nils-herrmann/clean_scirepeval huggingface dataset:
Social Network Sites: Users and Uses

Social network sites (SNS) have rapidly become very popular, challenging even the major portals and search engines in terms of usage and co mmercial value. This chapter introduces key SNS issues and reviews relevant academic resear ch from sociology, communication science, computer science, and information science. The chapter introduces a broad classification of SNS friendship and demonstrates t he range of types of SNS, each with its own unique combination of functionalities and objec tives. The users and uses for SNSs are also varied, both in terms of the broad range of re asons for using a site and also, at the microlevel, in terms of the understanding of the core co ncept of friending. The commonly discussed issues of privacy and security are reviewed, includ ing the extent to which they are taken seriously by users and SNS designers. New forms of electronic communication seem to always generate their own new language varieties an d SNS language is briefly discussed. The chapter is supported by a series of MySpace investi gations to illustrate key points and give additional information. Finally, the potential for p ogrammers to create small applications to run within SNSs or with SNS data is discussed and s peculations made about future developments. Table of contents
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.
Synthesizing Tabular Data using Generative Adversarial Networks

Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution. This paper presents, Tabular GAN (TGAN), a generative adversarial network which can generate tabular data like medical or educational records. Using the power of deep neural networks, TGAN generates high-quality and fully synthetic tables while simultaneously generating discrete and continuous variables. When we evaluate our model on three datasets, we find that TGAN outperforms conventional statistical generative models in both capturing the correlation between columns and scaling up for large datasets.
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints

We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems utilizing spatial-domain convolution or lower complexity systems designed for Winograd convolution. Furthermore, we explore the universal quantization and compression of these networks. In particular, the proposed framework produces one compressed model whose convolutional filters can be made sparse either in the spatial domain or in the Winograd domain. Hence, one compressed model can be deployed universally on any platform, without need for re-training on the deployed platform, and the sparsity of its convolutional filters can be exploited for further complexity reduction in either domain. To get a better compression ratio, the sparse model is compressed in the spatial domain which has a less number of parameters. From our experiments, we obtain 24.2×, 47.7× and 35.4× compressed models for ResNet-18, AlexNet and CT-SRCNN, while their computational cost is also reduced by 4.5×, 5.1× and 23.5×, respectively.
Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation

Sensor fusion is indispensable to improve accuracy and robustness in an autonomous navigation setting. However, in the space of end-to-end sensorimotor control, this multimodal outlook has received limited attention. In this work, we propose a novel stochastic regularization technique, called Sensor Dropout, to robustify multimodal sensor policy learning outcomes. We also introduce an auxiliary loss on policy network along with the standard DRL loss in order to reduce variance in actions of the multimodal sensor policy. Through extensive empirical testing, we demonstrate that our proposed policy can 1) operate with minimal performance drop in noisy environments and 2) remain functional even in the face of a sensor subset failure. Finally, through the visualization of gradients, we show that the learned policies are conditioned on the same latent input distribution despite having multiple and diverse observations spaces a hallmark of true sensorfusion. This efficacy of a multimodal sensor policy is shown through simulations on TORCS, a popular open-source racing car game. A demo video can be seen here: https://youtu.be/HC3TcJjXf3Q.
Current challenges and concepts in the preparation of root canal systems: a review.

Nickel-titanium rotary instruments are important adjuncts in endodontic therapy. This review attempts to identify factors that influence shaping outcomes with these files, such as preoperative root-canal anatomy and instrument tip design. Other, less significant factors include operator experience, rotational speed, and specific instrument sequence. Implications of various working length definitions and desired apical widths are correlated with clinical results. Despite the existence of one ever-present risk factor, dental anatomy, shaping outcomes with nickel-titanium rotary instruments are mostly predictable. Current evidence indicates that wider apical preparations are feasible. Nickel-titanium rotary instruments require a preclinical training period to minimize separation risks and should be used to case-related working lengths and apical widths. However, and despite superior in vitro results, randomized, clinical trials are required to evaluate outcomes when using nickel-titanium instruments.
Understanding MOOC students: motivations and behaviours indicative of MOOC completion

Massive open online courses (MOOCs) continue to appear across the higher education landscape, originating from many institutions in the USA and around the world. MOOCs typically have low completion rates, at least when compared with traditional courses, as this course delivery model is very different from traditional, fee-based models, such as college courses. This research examined MOOC student demographic data, intended behaviours and course interactions to better understand variables that are indicative of MOOC completion. The results lead to ideas regarding how these variables can be used to support MOOC students through the application of learning analytics tools and systems.
Cost Weighting for Neural Machine Translation Domain Adaptation

In this paper, we propose a new domain adaptation technique for neural machine translation called cost weighting, which is appropriate for adaptation scenarios in which a small in-domain data set and a large general-domain data set are available. Cost weighting incorporates a domain classifier into the neural machine translation training algorithm, using features derived from the encoder representation in order to distinguish in-domain from out-of-domain data. Classifier probabilities are used to weight sentences according to their domain similarity when updating the parameters of the neural translation model. We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting. Experiments on two large-data tasks show that both the traditional techniques and our novel proposal lead to significant gains, with cost weighting outperforming the traditional methods.
Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density

Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require “scaling-up” from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas.  Le lidar est en voie de devenir une technique de plus en plus populaire parmi les chercheurs pour le développement de modèles de prédiction des variables biophysiques de la forêt. Cependant, avant que cette technologie puisse être adoptée avec confiance pour le suivi à long terme au Canada, des modèles robustes pouvant être appliqués et validés pour des superficies de forêt vastes et complexes doivent être développés. Cela va exiger de passer des modèles courants développés à partir d’une forte densité de données lidar à une plus faible densité de données collectées à plus haute altitude. Cet article se penche sur l’effet de la diminution de l’espacement ponctuel moyen des échos individuels du lidar sur les modèles de variables biophysiques de la forêt. La validation des résultats a montré que les modèles à forte densité sont bien corrélés avec la hauteur dominante moyenne, la surface terrière, la fermeture du couvert et la biomasse aérienne moyenne (R2 = 0,84, 0,89, 0,60 et 0,91 respectivement). Les modèles à faible densité ne pouvaient pas correctement (R2 = 0,36) prédire la fermeture du couvert. Cependant, ils ont fourni des estimations légèrement meilleures pour la hauteur dominante moyenne, la surface terrière et la biomasse aérienne moyenne (R2 = 0,90, 0,91 et 0,92 respectivement). Des cartes ont été générées et validées pour toute la zone d’étude à partir de modèles à faible densité. La capacité des modèles à faible densité à cartographier correctement les variables biophysiques importantes est une indication positive de l’utilité des données lidar pour le suivi de vastes zones de forêt. [Traduit par la Rédaction] Thomas et al. 47
Relationship between risk and intention to purchase in an online context: role of gender and product category

Multiple studies have attempted to explain the online shopping behaviour of consumers both in Information Systems (IS) and Marketing literature. However, given the widening gap between actual and expected increase in Internet-enabled or web-based consumer purchase transactions, the need to investigate the underlying factors for on-line purchase behaviour assumes increased significance. Also, the gap between actual purchase behaviour of the consumer on Internet and that explained by existing research points to the possibility of some unexplained control variables influencing consumers’ online shopping behaviour. Building on past research, our study incorporates gender and product category as two control variables and unlike prior studies takes an integrative perspective by examining the interactional role of gender and product category on online shopping behaviour. Our study results show that relationship between perceived risk and intention to purchase is moderated by interaction of gender and product category. One major finding of this study, that perceived usefulness mediates the relationship between perceived risk and intention to purchase, has significant theoretical implications for technology acceptance model in Internet context. Our study also indicates perceived usefulness to be the primary determinant of on-line purchase behaviour and points to likely nonsignificant role of perceived ease of use in influencing purchase intention. We discuss these results and provide implications for both theory and research.
Coreference Resolution Using Competition Learning Approach

In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the singlecandidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. Furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution. The experimental results on MUC-6 and MUC-7 data set show that our approach can outperform those based on the singlecandidate model.
Traversing Knowledge Graphs in Vector Space

Path queries on a knowledge graph can be used to answer compositional questions such as “What languages are spoken by people living in Lisbon?”. However, knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new “compositional” training objective, which dramatically improves all models’ ability to answer path queries, in some cases more than doubling accuracy. On a standard knowledge base completion task, we also demonstrate that compositional training acts as a novel form of structural regularization, reliably improving performance across all base models (reducing errors by up to 43%) and achieving new state-of-the-art results.
Design and Development of Water Quality Monitoring System Based on Wireless Sensor Network in Aquaculture

College of Information and Electrical Engineering, China Agricultural University, Beijing, China Abstract. This paper presents a system framework taking the advantages of the WSN for the real-time monitoring on the water quality in aquaculture. We design the structure of the wireless sensor network to collect and continuously transmit data to the monitoring software. Then we accomplish the configuration model in the software that enhances the reuse and facility of the monitoring project. Moreover, the monitoring software developed to represent the monitoring hardware and data visualization, and analyze the data with expert knowledge to implement the auto control. The monitoring system has been realization of the digital, intelligent, and effectively ensures the quality of aquaculture water. Practical deployment results are to show the system reliability and real-time characteristics, and to display good effect on environmental monitoring of water quality.
The Human Semantic Potential: Spatial Language and Constrained Connectionism

One of the implications of connectionist work on language is that the "language mechanism" is not essentially different from mechanisms that perform other cognitive tasks. This leads naturally to the notion that language learning must be understood in the context of learning about the world more generally. It is difficult to think of how to constrain a modeling exercise that adopts this perspective, for such a system requires an encoding not only of some interesting part of language but also of the corresponding part of "the world." If one were to undertake this rather daunting task, a natural place to start would be with a subdomain of language in which the important semantic contrasts can be expressed in a two-dimensional visual world and to use low-resolution computer graphics for the encoding of this world. In The Human Semantic Potential, Regier takes precisely this approach, by focusing on the domain of closed-class spatial markers (e.g., prepositions such as above, below, left, right, in, and on in English). Bit map "movies" of a trajector that moves in relation to a landmark for several movie frames, or that simply exists, are used as the input to a connectionist learning model; the outputs are labels corresponding to prepositions. The scientific game plan is to construct a model that learns the semantics of spatial markers on the basis of exposure to examples in a given language; the learnable sets of markers under the model then constitute a typology of possible sets of spatial markers in languages of the world. Thus the model makes cross-linguistic typological predictions. Moreover, since the model generalizes from a subset of examples in each language to an assessment of the aptness of each word in all representable conditions, it makes language-internal predictions about the meanings that particular spatial markers can have. Given this framework, it seems reasonable to assess the project in terms of how much we learn about these two domains of prediction from it. In the former case-cross-linguistic typological prediction--the results are only mildly interesting: only a very small subset of the range of predictions that the model makes is revealed, apparently because of the assumed unanalyzability of the connectionist network at the core of the model. In the case of the second task--generalization from positive examples-the results are more appealing: Regier shows how a linguistically-motivated variation on a standard connectionist learning algorithm provides an effective mechanism for
Convolutional Neural Network for Paraphrase Identification

We present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Based on the insight that PI requires comparing two sentences on multiple levels of granularity, we learn multigranular sentence representations using convolutional neural network (CNN) and model interaction features at each level. These features are then the input to a logistic classifier for PI. All parameters of the model (for embeddings, convolution and classification) are directly optimized for PI. To address the lack of training data, we pretrain the network in a novel way using a language modeling task. Results on the MSRP corpus surpass that of previous NN competitors.
Anomalous Payload-Based Network Intrusion Detection

We present a payload-based anomaly detector, we call PAYL, for intrusion detection. PAYL models the normal application payload of network traffic in a fully automatic, unsupervised and very effecient fashion. We first compute during a training phase a profile byte frequency distribution and their standard deviation of the application payload flowing to a single host and port. We then use Mahalanobis distance during the detection phase to calculate the similarity of new data against the pre-computed profile. The detector compares this measure against a threshold and generates an alert when the distance of the new input exceeds this threshold. We demonstrate the surprising effectiveness of the method on the 1999 DARPA IDS dataset and a live dataset we collected on the Columbia CS department network. In once case nearly 100% accuracy is achieved with 0.1% false positive rate for port 80 traffic.
Parallelizing Skip Lists for In-Memory Multi-Core Database Systems

Due to the coarse granularity of data accesses and the heavy use of latches, indices in the B-tree family are not efficient for in-memory databases, especially in the context of today's multi-core architecture. In this paper, we study the parallelizability of skip lists for the parallel and concurrent environment, and present PSL, a Parallel in-memory Skip List that lends itself naturally to the multi-core environment, particularly with non-uniform memory access. For each query, PSL traverses the index in a Breadth-First-Search (BFS) to find the list node with the matching key, and exploits SIMD processing to speed up this process. Furthermore, PSL distributes incoming queries among multiple execution threads disjointly and uniformly to eliminate the use of latches and achieve a high parallelizability. The experimental results show that PSL is comparable to a readonly index, FAST, in terms of read performance, and outperforms ART and Masstree respectively by up to 30% and 5x for a variety of workloads.
NLP-driven citation analysis for scientometrics

This paper summarizes ongoing research in NLP (Natural Language Processing) driven citation analysis and describes experiments and motivating examples of how this work can be used to enhance traditional scientometrics analysis that is based on simply treating citations as a “vote” from the citing paper to cited paper. In particular, we describe our dataset for citation polarity and citation purpose, present experimental results on the automatic detection of these indicators, and demonstrate the use of such annotations for studying research dynamics and scientific summarization. We also look at two complementary problems that show up in NLP driven citation analysis for a specific target paper. The first problem is extracting citation context, the implicit citation sentences that do not contain explicit anchors to the target paper. The second problem is extracting reference scope, the target relevant segment of a complicated citing sentence that cites multiple papers. We show how these tasks can be helpful in improving sentiment analysis and citation based summarization. ∗This research was conducted while the authors were at University of Michigan. 2 Rahul Jha and others
Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods

We introduce a novel framework for the estimation of the posterior distribution of the weights of a neural network, based on a new probabilistic interpretation of adaptive subgradient algorithms such as AdaGrad and Adam. Having a confidence measure of the weights allows several shortcomings of neural networks to be addressed. In particular, the robustness of the network can be improved by performing weight pruning based on signal-to-noise ratios from the weight posterior distribution. Using the MNIST dataset, we demonstrate that the empirical performance of Badam, a particular instance of our framework based on Adam, is competitive in comparison to related Bayesian approaches such as Bayes By Backprop.