Characteristics of cybercrimes: evidence from Chinese judgment documents

Characteristics of cybercrimes: evidence from Chinese judgment document

A survey on deep learning based knowledge tracing

“Knowledge tracing (KT)” is an emerging and popular research topic in the field of online education that seeks to assess students’ mastery of a concept based on their historical learning of relevant exercises on an online education system in order to make the most accurate prediction of student performance. Since there have been a large number of KT models, we attempt to systematically investigate, compare and discuss different aspects of KT models to find out the differences between these models in order to better assist researchers in this field. The findings of this study have made substantial contributions to the progress of online education, which is especially relevant in light of the current global pandemic. As a result of the current expansion of deep learning methods over the last decade, researchers have been tempted to include deep learning strategies into KT research with astounding results. In this paper, we evaluate current research on deep learning-based KT in the main categories listed below. In particular, we explore (1) a granular categorisation of the technological solutions presented by the mainstream Deep Learning-based KT Models. (2) a detailed analysis of techniques to KT, with a special emphasis on Deep Learning-based KT Models. (3) an analysis of the technological solutions and major improvement presented by Deep Learning-based KT models. In conclusion, we discuss possible future research directions in the field of Deep Learning-based KT

Robust cross-network node classification via constrained graph mutual information

The recent methods for cross-network node classification mainly exploit graph neural networks (GNNs) as feature extractor to learn expressive graph representations across the source and target graphs. However, GNNs are vulnerable to noisy factors, such as adversarial attacks or perturbations on the node features or graph structure, which can cause a significant negative impact on their learning performance. To this end, we propose a robust graph domain adaptive learning framework RGDAL which exploits an information-theoretic principle to filter the noisy factors for cross-network node classification. Specifically, RGDAL utilizes graph convolutional network (GCN) with constrained graph mutual information and an adversarial learning component to learn noise-resistant and domain-invariant graph representations. To overcome the difficulties of estimating the mutual information for the non independent and identically distributed (non-i.i.d.) graph structured data, we design a dynamic neighborhood sampling strategy that can discretize the graph and incorporate the graph structural information for mutual information estimation. Experimental results on two real-world graph datasets demonstrate that RGDAL shows better robustness for cross-network node classification compared with the SOTA graph adaptive learning methods

Embedding Co in perovskite MoO3 for superior catalytic oxidation of refractory organic pollutants with peroxymonosulfate

A cobalt (Co)-doped perovskite molybdenum trioxide (α-MoO3) catalyst (Co-MO) was synthesized by a facile pyrolysis strategy and used for degrading various organic contaminants via peroxymonosulfate (PMS) activation. The doped Co was inserted in the inter space between the octahedron [MoO6], facilitating the growth of the α-MoO3 crystal on the [010] direction. This unique structure accelerated the activation of PMS as the Co-MO could function as a carrier for electron transfer to facilitate the Co(II)/Co(III) cycle in the Co-MO/PMS system. As a result, the Co-MO/PMS system showed noticeable activity for removing 100% bisphenol A (BPA) under a broad conditions within 30 min. The radical quenching test and electron paramagnetic resonance analysis revealed that singlet oxygen (1O2) was the main active species for BPA degradation in the Co-MO/PMS system, while free radicals, such as O2•-, SO4•- and •OH, were also produced as the intermediate species. Furthermore, the carrier mechanism may enable the Co-MO/PMS system maintain relatively high performance during repeat use, and also excellent adaptability was revealed by the well function in various water matrices and high activity in degrading various refractory organic pollutants. Our findings pave a useful avenue for the rational design of novel cobalt-doped catalysts with high catalytic performance toward wide environmental applications

Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among her friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of the whole network. Many user engagement studies have been conducted to find a set of critical (anchored) users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem called Anchored Vertex Tracking (AVT) in this paper, aiming to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored $k$-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem

Coupling Anion-Capturer with Polymer Chains in Fireproof Gel Polymer Electrolyte Enables Dendrite-Free Sodium Metal Batteries

Sodium metal batteries (SMBs) using gel polymer electrolytes (GPEs) with high theoretical capacity and low production cost are regarded as a promising candidate for high energy-density batteries. However, the inherent flammability of GPEs and uncontrolled Na dendrite caused by inferior mechanical properties and interfacial stability hinder their practical applications. Herein, an anion-trapping fireproof composite gel electrolyte (AT-FCGE) is designed through a chemical grafting–coupling strategy, where functionalized boron nitride nanosheets (M-BNNs) used as both nanosized crosslinker and anion capturer are coupled with poly(ethylene glycol)diacrylate in poly(vinylidene fluoride-co-hexafluoropropylene) matrix, to expedite Na+ transport and suppress dendrite growth. Experimental and calculation studies suggest that the anion-trapping effect of M-BNNs with abundant Lewis-acid sites can promote the dissociation of salts, thus remarkably improving the ionic conductivity and Na+ transference number. Meanwhile, the formation of highly crosslinked semi-interpenetrating network can effectively in situ encapsulate non-flammable phosphate without sacrificing the mechanical properties. Consequently, the resulting AT-FCGE shows significantly enhanced Na+ conductivity, mechanical properties, and excellent interfacial stability. The AT-FCGE enables a long-cycle stability dendrite-free Na/Na symmetric cell, and prominent electrochemical performance is demonstrated in solid-state SMBs. The approach provides a broader promise for the great potential of fire-retardant gel electrolytes in high-performance SMBs and the beyond

Associations between statins and adverse events in primary prevention of cardiovascular disease: Systematic review with pairwise, network, and dose-response meta-analyses

Objective To assess the associations between statins and adverse events in primary prevention of cardiovascular disease and to examine how the associations vary by type and dosage of statins. Design Systematic review and meta-analysis. Data sources Studies were identified from previous systematic reviews and searched in Medline, Embase, and the Cochrane Central Register of Controlled Trials, up to August 2020. Review methods Randomised controlled trials in adults without a history of cardiovascular disease that compared statins with non-statin controls or compared different types or dosages of statins were included. Main outcome measures Primary outcomes were common adverse events: self-reported muscle symptoms, clinically confirmed muscle disorders, liver dysfunction, renal insufficiency, diabetes, and eye conditions. Secondary outcomes included myocardial infarction, stroke, and death from cardiovascular disease as measures of efficacy. Data synthesis A pairwise meta-analysis was conducted to calculate odds ratios and 95% confidence intervals for each outcome between statins and non-statin controls, and the absolute risk difference in the number of events per 10 000 patients treated for a year was estimated. A network meta-analysis was performed to compare the adverse effects of different types of statins. An E max model based meta-analysis was used to examine the dose-response relationships of the adverse effects of each statin. Results 62 trials were included, with 120 456 participants followed up for an average of 3.9 years. Statins were associated with an increased risk of self-reported muscle symptoms (21 trials, odds ratio 1.06 (95% confidence interval 1.01 to 1.13); absolute risk difference 15 (95% confidence interval 1 to 29)), liver dysfunction (21 trials, odds ratio 1.33 (1.12 to 1.58); absolute risk difference 8 (3 to 14)), renal insufficiency (eight trials, odds ratio 1.14 (1.01 to 1.28); absolute risk difference 12 (1 to 24)), and eye conditions (six trials, odds ratio 1.23 (1.04 to 1.47); absolute risk difference 14 (2 to 29)) but were not associated with clinically confirmed muscle disorders or diabetes. The increased risks did not outweigh the reduction in the risk of major cardiovascular events. Atorvastatin, lovastatin, and rosuvastatin were individually associated with some adverse events, but few significant differences were found between types of statins. An E max dose-response relationship was identified for the effect of atorvastatin on liver dysfunction, but the dose-response relationships for the other statins and adverse effects were inconclusive. Conclusions For primary prevention of cardiovascular disease, the risk of adverse events attributable to statins was low and did not outweigh their efficacy in preventing cardiovascular disease, suggesting that the benefit-to-harm balance of statins is generally favourable. Evidence to support tailoring the type or dosage of statins to account for safety concerns before starting treatment was limited.</p

Global mortality of snakebite envenoming between 1990 and 2019

Snakebite envenoming is an important cause of preventable death. The World Health Organization (WHO) set a goal to halve snakebite mortality by 2030. We used verbal autopsy and vital registration data to model the proportion of venomous animal deaths due to snakes by location, age, year, and sex, and applied these proportions to venomous animal contact mortality estimates from the Global Burden of Disease 2019 study. In 2019, 63,400 people (95% uncertainty interval 38,900–78,600) died globally from snakebites, which was equal to an age-standardized mortality rate (ASMR) of 0.8 deaths (0.5–1.0) per 100,000 and represents a 36% (2–49) decrease in ASMR since 1990. India had the greatest number of deaths in 2019, equal to an ASMR of 4.0 per 100,000 (2.3—5.0). We forecast mortality will continue to decline, but not sufficiently to meet WHO’s goals. Improved data collection should be prioritized to help target interventions, improve burden estimation, and monitor progress

Global mortality of snakebite envenoming between 1990 and 2019

AbstractSnakebite envenoming is an important cause of preventable death. The World Health Organization (WHO) set a goal to halve snakebite mortality by 2030. We used verbal autopsy and vital registration data to model the proportion of venomous animal deaths due to snakes by location, age, year, and sex, and applied these proportions to venomous animal contact mortality estimates from the Global Burden of Disease 2019 study. In 2019, 63,400 people (95% uncertainty interval 38,900–78,600) died globally from snakebites, which was equal to an age-standardized mortality rate (ASMR) of 0.8 deaths (0.5–1.0) per 100,000 and represents a 36% (2–49) decrease in ASMR since 1990. India had the greatest number of deaths in 2019, equal to an ASMR of 4.0 per 100,000 (2.3—5.0). We forecast mortality will continue to decline, but not sufficiently to meet WHO’s goals. Improved data collection should be prioritized to help target interventions, improve burden estimation, and monitor progress

Global mortality of snakebite envenoming between 1990 and 2019

Snakebite envenoming is an important cause of preventable death. The World Health Organization (WHO) set a goal to halve snakebite mortality by 2030. We used verbal autopsy and vital registration data to model the proportion of venomous animal deaths due to snakes by location, age, year, and sex, and applied these proportions to venomous animal contact mortality estimates from the Global Burden of Disease 2019 study. In 2019, 63,400 people (95% uncertainty interval 38,900–78,600) died globally from snakebites, which was equal to an age-standardized mortality rate (ASMR) of 0.8 deaths (0.5–1.0) per 100,000 and represents a 36% (2–49) decrease in ASMR since 1990. India had the greatest number of deaths in 2019, equal to an ASMR of 4.0 per 100,000 (2.3—5.0). We forecast mortality will continue to decline, but not sufficiently to meet WHO’s goals. Improved data collection should be prioritized to help target interventions, improve burden estimation, and monitor progress