Reviewerly/exHarmony huggingface dataset:
Nuclei instance segmentation using a transformer-based graph convolutional network and contextual information augmentation

Nucleus instance segmentation is an important task in medical image analysis involving cell-level pathological analysis and is of great significance for many biomedical applications, such as disease diagnosis and drug screening. However, the high-density and tight-contact between cells is a common feature of most cell images, which poses a great technical challenge for nuclei instance segmentation. The latest research focuses on CNN-based methods for nuclei instance segmentation, which typically rely on bounding box regression and non-maximum suppression to locate nuclei. However, this frequently results in poor local bounding boxes for nuclei that are adhered or clustered together. In response to the challenges of high-density and tight-contact in cellular images, we propose a novel end-to-end nuclei instance segmentation model. Specifically, we first employ the Swin Transformer as the backbone network of our model, which captures global multi-scale information by combining the global modelling capability of transformers and the local modelling capability of convolutional neural networks (CNNs). Additionally, we integrate a graph convolutional feature fusion module (GCFM), that combines deep and shallow features to learn an affinity matrix. The module also adopts graph convolution to guide the network in learning the object-level local information. Finally, we design a hybrid dilated convolution module (HDC) and insert it into the backbone network to enhance the contextual information over a large range. These components assist the network in extracting rich features. The experimental results demonstrate that our algorithm outperforms several state-of-the-art models on the DSB2018 and LIVECell datasets.
A modified particle swarm optimization algorithm for a vehicle scheduling problem with soft time windows

This article constructed a vehicle scheduling problem (VSP) with soft time windows for a certain ore company. VSP is a typical NP-hard problem whose optimal solution can not be obtained in polynomial time, and the basic particle swarm optimization(PSO) algorithm has the obvious shortcoming of premature convergence and stagnation by falling into local optima. Thus, a modified particle swarm optimization (MPSO) was proposed in this paper for the numerical calculation to overcome the characteristics of the optimization problem such as: multiple constraints and NP-hard. The algorithm introduced the "elite reverse" strategy into population initialization, proposed an improved adaptive strategy by combining the subtraction function and "ladder strategy" to adjust inertia weight, and added a "jump out" mechanism to escape local optimal. Thus, the proposed algorithm can realize an accurate and rapid solution of the algorithm's global optimization. Finally, this article made typical benchmark functions experiment and vehicle scheduling simulation to verify the algorithm performance. The experimental results of typical benchmark functions proved that the search accuracy and performance of the MPSO algorithm are superior to other algorithms: the basic PSO, the improved particle swarm optimization (IPSO), and the chaotic PSO (CPSO). Besides, the MPSO algorithm can improve an ore company's profit by 48.5-71.8% compared with the basic PSO in the vehicle scheduling simulation.
Advanced Deep Learning Techniques for High-Quality Synthetic Thermal Image Generation

In this paper, we introduce a cutting-edge system that leverages state-of-the-art deep learning methodologies to generate high-quality synthetic thermal face images. Our unique approach integrates a thermally fine-tuned Stable Diffusion Model with a Vision Transformer (ViT) classifier, augmented by a Prompt Designer and Prompt Database for precise image generation control. Through rigorous testing across various scenarios, the system demonstrates its capability in producing accurate and superior-quality thermal images. A key contribution of our work is the development of a synthetic thermal face image database, offering practical utility for training thermal detection models. The efficacy of our synthetic images was validated using a facial detection model, achieving results comparable to real thermal face images. Specifically, a detector fine-tuned with real thermal images achieved a 97% accuracy rate when tested with our synthetic images, while a detector trained exclusively on our synthetic data achieved an accuracy of 98%. This research marks a significant advancement in thermal image synthesis, paving the way for its broader application in diverse real-world scenarios.
Multi-population artificial bee colony algorithm based on the nearest neighbour partition

The artificial bee colony (ABC) has shown great potential among many swarm intelligence optimisation algorithms (SIOAs). However, ABC still shows deficiencies in some aspects. The weak exploitation ability makes the original ABC hard to achieve promising results when dealing with complex optimisation problems. The roulette selection method may not work at the late search stage. To make up for these deficiencies, a modified multi-population ABC with the nearest neighbourhood partition (namely NNPMABC) is proposed in this paper. Firstly, a novel partition method is used to divide the swarm into several subgroups. Then, three improved search strategies and a new selection method based on the nearest neighbour partition are designed. In addition, a new search strategy is constructed for the scout bee stage. To prove the effectiveness of NNPMABC, 22 benchmark problems are tested. Results show NNPMABC performs the best among six ABCs.
Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks

Trust prediction facilitates the day-to-day functionality of diverse web-based applications, such as recommendation systems, market advertising and anomaly detection. However, existing works heavily rely on user-user trust interactions, which result in limited performance as the data sparsity. Previous studies have shown that the trust relationship between users is significantly affected by the category of items that the users interacted. In this paper, we propose a MetaTrust model, which generates redundant user-item interactions as the supplement of user-user trust to alleviate the data sparsity on trust prediction. Specifically, we propose category-aware metapaths, which generate abundant user-item-user interactions based on the common item category that users have interacted with. Further, Long Short Term Memory (LSTM) networks are utilized to mine features of multiple category-aware metapaths and their correlations. In order to filter the user-item-user interactions that are not related to the current task, the real trust relationship between users are embedd in the network with MLP. Finally, a multi-headed attention network is utilized to distinguish which metapath determines trust prediction between the current pair of users. Extensive experiments on three real-world dataset show that our proposed model can effectively achieve significant improvements over other competitive approaches and show the potential interpretability of trust building.
Detecting Breast Tumors in Tomosynthesis Images Utilizing Deep Learning-Based Dynamic Ensemble Approach

Digital breast tomosynthesis (DBT) stands out as a highly robust screening technique capable of enhancing the rate at which breast cancer is detected. It also addresses certain limitations that are inherent to mammography. Nonetheless, the process of manually examining numerous DBT slices per case is notably time-intensive. To address this, computer-aided detection (CAD) systems based on deep learning have emerged, aiming to automatically identify breast tumors within DBT images. However, the current CAD systems are hindered by a variety of challenges. These challenges encompass the diversity observed in breast density, as well as the varied shapes, sizes, and locations of breast lesions. To counteract these limitations, we propose a novel method for detecting breast tumors within DBT images. This method relies on a potent dynamic ensemble technique, along with robust individual breast tumor detectors (IBTDs). The proposed dynamic ensemble technique utilizes a deep neural network to select the optimal IBTD for detecting breast tumors, based on the characteristics of the input DBT image. The developed individual breast tumor detectors hinge on resilient deep-learning architectures and inventive data augmentation methods. This study introduces two data augmentation strategies, namely channel replication and channel concatenation. These data augmentation methods are employed to surmount the scarcity of available data and to replicate diverse scenarios encompassing variations in breast density, as well as the shapes, sizes, and locations of breast lesions. This enhances the detection capabilities of each IBTD. The effectiveness of the proposed method is evaluated against two state-of-the-art ensemble techniques, namely non-maximum suppression (NMS) and weighted boxes fusion (WBF), finding that the proposed ensemble method achieves the best results with an F1-score of 84.96% when tested on a publicly accessible DBT dataset. When evaluated across different modalities such as breast mammography, the proposed method consistently attains superior tumor detection outcomes.
How Do Social Media Contribute to the Emergence of “Extreme” Events? The Rise of Tour Paris 13: The “Sistine Chapel” of Street Art

Extraordinary events are exceptional and leave lasting memories in participants’ minds and sometimes in those of a broader audience. Digital technologies, and social media in particular, seem to be playing a growing role in the occurrence of such events. We analyze the physical interactions between actors and their digital exchanges via social media, which shaped the emergence of an event that developed around an unexpectedly successful, self-directed street art phenomenon. Interviews, onsite observations, and statistical analyses of social media traces reveal patterns in which the physical event and its digital counterpart recursively nurtured each other. These factors operated as a complex dissipative system in which independent actors interacted within and across physical and digital boundaries. Physical activities influenced digital activities, which in turn affected artistic creation and fueled the public’s eagerness to participate, transforming the event into a large art happening. Empirical observations reveal that the digitalization of the art show shaped the emergence of the event and stabilized it by placing it in a state of dynamic equilibrium. The event’s evolution, measured by social media activity, followed a Power Law, indicative of a phenomenon induced by multiple interdependent interactions. This signifies the existence of a statistically extreme, rare event.
Computation of a probabilistic uranium concentration map of Norway: A digital expert elicitation approach employing random forests and artificial neural networks

We compute the first probabilistic uranium concentration map of Norway. Such a map can support mineral exploration, geochemical mapping, or the assessment of the health risk to the human population. We employ multiple non-linear regression to fill the information gaps in sparse airborne and ground-borne uranium data sets. We mimic an expert elicitation by employing Random Forests and Multi-layer Perceptrons as digital agents equally qualified to find regression models. In addition to the regression, we use supervised classification to produce conservative and alarmistic classified maps outlining regions with different potential for the local occurrence of uranium concentration extremes. Embedding the introduced digital expert elicitation in a Monte Carlo approach we compute an ensemble of plausible uranium concentrations maps of Norway discretely quantifying the uncertainty resulting from the choice of the regression algorithm and the chosen parametrization of the used regression algorithms. We introduce digitated glyphs to visually integrate all computed maps and their associated uncertainties in a loss-free manner to fully communicate our probabilistic results to map perceivers. A strong correlation between mapped geology and uranium concentration is found, which could be used to optimize future sparse uranium concentration sampling to lower extrapolation components in future map updates.
tf.data service

Machine learning (ML) computations commonly execute on expensive specialized hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt. For cost efficiency, it is essential to keep these accelerators highly utilized. This requires preprocessing input data at the rate at which the accelerators can ingest and perform ML computations on the data. To avoid data stalls, the host CPU and RAM required for input data processing per accelerator core used for ML computations varies across jobs. Hence, the traditional approach of processing input data on ML accelerator hosts with a fixed hardware ratio leads to either under-utilizing the accelerators or the host CPU and RAM. In this paper, we address these concerns by building a disaggregated ML data processing system.
Mineralogy and Geochemistry of Anah and Euphrates Formations and Their Suitability for Portland Cement and Noora Industry at Al-Baghdadi and Haditha Areas, Western Iraq

The current study including geochemical and mineralogical aspects of 12 rock samples from Anah and Euphrates formations in two outcrops, The first outcrop is at Haditha city, with 12.5 meters thickness, and the second location is at Al-Baghdadi city, with 21 meters thickness. The mineralogical study by X-ray diffraction indicated that calcite is the main mineral in the rocks of Anah Formation, while calcite and dolomite exist in Euphrates Formation, where calcite is concentrated in the lower part of Euphrates Formation, while dolomite is common in the middle and upper parts of this formation, in addition to other minor minerals such as quartz and feldspar. Geochemical study was carried out by XRF to determine the major oxides(CaO, MgO, Fe2O3, Al2O3, SiO2, SO3, K2O, Na2O, MnO) and the suitability of Anah and Euphrates rocks as raw material for Portland cement and Al-Noura (lime) cement industries. According to the results it is found that Euphrates Formation in the Al-Baghdadi and Haditha outcrops is not suitable for the Portland cement, because of high percentage of MgO as a result of the high percentage of dolomitic limestone. The chemical analysis of the samples of the Anah Formation in Haditha outcrop are suitable for the Noura cement (lime) industry according to the Iraqi standard. The high content of magnesium in the Euphrates Formation at Al-Baghdadi area used as indicator that the rocks deposited in shallow marine and warm condition, and the environment was mostly isolated with warm condition. Low Sr content indicates shallow carbonate rocks. Thus, the sedimentary environment of the Anah Formation at Haditha outcrop was relatively shallower than that of the Euphrates Formation.
UX and Industry 5.0: A Study in Repairing Equipment Using Augmented Reality

Within the scope of industry 5.0, mixed reality (MR) is an eminent technology for problem solving and process optimization. The present research aimed to assess how users perceive an augmented reality (AR) application for diagnosis and intervention on industrial equipment. 18 participants with 3 different professional and technological backgrounds (general users (GU), information technology users (SD) and human centered design experts (HF)) took part in this study. Generally, the developed system was valued as slightly positive and some improvement points were identified. The results also revealed differences in the perceived User Experience (UX) between different user profiles. Also, there are important differences in perception depending on the person's analysis experience, with less rigor for new users and higher rigor for people who work with human factors, for example. This reinforce the concept of user centered design and the importance to understand the human being in its relation with the novel user interface and tendency to new technologies.
Communication Efficient Model-Aware Federated Learning for Visual Crowd Counting and Density Estimation in Smart Cities

Federated learning (FL) is an attractive paradigm where a number of users can improve their local models via sharing trained models or model increments with a central server, while the users' data is kept private. However, when model sizes are huge, FL incurs a significant communication overhead. In such scenarios, strategies that perform user sampling, so that only informative users communicate their models to the server, are desired. In this paper, we make several contributions on user sampling in FL. On the theoretical side, we consider a general framework that exhibits user heterogeneity across several dimensions: activation probabilities, gradient noise variance, number of updates per epoch, and communication channel quality. In this setting, we derive convergence rate of the FedAvg method. The rate explicitly characterizes the effects of heterogeneity and enables us to derive optimal user sampling probabilities in an offline setting, when the sampling probabilities are pre-computed. We then show how these derived probabilities naturally connect with existing optimized sampling strategies in an adaptive-online setting. On the practical side, we study visual crowd counting (VCC) as a representative deep learning application with huge-sized models. We provide an implementation of the FL system over real-world data across three pilot sites - Valetta, Trento and Novi Sad. The evaluation results demonstrate significant model accuracy benefits through employing FL over the multiple cities, and significant communication savings via non-uniform user sampling strategies.
3D video streaming in digital pathology using virtual reality

Nowadays the use of 3D visualization in pathology is already an existing technology, but its widespread is hindered by several technical obstacles. One of these is the memory required to store the samples required for 3D display and the computing capacity essential for display. In our article, we present a solution developed by us to solve these problems, which can be used to minimize the hardware resources required for 3D visualization. Our development enables users to perform 3D visualization on digitized pathological serial sections even with the help of their everyday laptop.
The Evolution of Technology use in Education Institutions: Learning Method Innovation

The rapid evolution of technological tools, particularly in the provision of multimedia materials, has continued to support the development of multimedia materials. Because of the benefits offered in increasing motivation and emotions in student learning, multimedia materials began to be used in the education system. Every educational revolution has coincided with the advancement of technological tools. As a result, educational institutions must become more competitive in their use of technology tools in order to produce quality students and graduates. The education revolution is also seen in tandem with the use of technology tools and the provision of multimedia materials. However, the use of technology tools in education must go through certain processes to ensure their effectiveness and usability. The use of technological tools that have not been thoroughly tested and have not gone through the research process has the potential to affect emotions in student learning. In particular, in order to conduct research on technology in education, researchers must first understand the evolution of the education industry. This study investigates the fundamentals of the education revolution that occurred from the era of education 1.0 to the era of education 4.0 in general.
Investigating Behavioural Intention Toward Adopting Artificial Intelligence Service Robots Technology in Hospitality in China

The pandemic accelerated the blooming of service robots in the hospitality industry in recent years in China. The researcher drew upon extended UTAUT2 theory and satisfaction to construct the conceptual framework and spent time on smartphones as mediating and moderating roles. Besides, IBM SPSS and SmartPLS were conducted to analyse the collected data with 310 valid responses through questionnaires. The results of this study contributed practical insights for relevant governmental departments and hotel operators as well as restaurant managers in their decision-making on whether to adopt AI unmanned services in the hospitality industry in China.
Navigating the Ethical and Legal Terrains of AI Tool Deployment: A Comparative Legal Analysis

The pervasive influence of technology has revolutionised numerous sectors across the globe, underscoring the need for a comprehensive evaluation of the tools that technology has engendered. Artificial Intelligence (AI), as a prominent technological tool, has exceeded expectations and proven immensely beneficial to humanity. Consequently, it is imperative to scrutinise the ethical and regulatory frameworks governing its application. This paper employs a doctrinal methodology to analyse the regulatory landscape and ethical guidelines surrounding AI technology in Nigeria and some selected jurisdictions. The findings highlight the necessity of establishing a robust governance system to govern the utilisation of this transformative technology. In light of the analysis, this study proposes several recommendations, including the imposition of stringent requirements for the provision and utilisation of AI tools, as well as the intensification of collaborations with relevant stakeholders.
The Interplay of Learning Organization and Digital Accessibility in Promoting Inclusion and Equity for Persons with Disabilities

With the increasing reliance on emerging technologies in the context of digital transformation, businesses aim to enhance service quality and adapt to changing customer needs. However, persons with disabilities still face barriers to employment due to negative attitudes, lack of accessibility, and discrimination. Therefore, there is a need to explore the challenges and opportunities associated with learning organization, digital accessibility, and disability in digital transformation, specifically focusing on employment for persons with disabilities in the human resources industry. This research agenda seeks to investigate the role of learning organization in enabling persons with disabilities for employment, identifying key challenges and opportunities associated with learning organization, digital accessibility, and disability in digital transformation. The contributions will provide valuable insights into policies and strategies that can promote accessibility, inclusion, and equity in the workplace for persons with disabilities through the lens of learning organization, contributing to the sustainable development goal of reducing inequality and promoting inclusive economic growth. This research agenda will be relevant for policymakers, employers, and persons with disabilities in the human resources industry.