Reviewerly/exHarmony huggingface dataset:
Development of a Smart Retail Management System Using IoT Technology

The retail industry has undergone a rapid transformation in recent years with the integration of IoT (Internet of Things) technology.IoT technology allows retailers to gain insights into customer behavior, optimize inventory management, and improve the overall shopping experience.This paper presents the development of a smart retail management system using IoT technology.The proposed system is designed to improve the efficiency and effectiveness of retail operations.The system consists of various IoT devices such as RFID readers, sensors, and cameras that collect data on customer behavior, inventory levels, and store conditions.The data collected is analyzed using machine learning algorithms to provide retailers with valuable insights into customer behavior, demand trends, and inventory levels.These insights can be used to optimize store operations, improve inventory management, and provide a better shopping experience for customers.The system also includes a mobile application that enables customers to access information about products, promotions, and discounts.The application uses beacon technology to provide personalized offers to customers based on their location and purchase history.Additionally, the system includes a dashboard for store managers that provides real-time data on inventory levels, sales trends, and customer traffic.The proposed system has the potential to revolutionize the retail industry by providing retailers with valuable insights into customer behavior and optimizing store operations.The system is scalable and can be easily adapted to meet the needs of different types of retail stores.Overall, this paper demonstrates the potential of IoT technology in the retail industry and provides a roadmap for the development of smart retail management systems.
Large-Resolution Difference Heterogeneous SAR Image Sea Ice Drift Tracking Using a Smooth Edge-Guide Super-Resolution Residual Network

Heterogeneous synthetic aperture radar (SAR) data contain more information, so the use of heterogeneous SAR images can potentially improve the performance of remote sensing applications. Feature tracking is crucial for using heterogeneous SAR data. However, feature tracking is a challenge using heterogeneous SAR images to harmonize high-resolution (HR) data with coarser data. In this paper, we propose a smooth edge-guide super-resolution recurrent residual learning network to uniform resolution of heterogeneous SAR image such that their features have more consistent representation. Our proposed framework contains a super-resolution network that aims to translate the low-resolution (LR) images into the HR ones to reduce their feature differences. The generated HR final image and the HR raw image can be considered homogeneous for sea ice drift tracking. Through several examples, we demonstrate the effectiveness of the method in the feature matching of images with a large-resolution difference images from different SAR sensors.
Multi-fidelity Bayesian Optimisation of Reactor Simulations using Deep Gaussian Processes

Coiled tube reactors under pulsed-flow conditions have been shown to provide promising mixing characteristics. In order to validate performance in an industrial setting, and investigate the underlying physics of successful mixing, coiled tube reactors must be optimised. In this work, we apply a novel framework to locate optimal solutions to this nonlinear, computationally expensive, and derivative-free problem. Our optimisation framework takes advantage of deep Gaussian processes to learn a multi-fidelity surrogate model. We apply this model within a novel Bayesian optimisation algorithm, using faster, less accurate, and potentially biased lower-fidelity simulations to enable faster reactor optimisation. We subsequently investigate the physical insights into the swirling flows of these optimal configurations, directly informing the design of future coiled-tube reactors under pulsed-flow conditions. We demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimisation problems, supporting the design of the next-generation of highly parameterised chemical reactors.
Inflated graph states refuting communication-assisted local-hidden-variable models

Standard Bell inequalities hold when distant parties are not allowed to communicate. Barrett et al. [Phys. Rev. A 75, 012103 (2007)] found correlations from Pauli measurements on certain network graphs to refute a local-hidden-variable (LHV) description, even allowing some communication along the graph. This has recently found applications in proving separation between classical and quantum computing, in terms of shallow circuits, and distributed computing. The correlations presented by Barrett et al. can be understood as coming from an extension of three party Greenberger-Horne-Zeilinger state correlations which can be embedded on a graph state. In this paper, we propose systematic extensions of any graph state, which we dub inflated graph states, such that they exhibit correlations which refute any communication-assisted LHV model. We further show the smallest possible such example, with a seven-qubit linear graph state, as well as specially crafted smaller examples with five and four qubits. The latter is the smallest possible violation using binary inputs and outputs.
STUDENTS’ READINESS AND CHALLENGES ON ONLINE LEARNING DURING COVID-19 PANDEMIC

The purpose of this paper is to examine the readiness on online learning during COVID-19 pandemic based on the conceptual frameworks adapted from Stauffer (2020), Butnaru et al. (2021), and (John Lemay et al., 2021). It is mainly quantitative in nature alongside some quantitative data. It examines the students’ readiness of shifting from the face-to-face teaching and learning to online learning. They are also asked to provide their experience of accessing online learning. The research data was collected at a local university in Malaysia through a survey comprises 71 student respondents and an interview with 10 student interviewees. The results of the research show that i) the students were exceled in computer skills but unfamiliar to the online platform; ii) students were satisfied with the internet network support quality; iii) few students used mono type online device to access online learning; iv) students did not have previous experience on online learning during high school and the students felt anxious before and during accessing online learning. The findings further reveal that the internet network support provided in the country is the factor of some students unable to access the online learning smoothly, they used more than one type of online device to access the online learning and felt anxiety on the disconnection of internet might affect their performance in assessments and presentation evaluation. The telcos are suggested to accelerate the upgrade of their internet network support quality in order to facilitate the success of the National 4IR policy in the country.
Supplemental Material: Seismic anisotropy indicates organized melt beneath the Mid-Atlantic Ridge aids seafloor spreading

&lt;p&gt;Additional information about the experiment and the shear-wave splitting analysis and its analyses and interpretation. It includes two supplementary figures showing local S-wave event locations, local S splitting delay times and their uncertainties against event depth, backazimuth, and event-to-station path length. Also included are two tables listing station details, individual and stacked teleseismic SKS results, and individual local S results. &lt;/p&gt;
Image inpainting via progressive decoder and gradient guidance

Abstract Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing multi-stage methods fail to effectively inpainting the larger missing areas, their common drawback is that the result of each stage is easily misguided by the wrong content generated in the previous stage. To solve this issue, in this paper, a novel one-stage generative adversarial network based on the progressive decoding architecture and gradient guidance. Firstly, gradient priors are extracted at the encoder stage to be passed to the decoding branch, and multiscale attention fusion group is used to help the network understand the image features. Secondly, multiple parallel decoding branches fill and refine the missing regions by top-down passing the reconstructed priors. This progressively guided repair avoids the detrimental effects of inappropriate priors. The joint guidance of features and gradient priors helps the restoration results contain the correct structure and rich details. And the progressive guidance is achieved by our fusion strategy, combining reimage convolution and design channel coordinate attention to fuse and reweight the features of different branches. Finally, we use the multiscale fusion to merge the feature maps at different scales reconstructed by the last decoding branch and map them to the image space, which further improves the semantic plausibility of the restoration results. Experiments on multiple datasets show that the qualitative and quantitative results of our computationally efficient model are competitive with those of state-of-the-art methods.
Llama 2 release will fuel competition in generative AI

Headline INT: Llama 2 will fuel competition in generative AI
TED-Face: Texture-Enhanced Deep Face Reconstruction in the Wild

We present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work—shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods.
Real-time Light Estimation and Neural Soft Shadows for AR Indoor Scenarios

We present a pipeline for realistic embedding of virtual objects into footage of indoor scenes with focus on real-time AR applications. Our pipeline consists of two main components: A light estimator and a neural soft shadow texture generator. Our light estimation is based on deep neural nets and determines the main light direction, light color, ambient color and an opacity parameter for the shadow texture. Our neural soft shadow method encodes object-based realistic soft shadows as light direction dependent textures in a small MLP. We show that our pipeline can be used to integrate objects into AR scenes in a new level of realism in real-time. Our models are small enough to run on current mobile devices. We achieve runtimes of 9ms for light estimation and 5ms for neural shadows on an iPhone 11 Pro.
AI solution to mammography imaging

Synopsis In recent years, AI technology in mammography has been developed to improve the detection of suspicious breast lesions. They serve to detect possible breast cancers and hence reduce time spent by the radiologists for analysis. This talk will highlight a trial run with Lunit INSIGHT MMG. In recent years, AI technology in mammography has been developed to improve the detection of suspicious breast lesions. They serve to detect possible breast cancers and hence reduce time spent by the radiologists for analysis. This talk will highlight a trial run with Lunit INSIGHT MMG.
Adult didactics – component of andragogy: peculiarities and basic categories

At the stage of rapid development of science and market of educational services for adults, a relatively new direction in education was formed - andragogy (pedagogy of adults).In the present time, andragogy occupies a place in the structure of education sciences as an independent category, possessing at the same time its own scientific apparatus, research methods, scientific approaches and principles. In this article, an attempt was made to substantiate the definition of basic concepts and categories of adult teaching: subject, tasks, functions. Special attention is paid to the principles of adult learning: priority of independent education, reliance on personal experience, individualization of learning, contextuality of learning, reliance on individual needs, considering them as motives and sources of designing the outcomes of education. At the same time, adult learning is considered within the framework of the following characteristics: andragogic self-reflexivity, decentralization of andragogic approach, deconstruction/reconstruction of andragogic theory, contextualization of adult learning and education.
Railroad Track Damage Detection Using CLAHE-KNN (CLAHE K-Nearest Neighbor)

This research is part of the development of the system, which aims to detect damage to railway tracks using the CLAHE-KNN model. This study aims to detect railway damage using the CLAHE-K-NN method and determine the effect of CLAHE on the K-Nearest Neighbor algorithm for railway damage detection. CLAHE is defined by Block Size (BS) and Clip Limit (CL). The dataset used in this study was railway imagery totaling 384 data. Based on the KNN method's experimental results, this method obtained an accuracy of 65.62%. In addition, the experimental results using KNN with CLAHE optimization get better accuracy by using clipLimit = 4.0 and tileGridSize = (4.8), which is 66.67%.
A Technical Investigation towards a Cloud-Based Signature Solution

This paper proposes a technical investigation towards a solution for a cloud-based signature architecture. Based on the main concepts and ideas available today in this field, our proposed approach integrates seven components to cover all the determined functional and non-functional requirements of such a system.
Requirements Analysis for a System for Certifying Online Content

This paper investigates the requirements for a system capable to authenticate (or certify) the content that is available online. Based on an analysis of the approaches used to fight against fake news and fake online content, our proposal is based on digital signatures, and integrates several key components to fulfill the determined functional requirements. This implies that both the origin of the text (the source) and the textual online content, can be trusted by anyone that is able to verify the applied electronic signature.
The Tale of Two MSMARCO - and Their Unfair Comparisons

The MS MARCO-passage dataset has been the main large-scale dataset open to the IR community and it has fostered successfully the development of novel neural retrieval models over the years. But, it turns out that two different corpora of MS MARCO are used in the literature, the official one and a second one where passages were augmented with titles, mostly due to the introduction of the Tevatron code base. However, the addition of titles actually leaks relevance information, while breaking the original guidelines of the MS MARCO-passage dataset. In this work, we investigate the differences between the two corpora and demonstrate empirically that they make a significant difference when evaluating a new method. In other words, we show that if a paper does not properly report which version is used, reproducing fairly its results is basically impossible. Furthermore, given the current status of reviewing, where monitoring state-of-the-art results is of great importance, having two different versions of a dataset is a large problem. This is why this paper aims to report the importance of this issue so that researchers can be made aware of this problem and appropriately report their results.
Intelligent Software Maintenance

Software systems are pervasive in our society. They play a vital role in our everyday life and are increasingly more and more complex. Software maintenance is one of the most important software development activities. It is estimated that more than 70% of the total cost of a software system is spent on maintenance activities. This cost can be reduced using Machine Learning (ML) and Artificial Intelligence (AI) in general. AI can be used in various stages of software maintenance, for example to identify design defects early on or code to reuse. We can analyse large amounts of data and identify patterns in a more efficient way by means of machine learning algorithms. In this chapter, we report about two studies that leverage (i) deep learning for code smell detection, and (ii) machine learning for reusable code search during software maintenance. We present the methodology that was followed and discuss the achieved results. We hope that practitioners and researchers reading the chapter will learn about the challenges and opportunities offered by AI, in the context of software maintenance, and that they will be able to successfully apply our proposed solutions to the context of their own software systems.
Skin Lesion Correspondence Localization in Total Body Photography

Longitudinal tracking of skin lesions - finding correspondence, changes in morphology, and texture - is beneficial to the early detection of melanoma. However, it has not been well investigated in the context of full-body imaging. We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in total body photography (TBP). Body landmarks or sparse correspondence are first created on the source and target 3D textured meshes. Every vertex on each of the meshes is then mapped to a feature vector characterizing the geodesic distances to the landmarks on that mesh. Then, for each lesion of interest (LOI) on the source, its corresponding location on the target is first coarsely estimated using the geometric information encoded in the feature vectors and then refined using the texture information. We evaluated the framework quantitatively on both a public and a private dataset, for which our success rates (at 10 mm criterion) are comparable to the only reported longitudinal study. As full-body 3D capture becomes more prevalent and has higher quality, we expect the proposed method to constitute a valuable step in the longitudinal tracking of skin lesions.
Aplikasi Database Untuk Mengelola Persediaan Barang di Toko Baju D’Clothing Menggunakan Pendekatan Entity Relationship Chart

D’Clothing store is one of the clothing stores located in Bogor that provides many choices for various ages, both women and men. Based on the interviews, D'Clothing store had some troubles in product management, starting from inventory data, suppliers data, sales data, et cetera. To overcome this problem, a new system is needed to facilitate management by utilizing technological products, which as creating a database. The output of this database is to make a form containing the name, stock, color, size, code, and price of the goods. In its preparation, there are several obstacles such as data being out of sync between the main table and other tables when building a relationship. After being analyzed, this issue happens due to the wrong placement of the primary key and records between tables. Another error is found in the placement of the formula when building the Query design. Therefore, it takes precision and logic to find a way out.
New YARN sharing GPU based on graphics memory granularity scheduling

As one of the most widely used cluster scheduling frameworks, Hadoop YARN only supported CPU and memory scheduling in the past. Furthermore, due to the widespread use of AI, the demand for GPU is also increasing. So Hadoop YARN V3.0 adds GPU scheduling, but the granularity is on the whole card yet, rather than finer-grained graphics memory scheduling. However, during daily training, although the graphics memory required by tasks may be much smaller than the whole GPU card, they will occupy the whole card, which results in wasted resources. To address this issue, Tensorflow provides the API for graphics memory control. Therefore, we propose to introduce this feature into Hadoop YARN so that it can support the heterogeneous scheduling: CPU, memory and graphics memory. Then we take HadoopV2.7 source code as the underlying architecture and design a new scheduler GSHARE. Compared with previous scheduling strategies, with 3 nodes, 3 GPU cards per node, and 12G graphics memory per card, GSHARE improves efficiency by up to 74% for Tensorflow tasks with 2G of graphics memory. Meanwhile, it minimizes the problem of wasted graphics memory caused by the inability to control graphics memory proportionally by the API of Tensorflow for multiple-card.
Capital Optical Character Recognition Using Neural Network Based on Gaussian Filter

Purpose: As digital technology advances, society needs to convert physical text into digital text. There are now many methods available for doing this. One of them is OCR (Optical Character Recognition), which can scan images [1]–[4] containing writing and turn them into digital text, making it easier to copy written text from an image. Text recognition in images is complex due to variations in text size, color, font, orientation, background, and lighting conditions.Methods: The technique of text recognition or optical character recognition (OCR) in images can be done using several methods, one of which is a neural network or artificial neural network. The artificial neural network method can help a computer make intelligent decisions with limited human assistance. Intelligent decisions can be made because the neural network can learn and model the relationship between nonlinear and complex input and output data. In this research, the scaled conjugated gradient is applied for optimization. SCG is very effective in finding the minimum value of a complex function, but it takes longer than some other optimization algorithms.Result/Findings: The dataset used is an image with a size of 28 x 28 which is changed in dimension to 784 x 1. This research uses 4000 epochs and obtained the best validation result at epoch 3506 with a value of 0.0087446. Results: From the statistical test results, the effect of perceived usefulness on ease of use has the highest level of influence, obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtained a test value of 1.2.Novelty: In this article, Gaussian filter is used as feature extraction to improve yield. Character detection results using a Gaussian filter are known to be almost 10% higher than those using only a neural network. The result with the Neural Network alone is 82.2%, while the Neural Network-Gaussian Filter produces 92.1%.