Miracle-dz/UniFAR-FTUs huggingface dataset:
Student Result Prediction in Covid-19 Lockdown using Machine Learning Techniques

Covid-19 virus has moved the world to a static state. This has confused the education field in conducting the end examinations. The school education has declared the results of a few classes as pass without conducting the end examinations. But, this may not be possible for all the education system, especially for final year college student results. For this reason, the prediction of result based on their previous performance is essential. Prediction can be effectively achieved by using machine learning algorithms. Machine learning automatically learns and improves from example and experience. Supervised machine learning algorithms Logistic regression and SVM are used for this work. The data set of 1460 students result of a college is considered for the study. Finally, the trained machine predicts accurately whether the student is eligible to acquire the degree or not and the same is viewed in the college portal.
An Approach to Skin Cancer Detection using Keras and Tensorflow

For humankind, skin cancer is a troubling illness. Given the rapid growth rate of skin cancer, its high treatment cost and death rate, the need for early detection of skin cancer has been increased. Now, the world has evolved in a way where skin cancer detection is possible by image pre-processing and machine learning methods. One well known and well worked method is Convolutional Neural Network (CNN). After segmentation of dermoscopic images, the features of the affected skin cells are extracted using feature extraction technique. We propose a convolutional neural network model to detect cancerous state of a person’s skin and classify them as malignant (melanoma) and benign (non-malignant). The above model’s architecture contains various layers which helps in reading the dataset by computer. Accurate results are always expected in these cases. We are using manual approach instead of automatic approach to overcome possible errors.
A literature review of the research on ferroelectric polymers

In this work, the research and development of ferroelectric polymers has been reviewed. The main focus is on properties of different types of ferroelectric polymers. Ferroelectrics of ceramic and thin film nature, are widely known for their use in several electronic devices. But the existence of ferroelectricity in polymers has made the ferroelectric polymers to be used in different electrical applications such as sensors, actuators, etc. The main theme of this paper is to report only the existing literature on ferroelectric-polymeric materials.
Enhancing new business development success a new portfolio management method for Fujifilm Tilburg

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
Examining the Effects of Food and Product Production Values and Production Added Value on Inflation Over the Years: Empirical Evidence for Turkey

In this study, it is aimed to examine the effects of food and product production values on inflation. In the study, the variables of the World Bank Country Reports between 1991-2019 Consumer Price Index, Wholesale Price Index, Food Production Index, Product Production Index and Production Value Added were used. According to the results obtained from the study, there is a statistically significant relationship between TUFE and TOFE and GUE, UUE and UKD variables (p <0.01). According to the results of controlled correlation analysis, the effects of food and product production indexes on consumer and wholesale inflation level are not statistically significant (p> 0.05). The effect of UKD and GUE parameters on inflation is statistically significant (p <0.05). Explanation power of both models is very high. According to the regression coefficients, UKD has a negative effect, and GUE has a positive effect. The results show that production has a positive effect on inflation, while production value added has a decreasing effect on consumer and wholesale prices. These results show that the production in our country is actually high cost and its added value is low.
ESG Performance and COVID-19 Pandemic: An Empirical Analysis of European Listed Firms

This paper aims to investigate the linkage between ESG performance and the impact of the COVID-19 pandemic on the stock performance of listed firms in Europe. We base on the Eurostoxx 600 index to select firms with available MSCI ESG Rating data and Sustainalytics ESG Ranking data, collected from the Bloomberg terminal. The final sample of 344 firms is then divided into two sub-samples with high and low ESG performance. We then conduct two analyses which are stock performance comparison and panel data regressions to analyze the impact of various factors in 2019 and 2020. Empirical results show that firms with high ESG performance have a lower volatility than those with low ESG performance in both periods. However, there is no evidence that the stock performance is higher for firms with high ESG performance. On the other hand, the different results obtained with panel data regressions when using the MSCI ESG Rating and Sustainalytics ESG Risk Rating implies that it is important to carefully investigate the methodology defined by each ESG rating agency. Furthermore, we find a signicant impact of COVID-19 factors on stock performance such as the number of cases, deaths, lockdown, and quantitative easing announcements. The sector and country effects are significant during the COVID-19 pandemic.
Physiologically-Regulated Adhesion of Hydrogels for Wound Dressing

Adhesion is an essential function of wound dressing materials, which allows the close contact with skin surface. However, the dressing replacement inevitably causes the secondary damage to unhealed wound. A wound dressing material having physiologically-regulated adhesion is thus expected to avert the secondary damage to patients. Herein, we report a hydrogel by covalently introducing cationic moieties into an elastic network. The cationic moieties are capable of electron-withdrawing that promotes a strong electrostatic interaction with polar groups of protein (high electron cloud density) from the wound skin tissue, enabling the adhesion of hydrogel on the skin surface. With the tissue metabolism, the nucleophilic skin surface forms a lipid layer that gradually destroys the electrostatic interaction and weakens the interfacial affinity. The adhesion energy can be reduced from 60 to &lt;10 J/m 2 in seven days, so that the dressing material can be removed from the wound area without bringing in the secondary damage. The cationic moieties also endow the hydrogel a high water swelling ratio, reaching 25200%, making it capable of absorption of a large amount of tissue exudate. In addition, the cationic moieties have an antibacterial effect that can decrease the infection possibility of the wound. The in-vivo tests demonstrate that the wound on a mouse back reaches physiological 100% healing after the hydrogel dressing treatment for 12 days. All results show that the introduction of cationic moieties makes the hydrogel more promising as a wound dressing.
The Fast Kernel Transform

Kernel methods are a highly effective and widely used collection of modern machine learning algorithms. A fundamental limitation of virtually all such methods are computations involving the kernel matrix that naively scale quadratically (e.g., constructing the kernel matrix and matrix-vector multiplication) or cubically (solving linear systems) with the size of the data set $N. $ We propose the Fast Kernel Transform (FKT), a general algorithm to compute matrix-vector multiplications (MVMs) for datasets in moderate dimensions with quasilinear complexity. Typically, analytically grounded fast multiplication methods require specialized development for specific kernels. In contrast, our scheme is based on auto-differentiation and automated symbolic computations that leverage the analytical structure of the underlying kernel. This allows the FKT to be easily applied to a broad class of kernels, including Gaussian, Matern, and Rational Quadratic covariance functions and physically motivated Green's functions, including those of the Laplace and Helmholtz equations. Furthermore, the FKT maintains a high, quantifiable, and controllable level of accuracy -- properties that many acceleration methods lack. We illustrate the efficacy and versatility of the FKT by providing timing and accuracy benchmarks and by applying it to scale the stochastic neighborhood embedding (t-SNE) and Gaussian processes to large real-world data sets.
Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80–120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points.
Spline Smoothing of 3D Geometric Data

Over the past two decades, we have seen an increased demand for 3D visualization and simulation software in medicine, architectural design, engineering, and many other areas, which have boosted the investigation of geometric data analysis and raised the demand for further advancement in statistical analytic approaches. In this paper, we propose a class of spline smoothers appropriate for approximating geometric data over 3D complex domains, which can be represented in terms of a linear combination of spline basis functions with some smoothness constraints. We start with introducing the tetrahedral partitions, Barycentric coordinates, Bernstein basis polynomials, and trivariate spline on tetrahedra. Then, we propose a penalized spline smoothing method for identifying the underlying signal in a complex 3D domain from potential noisy observations. Simulation studies are conducted to compare the proposed method with traditional smoothing methods on 3D complex domains.
Property-Aware Robot Object Manipulation: a Generative Approach

When transporting an object, we unconsciously adapt our movement to its properties, for instance by slowing down when the item is fragile. The most relevant features of an object are immediately revealed to a human observer by the way the handling occurs, without any need for verbal description. It would greatly facilitate collaboration to enable humanoid robots to perform movements that convey similar intuitive cues to the observers. In this work, we focus on how to generate robot motion adapted to the hidden properties of the manipulated objects, such as their weight and fragility. We explore the possibility of leveraging Generative Adversarial Networks to synthesize new actions coherent with the properties of the object. The use of a generative approach allows us to create new and consistent motion patterns. Besides, the informative content of the actions is preserved. Our results show that Generative Adversarial Nets can be a powerful tool for the generation of novel and meaningful transportation actions, which result effectively modulated as a function of the object weight and the carefulness required in its handling.
Amortized Generation of Sequential Counterfactual Explanations for Black-box Models

Explainable machine learning (ML) has gained traction in recent years due to the increasing adoption of ML-based systems in many sectors. Counterfactual explanations (CFEs) provide “what if” feedback of the form “if an input datapoint were x (cid:48) instead of x , then an ML-based system’s output would be y (cid:48) instead of y . ” CFEs are attractive due to their actionable feedback, amenability to existing legal frameworks, and ﬁdelity to the underlying ML model. Yet, current CFE approaches are single shot—that is, they assume x can change to x (cid:48) in a single time period. We propose a novel stochastic-control-based approach that generates sequential CFEs, that is, CFEs that allow x to move stochastically and sequentially across intermediate states to a ﬁnal state x (cid:48) . Our approach is model agnostic and black box. Furthermore, calculation of CFEs is amortized such that once trained, it applies to multiple datapoints without the need for re-optimization. In addition to these primary characteristics, our approach admits optional desiderata such as adherence to the data manifold, respect for causal relations, and sparsity—identiﬁed by past research as desirable properties of CFEs. We evaluate our approach using three real-world datasets and show successful generation of sequential CFEs that respect other counterfactual desiderata.
Meta-Learning to Compositionally Generalize

Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts. This property allows humans to create and interpret novel sentences, generalizing robustly outside their prior experience. Neural networks have been shown to struggle with this kind of generalization, in particular performing poorly on tasks designed to assess compositional generalization (i.e. where training and testing distributions differ in ways that would be trivial for a compositional strategy to resolve). Their poor performance on these tasks may in part be due to the nature of supervised learning which assumes training and testing data to be drawn from the same distribution. We implement a meta-learning augmented version of supervised learning whose objective directly optimizes for out-of-distribution generalization. We construct pairs of tasks for meta-learning by sub-sampling existing training data. Each pair of tasks is constructed to contain relevant examples, as determined by a similarity metric, in an effort to inhibit models from memorizing their input. Experimental results on the COGS and SCAN datasets show that our similarity-driven meta-learning can improve generalization performance.
Data-Efficient Instance Generation from Instance Discrimination

Generative Adversarial Networks (GANs) have significantly advanced image synthesis, however, the synthesis quality drops significantly given a limited amount of training data. To improve the data efficiency of GAN training, prior work typically employs data augmentation to mitigate the overfitting of the discriminator yet still learn the discriminator with a bi-classification (i.e., real vs. fake) task. In this work, we propose a data-efficient Instance Generation (InsGen) method based on instance discrimination. Concretely, besides differentiating the real domain from the fake domain, the discriminator is required to distinguish every individual image, no matter it comes from the training set or from the generator. In this way, the discriminator can benefit from the infinite synthesized samples for training, alleviating the overfitting problem caused by insufficient training data. A noise perturbation strategy is further introduced to improve its discriminative power. Meanwhile, the learned instance discrimination capability from the discriminator is in turn exploited to encourage the generator for diverse generation. Extensive experiments demonstrate the effectiveness of our method on a variety of datasets and training settings. Noticeably, on the setting of 2K training images from the FFHQ dataset, we outperform the state-of-the-art approach with 23.5% FID improvement.
Broadcasted Residual Learning for Efficient Keyword Spotting

Keyword spotting is an important research field because it plays a key role in device wake-up and user interaction on smart devices. However, it is challenging to minimize errors while operating efficiently in devices with limited resources such as mobile phones. We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load. Our method configures most of the residual functions as 1D temporal convolution while still allows 2D convolution together using a broadcasted-residual connection that expands temporal output to frequency-temporal dimension. This residual mapping enables the network to effectively represent useful audio features with much less computation than conventional convolutional neural networks. We also propose a novel network architecture, Broadcasting-residual network (BC-ResNet), based on broadcasted residual learning and describe how to scale up the model according to the target device's resources. BC-ResNets achieve state-of-the-art 98.0% and 98.7% top-1 accuracy on Google speech command datasets v1 and v2, respectively, and consistently outperform previous approaches, using fewer computations and parameters. Code is available at https://github.com/Qualcomm-AI-research/bcresnet.
On the Outage Capacity of the Massive MIMO Diversity Channel

We consider the massive Multiple Input Multiple Output (MIMO) diversity channel affected by independent and identically distributed Rayleigh fading, with linear processing at both transmitter and receiver sides, and analyze the outage capacity for large number of antennas. We first discuss the classical Single Input Multiple Output (SIMO) diversity channel that uses Maximal Ratio Combining (MRC) or Selection Combining (SC). For MRC, a numerical computation and a Gaussian Approximation (GA) are considered, whereas for SC an exact evaluation is possible. The analysis is then straightforwardly extended to the Multiple Input Single Output (MISO) system that uses Maximal Ratio Transmission (MRT) or transmit antenna selection. The general Multiple Input Multiple Output (MIMO) system that pursues full diversity is finally considered, with both optimal linear processing and simple antenna selection at both transmitter and receiver. If the number of antennas is sufficiently large on at least one side, the outage capacity of each considered diversity channel approaches that of a suitable reference Additive White Gaussian Noise (AWGN) channel with properly defined Signal-to-Noise Ratio (SNR), which provides a performance benchmark. This conclusion is valid for large but realistic number of antennas compatible with the assumption of independent fading.
A coarse-grained decomposition of surface geostrophic kinetic energy in the global ocean

We apply a coarse-grained decomposition of the ocean’s surface geostrophic flow derived from satellite and numerical model products. In the extra-tropics we find that roughly 60% of the global surface geostrophic kinetic energy is at scales between 100 km and 500 km, peaking at ≈ 300 km. Our analysis also reveals a clear seasonality in the kinetic energy with a spring peak. We show that traditional mean-fluctuation (or Reynolds) decomposition is unable to robustly disentangle lengthscales since the time mean flow consists of a significant contribution (greater than 50%) from scales < 500 km. By coarse-graining in both space and time, we find that every length-scale evolves over a wide range of time-scales. Consequently, a running time-average of any duration reduces the energy content of all length-scales, including those larger than 1000 km, and is not effective at removing length-scales smaller than 300 km. By contrasting our spatio-temporal analysis of numerical model and satellite products, we show that the AVISO gridded product suppresses temporal variations less than 10 days for all length-scales, especially between 100 km and 500 km. Plain Language Summary Traditionally, ‘eddies’ are identified as transient features that vary in time relative to a background time mean flow. As such, a ‘mean’ flow does not necessarily imply a large length-scale flow. For example, standing eddies or stationary meanders due to topographic interactions have little variation in time, but can still have significant energy at small length scales. Similarly, ‘eddy’, ‘time-varying’ or ’transient’ do not necessarily imply small length-scale, with examples including the large-scale transient motions from Rossby waves or fluctuations of the Kuroshio Current. Hence, the traditional time average approach offers no control over the specific physical length that divides oceanic flow into ‘large’ and ‘small’. That is, the length-scales constituting the large-scale flow cannot be varied/controlled by time averaging in a manner that is consistent with length-scales resolved in a coarse climate simulation. We consider a coarse-graining method to studying geostrophic ocean currents with this method consistent with our notions of ’mesoscale’ as defined by a length scale. Our approach is directly relevant to scale-aware parameterization requirements of coarse-resolution simulations, since we are directly focused on length-scales of the flow fields. To illustrate the coarse-graining method, and to add understanding to the oceanic flows analyzed here, we present the first ar X iv :2 10 6. 04 15 7v 1 [ ph ys ic s. ao -p h] 8 J un 2 02 1 A PREPRINT JUNE 9, 2021 global characterization of kinetic energy content and its temporal variation as decomposed by coarse-graining according to precisely defined length-scales.
Learning from Multiple Noisy Partial Labelers

Programmatic weak supervision creates models without hand-labeled training data by combining the outputs of heuristic labelers. Existing frameworks make the restrictive as-sumption that labelers output a single class label. Enabling users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision. We introduce this capability by deﬁning a probabilistic generative model that can estimate the underlying accuracies of multiple noisy partial labelers without ground truth labels. We show how to scale up learning, for example learning on 100k examples in one minute, a 300 × speed up compared to a naive imple-mentation. We also prove that this class of models is generically identiﬁable up to label swapping under mild conditions. We evaluate our framework on three text classiﬁcation and six object classiﬁcation tasks. On text tasks, adding partial labels increases average accuracy by 8.6 percentage points. On image tasks, we show that partial labels allow us to approach some zero-shot object classiﬁcation problems with programmatic weak supervision by using class attributes as partial labelers. On these tasks, our framework has accuracy comparable to recent embedding-based zero-shot learning methods, while using only pre-trained attribute detectors.