Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wearable Device for Indoor Imminent Danger Detection and Avoidance With Region-Based Ground Segmentation

Avoiding objects independently in indoor environments for individuals with severe visual impairment is one of the significant challenges in daily life. This paper presents a wearable application to help visually impaired people quickly build situational awareness and traverse safely. The system utilizes Red, Green, Blue, and Depth (RGB-D) camera and an Inertial Measurement Unit (IMU) to detect objects and the collision-free path in real-time. A region proposal module is presented to decide where to identify the ground from 3D point clouds. The segmented ground area can act as the traversable path, and its corresponding region in the image is removed to prevent detecting painted objects. The system can provide information about the category, distance, and direction of the detected objects by fusing the depth image and the neural network results. A 3D acoustic feedback mechanism is designed to improve the situational awareness for visually impaired people, and guild them traverse safely. The advantage of this system is that our 3D region proposal module can robustly propose the potential ground region and greatly reduce the computation cost of the ground segmentation. Besides, a typical machine-learning-based approach may miss objects because they could not be recognized, though they still may pose a danger. Another advantage of our approach is that the imminent danger detector can detect such unrecognizable objects to help users avoid a collision. Finally, experimental results demonstrate that the proposed system can be a useful indoor assistant tool to help blind individuals with collision avoidance and wayfinding.
Bridging the Gap between Theory, Practice, and Policy: A Decision-Making Process Based on Public Health Evidence Feasible in Multi-Stage Research on Biological Risk Factors in Poland

Stakeholder input into the decision-making process when developing public health programs and policies is crucial. This article presents an innovative approach, involving online participation with a wide group of stakeholders located in different geographic locations for policy consensus. The results of the project have been used to propose assumptions regarding a strategy for preventing blood-borne diseases in Poland. The research was conducted iteratively using a multi-stage qualitative methodology to explore risk assessment involving blood-borne infections. The final output of the study is a list of key problems/challenges and potential solutions associated with medical and nonmedical services that are connected to the breakage of tissue continuity. Qualitative research is rare in risk assessment, as priority is generally given to statistical data and endpoints. In addition to policy preparation for blood-borne illnesses, the methodology employed in the study can also be used to successfully explore other areas of public health.
Categorical Clustering Applied to the Discovery of Character Builds in TCTD2: the BaT Approach

This article describes an attempt to categorize character configurations of players of Tom Clancy’s the Division 2, conducted to highlight behavioral differences in approach to gameplay based on one's character build. Nine distinct character builds were extracted for maximum coherence and minimum variance and each build showed significant differences in separate measures of behavior such as playtime, character health and armor among other attributes. The proposed method was also able to recover builds recognized by social forums as well as discovering new ones. Appropriation of Character builds as categorical text-based data (BaT: Build as Text), provides a unique opportunity for game researchers to use a diverse set of input data which will in turn contribute to the improvement of the process of game design informed by player choices. Longitudinal observations in interconnection of obtained clusters may provide further insight into formation and evolution of gameplay types.
Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

Presently, the application of machine learning (ML) and data mining (DM) techniques have a vital role in healthcare systems and wisely convert all obtainable data into beneficial knowledge. It is proven from the literature works that a chance of 12% error remains in the diagnosis of the diseases by the medical practitioners. Moreover, for effective disease risk prediction in medical analysis, more emphasis is accorded to the area under the curve (AUC) with accuracy as an evaluation metric. However, the role of the AUC has not been previously characterized notably. In this research article, a novel feature reduction (NFR) model that is aligned with the ML and DM algorithms is proposed to reduce the error rate and further improve the performance. The proposed NFR model comprises of two approaches and uses the AUC in addition to the accuracy to achieve a robust and effective disease risk prediction. The first approach is based on a heuristic process evaluating performance by reducing features with respect to the improvement in the AUC besides the accuracy as evaluation metrics, working to obtain the best subset of highly contributing features in the prediction. The second approach evaluates the accuracy and AUC of all individual features and forms the subsets with the highest accuracies, AUCs, and least difference between them, which are combined in various combinations to achieve the best-reduced set of highly relevant features. For this purpose, the benchmarked public heart datasets of the ML repository of the University of California, Irvine (UCI) are tested; the results are promising. The highest accuracy and AUC achieved with the proposed NFR model are 95.52% and 99.20% with 41.67% feature reduction, respectively. The accuracy is 4.22% higher than recent existing research with a significant improvement of 25% in the performance of the running time of the algorithm.
MeToo: Sentiment Analysis using Neural Networks (Grand Challenge)

In the contemporary world, wide-range online social data has become a valuable tool in studying global movements and their impact on people as well as the people's impact on them. One such movement, #MeToo has led hitherto unknown victims of sexual assault to come out and openly share their personal experiences. While a lot of people have responded positively towards the movement, some have not. Our work attempts to study the data that we have gathered from Twitter and generate a better understanding of the MeToo movement's impact on society and vice versa. We use three different model architectures, a Convolutional Neural Network, a Recurrent Neural Network and a BERT transformer to classify the collected tweets on the basis of their attitude towards the people who have come out and the movement in general. The source to our GitHub project can be found at: https://github.com/ahmadkhan242/MeToo-
Improving the Performance of MCTS-Based µRTS Agents Through Move Pruning

The impressive performance of Monte Carlo Tree Search (MCTS) based game-playing agents in high branching-factor domains such as Go, motivated researchers to apply and adapt MCTS to even more challenging domains. Real-time strategy (RTS) games feature a large combinatorial branching factor and a real-time aspect that pose significant challenges to a broad spectrum of AI techniques, including MCTS. Various MCTS enhancements were proposed, such as the combinatorial multi-armed bandit (CMAB) based sampling, state/action abstractions, and machine learning. In this paper, we propose to employ move pruning as a way to improve the performance of MCTS-based agents in the context of RTS games. We describe a class of possibly detrimental player-actions and propose several pruning approaches targeting it. The experimentation results in µRTS indicate that this could be a promising direction.
Blind Audio Source Separation Using Two Expectation-Maximization Algorithms

The problem of multi-microphone blind audio source separation in noisy environment is addressed. The estimation of the acoustic signals and the associated parameters is carried out using the expectation-maximization algorithm. Two separation algorithms are developed using either deterministic representation or stochastic Gaussian distribution for modelling the speech signals. Under the deterministic model, the speech sources are estimated in the M-step by applying in parallel multiple minimum variance distortionless response (MVDR) beamformers, while under the stochastic model, the speech signals are estimated in the E-step by applying in parallel multiple multichannel Wiener filters (MCWF). In the simulation study, we generated a large dataset of microphone signals, by convolving speech signals, with overlapping activity patterns, by measured acoustic impulse responses. It is shown that the proposed methods outperform a baseline method in terms of speech quality and intelligibility.
Secure Transmission in Cognitive MIMO Networks Using Cooperative Jammer With Outdated CSI

In this paper, we investigate the secrecy performance of an underlay spectrum sharing cognitive MIMO networks with the help of a cooperative jammer (Charlie), in which the secondary transmitter (Alice) aims to communicate with secondary receiver (Bob) in the presence of an eavesdropper (Eve). In order to analyze the impact of the imperfect channel state information (CSI), the CSI of the links between secondary network and primary network, Alice and Bob, Charlie and Eve are considered to be outdated, respectively. We propose the transmit antenna selection-maximal-ratio combining/zero forcing beamforming scheme (TAS-MRC/ZFB) to improve the secrecy performance of the considered networks. Furthermore, the exact and the asymptotic closed-form expressions of secrecy outage probability are provided to analyze the impacts of the outdated CSI and the proposed TAS-MRC/ZFB on the considered networks. The results reveal that the outdated CSI between secondary network and primary network has a significant impact on secrecy performance of the considered networks in the low signal-to-noise ratio (SNR) region, the outdated CSI between Charlie and Eve will significantly deteriorate the secrecy performance of the considered networks in high SNR region. Finally, Monte Carlo simulations are carried out to verify our analysis.
On-Device Filter Design for Self-Identifying Inaccurate Heart Rate Readings on Wrist-Worn PPG Sensors

The ubiquitous deployment of smart wearable devices brings promises for an effective implementation of various healthcare applications in our everyday living environments. However, given that these applications ask for accurate and reliable sensing results of vital signs, there is a need to understand the accuracy of commercial-off-the-shelf wearable devices’ healthcare sensing components (e.g., heart rate sensors). This work presents a thorough investigation on the accuracy of heart rate sensors equipped on three different widely used smartwatch platforms. We show that heart rate readings can easily diverge from the ground truth when users are actively moving. Moreover, we show that the accelerometer is not an effective secondary sensing modality of predicting the accuracy of such smartwatch-embedded sensors. Instead, we show that the photoplethysmography (PPG) sensor’s light intensity readings are an plausible indicator for determining the accuracy of optical sensor-based heart rate readings. Based on such observations, this work presents a light-weight Viterbi-algorithm-based Hidden Markov Model to design a filter that identifies reliable heart rate measurements using only the limited computational resources available on smartwatches. Our evaluations with data collected from four participants show that the accuracy of our proposed scheme can be as high as 98%. By enabling the smartwatch to self-filter misleading measurements from being healthcare application inputs, we see this work as an essential module for catalyzing novel ubiquitous healthcare applications.
Analysis of Multilevel Electronic Balancer for AC Traction Substation with PS-PWM

The main goal of this paper is the analysis of an behavior for the electronic balancer of the AC traction substation. This balancer consists of three delta connected multilevel cascade H-bridge converters. The employed phase-shifted pulse width modulation causes the significant decrease of the current ripple due to multilevel nature of the voltage at the converter AC terminals. This paper describes the basic level of modulation technique for single H-bridge cell and analyzes low harmonic interferences of the converter. These results are extended to twelve cells H-bridge multilevel converter with 250 Hz switching frequency. The results of the analysis are based on a created model of the multilevel converter with the nominal power of 12.5 MVA (for traction substation use).
A Continuous Positioning Algorithm Based on RTK and VI-SLAM With Smartphones

The navigation technology has developed rapidly and immensely over the past few decades. Among the multiple navigation technologies, the representative and promising techniques are Real-time Kinematic (RTK) technique and Simultaneous Localization and Mapping (SLAM). RTK can provide real-time positioning results with high accuracy, while SLAM can not only locate the user but also construct a map of the new ambient. The first smartphone, the Xiaomi MI 8, equipped with the dual-frequency Global Navigation Satellite System (GNSS), hit the market in May 2018, providing valid carrier-phase measurements for RTK owe to the developer option of ”Force full GNSS measurements. ” Nevertheless, RTK underperforms in urban areas as the buildings and trees can block the satellite signals. RTK cannot even provide positioning results when the GNSS outage happens. However, SLAM can effectively make up the drawbacks of RTK as it utilizes no more information than the images. SLAM can also be combined with the Inertial Measurement Unit (IMU) called Visual-Inertial SLAM (VI-SLAM,) with the improvement of accuracy and robustness. Therefore, our study mainly aims to confirm the feasibility of continuous positioning based on RTK and VI-SLAM with the Xiaomi MI 8. An application is developed to execute the functions, including logging images, measurements of IMU, and GPS measurements in Receiver INdependent EXchange (RINEX) format with the Xiaomi MI 8. The performances of RTK with and without the assistance of VI-SLAM are assessed respectively in the urban area. The experimental results demonstrate that the combination of RTK and VI-SLAM based on smartphones can effectively provide continuous positioning results. We believe our application will facilitate research and development in relation to positioning algorithms. Readers have access to this application at https://github.com/Nronaldo/CIGRLogger.
The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things

The Internet of Things (IoT) is playing a vital role in shaping today? s technological world, including our daily lives. By 2025, the number of connected devices due to the IoT is estimated to surpass a whopping 75 billion. It is a challenging task to discover, integrate, and interpret processed big data from such ubiquitously available heterogeneous and actively natural resources and devices. Cluster analysis of IoT-generated big data is essential for the meaningful interpretation of such complex data. However, we often have very limited knowledge of the number of clusters actually present in the given data. The problem of finding whether clusters are present even before applying clustering algorithms is termed the assessment of clustering tendency. In this article, we present a set of useful visual assessment of cluster tendency (VAT) tools and techniques developed with major contributions from James C. Bezdek. The article further highlights how these techniques are advancing the IoT through large-scale IoT implementations.
A Grounded Coplanar Waveguide-Based Slotted Inverted Delta-Shaped Wideband Antenna for Microwave Head Imaging

A wideband grounded coplanar waveguide (GCPW) based patch antenna for head imaging is discussed in this paper. The proposed antenna is integrated with a slotted inverted delta-shaped main radiating patch, coplanar waveguide elements, and slotted partial ground. The prime objective of usages of coplanar waveguide elements is enhancing antenna effectiveness. In the prototype, examined different categories of slots in the ground and particularly rectangular and elliptical-shaped slots are used to improve the radiation directivity, gain, and efficiency. The optimized dimension of the antenna is $50\times 44\times 1.524$ mm3 by using the Rogers RO4350B substrate. The measured and simulated results demonstrate that the proposed prototype has a bandwidth of 2.01 GHz (1.70–3.71 GHz) with nearly directional radiation characteristics. The highest gain of the prototype is 5.65 dBi, and the maximum 93% radiation efficiency over the frequency band. The antenna has a lower group delay, higher fidelity factor (>95%), which are significant factors for microwave head imaging. Various design structures are performed to achieve the most satisfactory result. The proposed prototype is designed and analyzed by 3D CST 2019 simulator software. Later, the antenna is fabricated and measured to examine the performance. Thereafter, a single antenna and twelve-antennas array element are configured and placed surrounding the 3D realistic shaped Hugo head model to validate the effectiveness of the single antenna as well as the designed antennas array. The antenna has satisfactory field penetration into the human head tissues with a safe SAR (specific absorption rate). Less than 0.00233 W/kg SAR is attained over the operating frequency range, which is lower than the reported antennas. The Iteratively Corrected Delay Multiply and Sum (IC-DMAS) imaging algorithm will be applied for imaging purposes. The investigation through the literature of the statistical and measured data evident that the proposed antenna is apposite for the wideband microwave head imaging applications.
Application of Random-parameter Negative Binomial Model to Examine the Relationship between the Severity of Traffic Accident

Traffic accident injury is the eighth leading cause of death in the world, which seriously affects economic development and life stability. In order to explore the influencing factors of the severity of major road traffic accidents in China, potential influencing factors were selected from the statistical data of 366 major accidents. Fixed-parameter and random-parameter negative binomial model were respectively used to build models for accidents data. The results show that geographical zone, time and date of accident, road administrative grade, season, accident type, road alignment and number of traffic violation behavior are significantly related to the accident severity. Strict management of drivers is an effective way to improve the degree of accident injury.
Khayae (Meliaceae) specierum Nomenclator

RÉSUMÉ Le présent article constitue une mise au point nomenclaturale du genre Khaya A. Juss. , circonscrit à l'Afrique continentale, Madagascar et aux Comores. Ce genre contient un petit nombre de taxons, de quatre à six espèces selon les auteurs. Les noms publiés par le passé ne sont pas toujours rattachés à un type approprié ou nécessitent d'effectuer un choix parmi les syntypes. À la lumière de la bibliographie et de l'examen exhaustif des échantillons d'herbier, nous avons procédé à dix lectotypifications pour des taxons pour lesquels cela s'avérait nécessaire, ainsi qu'à une épitypification, afin de se conformer aux règles du Code international de Nomenclature botanique.
The Effect of 3d-Mri Modalities Mixture in Glioma Delimitation

today, image processing has become a very important issue in medical imaging field, which is constantly developing to facilitate the diagnosis of several diseases such as brain tumors, especially glioblastoma (GBM). The segmentation of glioblastoma tumors is an important early step in image analysis to characterize the tumor phenotypic features. This study describes a new approach for the detection and the delimitation of GBM using modalities mixture as a pre-processing step then Otsu multilevel thresholding and Neighborhood algorithm & maximum component. This proposed modalities mixture used three different MRI modalities which are Flair, T2 and T1. This approach has been tested on clinical database BRATS’2017. We report promising results. The Dice Similarity Coefficient metric for whole tumor was 0.88. the preprocessing step used increases the segmentation accuracy compared to the same technique without modalities mixture.
Optimal Parameters of Diffusion MRI measuring Corticospinal Tract Integrity in healthy subjects

Diffusion weighted imaging (DWI-MRI) is debatably the method of choice for characterizing brain microstructure non-invasively and in vivo in the chronic phase post-stroke. In fact, the degree of motor impairment after stroke is closely linked to the structural integrity of the corticospinal tract (CST). The aim of our study was to extract tract biophysical characteristics such as fractional anisotropy (FA) and Apparent Diffusion Coefficient (ADC) from the CST and to identify the optimal parameters of measuring CST integrity. We conduct experiments with ten healthy human subjects. For each subject, biophysical values were calculated for two Regions of Interest (the posterior limb of the internal capsule and the anterior pons) at two b-value (b=1000s/mm2 and b=3000 s/mm2). In this work, our results showed that the pons region more accurately predict CST integrity than the posterior limb of internal capsule using a b-value equal to 1000 s/mm2. FA and ADC are a promising metric for clinical applications.
Multi-Object tracking based on Kalman Filtering Combining Radar and Image Measurements

The purpose of this paper is to develop a tracking system. The designed platform is based on two kinds of physical sensors: a doppler radar module and HD Camera. All the measurements from those modules are processed using a data fusion method. In this work, a method based on the Gaussian mixture model is used for foreground detection (i.e. background subtraction). After that our move to a filtering step which is used to refine the detection results firstly obtained, then a tracking process is introduced. As a final stage, all the vision-based measurements are combined with the processed radar raw data. Here, the goal is to perfectly estimate the target velocity in the real time. Added to target 2D positions, this speed information is considered as a third dimension. This is very useful in many applications such as traffic control, robotics, autonomous vehicles etc. In this work a set of experiments is conducted in order to validate the developed tracking method.
Immune Parallel Artificial Bee Colony Algorithm For Spectrum Allocation In Cognitive Radio Sensor Networks

With the rapid development of technology, the continuous progress of communication technology has brought a very serious problem - the shortage of spectrum resources. A large part of the factors that cause the shortage of spectrum resources are related to the original spectrum allocation method. In order to further optimize the benefit of wireless spectrum and network efficiency, this paper proposes an immune parallel artificial bee colony algorithm (IPABCA) to optimize the spectrum allocation efficiency, and uses the classical graph coloring method to calculate the benefit and prove the superiority of group intelligent algorithm in solving the spectrum allocation problem. It can be seen from the simulation results that IPABCA is superior to the original ant colony optimization and particle swarm optimization in terms of total network benefit.