Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinician perception of pathological narcissism in females: a vignette-based study

The DSM-5 reports that up to 75% of those diagnosed with Narcissistic Personality Disorder (NPD) are males, which denotes that narcissism is a clinical phenomenon that operates differently in men and women. Vulnerable narcissism, which tends to be more prevalent in females and is currently under-appreciated in the DSM-5, may be diagnosed as other “vulnerable” disorders (e.g., Borderline Personality Disorder; BPD). The current study investigated gender differences in clinicians’ perceptions of narcissistic pathology. Adopting an online vignette-based study, clinicians (N = 108; 79 females) read clinical case vignettes of hypothetical patients and provided diagnostic ratings of existing personality disorders. Clinicians’ diagnostic ratings of NPD were concurrent with the vignette containing grandiose narcissism symptoms, irrespective of patient gender. However, when presented with a vulnerable narcissism vignette, clinicians were significantly more likely to attribute a BPD diagnosis in female patients, compared to male patients. Clinicians with a psychodynamic approach and more experience in practice were also more likely to label vulnerable narcissism symptoms as NPD, compared to those with a CBT approach and less experience in practice. The clinical implications of these results support the shift toward assessing personality dysfunction based on dimensional trait domains.
Teacher Advice-Seeking: Relating Centrality and Expertise in Middle School Mathematics Social Networks

Background/Context Teachers’ relationships with principals, instructional coaches, and other teachers have important implications for the improvement of their instructional practice and student learning. In particular, teachers who access content-specific instructional expertise through their social networks are more likely to exhibit and sustain evidence of instructional improvement; teachers who seek advice from colleagues with knowledge of both content and pedagogy have evidenced growth in their own knowledge and improved classroom instructional practice. Purpose/Objective In this paper, we extend the literature by examining how formal role relates to the relationship between expertise and advice-seeking in the context of urban middle school mathematics teachers’ social networks. Specifically, we first explore how network centrality varies across formal role group (i.e., teacher, instructional coach, and principal/assistant principal), and second, we investigate how centrality relates to expertise within each formal role group. Research Design We draw on a variety of data sources taken from a 4-year observational study of a sample of 30 schools and 533 teachers, coaches, and administrators in four large urban school districts. In particular, we rely on data from a network survey to document teachers’ advice-seeking behaviors, and we draw on the broader data set to document formal role and measures of expertise within each role group. Findings/Results The main findings are: (a) coaches were significantly more central than teachers, who are significantly more central than administrators; (b) teachers with greater expertise were more central; (c) while coach expertise was not related to centrality, teachers were more likely to nominate a coach if they perceived the coach to have expertise and be evaluative; and (d) administrators were rarely nominated. Conclusions/Recommendations Findings indicate that teachers are accessing information from those with expertise and experience, which suggests that advice-seeking among teachers may be self optimizing. Furthermore, teachers’ advice-seeking seems to be shaped both by their efforts to access expertise and in response to accountability pressures. This calls for caution against the misalignment of formal role and expertise. Our findings suggest that those in a social network whose social status is elevated to the formal role of coach are more sought out for advice, particularly if they are perceived to have evaluative power. This can inform what administrators can expect of teachers’ informal advice-seeking as well as how advice-seeking patterns are likely to shift if a teacher is made a coach.
Base pressure prediction in bluff-body potential-flow models

In a recent study by Yeung & Parkinson (1997), a wake width was proposed which allowed the bluff-body potential-flow model by Parkinson & Jandali (1970) to be extended to include the flow around an oblique flat plate. By incorporating this wake width in the momentum equation originally derived by Eppler (1954) for separated flow, the drag of the plate is related to its inclination and base pressure through a simple analytical condition. It allows the base pressure, which is usually treated as an empirical input, to be determined theoretically and thus the model becomes self-contained. Predictions of the base pressure, drag and width of wake are found to be in reasonable agreement with the experimental data. When applied to the symmetrical flow around a wedge of arbitrary vertex angle, similar agreement with experimental measurements is obtained as well. It is also demonstrated that this condition is compatible with the free-streamline models by Wu (1962) and Wu & Wang (1964) such that the corresponding predictions are in good agreement with experiment.
Performance of Battery-free BackCom in Uplink NOMA Systems with Joint Detection

In this paper, a framework for the performance evaluation of uplink Non-Orthogonal Multiple Access (NOMA) Backscattering Communication (BackCom) systems is proposed. A two-nodes bi-static scenario is considered, where the communication is affected by multi-path on both the forward and the backscattering links. The battery-free nature of the nodes is explicitly included in the model by introducing a node's activation power threshold. A power-domain NOMA approach is enforced, where multiplexing of interfering signals is performed by assigning a specific reflection coefficient to each node, based on Channel State Information (CSI) at the receiver. Moreover, two relevant detection schemes, namely the Successive Interference Cancellation (SIC) detector and the minimum distance joint detector (JD), are considered. Performance is evaluated in terms of average Bit Error Rate (BER) and aggregate successful decoding probability. Extensive simulations results examine the interplay among powering condition, reflection coefficients selection and interference rejection efficiency, and show a performance improvement offered by joint detection over SIC. Finally, a com-parison with state-of-the-art performance evaluation is included.
Resistant to Technology and Digital Banking Behavior Among Jakarta's Generation Z

When sophisticated technology is integrated into digital banking services with the goal of providing customers with an easy, fast, safe, and enjoyable experience, then details about customer behavior when receiving services are based on this sophisticated technology. Therefore, the goal of this study is to investigate how ease of use, usefulness, security, and self-efficacy affect resistance to technology or behavior when using digital banks among Jakarta's generation Z. In this quantitative study, partial least-square structural equation modeling (PLS-SEM) is used. Specifically, SmartPLS 3.0 is used to analyze the data collected from 157 participants. According to the findings of this study, participants consider the factor of resistance to digital banking technology to be more important than the impact on usage behavior. Issues related to security are also an important factor for customers when using technology such as digital banking services. Notably, the limitation of this research is that it does not use aspects of the technology used in each digital banking service. Furthermore, comparing users from different generations can help to improve the findings of this study.
A Review on Rate Splitting Multiple Access: Challenges and Future Directions

Rate splitting multiple access (RSMA) has turned up as a popular multiple access (MA) for the optimization of non-orthogonal transmissions for 5G and beyond 5G wireless communications. By strategically, RSMA manages the interference levels by decoding a part of the other user's message in a pair and treating the rest of the message as noise, thereby, maintaining the secrecy of the message. RSMA bridges the connection between two extreme MA schemes such as Space division multiple access (SDMA) and Non-orthogonal multiple access (NOMA) with different operation areas of low and high interference levels. RSMA can be classified as a generalized MA scheme and conventional MA schemes like orthogonal multiple access (OMA), NOMA, SDMA, multicasting, etc., can be employed as special cases of it. RSMA performance is optimal for different transmission schemes from information and communication theoretical findings. RSMA offers an increase in energy efficiency (EE), spectral efficiency (SE), coverage improvement, latency reduction, and enhanced degrees-of-freedom in comparison to conventional MA schemes, for both underloaded and overloaded regimes. Through this review, the RSMA and conventional MA schemes are discussed along with their potential advantages and disadvantages. In the end, a proposal is given for RSMA pertaining to research gaps arising out of existing literature.
Prediction of Early Heart Attack Possibility Using Machine Learning

The most vital or important organ in our body is the heart. Over the recent decades, cardiac diseases has been the primary cause of mortality worldwide. Our heart is employed to regulate and sustain blood flow. A data-driven prediction model that takes into consideration the risk factors for heart disease might be quite useful in the healthcare industry to reach an early diagnosis of heart disease. Clinical practitioners and academics are very interested in developing a reliable method for predicting cardiac disease. The research paper’s main focus is on those who are more prone to develop heart disease given certain medical criteria, therefore improves medical treatment and lowers costs. We have employed some of the machine learning to compare the accuracy of several machine learning techniques, and it is observed that Random Forest classifier outperformed with the accuracy of 87.9% comparing to other machine learning algorithms.
A Comprehensive Review of Machine Learning for Financial Market Prediction Methods

Financial market prediction is an important task for placing an investor's hard-earned money in the financial market to earn profit. Many parameters affect the financial market's valuation, making it volatile, which is challenging for investors. This review study gives a full overview of 53 research articles that were chosen based on the trend of machine learning algorithms, calculation methods, and other performance parameters. Primarily, it is seen that artificial neural network (ANN) and support vector machine (SVM) techniques are used for forecasting the financial market. For prediction purposes, stock selection is also an important task. A genetic algorithm (GA) is used to choose stocks, and it is a very important part of managing a portfolio. The K-means algorithm is used to create a group of stocks that have a similar pattern and behavior. Hybrid approaches also provide better results. This review paper makes it easier for researchers to understand the terms and key ideas of predicting the financial market using machine learning so they can make the right choices for their needs.
Numerical Simulation of a Laboratory Vortex

Abstract An axisymmetric numerical model has been developed to simulate Ward's (1972) laboratory experiments. It was shown by Davies-Jones (1976) that this experiment is more geophysically relevant than all previous experiments in that Ward's experiment exhibits both dynamical and geometrical similarity to actual tornadoes. Major results are 1) the core size versus inflow angle relationship agrees very nearly with Ward's measurements, 2) the numerical and laboratory surface pressure patterns are in agreement, and 3) it is demonstrated that the core radius is independent of the Reynolds number at high Reynolds number (Ward's data also exhibit this behavior). Based on this axisymmetric model some speculation concerning the nature of the asymmetric multiple vortex phenomenon is made. Furthermore, the numerical model allows the examination of the interior flow field. As a consequence, an explanation is offered in Section 6 for the double-walled structure sometimes observed in natural vortices. The experiments.
Disjoint-path routing in hypercubes and their variants

Modern supercomputers are massively parallel systems which connect hundreds of thousands of processor nodes, soon millions. It is thus critical to link this huge quantity of nodes according to a suitable topology in order to retain high performance. Interconnection networks used for this purpose are thus graphs with a high connectivity. However, due to physical restrictions, additional requirements such as the diameter of the network must be satisfied so as to achieve practicability. In this thesis, we will be addressing several routing problems inside different interconnection networks. Precisely, we shall focus on hypercubes and some of their variants, such as perfect hierarchical hypercubes. Inside these topologies, we shall describe solutions to disjoint-path routing problems such as the node-to-set disjoint-path routing problem or the set-to-set disjoint-path routing problem. Because of the very large and continuously increasing number of computing nodes inside massively parallel systems, we understand that data routing is a critical topic to retain high performance. At the same time, given the increasing complexity of the topologies used as interconnection network of modern supercomputers, routing is, likewise, becoming increasingly challenging and requires much attention. The routing problems addressed in this thesis are the node-to-set disjointpath routing problem and the set-to-set disjoint-path routing problem. The former one is about finding node-disjoint paths between one source node and a set of destination nodes inside a given interconnection network. The latter one is about finding node-disjoint paths between a set of source nodes and a set of destination nodes. Disjoint-path routing algorithms have two main advantages. First, generating disjoint paths is one solution to avoid notorious resource allocation issues of parallel systems such as deadlocks, livelocks or starvations. Second, considering the high number of nodes inside modern supercomputers, we understand that faults are likely to occur. In this context, generating disjoint paths maximises the probability of finding a fault-free path: in this case, one faulty node can effectively compromise at most one path. Chapter 1 presents the background of our research by introducing the current context of supercomputing and massively parallel systems, as well
Removing outliers using the L∞ Norm

Recently, there has been interest in solving geometric vision problems such as triangulation and camera resectioning using L∞ minimization. One key advantage of using the L∞ norm rather than the L2 norm is that the L∞ cost function has a single minimum unlike the commonly used L2 cost function which typically has multiple local minima. However, one drawback of using L∞ minimization is that it is not robust to outliers. By minimizing the L∞ norm instead of the L2 norm, we are, in essence, fitting the outliers and not the good data. Therefore, before one can perform L∞ optimization on a problem, it is first necessary to remove outliers. A popular (but generally unsound) method of removing outliers is to minimize the cost function using standard optimization techniques; then if the residual error is too great, remove the offending measurements and continue. Although this method can fail for simple L2 optimization problems, we show in this paper that for a wide class of L∞ problems it is a valid technique. It is proved that the set of measurements with greatest residual must contain at least one outlier. Thus, if we keep throwing out the measurements with greatest residual, we will eventually remove all outliers in the data. We test this hypothesis on the multiview reconstruction problem and show that even simple strategies for throwing out these maximum residual measurements are effective in removing outliers.
Bearing Error Diagnosis Using Deep Learning and Convolution Neural Network

Fault diagnostics and prognostics are essential issues. Industrial plants will be under a huge amount of pressure that maintains unpredictable interruption, system failures, and safety issues to a minimum, that necessitates identifying and eliminating potential issues as quickly as possible. Intelligent problem diagnosis is a promising technique because of its capacity to handle gathered signals quickly and effectively while also offering reliable diagnosis findings. Numerous authors have validated deep learning and machine learning approaches for identifying bearings failures, the findings have mostly been confined to tiny train and test datasets, with the input data modified to achieve high accuracy. In this article, original data of accelerometer sensor was loaded into unique periodic sequencing prediction algorithm that develops an edge fault diagnosis technique. We utilize identical frequency patterns as inputs to an innovative deep neural Long-Short-Term-Memory, Recurrent Neural Network to diagnosis bearings insufficiency at excellent accuracy inside the least time (CRNN). Without the use of database adjustment, the technique would acquire the maximum level of competence in the industry. The fault diagnostic method’s efficacy and applicability are demonstrated by comparing the findings to those of other intelligent fault detection systems using two widely known benchmark real vibration datasets.
Improved Faster-RCNN Based Biomarkers Detection in Retinal Optical Coherence Tomography Images

Optical coherence tomography (OCT) is an important ophthalmic imaging technique, which can generate high-resolution anatomical images and plays an important role in the detection of retinal biomarkers. However, the appearance of retinal biomarkers is complex, and some of these biomarkers differ greatly among different categories, while many features are similar. In addition, the boundaries of retinal biomarkers are often indistinguishable from the background. In this study, we propose a self-supervised contrastive boundary consistency network (SCB-Net) to detect retinal biomarkers in OCT images. A self-supervised contrastive classification module is proposed to improve the classification ability of the network between different categories of retinal biomarkers. Furthermore, in order to make the boundary of the retinal biomarkers located by the network closer to the ground truth, the boundary consistency is added on the basis of the original regressor to jointly constrain the boundary localization. The experimental results on a local dataset show that our proposed SCB-Net method achieves good detection performance compared with other detection methods.
MFSE: A Meta-Fusion Model for Polypharmacy Side-Effect Prediction with Graph Neural Networks

Despite being a very popular approach for treating complex diseases, polypharmacy can lead to increased risk of adverse side effects, many of which are observed after the drugs have been released in the market. Luckily, the significant increase in data availability of observed adverse side-effects has paved the way for machine learning approaches to assist in their prediction. In this work, we first present a novel framework for multi-relational link prediction with graph neural networks. Given a multi-relational graph, we create relation-specific vector representations for each node of the graph. With this approach, we create drug vector representations that are side-effect specific, by integrating external molecular and protein-target information with the drug information that is generated directly from the drug-drug interaction prediction graph. With our new meta-fusion approach, each information type is produced from a distinct G NN - based encoder architecture and then the integration is performed according to the side-effect type being predicted. While state-of-the-art models report maximum AUROC scores of 0.91, our technique reaches a score of 0.95. Also, we show that our fusion approach provides valuable external knowledge particularly to drug nodes in the prediction graph that have a smaller node degree.
Genetic Algorithm Based Energy Efficient and Load Balanced Clustering Approach for WSN

Consumption of energy in the wireless sensor networks is major constrain that restrict the impact of the application. It becomes crucial when a more nodes are deployed. Several energy-efficient solutions have been proposed by many researchers. Clustering is one of the most energy-efficient solution that has been proven for the large-size network. However, the performance of the clustering algorithm degrades because of the non-uniform cluster formation and non-uniform cluster heads distribution over the network. To resolve this problem, a Energy Efficient and Load Balanced Clustering Approach for Wireless Sensor Network Using Genetic Algorithm is presented in this paper. The proposed algorithm not only focused on the load balancing and uniform distribution of cluster head but also on the optimal cluster head selection which considers residual energy, inter-cluster, and intra-cluster communication distance. The performance parameters like, lifetime of the network and energy consumption of the proposed algorithm is analyzed with the existent algorithms. The outcomes of the experiment demonstrated that the presented algorithm performs better than the existent algorithms.
PreActo: Efficient Cross-Camera Object Tracking System in Video Analytics Edge Computing

Cross-camera real-time object tracking is one of the important, yet challenging applications of video analytics in edge computing environments. To provide accurate and efficient real-time tracking, a tracking target's future movements need to be predicted. Particularly, the destination camera and travel time of the target object are to be identified so that tracking duties can be handover-ed seamlessly. In this paper, we propose a collaborative cross-camera tracking system, called PreActo, with two key features: (1) ResNet-based trajectory learning to exploit the rich spatio-temporal information embedded within objects' moving patterns, which has not been utilized by the existing literature, and (2) collaboration between the edge server and the edge device for real-time trajectory prediction and tracking handover. To prove the validity of our proposed system, we evaluate PreActo on a video dataset leveraging real-world trajectories. Evaluation results show that the proposed system reduces up to $7\times$ the number of processed frames for handover, with $2\times$ lower latency while providing $1.5\times$ tracking precision improvement compared to the state-of-the-art.