Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior

Blockade of endothelin B receptor improves the efficacy of levetiracetam in chronic epileptic rats

AbstractPurposeTo elucidate the mechanisms that regulate p-glycoprotein (PGP) expression and function in pharmacoresistant epilepsy, we investigated the effect of an ETB receptor antagonist (BQ788) and a p38 mitogen-activated protein kinase (p38MAPK) inhibitor (SB202190) on intractable seizures in chronic epileptic rats.MethodsLithium-pilocarpine-induced chronic epileptic rats were used in the present study. Animals were given levetiracetam (LEV), LEV+SB202190, LEV+BQ788, SB202190 or BQ788 over a 3-day period using an osmotic pump. Seizure activity was recorded by video-EEG monitoring with 2h of recording per day at the same time of day. We also performed western blot after EEG analysis.ResultsCompared to control animals, PGP, ETB receptor and p38MAPK expression was increased in the hippocampus of epileptic animals. Neither SB202190 nor BQ788 affected the spontaneous seizure activity in epileptic rats. Three of ten rats were responders and achieved complete seizure control or significant reduction in seizure activity by LEV. In four of ten rats, seizure frequency was unaltered by LEV (non-responders). LEV+SB202190 reduced seizure duration, but not seizure frequency, in both responders and non-responders. LEV+BQ788 alleviated seizure frequency and seizure duration in both responders and non-responders. Compared to responders, PGP and ETB receptor expression was enhanced in the hippocampus of non-responders.ConclusionTo the best of our knowledge, these findings are the first indications of the role of ETB receptor in pharmacoresistant epilepsy. Therefore, the present data suggest that the regulation of the ETB receptor-mediated signaling pathway may be important for identification of new therapeutic strategies for improving antiepileptic drug efficacy

Malware Detection on Byte Streams of PDF Files Using Convolutional Neural Networks

With increasing amount of data, the threat of malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware of such type of malicious attacks. In this paper, we design a convolutional neural network to tackle the malware detection on the PDF files. We collect malicious and benign PDF files and manually label the byte sequences within the files. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. The proposed network is designed to interpret high-level patterns among collectable spatial clues, thereby predicting whether the given byte sequence has malicious actions or not. By experimental results, we demonstrate that the proposed network outperform several representative machine-learning models as well as other networks with different settings

File-level malware detection using byte streams

Abstract As more documents appear on the Internet, it becomes important to detect malware within the documents. Malware of non-executables might be more dangerous because people usually open them without worrying about inherent danger. Recently, deep learning models are used to analyze byte streams of the non-executables for malware detection. Although they have shown successful results, they are commonly designed for stream-level detection, but not for file-level detection. In this paper, we propose a new method that aggregates the stream-level results to get file-level results for malware detection. We demonstrate its effectiveness by experimental results with our annotated dataset, and show that it gives performance gain of 3.37–5.89% of F1 scores