Reviewerly/exHarmony huggingface dataset:
A collaborative filtering recommendation framework utilizing social networks

Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recommendation framework that employs social networks to generate more precise and pertinent recommendations. The framework is based on a modified version of the user-based collaborative filtering algorithm, which computes user similarity based on their ratings and social connections. The similarity measure is determined by a weighted combination of these two factors, with the weights learned through an optimization process. The framework is evaluated using a dataset of movie ratings and social connections between users. The findings reveal that the proposed approach outperforms traditional collaborative filtering methods regarding recommendation accuracy and relevance. Moreover, the framework can offer more diverse recommendations compared to traditional methods. In summary, the proposed framework integrates social networks to enhance the accuracy and relevance of collaborative filtering recommendations. The approach has various applications, including e-commerce, music, and movie recommendation, and can potentially address the issues of cold-start and sparsity in collaborative filtering.
Trustworthy decentralized collaborative learning for edge intelligence: A survey

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources. Decentralized Collaborative Learning (DCL) is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data. DCL has a wide range of applications in various domains, such as smart city and autonomous driving. However, DCL faces significant challenges in ensuring its trustworthiness, as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality, undermine learning reliability or violate data privacy. Therefore, it is crucial to design DCL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects: security, robustness, and privacy. We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of Trustworthy DCL (TDCL). Finally, we highlight open challenges and future directions for advancing TDCL research and practice.
Theorizing the subjectivizing powers of market-based technologies: Looking beyond coercion and seduction

Existing theorizations on how the proliferation of market-based technologies within universities come to foster so-called academic performer subjectivities have mainly drawn attention to their coercive and seductive powers. However, while these theorizations help explain why researchers either unwillingly adapt to, or identify with and cherish, their neoliberal ideals, they are less useful to explain recent empirical results showing that many researchers willingly comply yet are very critical of the very same ideals. Drawing upon an interview study of Swedish researchers, we address this theoretical gap in the literature by analytically disentangling three important qualities of the technologies per se, in terms of them producing performance numbers characterized by Specificness, Ongoingness, and Emptiness (SOE). These three qualities do not only have the dual power to interchangeably provoke bitter and sweet feelings, but also to foster the adoption of an academic performer subjectivity. In fact, it is precisely by provoking bittersweet feelings that these qualities break the sharp edges of pure coercion and seduction, thereby fostering a type of low-affective, yet highly persuasive form of reasoning about pros and cons of market-based technologies, which make their neoliberal ideals seem acceptable and reasonable at the end of the day.
Enhancement of efficiency of the training process with the use of digital technologies

The level of safety and efficiency of technological processes operation largely depends on the qualifications of operating personnel. In continuous processes of such industries as chemical, mining, processing, energy, etc., it is common practice to use computer simulators to master the skills of making the right decisions in emergency conditions or ability to choose an efficient mode of operation. With the aim of controlling the safety level of work, it is required to have an opportunity to evaluate the efficiency of methods and learning outcomes. There is no single approach to learning outcomes using simulator trainings at energy-providing enterprises with continuous and technological processes. The main task of such enterprises is to train students with a set of knowledge and skills that will ensure the required level of safety and operational efficiency. This research proposes the technique for training, which includes the sequence of theoretical and practical exercises, metrics for automatic recording of students' actions and criteria for assessing training efficiency. The authors have tested the proposed sequence of theoretical and practical exercises on the simulator on groups of engineering students. The results of the students' execution of practical tasks on the simulator have been recorded using the proposed metrics. Based on the results the authors have proposed the criteria to assess students' understanding of cause-and-effect relationships. The learning outcomes according to the technique and the proposed criteria have been verified with the help of blind test and the implementation of the final practical task on the simulator. The technique has been tested on different groups of students and it is obvious that due to the proposed metrics instructors are able to track the progress of learning. The research results can be applied at plants with continuous technological processes or in higher educational institutions. This practice-based approach to training can help to build the required set of knowledge and skills, which, in turn, will enhance the level of safety and operational efficiency. The engineering universities and enterprises of industry are currently implementing the proposed technique and computer simulator and it enables receiving feedback and updating of the technique for training operational personnel.
Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Medical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.In order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.We conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data.
Machine learning model identification and prediction of patients’ need for ICU admission: A systematic review

The emergency department (ED) triage process serves as a crucial first step for patients seeking acute care, This initial assessment holds crucial implications for patient survival and prognosis. In this study, a systematic review of the existing literature was performed to investigate the performance of machine learning (ML) models in recognizing and predicting the need for intensive care among ED patients.Four prominent databases (PubMed, Embase, Cochrane Library and Web of Science) were searched for relevant literature published up to April 28, 2023. The Prediction model study Risk of Bias Assessment Tool (PROBAST) was employed to evaluate the risk of bias and feasibility of prediction models.In ten studies, the main algorithms used were Gradient Boostin, Logistic Regressio, Neural Network, Support Vector Machines, Random Forest. The performance of each model was as follows: Gradient Boosting had a sensitivity range of 0.3 to 0.96, specificity range of 0.6 to 0.99, accuracy range of 0.37 to 0.99, precision range of 0.3 to 0.96, and AUC value range of 0.68 to 0.93; Logistic Regression had a sensitivity range of 0.46 to 0.97, specificity range of 0.28 to 0.99, accuracy range of 0.66 to 0.97, precision range of 0.27 to 0.63, and AUC value range of 0.72 to 0.97; Neural Networks had a sensitivity range of 0.45 to 0.96, specificity range of 0.58 to 0.99, accuracy range of 0.36 to 0.97, precision range of 0.27 to 0.96, and AUC value range of 0.67 to 0.91; Support Vector Machines had a sensitivity range of 0.49 to 0.83, specificity range of 0.94 to 0.98, accuracy range of 0.33 to 0.97, precision range of 0.53 to 0.94, and AUC values were not reported; Random Forests had a sensitivity range of 0.75 to 0.91, specificity range of 0.77 to 0.94, accuracy range of 0.35 to 0.77, precision range of 0.36 to 0.94, and AUC value of 0.83.ML models have demonstrated good performance in identifying and predicting critically ill patients in ED triage. However, because of the limited number of studies on each model, further high-quality prospective research is needed to validate these findings.
Supervised learning and pattern recognition in photonic spiking neural networks based on MRR and phase-change materials

Traditional computers are limited by the separation of memory and processor units, is difficult to achieve fast, efficient, and low-power computing. While photonic spiking neural networks (SNNs) can overcome these shortcomings, they encounter limitations in large-scale integration. Silicon photonics platform, compatible with mature Complementary Metal Oxide Semiconductor (CMOS) platforms, is a promising candidate for realizing large-scale photonic SNNs. In this work, we proposed an integrated photonic SNN by exploiting the photonic properties of phase-change material (PCM) Ge2Sb2Te5 (GST) and micro-ring resonators (MRR), and demonstrated its integrate-and-fire (IF) behavior. Based on a system-level behavioral model, we adopt an improved Tempotron-like ReSuMe supervised learning algorithm to train the proposed photonic SNNs and complete a pattern recognition task for the clock's 12 clockwise directions. Then the influence of different noise levels is considered, and the accuracy is close to 1 when the noise level is less than 0.2. We propose a photonic implementation of such an SNN system, and use wavelength division multiplexing to achieve a scalable architecture for the pattern recognition task. The collaborative design and optimization of hardware architecture and algorithm are realized, providing a theoretical basis for the realization of photonic SNN based on MRRs and PCM.
A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions

In recent years, there has been rapid development in machine learning for solving artificial intelligence tasks in various fields, including translation, speech, and image processing. These AI tasks are often interconnected rather than independent. One specific type of relationship is known as structural duality, which exists between multiple pairs of artificial intelligence tasks. The concept of dual learning has gained significant attention in the fields of machine learning, computer vision, and natural language processing. Dual learning involves using primitive tasks (mapping from domains X to Y) and dual tasks (mapping from domains Y to X) to enhance the performance of both tasks. In this study, we propose a general framework called Bi-Dual Inference by combining the principles of dual inference and dual learning. Our framework generates multiple dual models and a primal model by utilizing two dual tasks: sentiment analysis of input text and sentence generation of sentiment labels. We create these model pairs (primal model f, dual model g) by employing different initialization seeds and data access sequences. Each primal and dual model is considered as a distinct LSTM model. By reasoning about a single task with multiple similar models in the same direction, our framework achieves improved classification results. To validate the effectiveness of our proposed model, we conduct experiments on two datasets, namely NLPCC2013 and NLPCC2014. The results demonstrate that our model outperforms the optimal baseline model in terms of the F1 score, achieving an improvement of approximately 5%. Additionally, we provide parameter values for our proposed model, including model iteration analysis, α parameter analysis, λ parameter analysis, batch size analysis, training sentence length analysis, and hidden layer size setting. These experimental results further confirm the effectiveness of our proposed model.
Low-Illumination Image Enhancement Using Local Gradient Relative Deviation for Retinex Models

In order to obtain high-quality images, the application of low-illumination image enhancement techniques plays a vital role in enhancing the overall visual appeal. However, it is particularly difficult to enhance an image while maintaining the original information of the scene. The augmentation method based on Retinex theory is widely considered as one of the representative techniques for such problems, but this method still has some limitations. First of all, noise is easily ignored in the process of model building, and the robustness of the model needs to be improved. Secondly, the image decomposition is less effective, so that part of the image information is not effectively presented. Finally, the optimization procedure is computationally complicated. This paper introduces a novel approach for enhancing low-illumination images by utilizing the relative deviation of local gradients. The proposed method aims to address the challenges associated with low-illumination images and offers a solution to these issues. In this paper, local gradient relative deviation is used as a constraint term and a noise term is added to highlight the image texture and structure and improve the robustness of the models, considering that LP achieves piecewise smoothing with better sparsity compared to the sum norm commonly used by L1 and L2 norms. In this paper, the L2−LP norm is used to constrain the model, which smooths the illumination component and better preserves the details of the reflectance component. In addition, to efficiently solve the optimization problem, the alternating direction multiplier method is chosen to transform the optimization process into the solution of several sub-problems. In comparison to traditional Retinex models, the proposed method excels in its ability to simultaneously enhance the image and suppress noise effectively. The experimental outcomes demonstrate the effectiveness of the proposed model in enhancing both simulated and real data. This approach can be applied to low-illumination remote sensing images to obtain high-quality remote sensing image data.
A Multimodal User-Adaptive Recommender System

Traditional recommendation systems have predominantly relied on user-provided ratings as explicit input. Concurrently, visually aware recommender systems harness inherent visual cues within data to decode item characteristics and deduce user preferences. However, the untapped potential of incorporating item images into the recommendation process warrants investigation. This paper introduces an original convolutional neural network (CNN) architecture that leverages multimodal information, connecting user ratings with product images to enhance item recommendations. A central innovation of the proposed model is the User-Adaptive Filtering Module, a dynamic component that utilizes user profiles to generate personalized filters. Through meticulous visual influence analysis, the effectiveness of these filters is demonstrated. Furthermore, experimental results underscore the competitive performance of the approach compared to traditional collaborative filtering methods, thereby offering a promising avenue for personalized recommendations. This approach capitalizes on user adaptation patterns, enhancing the understanding of user preferences and visual attributes.
Encoder–Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation

The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state–of–the–art methods directly use ground truth depth maps for depth information fusion, which relies on highly developed and expensive depth sensors. Aiming to solve such a problem, we proposed a self–calibrated RGB-D image semantic segmentation neural network model based on an improved residual network without relying on depth sensors, which utilizes multi-modal information from depth maps predicted with depth estimation models and RGB image fusion for image semantic segmentation to enhance the understanding of a scene. First, we designed a novel convolution neural network (CNN) with an encoding and decoding structure as our semantic segmentation model. The encoder was constructed using IResNet to extract the semantic features of the RGB image and the predicted depth map and then effectively fuse them with the self–calibration fusion structure. The decoder restored the resolution of the output features with a series of successive upsampling structures. Second, we presented a feature pyramid attention mechanism to extract the fused information at multiple scales and obtain features with rich semantic information. The experimental results using the publicly available Cityscapes dataset and collected forest scene images show that our model trained with the estimated depth information can achieve comparable performance to the ground truth depth map in improving the accuracy of the semantic segmentation task and even outperforming some competitive methods.
Crafting a Museum Guide Using ChatGPT4

This paper introduces a groundbreaking approach to enriching the museum experience using ChatGPT4, a state-of-the-art language model by OpenAI. By developing a museum guide powered by ChatGPT4, we aimed to address the challenges visitors face in navigating vast collections of artifacts and interpreting their significance. Leveraging the model’s natural-language-understanding and -generation capabilities, our guide offers personalized, informative, and engaging experiences. However, caution must be exercised as the generated information may lack scientific integrity and accuracy. To mitigate this, we propose incorporating human oversight and validation mechanisms. The subsequent sections present our own case study, detailing the design, architecture, and experimental evaluation of the museum guide system, highlighting its practical implementation and insights into the benefits and limitations of employing ChatGPT4 in the cultural heritage context.
The Bibliometric Commingling of Metaverse and Non-fungible Tokens in Marketing

Historically, business organisations are always looking for a new approach to marketing and advertising their products to reach their target customers. The introduction of the Internet helped businesses to reach more potential customers than it was before. Currently, the newest technologies that have become the centre of attraction in marketing are metaverse and non-fungible tokens (NFTs). Marketing practitioners and scholars continue to show interest in technological metaverse and NFTs as an up-and-coming platform for enhancing consumer satisfaction experience virtually the same as the experience in physical stores. The aim of this study is to examine the impact of commingling of metaverse and NFTs in marketing with the lens of bibliometric analysis through R Bibliometrix. This study contributes to the marketing and financial technology literature by showing academic work's impact, quantity, and quality on metaverse and NFTs. The study sectionalises methodology, results, and conclusions and gives future research insights.
The Product’s Great Journey: A Multi-triangular Agile Approach

Within a constantly competitive scenario, productive organizations have been looking for alternatives to improve all their internal processes in order to try to stay alive among competitors, in terms of economic, environmental and social sustainability. Thus, it is no longer common for someone or some isolated sector of a company to make a decision on their own. The decisions became collegiate. However, some sectors end up having more relevant arguments at each stage of a product’s great journey. At every moment of the process, from conceptualization (cradle) to disposal (grave or reuse), some departments become protagonists, while others become supporting actors. In this approach, the great journey of a product could be divided into “triangle of forces” structured by stakeholder representatives. The objective of this specific paper is to evaluate the activities of the conceptualization phase of a product that is observed as the first of these triangles whose vertices are formed by representatives from the Marketing, Industrial Design and Product Engineering areas. The proposed triangle model aims to organize the decision-making process during the development of a new product. Assigning leadership responsibilities and support functions to each sector at each stage of the project. This proposal would transform the great product journey into a star formed by these triangles of power, creating an agile and interactive environment of simultaneous engineering. As a result, this study presents a proposal of fundamental activities involved in the initial triangle of the task force conceptualization phase and the interrelationships among the areas of Marketing, Industrial Design and Product Engineering that happen along these processes.