Miracle-dz/UniFAR-FTUs huggingface dataset:
Stopping the Insider at the Gates: Protecting Organizational Assets through Graph Mining

The increasing threat of insider attacks has resulted in a correlated increase in incentives to monitor trusted insiders. Measures of volumes of access, detailed background checks, and statistical characterizations of employee behaviors are all commonly used to mitigate the insider threat. These traditional approaches usually rely on supervised learning models or case studies to determine the critical features or attributes that can be used as indicators. Such approaches require labeled data for correct characterization of the threat. Yet regardless of the incentives to detect the insider threat, the incentives to share detailed labeled data on successful malicious insiders have proven inadequate. To address this challenging data environment, we developed an innovative approach that captures the temporal evolution of user-system interactions, to create an unsupervised learning framework to detect high-risk insider behaviors. Our method is based on the analysis of a bipartite graph of user and system interactions. The graph mining method detects increases in potential insider threat events following precipitating events, e.g., a limited restructuring. We apply our method to a dataset that comprises interactions between engineers and components in a software version control system spanning 22 years, and automatically detect statistically significant events. We find that there is statistically significant evidence for increasing anomalies in the committing behavior after precipitating events. Although these findings do not constitute detection of insider threat events per se, they reinforce the idea that insider operations can be motivated by the insiders’ environment and detected with the proposed method. We compare our results with algorithms based on volume-dependent statistics showing that our proposed framework outperforms those measures. This graph mining method has potential for early detection of insider threat behavior from user-system interactions, which could enable quicker mitigation.
Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease

Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists.
Multiple hypotheses at multiple scales for audio novelty computation within music

Novelty-based segmentation of audio signals has proven good performances for the estimation of boundaries of structural sections within music pieces. However, boundaries are detected only if structural sections satisfy the condition of sufficient acoustic inner-homogeneity. While this constraint is very restrictive and not representative of all musical contents, we propose in this paper to extend the detection of acoustic novelty to transitions between homogeneous and non-homogeneous sections and vice versa. Moreover, the length of the considered sections for the boundary detection is crucial, we also introduce a multi-scale novelty approach that allows to capture boundaries between sections of different temporal scales in a same segmentation. Evaluation of the combination of these two methods proves convincing results for temporal segmentation of music pieces. Embedding the algorithm in a music structure segmentation system, we show that performances can be consistently improved for this task.
Automatic vessel segmentation in wide-field retina images of infants with Retinopathy of Prematurity

The earliest signs of Retinopathy of Prematurity (ROP) are tortuosity and dilation of retinal vessels. Such vascular changes are considered of primary importance for the diagnosis and the follow-up of the disease. However, a widely accepted computerized system for their quantitative measurement is still missing. Images taken from a preterm baby's eye are often low-contrast, noisy, and blurred. Algorithms that have been successfully applied to analyze adult retinal images do not work well in ROP images. We propose here a novel method for the automatic extraction of vessel centerline in wide-field ROP retinal images, based on a sparse tracking scheme. After a set of seed points is identified all over the image, vessels are traced by connecting those seeds by means of minimum cost paths, whose weights depend on similarity features and alignment evaluated by a custom line operator. The performance of the method was assessed on a dataset of 20 images acquired with the RetCam fundus camera. A sensitivity of 0.78 and a false detection rate of 0.15 were obtained with respect to manual ground truth reference.
Study on the Evaluation Method of Gait Motion Including the Implicit Knowledge

The analysis of an implicit knowledge of medical doctors and physical therapists are important to evaluate a condition of patients in a quantitative way, however it is difficult to analyze it. In this paper, a part of an implicit knowledge of a walk is considered using the relation between five characteristcs obtained by data using high speed movie cameras and questionnaires obtained by the physical therapists. First, the data are classified using the Self-Organizing Map. Second, the relation between the five characteristic score that we have proposed to evaluate the gait motion using high speed movie cameras and questionnaire score obtained by the ptyshical therapists are discussed using the classificated map. As a result, it is clear that a part of the data is different, and that the motion of the upper part of the body in a walk and the timing of flection of the knee are focused on.
Pulse Shapes in High Purity Germanium Point Contact Detectors with Low Net Impurity Concentration

High Purity germanium point-contact detectors have low energy thresholds and excellent energy resolution over a wide energy range, and are thus widely used in nuclear and particle physics. In rare event searches, such as neutrinoless double beta decay, the point-contact geometry is of particular importance since it allows for pulse-shape discrimination, and therefore for a significant background reduction. In this paper we investigate the pulse-shape discrimination performance of ultra-high purity germanium point contact detectors. It is demonstrated that a minimal net impurity concentration is required to meet the pulse-shape performance requirements
Deep learning network for fully automatic left ventricle segmentation

The segmentation of left ventricle (LV) is an essential step for evaluation of LV structure and function, which is very important for some heart disease diagnosis. This study presents a new approach to segment LV endocardium and epicardium in all slices for two specific phase in cardiac cycle based on deep learning. The proposed method contains two step: (1) localization of region of interest (ROI) within whole image and removing unrelated areas to LV, (2) segmentation and delineation of LV based on the ROI extracted in previous step. Each step of the framework employ an isolated convolutional neural network (CNN). The experimental results demonstrated that the proposed network is capable of high precision LV segmentation. The model achieved 0.90 average dice measure for endocardium and 0.91 average dice measure for epicardium in middle slices.
A genetic approach to architectural pattern discovery

Architectural patterns represent reusable design of software architecture at a high level of abstraction. They can be used to structure new applications and to recover the modular structure of existing systems. Techniques like Architecture Compliance Checking (ACC) focus on testing whether realised artefacts adhere to the architecture. Typically, these techniques require a complete architecture as input. In this paper, we present a genetic approach to express and discover architectural patterns based on the allowed and disallowed dependencies between the pattern elements. Through static ACC, we validate the genuineness of the found instances. Initial validation shows the potential of the approach.
ISSUE The ice age ecologist: testing methods for reserve prioritization during the last global warming

Aim We play the role of an ice age ecologist (IAE) charged with conserving biodiversity during the climate changes accompanying the last deglaciation. We develop reserve-selection strategies for the IAE and check them against rankings based on modern data. Location Northern and eastern North America. Methods Three reserve-selection strategies are developed. (1) Abiotic: the IAE uses no information about species–climate relationships, instead maximizing the climatic and geographic dispersion of reserves. (2) Species distribution models (SDMs): the IAE uses boosted-regression trees calibrated against pollen data and CCSM3 palaeoclimatic simulations from 21 to 15 ka bp to predict modern taxon distributions, then uses these as input to the Zonation reserve-ranking program. (3) Rank-and-regress: regression models are used to identify climatic predictors of zonation rankings. All strategies are assessed against a Zonation ranking based on modern pollen distributions. Analysis units are ecoregions and grid cells. Results The abiotic strategy has a negative or no correlation between predicted and actual rankings. The SDM-based strategy fares better, with a significantly positive area-corrected correlation (r = 0.474, P < 0.001) between predicted and actual rankings. Predictive ability drops when grid cells are the analysis unit (r = 0.217, P = 0.058). Predictive ability for the rank-and-regress strategy is similar to the SDM results. Main conclusions For the IAE, SDMs improve the predictive ability of reserveselection strategies. However, predictive ability is limited overall, probably due to shifted realized niches during past no-analogue climates, new species interactions as species responded individually to climate change, and other environmental changes not included in the model. Twenty-first-century conservation planning also faces these challenges, and is further complicated by other anthropogenic impacts. The IAE’s limited success does not preclude the use of climate scenarios and niche-based SDMs when developing adaptation strategies, but suggests that such tools offer at best only a rough guide to identifying possible areas of future conservation value.
Self-referencing and Sensitivity Optimization in Photonic Crystal Slabs for Biosensing Applications

Self-Referencing and Sensitivity Optimization in Photonic Crystal Slabs for Biosensing Applications Ryan Schilling Supervisor: Dr. Ofer Levi Master of Applied Science Graduate Department of Edward S. Rogers Sr. Department of Electrical and Computer Engineering Institute of Biomaterials and Biomedical Engineering University of Toronto 2013 Photonic crystal slabs (PCS) are explored in the context of optofluidic refractive index (RI) sensing for portable, label-free, biosensing applications. The accuracy of RI sensors is limited by noise signals that cause a change in RI that cannot be differentiated from the signal of interest. For this reason self-referencing schemes that provide rejection of common mode signals, and an inherent temperature stabilization approach, are explored. A novel referencing method that allows for frequency shifts to be read out in the transmission power spectrum is proposed and characterized. In terms of improving sensing metrics the relevant characteristics of various PCS architectures are explored numerically. In addition, a novel suspended air-substrate device that offers greatly improved sensitivity is proposed and characterized. An experimental measurement near the theoretical detection limit for a PCS is demonstrated. In understanding measurement errors the crossed-polarization effect and its practical limitations are explored numerically.
Application of the Time Modulated Array in Satellite Communications

As a research hotspot in antenna arrays, the time modulated array (TMA) has advantages in pattern synthesis, direction finding, adaptive beamforming, and multi-beams, making it a potential application in satellite communications. This article illustrates how to utilize the TMA in satellite communications, to improve the performance of communication between the satellite and Earth. First, the overview of the TMA is provided. Second, direction finding by the TMA on the satellite is analyzed, which helps to detect and track users on Earth. Third, adaptive beamforming based on the TMA is discussed, which synthesizes the pattern of the generated harmonic component to point to mobile users. Fourth, the multibeam characteristic of the TMA is analyzed to simplify the complexity of a multi-beam system on the satellite. Finally, some conclusions are drawn, and areas of further research are put forward.
Improved Neighborhood Search for Collaborative Filtering

k -Nearest Neighbor ( k -NN) and other user-based collaborative ﬁltering (CF) algorithms have gained popularity because of the simplicity of their algorithms and performance. As the performance of such algorithms largely depends on neighborhood selection, it is important to select the most suitable neighborhood for each active user. Previous user-based CF simply relies on similar users or common experts in this regard; however, because users have different tastes as well as different expectations for expert advice, similar users or common experts may not always be the best neighborhood for CF. In search of a more suitable neighborhood, so-called personalized experts develop personalized expert features. Through experimentation, we show that personalized experts are different from similar users, common experts, or similar common experts. The personalized, expert-based CF algorithm outperforms k -NN and other user-based CF algorithms.
BEST1 protein stability and degradation pathways differ between autosomal dominant Best disease and autosomal recessive bestrophinopathy accounting for the distinct retinal phenotypes

Abstract Mutations in bestrophin-1 (BEST1) are associated with distinct retinopathies, notably three forms with autosomal dominant inheritance and one condition with an autosomal recessive mode of transmission. The molecular mechanisms underlying their distinct retinal phenotypes are mostly unknown. Although heterozygous missense mutations in BEST1 reveal dominant-negative effects in patients with autosomal dominant Best disease (BD), heterozygous mutations associated with autosomal recessive bestrophinopathy (ARB) display no disease phenotype. Here we show that the recessive mutations trigger a strong and fast protein degradation process in the endoplasmic reticulum (ER), thereby favoring a decreased stoichiometry of mutant versus normal BEST1 subunits in the assembly of the homo-pentameric BEST1 chloride channel. In contrast, dominant mutations escape ER-associated degradation and are subjected to a slightly delayed post-ER degradation via the endo-lysosomal degradation pathway. As a result, increased formation of a non-functional BEST1 channel occurs due to a roughly equimolar incorporation of normal and mutant BEST1 subunits into the channel complex. Taken together, our data provide insight into the molecular pathways of dominantly and recessively acting BEST1 missense mutations suggesting that the site of subcellular protein quality control as well as the rate and degree of mutant protein degradation are ultimately responsible for the distinct retinal disease phenotypes in BD and ARB.
Accelerating critical OS services in virtualized systems with flexible micro-sliced cores

Consolidating multiple virtual machines into a single server has been widely adopted in cloud computing to improve system utilization. However, the sharing of physical CPUs among virtual machines in consolidated systems poses a new challenge in providing an illusion of continuous CPU execution to the guest operating systems (OS). Due to the time-sharing of physical CPUs, the execution of a guest OS is periodically interrupted, while the guest OS may not be aware of the discontinuity of virtual time against the real time. The virtual time discontinuity problem causes the delayed processing of critical OS operations, such as interrupt handling and lock processing. Although there have been several prior studies to mitigate the problem, they address only a subset of symptoms, require the modification of guest OSes, or change the processor architecture. This paper proposes a novel way to comprehensively reduce the inefficiency of guest OS execution in consolidated systems. It migrates the short-lived critical OS tasks to dedicated micro-sliced cores, minimizing the delays caused by time sharing. The hypervisor identifies the critical OS tasks without any OS intervention, and schedules the critical code sections onto the dynamically partitioned cores at runtime. The dedicated micro-sliced cores employ a short sub-millisecond quantum to minimize the response latencies for consolidated virtual machines. By readily servicing the critical tasks, the proposed scheme can minimize the adverse artifact of virtual machine consolidation without any modification of guest OSes.
Evaluation of a modified 16-item Readiness for Interprofessional Learning Scale (RIPLS): Exploratory and confirmatory factor analyses

ABSTRACT The Readiness for Interprofessional Learning Scale (RIPLS) was developed to assess undergraduate readiness for engaging in interprofessional education (IPE). It has become an accepted and commonly used instrument. To determine utility of a modified 16-item RIPLS instrument, exploratory and confirmatory factor analyses were performed. Data used were collected from a pre- and post-intervention study involving 360 New Zealand undergraduate students from one university. Just over half of the participants were enrolled in medicine (51%) while the remainder were in pharmacy (27%) and nursing (22%). The intervention was a two-day simulation-based IPE course focused on managing unplanned acute medical problems in hospital wards (“ward calls”). Immediately prior to the course, 288 RIPLS were collected and immediately afterwards, 322 (response rates 80% and 89%, respectively). Exploratory factor analysis involving principal axis factoring with an oblique rotation method was conducted using pre-course data. The scree plot suggested a three-factor solution over two- and four-factor solutions. Subsequent confirmatory factor analysis performed using post-course data demonstrated partial goodness-of-fit for this suggested three-factor model. Based on these findings, further robust psychometric testing of the RIPLS or modified versions of it is recommended before embarking on its use in evaluative research in various healthcare education settings.
Automatic detection of CAP on central and fronto-central EEG leads via support vector machines

The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82%, specificity equal to 85.93%, accuracy equal to 84,05% and Cohen's kappa equal to 0.50.
Development of sugar palm yarn/glass fibre reinforced unsaturated polyester hybrid composites

This study investigates the effect of fibre hybridization for sugar palm yarn fibre with glass fibre reinforced with unsaturated polyester composites. In this work, unsaturated polyester resin are reinforced with fibre at a ratio of 70:30 wt% and 60:40 wt%. The hybrid composites were characterized in terms of physical (density and water absorption), mechanical (tensile, flexural and compression) and thermal properties through thermal gravimetry analysis (TGA). Density determination showed that density increased with higher wt% of glass fibre. The inherently higher density of glass fibre increased the density of hybrid composite. Resistance to water absorption is improved upon the incorporation of glass fibre and the hybrid composites were found to reach equilibrium absorption at days 4 and 5. As for mechanical performance, the highest tensile strength, tensile modulus, flexural strength, flexural modulus and compression strength were obtained from 40 wt% of fibres reinforcement with ratio of 50:50 wt% of sugar palm yarn fibre and glass fibre reinforced unsaturated polyester composites. The increase of glass fibre loading had a synergistic effect on the mechanical properties to the composites structure due to its superior strength and modulus. The thermal stability of hybrid composites was improved by the increase of onset temperature and the reduction of residues upon increase in temperature.
Structural Aspects of User Roles in Information Cascades

Social media plays an important role for the exchange and dissemination of information among its users. In turn, online users shape social media by their interactions, status and online behaviour in general. These aspects differ massively from user to user, which has an impact on the outcome of information diffusion. Users on social media have been categorised to user roles according to their online behaviour. While there has been a lot of research on user roles and information diffusion in isolation, their combination has not been researched much. In this paper, we study their correlation in particular whether particular user roles occur in specific structural positions in information cascades. By testing several hypotheses we could confirm that there is indeed a correlation of these two aspects. However, some user roles demonstrate diverse behaviour with regard to their activity patterns and need further investigation.