Query: Electronic media use and adolescent health and well-being: cross-sectional community study.

Relevant passages:
Internet paradox. A social technology that reduces social involvement and psychological well-being? <=SEP=> The Internet could change the lives of average citizens as much as did the telephone in the early part of the 20th century and television in the 1950s and 1960s. Researchers and social critics are debating whether the Internet is improving or harming participation in community life and social relationships. This research examined the social and psychological impact of the Internet on 169 people in 73 households during their first 1 to 2 years on-line. We used longitudinal data to examine the effects of the Internet on social involvement and psychological well-being. In this sample, the Internet was used extensively for communication. Nonetheless, greater use of the Internet was associated with declines in participants' communication with family members in the household, declines in the size of their social circle, and increases in their depression and loneliness. These findings have implications for research, for public policy and for the design of technology.

Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: a meta-analytic review of the scientific literature. <=SEP=> Research on exposure to television and movie violence suggests that playing violent video games will increase aggressive behavior. A metaanalytic review of the video-game research literature reveals that violent video games increase aggressive behavior in children and young adults. Experimental and nonexperimental studies with males and females in laboratory and field settings support this conclusion. Analyses also reveal that exposure to violent video games increases physiological arousal and aggression-related thoughts and feelings. Playing violent video games also decreases prosocial behavior.

Development of a Compendium of Energy Expenditures for Youth <=SEP=> BACKGROUND
This paper presents a Compendium of Energy Expenditures for use in scoring physical activity questionnaires and estimating energy expenditure levels in youth.


METHOD/RESULTS
Modeled after the adult Compendium of Physical Activities, the Compendium of Energy Expenditures for Youth contains a list of over 200 activities commonly performed by youth and their associated MET intensity levels. A review of existing data collected on the energy cost of youth performing activities was undertaken and incorporated into the compendium. About 35% of the activity MET levels were derived from energy cost data measured in youth and the remaining MET levels estimated from the adult compendium.


CONCLUSION
The Compendium of Energy Expenditures for Youth is useful to researchers and practitioners interested in identifying physical activity and energy expenditure values in children and adolescents in a variety of settings.

Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. <=SEP=> Two studies examined violent video game effects on aggression-related variables. Study 1 found that real-life violent video game play was positively related to aggressive behavior and delinquency. The relation was stronger for individuals who are characteristically aggressive and for men. Academic achievement was negatively related to overall amount of time spent playing video games. In Study 2, laboratory exposure to a graphically violent video game increased aggressive thoughts and behavior. In both studies, men had a more hostile view of the world than did women. The results from both studies are consistent with the General Affective Aggression Model, which predicts that exposure to violent video games will increase aggressive behavior in both the short term (e.g., laboratory aggression) and the long term (e.g., delinquency).

The CES-D Scale : A Self-Report Depression Scale for Research in the General Population <=SEP=> 

Chapter 8 Location-Based Social Networks : Users <=SEP=> In this chapter, we introduce and define the meaning of location-based social network (LBSN) and discuss the research philosophy behind LBSNs from the perspective of users and locations. Under the circumstances of trajectory-centric LBSN, we then explore two fundamental research points concerned with understanding users in terms of their locations. One is modeling the location history of an individual using the individual’s trajectory data. The other is estimating the similarity between two different people according to their location histories. The inferred similarity represents the strength of connection between two users in a locationbased social network, and can enable friend recommendations and community discovery. The general approaches for evaluating these applications are also presented.

Personality Traits and Music Genres: What Do People Prefer to Listen To? <=SEP=> Personality-based personalized systems are increasingly gaining interest as personality traits has been shown to be a stable construct within humans. In order to provide a personality-based experience to the user, users' behavior, preferences, and needs in relation to their personality need to be investigated. Although for a technological mediated environment the search for these relationships is often new territory, there are findings from personality research of the real world that can be used in personalized systems. However, for these findings to be implementable, we need to investigate whether they hold in a technologically mediated environment. In this study we assess prior work on personality-based music genre preferences from traditional personality research. We analyzed a dataset consisting of music listening histories and personality scores of 1415 Last.fm users. Our results show agreements with prior work, but also important differences that can help to inform personalized systems.

DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining <=SEP=> In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-TermMemory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and furthermining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery.

How to detect the Cuckoo Sandbox and to Strengthen it? <=SEP=> Nowadays a lot of malware are analyzed with virtual machines. The Cuckoo sandbox (Cuckoo DevTeam: Cuckoo sandbox. http://www.cuckoosandbox.org , 2013) offers the possibility to log every actions performed by the malware on the virtual machine. To protect themselves and to evande detection, malware need to detect whether they are in an emulated environment or in a real one. With a few modifications and tricks on Cuckoo and the virtual machine we can try to prevent malware to detect that they are under analyze, or at least make it harder. It is not necessary to apply all the modifications, because it may produce a significant overhead and if malware checks his execution time, it may detect an anomaly and consider that it is running in a virtual machine. The present paper will show how a malware can detect the Cuckoo sandbox and how we can counter that.

Synchronous Bidirectional Inference for Neural Sequence Generation <=SEP=> In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs in which left parts are much more accurate than right ones. In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. In addition, we propose two strategies for parameter optimization. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.

Towards Modeling False Memory With Computational Knowledge Bases <=SEP=> One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling.

Structural Models of Corporate Bond Pricing : An Empirical Analysis <=SEP=> This article empirically tests five structural models of corporate bond pricing: those of Merton (1974), Geske (1977), Longstaff and Schwartz (1995), Leland and Toft (1996), and Collin-Dufresne and Goldstein (2001). We implement the models using a sample of 182 bond prices from firms with simple capital structures during the period 1986–1997. The conventional wisdom is that structural models do not generate spreads as high as those seen in the bond market, and true to expectations, we find that the predicted spreads in our implementation of the Merton model are too low. However, most of the other structural models predict spreads that are too high on average. Nevertheless, accuracy is a problem, as the newer models tend to severely overstate the credit risk of firms with high leverage or volatility and yet suffer from a spread underprediction problem with safer bonds. The Leland and Toft model is an exception in that it overpredicts spreads on most bonds, particularly those with high coupons. More accurate structural models must avoid features that increase the credit risk on the riskier bonds while scarcely affecting the spreads of the safest bonds.

A survey and comparative study of data deduplication techniques <=SEP=> Increase in enormous amount of digital data needs more storage space, which in turn significantly increases the cost of backup and its performance. Traditional backup solutions do not provide any inherent capability to prevent duplicate data from being backed up. Backup of duplicate data significantly increases the backup time and unnecessary consumption of resources. Data Deduplication plays an important role in eliminating this redundant data and reducing the storage consumption. Its main intend is how to reduce more duplicates efficiently, removing them at high speed and to achieve good duplicate removal ratio. Many mechanisms have been proposed to meet these objectives. It has been observed that achieving one objective may affect the other. In this paper we classify and review the existing deduplication techniques. We also present evaluation of deduplication algorithms by measuring their performance in terms of complexity and efficiency with unstructured files. We propose an efficient means to achieve high deduplication ratio with minimum backup window. We also indicate some key issues need to focus in the future work.

Detecting concept change in dynamic data streams <=SEP=> In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as classification models generated need to be updated when significant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations with respect to one or more key performance factors such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach uses reservoir sampling to build a sequential change detection model that offers statistically sound guarantees on false positive and false negative rates but has much smaller computational complexity than the ADWIN concept drift detector. Extensive experimentation on a wide variety of datasets reveals that the scheme also has a smaller false detection rate while maintaining a competitive true detection rate to ADWIN.

Smarter Cities and Their Innovation Challenges <=SEP=> The transformation to smarter cities will require innovation in planning, management, and operations. Several ongoing projects around the world illustrate the opportunities and challenges of this transformation. Cities must get smarter to address an array of emerging urbanization challenges, and as the projects highlighted in this article show, several distinct paths are available. The number of cities worldwide pursuing smarter transformation is growing rapidly. However, these efforts face many political, socioeconomic, and technical hurdles. Changing the status quo is always difficult for city administrators, and smarter city initiatives often require extensive coordination, sponsorship, and support across multiple functional silos. The need to visibly demonstrate a continuous return on investment also presents a challenge. The technical obstacles will center on achieving system interoperability, ensuring security and privacy, accommodating a proliferation of sensors and devices, and adopting a new closed-loop human-computer interaction paradigm.

Differential Impairment of Remembering the Past and Imagining Novel Events after Thalamic Lesions <=SEP=> Vividly remembering the past and imagining the future (mental time travel) seem to rely on common neural substrates and mental time travel impairments in patients with brain lesions seem to encompass both temporal domains. However, because future thinking—or more generally imagining novel events—involves the recombination of stored elements into a new event, it requires additional resources that are not shared by episodic memory. We aimed to demonstrate this asymmetry in an event generation task administered to two patients with lesions in the medial dorsal thalamus. Because of the dense connection with pFC, this nucleus of the thalamus is implicated in executive aspects of memory (strategic retrieval), which are presumably more important for future thinking than for episodic memory. Compared with groups of healthy matched control participants, both patients could only produce novel events with extensive help of the experimenter (prompting) in the absence of episodic memory problems. Impairments were most pronounced for imagining personal fictitious and impersonal events. More precisely, the patients' descriptions of novel events lacked content and spatio-temporal relations. The observed impairment is unlikely to trace back to disturbances in self-projection, scene construction, or time concept and could be explained by a recombination deficit. Thus, although memory and the imagination of novel events are tightly linked, they also partly rely on different processes.

Inducing Probabilistic Grammars by Bayesian Model Merging <=SEP=> We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are incorporated by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are merged to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (‘Occam’s Razor’). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based n-grams, and stochastic context-free grammars.

Statistics: a brief overview. <=SEP=> The Accreditation Council for Graduate Medical Education sets forth a number of required educational topics that must be addressed in residency and fellowship programs. We sought to provide a primer on some of the important basic statistical concepts to consider when examining the medical literature. It is not essential to understand the exact workings and methodology of every statistical test encountered, but it is necessary to understand selected concepts such as parametric and nonparametric tests, correlation, and numerical versus categorical data. This working knowledge will allow you to spot obvious irregularities in statistical analyses that you encounter.

ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations <=SEP=> We describe PARANMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the nonEnglish side of a large parallel corpus, following Wieting et al. (2017). Our hope is that PARANMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks. To show its utility, we use PARANMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase generation.1

User-click modeling for understanding and predicting search-behavior <=SEP=> Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.
 Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.

Distributed intelligence for multi-camera visual surveillance <=SEP=> Latest advances in hardware technology and state of the art of computer vision and arti,cial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the di0erent components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed arti,cial intelligence composed of separate and autonomous modules is justi,ed by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and e4ciently built by modular software. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs <=SEP=> Bitcoin is a rising digital currency and exemplifies the growing need for systematically gathering and analyzing public transaction data sets such as the blockchain. However, the blockchain in its raw form is just a large ledger listing transfers of currency units between alphanumeric character strings, without revealing contextually relevant real-world information. In this demo, we present GraphSense, which is a solution that applies a graph-centric perspective on digital currency transactions. It allows users to explore transactions and follow the money flow, facilitates analytics by semantically enriching the transaction graph, supports path and graph pattern search, and guides analysts to anomalous data points. To deal with the growing volume and velocity of transaction data, we implemented our solution on a horizontally scalable data processing and analytics infrastructure. Given the ongoing digital transformation in financial services and technologies, we believe that our approach contributes to development of analytics solutions for digital currency ecosystems, which is relevant in fields such as financial analytics, law enforcement, or scientific research.

Layering concepts in anterior composite restorations. <=SEP=> SUMMARY
With increasing patient demands for esthetic dentition, composite resin restorations enjoy great popularity due to excellent esthetics, acceptable longevity, and relatively low costs. Shading concepts used by manufacturers, however, may be confusing to many clinicians.


PURPOSE
To review and describe the current main shading concepts, evaluate their potential for creating natural esthetics, and provide guidelines for application.

Distribution Free Prediction Bands <=SEP=> We study distribution free, nonparametric prediction bands with a special focus on their finite sample behavior. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band estimator by combining the idea of “conformal prediction” (Vovk et al., 2009) with nonparametric conditional density estimation. The proposed estimator, called COPS (Conformal Optimized Prediction Set), always has finite sample guarantee in a stronger sense than the original conformal prediction estimator. Under regularity conditions the estimator converges to an oracle band at a minimax optimal rate. A fast approximation algorithm and a data driven method for selecting the bandwidth are developed. The method is illustrated first in simulated data. Then, an application shows that the proposed method gives desirable prediction intervals in an automatic way, as compared to the classical linear regression modeling.

Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts <=SEP=> Social commerce has taken the e‐tailing world by storm. Business‐to‐consumer sites and, more important, intermediaries that facilitate shopping experience, continue to offer more and more innovative technologies to support social interaction among like‐ minded community members or friends who share the same shopping interests. Among these technologies, reviews, ratings, and recommendation systems have become some of the most popular social shopping platforms due to their ease of use and simplicity in sharing buying experience and aggregating evaluations. This paper studies the effect of electronic word of mouth (eWOM) communication among a closed community of book readers. We studied the entire market of Amazon Shorts e‐books, which are digital mi‐ croproducts sold at a low and uniform price. With the minimal role of price in the buying decision, social discussion via eWOM becomes a collective signal of reputation, and ultimately a significant demand driver. Our empirical study suggests that eWOM can be used to convey the reputation of the product (e.g., the book), the reputation of the brand (i.e., the author), and the reputation of complementary goods (e.g., books in the same category). Until newer social shopping technologies gain acceptance, eWOM technolo‐ gies should be considered by both e‐tailers and shoppers as the first and perhaps primary source of social buying experience.

EARNING INDEPENDENT CAUSAL MECHANISMS <=SEP=> Independent causal mechanisms are a central concept in the study of causality with implications for machine learning tasks. In this work we develop an algorithm to recover a set of (inverse) independent mechanisms relating a distribution transformed by the mechanisms to a reference distribution. The approach is fully unsupervised and based on a set of experts that compete for data to specialize and extract the mechanisms. We test and analyze the proposed method on a series of experiments based on image transformations. Each expert successfully maps a subset of the transformed data to the original domain, and the learned mechanisms generalize to other domains. We discuss implications for domain transfer and links to recent trends in generative modeling.

Experiences in Improving Risk Management Processes Using the Concepts of the Riskit Method <=SEP=> This paper describes experiences from two organizations that have used the Riskit method for risk management in their software projects. This paper presents the Riskit method, the organizations involved, case study designs, and findings from case studies. We focus on the experiences and insights gained through the application of the method in industrial context and propose some general conclusions based on the case studies.

Convolutional Recurrent Deep Learning Model for Sentence Classification <=SEP=> As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.

Predicting The Next App That You Are Going To Use <=SEP=> Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.

Osteology of Ischikauia steenackeri (Teleostei: Cypriniformes) with comments on its systematic position <=SEP=> The lakeweed chub Ischikauia steenackeri is a medium-sized, herbivorous fish and the sole extant member of the genus Ischikauia, which is endemic to Lake Biwa and the Yodo River drainage, Japan. In order to clarify its systematic position, the skeletal anatomy of I. steenackeri is described and its relationships with related genera are discussed. The present data suggest the monophyly of Ischikauia and seven cultrine genera (Culter, Chanodichthys, Megalobrama, Sinibrama, Hemiculter, Toxabramis, and Anabarilius) based on a unique character, the metapterygoid elongated dorsally. Additionally, our data suggest that Ischikauia is closely related to Culter, Chanodichthys, Megalobrama, and Sinibrama. This relationship is supported by three synapomorphies that are common to them: a narrow third infraorbital, dorsal extension of the third supraneural, and a large quadrate foramen.