Reviewerly/exHarmony huggingface dataset:
Downloading snapshots and creating stable R packages repositories using r-universe

&lt;strong&gt;Downloading repository snapshots&lt;/strong&gt; The new snapshot API lets you download a full copy of any CRAN-like repository on r-universe. You can use such a snapshot to mirror the entire CRAN-like repository on your own servers, or for example to build a stable, validated release of your package suite. The API endpoint is simply /api/snapshot and has several options to filter content.
An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis.
Audio-Visual Multi-modal Meeting Recording System

There exist two forms of speaker recognition in meeting recording systems: hardware recognition and software recognition, but the applicability of such two approaches in real meetings is not good enough and the hardware cost is too high. The main contribution of this paper is to use the theory of domain generalization to train the model and use contrast learning to improve the model migration learning ability, while this paper constructs a speaker recognition and meeting content transcription system based on deep learning audiovisual speech recognition (AVSR) model and speaker recognition model (SPR), which only needs a microphone and a camera to recognize the current speaker and use the system’s audiovisual speech recognition The speaker recognition module is used to transcribe the conference content.
Ontology-Based Explanations of Neural Networks for Collaborative Human-AI Decision Support Systems

Collaborative decision support systems, including both human experts and AI-based agents can lift decision support to a new level, helping end users to make informed and justified decisions in complex dynamic problem domains. However, human-AI collaboration still has some problematic aspects, one of which is the lack of trust to AI agents, caused by insufficient understanding of why these agents make certain decisions. In this paper, we analyze existing approaches to ontology-based explanation of neural AI agents. These approaches are then used to develop a conceptual model of neural explanation and human-AI interaction mechanisms aimed at explanation exchange. The proposed model will be used in the human-AI collaborative decision support system based on ontology-based neuro-symbolic AI.
Machine Learning Based Framework for Prediction of Photovoltaic Output Power

Energy Harvesting from various renewable energy sources has experienced rapid growth due to the detrimental environmental effects caused by the use of fossil fuels. Solar energy is a prominent energy resource widely employed for power generation in wide range of applications. The output power of a Photovoltaic (PV) panel depends significantly on environmental parameters, which results in irregular and variable output power. This unstable nature of output power has evolved as a notable issue. A precise framework for forecasting the output power of PV panel is essential. This manuscript proposes a hybrid ensemble Machine Learning (ML) model combining Support Vector Regression (SVR), Decision Tree Regression (DTR), and K-nearest Neighbor (KNN) as base learner with Linear Regressor (LR) as meta meta model to forecast short term PV panel output power. The proposed model is trained on selected features using Pearson's correlation coefficient and Principal Component Analysis (PCA) as feature selection techniques. In addition, results are obtained by computing various ML models to compare and evaluate the proposed work. The proposed model outperforms conventional ML models by achieving maximum coefficient of determination (R2) of 0.82 and minimum Mean Square Error (MSE) of 0.19.
Maintaining an R Package - Community Call Summary

In March we held a Community Call discussing the maintenance of R packages.This call included a starting presentation by Julia Silge followed by a discussion featuring panelists with a wide variety of backgrounds: Elin Waring, Erin Grand, Leonardo Collado-Torres and Scott Chamberlain.
Blockchain-Secure Mobility Data in Smart Campus

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution’s work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straightforward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel’s location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of data.
RFID Attendance System with Contagious Disease Prevention Module using Internet-of-Things Technology

This paper presents the development and implementation of an innovative Internet of Things (IoT)-based RFID attendance system integrated with a module focused on curbing the spread of contagious diseases in densely populated settings, such as schools and workplaces. Recognizing the limitations of existing radio frequency identification (RFID) attendance systems, an extensive analysis of prior works was conducted to identify crucial functionalities necessary for an effective solution. Leveraging the IoT paradigm, we devised a 3-layer architecture encompassing thermometer sensors, RFID technology, Arduino microcontrollers, Wi-Fi connectivity, and the message queuing telemetry transport (MQTT) protocol to facilitate seamless data transmission and storage. The resulting prototype enables real-time monitoring of feverish symptoms, serving as an early warning system for potential contagious illnesses while streamlining attendance management. Data collected from the IoT devices is securely stored in a centralized database and accessed through an intuitive information system embedded within the IoT application. Users can effortlessly view and manage attendance data, while administrators gain access to health-related metrics, enabling timely responses to health concerns. User evaluations of the developed system resulted in an outstanding “A” rating, validating its reliability, functionality, and user satisfaction. Future improvements involve real-world testing, scalability assurance, integration with health authorities for comprehensive data management, and automated alerts for potential disease outbreaks.
Robust Wavelet based Watermarking Using Particle Swarm Optimization

Abstract: From the 13th century we are using watermarking techniques and first time watermarking used by paper industry because the benefit of watermarking in this industry to unique identify of papers. Thus we can use this technique for authentication and copyright purpose. Watermarking is a pattern which is used to insert in a entity. When we insert any watermark then it will not change any functionality or structure of an entity. We can say digital entities like audio, video, computer programs, software, hardware like chips. Main purpose of watermark is to provide authenticity and illegal distribution of work attacks.
Contemporary challenges of consumption: a Kafkaesque and critical marketing perspective

Abstract Purpose Markets are sites of collaboration as well as contestation between their various actors, including firms and consumers. In this respect, a narrative that consumers are more empowered than ever has been in vogue. Drawing from Kafka’s literature and philosophy and the paradigm of critical marketing, this paper argues for a broadened perspective. Design/methodology/approach Using a selective and exploratory-review approach, six dimensions of the state of today’s consumer emerge. These are subsequently assessed and developed as manifestations challenging the empowerment thesis. Findings The depiction of intense subjugation in Kafka’s fiction and the lack of agency his characters portray furnish narrative frames to view the paradoxical state of contemporary consumption. Specifically, consumer choice and sovereignty, service failures and recoveries, privacy concerns, the capitalist nexus, transformation in service provisioning modes, and frontline employees’ lowered stakes comprise six analytical frames of consumers’ lives that depict their paradoxical simultaneity of empowerment and entrapment. Originality/value The work contributes to critical marketing theory in particular and critical philosophy in general. Specifically, the proposed framework of consumption challenges depicted herein can serve as a mid-range theoretical taxonomy. The exemplar states highlighted reveal managerial and policy challenges.
AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

Purpose The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features. Design/methodology/approach We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences. Findings Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively. Research limitation/implications This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations. Originality/value This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.
Descriptive Attributes for Language-Based Object Keypoint Detection

Multimodal vision and language (VL) models have recently shown strong performance in phrase grounding and object detection for both zero-shot and finetuned cases. We adapt a VL model (GLIP) for keypoint detection and evaluate on NABirds keypoints. Our language-based keypoints-as-objects detector GLIP-KP outperforms baseline top-down keypoint detection models based on heatmaps and allows for zero- and few-shot evaluation. When fully trained, enhancing the keypoint names with descriptive attributes gives a significant performance boost, raising AP by as much as 6.0, compared to models without attribute information. Our model exceeds heatmap-based HRNet’s AP by 4.4 overall and 8.4 on keypoints with attributes. With limited data, attributes raise zero-/one-/few-shot test AP by 1.0/3.4/1.6, respectively, on keypoints with attributes.
Fighting selection bias in statistical learning: application to visual recognition from biased image databases

AbstractIn practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performances on different population segments has highlighted the representativeness issues induced by a naive aggregation of the datasets. In this paper, we show how biasing models can remedy these problems. Based on the (approximate) knowledge of the biasing mechanisms at work, our approach consists in reweighting the observations, so as to form a nearly debiased estimator of the target distribution. One key condition is that the supports of the biased distributions must partly overlap, and cover the support of the target distribution. In order to meet this requirement in practice, we propose to use a low dimensional image representation, shared across the image databases. Finally, we provide numerical experiments highlighting the relevance of our approach.Keywords: Sampling biasselection effectvisual recognitionreliable statistical learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by the research chair ‘Good In Tech : Rethinking innovation and technology as drivers of a better world for and by humans’, under the auspices of the ‘Fondation du Risque’ and in partnership with the Institut Mines-Télécom, Sciences Po, Afnor, Ag2r La Mondiale, CGI France, Danone and Sycomore.
Development of 3D models with augmented reality for steel and reinforced concrete structure subjects

In the academic sphere of undergraduate Civil Engineering courses, students find it difficult to abstract three-dimensional visualisations from two-dimensional images presented in project documentation. The aim of this article is therefore to present an alternative to assist in the teaching and learning process of steel and reinforced concrete structural design content. Three-dimensional models of steel connections and reinforced concrete structural elements were developed with functionality for the application of Augmented Reality (AR). The models were developed in Revit software and exported to the Sketchfab platform, where it is possible to use the AR feature via an application. Additional information, such as texts, can be added to the models, which expands the possibilities for developing didactic activities. Both tools used proved to be very suitable for the purpose of developing digital objects for teaching engineering.
The Characteristics of Recycle Plaster of Paris at Different Drying Temperatures

The industry ceramic represents a large solid waste dumping and concerning environment damaged cause. As we know in Malaysia, ceramic industry very famous especially in Kuala Kangsar, Perak. According to Nawi (2016) the Department of Environment (DOE), 337,770 metric tons of plaster of Paris became solid waste in 2012, up from 80,000 metric tons in 2009. This shows an increase in solid waste is increasing. In this way, the aim of this study is to identify the chemical and physical properties that the used Plaster of Paris (POP) can be recycled as new plaster without affecting their casting performance. This paper showed the comparing the characteristic properties of New Plaster of Paris (NPOP) and Recycle Plaster of Paris (RPOP) at differences drying temperature. This experimental started with recycle used mould, crushing, sieving, drying in oven at variation temperature (150?,160?,170?,180?,190?) for 1 hour and tested with setting times, particle size, absorption test, porosity, and used the same ratio 40% of water and 60% material during the mixing process. The result show that all sample Recycles Plaster of Paris at variation temperature are performance and can be working similar New Plaster of Paris.
A novel multifunctional intelligent bed integrated with multimodal human–robot interaction approach and safe nursing methods

Abstract The authors propose a multifunctional intelligent bed (MIB) that integrates multiple modes of interaction to improve the welfare of mobility‐impaired users and reduce the workload of medical personnel. The MIB features independent autonomous omnidirectional movement, position adjustment, multi‐degree‐of‐freedom (DOF) movement regulation and posture memory functions to facilitate comfortable and convenient interaction for mobility‐impaired users. In particular, an integrated “MIB‐state perception‐interaction interfaces” system is established, and a bed fall risk detection algorithm and assisted get‐up‐transfer algorithm is proposed. By recognising and sharing human body state characteristics, nursing collaboration can be achieved with caregivers or other nursing robots. Comprehensive experiments demonstrate that the MIB is a novel MIB that is highly adaptable to the environment, convenient to interact with and safe. By integrating the proposed algorithms, daily safety monitoring, assisted get‐up and defecation tasks can be effectively accomplished. This technology demonstrates excellent applicability and promising prospects for implementation in hospitals, nursing centres and homes catering to elderly and disabled individuals with mobility impairments.
A Modified Binary Jaya Optimization Algorithm and its application in Feature Selection

In recent years rapid advancement of modern technologies has produced enormous and varied data which needs to be pre-processed before applying various machine learning techniques to gain valuable insight from the data. Feature Selection is an indispensable pre-processing step that helps to remove undesirable features which deteriorate the desired output from the various machine learning techniques. Further, it helps to wane the overall execution time. Metaheuristic algorithms have successfully applied as a wrapper approach for selecting those features which boost the overall outcome of machine learning techniques either in supervised or unsupervised form. The present work proposes a Modified Binary Jaya Optimization Algorithm as a wrapper for selecting the feature sub-set using K-NN as a classifier in a supervised Machine Learning task. In the proposed work, a unique initialization technique using Mutual information Coefficient as a Filter has been applied along with the Lévy Flight-based update mechanism, and a variable Mutation function is activated as the algorithm gets trapped in a locally optimal solution. The proposed work has been applied to ten significant benchmark classification datasets. The results show substantial improvement when compared with Binary Jaya Optimization Algorithm regarding average accuracy, precision, recall, F1-score, and feature size.
Extremely Low Bit-rate Image Compression via Invertible Image Generation

Image compression at extremely low bit-rates has always been a challenging task in bandwidth limited scenarios, such as aerospace and deep-sea explorations. Recent years have seen great success of deep learning in image compression, however, few of them are specially designed for extremely low bit-rate conditions. To solve this issue, in this paper, we propose a novel invertible image generation based framework for extremely low bit-rate image compression. The proposed framework is composed of three modules, including an invertible image generation (IIG) module, a generated image compression (GIC) module and a compressed image adjustment (CIA) module. The role of IIG module is to generate a compression-friendly image from the original image. In the IIG module, image generation and restoration are modelled as two mutually reversible processes to avoid the information loss. After the IIG module, the GIC module is employed to compress the generated images to save the coding bit-rates. After that, the CIA module is used to shrink the quality gap between the compressed generated image and the un-compressed image. Finally, the image from the CIA module is sent back to the IIG module to restore the original image. The experimental results on three different datasets show that the proposed framework achieves state-of-the-art performance in image compression with extremely low bit-rates. We also extend the proposed framework to feature compression towards object detection, which saves 90% bit-rates than the VVC standard with the same detection accuracy.
Hand gesture control accuracy through increased epoch and batch size

Abstract. The conventional method of mechanism control which requires physical contact is being replaced with remote control, especially with the advent of the Internet of things (IoT). Facial recognition is used to identify and authenticate faces from images or videos. It has many applications, including granting or restricting access to a facility, or secured areas, or preventing unauthorized users, including selective access. The accuracy of such a recognition and control system is very important and depends on how the system was modeled and adopted. In this study, facial images and gesture images were collated and modeled in a python neural network, then optimized using TensorFlow. The algorithm was compiled unto a raspberry pi for testing on a developed automatic gate. An effective method of achieving accuracy using the epoch is presented in this work. Five (0, 5, 10, 15, 20) batch number and epoch respectively were modeled to achieve accuracy for the system. The model was trained with five gestures; fist, palm, thumb up, thumb, and last finger. The gesture recognition accuracy achieved through the epoch was maximum at 99.97 when both batch number and epoch were set to 20. However, when no epoch was set, the accuracy was below 10.2, whereas there was no accuracy below 98% when the epoch was introduced. This depicts the importance of epoch in achieving accuracy in image recognition. It was also discovered that the higher the epoch and the batch number the greater the accuracy, but the processing would require a very high processing unit.