Image Mapping through Metadata

Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach.

Digital forensics is the digital equivalence of traditional crime investigations that leverages digital technologies to facilitate criminal investigations. The expertise and previous experience of an investigator or any security agent in traditional forensic investigations play an important role in the success, efficiency, and effectiveness of the investigation. Similarly employing the power of intelligence in the current computational resources in the digital investigation process will lead to more efficient and effective digital investigation results. This study builds on existing works in the literature to propose the use of clustering and classification machine learning algorithms on evolving database files to achieve intelligence in the digital investigation process and finally come up with a comprehensive block diagram as a general-purpose framework for intelligent forensic investigation.

Information technology systems are attacked by offenders using digital devices and networks to facilitate their crimes and hide their identities, creating new challenges for digital investigators. Malicious programs that exploit vulnerabilities also serve as threats to digital investigators. Since digital devices such as computers and networks are used by organisations and digital investigators, malicious programs and risky practices that may contaminate the integrity of digital evidence can lead to loss of evidence. For some reasons, digital investigators face a major challenge in preserving the integrity of digital evidence. Not only is there no definitive comprehensive model of digital forensic investigation for ensuring the reliability of digital evidence, but there has to date been no intensive research into methods of doing so. To address the issue of preserving the integrity of digital evidence, this research improves upon other digital forensic investigation model by creating a Comprehensive Digital Forensic Investigation Model (CDFIM), a model that results in an improvement in the investigation process, as well as security mechanism and guidelines during investigation. The improvement is also effected by implementing Proxy Mobile Internet Protocol version 6 (PMIPv6) with improved buffering based on Open Air Interface PIMIPv6 (OAI PMIPv6) implementation to provide reliable services during handover in Mobile Node (MN) and improve performance measures to minimize loss of data which this research identified as a factor affecting the integrity of digital evidence. The advantage of this is to present that the integrity of digital evidence can be preserved if loss of data is prevented. This research supports the integration of security mechanism and intelligent software in digital forensic investigation which assist in preserving the integrity of digital evidence by conducting experiments which carried out two different attack experiment to test CDFIM. It found that when CDFIM used security mechanism and guidelines with the investigation process, it was able to identify the attack and also ensured that the integrity of the digital evidence was preserved. It was also found that the security mechanism and guidelines incorporated in the digital investigative process are useless when the security guidelines are ignored by digital investigators, thus posing a threat to the integrity of digital evidence.

In today's era, efficiency of digital forensic investigation process is the biggest challenge for digital forensic community. The efficiency of investigation depends on the other various factors like storage capacity of digital media, proficiency of investigator and type of investigating case. Moreover, these storage devices do not store only investigating case related data but also stores accused personal and professional information which can lead a breach of privacy of accused. Very little attention has been given to make digital investigation process fully automated. In the Privacy Preserving Efficient Digital Forensic Investigation (PPEDFI) framework, we bridge the gap between efficiency and the privacy issue in digital forensic investigation process. The main contribution of the PPEDFI framework is to make the digital investigation process automated and shorten the turnaround time in evidence extraction process, thus saving the cost and time of digital investigation process. To evaluate the system, we conducted an investigation study namely: Digitized Document Fraud. The proposed framework was able to extract the evidence files and also rank them on the basis of their relevancy for being evidence.

From the digital forensics point of view, image forgery is considered as evidence that could provide a major breakthrough in the investigation process. Additionally, the development of storage device technologies has increased storage space significantly. Thus a digital investigator can be overwhelmed by the amount of data on storage devices that needs to be analysed. In this paper, we propose a model for classifying bulk JPEG images produced by the data carving process or other means into three different classes to solve the problem of identifying forgery quickly and effectively. The first class is JPEG images that contain errors or corrupted data, the second class is JPEG images that contain forged regions, and the third is JPEG images that have no signs of corruption or forgery. To test the proposed model, some experiments were conducted on our own dataset in addition to CASIA V2 image forgery dataset. The experiments covered different types of forgery technique. The results yielded around 88% accuracy rate in the classification process using five different machine learning methods on CASIA V2 dataset. It can be concluded that the proposed model can help investigators to automatically classify JPEG images, which reduce the time needed in the overall digital investigation process.

Formal concept analysis approach to understand digital evidence relationships

The advancement in information technology has changed our life significantly. No doubt, the Internet, smartphones, and social media have connected people and society. It has contributed to various aspects of our daily life. However, there is also a dark side of cyberspace, that is, cybercrime. It is becoming a global threat and needs technological solutions to combat. Whenever there is a crime, there is a law to curb the crime. The role of digital forensics is to understand the 'Who, What, Where and Why' of the incident. Digital forensics is a scientific process to collect, analyze, and present the evidence in a court of law. As it is used to link the person with criminal activities, it is crucial that the entire process of investigation be trustworthy. Managing the digital evidence and maintaining the chain of custody and integrity of digital evidence are utmost important. This chapter discusses the applications of blockchain technology to address the challenging issues faced by the digital investigation process and forensic readiness. The chapter also discusses the legal issues and admissibility of blockchain-based evidence in a court of law.

The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP)-CDM being one of the most prominent ones, allowing the standardization and harmonization of health care information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge. This challenge arises from the absence of a comprehensive standard representation for image-related information and subsequent image curation processes and their interlinkage with the respective clinical information. Successful resolution of this challenge holds the potential to enable imaging and clinical data to become harmonized, quality-checked, annotated, and ready to be used in conjunction, in the development of artificial intelligence models and other data-dependent use cases. To address this challenge, we introduce medical imaging (MI)-CDM-an extension of the OMOP-CDM specifically designed for registering medical imaging data and curation-related processes. Our modeling choices were the result of iterative numerous discussions among clinical and AI experts to enable the integration of imaging and clinical data in the context of the ProCAncer-I project, for answering a set of clinical questions across the prostate cancer's continuum. Our MI-CDM extension has been successfully implemented for the use case of prostate cancer for integrating imaging and curation metadata along with clinical information by using the OMOP-CDM and its oncology extension. By using our proposed terminologies and standardized attributes, we demonstrate how diverse imaging modalities can be seamlessly integrated in the future.

Many security professionals know about Image Metadata, but they do not really know how it can be used to protect from the threats and prove the authenticity of these images and the most significant aspect is the anonymity of perpetrator. The novelty of this paper lies on how to use a new algorithm of Image Metadata to detect internal intrusion and how to protect it in real time inside the cloud providers. In this paper, we propose a novel algorithm to use features and characteristics technologies of Image Metadata, namely, a novel family of Intrusion Detection Systems (IDS) built on Exchangeable Image File Format (EXIF) of Image Metadata. It can assist in eliminating the anonymity advantage of perpetrator which is considered extremely important for the Digital Investigation to detect the attacker in a real time and to prove the authenticity of the images Metadata against attackers.

In this era, the way people do business has switched to digital marketing. Accordingly, the purpose of this research is to find out the right digital marketing processes to improve business performance, with a focus on foods and beverages MSMEs in Semarang, Indonesia, motivated by the growth of digital marketing implemented by business owners. This research paper is based on a qualitative approach with a descriptive research method and used literature review, interviews, and observation to collect the data. This research examines the digital marketing processes of foods and beverages MSMEs in Semarang, which has rarely been focused on, and reveals that MSMEs that carried out more complete digital marketing processes have the potential to improve business performance.

Completing the Post Mortem Investigation

To guide the site staff to produce complex construction products with high quality, this paper proposes a three-dimensional digital technological process method for construction products. Firstly, the process information of construction products is processed from the perspective of quality control and quality management. Then, the geometric entities in the product construction process are modeled based on the building information model (BIM). Then, the process information is expressed and integrated into geometric entities by the model-based definition (MBD) technology, and the interactive files guiding product construction are output. Finally, the technological process expression example of the infill wall masonry project shows that the proposed digital technological process method can fully utilize the advantages of three-dimensional space and desktop virtual reality, help to deepen process understanding, improve product construction quality, reduce project cost, and provide new ideas for digital research in the construction industry.

The development and improvement to map agricultural land cover are major challenges for researchers. For the sustainable development of agronomics spatial information about agricultural practices plays a vital role. Remote sensing satellite imagery is a valuable aid in providing and understanding this spatial distribution of agricultural practices. The aim of this paper is to provide a better understanding of capabilities of satellite images for agricultural land cover mapping through the use of deep learning techniques. The global coverage , rich spectral and spatial information and repetitive nature of remote sensing(RS) data have made them effective tools for mapping crop extents and yield prediction. This paper explores wide ranging review of research papers and articles on deep learning algorithms for image processing and predictions in the field of agriculture. The DL algorithms has attained remarkable success in different fields of RS and its use in crop monitoring. This review systematically identified 40 research papers from peer reviewed scientific publications related to sensors, platforms, input features, training data, spatial distribution of study sites. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.

Raster map image is an important set of color images with similar patterns and textures presented in a limited number of colors. Manipulating this class of color images using conventional image filters may lead to a severe over-filtering problem, by which important details structures are over eliminated or degraded. Even if the statistical based algorithms have been recognized as a set of most efficient filters, their exponentially high memory consumption and computational cost can make them intractable in practice. To solve this operational difficulty, this work proposed a novel multi-layer image filtering approach transforming map image filtering into binary domain. It consists of three intuitive image operators: layer decomposition, binary image filters and layer merging. Experimental results demonstrated that the new proposed approach is very efficient for filtering of raster map image.

Formal and online education has come a long way and with changing times, new methods of teaching are being developed according to the need of the subject and the needs of the students. However certain age-old methods retain their charm with changing times and only emerge stronger. One such method is story telling. The purpose of this research paper is to work out how educators, students et al. round the world are using storytelling to support the digital educational process. This study provides a general framework about educational uses of storytelling in several subjects and describes the various perceptions of individuals in understanding storytelling. A survey was used to collect responses from a group of educators, students and others in educational settings to determine how they are using storytelling for online educational purposes. The results show the current situation of educational uses of storytelling and explore some of the benefits and challenges educators face in implementing storytelling in their institutions and teaching online classes. The paper also attempts to give solutions in bridge the digital divide through engaging storytelling. The research paper also explores reasons for storytelling's current popularity as a teaching and learning tool, then suggests how educators and students can work together to create a storytelling culture. To conclude, benefits for college kids who learn through storytelling are outlined.