Miracle-dz/UniFAR-FTUs huggingface dataset:
Modular Composition of Access Control Policies: A Framework to Build Multi-Site Multi-Level Combinations

We present general notions of access control policy composition using the CBAC model. We show that CBAC provides a uniform framework to define compositions of heterogeneous policies (e.g., RBAC and ABAC policies) as required in many practical situations. Compositions can be built both horizontally (where n given policies are combined to define a new policy) and vertically (where policies are extended by adding administration layers). We show that under some conditions on the operations used to build the composition, it is possible to ensure that the result preserves desirable properties (such as liveness and effectiveness). We also discuss mechanisms to detect and eliminate conflicts that may arise when composing policies originating from different sources.
Network (In)security: Leniency in Protocols' Design, Code and Configuration

Protocols are one of the founding pillars of network communication. Given their importance, protocols have received great attention not only from the research community but also from adversaries. Protocols, particularly their implementations, have been lucrative targets for adversarial attacks to induce network insecurity by compromising the guarantees that these implementations should provide. Most of these attacks can be traced back to the leniency in their design, code, or configuration. Finding leniency in implementations is challenging as these lenient instances are primarily tied to the semantics of the protocol and thus demand for techniques unlike existing approaches that we use for finding low-level memory corruption bugs. In this talk, I will discuss our experience and lesson learned in detecting leniency in different layers of the TCP/IP network protocol stack. First, I will show how leniency in the design of loss-based TCP congestion control schemes can be exploited by an attacker to manipulate the victim into taking actions favorable for the attacker. I will introduce our model-guided fuzzing approach to find such manipulation attacks in different TCP implementations part of mainstream operating systems (e.g., Linux, Windows). Next, I will focus on leniency in code, where an implementation exhibits noncompliance with its design. Specifically, I will talk about how lenient implementations of X.509 certificate validation in SSL/TLS libraries can be exploited by an attacker to mount impersonation attacks. Finally, I will highlight that it is not always the protocol's design or code, sometimes it is humans: the users and/or the IT (Information Technology) administrators. Specifically, I will present our multifaceted measurement study where we examined the WPA2-Enterprise Wi-Fi configurations prescribed in tertiary education institutes (TEIs) around the world. I will share our findings about the widespread insecure practices that can leave users of these institutes open to credential theft[2].
WiP: Metamodel for Continuous Authorisation and Usage Control

Access control has been traditionally used to protect data and privacy. Traditional access control models (e.g., ABAC, RBAC) cannot meet modern security requirements as technologies spread over heterogeneous and dynamic environments that need continuous monitoring. Modern models such as Usage Control (UCON) introduced the concept of continuous authorisation that has a lifecycle consisting of a series of phases through which the authorisation passes during its lifetime. However, such models assume a fixed lifecycle for all authorisations, so they cannot satisfy emerging technologies (e.g., smart vehicles, zero-trust, data flow), which require various and fine-grained lifecycles. Researchers have extended existing models to meet such requirements, but all solutions remain restrictive, as they are specially tailored for specific use-cases. In this paper, we propose an extensible model for continuous authorisations and usage control. The model enables its users to customise and dynamically configure the authorisation lifecycle as required by the use-case. This adds a layer of abstraction, forming a metamodel that can be instantiated into different flavours of continuous authorisation models, each addressing specific requirements. We also show that the authorisation lifecycle can be modelled as Deterministic Finite Automaton (DFA) and expressed in a structured language used by an evaluation engine to dynamically enact and manage the lifecycle. We layout the building blocks of the proposed metamodel and devise future research directions.
A Capability-based Distributed Authorization System to Enforce Context-aware Permission Sequences

Controlled sharing is fundamental to distributed systems. We consider a capability-based distributed authorization system where a client receives capabilities (access tokens) from an authorization server to access the resources of resource servers. Capability-based authorization systems have been widely used on the Web, in mobile applications and other distributed systems. A common requirement of such systems is that the user uses tokens of multiple servers in a particular order. A related requirement is the token may be used if certain environmental conditions hold. We introduce a secure capability-based system that supports "permission sequence" and "context". This allows a finite sequence of permissions to be enforced, each with their own specific context. We prove the safety property of this system for these conditions and integrate the system into OAuth 2.0 with proof-of-possession tokens. We evaluate our implementation and compare it with plain OAuth with respect to the average time for obtaining an authorization token and acquiring access to the resource.
The Hidden Technological Labour of Service Workers in Health and Beauty Shops

Advances in computing have started the digital transformation of work and there is hardly any occupational domain that is not affected by it. Yet, some jobs are still considered ”nontechnical” - such as for example the work of shop assistants in health and beauty retail. In this paper we take a focused look at the ways that advances in computing have also affected their work routines: Which technological labour do these ”nontechnical” workers perform on a daily basis? And why is this dimension of their work so often overlooked? To address these questions we present the results of an interdisciplinary qualitative study. The findings highlight the versatility of technologies employed and illustrate the sociotechnical complexity of the performed work tasks. Our discussion relates our insights to previous research on functional invisibility and offers a supplementing analysis of several hiding mechanisms. Thereby, the paper contributes a critical look that reveals the ways that keep technological labour downplayed, overlooked and undervalued in this particular female-dominated low-wage occupation of the service sector.
Making Space for Social Time: Supporting Conversational Transitions Before, During, and After Video Meetings

Unlike meetings in person, it is a well known but still unsolved problem that in traditional videoconferencing people just appear and disappear [56]. The lack of Conversational Transitions (CTs) is unnatural and also limits the both ritualistic and spontaneous small talk of collegiality and productivity that happen in transitional moments. We report a design investigation of the pros and cons of a CT-Space UI that that intertwines spatial and temporal metaphors to support a range of conversational transitions before, during, and after meetings, but, crucially, might also fit into standard periodic workplace practices rather than require all-day connection. We explore the comprehensibility of visual transitions in space, how spatial audio supports transitions, blending spatial and temporal metaphors, and fluid and visible group clustering. We argue that CTs should be a standard requirement for videoconferencing services.
From thinking fast to moving fast: motor control of fast limb movements in healthy individuals

Abstract The ability to produce high movement speeds is a crucial factor in human motor performance, from the skilled athlete to someone avoiding a fall. Despite this relevance, there remains a lack of both an integrative brain-to-behavior analysis of these movements and applied studies linking the known dependence on open-loop, central control mechanisms of these movements to their real-world implications, whether in the sports, performance arts, or occupational setting. In this review, we cover factors associated with the planning and performance of fast limb movements, from the generation of the motor command in the brain to the observed motor output. At each level (supraspinal, peripheral, and motor output), the influencing factors are presented and the changes brought by training and fatigue are discussed. The existing evidence of more applied studies relevant to practical aspects of human performance is also discussed. Inconsistencies in the existing literature both in the definitions and findings are highlighted, along with suggestions for further studies on the topic of fast limb movement control. The current heterogeneity in what is considered a fast movement and in experimental protocols makes it difficult to compare findings in the existing literature. We identified the role of the cerebellum in movement prediction and of surround inhibition in motor slowing, as well as the effects of fatigue and training on central motor control, as possible avenues for further research, especially in performance-driven populations.
On the Netlist Gate-level Pruning for Tree-based Machine Learning Accelerators

The technology advances in the recent years have led to the spread use of Machine Learning (ML) models in embedded systems. Due to the battery limitations of such edge devices, energy consumption has become a major problem. Tree-based models, such as Decision Trees (DTs) and Random Forests (RFs), are well-known ML tools that provide higher than standard accuracy results for several tasks. These tools are convenient for battery-powered devices due to their simplicity, and they can be further optimized with approximate computing techniques. This paper explores gate-level pruning for DTs and RFs. By using a framework that generates VLSI descriptions of the ML models, we investigate gate-level pruning to the mapped netlist generated after logic synthesis for three case studies. Several analyses on the energy- and area-accuracy trade-offs were performed and we found that we can obtain significant energy and area savings for small or even negligible accuracy drops, which indicates that pruning techniques can be applied to optimize tree-based hardware implementations.
Priority Based Prepaid Energy Meter Using IoT

Similar to the transformation of smartphones from landline phones, the energy meter is updated from manual reading meters to smart energy meters. Recently many prepaid energy meters are designed to reduce manual errors during meter readings and to avoid wastage of energy. In the proposed system, a prepaid smart energy meter is designed using IoT, where after a certain amount of energy is consumed; the meter allows the user to set their priority order in which the energy flows through their house. The priority is determined by the amount of energy used in each room. Maximum energy-consuming room can be given first priority and follows. A smartphone app can be used to set the priority. An alert message is sent to the user once the energy consumption crosses a certain amount of energy usage. The notification reached the user through Wi-Fi module when it is connected to the Arduino microcontroller. With the help of the internet, the system may be watched from anywhere at any time. If the user does not have access to the internet, GSM can be utilized to notify them.
A Review and Conceptual Framework of Digital Assistive Wearables and Consumer Hedonism

The purpose of this paper is to review past work in the area of digital assistive wearables and consumer hedonism. A theme-based systematic literature review was conducted in the area of digital assistive wearables and consumer hedonism. With the help of psychological reactance theory and the stereotype content model, a deeper understanding of the phenomenon was developed. This paper further provides a framework to explain the various psycho-social attributes that can make the product a hedonic choice. The conceptual framework offers a solution to product designers, marketers and researchers in the area of digital assisted wearables in moving it from a utilitarian product to a hedonic one.
Web Application Penetration Testing & Patch Development Using Kali Linux

Nowadays, safety is a first-rate subject for all applications. There has been an exponential growth year by year in the number of businesses going digital since the few decades following the birth of the Internet. In these technologically advanced times, cyber security is a must mainly for internet applications, so we have the notion of diving deeper into the Cyber security domain and are determined to make a complete project. We aim to develop a website portal for ease of communication between us and the end user. Utilizing the power of python scripting and flask server to make independent automated tools for detection of SQLI, XSS & a Spider(Content Discovery Tool). We have also integrated skipfish as a website vulnerability scanner to our project using python and Kali Linux. Since conducting a penetration test on another website without permission is not legal, we thought of building a dummy website prone to OS Command Injection in addition to the above-mentioned attacks. A well-documented report will be generated after the penetration test/ vulnerability scan. In case the website is vulnerable, patching of the website will be done with the user's consent.
Thermochemical seasonal heat storage for the built environment:a multi-scale investigation

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning

Scoliosis is a typical spinal disease that causes the spine to curve. Early treatment during the formation of the spine can greatly reduce the chances of health issues. Diagnosis of scoliosis relies on X-ray imaging, using X-ray images to diagnose lumbar, cervical, and thoracic spinal structures have traditionally proven difficult and time-consuming. Many clinical applications of spinal imaging require the accurate and robust identification of vertebrae from medical images. This paper presents an automated approach using deep learning to detect the spine’s curvature using its spinal column. Models of deep learning could be used to assist with the increasing volume of medical imaging data as well as provide initial interpretation of images gathered in primary care. Deep learning algorithms are a quicker and more efficient alternative to manual X-ray investigation for scoliosis detection. X-ray images of spine curvature are used to detect and classify scoliosis using a pre-trained EfficientNet model. In the first stage, the model was evaluated without augmentation, in which we achieved an accuracy of 78 %. In the second step, we augment the training data by using machine learning techniques, and after that, we achieved an accuracy of 86 %. Our findings show that the proposed automatic scoliosis identification method can accurately detect and classify spine curvature in X-ray images.
A feasibility study in the use of domestic water treatment residuals to remove phosphorus from wastewater

There is growing evidence that even low levels of phosphorus (<0.1mgL-1) entering natural water systems can cause eutrophication. Waste water treatment plants discharge over 23kT of phosphorus into UK surface waters per year. The Water Framework Directive requires that this be greatly reduced. Although many processes have been developed, they are often complex and energy intensive. This thesis aimed to assess how a novel process using dewatered water treatment works residuals (DWTR) could be engineered to maximize phosphorus removal from waste water treatment plant effluents while minimising system complexity and energy use. An extensive yearlong experiment was operated at two sites to investigate how phosphorus removal rates varied over time in relation to DWTR type, phosphorus concentration, hydraulic retention time (HRT) and scale. DWTR from eight different water treatment works were used in 35 experimental models of dimensions 0.1O x 1m and two meso scale beds 1x1x0.8m. The most significant factors effecting P removal rate were found to be DWTR type and media particle size. Total P removal varied between 58-95% for the 8 different DWTR over the year. Increasing particle size from 0.6-2 to 6-20mm reduced P adsorption capacity by 30% on average with 6 hours HRT and 5mgL-1 TP input. The key physical and chemical properties of the DWTR were measured to assess the effect of the parameters on the P removal ability of the media. No significant relationship (p<0.05) between the amount of Al, Fe, Ca or Mg in the media and its P removal ability were found. An idealised adsorption system using DWTR as the media would operate with 2 to 3 beds in series with a minimum HRT of 6 hours and maximum particle size of 6mm and depth of 1m. Computer models of such a system predict >95% TP removal for over two years of operation.
Online Personalization of Compression in Hearing Aids via Maximum Likelihood Inverse Reinforcement Learning

A key function of modern hearing aids is compression or mapping of sound to the residual hearing range of those suffering from hearing loss. This paper presents a machine learning approach to personalize compression in hearing aids in an online manner. The online feature of this approach allows it to be deployed in the field. The significance of this personalized compression lies in enabling preferred hearing outcomes relative to the one-size-fits-all prescriptive compression rationales that are currently being used. This personalization approach utilizes maximum likelihood inverse reinforcement learning to establish a model of a hearing aid user’s preference based on paired comparisons by the user. The results of the preference paired comparisons between the personalized and standard prescriptive settings from ten subjects indicate that personalized settings were preferred about 10 times more than the standard prescriptive settings. In addition, a word recognition comparison was also conducted showing that the personalized settings have no adverse impact on speech understanding in either quiet or in competing noise conditions.
A Composite Dataset of Lumbar Spine Images with Mid-Sagittal View Annotations and Clinically Significant Spinal Measurements

The modern computer-aided screening systems re-quire a large amount of well-annotated training data to produce robust and consistent diagnostic performance. Furthermore, the public datasets designed to evaluate automated spinal disorders screening frameworks lack quantitative labels, which are marked by expert radiologists and clinically validated by spinal surgeons. This paper presents a dataset containing high-resolution (and well-labeled) mid-sagittal views of lumbar spine magnetic resonance imaging (MRI) scans. These scans also contain vertebral body masks along with clinically significant spinal measurements, including lumbar height, intervertebral body distances, vertebral body sidewall dimensions, vertebral body superior and inferior end-plates dimensions, lumbar lordotic angles, and lumbosacral angles. The mid-sagittal view MRI scans within the proposed dataset were first procured, and then they were manually marked by the expert radiologists and validated by the expert spinal surgeons. Afterward, different spinal measurements were recorded, which serves as a benchmark to evaluate the autonomous frameworks for predicting spinal misalignments. In addition to this, the proposed dataset is, to the best of our knowledge, the first composite database that contains lumbar spine mid-sagittal images along with spinal attributes and detailed markings of radiologists duly verified by the spinal surgeons. The proposed dataset, unlike its competitors, also introduces a quantitative vote to the clinicians and researchers in the assessment process of lumbar spine disorders. Apart from this, the dataset is publicly available at https://data.mendeley. com/datasets/k3b363f3vz/2.
Oximeter with motion detection for alarm modification

A pulse oximeter which modifies the alarm condition when motion is detected. Basically, if the lack of a pulse is determined to be as a result of motion artifact, the generation of an alarm is postponed. In addition, the display indicates that motion is present and that the last reading is questionable due to the presence of motion. The invention also determines if motion artifact is present from the pulse oximeter detector signal itself. The ratio of the positive and negative peaks of the derivative of the pulse signal are compared to a motion/blood pulse threshold.
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques

Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts. First, power curve monitoring is targeted applying various types of Artificial Neural Networks to increase modeling accuracy. It is shown how the proposed method can significantly improve network reliability compared with existing models. Then, an advance technique is utilized to create a smoother dataset for network training followed by establishing dynamic ANFIS network. At this stage, designed network aims to predict power generation in future hours. Finally, a recursive principal component analysis is performed to extract significant features to be used as input parameters of the network. A novel fusion technique is then employed to build an advanced model to make predictions of turbines performance with favorably low errors.