title: Thermal resistance evaluation of die-attachment made of nano-composite Cu/Sn TLPS paste in SiC power module

abstract: In this paper, we demonstrate a high heat resistant bonding method by Cu/Sn transient liquid phase sintering (TLPS) method can be applied to die-attachment of silicon carbide (SiC)-MOSFET in high temperature operation power module. The die-attachment is made of nano-composite Cu/Sn TLPS paste. The die shear strength was 40 MPa for 3 × 3 mm2 SiC chip after 1,000 cycles of thermal cycle testing between −40 °C and 250 °C. This indicated a high reliability of Cu/Sn die-attachment. The thermal resistance of the Cu/Sn die-attachment was evaluated by transient thermal analysis using a sample in which the SiC-MOSFET (die size: 4.04 × 6.44 mm2) was bonded with Cu/Sn die-attachment. The thermal resistance of Cu/Sn die-attachment was 0.13 K/W, which was comparable to the one of Au/Ge die-attachment (0.12 K/W). The validity of nano-composite Cu/Sn TLPS paste as a die-attachment for high-temperature operation SiC power module is confirmed.


title: Complications caused by injection of dermal filler in Danish patients

abstract: Background ::: The usage of dermal fillers has increased significantly in recent years. Soft tissue augmentation with fillers helps to diminish the facial lines and to restore volume and fullness in the face at a relatively low cost. With the increasing number of treatments, the number of complications is likely to increase as well.


title: Recommendation sources on the intention to use e-books in academic digital libraries

abstract: Purpose – Few library studies have investigated recommendation classifications for e‐book (electronic book) usage, while none have directly compared what recommendation sources (word‐of‐mouth, advertising, and expert recommendation) might influence e‐book usage intentions. To fill this gap in the literature, the main purposes of this study are to: examine how users perceive the influence of recommendations on the intention to use e‐books for academic purposes; and to measure the level of the perception of trust and perceived risk when users receive e‐book recommendations from peers, advertisers, and experts.Design/methodology/approach – Data for this study were collected from 382 academic digital library users between the ages of 18 and 25. A multiple regression analysis was then conducted to identify the key causal relationships.Findings – The comparison of three recommendation sources (word‐of‐mouth, advertising, and expert recommendations) revealed that word‐of‐mouth (WOM) played a more important role ...


title: Location-aware Event Attendance System using QR Code and GPS Technology

abstract: Attendance process in a university’s event is time consuming and tracking the attendance can be harder. In this paper, a smart event attendance system for a university using QR code and GPS technology is proposed with objective to speed up the process of taking students’ attendance and tracking full attendance. The method of developing the system is based on two views; user view which is the mobile application used by the students, and admin view which is the web administration system used by the event organizer. From the evaluation, students’ attendance can be traced from the GPS location combine with QR code. The results indicate that full attendance increases as the system validates attendance through users’ identification, location and timestamp during user login and logout. The proposed system contributes to high satisfaction among the users that claim that the mobile application helps to speed up the event registration process.


title: Alpha-anonymization techniques for privacy preservation in social networks

abstract: Rapid growth and development of social networks has attracted the interest of the scientific community to utilize these huge datasets for research purpose. However, preserving the privacy of the users in the published data has also become an important concern. An adversary with very little background knowledge about the actors can extract personal information from the published data. To prevent such type of attacks, different anonymization models have been proposed for relational micro-data, which are further extended and adjusted to handle social network data. Preserving the structural properties of the raw graph is one of the most important aspects of social network anonymization. In this paper, we propose an (α, k) anonymity model based on the eigenvector centrality of the nodes present in the raw graph. We further extend the (α, k) anonymity model to propose (α, l) diversity model and (α, c, l) diversity model, which can also protect the sensitive attribute values associated with a particular actor. For anonymization purpose, we applied the noise node addition technique to generate the anonymized graphs. We tested our proposed algorithms with both synthetic dataset and real dataset. The results obtained show the effectiveness of our proposed algorithm in preserving the structural property of the raw graph. Our proposed methods add noise nodes efficiently so that they have minimal social importance.


title: Rein: Taming Tail Latency in Key-Value Stores via Multiget Scheduling

abstract: We tackle the problem of reducing tail latencies in distributed key-value stores, such as the popular Cassandra database. We focus on workloads of multiget requests, which batch together access to several data elements and parallelize read operations across the data store machines. We first analyze a production trace of a real system and quantify the skew due to multiget sizes, key popularity, and other factors. We then proceed to identify opportunities for reduction of tail latencies by recognizing the composition of aggregate requests and by carefully scheduling bottleneck operations that can otherwise create excessive queues. We design and implement a system called Rein, which reduces latency via inter-multiget scheduling using low overhead techniques. We extensively evaluate Rein via experiments in Amazon Web Services (AWS) and simulations. Our scheduling algorithms reduce the median, 95th, and 99th percentile latencies by factors of 1.5, 1.5, and 1.9, respectively.


title: The Social Psychology of Stigma.

abstract: This chapter addresses the psychological effects of social stigma. Stigma directly affects the stigmatized via mechanisms of discrimination, expectancy confirmation, and automatic stereotype activation, and indirectly via threats to personal and social identity. We review and organize recent theory and empirical research within an identity threat model of stigma. This model posits that situational cues, collective representations of one's stigma status, and personal beliefs and motives shape appraisals of the significance of stigma-relevant situations for well-being. Identity threat results when stigma-relevant stressors are appraised as potentially harmful to one's social identity and as exceeding one's coping resources. Identity threat creates involuntary stress responses and motivates attempts at threat reduction through coping strategies. Stress responses and coping efforts affect important outcomes such as self-esteem, academic achievement, and health. Identity threat perspectives help to explain the...


title: EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

abstract: Objective: Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support (CDS). Our objective is a general system that can extract and represent these knowledge contained in EMRs to support three CDS tasks: test recommendation, initial diagnosis, and treatment plan recommendation, with the given condition of one patient. Methods: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a single record. Three bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each task. One part of the bi-graph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bi-graph was regarded as a Markov random field to support the inference. Three lazy energy functions and one parameter-based energy function were proposed, as well as two knowledge representation learning-based energy functions, which can provide a distributed representation of medical entities. Three measures were utilized for performance evaluation. Results: On the initial diagnosis task, 80.11% of the test records identified at least one correct disease from top 10 candidates. Test and treatment recommendation results were 87.88% and 92.55%, respectively. These results altogether indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships in regards to knowledge level. Conclusion: Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require designing their energy functions individually.


title: Reward prediction in primate basal ganglia and frontal cortex

abstract: Abstract Reward information is processed in a limited number of brain structures, including fronto-basal ganglia systems. Dopamine neurons respond phasically to primary rewards and reward-predicting stimuli depending on reward unpredictability but without discriminating between rewards. These responses reflect ‘errors’ in the prediction of rewards in correspondence to learning theories and thus may constitute teaching signals for appetitive learning. Neurons in the striatum (caudate, putamen, ventral striatum) code reward predictions in a different manner. They are activated during several seconds when animals expect predicted rewards. During learning, these activations occur initially in rewarded and unrewarded trials and become subsequently restricted to rewarded trials. This occurs in parallel with the adaptation of reward expectations by the animals, as inferred from their behavioral reactions. Neurons in orbitofrontal cortex respond differentially to stimuli predicting different liquid rewards, without coding spatial or visual features. Thus, different structures process reward information processed in different ways. Whereas dopamine neurons emit a reward teaching signal without indicating the specific reward, striatal neurons adapt expectation activity to new reward situations, and orbitofrontal neurons process the specific nature of rewards. These reward signals need to cooperate in order for reward information to be used for learning and maintaining approach behavior.


title: Examining the Stability of Logging Statements

abstract: Logging statements (embedded in the source code) produce logs that assist in understanding system behavior, monitoring choke-points and debugging. Prior work showcases the importance of logging statements in operating, understanding and improving software systems. The wide dependence on logs has lead to a new market of log processing and management tools. However, logs are often unstable, i.e., the logging statements that generate logs are often changed without the consideration of other stakeholders, causing sudden failures of log processing tools and increasing the maintenance costs of such tools. We examine the stability of logging statements in four open source applications namely: Liferay, ActiveMQ, Camel and CloudStack. We find that 20–45% of their logging statements change throughout their lifetime. The median number of days between the introduction of a logging statement and the first change to that statement is between 1 and 17 in our studied applications. These numbers show that in order to reduce maintenance effort, developers of log processing tools must be careful when selecting the logging statements on which their tools depend. In order to effectively mitigate the issues that are caused by unstable logging statements, we make an important first step towards determining whether a logging statement is likely to remain unchanged in the future. First, we use a random forest classifier to determine whether a just-introduced logging statement will change in the future, based solely on metrics that are calculated when it is introduced. Second, we examine whether a long-lived logging statement is likely to change based on its change history. We leverage Cox proportional hazards models (Cox models) to determine the change risk of long-lived logging statements in the source code. Through our case study on four open source applications, we show that our random forest classifier achieves a 83–91% precision, a 65–85% recall and a 0.95–0.96 AUC. We find that file ownership, developer experience, log density and SLOC are important metrics in our studied projects for determining the stability of logging statements in both our random forest classifiers and Cox models. Developers can use our approach to determine the risk of a logging statement changing in their own projects, to construct more robust log processing tools, by ensuring that these tools depend on logs that are generated by more stable logging statements.


title: multithreaded context for robust conversational interfaces: Context-sensitive speech recognition and interpretation of corrective fragments

abstract: We focus on the issue of robustness of conversational interfaces that are flexible enough to allow natural "multithreaded" conversational flow. Our main advance is to use context-sensitive speech recognition in a general way, with a representation of dialogue context that is rich and flexible enough to support conversation about multiple interleaved topics, as well as the interpretation of corrective fragments. We explain, by use of worked examples, the use of our "Conversational Intelligence Architecture" (CIA) to represent conversational threads, and how each thread can be associated with a language model (LM) for more robust speech recognition. The CIA uses fine-grained dynamic representations of dialogue context, which supersede those used in finite-state or form-based dialogue managers. In an evaluation of a dialogue system built using this architecture we found that 87.9p of recognized utterances were recognized using a context-specific language model, resulting in an 11.5p reduction in the overall utterance recognition error rate, and a 13.4p reduction in concept error rate. Thus we show that by using context-sensitive recognition based on the predicted type of the user's next dialogue move, a more flexible dialogue system can also exhibit an improvement in speech recognition performance.


title: Mental models concepts for system dynamics research

abstract: Although “mental models” are of central importance to system dynamics research and practice, the field has yet to develop an unambiguous and agreed upon definition of them. To begin to address this problem, existing definitions and descriptions of mental models in system dynamics and several literatures related to cognitive science were reviewed and compared. Available definitions were found to be overly brief, general, and vague, and different authors were found to markedly disagree on the basic characteristics of mental models. Based on this review, we concluded that in order to reduce the amount of confusion in the literature, the mental models concept should be “unbundled” and the term “mental models” should be used more narrowly. To initiate a dialogue through which the system dynamics community might achieve a shared understanding of mental models, we propose a new definition of “mental models of dynamic systems” accompanied by an extended annotation that explains the definitional choices made and suggests terms for other cognitive structures left undefined by narrowing the mental model concept. Suggestions for future research that could improve the field's ability to further define mental models are discussed. © 1998 John Wiley & Sons, Ltd.


title: Field-aware Factorization Machines for CTR Prediction

abstract: Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.


title: Common and distinct neural correlates of personal and vicarious reward: A quantitative meta-analysis

abstract: Individuals experience reward not only when directly receiving positive outcomes (e.g., food or money), but also when observing others receive such outcomes. This latter phenomenon, known as vicarious reward, is a perennial topic of interest among psychologists and economists. More recently, neuroscientists have begun exploring the neuroanatomy underlying vicarious reward. Here we present a quantitative whole-brain meta-analysis of this emerging literature. We identified 25 functional neuroimaging studies that included contrasts between vicarious reward and a neutral control, and subjected these contrasts to an activation likelihood estimate (ALE) meta-analysis. This analysis revealed a consistent pattern of activation across studies, spanning structures typically associated with the computation of value (especially ventromedial prefrontal cortex) and mentalizing (including dorsomedial prefrontal cortex and superior temporal sulcus). We further quantitatively compared this activation pattern to activation foci from a previous meta-analysis of personal reward. Conjunction analyses yielded overlapping VMPFC activity in response to personal and vicarious reward. Contrast analyses identified preferential engagement of the nucleus accumbens in response to personal as compared to vicarious reward, and in mentalizing-related structures in response to vicarious as compared to personal reward. These data shed light on the common and unique components of the reward that individuals experience directly and through their social connections.


title: Development of unstable hips after treatment with the Tübingen splint: mid-term follow-up of 83 hip joints

abstract: Early diagnosis and treatment of hip dysplasia are widely accepted as major factors for beneficial outcome. However, modalities for reduction and retention as well as for imaging are currently under clinical investigation. Local and general risk factors, e.g., breech presentation and the family’s desire to avoid in-hospital treatment are major concerns in the decision-making process and consultation. For treatment of unstable hips in newborns the treatment with the Tübingen splint has proven good results in recent studies. However, mid- and long-term outcome studies are missing. We report on clinical results and X-ray parameter of initially unstable hips after treatment with the Tübingen splint at two time points: 12–24 and 24–48 months of age. Included were newborns with 83 unstable hips (type D, III, IV according to Graf) which were successfully treated with the Tübingen splint—used as reduction splint 24 h per day/7 days per week—until type I hips were documented by ultrasound examination. Measurments are based on routine pelvic X-ray control at the age of 12–24 and 24–48 months. The acetabular angle was determined and according to the Tönnis-Classification evaluated into: normal findings (< 1 s), slightly dysplastic findings (1–2 s) and severely dysplastic findings (> 2 s). Children with secondary hip dysplasia were not included in this series. In 2nd year of life, 45 hips (54.2%; initial hip type D: 47.4%, III: 63.2%, IV: 42.9%) of the formerly unstable hips show normal X-ray findings. Although final ultrasound showed normal findings, at this time point 28 hips (33.7%; initial hip type D: 34.2%, III: 31.6%, IV: 42.9%) were slightly dysplastic and 10 (12.0%%; initial hip type D: 18.4%, III: 5.3%, IV: 14.3%) still severely dysplastic. At the age of 24–48 months, the percentage of radiologic normal hips had increased to 61 hips (73.5%; initial hip type D: 68.4%, III: 81.6%, IV: 57.1%), the number of slightly (19 hips) and severely dysplastic (3 hips) hips had decreased 22.9% (initial hip type D: 28.9%, III: 15.8%, IV: 28.6%) respectively 3.6% (initial hip type D: 2.6%, III: 2.6%, IV: 14.3%). At this time no operative intervention was neccessary. Our mid-term data show on the one hand a good development of unstable hips after successful treatment with the Tübingen splint. On the other hand despite successful therapy and normal ultrasound findings at the end of treatment further imaging by X-ray are mandatory to close follow-up and to detect those which might need surgical correction of residual dysplasia.


title: RNA-Guided RNA Cleavage by a CRISPR RNA-Cas Protein Complex

abstract: Compelling evidence indicates that the CRISPR-Cas system protects prokaryotes from viruses and other potential genome invaders. This adaptive prokaryotic immune system arises from the clustered regularly interspaced short palindromic repeats (CRISPRs) found in prokaryotic genomes, which harbor short invader-derived sequences, and the CRISPR-associated (Cas) protein-coding genes. Here, we have identified a CRISPR-Cas effector complex that is comprised of small invader-targeting RNAs from the CRISPR loci (termed prokaryotic silencing (psi)RNAs) and the RAMP module (or Cmr) Cas proteins. The psiRNA-Cmr protein complexes cleave complementary target RNAs at a fixed distance from the 3' end of the integral psiRNAs. In Pyrococcus furiosus, psiRNAs occur in two size forms that share a common 5' sequence tag but have distinct 3' ends that direct cleavage of a given target RNA at two distinct sites. Our results indicate that prokaryotes possess a unique RNA silencing system that functions by homology-dependent cleavage of invader RNAs.


title: kNN CF: a temporal social network

abstract: Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour (kNN) algorithm has become the dominant method used in this context. Much research to date has focused on improving the performance of this algorithm, without considering the properties that emerge from manipulating the user data in this way. In order to understand the effect of kNN on a user-rating dataset, the algorithm can be viewed as a process that generates a graph, where nodes are users and edges connect similar users: the algorithm generates an implicit social network amongst the system subscribers. Temporal updates of the recommender system will impose changes on the graph. In this work we analyse user-user kNN graphs from a temporal perspective, retrieving characteristics such as dataset growth, the evolution of similarity between pairs of users, the volatility of user neighbourhoods over time, and emergent properties of the entire graph as the algorithm parameters change. These insights explain why certain kNN parameters and similarity measures outperform others, and show that there is a surprising degree of structural similarity between these graphs and explicit user social networks.


title: Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support.

abstract: Background ::: The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment. ::: ::: ::: Purpose ::: Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. ::: ::: ::: Methods ::: Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. ::: ::: ::: Conclusions ::: As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.


title: Distributed generation: challenges and possible solutions

abstract: This contribution starts from the observation that there is a renewed interest in small-scale electricity generation. The authors start with a discussion of the drivers behind this evolution indicating the major benefits and issues of small-scale electricity generation. Attention is paid to the impact of a massive penetration of distributed generation in the grid on the system safety and protection. An overview of the impact on voltage quality and stability is given, both static and dynamic. A practical example is discussed in order to show the problems and indicate solutions. Different types of generators and grid interfaces are treated. In a final chapter, an attempt is made to correctly define small-scale generation also commonly called distributed generation, embedded generation or decentralized generation.


title: Neuropsychological Outcomes of U.S. Veterans with Report of Remote Blast-Related Concussion and Current Psychopathology

abstract: This study explored whether remote blast-related MTBI and/or current Axis I psychopathology contribute to neuropsychological outcomes among OEF/OIF veterans with varied combat histories. OEF/OIF veterans underwent structured interviews to evaluate history of blast-related MTBI and psychopathology and were assigned to MTBI (n = 18), Axis I (n = 24), Co-morbid MTBI/Axis I (n = 34), or post-deployment control (n = 28) groups. A main effect for Axis I diagnosis on overall neuropsychological performance was identified (F(3,100) = 4.81; p = .004), with large effect sizes noted for the Axis I only (d = .98) and Co-morbid MTBI/Axis I (d = .95) groups relative to the control group. The latter groups demonstrated primary limitations on measures of learning/memory and processing speed. The MTBI only group demonstrated performances that were not significantly different from the remaining three groups. These findings suggest that a remote history of blast-related MTBI does not contribute to objective cognitive impairment in the late stage of injury. Impairments, when present, are subtle and most likely attributable to PTSD and other psychological conditions. Implications for clinical neuropsychologists and future research are discussed. (JINS, 2012, 18, 1-11). Language: en


title: S.: Integrating reinforcement learning with human demonstrations of varying ability

abstract: This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning significantly improve both learning time and policy performance. Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studies the impact of demonstration quality and quantity, as well as the effect of combining demonstrations from multiple teachers. Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration. The best performance was achieved by combining the best demonstrations from two teachers.


title: Corpus Based Classification of Text in Australian Contracts

abstract: Written contracts are a fundamental framework for commercial and cooperative transactions and relationships. Limited research has been published on the application of machine learning and natural language processing (NLP) to contracts. In this paper we report the classification of components of contract texts using machine learning and hand-coded methods. Authors studying a range of domains have found that combining machine learning and rule based approaches increases accuracy of machine learning. We find similar results which suggest the utility of considering leveraging hand coded classification rules for machine learning. We attained an average accuracy of 83.48% on a multi-class labeling task on 20 contracts combining machine learning and rule based approaches, increasing performance over machine learning alone.


Network Security


title: Abstract Dynamic causal modelling

abstract: In this paper we present an approach to the identification of nonlinear input‐state‐ output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic. © 2003 Elsevier Science (USA). All rights reserved.


title: New scaling limitation of the floating gate cell in NAND Flash Memory

abstract: As the scaling in NAND Flash Memory is progressed, the various interferences among the adjacent cells are more and more increased and the new phenomenon which is ignored until now has to be considered. In this paper, we will introduce the new program interference phenomenon which is generated between the program word line and the adjacent word lines along the bit-line. This new program interference is that the Vth's of the adjacent word lines along the bit-line are decreased while a word line is programming. Because this phenomenon is severely aggravated as the gate space is decreased, we have to consider this program interference for the future technology nodes.


title: Strategies to Improve Running Economy

abstract: Running economy (RE) represents a complex interplay of physiological and biomechanical factors that is typically defined as the energy demand for a given velocity of submaximal running and expressed as the submaximal oxygen uptake (VO2) at a given running velocity. This review considered a wide range of acute and chronic interventions that have been investigated with respect to improving economy by augmenting one or more components of the metabolic, cardiorespiratory, biomechanical or neuromuscular systems. Improvements in RE have traditionally been achieved through endurance training. Endurance training in runners leads to a wide range of physiological responses, and it is very likely that these characteristics of running training will influence RE. Training history and training volume have been suggested to be important factors in improving RE, while uphill and level-ground high-intensity interval training represent frequently prescribed forms of training that may elicit further enhancements in economy. More recently, research has demonstrated short-term resistance and plyometric training has resulted in enhanced RE. This improvement in RE has been hypothesized to be a result of enhanced neuromuscular characteristics. Altitude acclimatization results in both central and peripheral adaptations that improve oxygen delivery and utilization, mechanisms that potentially could improve RE. Other strategies, such as stretching should not be discounted as a training modality in order to prevent injuries; however, it appears that there is an optimal degree of flexibility and stiffness required to maximize RE. Several nutritional interventions have also received attention for their effects on reducing oxygen demand during exercise, most notably dietary nitrates and caffeine. It is clear that a range of training and passive interventions may improve RE, and researchers should concentrate their investigative efforts on more fully understanding the types and mechanisms that affect RE and the practicality and extent to which RE can be improved outside the laboratory.


title: Review of the Use of AI Techniques in Serious Games: Decision Making and Machine Learning

abstract: The video game market has become an established and ever-growing global industry. The health of the video and computer games industry, together with the variety of genres and technologies available, means that video game concepts and programmes are being applied in numerous different disciplines. One of these is the field known as serious games. The main goal of this paper is to collect all the relevant articles published during the last decade and create a trend analysis about the use of certain artificial intelligence algorithms related to decision making and learning in the field of serious games. A categorization framework was designed and outlined to classify the 129 papers that met the inclusion criteria. The authors made use of this categorization framework for drawing some conclusions regarding the actual use of intelligent serious games. The authors consider that over recent years enough knowledge has been gathered to create new intelligent serious games to consider not only the final aim but also the technologies and techniques used to provide players with a nearly real experience. However, researchers may need to improve their testing methodology for developed serious games, so as to ensure they meet their final purposes.


title: Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

abstract: The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.


Reverse engineering applications for recovery of broken or worn parts and re-manufacturing: Three case studies


Tactile sensing for mechatronics * a state of the art survey


title: Identifying Daily Electric Consumption Patterns from Smart Meter Data by Means of Clustering Algorithms

abstract: This paper presents clustering approaches applied on daily energy consumption curves of a building. Our aim is to identify a reduced set of consumption patterns for a tertiary building during one year. These patterns depend on the temperature throughout the year as well as the type of the day (working day, work-free day and school holidays). Two clustering approaches are used independently, namely the functional K- means algorithm, that takes into account the functional aspect of data and the Expectation-Maximization algorithm based on Gaussian Mixture Model (EM-GMM). The clustering results of the two algorithms are analyzed and compared. This study represents the first step towards the development of prediction models for energy consumption.


title: Wideband metal mount UHF RFID tag

abstract: This paper presents a wideband UHF RFID tag designed for operating on multiple materials including metal. We describe the antenna structure and present the comparison of modeling and simulation results with experimental data.


title: Securing modbus transactions using hash-based message authentication codes and stream transmission control protocol

abstract: Traditionally supervisory control and data acquisition (SCADA) networks were physically isolated, providing some inherent level of security; yet, as these networks slowly converged with both corporate intranets and the Internet, their security continually eroded. The gradual evolution of SCADA systems has introduced many novel and previously unknown security risks. During the advent of SCADA technologies, a heavy focus was put on providing system robustness, safety, and reliability. Because of this, widely deployed SCADA protocols like Modbus and DNP3 provide no inherent security controls. In this paper, we will propose a novel Modbus alternative capable of providing secure, backward-compatible Modbus message transmission using stream control transmission protocol and hash-based message authentication code technologies. This proposed protocol improvement ensures the availability and integrity of Modbus messages while providing a robust and secure mutual authentication mechanism. Improvements upon the legacy Modbus protocol aim to mitigate common SCADA protocol attack vectors.


title: Evaluating the visual quality of web pages using a computational aesthetic approach

abstract: Current Web mining explores useful and valuable information (content) online for users. However, there is scant research on the overall visual aspect of Web pages, even though visual elements such as aesthetics significantly influence user experience. A beautiful and well-laid out Web page greatly facilitates users' accessing and enhances browsing experiences.We use "visual quality (VisQ)" to denote the aesthetics of Web pages. In this paper, a computational aesthetics approach is proposed to learn the evaluation model for the visual quality of Web pages. First, a Web page layout extraction algorithm (V-LBE) is introduced to partition a Web page into major layout blocks. Then, regarding a Web page as a semi-structured image, features (e.g., layout,visual complexity, colorfulness) known to significantly affect the visual quality of a Web page are extracted to construct a feature vector. We present a multi-cost-sensitive learning for visual quality classification and a multi-value regression for visual quality score assignment. Our experiments compare the extracted features and conclude that the Web page's layout visual features (LV) and text visual features (TV) are the primary affecting factors toward Web page's visual quality. The performance of the learned visual quality classifier is close to some persons'. The learned regression function also achieves promising results.


title: Comparative and functional genomics of lactococci.

abstract: Whole-genome nucleotide sequencing has revolutionized the genetic, biochemical and molecular biology research on bacteria and indeed, many higher organisms. The genome sequences of the strains of two subspecies of Lactococcus lactis, L. lactis subsp. lactis and L. lactis subsp. cremoris, have been determined. These genomic sequences have permitted two important new approaches to be applied in the research of L. lactis. The analysis of the regulation of expression of all genes under specific circumstances at a given point in time is now possible by DNA microarray technology. The elucidation of the full protein complement of the organism as a function of intrinsic or external factors has been made possible by high-throughput protein identification and analysis techniques combined with the gene-derived know-how of the total protein encoding capacity of the genome. These techniques from the genomics arena, transcriptomics and proteomics, have been recently implemented in the study of various aspects of growth and functioning of L. lactis. In this paper we discuss a number of similarities and differences between the two lactococcal genome sequences and review the current status of genomics research in L. lactis. We also propose future directions with respect to both answering fundamental questions more quickly and more completely, as well as opening new avenues for biotechnological applications.


title: Constant Power Control of DFIG Wind Turbines With Supercapacitor Energy Storage

abstract: With the increasing penetration of wind power into electric power grids, energy storage devices will be required to dynamically match the intermittency of wind energy. This paper proposes a novel two-layer constant power control scheme for a wind farm equipped with doubly fed induction generator (DFIG) wind turbines. Each DFIG wind turbine is equipped with a supercapacitor energy storage system (ESS) and is controlled by the low-layer wind turbine generator (WTG) controllers and coordinated by a high-layer wind farm supervisory controller (WFSC). The WFSC generates the active power references for the low-layer WTG controllers according to the active power demand from or generation commitment to the grid operator; the low-layer WTG controllers then regulate each DFIG wind turbine to generate the desired amount of active power, where the deviations between the available wind energy input and desired active power output are compensated by the ESS. Simulation studies are carried out in PSCAD/EMTDC on a wind farm equipped with 15 DFIG wind turbines to verify the effectiveness of the proposed control scheme.


title: Spatial pyramid co-occurrence for image classification

abstract: We describe a novel image representation termed spatial pyramid co-occurrence which characterizes both the photometric and geometric aspects of an image. Specifically, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an image. The representation captures both the absolute and relative spatial arrangement of the words and, through the choice and combination of the predicates, can characterize a variety of spatial relationships. Our representation is motivated by the analysis of overhead imagery such as from satellites or aircraft. This imagery generally does not have an absolute reference frame and thus the relative spatial arrangement of the image elements often becomes the key discriminating feature. We validate this hypothesis using a challenging ground truth image dataset of 21 land-use classes manually extracted from high-resolution aerial imagery. Our approach is shown to result in higher classification rates than a non-spatial bagof- visual-words approach as well as a popular approach for characterizing the absolute spatial arrangement of visual words, the spatial pyramid representation of Lazebnik et al. [7]. While our primary objective is analyzing overhead imagery, we demonstrate that our approach achieves state-of-the-art performance on the Graz-01 object class dataset and performs competitively on the 15 Scene dataset.


title: As real as real ? Macroeconomic behavior in a large-scale virtual world

abstract: This article proposes an empirical test of whether aggregate economic behavior maps from the real to the virtual. Transaction data from a large commercial virtual world — the first such data set provided to outside researchers — is used to calculate metrics for production, consumption and money supply based on real-world definitions. Movements in these metrics over time were examined for consistency with common theories of macroeconomic change. The results indicated that virtual economic behavior follows real-world patterns. Moreover, a natural experiment occurred, in that a new version of the virtual world with the same rules came online during the study. The new world's macroeconomic aggregates quickly grew to be nearly exact replicas of those of the existing worlds, suggesting that `Code is Law': macroeconomic outcomes in a virtual world may be explained largely by design structure.


title: Smart parking system for Internet of Things

abstract: In this paper, we propose a smart parking system detecting and finding the parked location of a consumer's vehicle. Using ultrasonic and magnetic sensor, the proposed system detects vehicles in indoor and outdoor parking fields, accurately. Wireless sensor motes support a vehicle location service in parking lots using BLE.


RFID in cloud environment for Attendance monitoring system


title: BigSparse: High-performance external graph analytics

abstract: We present BigSparse, a fully external graph analytics system that picks up where semi-external systems like FlashGraph and X-Stream, which only store vertex data in memory, left off. BigSparse stores both edge and vertex data in an array of SSDs and avoids random updates to the vertex data, by first logging the vertex updates and then sorting the log to sequentialize accesses to the SSDs. This newly introduced sorting overhead is reduced significantly by interleaving sorting with vertex reduction operations. In our experiments on a server with 32GB to 64GB of DRAM, BigSparse outperforms other in-memory and semi-external graph analytics systems for algorithms such as PageRank, BreadthFirst Search, and Betweenness-Centrality for terabyte-size graphs with billions of vertices. BigSparse is capable of highspeed analytics of much larger graphs, on the same machine configuration.


A Comparison of the Elastic Properties of Graphene- and Fullerene-Reinforced Polymer Composites: The Role of Filler Morphology and Size


title: Stepped-reflector antenna for dual-band multiple beam Satellite communications payloads

abstract: A novel stepped-reflector antenna (SRA) suitable for dual-band multiple beam satellite payloads is introduced in this paper. The SRA system produces "flat-top" radiation patterns for receive beams and highly efficient Gaussian patterns for transmit beams over a geographic coverage region as seen by the geo-synchronous satellite. It combines the reflector improvements through the use of SRA with the feed horn advancements through the use of dual-band "high-efficiency horns" in order to realize an efficient multiple beam antenna (MBA) system supporting both downlink transmit and uplink receive signals of communication satellites. It is shown that the SRA provides a congruent set of spot beams on ground for both transmit and receive frequencies with the benefits of significantly improved edge-of-coverage gain, improved co-polar isolation among beams that re-use the same frequency channels, receive beam patterns that are less sensitive to satellite pointing error, and reduced number of reflector antennas when compared to conventional MBAs


title: Web Text-based Network Industry Classifications: Preliminary Results

abstract: Studies of market structure and product market competition are important in many disciplines, such as economics, finance, accounting and management. Reliable data for such studies is easily available for public firms (e.g., 10-K filings), but no reliable data exists for private firms. In this work we propose to mine the Internet Archive Wayback Machine, a digital archive of the World Wide Web, to build a database of 300,000 companies to support analyses of market structure, product market competition, and innovation. The goal of the WTNIC project is to download pages from the archive to build a profile for each company, and to use machine learning techniques to define similarity between companies based on similarity of their product and service offerings. This paper describes the challenges that must be overcome, our approach to overcome these challenges, and some preliminary results.


title: A Context-aware Attention Network for Interactive Question Answering

abstract: Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from statements and questions. However, almost all previous models have failed to consider detailed context information and unknown states under which systems do not have enough information to answer given questions. These scenarios with incomplete or ambiguous information are very common in the setting of Interactive Question Answering (IQA). To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available. Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts. When available, user's feedback is encoded and directly applied to update sentence-level attention to infer an answer. Extensive experiments on QA and IQA datasets quantitatively demonstrate the effectiveness of our model with significant improvement over state-of-the-art conventional QA models.


title: Testing the behavioral interaction and integration of attentional networks

abstract: One current conceptualization of attention subdivides it into functions of alerting, orienting, and executive control. Alerting describes the function of tonically maintaining the alert state and phasically responding to a warning signal. Automatic and voluntary orienting are involved in the selection of information among multiple sensory inputs. Executive control describes a set of more complex operations that include detecting and resolving conflicts in order to control thoughts or behaviors. Converging evidence supports this theory of attention by showing that each function appears to be subserved by anatomically distinct networks in the brain and differentially innervated by various neuromodulatory systems. Although much research has been dedicated to understanding the functional separation of these networks in both healthy and disease states, the interaction and integration among these networks still remain unclear. In this study, we aimed to characterize possible behavioral interaction and integration in healthy adult volunteers using a revised attention network test (ANT-R) with cue-target interval and cue validity manipulations. We found that whereas alerting improves overall response speed, it exerts negative influence on executive control under certain conditions. A valid orienting cue enhances but an invalid cue diminishes the ability of executive control to overcome conflict. The results support the hypothesis of functional integration and interaction of these brain networks.


title: A Tool for Security Measuring and Probe Management

abstract: Designing and maintaining correct security countermeasures to support trustworthiness require comprehensive understanding of current security posture, i.e., Situational Awareness (SA). Security SA means that a decision maker is aware of protected assets, existing vulnerabilities, and risk mitigation techniques. Applying security metrics offers a holistic way to gain Situational Awareness. However, it is a challenge to get input data for security measuring. Currently, systems produce the huge amount of security monitoring data, but this raw data is not in a uniform format or easily available for the measuring purposes. In this paper, we propose a Host-based Security Instrumentation System (HSIS) to facilitate the development of security measuring probes for uniform data collection. The proposed solution supports reusability of measuring probes and makes it possible to manage and adapt probes at operation time. Security administrators and auditors can support trustworthiness with the solution by evaluating systems' security state.


title: The Effects of Violent Video Games on Aggression: A Meta-Analysis.

abstract: Violent content video games such as Mortal Kombat and Doom have become very popular among children and adolescents, causing great concern for parents, teachers, and policy makers. This study cumulates findings across existing empirical research on the effects of violent video games to estimate overall effect size and discern important trends and moderating variables, Results suggest there is a smaller effect of violent video games on aggression than has been found with television violence on aggression. This effect is positively associated with type of game violence and negatively related to time spent playing the games, Directions for future programmatic research on video games are outlined. Language: en


title: Adapting user interfaces for gestural interaction with the flexible action and articulated skeleton toolkit

abstract: We present the Flexible Action and Articulated Skeleton Toolkit (FAAST), a middleware software framework for integrating full-body interaction with virtual environments, video games, and other user interfaces. This toolkit provides a complete end-to-end solution that includes a graphical user interface for custom gesture creation, sensor configuration, skeletal tracking, action recognition, and a variety of output mechanisms to control third party applications, allowing virtually any PC application to be repurposed for gestural control even if it does not explicit support input from motion sensors. To facilitate intuitive and transparent gesture design, we define a syntax for representing human gestures using rule sets that correspond to the basic spatial and temporal components of an action. These individual rules form primitives that, although conceptually simple on their own, can be combined both simultaneously and in sequence to form sophisticated gestural interactions. In addition to presenting the system architecture and our approach for representing and designing gestural interactions, we also describe two case studies that evaluated the use of FAAST for controlling first-person video games and improving the accessibility of computing interfaces for individuals with motor impairments. Thus, this work represents an important step toward making gestural interaction more accessible for practitioners, researchers, and hobbyists alike.


title: Low-frequency neuronal oscillations as instruments of sensory selection

abstract: Neuroelectric oscillations reflect rhythmic shifting of neuronal ensembles between high and low excitability states. In natural settings, important stimuli often occur in rhythmic streams, and when oscillations entrain to an input rhythm their high excitability phases coincide with events in the stream, effectively amplifying neuronal input responses. When operating in a ‘rhythmic mode’, attention can use these differential excitability states as a mechanism of selection by simply enforcing oscillatory entrainment to a task-relevant input stream. When there is no low-frequency rhythm that oscillations can entrain to, attention operates in a ‘continuous mode’, characterized by extended increase in gamma synchrony. We review the evidence for early sensory selection by oscillatory phase-amplitude modulations, its mechanisms and its perceptual and behavioral consequences.


HLearn : A Machine Learning Library for Haskell


title: Pattern separation, completion, and categorisation in the hippocampus and neocortex

abstract: The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 and also a large number of independent memories stored in CA3 include: a sparse distributed representation, representations that are independent due to the randomizing effect of the mossy fibres, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields, and in the dentate to CA3 connections that have diluted connectivity. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas. New results on competitive networks show which factors contribute to their ability to perform pattern separation, pattern clustering, and pattern categorisation, and how these apply in different hippocampal and neocortical systems.


Artificial Intelligence


title: A DUAL BAND CIRCULARLY POLARIZED RING ANTENNA BASED ON COMPOSITE RIGHT AND LEFT HANDED METAMATERIALS

abstract: In this paper a dual band circularly polarized antenna is designed, fabricated, and measured. Based on the concept of composite right and left handed (CRLH) metamaterials, the same phase constants and current distributions on the ring antenna are achieved at two frequency bands. Thus, the antenna has similar radiation patterns at both bands. The circular polarization is implemented by feeding two vertical ports from power dividers that provide equal magnitudes and quadrature phase excitations.


title: Superpixel Convolution for Segmentation

abstract: In this paper, we propose a novel segmentation algorithm based on convolutional neural networks (CNNs) on superpix-else CNNs are powerful methods for several computer vision tasks, but spatial information disappears through the pooling process. Moreover, since pooling compresses different types of pixels into a single value, pooling sometimes negatively affects the results of inference in segmentation task. We use superpixel pooling instead of general pooling to resolve this problem. However, general CNNs can't use superpixel images in which the adjacency relationships between pixels are broken. Therefore, we define CNNs and Dilated Convolution on superpixels. Finally, we show the effectiveness of proposed method on an HKU-IS dataset.


Modeling Botnet Propagation Using Time Zones


title: Edge Detection with Embedded Confidence

abstract: Computing the weighted average of the pixel values in a window is a basic module in many computer vision operators. The process is reformulated in a linear vector space and the role of the different subspaces is emphasized. Within this framework wellknown artifacts of the gradient-based edge detectors, such as large spurious responses can be explained quantitatively. It is also shown that template matching with a template derived from the input data is meaningful since it provides an independent measure of confidence in the presence of the employed edge model. The widely used three-step edge detection procedure - gradient estimation, non-maxima suppression, hysteresis thresholding - is generalized to include the information provided by the confidence measure. The additional amount of computation is minimal and experiments with several standard test images show the ability of the new procedure to detect weak edges.


title: A decision tree for soft tissue grafting.

abstract: Periodontal plastic surgery is commonly performed for esthetic and physiologic reasons, such as alleviating root sensitivity, root caries, and cervical abrasion and facilitating plaque control at the affected site. Currently, there is a lack of information regarding the most appropriate treatment method for the various clinical situations encountered. The aims of this paper are to review and discuss the various clinical situations that require soft tissue grafting and to attempt to provide recommendations for the most predictable technique. Using MEDLINE and The Cochrane Library, a review of all available literature was performed. Papers published in peer-reviewed journals written in English were chosen and reviewed to validate the decision-making process when planning for soft tissue grafting. A decision tree was subsequently developed to guide clinicians to choose the most appropriate soft tissue grafting procedure by taking into consideration the following clinical parameters: etiology, purpose of the procedure, adjacent interproximal bone level, and overlying tissue thickness. The decision tree proposed serves as a guide for clinicians to select the most appropriate and predictable soft tissue grafting procedure to minimize unnecessary mistakes while providing the ultimate desired treatment outcome.


title: X1000 real-time phoneme recognition VLSI using feed-forward deep neural networks

abstract: Deep neural networks show very good performance in phoneme and speech recognition applications when compared to previously used GMM (Gaussian Mixture Model)-based ones. However, efficient implementation of deep neural networks is difficult because the network size needs to be very large when high recognition accuracy is demanded. In this work, we develop a digital VLSI for phoneme recognition using deep neural networks and assess the design in terms of throughput, chip size, and power consumption. The developed VLSI employs a fixed-point optimization method that only uses +Δ, 0, and -Δ for representing each of the weight. The design employs 1,024 simple processing units in each layer, which however can be scaled easily according to the needed throughput, and the throughput of the architecture varies from 62.5 to 1,000 times of the real-time processing speed.


title: Design of ultrahigh-speed low-voltage CMOS CML buffers and latches

abstract: A comprehensive study of ultrahigh-speed current-mode logic (CML) buffers along with the design of novel regenerative CML latches will be illustrated. First, a new design procedure to systematically design a chain of tapered CML buffers is proposed. Next, two new high-speed regenerative latch circuits capable of operating at ultrahigh-speed data rates will be introduced. Experimental results show a higher performance for the new latch architectures compared to the conventional CML latch circuit at ultrahigh-frequencies. It is also shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed low-voltage applications.


title: An approach to interactive deep reinforcement learning for serious games

abstract: Serious games receive increasing interest in the area of e-learning. Their development, however, is often still a demanding, specialized and arduous process, especially when regarding reasonable non-player character behaviour. Reinforcement learning and, since recently, also deep reinforcement learning have proven to automatically generate successful AI behaviour to a certain degree. These methods are computationally expensive and hardly scalable to various complex serious game scenarios. For this reason, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts' knowledge to guide the learning process. Thereby, we aim to create a synergistic combination of experts and emergent cognitive systems. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a framework.


title: Ontology Engineering in a Networked World

abstract: The Semantic Web is characterized by the existence of a very large number of distributed semantic resources, which together define a network of ontologies. These ontologies in turn are interlinked through a variety of different meta-relationships such as versioning, inclusion, and many more. This scenario is radically different from the relatively narrow contexts in which ontologies have been traditionally developed and applied, and thus calls for new methods and tools to effectively support the development of novel network-oriented semantic applications. This book by Surez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The books third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work. The book primarily addresses two main audiences: students (and their lecturers) who need a textbook for advanced undergraduate or graduate courses on ontology engineering, and practitioners who need to develop ontologies in particular or Semantic Web-based applications in general. Its educational value is maximized by its structured approach to explaining guidelines and combining them with case studies and numerous examples. The description of the open source NeOn Toolkit provides an additional asset, as it allows readers to easily evaluate and apply the ideas presented.


title: Tooth bleaching with hydrogen peroxide and nano-hydroxyapatite: a 9-month follow-up randomized clinical trial

abstract: OBJECTIVES ::: The aim of this study was to compare the amount of tooth colour change, rebound rate and tooth sensitivity in patients submitted to a bleaching technique with 6% hydrogen peroxide (HP) with or without 2% nano-hydroxyapatite (n-HA). ::: ::: ::: METHODS ::: Sixty subjects were included in this examiner-blinded, randomized clinical trial using a 6% HP gel with or without 2% n-HA. Tooth colour and tooth sensitivity were analysed before and after treatment. All data were analysed statistically. ::: ::: ::: RESULTS ::: After bleaching, both treatments demonstrated significant improvements in tooth shade (P < 0.05 for both groups). At the 9-month recall, tooth shade remained significantly lighter than at baseline (P < 0.05 for both groups). However, a relapse of the tooth shade was observed compared with the immediate post-bleaching result (P < 0.05). 6% HP with 2% n-HA produced significantly lower sensitivity (P < 0.05) than the bleaching product without n-HA. Colour change evaluation resulted in no difference between the two groups. ::: ::: ::: CONCLUSION ::: Both treatments demonstrated significant improvements in tooth shade. The bleaching effectiveness of the tested products was comparable. The use of 6% HP with 2% n-HA reduced the incidence of sensitivity during the bleaching treatment compared to a bleaching agent that did not contain n-HA.


title: A review of conceptual clustering algorithms

abstract: Clustering is a fundamental technique in data mining and pattern recognition, which has been successfully applied in several contexts. However, most of the clustering algorithms developed so far have been focused only in organizing the collection of objects into a set of clusters, leaving the interpretation of those clusters to the user. Conceptual clustering algorithms, in addition to the list of objects belonging to the clusters, provide for each cluster one or several concepts, as an explanation of the clusters. In this work, we present an overview of the most influential algorithms reported in the field of conceptual clustering, highlighting their limitations or drawbacks. Additionally, we present a taxonomy of these methods as well as a qualitative comparison of these algorithms, regarding a set of characteristics desirable since a practical point of view, which may help in the selection of the most appropriate method for solving a problem at hand. Finally, some research lines that need to be further developed in the context of conceptual clustering are discussed.


title: Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway

abstract: A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assistance systems (ADAS), where the main challenge is the trade-off between computational speed and prediction accuracy for both non-merge and merge events. The GBNN algorithm employs a gated branch based on correlation analysis, scaled exponential linear units activation function, and adaptive moment estimation optimiser. The algorithm has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration's Next Generation Simulation (NGSIM). Input features are extracted from NGSIM and pre-processed by standardisation and principal component analysis. TensorFlow framework and Python are used as the development platform. Results show that the proposed GBNN algorithm with the Pearson correlation method has values of 97.7%, 96.3%, and 0.990 for non-merge accuracy, merge accuracy, and receiver operating characteristic score, respectively. It outperforms other traditional binary classifiers for MLC applications, and is more light-weight than a convolutional neural network (AlexNet) of deep learning algorithm. Owing to its compact architecture, the GBNN provides high accuracy and efficiency, demonstrating promising usage as an MLC suggestion system in ADAS.


title: Epi.q Lizard: a modular ground mobile robot

abstract: The paper discusses the functional design and the embodiment design of Epi.q Lizard, which is the last version of the Epi.q ground mobile robots family. Epi.q robots are equipped with underactuated stepping triple wheel locomotion units with epicyclical gearings, capable of switching between wheeled and legged locomotion without control intervention, depending on the dynamic condition of the vehicle. With respect to the previous versions, Epi.q Lizard is characterized by a higher level of modularity and can be reconfigured on the basis of the task requirements


Online Discussion and the Moral Pathway to Identity Politicization and Collective Action


Effects of Flipped Learning Using Online Materials in a Surgical Nursing Practicum: A Pilot Stratified Group-Randomized Trial


The Interactive Effect of Nonverbal Cues and Recipients ’ Personality


title: Cytotoxic, apoptic and necrotics effects of Agrocybe cylindracea (DC.) Maire on breast cancer cells MCF-7 and L929

abstract: The cytotoxicity, apoptotic and necrotic effects of Agrocybe cylindracea were investigated.The tissue fragments from taken dry basidiocarps were developed by tissue culture method in malt extract agar.Three pellets were taken from the secondary mycelium and its development completed in 7 days at 140 rpm at 27 °C in nutrient broth.In cytotoxicity studies, sample extract concentrations were applied to MCF-7 and L929 fibroblast cells as 5mg/ml, 2.5 mg/ml, 1.25 mg/ml,0.625 mg/ml and 0.3125 mg ml. The WST-1 solution was added and the absorbance was measured in a microplate reader at a wavelength of 440 nm as a result of the incubation. Apoptosis and necrosis were detected in the same cell line as the double staining experiment. Binary staining solution was prepared and MCF-7 and L929 Fibroblast cells were applied at the same concentrations.At the end of the incubation,measurements were done at 20X magnification in a fluorescence microscope with FITC and DAPI filter


title: Learning Robot Navigation Behaviors by Demonstration Using a RRT $$^{*}$$ ∗ Planner

abstract: This paper presents an approach for learning robot navigation behaviors from demonstration using Optimal Rapidly-exploring Random Trees (RRT\(^{*}\)) as main planner. A new learning algorithm combining both Inverse Reinforcement Learning (IRL) and RRT\(^{*}\) is developed in order to learn the RRT\(^{*}\)’s cost function from demonstrations. This cost function can be used later in a regular RRT\(^{*}\) for robot planning including the learned behaviors in different scenarios. Simulations show how the method is able to recover the behavior from the demonstrations.


title: Dental Implants of Cefuroxime axetil for the treatment of Periodontitis:A Technical Report

abstract: Dental implant for the treatment of periodontitis was developed for site specific delivery of Cefuroxime axetil a broad spectrum antibiotic. Cefuroxime axetil implants were prepared by solvent casting technique using ethyl cellulose and other co-polymers (HPMC-K4M or Eudragit RL100) in chloroform: dichloromethane (1:1) solvent with glycerol as plasticizers. Drug excipient compatibility was studied using FTIR and DSC. The films were evaluated for their thickness uniformity, folding endurance, weight uniformity, content uniformity, surface pH, invitro drug release and in-vitro antibacterial activity. In-vitro drug release was subjected to curve fitting using different equations and kinetic models to reveal release kinetics. The implants made from EC and HPMC-K4M batch W2 containing EC(400 mg), HPMC-K4M (100mg) and glycerol (0.4 ml) showed best result in respect to physical properties, %drug content (98.44 %) and %drug release in 6 days (95.96%). The implants made from EC and Eudragit RL -100, batch W8 containing EC (500 mg), Eudragit RL -100 (100mg) showed best result with respect to physical properties, %drug content (98.00%) and percent drug release in 7 days (96.78%). The in vitro drug release data showed that implants shows initially burst release followed by prolonged release. In vitro antibacterial activity was studied on S. aureus and E. coli organisms. The zone of inhibition for all the batches were found to be effectively higher in 48 hrs and then declined. W2 and W8 formulations showed better antibacterial effects with higher zones of inhibition. Stability studies revealed that the drug remained intact and stable in the periodontal implants during storage.


title: The bilingual adaptation: How minds accommodate experience.

abstract: According to some estimates, more than half of the world's population is multilingual to some extent. Because of the centrality of language use to human experience and the deep connections between linguistic and nonlinguistic processing, it would not be surprising to find that there are interactions between bilingualism and cognitive and brain processes. The present review uses the framework of experience-dependent plasticity to evaluate the evidence for systematic modifications of brain and cognitive systems that can be attributed to bilingualism. The review describes studies investigating the relation between bilingualism and cognition in infants and children, younger and older adults, and patients, using both behavioral and neuroimaging methods. Excluded are studies whose outcomes focus primarily on linguistic abilities because of their more peripheral contribution to the central question regarding experience-dependent changes to cognition. Although most of the research discussed in the review reports some relation between bilingualism and cognitive or brain outcomes, several areas of research, notably behavioral studies with young adults, largely fail to show these effects. These discrepancies are discussed and considered in terms of methodological and conceptual issues. The final section proposes an account based on "executive attention" to explain the range of research findings and to set out an agenda for the next steps in this field. (PsycINFO Database Record


title: Semi-Automatic Terminology Ontology Learning Based on Topic Modeling

abstract: Abstract Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user's query demonstrating the effectiveness of the proposed approach.


title: Recognizing human actions by attributes

abstract: In this paper we explore the idea of using high-level semantic concepts, also called attributes, to represent human actions from videos and argue that attributes enable the construction of more descriptive models for human action recognition. We propose a unified framework wherein manually specified attributes are: i) selected in a discriminative fashion so as to account for intra-class variability; ii) coherently integrated with data-driven attributes to make the attribute set more descriptive. Data-driven attributes are automatically inferred from the training data using an information theoretic approach. Our framework is built upon a latent SVM formulation where latent variables capture the degree of importance of each attribute for each action class. We also demonstrate that our attribute-based action representation can be effectively used to design a recognition procedure for classifying novel action classes for which no training samples are available. We test our approach on several publicly available datasets and obtain promising results that quantitatively demonstrate our theoretical claims.


title: Application of interactive videos in education

abstract: The paper presents teacher feedback on potential use of interactive videos in the learning processes. A traditional video often leaves the student as a passive viewer, while an interactive video will engage the student and demand interactions, thus activating the student. Interactive videos include the use of (embedded) quizzes, link-chains, interactive maps and interactive use of 3D-objects. The application areas differ between the different categories of interactive video. The main application areas for interactive videos with quiz are both summative and formative assessment. Interactive video with links has a variety of application areas, like reflections, attitude change, evaluation, case-based teaching, causal effects and nested stories. The main application areas of videos with interactive 3Dobjects are visualization, demonstration, presentation and exploration. The interviewees did not see a wide application area of videos with interactive maps, but problem solving activities were emphasized. Interactive videos in general would activate the student and raise awareness towards the video content. The paper also discusses the differences between interactive presentation tools and interactive video tools and presents the opportunities and weaknesses of a number of interactive video tools.


title: 4G as a Next Generation Wireless Network

abstract: User requirements are growing faster than ever and the limitations of the current mobile communication systems have forced the researchers to come up with more advanced and efficient technologies. 4G mobile technology is the next step in this direction. 4G is the next generation of wireless networks that will totally replace 3G networks. It is supposed to provide its customers with better speed and all IP based multimedia services. 4G is all about an integrated, global network that will be able to provide a comprehensive IP solution where voice, data and streamed multimedia can be given to users on an "Anytime, Anywhere" basis. At present we have many technologies each capable of performing functions like supporting voice traffic using voice over IP (VoIP), broadband data access in mobile environment etc., but there is a great need of deploying such technologies that can integrate all these systems into a single unified system. 4G presents a solution of this problem as it is all about seamlessly integrating the terminals, networks and applications. The race to implement 4G is accelerating as well as quite challenging. The aim of this paper is to highlight the benefits, challenges in deployment and scope of 4G technologies.


REPRESENTATION OF WEAKLY ADDITIVE OPERATORS 1


Does your Robot have Skills


title: Asymmetry of MTJ switching and its implication to STT-RAM designs

abstract: As one promising candidate for next-generation nonvolatile memory technologies, spin-transfer torque random access memory (STT-RAM) has demonstrated many attractive features, such as nanosecond access time, high integration density, non-volatility, and good CMOS process compatibility. In this paper, we reveal an important fact that has been neglected in STT-RAM designs for long: the write operation of a STT-RAM cell is asymmetric based on the switching direction of the MTJ (magnetic tunneling junction) device: the mean and the deviation of the write latency for the switching from low- to high-resistance state is much longer or larger than that of the opposite switching. Some special design concerns, e.g., the write-pattern-dependent write reliability, are raised by this observation. We systematically analyze the root reasons to form the asymmetric switching of the MTJ and study their impacts on STT-RAM write operations. These factors include the thermal-induced statistical MTJ magnetization process, asymmetric biasing conditions of NMOS transistors, and both NMOS and MTJ device variations. We also explore the design space of different design methodologies on capturing the switching asymmetry of different STT-RAM cell structures. Our experiment results proved the importance of full statistical design method in STT-RAM designs for design pessimism minimization.


title: Cybersecurity and Privacy Solutions in Smart Cities

abstract: The increasing proliferation and deployment of ICT in the infrastructure of cities has increased interest in smart cities. The long-term objective of a smart city is to enhance the quality of services provided to citizens and ultimately improve their quality of life. However, incorporating ICT opens up various security and privacy issues in smart cities, along with the people living in them. We briefly present the fundamental design concepts of a smart city and review recent smart city initiatives and projects. After identifying several security vulnerabilities and privacy issues within the context of smart cities that must be addressed, we then discuss various privacy and security solutions, recommendations, and standards for smart cities and their services.


title: A Transmitter or a Receiver Consisting of Two Strongly Coupled Resonators for Enhanced Resonant Coupling in Wireless Power Transfer

abstract: This paper proposes a novel resonator structure for efficiency and transferred power improvements: a transmitter (a receiver) that consists of two strongly coupled resonators. The two strongly coupled resonators are embedded within a transmitter device (a receiver device) and behave as a single resonator with enhanced performances. Unlike the conventional four-coil system, the first and the fourth resonators are also designed to have high loaded-Q and maximum cross couplings. Therefore, the first and the fourth resonators also take part in the coupled resonance with opposite-side resonators. This provides additional energy exchange path. The exact design guidelines are provided for each different resonance topology from analytical derivation. It is analyzed and experimentally demonstrated that the efficiency and the transferred power are increased by the proposed two-resonator technique. For a 30 cm × 25 cm parallel-resonant transmitter and an 18 cm × 16 cm parallel-resonant receiver at 13-cm distance, the efficiency and the transferred power with the proposed technique are 65.2% and 17.2 W, respectively, whereas those values without the proposed technique are only 37.3% and 6.2 W.


title: Ear-clipping Based Algorithms of Generating High-quality Polygon Triangulation

abstract: A basic and an improved ear clipping based algorithm for triangulating simple polygons and polygons with holes are presented. In the basic version, the ear with smallest interior angle is always selected to be cut in order to create fewer sliver triangles. To reduce sliver triangles in further, a bound of angle is set to determine whether a newly formed triangle has sharp angles, and edge swapping is accepted when the triangle is sharp. To apply the two algorithms on polygons with holes,"Bridge"edges are created to transform a polygon with holes to a degenerate polygon which can be triangulated by the two algorithms. Applications show that the basic algorithm can avoid creating sliver triangles and obtain better triangulations than the traditional ear clipping algorithm, and the improved algorithm can in further reduce sliver triangles effectively. Both of the algorithms run in O(n2) time and O(n) space.


title: Sleep in women across the life cycle from adulthood through menopause.

abstract: Studies of sleep across the life cycle in women have utilized both survey and polysomnographic techniques, but have tended to be of small sample size with diverse methodology. As a result, definitive conclusions about the impact of the menstrual cycle and use of oral contraceptives on sleep parameters cannot yet be made. Sleep disruption during pregnancy and postpartum is nearly universal, but effective and practical countermeasures are still needed. Longitudinal studies of sleep in the postpartum period are also lacking. Menopause is associated with insomnia due to several factors including hot flashes, mood disorders and increased sleep-disordered breathing. The use of hormone replacement therapy to treat sleep and other variables is an active area of investigation. In summary, much research is required to fully elucidate the impact of the life cycle on sleep parameters in women.


title: FPGA-based Implementation of an Ultrasonic Beacon for a Local Positioning System

abstract: Ultrasonic transducers have been often used to develop Local Positioning Systems (LPS) in the field of intelligent spaces. In these systems, it is necessary to design an ultrasonic beacon, which is distributed in the environment to cover a determined area where mobile robots or people can be located. This work describes the design and the implementation of an ultrasonic beacon in an FPGA-based platform. The design is flexible enough to allow the real-time configuration of the ultrasonic emission, according to parameters as efficient encoding or suitable modulation schemes. Furthermore, the beacons have available a I2C link, so they are in permanent communication for exchanging configuration parameters and synchronization, not only among them, but also with a central configuration module.


Improving Network Intrusion Detection Using Geometric Mean LDA


title: Visualizing non-functional requirements

abstract: Information systems can be visualized with many tools. Typically these tools present functional artifacts from various phases of the development life-cycle; these include requirements models, architecture and design diagrams, and implementation code. The syntactic structures of these artifacts are often presented in a textual language using symbols, or a graphical one using nodes and edges. In this paper, we propose a quality-based visualization scheme. Such a scheme is layered on top of these functional artifacts for presenting non-functional aspects of the system. To do this, we use quantified quality attributes. As an example, we visualize the quality attributes of trust and performance among various nonfunctional requirements of information systems.


title: Virtual reality vs. reality in engineering education

abstract: Virtual reality has become significantly popular in recent years. It is also widely used and more frequently implemented in education, training, and research. This article discusses the potential and the current state of virtual reality, and its tools in engineering education. The focus is put on some opportunities, challenges and dead ends of implementation faced by the authors. In this point of view, virtual reality is the future of creative learning, however it has its limits in terms of practical experiments, learning by doing, which is still more effective as virtual ones.


title: Cost-of-illness studies and cost-effectiveness analyses in anxiety disorders: A systematic review

abstract: Abstract Aims To review cost-of-illness studies (COI) and cost-effectiveness analyses (CEA) conducted for anxiety disorders. Methods Based on a database search in Pubmed, PsychINFO and NHS EED, studies were classified according to various criteria. Cost data were inflated and converted to 2005 US-$ purchasing power parities (PPP). Results We finally identified 20 COI and 11 CEA of which most concentrated on panic disorder (PD) and generalized anxiety disorder (GAD). Differing inclusion of cost categories limited comparability of COI. PD and GAD tended to show higher direct costs per case, but lower direct cost per inhabitant than social and specific phobias. Different measures of effectiveness severely limited comparability of CEA. Overall CEA analysed 26 therapeutic or interventional strategies mostly compared to standard treatment, 8 of them resulting in lower better effectiveness and costs than the comparator. Conclusions Anxiety disorders cause considerable costs. More research on phobias, more standardised inclusion of cost categories in COI and a wider use of comparable effectiveness measures (like QALYs) in CEA is needed.


title: GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction

abstract: In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single-view image. We found distance metrics, like Chamfer distance, were used in previous work to measure the difference of two point sets and serve as the loss function in point-based reconstruction. However, such point-point loss does not constrain the 3D model from a global perspective. We propose adding geometric adversarial loss (GAL). It is composed of two terms where the geometric loss ensures consistent shape of reconstructed 3D models close to ground-truth from different viewpoints, and the conditional adversarial loss generates a semantically-meaningful point cloud. GAL benefits predicting the obscured part of objects and maintaining geometric structure of the predicted 3D model. Both the qualitative results and quantitative analysis manifest the generality and suitability of our method.


title: Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields

abstract: Some existing CNN-based methods for single-view 3D object reconstruction represent a 3D object as either a 3D voxel occupancy grid or multiple depth-mask image pairs. However, these representations are inefficient since empty voxels or background pixels are wasteful. We propose a novel approach that addresses this limitation by replacing masks with “deformation-fields”. Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object. Each surface comprises a depth-map and corresponding deformation-field that ensures every pixel-depth pair in the depth-map lies on the object surface. These surfaces are then fused to form the full 3D shape. During training we use a combination of per-view loss and multi-view losses. The novel multi-view loss encourages the 3D points back-projected from a particular view to be consistent across views. Extensive experiments demonstrate the efficiency and efficacy of our method on single-view 3D object reconstruction.


title: A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network

abstract: Road detection is one of the most basic tasks of autonomous driving systems. At present, researches on this issue mainly take two kinds of data as input, i.e., LIDAR point clouds and RGB images from cameras. To make best use of the advantages and bypass the disadvantages of these two kinds of data, we propose a novel network, namely two- stream fusion fully convolutional network (TSF-FCN), which can take advantage of both the accurate location information from LIDAR point clouds and rich appearance information from RGB images. One stream of this network is LIDAR stream which aggregates multi-scale contextual information from LIDAR point clouds. The other stream is RGB stream which is used for extracting features from RGB images. To fuse the two streams, the feature maps of RGB stream are converted to a bird-view representation to concatenate with that of LIDAR stream. In this way, the two kinds of data can complement each other for detecting road. To verify the efficacy of our TSF-FCN, experiments are carried on KITTI- ROAD benchmark and competitive performance is achieved compared with state-of-the-art methods.


Efficient Parallel TLD on CPU-GPU Platform for Real-Time Tracking


title: USB PassOn: Secure USB Thumb Drive Forensic Toolkit

abstract: The flash memory market has grown at an extremely fast rate, when compared with all other types of semiconductors. Much of this can be owed to the rabid use of flash memory in portable media players like Apple iPod, but also to the growth of the cellular phone market, where over 1.2 billion handsets are expected to be sold worldwide this year alone, as well as other electronic devices.[8] Lately, corporation insiders steal confidential information using small USB thumb drive. Therefore USB thumb drives are becoming very important evidence that must be seized in digital crime investigation. In addition, because there are many kinds of USB thumb drives, we need some techniques which can identify specific drive. In this paper, we describe methods of digital evidence analysis about USB thumb drives. We also design and implement USB thumb drive analysis tool that offers various functions on a digital forensic view.


title: A Real-World Perspective of AOP

abstract: What is the real deal with AOP? Is it something that you should embrace or ignore? What do you gain with AOP, and what do you risk by adopting it? Let’s address these important questions from a practitioner’s point of view. We’ll start with AOP in the context of the typical hype cycle. This will give us a historical perspective on AOP evolution and indicate what lies ahead. We also look at the landscape, focusing on the current situation and changes in last few years. While AOP is a more general concept, because our focus is the real world use of it, we’ll focus on AspectJ—its most prominent implementation.


title: Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media

abstract: Many current Natural Language Processing [NLP] techniques work well assuming a large context of text as input data. However they become ineffective when applied to short texts such as Twitter feeds. To overcome the issue, we want to find a related newswire document to a given tweet to provide contextual support for NLP tasks. This requires robust modeling and understanding of the semantics of short texts. The contribution of the paper is two-fold: 1. we introduce the Linking-Tweets-toNews task as well as a dataset of linked tweet-news pairs, which can benefit many NLP applications; 2. in contrast to previous research which focuses on lexical features within the short texts (text-to-word information), we propose a graph based latent variable model that models the inter short text correlations (text-to-text information). This is motivated by the observation that a tweet usually only covers one aspect of an event. We show that using tweet specific feature (hashtag) and news specific feature (named entities) as well as temporal constraints, we are able to extract text-to-text correlations, and thus completes the semantic picture of a short text. Our experiments show significant improvement of our new model over baselines with three evaluation metrics in the new task.