Reviewerly/exHarmony huggingface dataset:
Nature of fluid and genetic affiliation of the Bakoshi-Kundila Au deposit, Nigeria: Evidence from trace elements in hydrothermal quartz

The Bakoshi-Kundila Au deposit is located in the Nigerian Shield in West Africa. In this deposit, Au-bearing pyrite contains inclusions of Au-rich hydrothermal quartz. Whether or not this co-precipitation relationship of quartz and pyrite is cogenetic and related to the gold fluid remains untested. The deposit's genetic affiliation is controversial. This study reports the results of textural and in situ chemical compositions of hydrothermal quartz, and sheds light on fluid composition, element substitutions into quartz structure, and inferred the deposit genetic type. We grouped the quartz into four generations: Qz1 and Qz2 (vein-type) and Qz3 and Qz4 (wallrock-type). The Qz2 is deformed, overprinted by late-stage fluid activity. The Qz1 is rich in Ga and Sr but poor in Al and Fe than Qz3 and Qz4: average Ga in ppm (Qz1 – 0.62, Qz3 – 0.08, Qz4 – 0.15), Sr (Qz1– 26.7, Qz3 – 4.59, Qz4 – 5.78), Al (Qz1– 16.7, Qz3 – 82.8, Qz4 – 136), Fe (Qz1– 131, Qz3 – 144, Qz4 – 133). However, the contents of low melting point chalcophile elements (LMCE) are higher and overlapped between Qz1 and Qz3 than those from Qz4: average Au in ppm (Qz1– 33.9, Qz3 – 38.2, Qz4 – 7.83), Cu (Qz1 – 13.1, Qz3 – 14.8, Qz4 – 5.95), Bi (Qz1 – 3.48, Qz3 – 0.25, Qz4 – 0.51). The LMCE in Qz1 displayed covariant relationships with Au (but poorly defined in Qz3 and Qz4), suggesting the likely presence of Au-Ag-(±Te)-bearing phases like petzite and argentum. Aluminum is the main trivalent element substituting Si4+ in Q3 and Qz4, whereas Fe3+ in Qz1. The charge-balanced cations are Li+, Na+, and K+ in Qz3 and Qz4, whereas Sr2+ in Qz1. The Al and Ti contents (in ppm) are higher and more variable in Qz3 (Al: 5.97-374, avg 82.8; Ti: 2.69-12.5, avg 6.60) and Qz4 (Al: 8.13-792, avg 136; Ti: 7.64-72.0, avg 20.2), which may imply fluctuation in fluid pH. These element concentrations are lower and less variable in Qz1 (Al: 5.25-47.8, avg 16.7; Ti: 3.41-18.0, avg 5.43), suggesting a less acidic and probably stable pH condition during its precipitation. The Qz1 has a lower average content (in ppm) of compatible elements (K – 15.7, Fe – 13.1, Ti – 5.43) than Qz3 (K – 25.9, Fe – 144, Ti – 6.60) and Qz4 (K – 63.0 Fe – 133, Ti – 20.2), implying that Qz3 and Qz4 precipitated at a higher temperature than Qz1. However, the contents of the incompatible elements (e.g., P, Ge, Rb, and Li) are not enriched in the supposed lately-formed Qz1 over Qz3 and Qz4, which is inconsistent. The inconsistency is also true between the wallrock type, with lower contents in low-temperature Qz3 than in high-temperature Qz4. Thus, temperature is considered not a major controlling factor during the formation of Bakoshi-Kundila quartz; instead, the pH condition and fluid-rock reaction are favored. The Bakoshi-Kundila quartz has Al-Ti contents akin to quartz from orogenic Au deposits.
Automating Conference Paper Assignment Using Classification Algorithms Incorporated with TF-IDF Vectorisation

Assign submitted papers to the right topics or reviewers with the right expertise in a research conference is important. However, the paper assignment process is presently a repetitive and time-consuming manual task. This study aims to automate the process by building a classification model incorporating TF-IDF vectorisation. We outline a data mining methodology consisting of data selection, text pre-processing, transformation, classification, evaluation, and optimisation. A data set of 1,000 conference papers with five research topics was carefully selected, pre-processed, and transformed into a TF-IDF vector representation. The TF-IDF vector was then applied to six popular document classification algorithms: Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree (DT) and Random Forest (RF). Initially, these algorithms with default parameters were evaluated and measured using F-measure. Then, the top three among them were further optimised using Grid Search. The results revealed SVM as the best-performing algorithm, with an F-measure score of 0.907.
Cellular Metaverse: Enhancing Real-Time Communications in Virtual World

In the next few years, various technologies including artificial intelligence, the Internet of Things, virtual reality, and augmented reality will coexist to realize the objectives of 6G wireless systems. In this paper, we propose a digital twin (DT) prototype of conventional wireless communication systems in a metaverse/virtual environment. We develop the modelling scenario of a cellular communications set-up in the Unity platform at first. Then, we establish the real-time communication between BS/s and user/s' in a metaverse environment after introducing some features such as cell coverage area, operating frequency, and global positioning system (GPS). With the aid of GPS, we continuously monitor the user/s' position in the metaverse environment. Moreover, we use the C# scripting tool in the metaverse environment to make all the modelling units functional and interconnected. The experiment results show that the user/s' location can be localized in the digital world, creating the DT of the real cellular communications set-up.
Development of OO-Do-Aware Parasite Egg Detection

The out-of-domain (OO-Do) problem occurs when a machine learning model is presented with test data that does not belong to any of the classes present in the training data. As a result, the model will always render an incorrect prediction, predict OO-Do as one of the trained classes. Parasitic infections can be a significant public health issue, and detecting and identifying parasite eggs in images can be a helpful technology for early diagnosis and treatment. However, a parasite egg detection model may face challenges when presented with out-of-domain (OO-Do) data, which includes images of unrelated objects such as cats, trees, or other irrelevant content. Previous research developed techniques for detecting out-of-domain samples in object detection to overcome this issue. These methods typically involve modifying a pre-trained model for object detection to improve its ability to detect samples outside the domain it was trained. To retain the performance of the original model while improving its ability to detect objects in OO-Do samples, we adopted a two-step approach to address the challenge of the out-of-domain samples. The first step involves classifying the test images using threshold strategies. The second step is employing object detection techniques to detect further and verify the out-of-domain samples. Object detection without threshold strategy and a two-step approach using SoftMax threshold achieved an F1-score of 77.30% and 70.60%, respectively. For out-of-domain image awareness, a two-step approach using SoftMax threshold obtained 57.97% F1-score compared to 29.94% F1-score of object detection without threshold strategy. This suggests that the proposed approach effectively addressed the out-of-domain problem in the context of parasite egg detection.
GAN vs Diffusion: Instance-Aware Inpainting on Small Datasets

Instance-aware inpainting is a crucial task in many fields such as fashion, entertainment, and photography. However, developing effective instance-aware inpainting methods that can generalize to various target instances is a significant challenge when large-scale datasets are not available. In this study, we compare the performance of two state-of-the-art approaches for instance-aware inpainting, namely instance-aware GAN (InstaGAN) and RePaint, a denoising diffusion probabilistic model, using small datasets. We chose these methods for comparison as GANs are widely used for image generation, while diffusion-based methods are gaining popularity for their ability to generate high-quality images. Our experiments show that RePaint outperforms InstaGAN in small-scale instance-aware inpainting tasks. RePaint utilizes a diffusion process that models the image pixel values as a random walk, which effectively removes noise and provides better results than InstaGAN's instance-aware GAN approach. The diffusion process also enables RePaint to handle a wide range of noise distributions, making it more versatile for inpainting tasks. Our results provide quantitative evidence that RePaint outperforms InstaGAN in small-scale instance-aware inpainting tasks, with a lower FID score and LPIPS score. These findings emphasize the importance of selecting the appropriate model for a given dataset and task.
Object-Oriented Cutout Data Augmentation for Tiny Object Detection

With the rapid development of deep learning, generic object detection has been widely applied in many fields of real life. However, the detection of tiny objects is still a challenging task due to fewer features and limited information in computer vision research. To overcome this limitation, we propose cutout data augmentation aiming at tiny objects that are prone to occlusion problems and occupy only small pixel areas in the image. Precisely, we perform a cutout that combines the traditional cutout method of randomly applying a mask to the image with the method of applying a cutout by dividing a specific area of the GT box corresponding to the category with the largest portion and the smallest in size of the dataset. By combining both techniques, we improve the occlusion problem while the semantic information of tiny objects is intact, making it more robust. Overall, the experiments achieve great results in improving accuracy on the tiny object dataset, VisDrone2019 [1].
A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%.
Gene-Ants: Ant Colony Optimization with Genetic Algorithm for Traveling Salesman Problem Solving

The Traveling Salesman Problem (TSP) is a well-known NP-hard problem that receives attention in many fields. To solve the NP-hard problems, Ant Colony Optimization (ACO) is a popular meta-heuristic that gives an effective solution of TSP but the limitation of ACO has an early stage of optimization and falls to the local optimal. To overcome the limitation of ACO, we use Genetic Algorithm (GA) that has the ability to avoid local optimal for improving the solution of ACO. In this paper, we present the ACO with the genetic operation from GA that is called Gene-Ants. Also, compare the result of the Gene-Ants algorithm with the basic ACO algorithm in the different TSP instances benchmark. The summarized results from the experiments show the Gene-Ants algorithm outperforms the basic ACO algorithm in terms of global optimal solution finding and convergence rate.
A Systemic Mapping of Methods and Tools for Performance Analysis of Data Streaming with Containerized Microservices Architecture

With the Internet of Things (IoT) growth and customer expectations, the importance of data streaming and streaming processing has increased. Data Streaming refers to the concept where data is processed and transmitted continuously and in real-time without necessarily being stored in a physical location. Personal health monitors and home security systems are examples of data streaming sources. This paper presents a systematic mapping study of the performance analysis of Data Streaming systems in the context of Containerization and Microservices. The research aimed to identify the main methods, tools, and techniques used in the last five years for the execution of this type of study. The results show that there are still few performance evaluation studies for this system niche, and there are gaps that must be filled, such as the lack of analytical modeling and the disregard for communication protocols’ influence.
Action Research for Industry Academia Collaboration : A replication Study

Collaboration between industry and academic communities requires considerable work but has the power to foster innovation. This relationship with joint trust promotes knowledge exchange that helps develop more qualified researchers and professionals. The Action Research (AR) method combines theory and practice, and studies involving industry-academia collaboration (IAC) have shown encouraging results. Nevertheless, further investigation is required to verify the effects of applying this method. This research investigates the perceptions of academic master’s and doctoral program students and professionals involved in projects that applied the AR method as a strategy to foster IAC. This article replicates a case study with different projects that conducted an AR in software companies. This study indicated high satisfaction among students (83%) when using action research in the course. All students considered the practical knowledge very relevant and would like to use the method again in other opportunities throughout their academic and professional life. This investigation showed that conducting IAC projects using the AR method within the industry in an educational context was challenging. That occurred due to the lack of experience in using empirical methods. Also, the professional’s unavailability delayed the results and, consequently, the activities in the project that already had a very tight schedule.
Analysing the state of the art of Blockchain application in Smart Cities: A bibliometric study

Due to social, economic, and environmental problems, the need arises to develop what is known as a Smart City allowing to improve the quality of people’s lives together with the application of emerging technologies such as Blockchain and providing improvements in different areas such as medical care, intelligent transport, or the supply chain management. The investigation analyses the association between Blockchain and Smart Cities using a Bibliometric Analysis, collected data from 384 articles published between 2018 and October 2022 with the topics ‘Blockchain’ and ‘Smart Cities’ from the Web of Science database. It has executed the VOSviewer program to appreciate the Bibliometric Analysis. The work has identified six research trends related with these fields.
Could the Topology of Virtual Processors Affect the Performance of a BSD-family OS Running in a VM?

Virtual machines are an essential technology in distributed and pervasive systems. One of its configurable parameters is the topology of the virtual processing system, which can potentially impact its performance. In this work, we verify how different virtual processing topologies affect the performance of VMs running BSD OSes. We conclude that in some types of application the topology does not affect the VM performance, while in others it does, and that the performance impact also depends on the OS adopted by the VM.
Optimizing FPGA-Based DNN Accelerator With Shared Exponential Floating-Point Format

In recent years, low-precision fixed-point computation has become a widely used technique for neural network inference on FPGAs. However, this approach has some limitations, as certain neural networks are difficult to quantify using fixed-point arithmetic, such as those involved in super-resolution scaling, image denoising, and other scenarios that lack sufficient conditions for fine-tuning. Furthermore, deploying a floating-point precision neural network directly on an FPGA would lead to significant hardware overhead and low computational efficiency. To address this issue, this paper proposes an FPGA-friendly floating-point data format that achieves the same storage density as int8 without sacrificing inference accuracy or requiring fine-tuning. Additionally, this paper presents an FPGA-based neural network accelerator that is compatible with the proposed format, utilizing DSP resources to increase the number of DSP cascading from 7 to 16, and solving the back-to-back accumulation issue of floating-point numbers. This design achieves comparable resource consumption and execution efficiency to those of 8-bit fixed-point accelerators. Experimental results demonstrate that the accelerator proposed in this study achieves the same accuracy as the native floating point on multiple neural networks without fine-tuning, and remains high computing performance. When deployed on the Xilinx ZU9P, the performance achieves 4.072 TFlops at 250 MHz, which outperforms the previous works, including the Xilinx official DPU.
Deep Active Learning for Multi-Label Classification of Remote Sensing Images

In this letter, we introduce deep active learning (AL) for multi-label classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for MLC of RS images. Unlike the existing AL query functions (which are defined for single-label classification or semantic segmentation problems), each query function in this paper is based on the evaluation of two criteria: i) multi-label uncertainty; and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the deep neural networks (DNNs) in correctly assigning multi-labels to each image. To assess this criterion, we investigate three strategies: i) learning multi-label loss ordering; ii) measuring temporal discrepancy of multi-label predictions; and iii) measuring magnitude of approximated gradient embeddings. The multi-label diversity criterion is associated to the selection of a set of images that are as diverse as possible to each other that prevents redundancy among them. To assess this criterion, we exploit a clustering based strategy. We combine each of the above-mentioned uncertainty strategies with the clustering based diversity strategy, resulting in three different query functions. All the considered query functions are introduced for the first time in the framework of MLC problems in RS. Experimental results obtained on two benchmark archives show that these query functions result in the selection of a highly informative set of samples at each iteration of the AL process.
Integrally Private Model Selection for Deep Neural Networks

Deep neural networks (DNNs) are one of the most widely used machine learning algorithms. In the literature, most of the privacy related work to DNNs focus on adding perturbations to avoid attacks in the output which can lead to significant utility loss. Large number of weights and biases in DNNs can result in a unique model for each set of training data. In this case, an adversary can perform model comparison attacks which lead to the disclosure of the training data. In our work, we first introduce the model comparison attack for DNNs which accounts for the permutation of nodes in a layer. To overcome this, we introduce a relaxed notion of integral privacy called $$\epsilon $$ -integral privacy. We further provide a methodology for recommending $$\epsilon $$ -Integrally private models. We use a data-centric approach to generate subsamples which have the same class-distribution as the original data. We have experimented with 6 datasets of varied sizes (10k to 7 million instances) and our experimental results show that our recommended private models achieve benchmark comparable utility. We also achieve benchmark comparable test accuracy for 4 different DNN architectures. The results from our methodology show superiority under comparison with three different levels of differential privacy.
Robust and Efficient Trajectory Planning for Formation Flight in Dense Environments

Formation flight has a vast potential for aerial robot swarms in various applications. However, the existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This article proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method that adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this article to coordinate the global planning and local trajectory optimizations.To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.
Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments

Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynablox</i> , a novel online mapping-based approach for robust moving object detection in complex unstructured environments. The central idea of our approach is to incrementally estimate high confidence free-space areas by modeling and accounting for sensing, state estimation, and mapping limitations during online robot operation. The spatio-temporally conservative free space estimate enables robust detection of moving objects without making any assumptions on the appearance of objects or environments. This allows deployment in complex scenes such as multi-storied buildings or staircases, and for diverse moving objects such as people carrying various items, doors swinging or even balls rolling around. We thoroughly evaluate our approach on real-world data sets, achieving 86% IoU at 17 FPS in typical robotic settings. The method outperforms a recent appearance-based classifier and approaches the performance of offline methods. We demonstrate its generality on a novel data set with rare moving objects in complex environments. We make our efficient implementation and the novel data set available as open-source.