Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Various Deep Learning Techniques for Spatio-Temporal Super-Resolution Reconstruction of Forced Isotropic Turbulent Flows

Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. This study performs super-resolution analysis on turbulent flow fields spatially and temporally using various state-of-the-art machine learning techniques like ESPCN, ESRGAN and TecoGAN to reconstruct high-resolution flow fields from low-resolution flow field data, especially keeping in mind the need for low resource consumption and rapid results production/verification. The dataset used for this study is extracted from the 'isotropic 1024 coarse' dataset which is a part of Johns Hopkins Turbulence Databases (JHTDB). We have utilized pre-trained models and fine tuned them to our needs, so as to minimize the computational resources and the time required for the implementation of the super-resolution models. The advantages presented by this method far exceed the expectations and the outcomes of regular single structure models. The results obtained through these models are then compared using MSE, PSNR, SAM, VIF and SCC metrics in order to evaluate the upscaled results, find the balance between computational power and output quality, and then identify the most accurate and efficient model for spatial and temporal super-resolution of turbulent flow fields.
ECOM'21: The SIGIR 2021 Workshop on eCommerce

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the world's largest web sites (e.g., Airbnb, Alibaba, Amazon, eBay, Facebook, Flipkart, Lowe's, Taobao, and Target). SIGIR has for several years seen sponsorship from eCommerce organisations, reflecting the importance of IR research to them. The purpose of this workshop is (1) to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) to determine how to use eCommerce's unique combination of free text, structured data, and customer behavioral data to improve search relevance, and (3) to examine how to build datasets and evaluate algorithms in this domain. Since eCommerce customers often do not know exactly what they want to buy (i.e. navigational and spearfishing queries are rare), recommendations are valuable for inspiration and serendipitous discovery as well as basket building.
Efficient Multi-scalable Network for Single Image Super Resolution

In computer vision, single-image super resolution has been an area of research for a significant period.Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration.Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges.Furthermore, previous works haven't placed enough emphasis on real-number scalability.Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size.In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations.It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space.Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model.Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.
GUIDED HYBRID GENETIC ALGORITHM FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

Context. One of the leading problems in the world of artificial intelligence is the optimization of complex systems, which is often represented as a nonlinear function that needs to be minimized. Such functions can be multimodal, non-differentiable, and even set as a black box. Building effective methods for solving global optimization problems raises great interest among scientists. Objective. Development of a new hybrid genetic algorithm for solving global optimization problems, which is faster than existing analogues. Methods. One of the crucial challenges for hybrid methods in solving nonlinear global optimization problems is the rational use of local search, as its application is accompanied by quite expensive computational costs. This paper proposes a new GBOHGA hybrid genetic algorithm that reproduces guided local search and combines two successful modifications of genetic algorithms. The first one is BOHGA that establishes a qualitative balance between local and global search. The second one is HGDN that prevents reexploration of the previously explored areas of a search space. In addition, a modified bump-function and an adaptive scheme for determining one of its parameters – the radius of the “deflation” of the objective function in the vicinity of the already found local minimum – were presented to accelerate the algorithm. Results. GBOHGA performance compared to other known stochastic search heuristics on a set of 33 test functions in 5 and 25dimensional spaces. The results of computational experiments indicate the competitiveness of GBOHGA, especially in problems with multimodal functions and a large number of variables. Conclusions. The new GBOHGA hybrid algorithm, developed on the basis of the integration of guided local search ideas and BOHGA and HGDN algorithms, allows to save significant computing resources and speed up the solution process of the global optimization problem. It should be used to solve global optimization problems that arise in engineering design, solving organizational and management problems, especially when the mathematical model of the problem is complex and multidimensional.
Comparison of Hierarchical and Non-Hierarchical Methods in Clustering Cities in Java Island using the Human Development Index Indicators year 2018

The Human Development Index (HDI) is a composite index to assess the developmental level of life quality in a particular region. In 2018, Java Island, which geographically has the most regencies/ municipalities in Indonesia, achieved human development with “high” status and was followed by all its regencies which have also achieved human development with “high” status. Therefore, research was carried out on how the characteristics inherent in the high HDI have been achieved in regencies on Java Island and grouping them so that it is easy to interpret regencies/ municipalities with homogeneous characteristics. This study used the hierarchical cluster method (single linkage, average linkage, and ward) and non-hierarchical cluster methods (K-Means and FCM). The results show that the best hierarchical cluster method is the average linkage method which forms four clusters where the regencies/ municipalities with the best characteristics (dimensions of education, health, and high purchasing power) are Kepulauan Seribu, Bogor, and 78 other regencies/ municipalities. Then, the best non-hierarchical method is the FCM method which forms two clusters, with a prominent characteristic is those city areas have better characteristics than district areas.
QoS and QoE aware multi objective resource allocation algorithm for cloud gaming

Cloud gaming is an innovative model that congregates video games. The user may have different Quality-of-Experience (QoE), which is a term used to measure a user’s level of satisfaction and enjoyment for a particular service. To guarantee general satisfaction for all users with limited cloud resources, it becomes a major issue in the cloud. This paper leverages a game theory in the cloud gaming model with resource optimization to discover optimal solutions to resolve resource allocation. The Rider-based harmony search algorithm (Rider-based HSA), which is the combination of Rider optimization algorithm (ROA) and Harmony search algorithm (HSA), is proposed for resource allocation to improve the cloud computing system’s efficiency. The fitness function is newly devised considering certain QoE parameters, which involves fairness index, Quantified experience of players (QE), and Mean Opinion Score (MOS). The proposed Rider-based HSA showed better performance compared to Potential game-based optimization algorithm, Proactive resource allocation algorithm, QoE-aware resource allocation algorithm, Distributed algorithm, and Yusen Li et al., with maximal fairness of 0.999, maximal MOS of 0.873, and maximal QE of 1.
Wandering and getting lost: the architecture of an app activating local communities on dementia issues

We describe the architecture of Sammen Om Demens (SOD), an application for portable devices aiming at helping persons with dementia when wandering and getting lost through the involvement of caregivers, family members, and ordinary citizens who volunteer.To enable the real-time detection of a person with dementia that has lost orientation, we transfer location data at high frequency from a frontend on the smartphone of a person with dementia to a backend system. The backend system must be able to cope with the high throughput data and carry out possibly heavy computations for the detection of anomalous behavior via artificial intelligence techniques. This sets certain performance and architectural requirements on the design of the backend.In the paper, we discuss our design and implementation choices for the backend of SOD that involve microservices and serverless services to achieve efficiency and scalability. We give evidence of the achieved goals by deploying the SOD backend on a public cloud and measuring the performance on simulated load tests.
A Framework for Proof-carrying Logical Transformations

In various provers and deductive verification tools, logical transformations are used extensively in order to reduce a proof task into a number of simpler tasks. Logical transformations are often part of the trusted base of such tools. In this paper, we develop a framework to improve confidence in their results. We follow a modular and skeptical approach: transformations are instrumented independently of each other and produce certificates that are checked by a third-party tool. Logical transformations are considered in a higher-order logic, with type polymorphism and built-in theories such as equality and integer arithmetic. We develop a language of proof certificates for them and use it to implement the full chain of certificate generation and certificate verification.
Mathematical model of the process of ultrafiltration concentration of secondary milk raw materials in tubular membrane devices with filtering elements of BTU 05/2 type

For the qualitative application of ultrafiltration processes for the concentration and purification of food solutions, both experimental studies and a mathematical description of the processes of the membrane separation process of solutions from the standpoint of the development of computational mathematical models are required. In this work, by analytical solution of equations, that is, by the method of finite differences, mathematical equations are solved. To obtain the system, the flow continuity equations, convective diffusion equations, Navier-Stokes equations and flow equations with boundary conditions were solved in order to build a mathematical model of the process of ultrafiltration protein concentration in cheese whey in the production of rennet cheeses. As a result of the analytical solution of the equations, a system of mathematical equations was obtained that allows one to construct a profile of changes in the flow rates of the solution along the cross-section of the intermembrane channel and to determine the protein concentration in cheese whey along the length of the tubular ultrafiltration element BTU 05/2 of industrial type. The obtained mathematical model makes it possible to theoretically describe the process of ultrafiltration protein concentration in cheese whey along the entire length of the membrane channel of the tubular element under laminar and transient regimes of solution flow. The resulting system of mathematical equations makes it possible to find the numerical values of the mass flow rate of cheese whey, make it possible to calculate the specific output flow when the transmembrane pressure changes and to calculate the concentration of solutes in the secondary milk raw materials on the left and right ultrafiltration membrane of the intermembrane channel. The adequacy of the developed mathematical model was carried out by comparing the calculated and experimental data on the specific output flow when the transmembrane pressure in the intermembrane channel changes from 0.1 to 0.25 MPa with ultrafiltration concentration of cheese whey. The deviation of the calculated data found by the mathematical model from experimental studies obtained on a semi-industrial tubular ultrafiltration plant BTU 05/2 using semipermeable membranes, in which the active layer is made of fluoroplastic, hemisulphone and polyethersulfone, did not exceed 10%.
Smart Attendance Management System Using Face Recognition

To Maintain the attendance record with day to day activities is a challenging task. The conventional method of calling name of each student is time consuming and there is always a chance of proxy attendance. The smart attendance management will replace the manual method, which takes a lot of time consuming and difficult to maintain. There are many biometric processes, in that face recognition is the best method. Here we are using the computer vision which is a field of deep learning that is used for the camera reading and writing and using TkInter to create a GUI application.
Review methods for breast cancer detection using artificial intelligence and deep learning methods

Nowadays, there are many related works and methods that use Neural Networks to detect the breast cancer. However, usually they do not take into account the training time and the result of False Negative (FN) while training the model. The main idea of this paper is to compare already existing methods for detecting the breast cancer using Deep Learning Algorithms. Moreover, since the breast cancer is one of the most common lethal cancers and early detection helps prevent complications, we propose a new approach and the use of the convolutional autoencoder. This proposed model has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60% and 93% respectively.
Towards autoscaling of Apache Flink jobs

Abstract Data stream processing has been gaining attention in the past decade. Apache Flink is an open-source distributed stream processing engine that is able to process a large amount of data in real time with low latency. Computations are distributed among a cluster of nodes. Currently, provisioning the appropriate amount of cloud resources must be done manually ahead of time. A dynamically varying workload may exceed the capacity of the cluster, or leave resources underutilized. In our paper, we describe an architecture that enables the automatic scaling of Flink jobs on Kubernetes based on custom metrics, and describe a simple scaling policy. We also measure the e ects of state size and target parallelism on the duration of the scaling operation, which must be considered when designing an autoscaling policy, so that the Flink job respects a Service Level Agreement.
Feedback control modulation for controlling chaotic maps

Based on existing feedback control methods such as OGY and Pyragas, alternative new schemes are proposed for stabilization of unstable periodic orbits of chaotic and hyperchaotic dynamical systems by suitable modulation of a control parameter. Their performances are improved with respect to: (i) robustness, (ii) rate of convergences, (iii) reduction of waiting time, (iv) reduction of noise sensitivity. These features are analytically investigated, the achievements are rigorously proved and supported by numerical simulations. The proposed methods result successful for stabilizing unstable periodic orbits in some classical discrete maps like 1-D logistic and standard 2-D Hénon, but also in the hyperchaotic generalized n-D Hénon-like maps.
Flow-based sampling for multimodal distributions in lattice field theory

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of methods to construct flow models for targets with multiple separated modes (i.e. theories with multiple vacua). We demonstrate the application of these methods to modeling two-dimensional real scalar field theory in its symmetry-broken phase. In this context we investigate the performance of different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC.
Quantum algorithms for multivariate Monte Carlo estimation

We consider the problem of estimating the expected outcomes of Monte Carlo processes whose outputs are described by multidimensional random variables. We tightly characterize the quantum query complexity of this problem for various choices of oracle access to the Monte Carlo process and various normalization conditions on its output variables and the estimates to be made. More specifically, we design quantum algorithms that provide a quadratic speed-up in query complexity with respect to the precision of the resulting estimates and show that these complexities are asymptotically optimal in each of our scenarios. Interestingly, we find that these speed-ups with respect to the precision come at the cost of a (up to exponential) slowdown with respect to the dimension of the Monte Carlo random variables, for most of the parameter settings we consider. We also describe several applications of our algorithms, notably in the field of machine learning, and discuss the implications of our results.
The Rise of Virtual Reality in Online Courses: Ethical Issues and Policy Recommendations

While ethical issues related to the adoption of Virtual Reality (VR) technology is analyzed across sectors from construction, architecture, retail, engineering, healthcare, less attention is paid to the ethical concerns in online courses. Using an inductive qualitative content analysis and observation in the business schools, this chapter aims to shed light on the ethical issues that may occur as results of use of VR technology in online courses. The findings indicate that the use of VR technology in online courses raises several technical and social/ethical issues. These issues comprise concerns related to record of personal data, which could be deployed in ways that threaten personal privacy, private neglect of users’ own real bodies and actual physical environments, and other moral and social security risks related with the way VR confuses the distinction between face-to-face and virtual learning. As these ethical issues raise questions about public policy, the chapter makes several recommendations that elaborate a platform for further discussion. It is argued that there is a need for a wider vision that looks beyond the teaching technological issues to those linked to students and teachers’ conducts, and institution policies.
Projective toric codes

Any integral convex polytope [Formula: see text] in [Formula: see text] provides an [Formula: see text]-dimensional toric variety [Formula: see text] and an ample divisor [Formula: see text] on this variety. This paper gives an explicit construction of the algebraic geometric error-correcting code on [Formula: see text], obtained by evaluating global section of the line bundle corresponding to [Formula: see text] on every rational point of [Formula: see text]. This work presents an extension of toric codes analogous to the one of Reed–Muller codes into projective ones, by evaluating on the whole variety instead of considering only points with nonzero coordinates. The dimension of the code is given in terms of the number of integral points in the polytope [Formula: see text] and an algorithmic technique to get a lower bound on the minimum distance is described.
Leveraging unsupervised image registration for discovery of landmark shape descriptor

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.
Recognition of Colombian Alphabeth in Sign Language Using Deep Learning Techniques

Abstract This work presents the development of an algorithm for sign language alphabet recognition Colombian in images, using the deep learning technique. For this, a data set consisting of each letter of the alphabet from A up to Z and a total of 4043 images with a resolution of 150×150 pixels of 3 channels, a red neural was built convolutional with 256 layers, with an accuracy of 75%.