Miracle-dz/UniFAR-FTUs huggingface dataset:
A WAVELET APPROACH FOR MEDICAL IMAGE DENOISING

Medical Images have always been vulnerable to high level components of noises. Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography and Ultrasound are among most popular techniques for producing medical images, during image capture and transmission noise is added in the images that decreases the image quality and leads to poor image analysis. Various denoising techniques are used to remove the noise or distortion from images while preserving the original quality of the image among which wavelet transform has been proved an efficient one in reducing the noise level. The aim of this paper to characterize the Gaussian noise in wavelet transforms subsequently a threshold based denoising algorithm has been developed using hard and soft thresholding techniques that works on Haar, Daubechies and Symlet Transforms. Firstly the image is decomposed using Haar and Daubechies and symlet transforms, and then the level of soft and hard threshold is selected for reducing the noise in the image and finally the comparison between them has been done on the basis of calculated PSNR& MSE of an image for every wavelet.
A Randomised Subspace Gauss-Newton Method for Nonlinear Least-Squares

We propose a Randomised Subspace Gauss-Newton (R-SGN) algorithm for solving nonlinear least-squares optimization problems, that uses a sketched Jacobian of the residual in the variable domain and solves a reduced linear least-squares on each iteration. A sublinear global rate of convergence result is presented for a trust-region variant of R-SGN, with high probability, which matches deterministic counterpart results in the order of the accuracy tolerance. Promising preliminary numerical results are presented for R-SGN on logistic regression and on nonlinear regression problems from the CUTEst collection.
An efficient cloud detection method for high resolution remote sensing panchromatic imagery

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems

Recently, graph neural networks have been gaining a lot of attention to simulate dynamical systems due to their inductive nature leading to zero-shot generalizability. Similarly, physics-informed inductive biases in deep-learning frameworks have been shown to give superior performance in learning the dynamics of physical systems. There is a growing volume of literature that attempts to combine these two approaches. Here, we evaluate the performance of thirteen different graph neural networks, namely, Hamiltonian and Lagrangian graph neural networks, graph neural ODE, and their variants with explicit constraints and different architectures. We briefly explain the theoretical formulation highlighting the similarities and differences in the inductive biases and graph architecture of these systems. We evaluate these models on spring, pendulum, gravitational, and 3D deformable solid systems to compare the performance in terms of rollout error, conserved quantities such as energy and momentum, and generalizability to unseen system sizes. Our study demonstrates that GNNs with additional inductive biases, such as explicit constraints and decoupling of kinetic and potential energies, exhibit significantly enhanced performance. Further, all the physics-informed GNNs exhibit zero-shot generalizability to system sizes an order of magnitude larger than the training system, thus providing a promising route to simulate large-scale realistic systems.
How Knowledge Science is Studied⎯a Vision from Conference Mining of the Relevant Knowledge Science Symposia

Abstract As knowledge engineering, knowledge management and even knowledge creation have been widely accepted in academics, business and industry, knowledge science is still a rather young area where diverse explorations are being undertaken. How to acquire a vision about knowledge science both efficiently and effectively is a problem. As the publications at those relevant conferences on the interesting topics represent the latest achievements and humans’ thinking about the topics, some hints may be acquired from those ideas (simply reflected by those keywords in each paper) and the authors. The so-called iView analysis toward the conference publications is developed to acquire the systemic vision of an interesting topic. Based on social network analysis technologies, iView analysis includes two basic steps. The first is to construct a pair of networks, a keyword network and an author network, based on paper information. Secondly, characteristics of both networks are analyzed, such as cutpoint, community structure, etc. from which to acquire some kind of collective explanations on the interesting topics under systemic perspectives. In this paper, an international symposium on knowledge science is tested to draw a rough picture of knowledge science studies. The information detected by network analysis could be pushed to conference organizers and participants for activities facilitation and to the other interested people to quickly acquire basic concepts or understanding of the concerned disciplines or topics, such as what are the major research topics? Who are the principal investigators? etc.
Application of furniture images selection based on neural network

In the construction of 2 million furniture image databases, aiming at the problem of low quality of database, a combination of CNN and Metric learning algorithm is proposed, which makes it possible to quickly and accurately remove duplicate and irrelevant samples in the furniture image database. Solve problems that images screening method is complex, the accuracy is not high, time-consuming is long. Deep learning algorithm achieve excellent image matching ability in actual furniture retrieval applications after improving data quality.
Body of Knowledge

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A novel algorithm for electronic image stabilization based on improved optical flow

Aiming at the problem of the jitter of video sequence recorded by the strapdown seeker in the terminal guidance process, a novel algorithm for electronic image stabilization based on improved optical flow is proposed. The algorithm uses Shi-Tomasi corner detection method to extract the image corner, and estimates the global motion parameters of jittery video sequence based on improved Pyramid LK optical flow which is designed. Then the Kalman smoothing global motion vector is adopted, which effectively makes compensation on the current image motion and retains the active movement while filtering random jitter parameters. Finally, stable image sequence output is achieved. The simulation test and the embedded platform for the actual test results indicate that the proposed algorithm has a good image stabilization effect on the translation, rotation and scaling motion of the random jittery video sequence recorded by the strapdown seeker, and possesses good robustness and real-time performance.
Enhancing cosmic shear with the multi-scale lensing PDF

We quantify the cosmological constraining power of the ‘lensing PDF’ - the one-point probability density of weak lensing convergence maps - by modelling this statistic numerically with an emulator trained on w CDM cosmic shear simulations. After validating our methods on Gaussian and lognormal ﬁelds, we show that ‘multi-scale’ PDFs - measured from maps with multiple levels of smoothing - offer considerable gains over two-point statistics, owing to their ability to extract non-Gaussian information: for a mock Stage-III survey, lensing PDFs yield 33% tighter constraints on the clustering parameter S 8 = σ 8 (cid:112) Ω m / 0 . 3 than the two-point shear correlation functions. For Stage-IV surveys, we achieve > 90% tighter constraints on S 8 , but also on the Hubble and dark energy equation of state parameters. Interest-ingly, we ﬁnd improvements when combining these two probes only in our Stage-III setup; in the Stage-IV scenario the lensing PDFs contain all information from the standard two-point statistics and more. This suggests that while these two probes are currently complementary, the lower noise levels of upcoming surveys will unleash the constraining power of the PDF.
Finding Triangles and Other Small Subgraphs in Geometric Intersection Graphs

We consider problems related to finding short cycles, small cliques, small independent sets, and small subgraphs in geometric intersection graphs. We obtain a plethora of new results. For example: * For the intersection graph of $n$ line segments in the plane, we give algorithms to find a 3-cycle in $O(n^{1.408})$ time, a size-3 independent set in $O(n^{1.652})$ time, a 4-clique in near-$O(n^{24/13})$ time, and a $k$-clique (or any $k$-vertex induced subgraph) in $O(n^{0.565k+O(1)})$ time for any constant $k$; we can also compute the girth in near-$O(n^{3/2})$ time. * For the intersection graph of $n$ axis-aligned boxes in a constant dimension $d$, we give algorithms to find a 3-cycle in $O(n^{1.408})$ time for any $d$, a 4-clique (or any 4-vertex induced subgraph) in $O(n^{1.715})$ time for any $d$, a size-4 independent set in near-$O(n^{3/2})$ time for any $d$, a size-5 independent set in near-$O(n^{4/3})$ time for $d=2$, and a $k$-clique (or any $k$-vertex induced subgraph) in $O(n^{0.429k+O(1)})$ time for any $d$ and any constant $k$. * For the intersection graph of $n$ fat objects in any constant dimension $d$, we give an algorithm to find any $k$-vertex (non-induced) subgraph in $O(n\log n)$ time for any constant $k$, generalizing a result by Kaplan, Klost, Mulzer, Roddity, Seiferth, and Sharir (1999) for 3-cycles in 2D disk graphs. A variety of techniques is used, including geometric range searching, biclique covers,"high-low"tricks, graph degeneracy and separators, and shifted quadtrees. We also prove a near-$\Omega(n^{4/3})$ conditional lower bound for finding a size-4 independent set for boxes.
Influence of Fin-tech on Customer Satisfaction: Empirical Evidence from Allied Bank of Pakistan

The purpose of this research is to examine the influence of financial technology on customer satisfaction in Allied bank of Pakistan. For this research the sample size of 183 respondents of Allied bank of Pakistan was selected using convenient sampling. The questionnaire was designed by 5 Likert-scale that was used to collect the data in order to examine the influence of financial technology on customer satisfaction. The models used were factor analysis, regression analysis, multiple regression analysis. The results of regression analysis showed that, there is a significant influence of safety reliability, Service Security and has positive and significant influence on customer satisfaction; whereas, Ease of Use and performance has positive and insignificant influence on customer satisfaction. Finally, it is recommended that, in the face of high-level competition in the banking industry and similarity of products offered by commercial banks establishing customer’s feedback system and evaluating its technology platform with current technological advancements for the better customer satisfaction.
A SURVEY OF RECOMMENDER SYSTEM TYPES AND ITS CLASSIFICATION

The current generation is finding it difficult to find the right information from the enormous amount of data they are presented with in the online platforms. It is hard to spent time online searching for information in such a scenario and it craves for the need of an information filtering system that could help them discover the information they seek. A research field that does this has emerged in the last few years called as recommender systems. A lot of extensive research is happening in the field which is trying to incorporate more attributes to give more precise and relevant personalised recommendations to a user. This paper is focused on reviewing some significant works in the three basic recommender system types including collaborative filtering, content based filtering and hybrid filtering. The paper also have identified and listed the major challenges faced by recommender systems. The main contribution of the paper is in proposing a novel hybrid recommender system which addresses the sparsity and serendipity drawback of recommender systems. The proposed method is expected to deliver more accurate, relevant and novel predictions.
Error analysis in German writing by Chinese Malaysian students

Transfer from a pre-existing linguistic repertoire to a target language is an important issue in foreign language acquisition. There are limited studies focusing on common German grammatical errors in the written form, both morphological and syntactical, that are made by Chinese Malaysian students in the Malaysia context. Furthermore, the correlation of these errors with structural differences between the students’ linguistic repertoire and the target language, i.e. German language should be given attention. The principal objective of this study is to ascertain the direct correlation between the influence of the students’ dominant language (Chinese) and the language of instruction (English) with the extent of grammatical errors made whilst acquiring the German language. Twenty Chinese Malaysian students participated in this study, and the data were collected from their compositions. The respondents were Chinese Malaysian students from the International Education College who have Chinese as their first language. The ungrammatical structures found in the written compositions of the students were analysed. This study used Contrastive Analysis to examine the differences between Mandarin Chinese, English and German. Error Analysis was used to identify the errors that had been made by the participants. This study was designed by the author based on the Interlanguage Theory. Common grammatical errors were categorised and analysed. The research findings prove that the learners were confused by the difficulties in German grammar. The study also reveals that the sources of errors were also intralingual and not just interlingual. Error Analysis provides information on the effectiveness of the teaching and the weakness of the participants. The results also reveal errors other than grammatical errors which should be given attention by the instructors or academics. The findings can help to enhance the teaching and learning of German as a foreign language in a class attended by learners who have Chinese and English as their previously acquired languages. This study also provides an insight into the nature of the learners’ erroneous sentence construction.
Toward Horizon-scale Accretion onto Supermassive Black Holes in Elliptical Galaxies

We present high-resolution, three-dimensional hydrodynamic simulations of the fueling of supermassive black holes in elliptical galaxies from a turbulent medium on galactic scales, taking M87* as a typical case. The simulations use a new GPU-accelerated version of the Athena++ AMR code, and they span more than six orders of magnitude in radius, reaching scales similar to that of the black hole horizon. The key physical ingredients are radiative cooling and a phenomenological heating model. We find that the accretion flow takes the form of multiphase gas at radii less than about a kpc. The cold gas accretion includes two dynamically distinct stages: the typical disk stage in which the cold gas resides in a rotationally supported disk, and relatively rare chaotic stages (≲10% of the time) in which the cold gas inflows via chaotic streams. Though cold gas accretion dominates the time-averaged accretion rate at intermediate radii, accretion at the smallest radii is dominated by hot virialized gas at most times. The accretion rate scales with radius as Ṁ∝r1/2 when hot gas dominates, and we obtain Ṁ≃10−4–10−3M⊙yr−1 near the event horizon, similar to what is inferred from EHT observations. The orientation of the cold gas disk can differ significantly on different spatial scales. We propose a subgrid model for accretion in lower-resolution simulations in which the hot gas accretion rate is suppressed relative to the Bondi rate by ∼(rg/rBondi)1/2 . Our results can also provide more realistic initial conditions for simulations of black hole accretion at the event horizon scale.
APLIKASI SISTEM ORDER ONLINE BERBASIS MOBILE ANDROID PADA OUTLET PIZZA HUT DELIVERY

Peningkatan penggunaan mobile saat ini sudah sangat berkembang dengan pesat terutama pada pengguna smartphone. Android salah satu smartphone yang mendominasi perkembangan tersebut. Android saat ini tidak hanya bisa membantu manusia dalam berkomunikasi tetapi juga dikembangkan untuk membantu mempermudah kerja manusia.Salah satunya dalam hal Informasi pemesanan di Outlet Pizza Hut Delivery. Aplikasi Order Online berbasis android ini dapat membantu customer dalam pesanan makanan siap saji.Dengan terintegrasi kedalam database, aplikasi ini memberikan informasi yang tersimpan dalam database tentang info terkini seputar kegiatan Order Online. Aplikasi ini dibangun dengan sebuah software dari anywhere software yakni basic4android, sebuah software untuk membuat aplikasi android dengan menggunakan bahasa basic. Database yang digunakan dalam sistem operasi android ini adalah MYSQL. Salah satu tujuan dibangun aplikasi ini adalah memberikan kemudahan kepada customer dalam pesan makanan siap saji.
Parallel Swarm Optimization Based Cluster for Emergency Message Dissemination in VANETs

In intelligent transportation system, emergency message has to be disseminated rapidly and extensively in order to inform as many vehicles in limited time. Hence, the broadcast storm problem is prone to occur in vehicular ad hoc networks. In the past, several approaches have been proposed to solve the problem. They are counter-, distance-, location-, cluster-, and probabilistic-based schemes. In this paper, we analyze existing cluster based protocols and present a novel scheme that is designed to form stable clusters, reduce the overhead of cluster maintenance during emergency message dissemination. The proposed clustering algorithm uses particle swarm optimization to assigns weight values for different factors during the cluster head election and cluster formation. It ensures the dissemination of the emergency message onto every part of the road, and when there is no relay vehicle, the emergency message will be reactivated. We use the GrooveNet simulator to demonstrate that the proposed scheme has a low collision probability, low overhead, more stable cluster, and a short end-to-end latency.
Lernfähige Instandhaltung

Kurzfassung Die zustandsorientierte Instandhaltung von Produktionsanlagen verspricht gegenüber anderen Instandhaltungsstrategien ein besseres Verhältnis von Verfügbarkeit zu Instandhaltungskosten. Zur Bestimmung und Prognose des Anlagenzustands wird in diesem Beitrag ein neuartiger Ansatz vorgestellt. Dieser Ansatz nutzt Methoden der Künstlichen Intelligenz, die auf einen lebenszyklusphasenübergreifenden, verteilten Datenbestand angewandt werden. Der Ansatz vereinfacht die Einführung der zustandsorientierten Instandhaltung durch maschinelle Lernverfahren und ist insbesondere für Anlagen mit einem bisher unbekannten Ausfallverhalten geeignet.
The Other side of ‘us’: Alterity construction and identification work in the context of planned change

How do we use the Other to make sense of who we are? A common assumption is that people positively affirm social identities by excluding an inferior Other. This article challenges that restricted notion by focusing on the variation and situational fluidity of alterity construction (othering) in identification work. Based on an ethnographic study of a change project in a public hospital, we examine how nurses, surgeons, medical secretaries, and external management consultants constructed Others/otherness. Depending on micro-situations, different actors reciprocally differentiated one another horizontally and/or vertically, and some also appropriated otherness in certain situations by either crossing boundaries or by collapsing them. The article contributes to theorizing on identification work and its consequences by offering a conceptualization of the variety of othering in everyday interaction. It further highlights relational agency in the co-construction of social identities/alterities. Through reciprocal othering, ‘self’ and ‘other’ mutually construct one another in interaction, enabled and constrained by structural contexts while simultaneously taking part in constituting them. As such, othering plays a key role in organizing processes that involve encounters and negotiations between different work- and occupational groups.