoretical as well as technical advances. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite cases with more than 99 percent outlier correspondences in city-scale models with several millions of 3D points.


title: Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach

abstract: The increasing amount of online content motivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-document summarization methods to multi-document summarization. The proposed methods are based on the hierarchical combination of single-document summaries, and achieves state of the art results.


title: Comprehensive Surgical Aesthetic Enhancement and Rejuvenation of the Perioral Region

abstract: There has been minimal attention paid in the literature to the aesthetics of the perioral area, either in youth or in senescence. Aging around the lips traditionally was thought to result from a combination of thinning skin surrounding the area, ptosis, and loss of volume in the lips. The atrophy of senescence was treated by adding volume to the lips and filling the deep nasolabial creases. There is now a growing appreciation for the role of volume enhancement in the perioral region and the sunken midface, as well as for dentition, in the resting and dynamic appearance of the perioral area (particularly in youth). In this article, the authors describe the senior author's (BG) preferred methods for aesthetic enhancement of the perioral region and his rejuvenative techniques developed over the past 28 years. The article describes the etiologies behind the dysmorphologies in this area and presents a problem-oriented algorithm for treating them.


title: How Integration helps on Cold-Start Recommendations

abstract: The RecSys Challenge 2017 focuses on recommending proper or potentially interested users for job postings. Different from traditional recommendation tasks, most of the items (jobs) to be recommended are cold-items without interaction history. To address this problem, this paper introduces an effective integration method. The whole framework consists integrations in 4 different levels: feature-level, model-level, data-level and approach-level. First, we extract features from users' and jobs' profiles and make further abstractions. Second, we improve the model of Wide and Deep Learning, a method that aggregates both embedding and numeric features. Particularly, to receive better performance, both online and offline user-item interactions are used to train the model. Other methods such as content-based Linear Regression, Item-neighbor, Historical Enhancement and Xgboost are also taken into consideration. Finally, we learn lessons from the field of social choice and experiments show that results of aggregation by voting from different methods give a better performance, receiving 29732 in offline test and 6903 in online test.


Chinese Grammatical Error Diagnosis Using Single Word Embedding.


title: The management of bee communities by intercropping with flowering basil (Ocimum basilicum) enhances pollination and yield of bell pepper (Capsicum annuum)

abstract: Enhancements of floral resources in surrounding landscape and in local crop fields are expected to mitigate adverse human impacts on pollinator diversity and pollination services. We evaluated whether intercropping with flowering basil (Ocimum basilicum) increases the local abundance and richness of bees and improves fruit and seed production of bell pepper (Capsicum annuum). Fields of bell pepper were divided into intercropping (basil mixed with pepper) and single-cropping plots (only pepper). We also grew bell peppers in protected greenhouse in the presence and absence of beehives. The richness and abundance of visiting bees to pepper flowers were greater in the intercropped plots than the single-cropping plots. The increase in the number of bees visiting the peppers was due to the stronger attraction of Paratrigona lineata, Apis mellifera and Tetragonisca angustula to basil in the intercropping plots. The pepper fruits produced in the intercropping were wider, longer, and heavier and developed more seeds than the fruits produced by single-cropping. The same differences were observed between the fruits grown in greenhouse with and without beehives of P. lineata. We demonstrate that agricultural systems designed to attract and retain pollinators are more productive than growing pure stands. Our results also highlight the role of provisioning floral resources to enhance bee conservation and management in anthropogenic ecosystems.


title: A tutorial overview of anomaly detection in hyperspectral images

abstract: In this paper, a tutorial overview on anomaly detection for hyperspectral electro-optical systems is presented. This tutorial is focused on those techniques that aim to detect small man-made anomalies typically found in defense and surveillance applications. Since a variety of methods have been proposed for detecting such targets, this tutorial places emphasis on the techniques that are either mathematically more tractable or easier to interpret physically. These methods are not only more suitable for a tutorial publication, but also an essential to a study of anomaly detection. Previous surveys on this subject have focused mainly on anomaly detectors developed in a statistical framework and have been based on well-known background statistical models. However, the most recent research trends seem to move away from the statistical framework and to focus more on deterministic and geometric concepts. This work also takes into consideration these latest trends, providing a wide theoretical review without disregarding practical recommendations about algorithm implementation. The main open research topics are addressed as well, the foremost being algorithm optimization, which is required for embodying anomaly detectors in real-time systems.


title: The multi-talented bacterial adenylate cyclases.

abstract: Bacterial pathogens produce a variety of toxins capable of altering the levels of cAMP in the cells of infected hosts. Moreover, cAMP is an important signaling molecule in many bacterial species, involved in regulation of gene expression in response to a variety of environmental stimuli. The genome of the opportunistic pathogen Pseudomonas aeruginosa encodes three adenylate cyclases. One of these is exoenzyme Y, which is translocated into the host cell via a type III secretion system (TTSS). The other two cyclases are CyaA and CyaB, that generate cAMP for intracellular signaling, and together with the cognate cAMP-binding protein Vfr, control the expression of the TTSS and several virulence factors. Using a mouse infection model, it was shown that CyaB, a membrane-bound class III adenylate cyclase plays a more prominent role in regulation of TTSS-encoding genes than CyaA. Given the wide distribution of the class III adenylate cyclases among bacteria, cAMP-dependent regulation of gene expression may have evolved as a conserved mechanism for sensing environmental signals ranging from nutritional content of the surrounding media to the presence of host tissues.


title: Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

abstract: We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging - even across different scanners. While investigating longi- tudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer's disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive func- tioning in the future.


title: Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data

abstract: Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.


title: Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

abstract: In this work we present a framework for the recognition of natural scene text. Our framework does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past. The deep neural network models at the centre of this framework are trained solely on data produced by a synthetic text generation engine -- synthetic data that is highly realistic and sufficient to replace real data, giving us infinite amounts of training data. This excess of data exposes new possibilities for word recognition models, and here we consider three models, each one "reading" words in a different way: via 90k-way dictionary encoding, character sequence encoding, and bag-of-N-grams encoding. In the scenarios of language based and completely unconstrained text recognition we greatly improve upon state-of-the-art performance on standard datasets, using our fast, simple machinery and requiring zero data-acquisition costs.


PHOTORELAXATION PROCESSES IN COVALENTLY LINKED INDOCARBOCYANINE AND THIACARBOCYANINE DYES


title: Facebook® and academic performance

abstract: There is much talk of a change in modern youth - often referred to as digital natives or Homo Zappiens - with respect to their ability to simultaneously process multiple channels of information. In other words, kids today can multitask. Unfortunately for proponents of this position, there is much empirical documentation concerning the negative effects of attempting to simultaneously process different streams of information showing that such behavior leads to both increased study time to achieve learning parity and an increase in mistakes while processing information than those who are sequentially or serially processing that same information. This article presents the preliminary results of a descriptive and exploratory survey study involving Facebook use, often carried out simultaneously with other study activities, and its relation to academic performance as measured by self-reported Grade Point Average (GPA) and hours spent studying per week. Results show that Facebook(R) users reported having lower GPAs and spend fewer hours per week studying than nonusers.


title: Recognizing and measuring self-regulated learning in a mobile learning environment

abstract: With the realization that more research is needed to explore external factors (e.g., pedagogy, parental involvement in the context of K-12 learning) and internal factors (e.g., prior knowledge, motivation) underlying student-centered mobile learning, the present study conceptually and empirically explores how the theories and methodologies of self-regulated learning (SRL) can help us analyze and understand the processes of mobile learning. The empirical data collected from two elementary science classes in Singapore indicates that the analytical SRL model of mobile learning proposed in this study can illuminate the relationships between three aspects of mobile learning: students' self-reports of psychological processes, patterns of online learning behavior in the mobile learning environment (MLE), and learning achievement. Statistical analyses produce three main findings. First, student motivation in this case can account for whether and to what degree the students can actively engage in mobile learning activities metacognitively, motivationally, and behaviorally. Second, the effect of students' self-reported motivation on their learning achievement is mediated by their behavioral engagement in a pre-designed activity in the MLE. Third, students' perception of parental autonomy support is not only associated with their motivation in school learning, but also associated with their actual behaviors in self-regulating their learning.


title: Cloud based Big Data Analytics: A Survey of Current Research and Future Directions

abstract: The advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed, and analyzed for information that can be used by organizations. Cloud computing provides an apt platform for big data analytics in view of the storage and computing requirements of the latter. This makes cloud-based analytics a viable research field. However, several issues need to be addressed and risks need to be mitigated before practical applications of this synergistic model can be popularly used. This paper explores the existing research, challenges, open issues, and future research direction for this field of study.


title: National policies that connect ICT-based education reform to economic and social development

abstract: Information and communication technology (ICT) is a principal driver of economic development and social change, worldwide. In many countries, the need for economic and social development is used to justify investments in educational reform and in educational ICT. Yet the connections between national development goals and ICT-based education reform are often more rhetorical than programmatic. This paper identifies the factors that influence economic growth and shows how they supported economic and social development in three national case studies: Singapore, Finland, and Egypt. It describes a systemic framework of growth factors and types of development that can be used to analyze national policies and connect ICT-based education reform to national economic and social development goals. And it discusses how the coordination of policies within and across ministries can support a nation’s efforts to improve economic and social conditions. The paper highlights special concerns and challenges of developing countries.


title: Dataset Augmentation with Synthetic Images Improves Semantic Segmentation

abstract: Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our approach is thus a promising solution to the problems of annotation and dataset collection.


title: Fingerprinting Mobile Devices Using Personalized Configurations

abstract: Abstract Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time


title: A look at several memory management units, TLB-refill mechanisms, and page table organizations

abstract: Virtual memory is a staple in modem systems, though there is little agreement on how its functionality is to be implemented on either the hardware or software side of the interface. The myriad of design choices and incompatible hardware mechanisms suggests potential performance problems, especially since increasing numbers of systems (even embedded systems) are using memory management. A comparative study of the implementation choices in virtual memory should therefore aid system-level designers.This paper compares several virtual memory designs, including combinations of hierarchical and inverted page tables on hardware-managed and software-managed translation lookaside buffers (TLBs). The simulations show that systems are fairly sensitive to TLB size; that interrupts already account for a large portion of memory-management overhead and can become a significant factor as processors execute more concurrent instructions; and that if one includes the cache misses inflicted on applications by the VM system, the total VM overhead is roughly twice what was thought (10--20% rather than 5--10%).


A Probabilistic Logic for Sequences of Decisions ∗


title: Reading Between the Lines: Overcoming Data Sparsity for Accurate Classification of Lexical Relationships

abstract: The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall.


title: ADMM ⊇ projective dynamics: fast simulation of general constitutive models

abstract: We apply the alternating direction method of multipliers (ADMM) optimization algorithm to implicit time integration of elastic bodies, and show that the resulting method closely relates to the recently proposed projective dynamics algorithm. However, as ADMM is a general-purpose optimization algorithm applicable to a broad range of objective functions, it permits the use of nonlinear constitutive models and hard constraints while retaining the speed, parallelizability, and robustness of projective dynamics. We demonstrate these benefits on several examples that include cloth, collisions, and volumetric deformable bodies with nonlinear elasticity.


First Enantioselective Synthesis of Vinyl Oxiranes from Aldehydes and Ylides Generated from Allyl Halides and Chiral Sulfides.


title: Three-dimensional sphericity, roundness and fractal dimension of sand particles

abstract: Particle morphological features at different scale levels hold the key to understanding the geological origin and mechanical behaviour of natural sands. In this context, it is necessary to characterise and quantify these morphological features by defining a series of reasonable descriptors. In this study, based on X-ray micro-computed tomographic (μCT) images collected from a series of image-processing techniques, the authors first introduced spherical harmonic analysis to reconstruct a three-dimensional (3D) realistic surface of the sand particles. Then 3D sphericity, roundness and fractal dimension were introduced to define the global form, local features and surface textures of the particle morphology. Based on the spherical harmonic–reconstructed surface, a novel framework was established to measure the descriptors of 3D sphericity, roundness and fractal dimension of sand particles. The 3D fractal dimension was an original descriptor used to characterise the fractal nature of the surface textures of r...


title: Semiconductor-based galvanic isolation

abstract: This paper presents the study on the semiconductor-based galvanic isolation. This solution delivers the differential-mode (DM) power via semiconductor power switches during their on states, while sustaining the common-mode (CM) voltage and blocking the CM leakage current with those switches during their off states. While it is impractical to implement this solution with Si devices, the latest SiC devices and the coming vertical GaN devices, however, provide unprecedented properties and thus can potentially enable the practical implementation. An isolated dc/dc converter based on the switched-capacitor circuit is studied as an example. The CM leakage current caused by the line input and the resulted touch current (TC) are quantified and compared to the limits in the safety standard IEC60950. To reduce the TC, low switch output capacitance and low converter switching frequency are needed. Then, discussions are presented on the TC reduction approaches and the design considerations to achieve high power density and high efficiency. A 400-V, 400-W prototype based on 1.7-kV SiC MOSFETs is built to demo the DM power delivery performance and showcase the CM leakage current problem. Further study on the CM leakage current elimination is needed to validate this solution.


title: Similarity-based machine learning methods for predicting drug–target interactions: a brief review

abstract: Computationally predicting drug ^ target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug ^ target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug ^ target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.


title: Security challenges of the internet of things

abstract: Internet of things (furthermore IoT) by definition refers to a networked interconnection of devices in everyday use that are often equipped with ubiquitous intelligence. It is based on processing of large quantities of data in order to provide useful information/service and enables an efficient regulatory policy in the area of IoT. One of the key challenges for the realization of the IoT includes security challenges, especially in the area of privacy and confidentiality among management of heterogeneities and limitations of network capacities. Those challenges will be based on information security management systems as well as on legal foundations. When considering the legal framework of security and privacy of the IoT, it has to be determined which model of regulation should be applied. Thereby, no traditional government regulation is actually appropriate for a global system such as IoT. The most important foundation consequently will be the regulatory foundations of European union (on the territory of EU) and, consequently building the model of self-regulation based partly on state and mostly on law international agreements which are to be considered as tools to govern the IoT. This paper gives an insight into the most important security challenges related to implementation of IoT and building appropriate regulatory framework. Adoption of the regulatory framework is crucial for the development of IoT services due to adequate legal security companies will get.


title: Discovering Hidden Factors of Variation in Deep Networks

abstract: Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification. But there has been less exploration in learning the factors of variation apart from the classification signal. By augmenting autoencoders with simple regularization terms during training, we demonstrate that standard deep architectures can discover and explicitly represent factors of variation beyond those relevant for categorization. We introduce a cross-covariance penalty (XCov) as a method to disentangle factors like handwriting style for digits and subject identity in faces. We demonstrate this on the MNIST handwritten digit database, the Toronto Faces Database (TFD) and the Multi-PIE dataset by generating manipulated instances of the data. Furthermore, we demonstrate these deep networks can extrapolate `hidden' variation in the supervised signal.


title: Modeling language and cognition with deep unsupervised learning: a tutorial overview

abstract: Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.


title: Robonaut 2 - The first humanoid robot in space

abstract: NASA and General Motors have developed the second generation Robonaut, Robonaut 2 or R2, and it is scheduled to arrive on the International Space Station in early 2011 and undergo initial testing by mid-year. This state of the art, dexterous, anthropomorphic robotic torso has significant technical improvements over its predecessor making it a far more valuable tool for astronauts. Upgrades include: increased force sensing, greater range of motion, higher bandwidth, and improved dexterity. R2's integrated mechatronic design results in a more compact and robust distributed control system with a fraction of the wiring of the original Robonaut. Modularity is prevalent throughout the hardware and software along with innovative and layered approaches for sensing and control. The most important aspects of the Robonaut philosophy are clearly present in this latest model's ability to allow comfortable human interaction and in its design to perform significant work using the same hardware and interfaces used by people. The following describes the mechanisms, integrated electronics, control strategies, and user interface that make R2 a promising addition to the Space Station and other environments where humanoid robots can assist people.


Chromatic polynomials of some sunflower mixed hypergraphs


Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks


title: GroupLens: an open architecture for collaborative filtering of netnews

abstract: Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.


title: Domain-driven design using meta-attributes: A DSL-based approach

abstract: Applying object-oriented domain-driven design in practice requires bridging the gaps that exist among the perceived domain class models of the key stakeholders involved. In this paper, we propose a domain-driven design method that uses meta-attributes with an aim to fill these gaps. Our method extends and generalises a previous work to use meta-attributes to build the domain class model. The meta-attributes are designed to not only make it easier for the designer and domain expert to collaboratively capture the domain-specific requirements in the model, but to ease the translation of the model to design specification. This specification is written in an object-oriented, internal DSL. To increase productivity, we define a generator function that realises the meta-mapping between the state and behaviour spaces of a domain class to automatically generate its behavioural specification. We demonstrate our method with an implementation in a prototyping tool for the domain class model.


title: Honey: its medicinal property and antibacterial activity

abstract: Indeed, medicinal importance of honey has been documented in the world's oldest medical literatures, and since the ancient times, it has been known to possess antimicrobial property as well as wound-healing activity. The healing property of honey is due to the fact that it offers antibacterial activity, maintains a moist wound condition, and its high viscosity helps to provide a protective barrier to prevent infection. Its immunomodulatory property is relevant to wound repair too. The antimicrobial activity in most honeys is due to the enzymatic production of hydrogen peroxide. However, another kind of honey, called non-peroxide honey (viz., manuka honey), displays significant antibacterial effects even when the hydrogen peroxide activity is blocked. Its mechanism may be related to the low pH level of honey and its high sugar content (high osmolarity) that is enough to hinder the growth of microbes. The medical grade honeys have potent in vitro bactericidal activity against antibiotic-resistant bacteria causing several life-threatening infections to humans. But, there is a large variation in the antimicrobial activity of some natural honeys, which is due to spatial and temporal variation in sources of nectar. Thus, identification and characterization of the active principle(s) may provide valuable information on the quality and possible therapeutic potential of honeys (against several health disorders of humans), and hence we discussed the medicinal property of honeys with emphasis on their antibacterial activities.


title: An improved bee colony optimization algorithm with an application to document clustering

abstract: The bee colony optimization (BCO) algorithm is proved to be one of the fast, robust and efficient global search heuristics in tackling different practical problems. Considering BCO algorithm in this paper, we utilize it for the data clustering, a fundamental problem that frequently arises in many applications. However, we discovered some obstacles in directly applying the ancient BCO to address the clustering problem and managed to change some basic behaviors of this swarm algorithm. In particular, we present an improved bee colony optimization algorithm, dubbed IBCO, by introducing cloning and fairness concepts into the BCO algorithm and make it more efficient for data clustering. These features give BCO very powerful and balanced exploration and exploitation capabilities to effectively guide the search process toward the proximity of the high quality solutions. In particular, the cloning feature allows it to take advantage of experiences gained from previous generations when generating new solutions. The problem of getting stuck in local optima still laid bare in the proposed improved version. As a result, to overcome the shortage of this swarm algorithm in searching locally, we hybridize it with the k-means algorithm to take advantage of fine tuning power of the widely used k-means algorithm which demonstrates good result in local searches. We propose four different hybridized algorithms based on IBCO and k-means algorithms and investigate the clustering results and convergence behavior of them. We empirically demonstrate that our hybrid algorithms alleviate the problem of sticking in a local solution even for large and high dimensional data sets such as document clustering. The results show that proposed algorithms are robust enough to be used in many applications compared to k-means and other recently proposed evolutionary based clustering algorithms including genetic, particle swarm optimization, ant colony, and bee based algorithms. HighlightsAn improved bee colony optimization algorithm by introducing cloning and fairness concepts into the algorithm.An efficient document clustering algorithm based on the proposed improved bee colony algorithm.Hybrid clustering algorithms to gain from the explorative power of the proposed improved algorithm and exploitive power of the k-means algorithm.An exhaustive set of experiments on various standard datasets.


title: AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction

abstract: Android smartphones are becoming increasingly popular. The open nature of Android allows users to install miscellaneous applications, including the malicious ones, from third-party marketplaces without rigorous sanity checks. A large portion of existing malwares perform stealthy operations such as sending short messages, making phone calls and HTTP connections, and installing additional malicious components. In this paper, we propose a novel technique to detect such stealthy behavior. We model stealthy behavior as the program behavior that mismatches with user interface, which denotes the user's expectation of program behavior. We use static program analysis to attribute a top level function that is usually a user interaction function with the behavior it performs. Then we analyze the text extracted from the user interface component associated with the top level function. Semantic mismatch of the two indicates stealthy behavior. To evaluate AsDroid, we download a pool of 182 apps that are potentially problematic by looking at their permissions. Among the 182 apps, AsDroid reports stealthy behaviors in 113 apps, with 28 false positives and 11 false negatives.


title: Using Ideas of Kolmogorov Complexity for Studying Biological Texts

abstract: Kolmogorov complexity furnishes many useful tools for studying different natural processes that can be expressed using sequences of symbols from a finite alphabet (texts), such as genetic texts, literary and music texts, animal communications, etc. Although Kolmogorov complexity is not algorithmically computable, in a certain sense it can be estimated by means of data compressors. Here we suggest a method of analysis of sequences based on ideas of Kolmogorov complexity and mathematical statistics, and apply this method to biological (ethological) "texts." A distinction of the suggested method from other approaches to the analysis of sequential data by means of Kolmogorov complexity is that it belongs to the framework of mathematical statistics, more specifically, that of hypothesis testing. This makes it a promising candidate for being included in the toolbox of standard biological methods of analysis of different natural texts, from DNA sequences to animal behavioural patterns (ethological "texts"). Two examples of analysis of ethological texts are considered in this paper. Theses examples show that the proposed method is a useful tool for distinguishing between stereotyped and flexible behaviours, which is important for behavioural and evolutionary studies.


title: Breaking the filter bubble: democracy and design

abstract: It has been argued that the Internet and social media increase the number of available viewpoints, perspectives, ideas and opinions available, leading to a very diverse pool of information. However, critics have argued that algorithms used by search engines, social networking platforms and other large online intermediaries actually decrease information diversity by forming so-called "filter bubbles". This may form a serious threat to our democracies. In response to this threat others have developed algorithms and digital tools to combat filter bubbles. This paper first provides examples of different software designs that try to break filter bubbles. Secondly, we show how norms required by two democracy models dominate the tools that are developed to fight the filter bubbles, while norms of other models are completely missing in the tools. The paper in conclusion argues that democracy itself is a contested concept and points to a variety of norms. Designers of diversity enhancing tools must thus be exposed to diverse conceptions of democracy.


title: Top-N Recommendation on Graphs

abstract: Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.


title: Projective simulation for artificial intelligence

abstract: We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.


title: Impact Analysis of Flow Shaping in Ethernet-AVB/TSN and AFDX from Network Calculus and Simulation Perspective

abstract: Ethernet-AVB/TSN (Audio Video Bridging/Time-Sensitive Networking) and AFDX (Avionics Full DupleX switched Ethernet) are switched Ethernet technologies, which are both candidates for real-time communication in the context of transportation systems. AFDX implements a fixed priority scheduling strategy with two priority levels. Ethernet-AVB/TSN supports a similar fixed priority scheduling with an additional Credit-Based Shaper (CBS) mechanism. Besides, TSN can support time-triggered scheduling strategy. One direct effect of CBS mechanism is to increase the delay of its flows while decreasing the delay of other priority ones. The former effect can be seen as the shaping restriction and the latter effect can be seen as the shaping benefit from CBS. The goal of this paper is to investigate the impact of CBS on different priority flows, especially on the intermediate priority ones, as well as the effect of CBS bandwidth allocation. It is based on a performance comparison of AVB/TSN and AFDX by simulation in an automotive case study. Furthermore, the shaping benefit is modeled based on integral operation from network calculus perspective. Combing with the analysis of shaping restriction and shaping benefit, some configuration suggestions on the setting of CBS bandwidth are given. Results show that the effect of CBS depends on flow loads and CBS configurations. A larger load of high priority flows in AVB tends to a better performance for the intermediate priority flows when compared with AFDX. Shaping benefit can be explained and calculated according to the changing from the permitted maximum burst.


title: Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

abstract: We consider the problem of \emph{fully decentralized} multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network. Specifically, we assume that the reward functions of the agents might correspond to different tasks, and are only known to the corresponding agent. Moreover, each agent makes individual decisions based on both the information observed locally and the messages received from its neighbors over the network. Within this setting, the collective goal of the agents is to maximize the globally averaged return over the network through exchanging information with their neighbors. To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large. Under the decentralized structure, the actor step is performed individually by each agent with no need to infer the policies of others. For the critic step, we propose a consensus update via communication over the network. Our algorithms are fully incremental and can be implemented in an online fashion. Convergence analyses of the algorithms are provided when the value functions are approximated within the class of linear functions. Extensive simulation results with both linear and nonlinear function approximations are presented to validate the proposed algorithms. Our work appears to be the first study of fully decentralized MARL algorithms for networked agents with function approximation, with provable convergence guarantees.


title: Using algebraic functions of views for indexing-based object recognition

abstract: Current indexing-based approaches build the hash table using either a large number of reference views or 3D models. In this paper, we propose building the hash table using algebraic functions of views. During preprocessing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. These views are computed using algebraic functions of a small number of reference views which contain the group. Fundamental to this procedure is a methodology based on singular value decomposition and interval arithmetic for estimating the ranges of values that the parameters of algebraic functions can assume. During recognition, scene groups are used to retrieve from the hash table the model groups that might have produced them. Using algebraic functions of views for indexing-based recognition offers a number of advantages. First of all, the hash table can be built easier without requiring 3D models or a large number of reference views. Second, recognition does not rely on the similarities between new and reference views. Third, verification becomes simpler. Finally, the approach is more general and extendible.


title: Space and frequency correlation characteristics of L‐band multipath fading due to sea surface reflection

abstract: When maritime satellite communication is conducted at low elevation angles, fading due to sea surface reflection becomes a problem. For suppression of the fading, space diversity and frequency diversity may be employed. This paper studies the correlation characteristics of multipath fading due to sea reflection in the spatial and frequency domains in L-band operation. This is important in investigating the diversity effect. First, from the theoretical model of sea surface reflection, the correlation scattering cross sections of the coherent and incoherent components of the reflected wave are obtained. Next, from numerical calculations, the correlation characteristics are obtained with the antenna elevation angle and the height over the sea as the parameters. It is found that in space diversity good diversity effects can be expected from two antennas- separated in the vertical direction by about 40 cm. It is also seen that fading reduction cannot be expected with frequency diversity in the case of narrowband communications such as in maritime satellite communications. The effects of various fading reduction measures in the up and down links are considered.


title: Filtered Patent Maps for Predicting Diversification Paths of Inventors and Organizations.

abstract: In a patent technology network map, almost all pairs of technology classes are connected, whereas most of the connections are extremely weak. This observation suggests the need and also the possibility to filter the network map by removing the negligible and noisy links. But link removal may reduce the power of the network for predicting the cross-field patent portfolio diversification of inventors and inventing organizations. This paper proposes a metric for such predictive power of a patent network, and a method that allows one to objectively choose a best tradeoff between predictive power and the removal of weak links. We show the results that identify filtered networks below the optimal tradeoff, and also remove a degree of arbitrariness compared with other filtering treatments from the literature. On that basis, we further demonstrate the use of filtered technology maps to visualize and analyze the main paths of patent portfolio diversification of a prolific inventor (Leonard Forbes) and a technology company (Google Inc.).


Atherosclerosis: Why Do We Pretend the Pathogenesis Is Mysterious?


title: Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems

abstract: Automated vehicle detection plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion or unstable bandwidth, especially in regard to wireless systems. This often hinders the detection of moving vehicles in variable bit-rate video streams. This paper presents a novel approach for vehicle detection based on probabilistic neural networks through artificial neural networks, which can accurately detect moving vehicles not only in high bit-rate video streams but also in low bit-rate video streams. The overall results of detection accuracy analyses demonstrate that the proposed approach has a substantially higher degree of both qualitative and quantitative efficacy than other state-of-the-art methods. For instance, the proposed method achieved Similarity and F 1 accuracy rates that were up to 61.75% and 69.38% higher than the other compared methods, respectively.


title: Trajectory planning and four-leg coordination for stair climbing in a quadruped robot

abstract: We report on the algorithm of trajectory planning and four leg coordination for quasi-static stair climbing in a quadruped robot. The development is based on the geometrical interactions between the robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. In addition, a brief study on stability of the robot is also reported. Finally, simulation and experimental test are also executed to evaluate the performance of the algorithm.


title: Three-Dimensional Stacked Nanophotonic Network-on-Chip Architecture with Minimal Reconfiguration

abstract: As throughput, scalability, and energy efficiency in network-on-chips (NoCs) are becoming critical, there is a growing impetus to explore emerging technologies for implementing NoCs in future multicore and many-core architectures. Two disruptive technologies on the horizon are nanophotonic interconnects (NIs) and 3D stacking. NIs can deliver high on-chip bandwidth while delivering low energy/bit, thereby providing a reasonable performance-per-watt in the future. Three-dimensional stacking can reduce the interconnect distance and increase the bandwidth density by incorporating multiple communication layers. In this paper, we propose an architecture that combines NIs and 3D stacking to design an energy-efficient and reconfigurable NoC. We quantitatively compare the hardware complexity of the proposed topology to other nanophotonic networks in terms of hop count, network diameter, radix, and photonic parameters. To maximize performance, we also propose an efficient reconfiguration algorithm that dynamically reallocates channel bandwidth by adapting to traffic fluctuations. For 64-core reconfigured network, our simulation results indicate that the execution time can be reduced up to 25 percent for Splash-2, PARSEC, and SPEC CPU2006 benchmarks. Moreover, for a 256-core version of the proposed architecture, our simulation results indicate a throughput improvement of more than 25 percent and energy savings of 23 percent on synthetic traffic when compared to competitive on-chip electrical and optical networks.


title: Why do bad moods increase self-defeating behavior? Emotion, risk taking, and self-regulation

abstract: Increased risk taking may explain the link between bad moods and self-defeating behavior. In Study 1, personal recollections of self-defeating actions implicated bad moods and resultant risky decisions. In Study 2, embarrassment increased the preference for a long-shot (high-risk, high-payoff) lottery over a low-risk, low-payoff one. Anger had a similar effect in Study 3. Study 4 replicated this and showed that the effect could be eliminated by making participants analyze the lotteries rationally, suggesting that bad moods foster risk taking by impairing self-regulation instead of by altering subjective utilities. Studies 5 and 6 showed that the risky tendencies are limited to unpleasant moods accompanied by high arousal; neither sadness nor neutral arousal resulted in destructive risk taking. Language: en


title: Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community

abstract: Database URL ::: https://citrusgreening.org/.


title: A Prosody-Only Decision-Tree Model For Disfluency Detection

abstract: Speech disfluencies (filled pauses, repetitions, repairs, a nd false starts) are pervasive in spontaneous speech. The ability to detect and correct disfluencies automatically is important for eff ective natural language understanding, as well as to improve speech models in general. Previous approaches to disfluency detect ion have relied heavily on lexical information, which makes them less applicable when word recognition is unreliable. We have developed a disfluency detection method using decision tree classifiers that use only local and automatically extracted pros odic features. Because the model doesn’t rely on lexical informa tion, it is widely applicable even when word recognition is unreliable. The model performed significantly better than chance a t detecting four disfluency types. It also outperformed a lang uage model in the detection of false starts, given the correct tra nscription. Combining the prosody model with a specialized language model improved accuracy over either model alone for the detection of false starts. Results suggest that a prosody-only mo del can aid the automatic detection of disfluencies in spontaneo us speech.


title: Social Media Technology and Government Transparency

abstract: Researching and evaluating social media initiatives is essential to encourage usage, continually improve the tools and policies governing their use, and develop governance processes that incorporate social media participation in its many forms.


title: Automatic information extraction from large websites

abstract: Information extraction from websites is nowadays a relevant problem, usually performed by software modules called wrappers. A key requirement is that the wrapper generation process should be automated to the largest extent, in order to allow for large-scale extraction tasks even in presence of changes in the underlying sites. So far, however, only semi-automatic proposals have appeared in the literature.We present a novel approach to information extraction from websites, which reconciles recent proposals for supervised wrapper induction with the more traditional field of grammar inference. Grammar inference provides a promising theoretical framework for the study of unsupervised---that is, fully automatic---wrapper generation algorithms. However, due to some unrealistic assumptions on the input, these algorithms are not practically applicable to Web information extraction tasks.The main contributions of the article stand in the definition of a class of regular languages, called the prefix mark-up languages, that abstract the structures usually found in HTML pages, and in the definition of a polynomial-time unsupervised learning algorithm for this class. The article shows that, differently from other known classes, prefix mark-up languages and the associated algorithm can be practically used for information extraction purposes.A system based on the techniques described in the article has been implemented in a working prototype. We present some experimental results on known Websites, and discuss opportunities and limitations of the proposed approach.


title: Pipelined back-propagation for context-dependent deep neural networks,” Interspeech

abstract: The Context-Dependent Deep-Neural-Network HMM, or CDDNN-HMM, is a recently proposed acoustic-modeling technique for HMM-based speech recognition that can greatly outperform conventional Gaussian-mixture based HMMs. For example, a CD-DNN-HMM trained on the 2000h Fisher corpus achieves 14.4% word error rate on the Hub5’00-FSH speakerindependent phone-call transcription task, compared to 19.6% obtained by a state-of-the-art, conventional discriminatively trained GMM-based HMM. That CD-DNN-HMM, however, took 59 days to train on a modern GPGPU—the immense computational cost of the minibatch based back-propagation (BP) training is a major roadblock. Unlike the familiar Baum-Welch training for conventional HMMs, BP cannot be efficiently parallelized across data. In this paper we show that the pipelined approximation to BP, which parallelizes computation with respect to layers, is an efficient way of utilizing multiple GPGPU cards in a single server. Using 2 and 4 GPGPUs, we achieve a 1.9 and 3.3 times end-to-end speed-up, at parallelization efficiency of 0.95 and 0.82, respectively, at no loss of recognition accuracy.


title: CLOSET+: searching for the best strategies for mining frequent closed itemsets

abstract: Mining frequent closed itemsets provides complete and non-redundant results for frequent pattern analysis. Extensive studies have proposed various strategies for efficient frequent closed itemset mining, such as depth-first search vs. breadthfirst search, vertical formats vs. horizontal formats, tree-structure vs. other data structures, top-down vs. bottom-up traversal, pseudo projection vs. physical projection of conditional database, etc. It is the right time to ask "what are the pros and cons of the strategies?" and "what and how can we pick and integrate the best strategies to achieve higher performance in general cases?"In this study, we answer the above questions by a systematic study of the search strategies and develop a winning algorithm CLOSET+. CLOSET+ integrates the advantages of the previously proposed effective strategies as well as some ones newly developed here. A thorough performance study on synthetic and real data sets has shown the advantages of the strategies and the improvement of CLOSET+ over existing mining algorithms, including CLOSET, CHARM and OP, in terms of runtime, memory usage and scalability.


title: A Survey on Load Testing of Large-Scale Software Systems

abstract: Many large-scale software systems must service thousands or millions of concurrent requests. These systems must be load tested to ensure that they can function correctly under load (i.e., the rate of the incoming requests). In this paper, we survey the state of load testing research and practice. We compare and contrast current techniques that are used in the three phases of a load test: (1) designing a proper load, (2) executing a load test, and (3) analyzing the results of a load test. This survey will be useful for load testing practitioners and software engineering researchers with interest in the load testing of large-scale software systems.


title: Amortized efficiency of list update and paging rules

abstract: In this article we study the amortized efficiency of the “move-to-front” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes t(i) time, we show that move-to-front is within a constant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, do not share this property. We generalize our results to show that move-to-front is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to move-to-front is the “least recently used” (LRU) replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the off-line paging rule (Belady's MIN algorithm) by a factor that depends on the size of fast memory. No on-line paging algorithm has better amortized performance.


title: Ecology and evolution of the flu

abstract: Influenza (flu) is a common infectious disease, but it is unusual in that the primary timescales for disease dynamics (epidemics) and viral evolution (new variants) are roughly the same. Recently, extraordinarily reliable phylogenetic reconstructions of flu virus evolution have been made using samples from both extant and extinct strains. In addition, because of their public health importance, flu epidemics have been monitored throughout the period over which the phylogenetic trees extend. In parallel with this empirical work, theoretical ecologists have developed mathematical and computational models that elucidate many properties of multistrain systems. In the future, to unravel and interpret the complex interactions between ecological and evolutionary forces on flu dynamics, the documented evolution of the virus must be related to the observed population dynamics of the disease. New theoretical insights are also required to simplify model structures and facilitate predictions that can be tested with accessible data.


title: A novel genetic algorithm considering measures and phrases for generating melody

abstract: Composing music through evolutionary algorithms has received increasing attention recently. To establish a standard of composing, some studies were proposed on the basis of analysis on musicians, statistics of music details, and rule of thumbs. These methods have achieved some promising results; however, generating melody is still a formidable challenge to computer composition because of the considerable permutations of notes. This study develops a genetic algorithm (GA) based on music theory to generate melody. In particular, we use the rhythm of existing songs as the basis to generate new compositions instead of generating music from scratch; that is, the GA keeps the rhythm of an existing song and rearranges the pitches of all notes for a new composition. Three crossover operators are further proposed to improve the performance of GA on composition. The experimental results show that the GA can achieve satisfactory compositions. The three crossover operators outperform 2-point crossover in the fitness of resultant compositions.


title: Harvesting rainfall energy by means of piezoelectric transducer

abstract: In this paper a detailed study on the piezoelectric energy harvesting of rainfall is presented. Different features have been taken into account in order to define the limits in this energy harvesting. Only commercial transducers have been considered: a lead zirconate titanate and polyvinylidene difluoride transducer.


title: Accounting Restatements and External Financing Choices

abstract: There is little research on how accounting information quality affects a firm’s external financing choices. In this paper, we use the occurrence of accounting restatements as a proxy for the reduced credibility of accounting information and investigate how restatements affect a firm’s external financing choices. We find that for firms that obtain external financing after restatements, they rely more on debt financing, especially private debt financing, and less on equity financing. The increase in debt financing is more pronounced for firms with more severe information problems and less pronounced for firms with prompt CEO/CFO turnover and auditor dismissal. Our evidence indicates that accounting information quality affects capital providers’ resource allocation and that debt holders help alleviate information problems after accounting restatements.


title: Semantic Photo Retargeting Under Noisy Image Labels

abstract: With the popularity of mobile devices, photo retargeting has become a useful technique that adapts a high-resolution photo onto a low-resolution screen. Conventional approaches are limited in two aspects. The first factor is the de-emphasized role of semantic content that is many times more important than low-level features in photo aesthetics. Second is the importance of image spatial modeling: toward a semantically reasonable retargeted photo, the spatial distribution of objects within an image should be accurately learned. To solve these two problems, we propose a new semantically aware photo retargeting that shrinks a photo according to region semantics. The key technique is a mechanism transferring semantics of noisy image labels (inaccurate labels predicted by a learner like an SVM) into different image regions. In particular, we first project the local aesthetic features (graphlets in this work) onto a semantic space, wherein image labels are selectively encoded according to their noise level. Then, a category-sharing model is proposed to robustly discover the semantics of each image region. The model is motivated by the observation that the semantic distribution of graphlets from images tagged by a common label remains stable in the presence of noisy labels. Thereafter, a spatial pyramid is constructed to hierarchically encode the spatial layout of graphlet semantics. Based on this, a probabilistic model is proposed to enforce the spatial layout of a retargeted photo to be maximally similar to those from the training photos. Experimental results show that (1) noisy image labels predicted by different learners can improve the retargeting performance, according to both qualitative and quantitative analysis, and (2) the category-sharing model stays stable even when 32.36p of image labels are incorrectly predicted.


title: VoIP steganography and its Detection—A survey

abstract: Steganography is an ancient art that encompasses various techniques of information hiding, the aim of which is to secret information into a carrier message. Steganographic methods are usually aimed at hiding the very existence of the communication. Due to the rise in popularity of IP telephony, together with the large volume of data and variety of protocols involved, it is currently attracting the attention of the research community as a perfect carrier for steganographic purposes. This paper is a survey of the existing VoIP steganography (steganophony) methods and their countermeasures.


title: A finger vein recognition system

abstract: The most vital requirement in today's world of spoofing attacks is the high security. The development in consumer electronics demands for high security with high accuracy and high speed of authentication. Human behavioural and physiological features in biometrics has the large scope as a solution for security issues. However, the existing biometric systems are highly complex in terms of time or space or both, and thus not suitable in very high security. Thus an embedded finger-vein recognition system for authentication is proposed. The system is to be implemented using novel finger vein recognition algorithm and lacunae, fractal dimension and gabor filter are the algorithms used for feature extraction and the matching of the extracted feature is done using the distance classifier. The analysis is done using the various features from which the kurtosis, range shows large variation from person to person. Based on this analysis finger vein recognition becomes easier and reliable.


title: A Paradigm for Situated and Goal-Driven Language Learning

abstract: A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research in natural language dialogue should focus on designing communicative agents which can integrate themselves into these contexts and productively collaborate with humans. In this abstract, we propose a general situated language learning paradigm which is designed to bring about robust language agents able to cooperate productively with humans.


title: Religiosity as a predictor of in-group favoritism within and between religious groups

abstract: Abstract It was hypothesized that religiosity is positively associated with religious in-group favoritism. This hypothesis was tested using the second wave of data from the Midlife in the United States representative survey of middle adulthood. The sample included White participants from four religious groups (Baptists, Catholics, Methodists, and Jews). Consistent with the hypothesis, when analyzing the full sample and within each of the four religious groups, religiosity was predictive of in-group favoritism. However, while differences between religious groups in in-group favoritism emerged, and remained when controlling for the previously found group differences in intelligence and personality, the group differences in in-group favoritism were not mediated by religiosity. For example, while Baptists scored high in both religiosity and in-group favoritism, Jews scored low in religiosity yet high in in-group favoritism. Possible explanations for these findings are discussed, such as genetic similarity among group members.


title: Cognitive Performance , Sleepiness , and Mood in Partially Sleep Deprived Adolescents : The Need for Sleep Study

abstract: Summary ::: Retrieving false information can have serious consequences. Sleep is important for memory, but voluntary sleep curtailment is becoming more rampant. Here, the misinformation paradigm was used to investigate false memory formation after 1 night of total sleep deprivation in healthy young adults (N = 58, mean age ± SD = 22.10 ± 1.60 years; 29 males), and 7 nights of partial sleep deprivation (5 h sleep opportunity) in these young adults and healthy adolescents (N = 54, mean age ± SD = 16.67 ± 1.03 years; 25 males). In both age groups, sleep-deprived individuals were more likely than well-rested persons to incorporate misleading post-event information into their responses during memory retrieval (P < 0.050). These findings reiterate the importance of adequate sleep in optimal cognitive functioning, reveal the vulnerability of adolescents' memory during sleep curtailment, and suggest the need to assess eyewitnesses' sleep history after encountering misleading information.


title: Effect of Manipulated Amplitude and Frequency of Human Voice on Dominance and Persuasiveness in Audio Conferences

abstract: We propose to artificially manipulate participants' vocal cues, amplitude and frequency, in real time to adjust their dominance and persuasiveness during audio conferences. We implemented a prototype system and conducted two experiments. The first experiment investigated the effect of vocal cue manipulation on the perception of dominance. The results showed that participants perceived higher dominance while listening to a voice with a high amplitude and low frequency. The second experiment investigated the effect of vocal cue manipulation on persuasiveness. The results indicated that a person with a low amplitude and low frequency voice had greater persuasiveness in conversations with biased dominance, while there was no statistically significant difference in persuasiveness in conversations with unbiased dominance.


title: Watch Me Code: Programming Mentorship Communities on Twitch.tv

abstract: Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.