Multi-shape symmetric encryption mechanism for nongeneric attacks mitigation

Static cyphers use static transformations for encryption and decryption. Therefore, the attacker will have some knowledge that can be exploited to construct assaults since the transformations are static. The class of attacks which target a specific cypher design are called Non-Generic Attacks. Whereby, dynamic cyphers can be utilised to mitigate non-generic attacks. Dynamic cyphers aim at mitigating non-generic attacks by changing how the cyphers work according to the value of the encryption key. However, existing dynamic cyphers either degrade the performance or decrease the cypher’s actual security. Hence, this thesis introduces a Multi-Shape Symmetric Encryption Mechanism (MSSEM) which is capable of mitigating non-generic attacks by eliminating the opponents’ leverage of accessing the exact operation details. The base cyphers that have been applied in the proposed MSSEM are the Advanced Encryption Standard (AES) competition finalists, namely Rijndael, Serpent, MARS, Twofish, and RC6. These cyphers satisfy three essential criteria, such as security, performance, and expert input. Moreover, the modes of operation used by the MSSEM are the secure modes suggested by the National Institute of Standards and Technology, namely, Cipher Block Chaining (CBC), Cipher Feedback Mode (CFB), Output Feedback Mode (OFB), and Counter (CTR). For the proposed MSSEM implementation, the sender initially generates a random key using a pseudorandom number generator such as Blum Blum Shub (BBS) or a Linear Congruential Generator (LCG). Subsequently, the sender securely shares the key with the legitimate receiver. Besides that, the proposed MSSEM has an entity called the operation table that includes sixty different cypher suites. Each cypher suite has a specific cypher and mode of operation. During the run-time, one cypher suite is randomly selected from the operation table, and a new key is extracted from the master key with the assistance of SHA-256. The suite, as well as the new key, is allowed to encrypt one message. While each of the messages produces a new key and cypher suite. Thus, no one except communicating parties can access the encryption keys or the cypher suites. Furthermore, the security of MSSEM has been evaluated and mathematically proven to resist known and unknown attacks. As a result, the proposed MSSEM successfully mitigates unknown non-generic attacks by a factor of 2−6. In addition, the proposed MSSEM performance is better than MODEM since MODEM generates 4650 milliseconds to encrypt approximately 1000 bytes, whereas MSSEM needs only 0.14 milliseconds. Finally, a banking system simulation has been tested with the proposed MSSEM in order to secure inbound and outbound system traffic

Linking social media, medical literature, and clinical notes using deep learning.

Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic medical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, and our key is finding concepts in the medical text. The National Library of Medicine (NLM) introduces a Unified Medical Language System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and apply supervised and semi-supervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer learning. The results include an integrated, end-to-end, web-based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts

Computer-Assisted Interactive Documentary and Performance Arts in Illimitable Space

This major component of the research described in this thesis is 3D computer graphics, specifically the realistic physics-based softbody simulation and haptic responsive environments. Minor components include advanced human-computer interaction environments, non-linear documentary storytelling, and theatre performance. The journey of this research has been unusual because it requires a researcher with solid knowledge and background in multiple disciplines; who also has to be creative and sensitive in order to combine the possible areas into a new research direction. [...] It focuses on the advanced computer graphics and emerges from experimental cinematic works and theatrical artistic practices. Some development content and installations are completed to prove and evaluate the described concepts and to be convincing. [...] To summarize, the resulting work involves not only artistic creativity, but solving or combining technological hurdles in motion tracking, pattern recognition, force feedback control, etc., with the available documentary footage on film, video, or images, and text via a variety of devices [....] and programming, and installing all the needed interfaces such that it all works in real-time. Thus, the contribution to the knowledge advancement is in solving these interfacing problems and the real-time aspects of the interaction that have uses in film industry, fashion industry, new age interactive theatre, computer games, and web-based technologies and services for entertainment and education. It also includes building up on this experience to integrate Kinect- and haptic-based interaction, artistic scenery rendering, and other forms of control. This research work connects all the research disciplines, seemingly disjoint fields of research, such as computer graphics, documentary film, interactive media, and theatre performance together.Comment: PhD thesis copy; 272 pages, 83 figures, 6 algorithm

Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words

The state-of-the-art image analysis algorithms offer a unique opportunity to extract semantically meaningful features from medical images. The advantage of this approach is automation in terms of content-based image retrieval (CBIR) of medical images. Such an automation leads to more reliable diagnostic decisions by clinicians as the direct beneficiary of these algorithms. Digital pathology (DP), or whole slide imaging (WSI), is a new avenue for image-based diagnosis in histopathology. WSI technology enables the digitization of traditional glass slides to ultra high-resolution digital images (or digital slides). Digital slides are more commonly used for CBIR research than other modalities of medical images due to their enormous size, increasing adoption among hospitals, and their various benefits offered to pathologists (e.g., digital telepathology). Pathology laboratories are under constant pressure to meet increasingly complex demands from hospitals. Many diseases (such as cancer) continue to grow which creates a pressing need to utilize existing innovative machine learning schemes to harness the knowledge contained in digital slides for more effective and efficient histopathology. This thesis provides a qualitative assessment of three popular image analysis techniques, namely Local Binary Pattern (LBP), Bag of visual Words (BoW), and Convolution Neural Networks (CNN) in their abilities to extract the discriminative features from gigapixel histopathology images. LBP and BoW are well-established techniques used in different image analysis problems. Over the last 5-10 years, CNN has become a frequent research topic in computer vision. CNN offers a domain-agnostic approach for the automatic extraction of discriminative image features, used for either classification or retrieval purposes. Therefore, it is imperative that this thesis gives more emphasis to CNN as a viable approach for the analysis of DP images. A new dataset, Kimia Path24 is specially designed and developed to facilitate the research in classification and CBIR of DP images. Kimia Path24 is used to measure the quality of image features extracted from LBP, BoW, and CNN; resulting in the best accuracy values of 41.33%, 54.67%, and 56.98% respectively. The results are somewhat surprising, suggesting that the handcrafted feature extraction algorithm, i.e., LBP can reach very close to the deep features extracted from CNN. It is unanticipated, considering that CNN requires much more computational resources and efforts for designing and fine-tuning. One of the conclusions is that CNN needs to be trained for the problem with a large number of training images to realize its comprehensive benefits. However, there are many situations where large, balanced, and the labeled dataset is not available; one such area is histopathology at present

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

A Peer-to-Peer Network Framework Utilising the Public Mobile Telephone Network

P2P (Peer-to-Peer) technologies are well established and have now become accepted as a mainstream networking approach. However, the explosion of participating users has not been replicated within the mobile networking domain. Until recently the lack of suitable hardware and wireless network infrastructure to support P2P activities was perceived as contributing to the problem. This has changed with ready availability of handsets having ample processing resources utilising an almost ubiquitous mobile telephone network. Coupled with this has been a proliferation of software applications written for the more capable `smartphone' handsets. P2P systems have not naturally integrated and evolved into the mobile telephone ecosystem in a way that `client-server' operating techniques have. However as the number of clients for a particular mobile application increase, providing the `server side' data storage infrastructure becomes more onerous. P2P systems offer mobile telephone applications a way to circumvent this data storage issue by dispersing it across a network of the participating users handsets. The main goal of this work was to produce a P2P Application Framework that supports developers in creating mobile telephone applications that use distributed storage. Effort was assigned to determining appropriate design requirements for a mobile handset based P2P system. Some of these requirements are related to the limitations of the host hardware, such as power consumption. Others relate to the network upon which the handsets operate, such as connectivity. The thesis reviews current P2P technologies to assess which was viable to form the technology foundations for the framework. The aim was not to re-invent a P2P system design, rather to adopt an existing one for mobile operation. Built upon the foundations of a prototype application, the P2P framework resulting from modifications and enhancements grants access via a simple API (Applications Programmer Interface) to a subset of Nokia `smartphone' devices. Unhindered operation across all mobile telephone networks is possible through a proprietary application implementing NAT (Network Address Translation) traversal techniques. Recognising that handsets operate with limited resources, further optimisation of the P2P framework was also investigated. Energy consumption was a parameter chosen for further examination because of its impact on handset participation time. This work has proven that operating applications in conjunction with a P2P data storage framework, connected via the mobile telephone network, is technically feasible. It also shows that opportunity remains for further research to realise the full potential of this data storage technique

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

Perception-driven approaches to real-time remote immersive visualization

In remote immersive visualization systems, real-time 3D perception through RGB-D cameras, combined with modern Virtual Reality (VR) interfaces, enhances the user’s sense of presence in a remote scene through 3D reconstruction rendered in a remote immersive visualization system. Particularly, in situations when there is a need to visualize, explore and perform tasks in inaccessible environments, too hazardous or distant. However, a remote visualization system requires the entire pipeline from 3D data acquisition to VR rendering satisfies the speed, throughput, and high visual realism. Mainly when using point-cloud, there is a fundamental quality difference between the acquired data of the physical world and the displayed data because of network latency and throughput limitations that negatively impact the sense of presence and provoke cybersickness. This thesis presents state-of-the-art research to address these problems by taking the human visual system as inspiration, from sensor data acquisition to VR rendering. The human visual system does not have a uniform vision across the field of view; It has the sharpest visual acuity at the center of the field of view. The acuity falls off towards the periphery. The peripheral vision provides lower resolution to guide the eye movements so that the central vision visits all the interesting crucial parts. As a first contribution, the thesis developed remote visualization strategies that utilize the acuity fall-off to facilitate the processing, transmission, buffering, and rendering in VR of 3D reconstructed scenes while simultaneously reducing throughput requirements and latency. As a second contribution, the thesis looked into attentional mechanisms to select and draw user engagement to specific information from the dynamic spatio-temporal environment. It proposed a strategy to analyze the remote scene concerning the 3D structure of the scene, its layout, and the spatial, functional, and semantic relationships between objects in the scene. The strategy primarily focuses on analyzing the scene with models the human visual perception uses. It sets a more significant proportion of computational resources on objects of interest and creates a more realistic visualization. As a supplementary contribution, A new volumetric point-cloud density-based Peak Signal-to-Noise Ratio (PSNR) metric is proposed to evaluate the introduced techniques. An in-depth evaluation of the presented systems, comparative examination of the proposed point cloud metric, user studies, and experiments demonstrated that the methods introduced in this thesis are visually superior while significantly reducing latency and throughput