In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.
Abstract
New Attacks are increasingly used by attackers every day but many of them are not detected by Intrusion Detection Systems as most IDS ignore raw packet information and only care about some basic statistical information extracted from PCAP files. Using networking programs to extract fixed statistical features from packets is good, but may not enough to detect nowadays challenges. We think that it is time to utilize big data and deep learning for automatic dynamic feature extraction from packets. It is time to get inspired by deep learning pre-trained models in computer vision and natural language processing, so security deep learning solutions will have its pre-trained models on big datasets to be used in future researches. In this paper, we proposed a new approach for embedding packets based on character-level embeddings, inspired by FastText success on text data. We called this approach FastPacket. Results are measured on subsets of CIC-IDS-2017 dataset, but we expect promising results on big data pre-trained models. We suggest building pre-trained FastPacket on MAWI big dataset and make it available to community, similar to FastText. To be able to outperform currently used NIDS, to start a new era of packet-level NIDS that can better detect complex attacks
Purpose
The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.
Design/methodology/approach
A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.
Findings
MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.
Originality/value
The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.
Now a day's application deal with Big Data has tremendously been used in the popular areas. To tackle with such kind of data various approaches have been developed by researchers in the last few decades. A recent investigated techniques to factored the data matrix through a known latent factor in a lower size space is the so called matrix factorization. In addition, one of the problems with the NMF approaches, its randomized valued could not provide absolute optimization in limited iteration, but having local optimization. Due to this, the authors have proposed a new approach that considers the initial values of the decomposition to tackle the issues of computationally expensive. They have devised an algorithm for initializing the values of the decomposed matrix based on the PSO. In this paper, the auhtors have intended a genetic algorithm based technique while incorporating the nonnegative matrix factorization. Through the experimental result, they will show the proposed method converse very fast in comparison to other low rank approximation like simple NMF multiplicative, and ACLS technique.
AbstractWith the increasing connectivity of devices, the amount of data that is recorded and ready for analysis is growing correspondingly. This is also the case for shop floors in flexible sheet metal handling and production. With the growing need for flexibility in production, the availability of machine tools is imminent. This paper shows different approaches that a classical manufacturing systems company such as TRUMPF takes in applying data mining techniques to address the new challenges which come with the Internet of things. In addition to classical methods, a new approach is introduced that does not need any alteration of the machine or its interfaces.
ELTA: New Approach in Designing Business Intelligence Solutions in Era of Big Data