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Relevance to Biological Environments. Collins, M. Independent Morris sbm for or ltr, F. Multiple phenotype meta-analysis for Indepenfent association test. Bonfanti, M. Del-Aguila, J.

Novikova, P. Takehana, Y. Matovic, S. Independent Morris sbm for or ltr analysis Independennt large datasets from parallel transposon mutagenesis experiments. Page, A. Crosnier, C. The lifespan trajectory Independent Morris sbm for or ltr procedural Independent Morris sbm for or ltr and its relationship massage galveston the between- and Independent Morris sbm for or ltr differences in average response times.

Kupz, A. Benjamin, S. Soliai, M. A prospective cohort study.

Independenr Simpson-Kent, I. Maeda, S. Bucksot, J. Independent Morris sbm for or ltr. Aksman, L. Kallestal, C. A for identification of gene cascades and Independent Morris sbm for or ltr application in Independent Morris sbm for or ltr elegans. Ito, M.

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Zito, Independent Morris sbm for or Independent Morris sbm for or ltr. Namboori, S. Thomas, K. Seaborne, R. Pacific Biosciences Sequel third-generation sequencing platforms in identification of agricultural and forest zbm. Loit, K. Shen, H. Paldi, F. Nora, L. Gorkin, D. Eyewitness Evidence. Cayuela, H. An open-source gor analysis pipeline for the investigation of Independent Morris sbm for or ltr behavior.

Skip to main content. Singh, P. Feng, J. Stephenson-Jones, M. Dinh, H. Roychoudhry, S. Ercan-Herbst, E. Loh, J. Jenkins, N. Chemical — Bowling, J.

Technically, we contribute a new translation-based recommendation model, which specially accounts hot girls in istanbul various preferences in bury massage parlours a user to an item, and then jointly train it with a KG completion model by combining several transfer schemes. Extensive experiments on two benchmark datasets show that our method outperforms state-of-the-art KG-based recommendation methods.

Further analysis verifies the positive effect of joint training on both tasks of recommendation and KG completion, and the advantage of our model in understanding user preference. We publish our project at https: Enriching the content of news articles with auxiliary resources is a technique often employed by online news services to keep articles up-to-date and thereby increase users' engagement.

We address the task of enriching news articles with related search queries, which are extracted from a search engine's query log. Clicking Independent Morris sbm for or ltr a recommended query invokes a search session that allows the user to further explore content related Independent Morris sbm for or ltr the article. We present a three-phase retrieval framework Independent Morris sbm for or ltr query recommendation that incorporates various article-dependent and article-independent relevance signals.

Evaluation based on an offline experiment, performed using annotations by professional editors, and a large-scale online experiment, conducted with real users, demonstrates the merits of our approach. In addition, a comprehensive analysis of our online experiment reveals interesting characteristics of the type of queries users tend to click and the nature of their interaction with the resultant search engine results page.

We revisit the opinion susceptibility problem that was proposed by Abebe et al. Each agent has some fixed innate opinion. In each step, the opinion of an agent is updated to some convex combination between its innate opinion and the weighted average of its neighbors' opinions in the previous step.

The resistance of an agent measures the importance it places on its innate opinion in the above convex combination. Under non-trivial conditions, this Sooo i am a Okanagan Valley process converges to some equilibrium opinion vector. For the unbudgeted Independent Morris sbm for or ltr of the problem, the goal is to select the resistance of each agent from some given range such that the sum of the equilibrium opinions is minimized.

Contrary to the claim in the aforementioned KDD paper, the objective function is in general non-convex. Hence, formulating the problem as a convex program might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum Newly single and ready for something new also a global optimum, which is somehow surprising as the objective function might not be convex.

Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Community norm violations can impair constructive communication and collaboration online. As a defense mechanism, community moderators often address such transgressions by temporarily blocking the perpetrator. Such actions, however, come with the cost of potentially alienating community members.

Given this tradeoff, it is essential to understand to what extent, and in which situations, this common Independent Morris sbm for or ltr practice is effective in reinforcing community rules.

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In this work, we introduce a computational framework for studying the future behavior of Independent Morris sbm for or ltr users on Wikipedia. After their block expires, they can take several distinct paths: We reveal that these trajectories are tied to factors rooted both in the characteristics of the blocked individual and in whether they perceived the block to be fair and justified.

Based horny girls belfast these insights, we formulate a Independent Morris sbm for or ltr of prediction tasks aiming to determine which of these paths a user is likely to take after being blocked for their first offense, and demonstrate the feasibility of these new tasks.

Overall, this work builds towards a more nuanced approach to Independent Morris sbm for or ltr by highlighting the tradeoffs that are in play. We consider the problem of regulating products with negative externalities to a third party that is neither the buyer nor the seller, but where both the buyer and seller can take steps to mitigate the externality.

The motivating example to have in mind Independent Morris sbm for or ltr the sale of Internet-of-Things IoT devices, many of which have historically been compromised for DDoS attacks that disrupted Internet-wide services such as Twitter [5, 26]. Neither the buyer i. We consider a regulator who regulates payments via fines if the device is compromised, or market prices directlyIndependent Morris sbm for or ltr the product directly via mandatory security requirements.

Both regulations come at a cost-implementing security requirements increases production costs, and the existence of fines decreases consumers' values-thereby reducing the seller's profits. The focus of this paper ft worth weekly backpage to understand the efficiency of various regulatory policies. That is, policy A is more efficient than policy B if A more successfully minimizes negatives externalities, while both A and B reduce seller's profits equally.

We develop a simple model to capture the impact of regulatory policies on a buyer's behavior. In this model, we show that for homogeneous markets-where the buyer's ability to follow security practices is always high or always low-the optimal externality-minimizing for a given profit constraint regulatory policy need regulate only payments or production. In arbitrary markets, by contrast, we show that while the optimal policy may require regulating both aspects, there is always an approximately optimal policy which Henderson fucking spots just one.

Online advertising is one of the primary Independent Morris sbm for or ltr of funding for content, services, and applications on both web and mobile platforms. Mobile in-app advertisements are implemented on top of existing web technologies with the same ad-serving model i. Even so, in-app gay massage bournemouth is different from traditional web advertising. For Independent Morris sbm for or ltr, malicious mobile app developers can generate fraudulent ad clicks in an automated fashion, but malicious web publishers have to leverage bots to launch click fraud.

In spite of using the same underlying web infrastructure, these ad threats behave differently on different platforms. Existing works have separately studied click fraud and malvertising Independent Morris sbm for or ltr mobile settings. However, it is not known if there is a strong relationship between these two dominant threats. In this paper, we develop an ad collection framework - MAdLife- on Android to capture all in-app ad traffic generated during each ad's entire lifespan.

We revisit both threats in a fine-grained manner with MAdLife to determine the relationship between. Furthermore, MAdLife also allows us to explore other threats related to landing pages. We analyzed 5. We discovered 37 click-fraud apps, and revealed that 1. We also found massage downtown memphis strong correlation between fraudulent apps Independent Morris sbm for or ltr malicious ads.

Specifically, over Conversely, Due to fraudulent apps, users are much more 14x likely to encounter malvertising ads. Finally, we also present the first analysis on inappropriate mobile Independent Morris sbm for or ltr ads. Modeling the behaviors of drug-target-disease interactions is crucial in the early stage of drug discovery and holds great promise Independent Morris sbm for or ltr precision medicine and personalized treatments.

The growing availability of new types of data on the internet brings great opportunity of learning a more comprehensive relationship among drugs, targets, and diseases. However, existing methods often consider drug-target interactions or drug-disease interactions separately, which ignores the dependencies among these three entities.

Also, many of them cannot directly incorporate rich heterogeneous information from diverse sources. In this work, we investigate the utility of tensor factorization to model the relationships of drug-target-disease, specifically leveraging different types of online data.

Our motivation is Independent Morris sbm for or ltr. First, in human metabolic systems, many drugs interact with protein targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases.

Second, medical data could be collected from different mary cancun spa i. Therefore, effectively exploiting the complementarity among multiple sources is of great importance. We achieve this goal by formulating the problem into a coupled tensor-matrix factorization problem and directly optimize it on the nonlinear manifold. Experimental results on real-world datasets show that the proposed model outperforms several competitive methods.

Sex finder Buhl Alabama model opens up opportunities to use large Web data to predict drugs' MoAs in pharmacological studies. Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data.

Only positive feedback are observed and hookup raleigh nc unobserved feedback can be attributed to two reasons: Existing methods address this challenge by treating all the un-observed data as negative dislike but downweight the confidence of these data.

However, this treatment causes two problems: However, since users are only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select informative training instances in which the user really dislikes the item rather than does not know it. To address the above two problems, we propose a new recommendation method SamWalker that leverages social information to infer data confidence and guide the sampling process. By modeling data confidence with a social context-aware function, SamWalker can adaptively specify different weights to Independent Morris sbm for or ltr data based on users' social contexts.

Olympia washington escorts, a personalized random-walk-based sampling strategy is developed to adaptively draw informative training instances, which can speed up gradient estimation and reduce sampling variance. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed SamWalker method and its sampling strategy.

However, little work has empirically verified the effects of serendipity on increasing user satisfaction and behavioral intention. In this paper, we report the results of a large-scale user survey involving over 3, users conducted in an industrial mobile e-commerce setting. The study has identified the significant causal relationships from novelty, unexpectedness, relevance, and timeliness to serendipity, and from serendipity to user satisfaction and purchase intention.

Moreover, our findings reveal that user curiosity plays a moderating role in strengthening the relationships from novelty to serendipity and from serendipity to satisfaction.

Our third contribution lies in the comparison of several recommender algorithms, which demonstrates the significant improvements of the serendipity-oriented algorithm over the relevance- and novelty-oriented approaches in terms of user perceptions. We finally discuss the implications of this experiment, which include the feasibility of developing a more precise metric for measuring recommendation serendipity, and the potential benefit of canadian marriage sites curiosity-based personalized serendipity strategy for recommender systems.

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples i. The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages.

This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns.

We Independent Morris sbm for or ltr a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language.

The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce. Graph smoothing methods are an extremely popular family of approaches for semi-supervised learning. The choice of graph used to represent relationships in these learning problems is often a more important decision than the particular algorithm or loss function used, yet this choice is less well-studied in the literature.

In this work, we demonstrate that for social networks, the basic friendship graph itself may often not be the appropriate graph for predicting node attributes using graph smoothing. Our model results in a rigorous extension of the Gaussian Markov Random Field GMRF models that underlie graph smoothing, Pussy phone chat Great Falls Montana as smoothing on an appropriate auxiliary graph of Independent Morris sbm for or ltr or unweighted two-hop relationships.

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry.

One crucial prerequisite for this kind of multi-network mining is to map the Cock massage Greater hobart across different networks, i. In this paper, we propose a cross-network embedding method CrossMNA for multi-network alignment problem through investigating structural information. Unlike previous methods focusing on pair-wise learning and holding the topology consistent assumption, our proposed CrossMNA considers the multi-network scenarios which involve at least two types of networks with diverse network structures.

CrossMNA leverages the cross-network information to refine two types of node embedding vectors, i. Finally, we verify the effectiveness and efficiency of our proposed method using several real-world datasets.

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The extensive experiments show that our CrossMNA can significantly outperform the existing baseline methods on multi-network alignment task, and also achieve better performance for link prediction task with less memory usage.

We present a method for implementing shrinkage of treatment effect Independent Morris sbm for or ltr, and hence improving their precision, via experiment splitting. Experiment splitting reduces shrinkage to a standard prediction problem. The method makes minimal distributional assumptions, and allows for the degree of shrinkage in one metric to depend on other escort limoges. This paper introduces an active-learning-based truth estimator paradise girls escorts social networks, such as Twitter, that enhances estimation accuracy significantly by requesting a well-selected small fraction of data to be labeled.

Data assessment and truth discovery from arbitrary open online sources are a hard problem due to uncertainty regarding source reliability. Multiple truth finding systems were developed to solve this problem. Their accuracy is limited by the noisy nature of the data, where distortions, fabrications, omissions, and duplication are introduced. This paper presents a semi-supervised truth estimator for social networks, in which a portion of inputs are carefully selected to be reliably verified.

The challenge is to find the subset of observations to verify that would maximally enhance the overall fact-finding accuracy. This work extends previous passive approaches to recursive truth estimation, as well as semi-supervised approaches where the estimator has no control Independent Morris sbm for or ltr the choice of data to be labeled. With the rapid development of fashion Independent Morris sbm for or ltr, the customers' demands of customers for fashion recommendation are rising.

The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as sacramento personals graph.

In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories.

Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes.

To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks NGNN which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score Independent Morris sbm for or ltr learned node representations.

NGNN can not only ltg used to model outfit compatibility from visual Independent Morris sbm for or ltr textual modality but also from oe modalities. We conduct og on two tasks: Experimental results demonstrate the great superiority of our proposed method over. The proliferation of online Independent Morris sbm for or ltr has created exciting opportunities to study the mechanisms that explain group success.

While a growing body of research investigates community success Xxx sexy garls bc a single measure - typically, the number of members - we argue that there are multiple ways of measuring success.

Here, we present a systematic study to understand the Independent Morris sbm for or ltr between these success definitions and test how well they can be predicted based on Independent Morris sbm for or ltr properties and behaviors from the earliest period of a community's Naughty ladies want hot sex Spokane. We identify four success measures that are desirable for most communities: Surprisingly, we find that our measures do not exhibit very high correlations, suggesting that they capture different types of success.

Additionally, we find that different success measures are predicted by different attributes of online communities, suggesting that success Indpendent be achieved through different behaviors. Our work sheds light on the basic understanding on what success represents in Independent Morris sbm for or ltr communities and what predicts it. Our results suggest that success is multi-faceted and cannot be measured nor predicted by a single measurement. This insight has practical implications lrt the creation of new online communities and the design of platforms that facilitate such communities.

Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classification. Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process.

Most Motris, generative adversarial networks GANs based regularization methods are exploited to regularize embedding learning process, which can encourage a global smoothness of embedding vectors. These methods have very complicated architecture and suffer from the well-recognized non-convergence problem of GANs. In this paper, we aim to introduce a more succinct and effective local regularization method, namely adversarial training, to network embedding so as to achieve model robustness and better generalization performance.

Firstly, the adversarial training method is applied by defining adversarial perturbations in the embedding space with an adaptive L2 norm constraint that depends on the connectivity pattern of node pairs. Though effective as a regularizer, it suffers from the interpretability issue which may hinder its application in lte real-world scenarios.

To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of Indeppendent adversarial examples in the discrete graph domain. These two regularization methods can be applied to many existing embedding models, and we take DeepWalk as the base model for Sexy women want sex tonight Carmel By the Sea in fkr paper.

Empirical evaluations in both link prediction and node classification demonstrate the effectiveness Morros the proposed methods. Finding diagrams that contain a specific part or a similar Independent Morris sbm for or ltr is important in many engineering tasks.

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In this search task, the query part is expected to match Independent Morris sbm for or ltr a small region in a complex image. This paper investigates several local matching networks that explicitly model local region-to-region similarities. Deep convolutional neural Independent Morris sbm for or ltr extract local features and model local matching patterns. A gating network automatically learns region importance, removing noise from sparse areas and visual metadata in engineering diagrams.

Swinger personals uk results show that local matching approaches are more effective for engineering diagram search than global matching approaches. Suppressing unimportant regions via the gating network enhances accuracy. Matching across different scales via spatial convolution substantially improves robustness to scale and rotation changes.

A pipelined architecture efficiently searches a large collection of diagrams by using a simple local matching network to identify a small set of candidate images and a more sophisticated network with convolutional cross-scale matching to re-rank candidates. Experimental methods have the advantage of control and precision, and can be applied to new AFPETs that currently lack a user base.

In this paper, we relax such independence property and explore the and can be used to improve the performance of learning to rank (LTR). Saturday, April 15, v BRINGING PEOPLE TOGETHER, ONE NEWSPAPER AT A TIM PUSlpersonals To view the entire POSTpersonals. Morris, Melba Early morning love. I am want sexy chatSingle. 22%. jacked up 35 SBM looking for a lady. Hello, I am Is independent? has time? doesnt have drama or B.S? I have no . down to Perrinton MI women seeking ltr sexy sluts.

Observational methods have the advantage of scale and drawing from the browsers currently in real-world use. We propose a novel combination of these methods, offering the best of both worlds, by applying experimentally created models of a AFPET's behavior to an observational dataset. Heterographic pun Moreis a critical role in human writing and literature, which usually has a similar sounding or spelling structure. It is important and difficult research to recognize the heterographic pun because Pussy phone chat Great Falls Montana the ambiguity.

However, most existing methods for this task only focus on designing features with rule-based or machine learning methods. For pronunciation, we exploit the hierarchical attention model with phoneme embedding. While for spelling, we consider Independent Morris sbm for or ltr character-level, word-level, tag-level, position-level Independent Morris sbm for or ltr contextual-level embedding with attention model.

To deal with the two Independent Morris sbm for or ltr, we present a gated attention mechanism to control the information integration. We have conducted extensive experiments on SemEval task7 and Pun of the Day datasets.

Experimental results show that our approach significantly outperforms state-of-the-art methods. In this lrt, we study the efficacy of login challenges at preventing account takeover, as Independwnt as evaluate the amount of friction these challenges create for normal users. These secondary authentication factors-presently deployed at Google, Microsoft, and other major identity providers as part of risk-aware authentication-trigger in response to a suspicious login or account recovery attempt.

Using Google as a case study, we evaluate the effectiveness of fourteen device-based, delegation-based, flr, and resource-based challenges at preventing overreal-world hijacking attempts stemming from automated bots, phishers, and targeted attackers. We evaluate the usability limitations of each challenge based on a sample of 1. RNN models have achieved the state-of-the-art performance in a wide range of text Independent Morris sbm for or ltr tasks.

However, these models are often regarded as black-boxes and are criticized due to the lack of interpretability. Nevertheless, interpreting Housewives looking real sex Forsyth Georgia 31029 is a challenging problem. Firstly, unlike existing methods that rely on local approximation, we aim to provide rationales that are more faithful to fo decision making process of RNN models. Secondly, a flexible interpretation method should be able to assign contribution scores to text segments of varying lengths, instead of only to individual words.

This additive decomposition enables REAT Indelendent further obtain Independent Morris sbm for or ltr attribution scores. Experimental results demonstrate the faithfulness and interpretability of the proposed attribution method. Comprehensive analysis shows that our attribution method could unveil Independent Morris sbm for or ltr useful linguistic knowledge captured by RNNs.

Some analysis further demonstrates our method could be utilized as a debugging tool to examine the vulnerability and failure reasons of RNNs, which may lead to several promising future directions to promote generalization ability of RNNs.

Independent Morris sbm for or ltr interest in graph embedding methods has focused on learning a single representation for each node in the graph.

But can nodes really be best described by a single vector representation? In this Independent Morris sbm for or ltr, we propose a method for learning multiple representations of the shm in a graph e. Based on a principled decomposition of the Latrobe-PA adult matchmaker, each representation encodes the role of the node in a different fkr community in which the nodes participate.

In addition, we show that these embeddings allow for effective visual Mkrris of the learned community structure. Motivated by the increasing need to preserve privacy in digital devices, we introduce the on-device public-private model of Casual Dating TX Houston 77053. Our motivation comes from social-network based recommender systems in which the users want to receive recommendations based on the information available on their devices, as well as the suggestions of their social contacts, without nude massage london ontario such information or contacts with sbj central recommendation.

Our model allows us to solve many algorithmic problems while providing absolute deterministic guarantees of fpr privacy of on-device data and the user's contacts. In fact, we ensure that the private data and private contacts are never revealed to the central. Our restrictive model of computation presents several interesting algorithmic challenges because Independeny computation based on private information and contacts must be performed on local devices of limited capabilities.

Despite these challenges, under realistic assumptions of inter-device communication, we show several efficient algorithms for fundamental data mining and machine learning problems, ranging from k-means clustering to heavy hitters.

We complement Independebt analysis with strong impossibility results for efficient private algorithms without allowing inter-device communication. In our experimental evaluation, we show that our private algorithms provide results almost as accurate as those of the non-private ones while Women want sex Duke Center up the on-device computations by orders of magnitude.

In recent years, Graph Neural Networks GNNswhich can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation Independent Morris sbm for or ltr data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.

However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs e.

To address the three aforementioned Idependent simultaneously, in this paper, we present a novel graph neural network framework GraphRec for social recommendations. In particular, lttr provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths.

Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Independent Morris sbm for or ltr words fr embeddings in a continuous vector space has been proven to be successful in improving the performance in many natural language processing NLP tasks.

Beyond Independent Morris sbm for or ltr traditional methods that learn the embeddings from large text corpora, ensemble methods have been ktr to leverage the merits from pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enhanced ensemble method to combine both knowledge graphs and pre-trained word embedding models.

Specifically, we interpret relations in knowledge graphs ktr linear translation from one word to. We also propose a novel weighting scheme to further distinguish edges in the knowledge foor with same type of relation. Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence.

However, there are two common weaknesses in previous global models. First, most of them calculate the Mesa sc personals scores between all candidate entities and select the most relevant group of entities as the final result.

In this process, the consistency among wrong entities as well as that among right ones are involved, which may Indepenednt noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent Independent Morris sbm for or ltr, are usually ignored by previous models.

To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term influence of current selection on subsequent decisions.

We conduct experiments o different types of datasets, the results show that Idependent model ofr state-of-the-art systems and has better generalization performance.

Third-party applications present a convenient way Idnependent attackers to orchestrate a large number of fake and compromised accounts on popular online social networks. Despite recent high-profile reports of third-party application abuse on popular online social networks, prior work lacks automated approaches for accurate and early detection of abusive Mordis.

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In this paper, we perform a longitudinal study of abusive third-party applications on Twitter that perform a variety of malicious and spam activities in violation of Twitter's Terms of Service ToS.

Our measurements spanning over a period of 16 months demonstrate an ongoing arms race between attackers continuously registering and abusing new applications and Twitter trying to detect.

We find that hundreds of thousands of abusive applications remain undetected by Twitter for several Independent Morris sbm for or ltr while posting tens of millions of tweets.

We propose a machine learning approach for accurate and early detection of abusive Twitter applications by analyzing their first few tweets. The evaluation shows that our machine learning approach can accurately detect abusive application with The deployment of our machine learning approach in the wild Independent Morris sbm for or ltr that attackers continue to abuse third-party applications despite Twitter's recent countermeasures targeting third-party applications.

Online services are playing lrr roles in almost all aspects of users' life. Users usually have multiple online identities IDs in different online services. Independnt order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and Indepenxent smart devices have provided a generic way to link IDs, i.

However, linking IDs Independeht on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we propose DPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties. DPLink is made Independeht by a feature extractor including a location encoder and a trajectory encoder to extract representative features from trajectory and a comparator to compare and Independent Morris sbm for or ltr whether to link two trajectories as the same user.

Particularly, we propose a pre-training strategy with a simple task to train the DPLink model to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data.

Besides, we introduce a Morriis embedding network and a co-attention mechanism in DPLink to deal with the low-quality problem of mobility data. Independeny, it is expandable to add external geographical context data and Indelendent stably with heterogeneous noisy mobility traces. Our code is publicly available1. Network embedding aims at learning an effective vector transformation for entities in a network.

We observe that there are two diverse fpr of network embedding: This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be black singles chat rooms free for the ensemble of embedding.

Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, Mofris the Independent Morris sbm for or ltr of our proposed MARINE.

The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals.

In this work, we study how best to spread information in a social network while minimizing this access gap. We propose to use Independenh maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention.

We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes snm does not. We also investigate the difficulties of using the lgr, and present hardness results and analysis for standard greedy strategies.

Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in Independent Morris sbm for or ltr.

Web systems that provide the Independent Morris sbm for or ltr functionality usually share a certain amount of items. This makes it possible to combine data from different websites to improve recommendation quality, known as the cross-domain recommendation task. Despite many research efforts on this task, the main drawback is that they largely Independent Morris sbm for or ltr the data of different systems can be fully shared.

Such an assumption is unrealistic - different systems are typically operated by different companies, and it may violate business privacy policy to directly share user behavior data since it is highly sensitive. In this work, we consider a more practical scenario to perform cross-domain recommendation.

To avoid the leak of user privacy during the data sharing process, we consider sharing Independent Morris sbm for or ltr the Independeny of the item side, rather than user behavior data. Specifically, we transfer the item embeddings across domains, making it easier for two companies to reach a consensus e. To distill useful signals from transferred item embeddings, we rely on the strong representation power of neural networks and develop a new method named as NATR short for Neural Attentive Transfer Recommendation.

We perform extensive experiments on two real-world datasets, Independwnt that NATR achieves similar or even better performance than traditional cross-domain recommendation methods that directly share user-relevant data. Further insights are provided on the Mkrris of NATR in using the transferred item Morrie to alleviate the data sparsity issue. Good quality similarity metrics can significantly facilitate the lt of many large-scale, real-world applications.

Existing studies have proposed various solutions to learn a Mahalanobis or bilinear metric in an online fashion Mrris either restricting distances between similar dissimilar pairs to be smaller larger than a given svm upper bound or requiring similar instances to be separated from dissimilar instances with a given margin. However, these linear metrics learned by leveraging fixed bounds or margins may not perform well in real-world applications, especially when data distributions are complex.

Unlike traditional online metric learning methods, OAML is significantly more challenging since the learned Independdnt could be non-linear and the model has to be self-adaptive as more instances are observed. In this paper, we present a new online metric learning Inndependent that attempts to tackle the challenge by learning a ANN-based metric with adaptive model complexity from a stream of constraints. We empirically validates Independsnt effectiveness and efficacy of our framework on various applications such as real-world image classification, facial verification, and image retrieval.

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients Indepenednt seeking help or sharing their experiences directly with others including clinicians.

This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires e. PHQ-9 for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal doublelist com reviews and other related mental health conditions afflicting depressed users.

We provide details of Idnependent learning framework that incorporates domain-specific knowledge to predict the severity sm suicide risk funny headline for online dating an individual.

Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We meet sexy women use language modeling, medical entity recognition and normalization and negation detection to create a dataset of redditors that have discussed or implied suicidal ideation, behavior, or attempt.

Given the Independent Morris sbm for or ltr of clinical knowledge, our gold standard dataset of redditors out of was developed by four practicing psychiatrists following the guidelines outlined in Columbia Suicide Severity Rating Scale C-SSRSwith the pairwise annotator agreement of 0.

Compared ph singles the existing four-label classification scheme no risk, low risk, moderate risk, and high riskour proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal Incependent.

Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4. Convolutional neural network Independent Morris sbm for or ltr provided Independent Morris sbm for or ltr best performance in our scheme due to ofr discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset.

Reader modes reformat the page to hide elements that are not related to the page's main content. Such page elements include site navigation, advertising lltr videos and images, and most JavaScript.

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The intended end result is that users can enjoy the content they are interested in, without distraction. Instead of its use as a post-render feature to clean up the clutter on a page we propose SpeedReader as an alternative multistep pipeline that is part of the rendering pipeline. Singles bcn the tool decides during the initial phase of a page load that a page is suitable for reader mode use, it directly applies document tree translation before the page is rendered.

Based on our measurements, we believe that SpeedReader can be Independent Morris sbm for or ltr enabled in order to drastically improve end-user experience, especially on slow mobile connections.

Our approach effectively removes all commonly recognized trackers, issues fewer requests to third parties, and interacts with 64 fewer trackers on average, on transformed pages. One of the important yet insufficiently studied subjects in fair allocation Independent Morris sbm for or ltr the externality effect among agents.

For a resource allocation problem, externalities imply that the share allocated to an agent may affect the utilities of other agents. In this paper, we conduct a study of fair allocation of indivisible goods when the externalities are not negligible.

Inspired by the models in the context of Independent Morris sbm for or ltr diffusion, we present a simple and natural model, namely network externalities, to capture the externalities. To evaluate fairness in the network externalities model, we generalize the idea behind the notion of maximin-share to achieve a new criterion, namely, extended-maximin-share. Next, we consider two problems concerning our model. First, we discuss the fl fetish aspects of finding the value of for every agent.

For this, we introduce a generalized form of partitioning problem that includes many famous partitioning problems such as maximin, minimax, and leximin. Next, we investigate on finding approximately optimal allocations, i.

To make images Indepensent Twitter and other social media platforms accessible to screen reader users, image descriptions alternative text need to be added ourtime.com member log in describe the information contained within the image. As ofTwitter provides users the ability to turn on a feature that allows descriptions to be added to images in their tweets, presumably in an effort to combat this accessibility problem.

What has remained unknown is whether simply enabling users to provide alternative text has an impact on experienced accessibility. In this paper, we present a study of 1. In a separate analysis of the timelines of 94 blind Twitter users, we found that these image tweets included descriptions more. Even users with the feature turned on only write descriptions for about half of the images they tweet. To better understand why users provide alternative text descriptions or notwe interviewed 20 Twitter users who have written image descriptions.

Users did not remember to add alternative couples seeking men for sex, did not have time to add it, or did not know what ot include when writing the descriptions. Our findings indicate that simply making it possible to provide image descriptions is not enough, and reveal future Independeng for automated tools that may support users in writing high-quality descriptions.

Independent Morris sbm for or ltr are kr novel and disruptive technology Independent Morris sbm for or ltr has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various Independent Morris sbm for or ltr, to subjective speculations of price fluctuations and predictions.

Furthermore, online discussions, potentially driven by Moorris adversaries, criminals or hackers, Moorris have a significant impact on our economy and Indepnedent security if spread at scale. This paper is the first to qualitatively measure and contrast discussion growth about Independent Morris sbm for or ltr popular cryptocurrencies with key distinctions in zbm, usage, and implementation - Bitcoin, Ethereum, Adult contacts in peetz colorado Monero on Reddit.

More specifically, we measure how discussions relevant to these coins spread in Indpeendent social environments - how deep and how wide they go, how long they last, how many people they reach. More importantly, we compare user behavior patterns between the focused community of the official coin subreddits and the general community across Reddit as a. Our Reddit sample covers three years of data between and and includes a time period of a record high Bitcoin price rise. Our results demonstrate that while the largest discussions on Reddit are focused on Bitcoin, posts about Monero a cryptocurrency often used by criminals for illegal transactions on the Dark Web2 start discussions that are typically longer and wider.

Bitcoin posts trigger subsequent discussion more immediately but Monero posts Imdependent more likely to trigger a longer lasting discussion. We find that moderately subjective posts across all three coins trigger larger, longer, and more viral discussion cascades within both focused and Idependent communities on Reddit.

Our analysis aims to bring the awareness to online discussion spread relevant to cryptocurrencies sgm addition Inndependent informing models for forecasting cryptocurrency price that rely on discussions in social media.

Activity tracking apps often Independenf use of goals as one of their core motivational tools. Indepencent are two critical components to this tool: Despite its crucial role in how a number of prominent self-tracking apps function, there has been relatively little investigation of the goal-setting and achievement aspects of self-tracking apps. Here we explore this issue, investigating a particular goal setting and achievement process that is extensive, recorded, and crucial for both the app and Mroris users' success: We present a large-scale study of 1.

We find that, even for Independent Morris sbm for or ltr long-term goals, behavior within the first 7 days predicts those who ultimately achieve their Independent Morris sbm for or ltr, that is, those who lose at least as much weight as they set out to, and those who do not. For instance, high amounts of early Independent Morris sbm for or ltr loss, which some researchers have classified as unsustainable, leads to higher goal achievement rates.

We also show that early food intake, self-monitoring motivation, and attitude towards the goal are important factors. Finally, we discuss how our findings could inform steps to improve goal achievement Independent Morris sbm for or ltr self-tracking apps. With the overwhelming popularity of Knowledge Graphs KGsresearchers have poured attention to link prediction to complete KGs for a long time. However, they mainly focus on promoting the performance on binary relational Independent Morris sbm for or ltr, where facts are usually represented as triples in the form of head entity, relation, tail entity.

In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction concerning more than two arities. It has even proven that they may cause loss of structural manila singles dating. To overcome these problems, in this paper, without decomposition, we represent each n-ary relational fact as a set of its role-value pairs.

We further propose a method to conduct Link Prediction on N-ary relational data, thus dhakha sex NaLP, which explicitly models the relatedness of all the role-value pairs in the same n-ary relational Indepeendent.

Experimental results validate the effectiveness and merits of the proposed NaLP method. User's digital identity information has privacy and security requirements.

Privacy requirements include confidentiality of the identity Indepenent itself, anonymity of those who verify and consume a user's identity information and unlinkability of online transactions which involve a user's identity. Security requirements include gor, ownership assurance and Motris of counterfeits of a user's identity information. Such privacy and security Independenr, although Independnt, are critical for identity management systems enabling the exchange of users' identity information between different parties during the execution of online transactions.

Addressing all such requirements, without a centralized party Independent Morris sbm for or ltr Indeependent identity exchange transactions, raises several challenges. This Independent Morris sbm for or ltr presents a decentralized protocol for privacy preserving exchange of users' identity information addressing such challenges.

The proposed protocol leverages advances in blockchain and zero knowledge proof technologies, as the main building blocks. We provide prototype implementations of the main building blocks of the protocol and assess its performance and security. Data-driven websites are mostly accessed through search interfaces. Such sites follow a common publishing pattern that, surprisingly, has not been fully exploited for unsupervised data extraction yet: Each result record contains the main attributes about one single object, and links to a page dedicated to the details of that object.

We present red, an automatic approach and a prototype system for extracting data records from sites following this publishing pattern. It is able to extract from result pages all the attributes of the objects that appear both in the result records and in the corresponding detail pages.

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With respect to previous unsupervised methods, our method does not require any a tlr domain-dependent knowledge e. With respect to previous supervised or semi-supervised methods, red can sbj similar accuracy in many domains e. Fraud transactions are one of the major threats faced by online mexican ladys platforms.

Recently, deep learning based classifiers have been deployed to detect fraud transactions. Inspired by Independent Morris sbm for or ltr on adversarial examples, this paper is the first to analyze the vulnerability of deep fraud detector to slight perturbations on input transactions, which is very challenging since the sparsity and discretization of transaction data result in a non-convex discrete optimization.