Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Predictive values of carotid high-resolution magnetic resonance imaging for large 1 embolus shedding in carotid artery stenting

3 Background/aim: Embolus shedding is one of the important complications in carotid 4 artery stenting (CAS). Carotid high-resolution magnetic resonance imaging (HR-MRI) 5 is often used to directly reflect important biological characteristics, such as plaque size 6 and composition, as well as the structure of the carotid artery wall. 7 The aim of this study was to investigate the predictive values of carotid HR-MRI for 8 large embolus shedding in CAS. 9 Materials and methods: In total, 195 patients with carotid stenosis were enrolled. 10 Preoperative carotid HR-MRI was performed to define the nature of the carotid plaques. 11 CAS was performed in all patients, and intraoperative embolic protection devices were 12 used to collect the shed emboli. According to the diameter and number of shed emboli, 13 the patients were divided into the small-embolus group (group X) and large-embolus 14 group (group Y). Logistic regression analysis was used to analyze the risk factors of 15 large embolus shedding. 16 Results: Group Y included 58 patients, and group X included 137 patients. Age, 17 stenosis length, smoking, and ≥3 transient cerebral ischemic attacks were risk factors for 18 large embolus shedding. Two cases of shed large emboli developed from stable plaques, 19 and 56 cases of large emboli developed from vulnerable plaques. When vulnerable 20 plaques were associated with more risk factors, the incidences of large embolus 21 shedding in cases with vulnerable plaques combined with 0, 1, 2, 3, and 4 risk factors 22 were 44 % (4/9), 68.1% (15/22), 72.2% (13/18), 76.5% (13/17), and 84.6% (11/13), 23 respectively 24
A Novel Localization Technique Using Luminous Flux

As global navigation satellite system (GNNS) signals are unable to enter indoor spaces, substitute methods such as indoor localization-based visible light communication (VLC) are gaining the attention of researchers. In this paper, the systematic investigation of a VLC channel is performed for both direct and indirect line of sight (LoS) by utilizing the impulse response of indoor optical wireless channels. In order to examine the localization scenario, two light-emitting diode (LED) grid patterns are used. The received signal strength (RSS) is observed based on the positional dilution of precision (PDoP), a subset of the dilution of precision (DoP) used in global navigation satellite system (GNSS) positioning. In total, 31 × 31 possible positional tags are set for a given PDoP configuration. The values for positional error in terms of root mean square error (RMSE) and the sum of squared errors (SSE) are taken into consideration. The performance of the proposed approach is validated by simulation results according to the selected indoor space. The results show that the position accuracy enhanced is at short range by 24% by utilizing the PDoP metric. As confirmation, the modeled accuracy is compared with perceived accuracy results. This study determines the application and design of future optical wireless systems specifically for indoor localization.
Deliberate Control or Reduced Fecundability?

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TRUST IN DOUBT: CONSUMING IN A POST-TRUTH WORLD Consuming Information from Sources Perceived as Biased versus Untrustworthy: Parallel and Distinct Influences

Consumer research has examined whether perceptions of ulterior motives behind marketing result in greater consumer skepticism and reduced persuasion. Yet skepticism could stem from perceiving a message source as untrustworthy or as biased. The possibility of source bias has been relatively overlooked or conflated with untrustworthiness. Yet recent research has demonstrated that consumers perceive source bias and untrustworthiness differently. Sources are viewed as biased when they have a skewed perception but as untrustworthy when they are dishonest. Bias and untrustworthiness can serve as independent reasons to view a source as lacking credibility and thus can undermine persuasiveness. However, when sources switch positions, perceived bias and untrustworthiness can have different influences on surprise and different downstream consequences for the persuasiveness of the new message. Unique and common antecedents of bias versus untrustworthiness are discussed, as well as implications for con-
Enhancing Downloading Time By Using Content Distribution Algorithm

A traditional model in peer-to-peer (P2P) content distribution is that of a large community of intermittently connected nodes that cooperate to share files. Because nodes are intermittently connected, the P2P community must replicate and replace files as a function of their popularity to achieve satisfactory performance. In this paper, I develop a technique to enhance the downloading time with the aid of content distribution technique in the Peer-to-Peer architecture. In this study each peer will be sending video file request to the server. Server has the capability to manage multiple requests with the help of defined number of Caches. Each video file will be partitioned into equal sized chunks and assembly information file like a torrent file in the BitTorrent application. The peer requested movie will be distributed from any of the available caches by equal sized chunks. The service mode have both server and helper mode. The Enhanced Automated Mode Selection (EAMS) algorithm will help server to select the correct service mode to serve in the peer community. This will benefit server to avail the requested file else will block the request until the availability of the file to utilize the peer bandwidth.
Moho structure of Australia from probabilistic inversion of teleseismic P-wave coda autocorrelation

Summary The autocorrelation of the seismic transmission response of a layered medium (autocorrelogram), in the presence of a free surface, corresponds to the reflection response. Despite many studies on the imaging of local structures through retrieval and forward modelling of stacked autocorrelograms, there is limited work on the inversion of these data. In this study, we show that the probabilistic inversion of autocorrelograms is efficient, and can be used as an alternative imaging tool when other approaches are not applicable. Here, we calculate autocorrelograms of teleseismic P-wave coda recorded on more than 1200 permanent and temporary seismic stations across Australia, and utilise a Bayesian framework to invert these data for imaging the Moho structure. The results show patterns of structures that are consistent with those seen in the Australian Seismological Reference Model (AuSREM).
An Investigation of the Efficacy of Using Strain Gauge Arrays to Measure Axial and Shear Femur Forces in Post-Mortem Human Subjects

Lower extremity injuries are prevalent in motor vehicle collisions. Therefore, it is necessary to accurately quantify lower extremity kinetics in order to evaluate strategies for mitigating these injuries. Current methods for quantifying lower extremity loads in post-mortem human subjects (PMHS) have limitations that could affect the accuracy of the measured loads. Hence, the purpose of this study is to evaluate the efficacy of using strain gauge arrays (SGAs) to quantify axial and anterior-posterior shear femur forces in PMHS during fullscale frontal sled tests. Six PMHS were instrumented with SGAs to measure bilateral axial and shear femur forces before undergoing frontal sled tests. Several calibration procedures were performed to convert the SGA outputs (mV) to forces. The resulting forces were compared to knee bolster reaction forces and femur forces from matched sled tests performed using anthropomorphic test devices. Overall, the PMHS SGAs measured reasonable axial forces during the sled tests, but more work is necessary to validate the shear forces. Given the degree of crosstalk observed during the calibration procedure, future studies should use axial, anterior-posterior shear, and medial-lateral shear SGAs together to facilitate full crosstalk compensation. Furthermore, precise post-test calibrations capable of evaluating crosstalk should be performed on long bones instrumented with SGAs.
Computer-generated Gothic Tracery with a Motif-oriented Approach

Drawing decorative ornaments as computer-generated imagery (CGI) is both timeand energy-intensive because it usually involves complicated motifs. Therefore, a procedural approach that is applicable to a variety of fields may be useful for drawing ornaments efficiently. Consequently, an algorithmic approach for generating complicated ornaments has been proposed in this study. This research specifically focuses on Gothic tracery, which is widely found in European architecture from the 12th to 15th centuries. Tracery constitutes stonework elements that support glass panes in windows. Although Gothic tracery comprises a wide variety of complicated patterns, it usually consists of combinations of circles with a few straight lines. These combinations and geometric operations can generate various motifs. However, it is difficult to extract the rules for obtaining desirable patterns from circle arrangements because of variations in geometric situations. Therefore, we attempted through this study to draw Gothic tracery using a motif-oriented approach whereby an algorithm automatically seeks an appropriate position in a closed 2D space for placement of the motif. Thus, the algorithm determines the arrangement of circles required to draw the motif. This algorithm can produce some principal Gothic-style motifs automatically, and recursive execution of the algorithm can generate complicated patterns.
A SURVEY-BASED MEASURE OF OUTPUT GAP FOR PORTUGAL

Given the importance of output gap estimations, particularly in the context of budgetary surveillance, policy setting and economic monitoring, a new measure of output gap for Portugal is developed. The methodology is grounded in Szӧrfi (2015) framework, which is based on qualitative data. It integrates information about manufacturing, services and construction sectors, providing a representative measure of total economy slack. The survey-based estimates revealed robust stability properties when considering revisions in the projections of the main international institutions, namely the OCDE, the IMF and the EC. Both in real-time and nearly real-time estimations, the survey-based approach performed better than these organizations. It also performed well in 1, 2 and 3 year lagged estimations. Key-Words: Output gap, slack, survey data JEL Classification: E63, E66 The opinions expressed in the article are authors’ own responsibility and do not necessarily match those of the institution. All errors and omissions are the authors’ own responsibility. Contacts: Diogo.silva@gpeari.min-financas.pt Carla.monteiro@gpeari.min-financas.pt Joao.leal@gpeari.min-financas.pt Eduardo.gutierrez. 2014@novasbe.pt
Heat Map Segmentation

Many geospatial datasets can be represented as a heat map, such as rainfall, population density, terrain elevation, and others. These heat maps tend to form clusters of high density areas among a background of low density areas. This gem presents an automatic way to detect such clusters, and segment the heat map into areas. Experiments are conducted for two datasets which correlate to population density and show that the segmentation aligns with metropolitan areas and is stable to the choice of dataset. The segmentation described in this gem can potentially aid geospatial algorithms by supplying a smart divide-and-conquer strategy, such that the algorithm does not need to run for the entire Earth, but rather there can be a fine-grained model for each area.
Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties.
A Survey of Layered Approach to Threats and Countermeasures

In today's technological world it is extremely essential to identify the valuable assets of an organization, possible dangerous threats and state of the art defenses. It is necessary to raise the awareness of sophisticated threat among every employee of an organization. In this paper, we have elaborated most risky and smart threat that may trick user to get access of organization assets. It discusses information assurance component of an organization. It further discussed how layered approach is applied to organizations through Information Assurance elements. Each layer must be working correctly to ensure security.
TMI Rain Rate Estimation Over Land and Ocean Utilizing Convective and Stratiform Discrimination

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima in a regional map containing a Mesoscale Convective System (MCS). This is because of relatively small footprint size (~ 5.5 kin) and strong extinction properties in this channel of the TMI. A map of rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85 minima, produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we infer from TMI data the presence of three different kinds of thunderstorms or Cbs. They are young, mature, and decaying Cbs that have a scale of about 20 km on the average. Two parameters enable us to infer these three kinds of Cbs objectively: a) the magnitude of scattering depression deduced from local T85 minima and b) the mean horizontal gradient of T85 around such minima. Knowing the category of a given Cb, we can estimate the rain rate associated with it. Such estimation is done with the help of relationships linking T85 minimum to rain rate in each Cb type. Similarly, a weak background rain rate in all the areas where T85 is less than 260 K is deduced with another relationship linking T85 to rain rate. In our rain retrieval model, this background rain constitutes the stratiforrn rain where the Cbs are absent. Initially, these relationships are optimized or tuned utilizing the PR and TMI data of a few MCS events. After such tuning, the model is applied to independent MCS cases. The areal distribution of light (1-10 mmhrl), moderate (10-20 mmhrl), and intense (> 20 mmhr _) rain rates are retrieved satisfactorally. Accuracy in the estimates of the light, moderate and intense rain areas and the mean rain rates associated with such areas in these independent MCS cases is on the average about 15%. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics. TMI Rain Rate Estimation m
Intergenerational mobility in education: Estimates of the worldwide variation

This paper uses the recently published “Global Database on Intergenerational Mobility” to present a detailed picture of the chances of such mobility around the world. The empirical results obtained from transition matrices and linear regression models, being furthermore based on harmonized data for the education attainments of children and their parents across 148 countries worldwide, point to three main conclusions: First, the likelihood of attaining intergenerational mobility differs widely across countries and world regions. Second, intergenerational persistence in education level is particularly strong in least-developed countries. Third and finally, and perhaps most importantly, the mobility gap between rich and poor countries has increased over time.
A multilevel simulation framework for highly automated harvest processes enabled by environmental sensor systems

This article proposes the concept of a simulation framework for environmental sensors with multilevel abstraction in agricultural scenarios. The implementation case study is a simulation of a grain-harvesting scenario enabled by LiDAR sensors. Environmental sensor models as well as kinematics and dynamic behavior of machines are based on the robotics simulator Gazebo. Models for powertrain, machine process aggregates and peripheral simulation components are implemented with the help of MATLAB/ Simulink and with the robotics middleware Robot Operating System (ROS). This article deals with the general concept of a multilevel simulation framework and in particular with sensor and environmental modeling.
Hacking a Smart Robot Vacuum Cleaner

IoT devices can be found in almost any type of situation as the availability and viability of them has surged in the last decade with technological advancements. The purpose of this project is to investigate how secure these types of devices, in particular a robot vacuum cleaner, actually are if an ill intended actor tries to interfere with the device. Different methods used in the sphere of threat modeling and penetration testing were applied and tested with the result coming back positive. The robot vacuum cleaner was successfully compromised and the privacy of the owner could be violated applying the attacks used. The current way of thinking about privacy and security of IoT devices could therefore need to be reviewed.
Trajectory Kernel for Bayesian Optimization

In this thesis we investigate the performance of two trajectory kernels used by Bayesian optimization to solve robotic reinforcement learning tasks. Furthermore, we contribute a trajectory kernel to a Bayesian optimization algorithm, which optimizes over a local search space. Bayesian optimization has proven to be particularly effective in the area of reinforcement learning tasks in recent years [1], but also has some disadvantages. When it comes to higher dimensional problems, Bayesian optimization does not scale well due to the search space that needs to be optimized globally. In addition, commonly used standard kernels in Bayesian optimization only detect similarities in policy parameters, but not in behaviour patterns. For this reason, we implement trajectory kernels to the Gaussian process used by Bayesian optimization. These kernels exploit trajectory data generated by the reinforcement learning agent, to derive a more precise measure than policy parameter values. To compensate for the global nature of Bayesian optimization, we use the algorithm proposed by [2], which restricts the search space to a local area. In this thesis we show that restricting the search space enhances the learning performance in the reinforcement learning tasks of Cart Pole, Acrobot and Mountain Car. Based on our results, our trajectory kernel performs slightly better at its peak performance level compared to the standard kernels squared exponential and Matérn 5/2. The trajectory kernel adapted from [1] performs worse than the standard kernels.
A Visualization System using the Database of Electric Vehicles’ Energy Consumption and a Showing of Information on the base of Driving Situations

We proposed the model of database that accumulates daily driving logs acquired by vehicle-mounted simple sensors. Driving logs are normalized by energy consumption model of electric vehicles (EVs). And, we proposed the visualization system for retrieving useful information using this database. In this paper, we visualize effectiveness of replacing an existent vehicle with an EV. For example, we can know tendencies of roads on which we often lost electric energy. For validating efficacy of this visualization system, we have collected driving logs and visualized various information. As a result, we confirmed that we know tendencies of roads by collecting many logs.
AUTOMATIC DISCOVERY OF SIMILAR WORDS BY SUBSTITUTE VECTORS

Patterns between words are generally used for automatic information extraction. However, the patterns can only find related words close to each other. In this study, a method based on substitute vectors can overcome of this difficulty. Firstly, the word sets having the same substitute vector are constructed. Then, similar word sets are obtained according to the number of co-occurring sets. In this sets, semantically relatedness ratio is above 70%. The proposed method is unsupervised. Because, it does not require any seed words manually labeled.
Augmenting Emotions from Speech with Generative Music: Emotion Transformation & Prototype Evaluation

The present essay is an activity report about an interdisciplinary student project concerned with a software prototype recognizing affect from human speech and transforming it to congruently perceived generative music. Besides motivating relevant use cases and research from domains like medical sciences and human computer interaction, an emotion recognition and processing pipeline is proposed to map recognized emotions into the Circumplex Model as a pre-stage for music generation. For the latter, existing music algorithms and related research are outlined and an object oriented implementation demonstrated, which is capable of composing and playing music dynamically according to according emotional information. The complete prototype, including emotion recognition from speech plus music generation and all steps in between, was evaluated in a user study. Its results are outlined at the end of this work. They strongly indicate that music created by the utilized approach is perceived as emotionally similar to affective speech. Concludingly, further necessary work is discussed to make the prototype ready for use in real world scenarios, that are especially related to research in medical and psychological domains as well as solutions from media industry for supportive musical accompaniment of artistical verbal stages.
Convoluted Feelings Convolutional and recurrent nets for detecting emotion from audio data

We present a Convolutional Neural Network (CNN) model to extract features from audio data. We apply this model to the task of classifying emotion from speech data. Classification on features extracted from our single-layer CNN model (1024 filters) outperforms classification on typically extracted acoustic features. We achieve an accuracy of 50% for 7-class classification using CNN-extracted features on 500ms audio patches. In addition, we predict emotion with 70% accuracy using LSTMs (Long Short Term Memory) to get context over time.