Financial Malware Detect With Job Anomaly

It is well-known that financial frauds, such as money laundering, also facilitate terrorism or other illegal activity. A lot of this kind of this kind of illicit dealings entails a complicated trading and financial exchange, and that makes it impossible to uncover the frauds. Additionally, dynamic financial networks and features can be leveraged for trading. The trading network shows the relationship between organizations, thereby allowing investigators to identify fraudulent activity; while entity features filter out fraudulent behavior. Thus, the characteristics of the network and characteristics include knowledge that has the ability to enhance fraud identification. However, most of the current approaches operate on either networks or content. In this study, we propose a novel approach, dubbed CoDetect, that capitalizes on network and feature details. Another excellent aspect of the CoDetect is that it is able to simultaneously track both financial transactions and patterns of fraud. Extensive laboratory testing on both synthetic evidence and actual cases demonstrates the framework's capacity to tackle financial fraud

Impact Of Artificial Intelligence And Big Data On The Oil And Gas Industry In Nigeria

This paper examines the concept of Artificial intelligence and Big Data as a field of study and its Impact on the oil and gas industry. Artificial Intelligence refers to the concept having of Computer systems that can perform tasks that would typically require human intelligence. Some such tasks are visual perception, speech recognition, decision-making and translation between languages, amongst others. “Big data” or Big Data analytics is a term often used to describe a huge or somewhat overwhelming data size that exceeds the capacity of both humans and the traditional software to process within an acceptable time and value. There is a big interface between the two concepts. AI does not stand alone; it requires big data for efficiency. AI and Big Data have brought about great impact across different industries and organizations. In the oil and gas industry, there have been an increasing installation of data recording sensors, hence data acquisition in exploration, drilling and production aspects of the industry. The industry is gradually making use of this huge data set by processing them using AI enabled tools and software to arrive at smart decisions that bring efficiency to operations in the industry. Some of such areas are analysis of seismic and micro-seismic data, improvement in reservoir characterization and simulation, reduction in drilling time and increasing drilling safety, optimization of pump performance, amongst others. Some of the solutions listed above have been successfully implemented in Nigeria, mostly by the international oil companies and some additional areas have also been impacted: managing asset integrity, tubular tally for drilling operations using RFID and the licensing and permit system by DPR. The industry has fully embraced the AI and Big Data concept, the future is very bright for more innovative solutions. However, there are still a few challenges especially in Nigeria. Some of these challenges include lack of local skilled manpower, poor data culture, security challenges in the industry’s operating areas, limited availability of good quality data, and understanding the complexity of the concept

Machine learning effects on the norwegian oil and gas industry

The downturn in the Norwegian oil industry in recent years has led to a revaluation of the sector. Out of this turmoil, a new surge of innovation appeared. This paper explores the innovation effects machine learning (ML) technology has brought to the Norwegian oil and gas industry (NOGI) using a qualitative approach through conducting semi-structured qualitative interviews. These interviews focus on five unique perspectives within the industry. These perspectives represent the unique interplay between private and public actors on the Norwegian continental shelf (NCS). The interviews discuss the value of big data, the use of ML in optimizing extraction processes and finding more sustainable approaches to detecting oil and gas. After presenting the five perspectives in the analysis, similarities and differences are discussed in light of the role the actors i.e. the companies play on the NCS. Interviewees expressed their enthusiasm and aversions about using new technologies to secure competitive advantages, despite most companies developing similar uses of ML. Throughout the analysis, background information from website searches and analyses are used to provide context for the interview data. The results show that the use of data, advanced analytics and various forms of ML create opportunities to fundamentally reimagine how and where work gets done and that there are possibilities of finding safer, more cost efficient and more sustainable approaches to the work currently being done through ML in the NOGI. The study shows that ML has brought disruptive innovation to the NOGI that enhances competitive advantages

External servers security

Romero Barrero, D. (2010). External servers security. http://hdl.handle.net/10251/9111.Archivo delegad

Detectors for the James Webb Space Telescope Near-Infrared Spectrograph I: Readout Mode, Noise Model, and Calibration Considerations

We describe how the James Webb Space Telescope (JWST) Near-Infrared Spectrograph's (NIRSpec's) detectors will be read out, and present a model of how noise scales with the number of multiple non-destructive reads sampling-up-the-ramp. We believe that this noise model, which is validated using real and simulated test data, is applicable to most astronomical near-infrared instruments. We describe some non-ideal behaviors that have been observed in engineering grade NIRSpec detectors, and demonstrate that they are unlikely to affect NIRSpec sensitivity, operations, or calibration. These include a HAWAII-2RG reset anomaly and random telegraph noise (RTN). Using real test data, we show that the reset anomaly is: (1) very nearly noiseless and (2) can be easily calibrated out. Likewise, we show that large-amplitude RTN affects only a small and fixed population of pixels. It can therefore be tracked using standard pixel operability maps.Comment: 55 pages, 10 figure