Miracle-dz/UniFAR-FTUs huggingface dataset:
USING DEMOGRAPHIC SUBGROUP AND DUMMY VARIABLE EQUATIONS TO PREDICT

The purpose of this study was to determine whether adjustments for the differential prediction observed among sex, racial/ethnic, or age subgroups in one freshman class at a college could be used to improve prediction accuracy for these subgroups infuturefreshman classes. For older students, dummy variable and separate subgroup prediction equations were found, on cross-validation, to be more accurate than the total group equations. For sex subgroups, dummy variable and separate subgroup equations were only moderately effective in improving prediction accuracy. For racial/ethnic subgroups, they were more often than not less accurate, on cross-validation, than total group equations. Among all three kinds of demographic subgroupings, shifts over time in colleges' mean grade averages were found to be a more important source of prediction bias than differential prediction.
Fuzzy KNN Implementation for Early Parkinson's Disease Prediction

Most machine learning models have had tremendous success in implementing prediction analysis on dePD end diseases such as brain tumors, making it an ambitious goal to apply machine learning to medical research discoveries. In the case of Parkinson's disease, for example, early diagnosis and understanding might allow patients to adopt preventative measures before the onset of clinical symptoms. In cases when no effective therapies exist, machine learning mPDe provides a means of making an early diagnosis and thereby improving patient outcomes. A loss of brain function, like in the case of Dementia, impairs the ability of the rest of the body to function normally. In medicine, the application of machine learning models is known as “quantum intelligence,” and it is used to determine which drug combinations work best. In terms of technology and automation, quantum computing or intelligence is among the highest. We're attempting to apply quantum intelligence to a dataset about Parkinson's disease. Models of machine learning and deep learning, such as random forests, decision trees, convolutional neural networks, recurrent neural networks, and so on, are applied to a variety of generic datasets. OASIS is a specialized neuroimaging dataset with a variety of MRI patient dimensions. This can be incorporated into future studies. We are employing 3D brain pictures to diagnose the tumour using machine learning models, and MRI has multiple facets to be taken into account before arriving at a diagnosis. Magnetic Resonance Imaging (MRI) has two variations, known as T1 and T2. When it comes to fMRI, PET, and similar technologies, we've got you covered. Decision trees and random forest algorithms from machine learning, along with neural networks and RNNs from deep learning, were used to attain the desired accuracy.
Energy dependence and fluctuations of anisotropic flow in Pb-Pb collisions at sNN=5.02 and 2.76 TeV

: Measurements of anisotropic (cid:13)ow coe(cid:14)cients with two- and multi-particle cumulants for inclusive charged particles in Pb{Pb collisions at p s NN = 5 : 02 and 2.76 TeV are reported in the pseudorapidity range j (cid:17) j < 0 : 8 and transverse momentum 0 : 2 < p T < 50 GeV/ c . The full data sample collected by the ALICE detector in 2015 (2010), corresponding to an integrated luminosity of 12.7 (2.0) (cid:22) b (cid:0) 1 in the centrality range 0{80%, is analysed. Flow coe(cid:14)cients up to the sixth (cid:13)ow harmonic ( v 6 ) are reported and a de-tailed comparison among results at the two energies is carried out. The p T dependence of anisotropic (cid:13)ow coe(cid:14)cients and its evolution with respect to centrality and harmonic number n are investigated. An approximate power-law scaling of the form v n ( p T ) (cid:24) p n= 3 T is observed for all (cid:13)ow harmonics at low p T (0 : 2 < p T < 3 GeV/ c ). At the same time, the ratios v n =v n=mm are observed to be essentially independent of p T for most centralities up to about p T = 10 GeV/ c . Analysing the di(cid:11)erences among higher-order cumulants of elliptic (cid:13)ow ( v 2 ), which have di(cid:11)erent sensitivities to (cid:13)ow (cid:13)uctuations, a measurement of the standardised skewness of the event-by-event v 2 distribution P ( v 2 ) is reported and constraints on its higher moments are provided. The Elliptic Power distribution is used to parametrise P ( v 2 ), extracting its parameters from (cid:12)ts to cumulants. The measurements are compared to di(cid:11)erent model predictions in order to discriminate among initial-state models and to constrain the temperature dependence of the shear viscosity to entropy-density ratio.
Adaptive Domestic Waste Segregation using Image Classification

Urban India currently generates 62 million tons of waste per year; by 2030, this figure is expected to rise to 165 million tons. People rarely make arrangements to provide suitable dustbins for segregated garbage in both residential and commercial contexts. As a result, the goal is to automate waste sorting at the source while minimizing human interaction. Image classification technique is used to quickly and effectively classify waste in the trash can. Unlike object detection of a specific entity, where objects of that entity do have some comparable traits and attributes, garbage can be of any dimension, item, colour, or texture. This makes the task of detecting garbage objects difficult. The effectiveness of various pre-trained convolutional neural networks for garbage categorization is compared and tested in this study.
Classical and quantum properties of variable-coordination number Josephson junction arrays

OF THE DISSERTATION Classical and Quantum Properties of Variable-Coordination Number Josephson Junction Arrays By JOSHUA PARAMANANDAM Dissertation Director: Professor Michael Gershenson Josephson junction arrays are a quintessential platform which has been widely used in the study a wide range of problems in classical and quantum dynamics. In this thesis, we introduce a Josephson lattice with a new topology which allows the adjustment of the coordination number. For large coordination numbers, the lattice is amenable to mean field predictions. We have employed this lattice to study classical phase transitions. The lattice has been studied by Monte-Carlo simulations to determine TC . The experimental determination of TC agrees with numerical studies and are close to the mean field predictions. We have also studied the magnetic field dependence of the transition temperatures using a tight-binding model. The experimental results agree fairly well with the results of numerical calculations. The classical dynamics of this lattice has been simulated with RCSJ model and are compared with the switching dynamics of the array. We have studied both zero field and field-tuned quantum phase transitions the VZN lattice. The zero-field transition occurs at a critical value of the control parameter g smaller than the disorder-free case. When frustrated by the magnetic field, the arrays demonstrated several quantum phase transitions at different critical values of the resistance between RC = 3 − 10kΩ, which is in line with earlier observations. In particular, with increasing B we
Plant Classification based on Grey Wolf Optimizer based Support Vector Machine (GOS) Algorithm

Leaves are the primary identifying feature of trees and other plants. Many of these plants are used in the pharmaceutical industry as industrial crops. Growing automation in industries including commerce and medicine has made accurate leaf identification crucial. Leaves are typically classified according to morphological or genetic characteristics. As a result of their numerous physical differences, however, it is becoming increasingly difficult to categorize the diverse leaf cultivars that exist. Several evolutionary shifts over the past several decades have resulted in an increase in the number of variants of a certain leaf type. To manually sift and identify these leaves is a laborious process. A novel hybrid GOS algorithm is proposed in this study for detecting leaves based on their shape, color, and texture. Three types of leaves (apple, cucumber, and mango) are used as examples, and features for each are extracted using Image Processing techniques, before being optimized with the Grey Wolf Optimizer and finally classified with the SVM (Support Vector Machine) classifier algorithm. Experimental results show that the proposed GOS work improves upon the SVM classifier, with a classification accuracy of 96.83 percent.
Understanding User Behaviour with Web Session Clustering and User Engagement Metrics

Clustering technique is one of the mining techniques in web usage mining to discover user navigational behaviour of similar patterns from a weblog. A weblog is preprocessed to retrieve the web sessions that can be assigned to the clustering techniques. Clustering will group similar user access patterns from web sessions. The main purpose of web session clustering is to evaluate how engaged the users are with the website. Therefore, there are two important parts presented in this paper, which are the performance analysis of different clustering techniques and the utilization of user engagement metrics to describe the user behaviour of each cluster. The clustering techniques, namely k-means, k-medoids, bisecting k-means, and fuzzy c-means are applied on two different weblogs. The clustering qualities of the techniques are then compared using cluster validity indices. Two engagement metrics, which are the number of clicks and the number of sessions are calculated for each cluster to discover its user behaviour.
Experiments on the Laminar/Turbulent Transition of Liquid Flows in Rectangular Microchannels

The advances of micro-fabrication techniques allow for the manufacturing of micro-heat exchangers or micro-reactors. These micro-devices are characterized by a large surface-to-volume ratio and, hence, allow for the transfer of large heat fluxes or offer large catalytic surfaces for reactions. The design and optimization of such micro-devices heavily relies on correlations for pressure drop and heat transfer, as well as on information on the laminar/turbulent transition. As these questions are still discussed controversially in literature, a careful investigation appears highly desirable. This paper concentrates on rectangular stainless steel micro-channels with a hydraulic diameter of about 133 μm. Three aspect ratios of 1:1, 1:2, 1:5 are studied, whereas the hydraulic diameter is kept constant. The average roughness depth of the channel walls is about 1–2 μm in general, and specific channels are of roughness depth of about 25 μm. Filtered and degassed de-ionized water is driven at pressure differences up to 20 bar through the channels, allowing for Reynolds numbers up to 4000. The measuring techniques allow for a highly accurate determination of the mass flow rate (precision weighting), the temperatures at inlet and outlet, the pressure drop, and the time-resolved velocity field (μPIV). The measured quantities consistently show that the laminar/turbulent transition for smooth channels is in the Reynolds number range of 1900–2200, which is in agreement with findings for macroscopic channels. The influence of rough channel walls appears particularly strong for the micro channels of aspect ratio 1:5 (Reynolds number of about 1000). This raises the question of whether the relative roughness remains the relevant parameter at extreme aspect ratios. In this article, we focus on the μPIV results.
NAGNet: A Convolutional Neural Network for Real-Time Sentiment Analysis of Students

Students' emotional state during class can reflect their understanding of classroom knowledge. Analysing the emotional data of students is helpful for ensuring that teachers understand students' learning effect and improve their teaching quality. With the rapid development of information education, student emotion analysis has been widely studied in the field of educational technology. However, due to the complexity and inconsistency of educational scenes, traditional facial expression recognition algorithms have low accuracy, losing information while extracting facial expression features, and student emotion analysis faces several technical issues. Therefore, based on the theory of deep learning, this paper proposes a novel network model NAGNet for recognizing and analysing students' expressions in classroom environments, to assist teachers in carrying out reasonable teaching effect evaluations. In this model, Res2Net50 is used as the backbone network to enhance the feature extraction ability of the model. Moreover, non-local attention module is added to integrate the global facial expression feature information to achieve fine-grained sentiment analysis. In addition, we replace the original pooling algorithm with generalized mean (GeM) pooling to pool the features of the last layer. The experimental results on the public affection dataset FERPlus, which includes eight emotions, show that the NAGNet model achieves an accuracy of 89.3%. Thus, the proposed model has higher accuracy than existing methods. In a real-world application scenario, the NAGNet model can analyse and detect students' emotions in real time. What's more, it has good robustness.
Energy Systems Test Case Discovery Enabled by Test Case Profile and Repository

Smart energy systems comprise multiple domains like power, thermal, control, information, and communication technology, which increases the complexity of research and development studies. This expansion also requires larger and ever so complex experimental pilot environments driving the demand for geographically distributed multi-research infrastructure tests. The Holistic Test Description approach supports the design of multi-domain and multi-research infrastructure tests by orga-nizing the test cases into comprehensive segments, ensuring all relevant items for testing are covered. These test cases eventually form a pool, which to understand holistically would require studying and reading all the descriptions. This work proposes therefore the concept of Test Case Profiles to improve test case discovery and the structured creation of them. Test Case Profiles add further structure to the indexing in test case repositories. Along with the proposed indexing method, four different use cases are introduced to motivate additional applications of the proposed concept.
A Fusion Network for Non-Uniform Deblurring*

In the field of computer vision, non-uniform image deblurring is a crucial and difficult task. By learning features from receptive fields, existing image deblurring algorithms have made advanced progress. However, non-local feature representations, which depict the global data distribution of blurry images are not taken into account. In this paper, we investigate non-uniform task by integrating local and non-local features. Specifically, we develop a DRDDBU (Dense-in-Residual Dense Dilation Blocks Unit) and a SAB (Scale Attention Block) to implement local and non-local, respectively. DRDDBU has the virtue of dense connections embodied in locally dense blocks and globally dense connections, which reuses and enhances all intermediate features. SAB is developed to preserve significant and suppress irrelevant responses for generating latent images. In addition, multiple loss functions are proposed to enhance network training and encourage convergence. Subjective and objective comparison experiments on various datasets are done to illustrate the efficiency of the suggested strategy. On synthetic datasets and real images, our non-uniform deblurring method outperforms state-of-the-art (SOTA) methods.
Model Predictive Control of a Single Stage Power Factor Correction for Inductive Power Transfer Systems

Single-phase AC-DC inductive power transfer (IPT) converters are widely used in grid-connected systems, such as wireless electric vehicle (EV) chargers. Traditional two-stage topology with an AC-DC power factor correction (PFC) rectifier stage and a DC-DC IPT converter stage cannot achieve the highest overall efficiency and the lowest production cost. In this paper, the maximum usage of a high frequency inverter is provided in dynamic inductive power transfer applications by optimization of power factor (PF) considering the limitation of the inverter VA capability. In addition, a compact single- phase and single- stage AC-DC converter in the secondary side has presented. As salient feature, the control system does not need data transfer between the primary and the secondary sides to achieve its objectives. Also, in order to significantly increase the system dynamics, move blocking model-based predictive control is applied to secondary side PF correction converter. In order to evaluate the effectiveness of the introduced method under the critical conditions, it is assumed that the secondary-side operates out of resonant frequency, due to aging or failure issues. The simulation results confirm the desirable performance of the proposed topology.
Procedural Fairness And Communication Satisfaction As Factors In Mission Attachment Of Employees: An Applied Research In A Motorcycle Firm

Bu makale, isgorenlerin orgutsel misyon bagliligi ile iletisim tatmini ve islem adaleti algilamalari arasindaki iliskinin bir motosiklet isletmesinde yapilan uygulama ile ortaya konmasini konu edinmektedir. Arastirmada amaca ve icerige uygun olarak hazirlanmis bir anket calismasi kullanilmistir. Arastirmada toplam 106 adet anket kullanilirken, anketlerin geri donusum orani % 81.5 olarak gerceklesmistir. Arastirma sonuclari isgorenlerin orgutsel misyon bagliligi ile iletisim tatminleri arasinda pozitif ve cok guclu bir iliski oldugu gercegini ortaya koymaktadir. Ayni sekilde isgorenlerin orgutsel misyon bagliligi ile islem adaleti algilamalari arasinda da poztif yonlu bir iliski oldgu arastirmada belirlenmistir. Arastirma sonuclari orgutsel misyon bagliligindaki degisimin % 53.7’sinin iletisim tatmini, islem adaleti algilamalari ile cinsiyet ve toplam calisma suresi degiskenlerine bagli oldugunu gostermektedir. Orgutsel misyon bagliligi uzerindeki en buyuk etkiye iletisim tatmini degiskeninin sahip oldugu ve bunu islem adaleti algilamalari degiskeninin takip ettigi de arastirmanin bir diger sonucudur. Isgorenlerin orgutsel misyon bagliliginin iletisim tatmini ve islem adaleti algilamalarinin desteklenmesi ile artirilabilecegi de arastirmanin temel sonucu olarak one cikmaktadir
Ghost Expectation Point with Deep Reinforcement Learning in Financial Portfolio Management

Reinforcement learning algorithms have a wide range of applications in diverse areas, such as portfolio management, automatic driving, and visual object detection. This paper introduces a novel network architecture Ghost expectation point (GXPT) embedded in a deep reinforcement learning framework based on GhostNet, which is constructed using convolutional neural networks and ghost bottleneck modules. The Ghost bottleneck module can generate many Ghost feature maps, improving the ability of the network to extract information from the real-world market. Furthermore, the number of parameters and floating point operations (FLOPs) is reduced. We use the GXPT to realize Jiang et al.’s Ensemble of Identical Independent Evaluators (EIIE) framework. In the EIIE framework, GhostNet is adapted to implement Identical Independent Evaluators to evaluate the growth potential of each asset. In our experiments, we chose the Accumulated Portfolio Value (APV) and the Sharpe Ratio (SR) to assess the efficiency of our strategy in the back-test. It is found that our strategy is at least 5.11% and 29.9% higher than the comparison strategies in APV and SR, respectively.
Preserving Mental Health Information in Speech Anonymization

Widespread speech technologies may change the dynamics of mental health (MH) diagnosis, monitoring, and care by tracking spectrotemporal patterns of speech in a personalized manner. Yet, these technologies have given rise to a large public debate about the ability of users to protect their privacy. This work examines a speech anonymization algorithm that preserves information that is diagnostic of the MH condition of the speaker and representative of the phonological content of speech, while suppressing characteristics related to the speaker identity. The proposed anonymization algorithm relies on an auto-encoder that conducts an identity mapping between the original input, represented as the superposition of Mel-spectrogram coefficients and posteriogram (PPG) vectors, and the reconstructed output. The auto-encoder is trained in an adversarial manner to minimize the loss corresponding to MH-related information and maximize the loss corresponding to the identity (ID) of a speaker. The proposed algorithm is evaluated on data from healthy participants and patients with depression from the Distress Analysis Interview Corpus Wizard of Oz (DAIC-WoZ) dataset. Results indicate that the speech signals synthesized by the proposed anonymization algorithm have higher word error rate (WER), as calculated between the original speech transcripts and transcripts from the anonymized speech signals obtained via an automatic speech recognizer (ASR), and are rated as being less comprehensible by MTurk listeners compared to the original signals. However, the intelligibility of the synthesized speech appears equivalent to baseline speech anonymization algorithms that solely suppress the speaker ID without considering MH-related information, or rely on a cascade of signal processing methods that perform consecutive speech transformations. The proposed algorithm can further effectively suppress information related to the speaker ID, since the corresponding anonymized signals achieve 15.9% reduction in speaker classification accuracy compared to the original speech signals. Finally, the anonymized speech signals yield similar performance to the original speech when they are used as an input for estimating degree of depression severity, and superior performance compared to conventional speech anonymization algorithms that do not consider the preservation of MH-related information as an optimization criterion.
ARIMA Model based Time Series Modelling and Prediction of Foreign Exchange Rate against US Dollar

Exchange rate forecasting has proven challenging for players like traders and professionals in this current financial industry. Econometric and statistical models are often utilized in the analysis and forecasting of foreign exchange rate. Governments, financial organizations, and investors prioritize analyzing the future behaviour of currency pairs because this analyzing technique is being utilized to understand a country's economic status and to make a decision on whether to do any transactions of goods from that country. Several models are used to predict this kind of time-series with adequate accuracy. However, because of the random nature of these time series, strong predicting performance is difficult to achieve. During the Covid-19 situation, there is a drastic change in the exchange rate worldwide. This paper examines the behaviour of Australia's (AUD) daily foreign exchange rates against the US Dollar from January 2016 to December 2020 and forecasts the 2021 exchange rate using the ARIMA model. For better accuracy, technical indicators such as Interest Rate Differential, GDP Growth Rate and Unemployment Rate are also taken into account. In exchange rate forecasting, there are various types of performance measures based on which the accuracy of the forecasted result is computed. This paper examines seven performance measures and found that the accuracy of the forecasted results is adequate with the actual data.