Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Security Situation Assessment Model Based on Extended Hidden Markov

A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective.
Reply to Liu: The disassembly of the actin cytoskeleton is an early event during NETosis

We used quantitative, high-resolution live-cell imaging of neutrophil-like human cells (dHL60) and mouse and human blood-derived polymorphonuclear neutrophils (PMNs) stimulated with ionomycin, lipopolysaccharide (LPS), or Candida albicans to identify 13 cellular events occurring during neutrophil extracellular trap formation (NETosis) (1). Following thousands of single cells over time showed that these events occurred in a specific order, with actin disassembly preceding all other cellular events observed, indicating that actin disassembly is an early event in NETosis (1). In their letter to the editor, Liu challenges our conclusion (2). Liu uses flow cytometry of PMA-stimulated cells stained with “RFP-labeled phalloidin” to show that, at the population level, phalloidin fluorescence increases 60 min after addition of PMA and returns to basal level at 180 min, concluding that actin disassembly is not an early event of NETosis (2). The points below support our published contention that … [↵][1]1To whom correspondence may be addressed. Email: watermancm{at}nhlbi.nih. gov. [1]: #xref-corresp-1-1
An Ideal Team Is More than a Team of Ideal Agents

The problem of building a team to perform a complex task is often more than an optimal assignment of subtasks to agents based on individual performances. Subtasks may have subtle dependencies and relations that affect the overall performance of the formed team. This paper investigates the dependencies between subtasks and introduces some desired qualities of teams, such as preserving privacy or fairness. It proposes algorithms to analyze and build teams by taking into account the dependencies of assigned subtasks and agent performances. The performance of the algorithms are evaluated experimentally based on a multiagent system that is developed to answer complex queries. We show that by improving an initial team iteratively, the algorithm obtains teams with higher performance.
Personalized Automated Machine Learning

Automated Machine Learning (AutoML) is the challenge of ﬁnding machine learning models with high predictive performance without the need for specialized data scientists. Existing approaches optimize a pipeline of pre-processing, feature engineering, model selection and hyperparameter optimization, and assume that the user is fully aware of the choice of the underlying metric (such as precision, recall or F1-measure). However, end-users are often unaware of the actual implications of choosing a metric, as the resulting models often signiﬁcantly vary in their predictions. In this work, we propose a framework to personalise AutoML for individual end-users by learning a designated ranking model from pairwise user preferences and using the latter as the metric function for state-of-the-art AutoML systems. Given a set of possible metrics, we generate candidate models by repeatedly running AutoML with combinations of the former and have the user choose between pairs of resulting models. We use RankNet to learn a personalized ranking function for the end-user, which is used as loss function for ﬁnal run of a standard AutoML system. To evaluate our proposed framework we deﬁne three preferences a user could pursue and show that a ranking model is able to learn these preferences from pairwise comparisons. Furthermore, by changing the metric function of AutoML we show that a personalized preference is able to improve machine learning pipelines. We evaluated the ability of learning a personalized preference and the entire framework on several OpenML multi-class classiﬁcation datasets.
Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks

Network disintegration has been an important research hotspot in complex networks for a long time. From the perspective of node attack, researchers have devoted to this field and carried out numerous works. In contrast, the research on edge attack strategy is insufficient. This paper comprehensively evaluates the disintegration effect of each structural similarity index when they are applied to the weighted-edge attacks model. Experimental results show that the edge attack strategy based on a single similarity index will appear limited stability and adaptability. Thus, motivated by obtaining a stable disintegration effect, this paper designs an edge attack strategy based on the ordered weighted averaging (OWA) operator. Through final experimental results, we found that the edge attack strategy proposed in this paper not only achieves a more stable disintegration effect on eight real-world networks, but also significantly improves the disintegration effect when applied on a single network in comparison with the original similarity index.
Capturing Attraction Distribution: Sequential Attentive Network for Dwell Time Prediction

In article recommendation, the dwell time is an important metric to measure user engagement on content and has been widely used as a proxy for user satisfaction. Therefore, predicting the dwell time is very helpful for making better recommendations and improving user experience. Modeling the interaction between user and content is the key for dwell time prediction. However, conventional methods usually model the content with document-level representation in a non-personalized way, which ignores the natural reading process of the reader and the reader attraction in sub-document level, this might lead to a bias for analyzing the user reading behavior. Since the attraction level of different parts is different for the user, the user attention changes dynamically while reading. The former content affects the reading for the latter content via the change of attraction level. Therefore, considering the attraction level of each part of the article, i.e., attraction distribution, is quite necessary for content modeling. In this paper, we propose the S equential A ttentive N etwork (SAN) for dwell time prediction, which effectively models the attraction distribution of the article reading process. We collect the data from WeChat, a widely-used mobile app in China, for experiments. The results demonstrate the advantages of our model over several competitive baselines on dwell time prediction.
Anomaly Detection Forest

In many applications of anomaly detection, data with labeled anomalies may not be readily available. When designing a system for detection of malicious network activity, for instance, comprehensive malicious data can not be obtained since attacks constantly evolve and hence change their pattern. In such cases, an automatic anomaly detection system needs, at least partly, to rely on one-class learning, where training data exclusively contains normal instances. In this paper, we present a new anomaly detection algorithm, the Anomaly Detection Forest, optimized for the one-class learning problem. The algorithm is an ensemble of binary trees, where each tree is trained on a random subset and where the location of empty leaves deﬁne the anomaly score attributed to a data point. Our experimental results on a set with 14 public datasets show that the new algorithm outperforms the state-of-the-art algorithms Isolation Forest and One-Class Random Forest for the task of anomaly detection in the one-class learning setting.
Differences in Population Density of 3 Rodent Species Between Natural Restored and Red Pine Silvicultured Forests after Forest fire

This study was conducted to clarify the differences in rodents population densities between natural restored and red pine silvicultured forests after forest fire in Samcheok, Gangwon Province, Korea from March to December 2008. One ha size of 3 study plots were set up in each natural restored and silvicultured stand. We trapped the small rodents during 4 consecutive nights every 2 months in each stand. Understory coverage and number of shrub stems were higher in silvicultured stand than in natural restored stand. Coverage of overstory, suboverstory and midstory, number of tree stem, woody seedling stems and dead wood, and amount of coarse woody debris were higher in natural restored stand than in silvicultured stand. Six hundred eighty eight individuals of four species, such as Apodemus agrarius, A. peninsulae, Eothenomys regulus and Tamias sibiricus were captured in our study. Number of captured small rodents were higher in natural restored stand than in silvicultured stand. Also, species compositions were differed in both stands. The captured number of A. agrarius and A. peninsulae were most highest in April and December. E. regulus were shown higher number of captured in April and June, and T. sibiricus were in June and October. Removal of coarse woody debris and silvicultural practice would not be good for the inhabitation of small rodents. For the conservation of small rodents diversity, management of understory and canopy would be needed in forest fired area.
Adaptive Local Neighbors for Transfer Discriminative Feature Learning

In Domain Adaptation (DA), how to reduce the distributional differences across domains and preserve the data structures are two critical issues to obtain domain-invariant features. Existing DA methods either preserve the Local Manifold Structure (LMS) or the Global Discriminative Consistency (GDC), while fail to take those two metrics into account simultaneously. Therefore, the extracted features are either short of discriminative ability or sensitive to the multimodally distributed data. Moreover, the local neighbored relationships among data points are mostly established in original data space, which is unreliable, especially for data with large noises. Therefore, this paper proposes a novel DA approach, i.e., Adaptive Local Neighbors for Transfer Discriminative Feature Learning, to leverage LMS and GDC into a uniﬁed transfer feature learning model, where we only focus on the GDC between the local neighbors, so that the extracted features are more discriminative and robust to the multimodally distributed data. Moreover, the data points’ local neighbors are revealed adaptively in the learned subspace so that it is insensitive to the data noises. Compared with the state-of-the-art methods, the proposed approach achieves higher performance for different cross-domain image classiﬁcation tasks, especially 3.0% improved for Ofﬁce10+Caltech10 dataset.
Public Discourse and Socially Responsible Market Behavior

We investigate the causal impact of public discourse on socially responsible market behavior. We conduct laboratory market experiments with products that differ in their production costs and social impact, and provide market actors and impacted third parties with the opportunity to discuss appropriate market behavior. Across two studies that vary characteristics of the discourse, the external impact and the participants, we find that public discourse substantially increases market social responsibility. Our findings suggest that discussions and campaigns focusing on appropriate market behavior can be powerful tools for shaping responsible norms governing market conduct and addressing inefficiencies due to market failures.
Incorporating Semantic Dependencies Extracted from Knowledge Graphs into Joint Inference Template-Based Information Extraction

In template-based information extraction, a template is described by a predeﬁned set of slots that need to be ﬁlled with entities mentioned in a text. Compared to traditional relation extraction that aims at classifying binary relations involving a pair of entities only, template-based slot-ﬁlling is of higher complexity as interdependencies between slot-ﬁller values need to be taken into account. To model these dependencies, we tackle the slot-ﬁlling task as a joint inference problem and build on factorized distributions as used in conditional random ﬁelds. Dependencies are often described by textual features only, but in some cases they are of semantic nature and not directly expressed in text. To exploit this potential, we propose the incorporation of semantic dependencies extracted from knowledge graphs into an information extraction model. Dependencies are extracted from the variable bindings of queries executed over a knowledge graph and capture semantic soft constraints that are weighted as part of model training. We evaluate our approach on ﬁve distantly supervised labeled datasets extracted from Wikipedia/DBpedia and compare our approach to a most frequent entity baseline as well as a purely textual model. We show that there is an overall positive impact of integrating factual background knowledge in all datasets, yielding an improvement of up to 10 points in F 1 .
A Framework for Exploratory Query Answering with Ontologies

In the ontology-based data access paradigm, ontologies act as an interface to data sources, facilitating query formulation by providing a vocabulary closer to the users’ understanding. However, accessing data in large and unfamiliar sources can still be hard. In practice, users may find that the formulated queries have too many answers to be informative, or at the other extreme, they have very few or no answers at all. Refining queries can be difficult for users who may not know how to formulate their information needs according to the data. Moreover, changes in the query may have no effect on the answers, or dramatically affect them in unexpected ways. Manual exploration via iterative query evaluation can be computationally very costly if each minor query variation is evaluated independently and from scratch. Towards addressing this problem, we propose a framework for exploring datasets using ontology-mediated queries (OMQs). Rather than formulating a precise conjunctive query (CQ), the user writes a template CQ where parts may be marked to indicate that they may be relaxed or constrained during the exploration. From such a template we build what we call a query space: a set of OMQs that are ordered according to their specificity (or generality). The OMQs in the space are obtained from the template using a fixed set of reformulation rules that modify CQs to be more general or more specific w.r. t. O and a given dataset A. Navigating the query space from one query to a more general (or specific one) allows then users to explore the dataset. The reformulation rules were proposed in our previous work [1], but here their application is restricted by the template and yields a finite space of queries that enables data exploration by choosing among the automatically generated reformulations those which modify the answers in a minimal way. Our framework can be realized in Datalog. We describe how a query space can be generated using Datalog rules. The Datalog program is evaluated over the data and produces a structure (i.e., compilation) that contains all OMQs in the space and their answers, and which can be used to navigate between
Learning Kernel-Based Embeddings in Graph Neural Networks

We investigate whether Graph Convolutional Neural Networks (GCNNs) may beneﬁt from incorporating information conveyed by a state-of-the-art graph kernel in the learning process. We propose a GCNN architecture and a training procedure based on multi-task learning, where we provide supervision not only from the graph labels, but also from the kernel to each layer of the network, achieving state-of-the-art performances on many real-world datasets. We conduct an ablation study to analyze the impact on the predictive performances of each part of our proposal, including a simpliﬁed version of our multi-task learning formulation that can, in principle, be applied with a broad family of graph embeddings. Finally, we study how to improve the performance of a model considering graphs coming from related datasets into the training procedure in a semi-supervised learning fashion.
Determinants of public perception of elite sport

ABSTRACT Elite sports are associated with values such as fair competition, striving to become better and challenging oneself. These are considered as social benefits. However, integrity issues relating to misuse of doping or corruption challenge this. This paper examines the determinants of public perception of elite sport by means of a survey. Logistic regression modelling assesses the effects of trust, athletes as role models, perceived costs and benefits on public perception of elite sports. The results reveal that perceived benefits have a positive effect on all public perception measures whereas perceived costs, trust in key actors of the elite sport system and viewing athletes as role models have only an effect on selected perception measures. Sports managers should emphasise on perceived benefits in their communication strategy and use athletes as role models in their communication strategies to strengthen a positive perception of elite sports.
Effects of nitrogen fertilizer rate on leaf color of chimera Hosta 'Gold standard'

The effects of four nitrogen levels(0,1,2,3 g/kg soil) on leaf color,chlorophyll contents,total N contents,key enzymes activities of nitrogen metabolism and chloroplast ultrastructure of chimera Hosta 'Gold standard' were studied.Nitrogen levels mainly influenced the colour of the yellow leaf area of Hosta 'Gold standard'. The chimera characteristics were maintained under 0 and 1 g/kg nitrogen levels.There were no significant differences in chlorophyll contents,total N contents,and activities of key enzymes of nitrogen metabolism.The formation of chloroplasts which lack normal grana stacking was severely inhibited in the yellow leaf area.Under 2 and 3 g/kg nitrogen levels,the yellow leaf area became green.Compared with the indexes under 0 and 1 g/kg nitrogen levels,chlorophyll contents,total N contents and activities of key enzymes of nitrogen metabolism were significantly increased.Chloroplast ultrastructure was fully developed and normal thylakoid lamellar structure was formed.
Weighted LARS for Quantitative Stream Reasoning

We extend LARS, which is a recent stream reasoning framework based on ASP, to weighted LARS (wLARS), where formulae are interpreted as algebraic expressions over semirings. This adds the ability to express quantitative measures of many different natures and to approach respective reasoning problems such as probabilistic reasoning, preferential reasoning and quantitative queries in a uniform manner. Notably, well-known quantitative ASP extensions can be formalized using wLARS, thus lifting them to the streaming setting. We identify a relevant wLARS fragment that is equivalent to weighted automata, which consequently gives us a rule-based language for expressing behaviors of such automata. Furthermore, we analyze evaluating wLARS formulae, showing that brave preferential reasoning is PSPACE-resp. Σ p 3 -complete in relevant settings.
Negative-Aware Training: Be Aware of Negative Samples

Negative samples, whose class labels are not included in training sets, are commonly classiﬁed into random classes with high conﬁdence and this severely limits the applications of traditional models. To solve this problem, we propose an approach called Negative-Aware Training (NAT), which introduces negative samples and trains them along with the original training set. The object function of NAT forces the classiﬁer to output equal probability for each class on negative samples, other settings stay unchanged. Moreover, we introduce NAT into GAN and propose NAT-GAN, in which discriminator distinguishes between both generated samples and negative samples. With the assist of NAT, NAT-GAN can ﬁnd more accurate decision boundaries, thus converges steadier and faster. Experimental results on synthesis and real-word datasets demonstrate that: 1) NAT gets better performance on negative samples in accordance with our proposed negative conﬁdence rate metric. 2) NAT-GAN gets better quality scores than several traditional GANs and achieves state-of-the-art Inception Score (9.2) on CIFAR 10. Our demo and code are available at https://natpaper.github. io.
Rewrite or Not Rewrite? ML-Based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

Query-driven reasoning techniques with Datalog rules, like Magic Sets (MS), are ideal for implementing query answering on Knowledge Graphs (KGs). For some queries, executing a rewriting procedure like MS is the best choice, but for others a non-rewriting procedure like Query-subquery (QSQ) can be faster. Choosing beforehand which procedure should be used is not trivial and mistakes can be costly. To address this problem, we describe a first-of-its-kind method that builds a Machine Learning (ML) model to predict whether a query should be answered with MS or with QSQ. Experiments on several well-known KGs show that our method can return accurate predictions, and this leads to a significant reduction of the response time of query answering.
Lifting Majority to Unanimity in Opinion Diffusion

In this paper, we study an information exchange process in which a network of individuals exchanges a binary opinion. In the process, the individuals change their opinions only if a majority of their neighbours have the opposite opinion and they do it synchronously. Motivated by applications in multiagent systems, distributed computing, and social science, our goal is to derive graph-theoretic features of the network that guarantee whenever a majority of individuals initially have the same opinion, they will eventually spread the opinion to all individuals. We tackle the problem by ﬁrst introducing a graph-theoretic notion called controlling set which is capable of characterising the information exchange process and, by exploiting the notion, we obtain a series of lower and upper bounds on the in-degree of vertices as well as lower bound on the size of certain neighbourhoods for guaranteeing the majority to unanimity behaviour.
Interpolated corrected curvature measures for polygonal surfaces

A consistent and yet practically accurate definition of curvature onto polyhedral meshes remains an open problem. We propose a new framework to define curvature measures, based on the Corrected Normal Current, which generalizes the normal cycle: it uncouples the positional information of the polyhedral mesh from its geometric normal vector field, and the user can freely choose the corrected normal vector field at vertices for curvature computations. A smooth surface is then built in the Grassmannian ℝ3 × 𝕊2 by simply interpolating the given normal vector field. Curvature measures are then computed using the usual Lipschitz–Killing forms, and we provide closed‐form formulas per triangle. We prove a stability result with respect to perturbations of positions and normals. Our approach provides a natural scale‐space for all curvature estimations, where the scale is given by the radius of the measuring ball. We show on experiments how this method outperforms state‐of‐the‐art methods on clean and noisy data, and even achieves pointwise convergence on difficult polyhedral meshes like digital surfaces. The framework is also well suited to curvature computations using normal map information.