Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Effective Behavioural Biometrics for Mobile Devices

Mobile devices store immense amounts of sensitive data, making them attractive targets for attackers. The first line of defence against attacks is authentication --- verifying the identity of an agent accessing the device. We examine behavioural biometrics as an effective authentication mechanism on mobile devices. Behavioural biometrics model an agent's behaviours to establish their identity. We construct an authentication system based on profiling the device sensors (touch screen, accelerometer and gyroscope) during a swipe, defined as a quick, simple movement across a touch screen. In addition, we explore the relationship between problem setting and evaluation methodology in authentication systems, producing a list of requirements necessary for accurate evaluation. Finally, we perform a user study to ascertain the effectiveness of our behavioural biometric mechanism. We conclude that this system is resistant to attacks which trivially bypass standard mechanisms such as PINs, while also potentially lightening the usability load imposed by authentication.
Assessment of anti-bacterial activity of non-thermal plasma in sterilization of infectious wastes

In today's world, the production of hospital wastes and their adverse effects such as infectious outbreaks and resistance to treatment is an important issue. Therefore, it's vital to find a new and efficient method to manage such wastes. In this study, the ability of dielectric barrier discharge (DBD) plasma to deactivate Pseudomonas aeruginosa and Staphylococcus aureus bacteria was assessed. The bacteria were treated with DBD plasma after cultivation in liquid milieu, and then dried in a sterile air stream. The results showed that for both bacteria, the number of deactivated colonies increased proportionally to the time of treatment. First, it occurred rapidly, and then the number of active colonies decreased at a slower speed. Also, increasing the plasma duty cycle in the same treatment time led to more deactivated colonies. This increase was more significant in the Pseudomonas aeruginosa bacteria, and changes for the Staphylococcus aureus was slight.
FACILITATING MATHEMATICAL UNDERSTANDING IN THREE-DIMENSIONAL GEOMETRY USING THE SOLO TAXONOMY

This study aims to examine: (1) an increase in mathematical understanding of students whose learning with SOLO taxonomy of Superitem techniques compared to conventional learning, (2) completeness of mathematical understanding of students using SOLO taxonomy of Superitem techniques, (3) student attitudes toward learning using the SOLO taxonomy of Superitem techniques. The study was a quasi-experimental study in one of the high schools in Bandung with a research design control group design posttest. The population is all class X high school students. The sample was taken by the Purposive Random Sampling technique as much as two classes. Data collection is done by giving tests and non-tests. The written test is mathematical understanding in the concept of three dimensional geometry. Non tests are attitude questionnaires with a Likert scale. The results showed (1) an increase in mathematical of students whose learning using SOLO taxonomy with Superitem techniques was higher than that of students with conventional learning methods, (2) classical mastery learning in experimental classes was achieved, and (3) positive student attitudes in all indicator.
Antibacterial, Antidiabetic and Brine Shrimp Lethality Activities of Some Selected Medicinal Plants from Kavrepalanchok District of Nepal

The methanol extracts of nine medicinal plants collected from Kavrepalanchok district of Nepal were subjected to their potential bioactivity viz. antibacterial, antidiabetic and toxicity tests. Antibacterial property of the extracts was evaluated against Gram-positive and Gram-negative bacteria by agar well diffusion method and antidiabetic activity was investigated by α-amylase inhibition assay. The toxicity of plant extracts was assessed by brine shrimp lethality test. All the nine different plant extracts showed antibacterial activity with the zone of inhibition (ZOI) ranging from 5 to 35 mm. Among the studied plant extracts, Psidium guajava showed the highest ZOI against Salmonella typhi (35 mm) while Melia azedarach was most effective against Staphylococcus aureus (22 mm). The percentage of α- amylase enzyme inhibition was found up to 88.56 ± 3.50 in dose dependent manner. The enzyme inhibitory concentration IC50 value of standard, acarbose was 166.01 μg/mL while the most effective anti-diabetic plant extract of Utrica dioica has 186.67 μg/mL. Moreover, various plant extracts depicted various levels of toxic activities; Curcuma longa demonstrated significant toxicity to Artemia salina with LC50 value 62.10 μg/mL, while Agerantina adenophora, P. guajava and M. azedarach showed moderate toxicity with 103.77, 109.37 and 383.58 μg/mL, respectively.
Geometric Deep Learning for Volumetric Computational Fluid Dynamics

This master thesis explores ways to apply geometric deep learning to the field of numerical simulations with an emphasis on the Navier-Stokes equations. With the recent success of Deep Learning, there should be room for experimentation also in the field of fluid simulations. Here we lead such an experiment. We propose an end-to-end differentiable architecture that allow object-to-mesh predictions of fluid simulations. We provide a comparison with a baseline and visual results on three different datasets: airfoils, backward facing steps and winged drones.
EVALUATION OF THE STUDIES ON TECHNOLOGY LEADERSHIP: A CONTENT ANALYSIS

Abstract A technology leader is the person who establishes the relationship between technology and leadership by trying to reconcile human and information technology components as the most important in this process by taking an active role in executing the technology. This study aims to determine the current trends in the published articles on technology leadership in order to provide a review on these studies. Data of the study were obtained from ERIC database by searching “technology leadership” and a total number of 53 articles were obtained. The obtained data were examined based on content analysis criteria including year of publication, country, research method, subject, sample, data collection tool and data analysis method. Data were analyzed and provided with frequency and percentages and shown with tables and figures. Results are discussed with relevant literature and recommendations for further research and practices are provided. Keywords: technology leadership, technology, content analysis
Exogenous application of gibberellic acid mitigates drought-induced damage in spring wheat

Drought stress is a major problem in wheat production but it could be managed by using various exogenous protectants such as gibberellic acid (GA). Although GA is a plant growth hormone, it shows a potential to protect the plant in stress conditions. To investigate the possible role of GA in mitigating drought stress, we treated wheat (Triticum aestivum ‘BARI Gom-21’) seedlings with a GA spray under semihydroponic conditions. In the experiment, the combined effect of GA and drought stress (induced by 12% polyethylene glycol) was studied after 48 h and 72 h. In the absence of exogenous GA, drought-stressed wheat seedlings showed various physiological and biochemical changes in a time-dependent manner. Malondialdehyde (MDA), hydrogen peroxide (H2O2) and free proline (Pro) concentrations were increased, whereas catalase (CAT) and ascorbate peroxidase (APX) activities were reduced under drought stress. Gibberellic acid played a role in restoring the ascorbate (AsA) level, decreased the reduced/oxidized glutathione (GSH/GSSG) ratio and reduced monodehydroascorbate reductase (MDHAR) and dehydroascorbate reductase (DHAR) activities. Gibberellic acid significantly affected the glyoxalase system. Under drought stress, the methylglyoxal (MG) concentration was increased but GA application stimulated glyoxalase I (Gly I) and glyoxalase II (Gly II) activities to protect the wheat seedlings against stress. The study concluded that the severity of drought stress in wheat depends on the growth stage and it increases with an increase in the duration of stress, whereas exogenous GA helped the seedlings to survive by upregulating antioxidant defense mechanisms and the glyoxalase system.
Similarities between acquired dyslexia, developmental dyslexia and early reading

This thesis compared responses to onsets and offsets of stimuli (visual, auditory, phonemic and orthographic) in children with developmental dyslexia (DDs), adults with acquired dyslexia (ADs) and early readers (ERs). Similarities were observed between the reading characteristics of ERs who demonstrated an incomplete mapping between letters and sounds and a group of ADs. Cluster analysis identified children of 'low', 'intermediate' (IRs) and 'high' alphabetic ability in two samples of early readers (4 to 6-year-olds). IRs showed an alphabetic profile similar to those expected of Ehri's (1995) partial alphabetic phase. There was a lack of discrimination between auditory onset and offset in IRs, DDs and ADs that suggested a basis for problems in establishing reliable phonological representations. DDs showed deficits in sensitivity to the first of contiguous trios of tones and of phonemes, and to the last in a series of three lights, compared to chronological age controls (CAs). DDs were similar to RAs (reading age controls) on most tasks. ADs were slower and less accurate in temporal processing tasks than their CAs but showed similarities to their RAs. This supports the notion of a cognitive delay in developmental dyslexia and a regression to an immature cognitive state in acquired dyslexia. Cluster analysis of ERs, ADs and DDs on alphabetic measures demonstrated that, whereas many of the DDs (10/22) were able to progress to a more advanced phase of reading, most (8/9) ADs clustered with the IRs, suggesting that many ADs may be trapped within a cognitive state comparable to Ehri's partial alphabetic phase. A cognitive model of both acquired and developmental dyslexia is proposed whereby, despite different biological causes for the two conditions, a failure to fully complete alphabetic links due to the dominance of the whole-word level of representation may form the basis of both dyslexias.
Intra–group Work Patterns in Final Assembly of Motor Vehicles

Discusses experiences from the design of parallellized flow assembly systems. Also discusses factors influencing the performance of collective work and relates these to the flow parameters in the Saab Automobile′s final assembly plant in Malmo and the Volvo Uddevalla final assembly plant. Illustrates this discussion by empirical data from the shopfloor of the Volvo Uddevalla plant. Focuses mainly on the evolution of the intra‐group work pattern from the start in the training workshop to the final intra‐group work pattern and the assembly performance achieved before the closing down of the plant. Reports on management′s initial design assumptions communicated during the design of the Uddevalla plant. Comments on and revises these design assumptions, since they were far too simple and lacked the support of theoretical analysis and empirical evidence. The lack of a scientific explanation for the production and work organization principles communicated within Volvo might be one of the reasons for the closing .
A two-dimensional MoS2/C3N broken-gap heterostructure, a first principles study

van der Waals (vdW) heterojunctions are of interest in two-dimensional electronic and optoelectronic devices. In this work, first-principles calculations were used to study the atomic and electronic properties of the MoS2/C3N vdW heterojunction. The results show that there is no overlap of the band gaps for the MoS2 and C3N monolayers in the heterojunction, indicating the MoS2/C3N vdW heterostructure has a type III alignment. The MoS2/C3N vdW heterostructure is a broken-gap heterojunction. The effects of biaxial strain and external electric field on the band structure of the vdW heterostructure were also investigated. The alignment type cannot be changed, but the band overlap can be tuned. The present work reveals that the MoS2/C3N heterostructures are quite favorable for applications in tunneling devices based on the broken-gap heterostructures.
Factor analysis of dynamic PET images

Thanks to its ability to evaluate metabolic functions in tissues from the temporal evolution of a previously injected radiotracer, dynamic positron emission tomography (PET) has become an ubiquitous analysis tool to quantify biological processes. Several quantification techniques from the PET imaging literature require a previous estimation of global time-activity curves (TACs) (herein called \textit{factors}) representing the concentration of tracer in a reference tissue or blood over time. To this end, factor analysis has often appeared as an unsupervised learning solution for the extraction of factors and their respective fractions in each voxel. Inspired by the hyperspectral unmixing literature, this manuscript addresses two main drawbacks of general factor analysis techniques applied to dynamic PET. The first one is the assumption that the elementary response of each tissue to tracer distribution is spatially homogeneous. Even though this homogeneity assumption has proven its effectiveness in several factor analysis studies, it may not always provide a sufficient description of the underlying data, in particular when abnormalities are present. To tackle this limitation, the models herein proposed introduce an additional degree of freedom to the factors related to specific binding. To this end, a spatially-variant perturbation affects a nominal and common TAC representative of the high-uptake tissue. This variation is spatially indexed and constrained with a dictionary that is either previously learned or explicitly modelled with convolutional nonlinearities affecting non-specific binding tissues. The second drawback is related to the noise distribution in PET images. Even though the positron decay process can be described by a Poisson distribution, the actual noise in reconstructed PET images is not expected to be simply described by Poisson or Gaussian distributions. Therefore, we propose to consider a popular and quite general loss function, called the $\beta$-divergence, that is able to generalize conventional loss functions such as the least-square distance, Kullback-Leibler and Itakura-Saito divergences, respectively corresponding to Gaussian, Poisson and Gamma distributions. This loss function is applied to three factor analysis models in order to evaluate its impact on dynamic PET images with different reconstruction characteristics.
Structuring Reference Architectures for the Industrial Internet of Things

The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.
QUANTIFYING AND PREDICTING USER REPUTATION IN A NETWORK SECURITY CONTEXT

Title of Dissertation: QUANTIFYING AND PREDICTING USER REPUTATION IN A NETWORK SECURITY CONTEXT Margaret Stephanie Gratian, Doctor of Philosophy, 2019 Dissertation directed by: Associate Professor Michel Cukier, Department of Mechanical Engineering Reputation has long been an important factor for establishing trust and evaluating the character of others. Though subjective by definition, it recently emerged in the field of cybersecurity as a metric to quantify and predict the nature of domain names, IP addresses, files, and more. Implicit in the use of reputation to enhance cybersecurity is the assumption that past behaviors and opinions of others provides insight into the expected future behavior of an entity, which can be used to proactively identify potential threats to cybersecurity. Despite the plethora of work in industry and academia on reputation in cyberspace, proposed methods are often presented as black boxes and lack scientific rigor, reproducibility, and validation. Moreover, despite widespread recognition that cybersecurity solutions must consider the human user, there is limited work focusing on user reputation in a security context. This dissertation presents a mathematical interpretation of user cyber reputation and a methodology for evaluating reputation in a network security context. A user’s cyber reputation is defined as the most likely probability the user demonstrates a specific characteristic on the network, based on evidence. The methodology for evaluating user reputation is presented in three phases: characteristic definition and evidence collection; reputation quantification and prediction; and reputation model validation and refinement. The methodology is illustrated through a case study on a large university network, where network traffic data is used as evidence to determine the likelihood a user becomes infected or remains uninfected on the network. A separate case study explores social media as an alternate source of data for evaluating user reputation. User-reported account compromise data is collected from Twitter and used to predict if a user will self-report compromise. This case study uncovers user cybersecurity experiences and victimization trends and emphasizes the feasibility of using social media to enhance understandings of users from a security perspective. Overall, this dissertation presents an exploration into the complicated space of cyber identity. As new threats to security, user privacy, and information integrity continue to manifest, the need for reputation systems and techniques to evaluate and validate online identities will continue to grow. QUANTIFYING AND PREDICTING USER REPUTATION IN A NETWORK SECURITY CONTEXT
High‐Precision Measurement of Stable Cr Isotopes in Geological Reference Materials by a Double‐Spike TIMS Method

Chromium (Cr) isotopes have been widely used in various fields of Earth and planetary sciences. However, high‐precision measurements of Cr stable isotope ratios are still challenged by difficulties in purifying Cr and organic matter interference from resin using double‐spike thermal ionisation mass spectrometry. In this study, an improved and easily operated two‐column chemical separation procedure using AG50W‐X12 (200–400 mesh) resin is introduced. This resin has a higher cross‐linking density than AG50W‐X8, and this higher density generates better separation efficiency and higher saturation. Organic matter from the resin is a common cause of inhibition of the emission of Cr during analysis by TIMS. Here, perchloric and nitric acids were utilised to eliminate organic matter interference. The Cr isotope ratios of samples with lower Cr contents could be measured precisely by TIMS. The long‐term intermediate measurement precision of δ53/52CrNIST SRM 979 for BHVO‐2 is better than ± 0.031‰ (2s) over one year. Replicated digestions and measurements of geological reference materials (OKUM, MUH‐1, JP‐1, BHVO‐1, BHVO‐2, AGV‐2 and GSP‐2) yield δ53/52CrNIST SRM 979 results ranging from −0.129‰ to −0.032‰. The Cr isotope ratios of geological reference materials are consistent with the δ53/52CrNIST SRM 979 values reported by previous studies, and the measurement uncertainty (± 0.031‰, 2s) is significantly improved.
Lightweight Security Protocols for Internet of Things: A Review

Smart devices and IoT applications have been widely used in many fields of social living, social production, home and industrial automation which have made people’s life efficient and convenient. But in recent years, ubiquitous deployment of some tools and technologies has raised several concerns related to the privacy and security in IoT enabled smart environment. The present paper presents a range of various existing as well as proposed lightweight security protocols. Security protocols are designed to make secure communication within IoT enabled environment with less computation and storage cost. Lightweight protocols are characterized by relatively small overhead. The paper also presents a comparative study of various lightweight security protocols for IoT, proposed by many researchers in recent years. Lightweight cryptography algorithms used for designing lightweight security protocols have also been explored in present paper.
MODFLOW’s River Package: Part 2: Correction, Combining Analytical and Numerical Approaches

Most widely used integrated hydrologic models still describe the flow interaction between streams and aquifers using primitive early concepts. In the previous article the shortcomings of the methodology were shown in great details. In this second part means are presented by which improvements can be introduced into the procedures. Accuracy and numerical efficiency will be improved. The article describes in details the proposed alternatives for both the saturated and the unsaturated connections. In the article reference is made specifically to the code MODFLOW. Most of the other integrated hydrologic models used for large-scale regional studies apply essentially the same methodology to estimate seepage.
Robot following ahead of the leader and learning human-relevant navigational cues

As robots become more involved in our everyday lives, we may find it useful to design new methods to interact with them. In this thesis, we design two applications to facilitate human-robot interaction. The first application is an autonomous mobile robot that follows a walking user while staying ahead of them. Despite several useful applications for autonomous push-carts, this problem has received much less attention than the easier problem of following from behind. In contrast to previous work, we use multi-modal person detection and a human-motion model that considers obstacles to predict the future path of the user. We implement the system with a modular architecture of an obstacle mapper, a human tracker, a human motion model, a robot motion planner and a robot motion controller. We report on the performance of the robot in real world experiments. We believe that approaches to this largely overlooked problem could be useful in real industrial, domestic and entertainment applications in the near future. The second application is a novel system for describing the navigational cues ahead of the robot. We train a Convolutional Neural Network (CNN) architecture on 2D LiDAR data and occupancy grid maps to detect navigational objects during robot movement through an indoor environment. These navigational objects include closed-rooms (room with closed doors), open-rooms (room with open doors) and intersections. On top of this network, our system uses a tracking module that improves the detection accuracy of the system by clustering and recording each detection of the model. This tracking module also enables us to describe the robot navigational cues. We evaluate the system in both simulation and the real world. We compare the combination of 2D LiDAR data and occupancy grid maps and using each of them alone.