Research on improved evidence theory based on multi-sensor information fusion

In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the basic probability distribution of each focal element to obtain a new evidence source. Then the concept of credibility is introduced, and the average support of evidence credibility and evidence focal element is used to improve the synthesis rule, so as to obtain the fusion result. Compared with other algorithms, the proposed algorithm can solve the problems existing in DS evidence theory when dealing with highly conflicting evidence to a certain extent, and the fusion results are more reasonable and the convergence speed is faster.

Digital Forensics Analysis of Ubuntu Touch on PinePhone

New smartphones made by small companies enter the technology market everyday. These new devices introduce new challenges for mobile forensic investigators as these devices end up becoming pertinent evidence during an investigation. One such device is the PinePhone from Pine Microsystems (Pine64). These new devices are sometimes also shipped with OSes that are developed by open source communities and are otherwise never seen by investigators. Ubuntu Touch is one of these OSes and is currently being developed for deployment on the PinePhone. There is little research behind both the device and OS on what methodology an investigator should follow to reliably and accurately extract data. This results in potentially flawed methodologies being used before any testing can occur and contributes to the backlog of devices that need to be processed. Therefore, in this paper, the first forensic analysis of the PinePhone device with Ubuntu Touch OS is performed using Autopsy, an open source tool, to establish a framework that can be used to examine and analyze devices running the Ubuntu Touch OS. The findings include analysis of artifacts that could impact user privacy and data security, organization structure of file storage, app storage, OS, etc. Moreover, locations within the device that stores call logs, SMS messages, images, and videos are reported. Interesting findings include forensic artifacts, which could be useful to investigators in understanding user activity and attribution. This research will provide a roadmap to the digital forensic investigators to efficiently and effectively conduct their investigations where they have Ubuntu Touch OS and/or PinePhone as the evidence source.

Research on Improved DS Evidence Theory

In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved DS evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the BPA of each focal element to obtain a new evidence source. Then, the concept of credibility is introduced, and the synthetic rules are improved by using the credibility of evidence and the average support of evidence focal elements, so as to obtain the fusion results. The results show that compared with other algorithms, this algorithm can solve the problems of DS evidence theory in dealing with highly conflicting evidence to a certain extent, and the fused results are more reasonable and converge faster.

ANTI-FORENSICS: COUNTERING SENSOR NOISE CAMERA IDENTIFICATION

In simple terms, Anti-Forensics can be told as the techniques used to counter forensic analysis done by forensic investigators. This paper mainly focuses on some of the most used anti-forensics techniques along with the challenges the forensics investigator faces. There are many tools and techniques available that when used properly can be highly effective against the forensic analysis techniques. Various tools assist you against various anti-forensics techniques like Elimination of evidence source, Data hiding, and Trail obfuscation. These techniques are used mainly to make the investigation consume more time and money. Sensor Noise Camera Identification is a way to link a photo with the camera the photo was taken from using a noise signature that is unique for every camera. KEYWORDS: Anti-Forensics (AF), Forensic Analysis, Anti-Forensic Techniques, Sensor Noise Camera Identification

Multiple Modern Methods of Blood Pressure Measurement and the 2018 European Society of Hypertesion Guidelines

The new international guidelines on hypertension management have been issued in the past few years. The AHA (2017) and ESH (2018) Guidelines are similar in many key aspects. However, they differ substantially in the area of blood pressure (BP) measurement methodology. In this article, we aim to explain the ESC Guidelines position, specifically the conservative BP thresholds and a wider use of ambulatory BP measurement methods. In our opinion, the main reason behind this position is the introduction and a relatively widespread use of automatic office BP measurement (AOBP). On one hand, this method has questioned the results of the SPRINT trial – the key evidence source for the AHA Guidelines. On the other hand, AOBP has challenged the concept of “traditional office BP measurement”. Therefore, it is important to be aware of the current multiplicity of BP measurement methods and respective threshold values recommended for each method. It is essential to perform the office BP measurement correctly and thoroughly. Throughout all stages of the hypertensive patient management (diagnosis, antihypertensive treatment choice, and long-term observation), the use of out-of-clinic BP measurement is strongly advisable.

Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.