Background: The yearly incidence of people with Spinal Cord Injury (SCI) is between250,000 and 500,000, according to the World Health Organization (WHO). The injury often reduces the ability to walk. Various consequences affect the nervous system and, thus, the entire body. Therefore, the patient population with SCI is highly heterogeneous also in their gait patterns. Multiple tools are used to classify and understand the walking impairments caused by the injury. Objective: To underline the added value brought by the integration of 3D gait analysis to more standard methods (GDI, GPS, GVS, spatiotemporal parameters, ASIAgrade, muscle strength, and spasticity) in the evaluation and interpretation of gait patterns of subjects with SCI. Methods: 3D gait analysis with a passive optical motion capture system (Vicon)and four force plates was performed in 7 control subjects and 3 with SCI. The model used for marker placement and pre-processing was CGM 2.3. Matlab was used to analyze and plot the kinematic and kinetic joints’ data and calculate the GDI, GPS, and GVS indexes and spatiotemporal parameters for subjects with SCI and the control group. A specialized physiotherapist conducted the clinical assessment of the patients with SCI in a rehabilitation center. This included: ASIA grade and review, muscle strength, and spasticity with Daniels Whorthingham scale and Modified Ashworth scale, respectively. The evaluation of the result was qualitative. Results: The integration of 3D gait analysis show further understanding in the assessment of walking impairments. The indexes resumed the impairments and classified the subjects but lacked temporal and functional perspective. Gait phases and spatiotemporal parameters suggested difficulties in stability and balance but could not identify the problem’s origin. Lastly, clinical assessment enlightened the singular motor and sensory function impairments. 3D gait analysis contextualized these results identifying gait patterns and functional implications. Conclusion: Integrating 3D gait analysis might give a deeper understanding of subjects with SCI’s gait strategies and impairments. Indeed this complex technique links the other methods’ results, contextualizing them and gaining information.

Memory corruption attacks have existed in C and C++ for more than 30 years, and over the years many defenses have been proposed. In addition to that, a new class of attacks, Spectre, has emerged that abuse speculative execution to leak secrets and sensitive data through micro-architectural side channels. Many defenses have been proposed to mitigate Spectre as well. However, with every new defense a new attack emerges, and then a new defense is proposed. This is an ongoing cycle between attackers and defenders. There exists many defenses for many different attack avenues. However, many suffer from either practicality or effectiveness issues, and security researchers need to balance out their compromises. Recently, many hardware vendors, such as Intel and ARM, have realized the extent of the issue of memory corruption attacks and have developed hardware security mechanisms that can be utilized to defend against these attacks. ARM, in particular, has released a mechanism called Pointer Authentication in which its main intended use is to protect the integrity of pointers by generating a Pointer Authentication Code (PAC) using a cryptographic hash function, as a Message Authentication Code (MAC), and placing it on the top unused bits of a 64-bit pointer. Placing the PAC on the top unused bits of the pointer changes its semantics and the pointer cannot be used unless it is properly authenticated. Hardware security features such as PAC are merely mechanisms not full fledged defences, and their effectiveness and practicality depends on how they are being utililzed. Naive use of these defenses doesn't alleviate the issues that exist in many state-of-the-art software defenses. The design of the defense that utilizes these hardware security features needs to have practicality and effectiveness in mind. Having both practicality and effectiveness is now a possible reality with these new hardware security features. This dissertation describes utilizing hardware security features, namely ARM PAC, to build effective and practical defense mechanisms. This dissertation first describes my past work called PACTight, a PAC based defense mechanism that defends against control-flow hijack- ing attacks. PACTight defines three security properties of a pointer such that, if achieved, prevent pointers from being tampered with. They are: 1) unforgeability: A pointer p should always point to its legitimate object; 2) non-copyability: A pointer p can only be used when it is at its specific legitimate location; 3) non-dangling: A pointer p cannot be used after it has been freed. PACTight tightly seals pointers and guarantees that a sealed pointer cannot be forged, copied, or dangling. PACTight protects all sensitive pointers, which are code pointers and pointers that point to code pointers. This completely prevents control-flow hijacking attacks, all while having low performance overhead. In addition to that, this dissertation proposes Scope-Type Integrity (STI), a new defense policy that enforces pointers to conform to the programmer's intended manner, by utilizing scope, type, and permission information. STI collects information offline about the type, scope, and permission (read/write) of every pointer in the program. This information can then be used at runtime to ensure that pointers comply with their intended purpose. This allows STI to defeat advanced pointer attacks since these attacks typically violate either the scope, type, or permission. We present Runtime Scope-Type Integrity (RSTI). RSTI leverages ARM Pointer Authentication (PA) to generate Pointer Authentication Codes (PACs), based on the information from STI, and place these PACs at the top bits of the pointer. At runtime, the PACs are then checked to ensure pointer usage complies with STI. RSTI overcomes two drawbacks that were present in PACTight: 1) PACTight relied on a large external metadata for protection, whereas RSTI uses very little metadata. 2) PACTight only protected a subset of pointers, whereas RSTI protects all pointers in a program. RSTI has large coverage with relatively low overhead. Also, this dissertation proposes sPACtre, a new and novel defense mechanism that aims to prevent Spectre control-flow attacks on existing hardware. sPACtre is an ARM-based defense mechanism that prevents Spectre control-flow attacks by relying on ARM's Pointer Authentication hardware security feature, annotations added to the program on the secrets that need to be protected from leakage and a dynamic tag-based bounds checking mechanism for arrays. We show that sPACtre can defend against these attacks. We evaluate sPACtre on a variety of cryptographic libraries with several cryptographic algorithms, as well as a synthetic benchmark, and show that it is efficient and has low performance overhead Finally, this dissertation explains a new direction for utilizing hardware security features to protect energy harvesting devices from checkpoint-recovery errors and malicious attackers. / Doctor of Philosophy / In recent years, cyber-threats against computer systems have become more and more preva- lent. In spite of many recent advancements in defenses, these attacks are becoming more threatening. However, many of these defenses are not implemented in the real-world. This is due to their high performance overhead. This limited efficiency is not acceptable in the real-world. In addition to that, many of these defenses have limited coverage and do not cover a wide variety of attacks. This makes the performance tradeoff even less convincing. Thus, there is a need for effective and practical defenses that can cover a wide variety of attacks. This dissertation first provides a comprehensive overview of the current state-of-the-art and most dangerous attacks. More specifically, three types of attacks are examined. First, control-flow hijacking attacks, which are attacks that divert the proper execution of a pro- gram to a malicious execution. Second, data oriented attacks. These are attacks that leak sensitive data in a program. Third, Spectre attacks, which are attacks that rely on sup- posedly hidden processor features to leak sensitive data. These "hidden" features are not entirely hidden. This dissertation explains these attacks in detail and the corresponding state-of-the-art defenses that have been proposed by the security research community to mitigate them. This dissertation then discusses effective and practical defense mechanisms that can mitigate these attacks. The dissertation discusses past work, PACTight, as well as its contributions, RSTI and sPACtre, presenting the full design, threat model, implementation, security eval- uation and performance evaluation of each one of these mechanisms. The dissertation relies on insights derived from the nature of the attack and compiler techniques. A compiler is a tool that transforms human-written code into machine code that is understandable by the computer. The compiler can be modified and used to make programs more secure with compiler techniques. The past work, PACTight, is a defense mechanism that defends against the first type of attacks, control-flow hijacking attacks, by preventing an attacker from abusing specific code in the program to divert the program to a malicious execution. Then, this dissertation presents RSTI, a new defense mechanism that overcomes the limitations of PACTight and extends it to cover data oriented attacks and prevent attackers from leaking sensitive data from the program. In addition to that, this dissertation presents sPACtre, a novel defesnse mechanism that defends against Spectre attacks, and prevents an attacker from abusing a processor's hidden features. Finally, this dissertation briefly discusses a possible future direction to protect a different class of devices, referred to as energy-harvesting devices, from attackers.

Real estate professionals, such as investors, owner-occupants, and lenders who are involved in the investment decision-making process are increasingly interested in sustainability and energy efficiency investment. However, current tools and techniques, both technical and financial, typically used for assessing sustainability on their own are unable to provide comprehensive and reliable financial information required for making high-quality investment decisions. Sustainability investment often includes non-cost benefits, value implications, as well as substantial risk and uncertainty that current methods do not simultaneously incorporate in their assessment process. Through a combined quantitative and qualitative approach, this research creates a new systematic assessment process to consider both cost and non-cost savings and therefore the true financial performance of a set of sustainable options in the context of value and risk, while explicitly deriving and including various uncertainties inherent in the process. The framework integrates assessment tools of technical decision-makers with those of investment decision-makers into a single platform to improve the quality of financial performance projections, and therefore, investment decisions concerning sustainable options in real estate. A case study is then conducted to test and demonstrate the numeric application of the proposed framework in the context of a non-green office building. The case study presents how to connect the technical outcomes to financial inputs, present the information, and estimate the true financial performance of a green retrofit option, where incremental value and uncertainty have been modeled and included. Three levels of financial analysis are performed to estimate the distribution of financial outcomes including: 1) Cost-based level-1: only energy related costs savings were considered; 2) Cost-based level-2: the non-energy cost savings, including heath and productivity, were also considered; and 3) Value-based level: the value implications of the green retrofit option were considered in addition to items in level 2. As a result of applying the proposed framework when evaluating sustainability investment options, many investment opportunities that were otherwise ignored may be realized, and therefore, the breadth and depth of sustainability investment in real estate will increase. / Ph. D.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.

<p dir="ltr">As cameras increasingly populate urban environments for surveillance, the threat of data breaches and losses escalates as well. The rapid advancements in generative Artificial Intelligence have greatly simplified the replication of individuals’ appearances from video footage. This capability poses a grave risk as malicious entities can exploit it for various nefarious purposes, including identity theft and tracking individuals’ daily activities to facilitate theft or burglary.</p><p dir="ltr">To reduce reliance on video surveillance systems, this study introduces Urban Seismic Event Detection (USED), a deep learning-based technique aimed at extracting information about urban seismic events. Our approach involves synthesizing training data through a small batch of manually labelled field data. Additionally, we explore the utilization of unlabeled field data in training through semi-supervised learning, with the implementation of a mean-teacher approach. We also introduce pre-processing and post-processing techniques tailored to seismic data. Subsequently, we evaluate the trained models using synthetic, real, and unlabeled data and compare the results with recent statistical methods. Finally, we discuss the insights gained and the limitations encountered in our approach, while also proposing potential avenues for future research.</p>

No / In this article we assess the abilities of a new electromagnetic (EM) system, the CMD Mini-Explorer, for prospecting of archaeological features in Ireland and the UK. The Mini-Explorer is an EM probe which is primarily aimed at the environmental/geological prospecting market for the detection of pipes and geology. It has long been evident from the use of other EM devices that such an instrument might be suitable for shallow soil studies and applicable for archaeological prospecting. Of particular interest for the archaeological surveyor is the fact that the Mini-Explorer simultaneously obtains both quadrature (conductivity') and in-phase (relative to magnetic susceptibility') data from three depth levels. As the maximum depth range is probably about 1.5m, a comprehensive analysis of the subsoil within that range is possible. As with all EM devices the measurements require no contact with the ground, thereby negating the problem of high contact resistance that often besets earth resistance data during dry spells. The use of the CMD Mini-Explorer at a number of sites has demonstrated that it has the potential to detect a range of archaeological features and produces high-quality data that are comparable in quality to those obtained from standard earth resistance and magnetometer techniques. In theory the ability to measure two phenomena at three depths suggests that this type of instrument could reduce the number of poor outcomes that are the result of single measurement surveys. The high success rate reported here in the identification of buried archaeology using a multi-depth device that responds to the two most commonly mapped geophysical phenomena has implications for evaluation style surveys. Copyright (c) 2013 John Wiley & Sons, Ltd.

Background: The traditional way of product promotion is no longer enough to attract the emerging consumer class; high-quality short video platforms have brought more business opportunities and dividends, providing a new channel for product marketing and promotion. The short video platform of TikTok has strong traffic attributes and intuitive product presentation, and many brands and merchants have shifted their product marketing and promotion positions here. However, brands and merchants lack a deep understanding of the internal mechanism of the influence of TikTok's short videos on the purchase intention of the consumer, cannot grasp the pain points of consumers' demand well, and lack attention to the online shopping experience of consumers. The existing researches mainly analyze the marketing strategies of short videos, and there are insufficient researches on consumers' purchase intention in the context of short videos on TikTok. Purpose: This thesis takes the consumer of TikTok short videos as the research object. Based on SOR theory, three main characteristics of TikTok short videos are selected as independent variables: entertainment, interactivity, and personalization. Meanwhile, social presence is introduced as the intermediary variable to explore the mechanism of influence of TikTok short videos' characteristics on consumers' purchase intention. Method: In this thesis, the questionnaire survey method was used to collect data, and 207 valid questionnaires were collected. Finally, SPSS26.0 statistical analysis software was used to process and analyze the data. Conclusion: The empirical results show that: (1) the entertainment, interactivity, and individuation of TikTok short videos have a positive impact on the purchase intention of the consumer; (2) TikTok short video entertainment, interactive, personalized positive impact on the consumer's sense of social presence; (3) Social presence plays a partial mediating role in the influence of entertainment, interactivity and personalization on purchase intention. According to the research conclusions, this thesis puts forward relevant suggestions for guiding enterprises to use short video platforms to carry out targeted and efficient precision marketing, stimulate the consumer's purchase intention, and maximize the use of short video as a differentiated marketing tool to cultivate their core competitiveness.

Cancer is a leading cause of death globally, and early detection is crucial for better outcomes. This research aims to improve Region Of Interest (ROI) segmentation and feature extraction in medical image analysis using Radiomics techniques with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance Test. The dataset consisted of CT and PET-CT DICOM images from 131 lung cancer patients. Results showed that PCA identified 14 features, Hierarchical Clustering 17, t-SNE 58, and ISOMAP 40, with texture-based features being the most critical. This study highlights the potential of integrating Radiomics and unsupervised learning techniques to enhance cancer prediction from medical images.

Объект – коммуникативная сфера российских предпринимателей. Предмет – различия в сформированности коммуникативных средств у предпринимателей и специалистов в найме. Диссертация состоит из введения, двух глав, заключения и списка использованной литературы, включающего 117 наименований. Общий объём работы составляет 81 страницу. Во введении раскрывается актуальность темы исследования, отражается проблематика, поставлены цели и задачи, определены объект и предмет диссертации, указаны методы и методики, используемые автором в исследовании. Также, автором отражены практическая значимость и научная новизна работы. В первой главе рассмотрена история исследования психологии предпринимателя, автором выделен ряд ключевых методик, которые позволяют дать оценку психологическому портрету предпринимателя. Также, в главе определены основные черты российского предпринимателя Выделяя ряд подходов к пониманию сущности коммуникации, автором рассмотрена коммуникативная сфера, указаны её виды и особенности. В главе определены коммуникативные особенности предпринимателей. Во второй главе проведено исследование коммуникативных умений предпринимателя на основе ряда методик. В исследовании выявлены значимые различия в коммуникативных навыках и организаторских способностях предпринимателей и специалистов, работающих по найму. Автором на практике подтверждены заявленные гипотезы. В заключении в обобщённом виде представлены все основные выводы теоретической и эмпирической глав, обоснована практическая значимость, автором описаны возможные перспективы применения работы в разных сферах, определены дальнейшие возможности для изучения данной темы. / The object is the communicative sphere of Russian entrepreneurs. The subject is the differences in the formation of communication tools among entrepreneurs and hiring specialists. The dissertation consists of an introduction, two chapters, a conclusion and a list of references, including 117 titles. The total amount of work is 81 pages. The introduction reveals the relevance of the research topic, reflects the problems, sets goals and objectives, defines the object and subject of the dissertation, indicates the methods and techniques used by the author in the study. The author also reflects the practical significance and scientific novelty of the work. In the first chapter, the history of the study of the psychology of an entrepreneur is considered, the author identifies a number of key techniques that allow us to assess the psychological portrait of an entrepreneur. Also, the chapter defines the main features of the Russian entrepreneur, highlighting a number of approaches to understanding the essence of communication, the author considers the communicative sphere, indicates its types and features. The chapter defines the communicative features of entrepreneurs. In the second chapter, a study of the communicative skills of an entrepreneur is conducted on the basis of a number of techniques. The study revealed significant differences in the communication skills and organizational abilities of entrepreneurs and self-employed professionals. The author has confirmed the stated hypotheses in practice. In conclusion, all the main conclusions of the theoretical and empirical chapters are summarized, their practical significance is substantiated, the author describes possible prospects for applying the work in different fields, and identifies further opportunities for studying this topic.

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs. / The data and image files accompanying this thesis are not available online.