Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyber Insider Threats Situation Awareness Using Game Theory and Information Fusion-based User Behavior Predicting Algorithm

Cyber insider threat is a difficult problem because it is always covered by a legal identity. Researchers have proposed many methods to deal with this kind of problem which are model-based, graph-based and access control-based algorithms. However, many of these methods are dependent upon traditional IDS which are impacted by false positive rate and not suitable for insider problem any more. Some other game-based methods are dependent on assumption that insiders’ decisions are optimal and rational. Nevertheless, this kind of algorithm can not handle some irrational insider’s behavior and determine when a round of interaction starts or ends for system defender. In this paper, we proposed our algorithm for insider threat situation awareness, which is based on game theory and information fusion. We use dynamic Bayesian network (DBN) structure and exact inference to acquire and fuse different type of insider information for behavior analysis and avoid traditional IDS shortcoming, finally we obtain situation awareness or prediction trend of insider’s future actions by quantal response equilibrium (QRE) calculation. Simulation experiment results indicate that our algorithm has better convergence and precision than other same algorithm even though we should pay additional but accepted computation cost.
QoE Issues of OTT Services over 5G Network

Currently , 4G network becomes commercial in large scale around the world and the industry has started the fifth-generation mobile communication technology (5G) research. Compared to 4G network, 5G network will support larger mobility as well as higher transmission rate, higher user experience rate, energy efficiency, spectrum efficiency and so forth. All of these will boost a variety of multimedia services, especially for Over-The-Top (OTT) services. So for, OTT services have already gained great popularity and contributed to large traffic consumption, which propose a challenge for operators. As OTT services are designed to deliver over the best effort Internet, the QoE management solutions for traditional multimedia services are obsolete, which propose new challenges in QOE management aspects for network and service providers, especially for the 4G and future 5G network. This paper attempts to present the technical challenges faced by 5G network from QoE management perspective of OTT services. Our objective is to enhance the user experience of OTT services and improve network efficiency. We analysis the characteristics and QoE factors of OTT services over 5G wireless network. With the QoE factors and current QoE management situation, we summarize OTT services QoE quantification and evaluation methods, present QoE-driven radio resource management and optimization solutions. Then, we propose a framework and whole evaluation procedure which aim at obtaining the accurate user experience value as well as improving network efficiency and optimizing the user
Modelling and performance analysis of component-based systems

Assessment of dependability of component-based systems must include verification of temporal and performance requirements as they can be of primary importance for many real-time and embedded systems. This paper uses labelled timed Petri nets as models of the behaviour of components at their interfaces. These component models are systematically composed into an integrated model of the system which is used for verification of temporal characteristics and performance analysis.
Partial iris image recognition using wavelet based texture features

Now a day's security is the key issue all over in the world and iris biometric is shown to be the most authentic and accurate for human identification. In this paper, an effective partial iris image recognition is described based on wavelet texture features where capturing the entire iris may not be feasible. Daubechies 4-level Db8 wavelet transform is applied to two small portions namely left and right sub-images of the irises to extract distinctive iris features which is comprised of only 510 bits whereas existing approaches extracted as many as 1024 bits to represent the unique feature vector. Hamming distance similarity measurement technique was employed for recognition. The experimental results show that it is possible to use only significant portion (left and right sub-image) of the irises for human identification.
A Parallel Algorithm with the Search Space Partition for the Pickup and Delivery with Time Windows

The pickup and delivery problem with time windows (PDPTW) is an NP-hard optimization problem of serving transportation requests using a limited number of vehicles. Its main objective is to minimize the number of delivering trucks, whereas the secondary objective is to decrease the distance traveled during the service. A feasible routing schedule must satisfy the time window, capacity and precedence constraints. In this paper, we propose to partition the search space in our parallel guided ejection search algorithm (P-GES) to minimize the fleet size in the PDPTW. The introduced techniques help decrease the convergence time of the algorithm without affecting the quality of results. An extensive experimental study (comprising nearly 52,000 CPU hours on an SMP cluster) performed on the Li and Lim's benchmark set shows that the parallel algorithm is effective, and is able to retrieve very high-quality results. We report 10 new world's best solutions obtained using P-GES enhanced with the proposed search space partition approaches.
Neural substrates of subphonemic variation and lexical competition in spoken word recognition

ABSTRACT In spoken word recognition, subphonemic variation influences lexical activation, with sounds near a category boundary increasing phonetic competition as well as lexical competition. The current study investigated the interplay of these factors using a visual world task in which participants were instructed to look at a picture of an auditory target (e.g. peacock). Eyetracking data indicated that participants were slowed when a voiced onset competitor (e.g. beaker) was also displayed, and this effect was amplified when acoustic-phonetic competition was increased. Simultaneously-collected fMRI data showed that several brain regions were sensitive to the presence of the onset competitor, including the supramarginal, middle temporal, and inferior frontal gyri, and functional connectivity analyses revealed that the coordinated activity of left frontal regions depends on both acoustic-phonetic and lexical factors. Taken together, results suggest a role for frontal brain structures in resolving lexical competition, particularly as atypical acoustic-phonetic information maps on to the lexicon.
BASED ON FAST SPARSE RECOVERY TECHNIQUES

In the field of space-time adaptive processing (STAP), spare recovery type STAP (SR-STAP) algorithms exploit formulation of the clutter estimation problem in terms of sparse representation of a small number of clutter positions among a much larger number of potential positions in the angle-Doppler plane, and provide an effective approach to suppress the clutter especially in very short snapshots. However, it differs from many situations encountered by other SR application fields in the following ways: (i) it does not require to obtain the exact solution; (ii) it highly requires low-complexity approaches. In this paper, we focus on the performance analysis and parameters setting of STAP algorithms based on five representative fast SR techniques, namely, the compressive sampling matching pursuit, the sparse reconstruction by separable approximation, the fast iterative shrinkage-thresholding algorithm, the focal underdetermined system solution and the smoothed l0 norm method.
Microsimulating Automobile Markets: Evolution of Vehicle Holdings and Vehicle-Pricing Dynamics

Vehicle ownership decisions are central to estimates of emissions, gas-tax revenues, energy security, pavement management, and other concerns. This work combines an auction-style microsimulation of vehicle prices and random-utility-maximizing choices in order to produce a market model for the evolution of new and used personal-vehicle fleets. All available vehicles compete directly, with demand, supply and price signals endogenous to the model. The framework is described, analyzed and implemented to show its capabilities in predicting outcomes of varying inputs. Application of the model system using Austin, Texas survey data (for behavioral parameters and a synthetic population) over a 20-year period highlight the model’s flexibility and reasonable response to multiple inputs, as well as potential implementation issues.
Rendering of large-scale 3D terrain point cloud based on out-of-core

In this paper, we provide a new method for rendering of large-scale 3D terrain point cloud based on bounding volume hierarchy (BVH), level of detail (LOD) and out-of-core algorithm. First, large-scale 3D terrain point clouds are partitioned into several same size small blocks according to its size. Then small blocks are reconstructed to mesh models and added texture mappings. Thirdly, we use BVH to organize scene data and create two levels to handle node loading and computer graphics rendering separately. Fourthly, we use view-dependent out-of-core method to determine which part of meshes should be rendering or deleted from the memory. In the last of the paper, we use terrain models reconstructed from point clouds to test that our method has higher usage efficiency of computer memory and rendering rates.
A study of the impact of HOG and LBP based temporal association on far infrared pedestrian detection

In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits from a fast pedestrian candidate selection given by a three dimensional filtering of bounding boxes that are on the road, it gathers the accuracy of aggregated channel feature pedestrian detector and it is enhanced with a temporal based reasoning mechanism that allows an accurate identification of real pedestrians in the scene. Within the context of the proposed model we study the effects of different association rules for the detected pedestrians. The evaluation of the proposed algorithm proves its benefits, focused on the reduction of the false positives rate.
Literacy and Digital Problem -solving Skills in the 21st Century: What PIAAC Says about Educators in the United States, Canada, Finland and Japan

ABSTRACT The purpose of our study is to delve into the education gap between the United States and some countries by examining the literacy and digital problem-solving skills of American educators, and comparing their performance with that of their peers from Canada, Finland, and Japan. We use PIAAC data collected by the Organization for Economic Co-operation and Development (OECD), and define educators as professionals with the highest level of qualification in the area of teacher training and education science. Our findings show that, internationally, U.S. educators are at a great disadvantage vis a vis their peers in Finland and Japan in terms of literacy, and they rank the lowest in digital problem-solving skills among the four studied countries. Other key findings include (a) in Canada and Finland, educators perform significantly higher in literacy than non-educators in their respective countries; (b) young educators in the United States (under age 35) lag far behind the same age group in Finland, Japan, and Canada; and (c) in all four countries, digital problem-solving skills of educators tend to decrease as their age increases. All statistical analyses are based on regression using sampling weights.
Model development to predict perceived degree of naturalness

This paper presents the development of a mathematical model to predict the perception of naturalness for a range of materials, based on an understanding of the relationship between the physical attributes of the material and the human sensory inputs. The work is being carried out under an European Union project called 'Measurement of Naturalness' (MONAT), which focuses on understanding the relationships between the physical properties of natural and synthetic materials and the visual and tactile sensory processes that lead to perceptual judgments of naturalness. Integral to the project is the development of novel measurement facilities with dynamic ranges and sensitivities that are relevant for the human sensory systems. The input data to the model are derived from psychophysical and physical studies on pre-selected wood, textile and stone samples. Keyword s: regression, sensory metrology, perception
The application of GPU particle tracing to diffusion tensor field visualization

In this paper we introduce GPU particle tracing for the visualization of 3D diffusion tensor fields. For about half a million particles, reconstruction of diffusion directions from the tensor field, time integration and rendering can be done at interactive rates. Different visualization options like oriented particles of diffusion-dependent shape, stream lines or stream tubes facilitate the use of particle tracing for diffusion tensor visualization. The proposed methods provide efficient and intuitive means to show the dynamics in diffusion tensor fields, and they accommodate the exploration of the diffusion properties of biological tissue.
Exploring the Use of Suspension in High School Codes of Conduct for Mild, Moderate, and Severe Behaviors.

This Dissertation is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in Dissertations by an authorized administrator of Loyola eCommons. For more information, please contact ecommons@luc.edu. iii ACKNOWLEDGEMENTS The completion of this dissertation would not have been possible without the support and guidance of many individuals. I would like to thank my dissertation chair, Dr. Pam Fenning, for your support, time, and encouragement throughout my time at Loyola University. To my committee members, Dr. Lynne Golomb and Dr. Erica Hartman, for your knowledge, feedback, and time during this project. I would not have been able to complete my dissertation without the help and assistance from my wonderful committee. whose support and humor were vital in completing this project and my Ph.D. To my family, who believed in me and supported me throughout this process. Finally, to my closest friends, Shelly Rees and Erica Hartman for their support, love, and friendship during my long seven years in graduate school. To everyone that has a dream and the courage to live life to the fullest.
Incidence of vascular dementia in Rochester, Minn, 1985-1989.

OBJECTIVE To examine the contribution of cerebrovascular disease to dementia. METHODS We used the records-linkage system of the Rochester Epidemiology Project to ascertain incident cases of dementia in Rochester, Minn, for 1985 through 1989. We defined dementia using the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. To define dementia types, we reviewed neuroimaging reports, which were available for two thirds of dementia cases, in addition to medical histories and neurologic examination results. Vascular dementia (VaD) was defined by 1 of the following criteria: dementia onset or worsening within 3 months of a clinical stroke or bilateral gray matter infarctlike lesions shown by imaging that fulfilled specified location criteria (critical imaging lesions). RESULTS We found 482 incident cases of dementia. Overall, 10% of patients had onset or worsening of their dementia within 3 months of a stroke. Eleven percent of the incident dementia cases had bilateral gray matter lesions on imaging that were considered critical. Eighteen percent of patients had one or the other of these features (VaD by our criteria), but only 4% of patients had both. The incidence rate of VaD increased steeply with advancing age and was similar in men and women. Our incidence rates were similar to those from a recent European meta-analysis. CONCLUSION The presence of either a stroke temporally related to dementia onset or worsening or of critical imaging lesions was common among dementia patients, whereas the occurrence of both features together was rare.
Ligand Valency Affects Transcytosis, Recycling and Intracellular Trafficking Mediated by the Neonatal Fc Receptor

The neonatal Fc receptor (FcRn) transports IgG across epithelial cell barriers to provide maternal antibodies to offspring and serves as a protection receptor by rescuing endocytosed IgG and albumin from lysosomal degradation. Here we describe the generation of polarized Madin–Darby canine kidney (MDCK) cells expressing rat FcRn (rFcRn) to investigate the potential requirement for ligand bivalency in FcRn‐mediated transport. The rFcRn‐MDCK cells bind, internalize and bidirectionally transcytose the bivalent ligands IgG and Fc across polarized cell monolayers. However, they cannot be used to study FcRn‐mediated transport of the monovalent ligand albumin, as we observe no specific binding, internalization or transcytosis of rat albumin. To address whether ligand bivalency is required for transport, the ability of rFcRn to transcytose and recycle wild‐type Fc homodimers (wtFc; two FcRn‐binding sites) and a heterodimeric Fc (hdFc; one FcRn‐binding site) was compared. We show that ligand bivalency is not required for transcytosis or recycling, but that wtFc is transported more efficiently than hdFc, particularly at lower concentrations. We also demonstrate that hdFc and wtFc have different intracellular fates, with more hdFc than wtFc being trafficked to lysosomes and degraded, suggesting a role for avidity effects in FcRn‐mediated IgG transport.
Evaluation of El Niño-Southern Oscillation in the ACCESS coupled model simulations for CMIP5

One of the key performance measures for Coupled General Circulation Models (CGCMs) is their ability to realistically simulate the prominent modes of climate variability. Here, we investigate the realism of El Niño–Southern Oscillation (ENSO), the dominant mode of observed interannual climate variability, as simulated by the Australian Community Climate and Earth System Simulator (ACCESS). We examine the key ENSO properties calculated from observations and from the pre-industrial control and historical simulations performed by versions 1.0 and 1.3 of the ACCESS coupled model (ACCESS-CM), as part of the Coupled Model Intercomparison Project phase 5 (CMIP5). The ENSO properties examined are the spatial structure and timescales, phase locking to the annual cycle, growth and decay of Niño3 SST anomalies, and the coupled ENSO evolution, as well as the ENSO induced teleconnection patterns (as diagnosed by linear regressions of the three-dimensional atmospheric circulation field, rainfall, sea-level pressure, and the upper-tropospheric geopotential heights onto the Niño3 index). The coupled evolutions of SST, zonal wind stress, and the thermocline depth during a typical ENSO event are used to illustrate the essential features of ENSO dynamics. Our results show that both versions of the ACCESS-CM perform reasonably well in simulating the ENSO properties and teleconnections. In particular, the spatial structure, timescales, phase locking, and the growth and decay rates of ENSO are well simulated. The evolution of SST, zonal wind stress, and the thermocline depth during ENSO events is also reasonably well simulated, although the SST (zonal wind stress) response is found to be stronger (weaker) than observed. The simulated ENSO teleconnection patterns are also mostly realistic, despite some adverse impact of the equatorial Pacific cold SST bias on the local precipitation response. The present study shows that the ACCESS-CMs simulate the key ENSO properties (e.g. amplitude and spatial structure, timescales, and atmospheric teleconnections) better than most of the previous generations of CGCMs.
Not “Pulling up the Ladder”: Women Who Organize Conference Symposia Provide Greater Opportunities for Women to Speak at Conservation Conferences

The scientific community faces numerous challenges in achieving gender equality among its participants. One method of highlighting the contributions made by female scientists is through their selection as featured speakers in symposia held at the conferences of professional societies. Because they are specially invited, symposia speakers obtain a prestigious platform from which to display their scientific research, which can elevate the recognition of female scientists. We investigated the number of female symposium speakers in two professional societies (the Society of Conservation Biology (SCB) from 1999 to 2015, and the American Society of Ichthyologists and Herpetologists (ASIH) from 2005 to 2015), in relation to the number of female symposium organizers. Overall, we found that 36.4% of symposia organizers and 31.7% of symposia speakers were women at the Society of Conservation Biology conferences, while 19.1% of organizers and 28% of speakers were women at the American Society of Ichthyologists and Herpetologists conferences. For each additional female organizer at the SCB and ASIH conferences, there was an average increase of 95% and 70% female speakers, respectively. As such, we found a significant positive relationship between the number of women organizing a symposium and the number of women speaking in that symposium. We did not, however, find a significant increase in the number of women speakers or organizers per symposium over time at either conference, suggesting a need for revitalized efforts to diversify our scientific societies. To further those ends, we suggest facilitating gender equality in professional societies by removing barriers to participation, including assisting with travel, making conferences child-friendly, and developing thorough, mandatory Codes of Conduct for all conferences.
Programming Many-Cores on Different Levels of Abstraction

Many-core hardware is targeted specifically at obtaining high-performance. However, obtaining high-performance is often challenging because hardware-specific details have to be taken into account. This leads to low-level code that is difficult to read, maintain, and port to other architectures. This is a well-recognized problem and there are many programming models that try to balance a high-level programming model with high-performance. We propose an entirely different, novel approach that allows programmers to define and choose their own level of abstraction: a high level of abstraction for readability and portability, and user-defined, lower levels to incorporate more hardware-specific details to obtain higher performance.
A mechanism for identifying the guilt agent in a network using vector quantization and skew Gaussian distribution

The recent state of art technologies has facilitated towards the ease of transfer of information from source to destination in a most comfortable and optimized medium. The channel transmitting discrepancies are rotted out by the sophisticated mechanisms that are being used currently using fiber optical cables etc… However sophisticated transmission mechanisms are available, the data transmission is always under threat due to intruder’s hackers and guilt agents, who try to either steal the information, override the information or share the information to miscreants. Therefore techniques are to be developed to identify these guilt agents so that the data transmission can be transformed in a more secure way without any pitfalls. This paper addresses in this direction by proposing models based on vector quantization and statistical models.