Reviewerly/exHarmony huggingface dataset:
Implementation of an e-medical portal system for healthcare services in Calabar

This article is about the implementation of a web-based e-medical patient portal to facilitate the interaction between the hospital, the medical practitioners and the patients. The aim of this study is to develop a guideline that can be used to implement a web-based e-medical portal. An E-medical portal is an information system that provides to individuals the access to their personal health records and many other services related to healthcare. To achieve the aim, two questions were formulated: (1) what are the requirements for the implementation of interactive systems (portal) between the medical practitioners and patients? and (2) what are the factors that influences an interactive systems (portal) that can be used by both medical practitioners and patients? The study was conducted using the qualitative method, with an inductive approach. The Data was collecting through Documentation using the desk research method as tool of collection. From the analysis, three important factor were find and discussed.
Simultaneous local spectral, colorimetric, and topographic characterization of laser-induced colored stainless steel with low coherence interference microscopy

Laser marking is a well-established advanced technology for changing the color properties of an object. The spectral response or color information induced by the laser is generally measured with a spectrometer or an ellipsometer. These instruments only provide an average response over an area of a few thousand µm² at best. However, color laser marking is a local process that modifies the structure of the metal at a micrometer scale. In this work, we propose a technique for extracting simultaneously the 3D topography and the 2D spectral response from 460 nm to 900 nm, providing color information over this spectral range, in a single measurement. The proposed technique uses a low-coherence interference microscope to provide a laterally diffraction-limited measurement (∼1 µm) over a large field of view (up to 200 × 200 µm²) with the advantages of being rapid (<3 min) and contactless. Through the Fourier Transform analysis of the measured interferogram, a multimodal characterization (quantitative spectra, color (xy) and topography) of reflective samples can be achieved. Results obtained on laser-induced colored stainless steel are reported.
FunkNN: Neural Interpolation for Functional Generation

Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? Existing MLP-based architectures generate worse samples than the grid-based generators with favorable convolutional inductive biases. Models that focus on generating images at different scales do better, but employ complex architectures not designed for continuous evaluation of images and derivatives. We take a signal-processing perspective and treat continuous image generation as interpolation from samples. Indeed, correctly sampled discrete images contain all information about the low spatial frequencies. The question is then how to extrapolate the spectrum in a data-driven way while meeting the above design criteria. Our answer is FunkNN -- a new convolutional network which learns how to reconstruct continuous images at arbitrary coordinates and can be applied to any image dataset. Combined with a discrete generative model it becomes a functional generator which can act as a prior in continuous ill-posed inverse problems. We show that FunkNN generates high-quality continuous images and exhibits strong out-of-distribution performance thanks to its patch-based design. We further showcase its performance in several stylized inverse problems with exact spatial derivatives.
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning

Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains. For instance, training a wildfire classifier on satellite imagery requires curating a massive and diverse dataset, which is an expensive and time-consuming process that can span from weeks to months. Searching for relevant examples in over 40 petabytes of unlabelled data requires researchers to manually hunt for such images, much like finding a needle in a haystack. We present a no-code end-to-end pipeline, Curator, which dramatically minimizes the time taken to curate an exhaustive labeled dataset. Curator is able to search massive amounts of unlabelled data by combining self-supervision, scalable nearest neighbor search, and active learning to learn and differentiate image representations. The pipeline can also be readily applied to solve problems across different domains. Overall, the pipeline makes it practical for researchers to go from just one reference image to a comprehensive dataset in a diminutive span of time.
Leveraging World Knowledge in Implicit Hate Speech Detection

While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.
Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimization

Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computationally demanding, we investigate the use of simulators with different levels of fidelity. As a first step, we express the overall safety specification in terms of environmental parameters and structure this safety specification as an optimization problem. We propose a multi-fidelity falsification framework using Bayesian optimization, which is able to determine at which level of fidelity we should conduct a safety evaluation in addition to finding possible instances from the environment that cause the system to fail. This method allows us to automatically switch between inexpensive, inaccurate information from a low-fidelity simulator and expensive, accurate information from a high-fidelity simulator in a cost-effective way. Our experiments on various environments in simulation demonstrate that multi-fidelity Bayesian optimization has falsification performance comparable to single-fidelity Bayesian optimization but with much lower cost.
Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data

Deep learning methods in the literature are commonly benchmarked on image data sets, which may not be suitable or effective baselines for non-image tabular data. In this paper, we take a data-centric view to perform one of the first studies on deep embedding clustering of tabular data. Eight clustering and state-of-the-art embedding clustering methods proposed for image data sets are tested on seven tabular data sets. Our results reveal that a traditional clustering method ranks second out of eight methods and is superior to most deep embedding clustering baselines. Our observation aligns with the literature that conventional machine learning of tabular data is still a robust approach against deep learning. Therefore, state-of-the-art embedding clustering methods should consider data-centric customization of learning architectures to become competitive baselines for tabular data.
High Resolution Modeling and Analysis of Cryptocurrency Mining's Impact on Power Grids: Carbon Footprint, Reliability, and Electricity Price

Blockchain technologies are considered one of the most disruptive innovations of the last decade, enabling secure decentralized trust-building. However, in recent years, with the rapid increase in the energy consumption of blockchain-based computations for cryptocurrency mining, there have been growing concerns about their sustainable operation in electric grids. This paper investigates the tri-factor impact of such large loads on carbon footprint, grid reliability, and electricity market price in the Texas grid. We release open-source high-resolution data to enable high-resolution modeling of influencing factors such as location and flexibility. We reveal that the per-megawatt-hour carbon footprint of cryptocurrency mining loads across locations can vary by as much as 50% of the crude system average estimate. We show that the flexibility of mining loads can significantly mitigate power shortages and market disruptions that can result from the deployment of mining loads. These findings suggest policymakers to facilitate the participation of large mining facilities in wholesale markets and require them to provide mandatory demand response.
Deep Transfer Learning and Intelligent Item Packing in Retail Management

The main aim of this research is to facilitate recognition of plant items products packing service and overcome the challenge of classifying human eye without relying on human memory, and therefore, to facilitate the management of the retail market and improve the shopping experience of con- sumers in the Sultanate of Oman. Deep transfer learning (DTL) was used from pre-trained network on ImageNet of convolutional neural networks (CNN) architecture through train and improve and using fine-tuning to be able to detect plant items products before they are packaged. The results of this study revealed that the best performance was chosen among five pre-trained networks, and it was MobileNetV2 model, which showed accuracy with (99%). Our model, it is pre-trained MobileNetV2 models and fine-tuned matched with the actual product, where all volunteer shoppers and their number six were able to rate except shopper #six (94%). Traditional packing vending system with the actual product was able to rate (72%).
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses.HIER achieves this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since the geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER is evaluated on four standard benchmarks, where it consistently improved the performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.
An Approach for Energy-Efficient Resource Allocation Through Early Planning of Virtual Machines to Servers in Cloud Server Farms

Carbon dioxide emissions are a significant source of pollution in the atmosphere. The innovative hardware plays a fundamental part in the carbon release. This is huge in light of the fact that power utilization by mechanical apparatus has surpassed 40 k TWh in 2021 from 10 k TWh in 2000, and is expanding step by step. Server farms in the cloud processing climate have involved a significant imperative situation in this class of mechanical hardware. In cloud computing, computational assets are leased staying away from gigantic ventures on the business part. Because of this alluring contribution, reception and sending of Cloud computing has become exceptionally famous among ventures as well as in the exploration local area. Increased use of Cloud computing, on the other hand, has resulted in higher energy consumption and carbon emissions into the atmosphere. A server farm is a collection of servers with a big number of them, and there are a lot of them all over the world. In the modern era of cloud computing, energy consumption is currently viewed as one of the most significant evaluation challenges. Service level agreements are contracts between a customer and a vendor in which the vendor agrees to particular service qualities such as quality, availability, and accountability. One of the major challenges identified is reducing the amount of power consumed by server farms without affecting the quality of service (QoS). Consequently, it is proposed to optimize resource allocation and minimize energy consumption for the cloud environment. This is completed by limiting dynamic servers in a server farm without compromising the exhibition of undertakings and client prerequisites. To approve the productivity of the proposed calculations, CloudSim is utilized with certifiable responsibility follows which are gathered from in excess of 1000 PlanetLab virtual machines.
Operational Quantum Mereology and Minimal Scrambling

In this paper we will attempt to answer the following question: what are the natural quantum subsystems which emerge out of a system's dynamical laws? To answer this question we first define generalized tensor product structures (gTPS) in terms of observables, as dual pairs of an operator subalgebra $\cal A$ and its commutant. Second, we propose an operational criterion of minimal information scrambling at short time scales to dynamically select gTPS. In this way the emergent subsystems are those which maintain the longest informational identity. This strategy is made quantitative by defining a Gaussian scrambling rate in terms of the short-time expansion of an algebraic version of the Out of Time Order Correlation (OTOC) function i.e., the $\cal A$-OTOC. The Gaussian scrambling rate is computed analytically for physically important cases of general division into subsystems, and is shown to have an intuitive and compelling physical interpretation in terms of minimizing the interaction strength between subsystems, and maximizing the effectiveness of error-correcting codes.
Policy Optimization to Learn Adaptive Motion Primitives in Path Planning with Dynamic Obstacles

This paper addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles -- a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub problems and combining the local solutions into the global one. The crux of any planning method for non-holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot environments. We show that POLAMP is able to plan collision-free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the-art competitors.
Can 5th Generation Local Training Methods Support Client Sampling? Yes!

The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are reasonably well understood, the third component, whose role is to reduce the number of communication rounds needed to train the model, resisted all attempts at a satisfactory theoretical explanation. Malinovsky et al. (2022) identified four distinct generations of LT methods based on the quality of the provided theoretical communication complexity guarantees. Despite a lot of progress in this area, none of the existing works were able to show that it is theoretically better to employ multiple local gradient-type steps (i.e., to engage in LT) than to rely on a single local gradient-type step only in the important heterogeneous data regime. In a recent breakthrough embodied in their ProxSkip method and its theoretical analysis, Mishchenko et al. (2022) showed that LT indeed leads to provable communication acceleration for arbitrarily heterogeneous data, thus jump-starting the $5^{\rm th}$ generation of LT methods. However, while these latest generation LT methods are compatible with DS, none of them support CS. We resolve this open problem in the affirmative. In order to do so, we had to base our algorithmic development on new algorithmic and theoretical foundations.
Cross Version Defect Prediction with Class Dependency Embeddings

Software Defect Prediction aims at predicting which software modules are the most probable to contain defects. The idea behind this approach is to save time during the development process by helping find bugs early. Defect Prediction models are based on historical data. Specifically, one can use data collected from past software distributions, or Versions, of the same target application under analysis. Defect Prediction based on past versions is called Cross Version Defect Prediction (CVDP). Traditionally, Static Code Metrics are used to predict defects. In this work, we use the Class Dependency Network (CDN) as another predictor for defects, combined with static code metrics. CDN data contains structural information about the target application being analyzed. Usually, CDN data is analyzed using different handcrafted network measures, like Social Network metrics. Our approach uses network embedding techniques to leverage CDN information without having to build the metrics manually. In order to use the embeddings between versions, we incorporate different embedding alignment techniques. To evaluate our approach, we performed experiments on 24 software release pairs and compared it against several benchmark methods. In these experiments, we analyzed the performance of two different graph embedding techniques, three anchor selection approaches, and two alignment techniques. We also built a meta-model based on two different embeddings and achieved a statistically significant improvement in AUC of 4.7% (p < 0.002) over the baseline method.
Pengaruh Perhatian Orang Tua Terhadap Prestasi Belajar Matematika

This research is expected to be useful for the following parties: (1) For schools, namely as input material for schools that are used as research locations, in an effort to improve to improve the quality and achievement of students' mathematics learning. (2) For teachers as a consideration for teachers to improve student learning outcomes in the subjects they teach. (3) For students it can be used as a reference material in generating interest in learning, as well as methods that can improve methods that can improve student learning outcomes. (4) Subsequent research materials as reference material for further researchers discussing research topics relevant to this research.
Learning Multimodal Data Augmentation in Feature Space

The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modality tasks like image classification. Indeed, it is particularly difficult to augment each modality while preserving the overall semantic structure of the data; for example, a caption may no longer be a good description of an image after standard augmentations have been applied, such as translation. Moreover, it is challenging to specify reasonable transformations that are not tailored to a particular modality. In this paper, we introduce LeMDA, Learning Multimodal Data Augmentation, an easy-to-use method that automatically learns to jointly augment multimodal data in feature space, with no constraints on the identities of the modalities or the relationship between modalities. We show that LeMDA can (1) profoundly improve the performance of multimodal deep learning architectures, (2) apply to combinations of modalities that have not been previously considered, and (3) achieve state-of-the-art results on a wide range of applications comprised of image, text, and tabular data.
Abelian and consta-Abelian polyadic codes over affine algebras with a finite commutative chain coefficient ring

In this paper, we define Abelian and consta-Abelian polyadic codes over rings defined as affine algebras over chain rings. For that aim, we use the classical construction via splittings and multipliers of the underlying Abelian group. We also derive some results on the structure of the associated polyadic codes and the number of codes under these conditions.
A Survey on Discrete Gaussian Samplers in Lattice Based Cryptography

Lattice-based cryptography is one of the most competitive algorithms in post-quantum algorithms. The discrete Gaussian sampler is a fundamental building block in lattice-based cryptography, but it is still challenging to construct a generic, efficient and secure discrete Gaussian sampler. In this work, we survey the existing discrete Gaussian samplers and summarize the characteristics and improvements of each sampler in detail. In addition, we discuss the evaluation criteria for samplers which we believe that a good scheme should use less precision to achieve the same level of security. The survey can help the reader to focus on the development of discrete Gaussian samplers and apply the discrete Gaussian sampler to lattice-based cryptography in a black-box manner.
Relative hulls and quantum codes

Given two $q$-ary codes $C_1$ and $C_2$, the relative hull of $C_1$ with respect to $C_2$ is the intersection $C_1\cap C_2^\perp$. We prove that when $q>2$, the relative hull dimension can be repeatedly reduced by one, down to a certain bound, by replacing either of the two codes with an equivalent one. The reduction of the relative hull dimension applies to hulls taken with respect to the $e$-Galois inner product, which has as special cases both the Euclidean and Hermitian inner products. We give conditions under which the relative hull dimension can be increased by one via equivalent codes when $q>2$. We study some consequences of the relative hull properties on entanglement-assisted quantum error-correcting codes and prove the existence of new entanglement-assisted quantum error-correcting maximum distance separable codes, meaning those whose parameters satisfy the quantum Singleton bound.