Bayesian Learning Networks Approach to Cybercrime Detection

The growing dependence of modern society on telecommunication and information networks has become inevitable. The increase in the number of interconnected networks to the Internet has led to an increase in security threats and cybercrimes such as Distributed Denial of Service (DDoS) attacks. Any Internet based attack typically is prefaced by a reconnaissance probe process, which might take just a few minutes, hours, days, or even months before the attack takes place. In order to detect distributed network attacks as early as possible, an under research and development probabilistic approach, which is known by Bayesian networks has been proposed. This paper shows how probabilistically Bayesian network detects communication network attacks, allowing for generalization of Network Intrusion Detection Systems (NIDSs). Learning Agents which deploy Bayesian network approach are considered to be a promising and useful tool in determining suspicious early events of Internet threats and consequently relating them to the following occurring activities.Peer reviewe

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

Late Effects of Ionizing Radiation on Normal Microvascular Networks

Therapeutic doses of ionizing radiation result in changes in the structure and function of microvascular networks in normal tissue. Previously, we reported on the early effects of ionizing radiation on microvascular networks at 3, 7, and 30 days post-irradiation [1-3]. Data from the early time points suggested that ionizing radiation significantly alters the structure and function of microvascular networks and interferes with the normal processes of vessel maturation. Here, we present our findings on the late effects of ionizing radiation on normal tissue microvasculature at 60, 120, and 180 days post-irradiation. The cremaster muscle of Golden Syrian hamster was locally irradiated (single 10Gy dose, delivered at 2 Gy/min). Microvascular networks were selected in reference to a well-defined location in the tissue to reduce heterogeneity due to spatial variation. Intravital microscopy was used to measure both structural and functional parameters. Geographic Information Systems (GIS) technology was used to establish network topology. At all late time points, the diameter of irradiated vessels was significantly larger than control. Red blood cell velocity in irradiated vessels showed a significant decrease from controls at 120 days post-irradiation and an increase at 180 days post-irradiation. Others parameters such as lineal density, tortuosity, vessel length, and vessel tone showed no significant difference between control and irradiated vessels. The hamster cremaster muscle proved to be an effective model in examining the effects of radiation on normal microvascular tissue. Together our early effect and late effect studies suggest that significant changes occur in structural and functional parameters of irradiated microvascular networks and, hence, that radiation therapy may alter the oxygen delivery capacity of normal tissue microvascular networks

Platelet-Rich Plasma as an Autologous and Proangiogenic Cell Delivery System

Angiogenesis is a key factor in early stages of wound healing and is crucial for the repair of vascularized tissues such as the bone. However, supporting timely revascularization of the defect site still presents a clinical challenge. Tissue engineering approaches delivering endothelial cells or prevascularized constructs may overcome this problem. In the current study, we investigated platelet-rich plasma (PRP) gels as autologous, injectable cell delivery systems for prevascularized constructs. PRP was produced from human thrombocyte concentrates. GFP-expressing human umbilical vein endothelial cells (HUVECs) and human bone marrow-derived mesenchymal stem cells (MSCs) were encapsulated in PRP gels in different proportions. The formation of cellular networks was assessed over 14 days by time-lapse microscopy, gene expression analysis, and immunohistology. PRP gels presented a favorable environment for the formation of a three-dimensional (3D) cellular network. The formation of these networks was apparent as early as 3 days after seeding. Networks increased in complexity and branching over time but were only stable in HUVEC-MSC cocultures. The high cell viability together with the 3D capillary-like networks observed at early time points suggests that PRP can be used as an autologous and proangiogenic cell delivery system for the repair of vascularized tissues such as the bone

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

Analysis of Antibiotic Exposure and Early-Onset Neonatal Sepsis in Europe, North America, and Australia.

IMPORTANCE Appropriate use of antibiotics is life-saving in neonatal early-onset sepsis (EOS), but overuse of antibiotics is associated with antimicrobial resistance and long-term adverse outcomes. Large international studies quantifying early-life antibiotic exposure along with EOS incidence are needed to provide a basis for future interventions aimed at safely reducing neonatal antibiotic exposure. OBJECTIVE To compare early postnatal exposure to antibiotics, incidence of EOS, and mortality among different networks in high-income countries. DESIGN, SETTING, AND PARTICIPANTS This is a retrospective, cross-sectional study of late-preterm and full-term neonates born between January 1, 2014, and December 31, 2018, in 13 hospital-based or population-based networks from 11 countries in Europe and North America and Australia. The study included all infants born alive at a gestational age greater than or equal to 34 weeks in the participating networks. Data were analyzed from October 2021 to March 2022. EXPOSURES Exposure to antibiotics started in the first postnatal week. MAIN OUTCOMES AND MEASURES The main outcomes were the proportion of late-preterm and full-term neonates receiving intravenous antibiotics, the duration of antibiotic treatment, the incidence of culture-proven EOS, and all-cause and EOS-associated mortality. RESULTS A total of 757 979 late-preterm and full-term neonates were born in the participating networks during the study period; 21 703 neonates (2.86%; 95% CI, 2.83%-2.90%), including 12 886 boys (59.4%) with a median (IQR) gestational age of 39 (36-40) weeks and median (IQR) birth weight of 3250 (2750-3750) g, received intravenous antibiotics during the first postnatal week. The proportion of neonates started on antibiotics ranged from 1.18% to 12.45% among networks. The median (IQR) duration of treatment was 9 (7-14) days for neonates with EOS and 4 (3-6) days for those without EOS. This led to an antibiotic exposure of 135 days per 1000 live births (range across networks, 54-491 days per 1000 live births). The incidence of EOS was 0.49 cases per 1000 live births (range, 0.18-1.45 cases per 1000 live births). EOS-associated mortality was 3.20% (12 of 375 neonates; range, 0.00%-12.00%). For each case of EOS, 58 neonates were started on antibiotics and 273 antibiotic days were administered. CONCLUSIONS AND RELEVANCE The findings of this study suggest that antibiotic exposure during the first postnatal week is disproportionate compared with the burden of EOS and that there are wide (up to 9-fold) variations internationally. This study defined a set of indicators reporting on both dimensions to facilitate benchmarking and future interventions aimed at safely reducing antibiotic exposure in early life