Reviewerly/exHarmony huggingface dataset:
Autoeficacia académica y percepción de la educación virtual en estudiantes universitarios durante la pandemia

The pandemic situation and the restriction measures that are well known to all forced university students to adapt to the new remote or virtual modality or otherwise suspend their studies. This new modality of teaching-learning in some students was carried out satisfactorily, although in other cases, due to extrinsic factors such as the lack of technological tools to achieve adequate connectivity, and intrinsic factors such as academic self-efficacy, which is understood as the very perception of the ability to be able to carry out studies and perform satisfactorily, the results were not optimal. The present investigation, started in the year 2022, sought to describe the level of academic self-efficacy of a group of university students, as well as the perception they had about the virtual education received during the COVID-19 pandemic period. The Robles Academic Self-efficacy Scale (2019) and a survey on opinions regarding the level of satisfaction with remote education were used as a study instrument. It was found that the level of academic self-efficacy of the evaluated students is within a lower than average range; Age is a relevant factor and it showed statistically significant differences in terms of the level of academic self-efficacy, especially in the groups of students aged 27 and over. Almost half, 46.02% of the sample, considered that this modality of virtual edu m them; and finally, more than half (51.79%) prefer the so-called mixed or hybrid education.
Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise

We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several popular nonlinearity choices, like clipped, normalized, signed, or quantized gradient, but we also consider novel nonlinearity choices. We establish for the considered class of methods strong convergence guarantees assuming a strongly convex cost function with Lipschitz continuous gradients under very general assumptions on the gradient noise. Most notably, we show that, for a nonlinearity with bounded outputs and for the gradient noise that may not have finite moments of order greater than one, the nonlinear SGD’s mean squared error (MSE), or equivalently, the expected cost function’s optimality gap, converges to zero at rate , . In contrast, for the same noise setting, the linear SGD generates a sequence with unbounded variances. Furthermore, for general nonlinearities that can be decoupled componentwise and a class of joint nonlinearities, we show that the nonlinear SGD asymptotically (locally) achieves an rate in the weak convergence sense and explicitly quantify the corresponding asymptotic variance. Experiments show that, while our framework is more general than existing studies of SGD under heavy-tail noise, several easy-to-implement nonlinearities from our framework are competitive with state-of-the-art alternatives on real datasets with heavy-tail noises.
The Role of Community Pharmacies in Smart Cities: A Brief Systematic Review and a Conceptual Framework

Community pharmacies are responsible for buying, stoking, and dispensing medicines. These businesses play a central role in healthcare systems. Study aim: To define possible contributions of community Pharmacies in smart cities. Research question: How can community Pharmacies contribute to smart cities? Methods: A brief systematic review was carried out. Three databases were browsed: PubMed, SciELO and Google Scholar. Databases were conveniently selected. Inclusion criteria: any study specifically about the involvement of community pharmacies in smart cities. Results: Only, 5 papers were selected (5 out of 104). Selected papers were about the safety transport of medicines, e-prescribing, electronic product information, use of Information and Communication Technology (ICT) and pharmaceutical services. Discussion: Community pharmacies can provide diverse health services in smart cities, such as collecting patients’ clinical data, re-dispensing of reused medicines, applying diagnostic tools, generating automatic alerts about precautions/contra indications of medicines, follow-up of patients, and/or alerting patients for the necessity of routine exams. Conclusion: The number of studies on the present topic was very limited. The massive and coordinated integration of community pharmacies and pharmaceutical services in smart cities is lacking. ICTs, artificial intelligence, and internet of things, among others seem to be crucial for the integration of community pharmacies in smart cities. Further studies are recommended.
High-rate continuous-variable measurement-device-independent quantum key distribution

We report the first continuous-variable measurement-device-independent QKD system generating secret keys at 5 MBaud without frequency and phase locking. We achieve this using a relay structure based on a polarization 90-degree optical hybrid and well-designed DSP.
An Improved Image Encryption Consuming Fusion Transmutation and Edge Operator

The field of cryptography oversees the development of methods for transforming information between coherent and incoherent formats. Encryption and decryption techniques controlled by keys maintain the privacy of the substance and who can access it. Private key cryptography refers to methods of encryption and decryption that employ the same secret key. The alternative is public key cryptography, wherever the encryption and decryption keys are different. It is essential for the sanctuary of any crypto scheme that the confusion and diffusion properties be met. While the diffusion property rearranges the pixels in an image, the confusion property simply replaces the pixel values. In-depth discussion of a genetic-algorithm-based hybrid approach to secure and complex three-dimensional chaos-based image encryption (SCIE) has been presented. Here, we use mathematics edge, multipoint edges operator, and coupled transmutation operatives to accomplish permutation. In this method, a key stream is created using a 3D CSI (Compound Sine and ICMIC) map. Using a private key, hybrid operators are used to encrypt data. Several metrics were considered while evaluating the suggested algorithm's efficacy, including the UACI (Unified Average Change Intensity), correlation constant, NPCR (Net Pixel Change Rate). Experiments with the same have shown promising results in protecting real-time photos.
The Use of Spelling Bee to Improve Students’ English Vocabulary Mastery at SMP Negeri 11 Makassar

This research discussed about “The Use of Spelling Bee to Improve Students’ English Vocabulary Mastery at SMPN 11 Makassar”. The objectives of this research are to find out the improvement and interest of the students’ English vocabulary after using spelling bee. The researcher used quasi-experimental design with non-equivalent control group design. The population of this research of the seventh year students at SMPN 11 Makassar. It consisted of 40 students which divided into two group of class where 20 students from VII A as experiment class and 20 students from VII B as a control class. The independent variable of this research is spelling bee and the dependent variable of this research was students’ vocabulary mastery. The instrument of this research was vocabulary test used pre-test and post-test. This research used purposive sampling technique. The results of the research showed that score test spelling bee on pre-test in experiment class is 64.80 and score test pre-test in control class is 53.55. The score post-test in experiment class is 78.85 and score post-test in control class is 50.75. Based on the result of the questionnaire, the researcher concluded that H1 was accepted and H0 is rejected it means that using spelling bee was affective to improve the students English vocabulary of the seventh grade students at SMPN 11 Makassar. Therefore, the rating mean score questionnaire is 62.75, put in the category fairly
Can Synthetic Images Improve CNN Performance in Wound Image Classification?

For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.
Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Abstract The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today’s production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today’s production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL.
A majorization–minimization-based method for nonconvex inverse rig problems in facial animation: algorithm derivation

Abstract Automated methods for facial animation are a necessary tool in the modern industry since the standard blendshape head models consist of hundreds of controllers, and a manual approach is painfully slow. Different solutions have been proposed that produce output in real-time or generalize well for different face topologies. However, all these prior works consider a linear approximation of the blendshape function and hence do not provide a high-enough level of detail for modern realistic human face reconstruction. A second-order blendshape approximation leads to higher fidelity facial animation but generates a non-linear least squares optimization problem with high dimensionality. We derive a method for solving the inverse rig in blendshape animation using quadratic corrective terms, which increases accuracy. At the same time, due to the proposed construction of the objective function, it yields a sparser estimated weight vector compared to the state-of-the-art methods. The former feature means lower demand for subsequent manual corrections of the solution, while the latter indicates that the manual modifications are also easier to include. Our algorithm is iterative and employs a Majorization–Minimization paradigm to cope with the increased complexity produced by adding corrective terms. The surrogate function is easy to solve and allows for further parallelization on the component level within each iteration. This paper is complementary to an accompanying paper (Racković et al. arxiv preprint. https://arxiv.org/abs/2302.04843 , 2023) where we provide detailed experimental results and discussion, including highly-realistic animation data, and show a clear superiority of the results compared to the state-of-the-art methods.
IMAGE COMPRESSION USING DEEPAUTOENCODER

The rapid emergence of several online platforms has led to the generation of an enormous amount of data, mostly in the form of images and videos.Multimedia data including graphical, audio and video data in the uncompressed form needs noticeable amount of storage space and bandwidth for its transmission.TO overcome the problem of storage space and bandwidth, the necessity of suitable image compression methods has become a necessity.Image compression is the reduction in the dimension of an image for beneficial storage and transmission.Various methodologies had immerged to solve this problem, but mostly suffered a major drawback, that is the reconstructed image suffers significant data loss.A convolutional autoencoder model has been created with 20 different layers and filters to get a better image compression model.The proposed model can be implemented using Python, Keras, and Tensorflow backend.
The general framework for few-shot learning by kernel HyperNetworks

Abstract Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting, where only one element represents each class. We propose the general framework for few-shot learning via kernel HyperNetworks—the fusion of kernels and hypernetwork paradigm. Firstly, we introduce the classical realization of this framework, dubbed HyperShot. Compared to reference approaches that apply a gradient-based adjustment of the parameters, our models aim to switch the classification module parameters depending on the task’s embedding. In practice, we utilize a hypernetwork, which takes the aggregated information from support data and returns the classifier’s parameters handcrafted for the considered problem. Moreover, we introduce the kernel-based representation of the support examples delivered to hypernetwork to create the parameters of the classification module. Consequently, we rely on relations between the support examples’ embeddings instead of the backbone models’ direct feature values. Thanks to this approach, our model can adapt to highly different tasks. While such a method obtains very good results, it is limited by typical problems such as poorly quantified uncertainty due to limited data size. We further show that incorporating Bayesian neural networks into our general framework, an approach we call BayesHyperShot, solves this issue.
On the Semantics of Risk Propagation

Risk propagation encompasses a plethora of techniques for analyzing how risk "spreads" in a given system. Albeit commonly used in technical literature, the very notion of risk propagation turns out to be a conceptually imprecise and overloaded one. This might also explain the multitude of modeling solutions that have been proposed in the literature. Having a clear understanding of what exactly risk is, how it be quantified, and in what sense it can be propagated is fundamental for devising high-quality risk assessment and decision-making solutions. In this paper, we exploit a previous well-established work about the nature of risk and related notions with the goal of providing a proper interpretation of the different notions of risk propagation, as well as revealing and harmonizing the alternative semantics for the links used in common risk propagation graphs. Finally, we discuss how these results can be leveraged in practice to model risk propagation scenarios.
SNRepair: Systematically Addressing Sensor Faults and Self-Calibration in IoT Networks

An accurate and robust technique for sensor fault diagnosis proves useful for an uninterrupted supply of correct monitoring data across the Internet-of-Things (IoT) network. The manual checking and calibration of thousands of sensors deployed in the IoT network is a challenging task. Furthermore, most calibration techniques require additional hardware support for calibration. To address these issues, a unique IoT-based framework, SNRepair, has been proposed, which uses modified deep reinforcement learning (DRL) for detecting different types of sensor faults, such as bias, drift, complete failure (CF), and precision degradation (PD). This technique is also capable of self-calibrating the faulty sensors automatically using the historical healthy data of the same sensor. The fault detection module identifies four types of sensor faults with 96.17% accuracy. The self-calibration module can calibrate the faulty sensors within a few seconds, thereby ensuring the uninterrupted availability of accurate monitoring information. The proposed model is robust as it can work efficiently even in noisy environments with accurate results.
Engineering user-centered explanations to query answers in ontology-driven socio-technical systems

The role of explanations in intelligent systems has in the last few years entered the spotlight as AI-based solutions appear in an ever-growing set of applications. Though data-driven (or machine learning) techniques are often used as examples of how opaque (also called black box) approaches can lead to problems such as bias and general lack of explainability and interpretability, in reality these features are difficult to tame in general, even for approaches that are based on tools typically considered to be more amenable, like knowledge-based formalisms. In this paper, we continue a line of research and development towards building tools that facilitate the implementation of explainable and interpretable hybrid intelligent socio-technical systems, focusing on features that users can leverage to build explanations to their queries. In particular, we present the implementation of a recently-proposed application framework (and make available its source code) for developing such systems, and explore user-centered mechanisms for building explanations based both on the kinds of explanations required (such as counterfactual, contextual, etc.) and the inputs used for building them (coming from various sources, such as the knowledge base and lower-level data-driven modules). In order to validate our approach, we develop two use cases, one as a running example for detecting hate speech in social platforms and the other as an extension that also contemplates cyberbullying scenarios.
Content-Based Image Recovery System with the Aid of Median Binary Design Pattern

Content-based image retrieval is a technique for locating images in vast, unlabeled image collections (CBIR).However, users are not happy with the traditional methods of information retrieval.Additionally, the number of consumer-accessible pictures and online production and distribution channels are expanding.Consequently, permanent and widespread digital image processing occurs across numerous industries.As a result, acquiring quick access to these big image databases and extracting identical images from sizable groups of photographs from a specific image (Query) create significant problems that call for efficient solutions.Calculations related to similarity and feature representation are crucial to a CBIR system's effectiveness.Color, shape, texture, and gradient are some essential features that can be utilized to portray an image.Local Binary Pattern (LBP) is a modest and successful texture controller that marks the pixels of an image by controlling the part of every pixel and deciphering the outcome as a binary value.The Local Binary Pattern (LBP) approach is acquainted with grey-level images to characterize color images as the pattern's dimensionality is enhanced.The current study proposes the 'Median Binary Pattern', which incorporates the multichannel decoded Local Binary Pattern (mdLBP) utilized to portray color images.For consolidating LBPs from more than one channel to make the descriptor noise-robust, two structures, specifically adder and decoder-based structures, and a noise-robust binary pattern called the 'Median Binary Pattern'.Compared with existing approaches, the proposed method achieved Average Recovery precision (ARP) and Average Recovery rate (ARR) of 68.1 and 33.55, respectively, with Noise Robust Binary Patterns.
Synthetic Minority Oversampling Technique for Efforts to Improve Imbalanced Data in Classification of Lettuce Plant Diseases

In this study we classified lettuce plant diseases. These plant diseases are available in the form of images that have been converted in .csv format to be classified. These plant diseases are available in the form of images that have been converted in .csv format to be classified. Image These plant diseases have been divided into several classes or categories. Then we determine the features of each row and column of the dataset. Each line in the CSV file represents one image, and each column represents one feature Each line in the CSV file represents one image, and each column represents one feature. Then a label is made for each line in the CSV file, namely the class or category where the images are grouped. Thus, so that we get datasets that are ready to be processed with machine learning. However, in processing the dataset, we get imbalanced data. So we added the Synthetic Minority Over-sampling Technique (SMOTE) method to overcome the imbalance that occurs. So that the data can be classified using several algorithms to find the best accuracy.
Prediction model of photovoltaic output power based on VMD-EMD-BiLSTM

The accuracy of photovoltaic (PV) power prediction is significantly influenced by the high complexity and volatility of the PV sequence. The existing methods for predicting photoelectric power are difficult to effectively mine and analyze the internal variation law of data. To improve the accuracy of PV power prediction, a new method is proposed that first performs variational mode decomposition (VMD) and empirical mode decomposition (EMD), and then establishes a bi-directional long and short-term memory neural network (BiLSTM) for PV output power prediction. The proposed method extracts the amplitude and frequency characteristics of the PV output power series through VMD. After that, the residual term with strong non-stationarity is generated, which still has more sequence characteristics. The residual term is then decomposed by EMD for the second time to extract more features. Finally, the BiLSTM model is established to conduct bidirectional mining for PV power data and predict PV output power. The actual PV data is used to test the experimental results, which show that the proposed VMD-EMD-BiLSTM prediction model has better prediction performance.