Reviewerly/exHarmony huggingface dataset:
Comprehensive evaluation of hydro-chemical processes, suitability, health risks, and sources of groundwater contamination using compositional data analysis, Nizampur basin, Pakistan

This study aimed to evaluate the level of contamination, health risks, hydro-chemical processes, and sources of contaminants in the groundwater of the Nizampur basin, northwestern Pakistan. Forty-eight (n = 48) groundwater samples from predefined locations were collected and analyzed using the standard procedure for a total of 13 parameters. Statistical techniques in the compositional data analysis perspective were used for the identification of sources. The results showed that TDS, Turbidity, SO4, and NO3 concentrations surpassed the limits in 44, 29, 25, and 48% of samples, respectively. The nitrate pollution index indicated that 48% of the samples were contaminated with nitrates, while the composite index PIG showed that only 22% of the samples indicated low pollution. A close association between the land use/land cover of the study area and NO3 contamination was observed. In general, the groundwater of the region could be categorized as fresh-very hard water (87.5%), the major hydro-chemical facies were, Ca–Mg–HCO3, followed by mixed Ca–Mg–Cl and Ca–Cl facies. The Van Wirdum diagram showed that groundwater was mainly influenced by rock weathering and profound human impact. Similarly, it was assessed using chloro-alkaline indices that reverse ion exchange is a governing hydro-chemical process. NO3 concentrations in the area have the potential to cause non-cancer ailments in 75% and 47% of the samples in children and adults respectively. Based on the assessments made using the compositional data analysis it was inferred that NO3, SO4, Cl, and Turbidity originated from human sources, while the rest of the parameters have natural/geogenic sources.
Water–Rock Interactions across Volcanic Aquifers of the Lece Andesite Complex (Southern Serbia): Geochemistry and Environmental Impact

The study of aquifers of the Lece andesite complex (LAC) and its surroundings yielded a new procedural stepwise analysis that allowed the assessment of the origin of elements, particularly in areas affected by both anthropogenic and natural influences. The methodology uses the mineralogical composition of the rocks, including the elements available in rocks and groundwater. This study analyzes the element ratios B/Cl−, Na+/Ca2+, Ca2+/Mg2+, HCO3−/Cl−, and Na+/Na+ + Cl−; the correlations are coupled with a statistical analysis. In addition to reevaluating the already published water content, we provide an important new dataset. The results show that the main source of the water contamination with the elements B, F, U, As, Cu, Fe, Zn, Co, and Ni is the processes occurring at the contact between the groundwaters and non-altered/altered (propylitized) andesite rocks. This is also observed in the waters extracted from crystalline schists. The results may help develop an efficient use and assessment of the qualitative water potential of the LAC reservoirs. Similarly, the results highlight the applicability of the groundwaters, facilitating their regional research and use, further encouraging new initiatives for the preservation and protection of human and animal health.
A Comprehensive Review on Multiple Instance Learning

Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. Many Algorithms based on MIL have been put forth over the years. In this paper, we will discuss MIL, the background of MIL and its application in multiple domains, some MIL-based methods, challenges, and lastly, the conclusions and prospects.
Real-Time Clustering Based on Deep Embeddings for Threat Detection in 6G Networks

Trials and deployments of sixth Generation (6G) wireless networks, delivering extreme capacity, reliability, and efficiency, are expected as early as 2030.Attempts from both industry and academia are trying to define the next generation network infrastructure.6G will set in motion the fourth industrial revolution, delivering global, integrated intelligence.In this scenario, Artificial Intelligence (AI)-assisted architecture for 6G networks will realize knowledge discovery, automatic network adjustment and intelligent service provisioning.The long-term vision for implementing 6G security is to implement an autonomous, self-learning AI-assisted architecture that can perform threat mitigation without disrupting the normal use, enabling the resilience and reliability of the network and fulfilling security automation.This work proposes a first implementation of a proactive threat discovery service to be deployed at 6G base stations, paving the way for collective network intelligence in the context of cybersecurity mechanisms.Specifically, a fully unsupervised Deep Learning (DL) model is presented, able to recognize both Denial of Service (DoS) Hulk and DoS Goldeneye, with 97.0% and 92.2% mean F1-score respectively.
UAV Target Detection Algorithm Based on Improved YOLOv8

Since UAVs usually fly at higher altitudes, resulting in a more significant proportion of small targets after imaging, this poses a challenge to the target detection algorithm at this stage; in addition, the high-speed flight of UAVs causes a sense of blurring on the detected objects, which leads to difficulties in target feature extraction. To address the two problems presented above, we propose a UAV target detection algorithm based on improved YOLOv8. First, the small target detection structure (STC) is embedded in the network, which acts as a bridge between shallow and deep features to improve the collection of semantic information of small targets and enhance detection accuracy. Second, using the feature of global information of UAV imaging-focused targets, the global attention GAM is introduced to the bottom layer of YOLOv8m’s backbone to prevent the loss of image feature information during sampling and thus increase the algorithm’s detection performance by feeding back feature information of different dimension. The modified model effectively increases the detection of tiny targets with an mAP value of 39.3%, which is 4.4% higher than the baseline approach, according to experimental results on the VisDrone2021 dataset, and outperforms mainstream algorithms such as SSD and YOLO series, effectively increasing the detection performance of UAVs for small targets.
Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks

Due to the need to know the availability of solar resources for the solar renewable technologies in advance, this paper presents a new methodology based on computer vision and the object detection technique that uses convolutional neural networks (EfficientDet-D2 model) to detect clouds in image series. This methodology also calculates the speed and direction of cloud motion, which allows the prediction of transients in the available solar radiation due to clouds. The convolutional neural network model retraining and validation process finished successfully, which gave accurate cloud detection results in the test. Also, during the test, the estimation of the remaining time for a transient due to a cloud was accurate, mainly due to the precise cloud detection and the accuracy of the remaining time algorithm.
A 2D Heterostructure‐Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing

The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.
Investigating applications of Artificial Intelligence in university libraries of Pakistan: An empirical study

This study aims to investigate the applications of Artificial Intelligence (AI) in the university libraries of Pakistan. Following the explanatory sequential mixed-methods approach, this study was completed in two phases. In the first phase, quantitative data was collected from 237 university librarians from across Pakistan. In the second phase, 10 purposefully selected university librarians were interviewed. The results reveal that Pakistani university libraries are using limited AI-based library services including text-to-speech and speech-to-text technologies, Google Assistant to search by voice command, Radio Frequency Identification (RFID) system for self-checkout, check-in, and security purposes, and intelligent data analysis for collection management. This study explored key factors that influence implementation of AI applications including highly integrated technological infrastructure, funding/cost associated with AI, collaboration between AI experts and professionals, and library users' feedback. The major identified challenges were the requirement of a highly networked and integrated environment, lack of budget, high cost of AI technologies, and lack of staff expertise. This study claims to implement the first comprehensive survey of AI applications in Pakistani university libraries at the national level. The research findings would support library and university administrators to integrate AI applications in libraries not only in Pakistan but also in other developing countries.
Progressive Scale-Aware Network for Remote Sensing Image Change Captioning

Remote sensing (RS) images contain numerous objects of different scales, which poses significant challenges for the RS image change captioning (RSICC) task to identify visual changes of interest in complex scenes and describe them via language. However, current methods still have some weaknesses in sufficiently extracting and utilizing multi-scale information. In this paper, we propose a progressive scale-aware network (PSNet) to address the problem. PSNet is a pure Transformer-based model. To sufficiently extract multi-scale visual features, multiple progressive difference perception (PDP) layers are stacked to progressively exploit the differencing features of bitemporal features. To sufficiently utilize the extracted multi-scale features for captioning, we propose a scale-aware reinforcement (SR) module and combine it with the Transformer decoding layer to progressively utilize the features from different PDP layers. Experiments show that the PDP layer and SR module are effective and our PSNet outperforms previous methods.
Concept Distillation in Graph Neural Networks

The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Distillation Module, the first differentiable concept-distillation approach for graph networks. The proposed approach is a layer that can be plugged into any graph network to make it explainable by design, by first distilling graph concepts from the latent space and then using these to solve the task. Our results demonstrate that this approach allows graph networks to: (i) attain model accuracy comparable with their equivalent vanilla versions, (ii) distill meaningful concepts achieving 4.8% higher concept completeness and 36.5% lower purity scores on average, (iii) provide high-quality concept-based logic explanations for their prediction, and (iv) support effective interventions at test time: these can increase human trust as well as improve model performance.
A comprehensive review of blockchain technology: Underlying principles and historical background with future challenges

Blockchain is a distributed digital ledger technology that has revolutionized businesses, industries, and commerce by eliminating the need for a central storage and control authority. Blockchain presents time-stamped and immutable blocks of data that are not owned by any single entity but rather managed by a group of nodes or computers where each block is secured and linked using cryptographic principles. The immutable and decentralized nature of blockchain has redefined trust, ownership, identity, and financial systems by providing a secure, fast, transparent, and pseudo-anonymous solution. This paper provides a comprehensive review of blockchain technology focusing on the historical background, underlying principles, and the sudden rise in the popularity of blockchain technology. The paper also discusses the various consensus algorithms of blockchain technology. Next, the paper focuses on the various application areas and prospective use cases of blockchain technology with the underlying challenges and issues. Further, the paper presents some unconventional use cases of blockchain technology. The study also reviews state-of-the-art articles to provide a comprehensive overview of the various aspects of blockchain technology in varied domains. The comparison between traditional database systems and blockchain technology is presented, and the appropriate scenarios where blockchain-based solutions may or may not provide the best solutions are also discussed. Further, it discusses some of the most infamous security breaches that impacted the blockchain industry in the recent past.
Unveiling the Future of Oral Squamous Cell Carcinoma Diagnosis: An Innovative Hybrid AI Approach for Accurate Histopathological Image Analysis

Oral cancer poses a formidable global health threat, demanding urgent attention to combat its devastating impact. Timely detection of oral squamous cell carcinoma (OSCC) is pivotal for successful treatment and improved survival rates. However, manual histopathological analysis, reliant on the expertise of medical practitioners, can be time-consuming and vulnerable to subjective discrepancies. To surmount these challenges and elevate diagnostic outcomes, this research explores the transformative potential of artificial intelligence (AI) in OSCC diagnosis. Three distinct methodologies, namely Gabor + CatBoost, ResNet50 + CatBoost, and Gabor+ ResNet50 + CatBoost, were implemented to leverage the power of AI. By extracting 32 low-level features from the Gabor Filter and 100,532 high-level features from the ResNet50 model, the study adopts principal component analysis (PCA) to mitigate overfitting, retaining the top 4096 components. The extracted features underwent individual classification using CatBoost, followed by concatenation and image classification. Remarkably, the third strategy, which synergized Gabor filtering with ResNet50 feature extraction, along with CatBoost classification, demonstrated the most exceptional performance. Achieving an impressive accuracy of 94.92%, 95.51% precision, 84.30% sensitivity, 95.54% specificity, 94.90% F1 score, and 94.9% AUC, these AI-based approaches herald a new era of accurate and efficient OSCC diagnosis.
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning

Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute time-varying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are useful to visualize the transport mechanisms of passive tracers advecting with the flow. However, many vehicles and mobile sensors are not passive, but are instead actuated according to some intelligent trajectory planning or control law; for example, model predictive control and reinforcement learning are often used to design energy-efficient trajectories in a dynamically changing background flow. In this work, we investigate the use of FTLE on such controlled agents to gain insight into optimal transport routes for navigation in known unsteady flows. We find that these controlled FTLE (cFTLE) coherent structures separate the flow field into different regions with similar costs of transport to the goal location. These separatrices are functions of the planning algorithm’s hyper-parameters, such as the optimization time horizon and the cost of actuation. Computing the invariant sets and manifolds of active agent dynamics in dynamic flow fields is useful in the context of robust motion control, hyperparameter tuning, and determining safe and collision-free trajectories for autonomous systems. Moreover, these cFTLE structures provide insight into effective deployment locations for mobile agents with actuation and energy constraints to traverse the ocean or atmosphere.
Evaluation model and algorithm optimization of intelligent manufacturing system on the basis of BP neural network

Intelligent manufacturing is a key direction for the development of the manufacturing industry. Since the level of intelligent manufacturing varies among enterprises, building a scientific and reliable evaluation model is the key to evaluate enterprises. The study optimizes the Back Propagation (BP) neural network based on the Firefly Algorithm (FA) and innovatively introduces the Sparrow Search Algorithm (SSA), which changes the search mechanism of the BP and improves the traditional BP affected by the local optimum. of the traditional BP. In the experimental results, the average error of the improved model is 1.34% and 0.41% under the optimal preset parameters, which is significantly better than other advanced algorithms. It is verified that the improved evaluation model in this study has lower error and better effect, which has better application value in the evaluation of intelligent manufacturing system.
A Methodology and Open-Source Tools to Implement Convolutional Neural Networks Quantized with TensorFlow Lite on FPGAs

Convolutional neural networks (CNNs) are used for classification, as they can extract complex features from input data. The training and inference of these networks typically require platforms with CPUs and GPUs. To execute the forward propagation of neural networks in low-power devices with limited resources, TensorFlow introduced TFLite. This library enables the inference process on microcontrollers by quantizing the network parameters and utilizing integer arithmetic. A limitation of TFLite is that it does not support CNNs to perform inference on FPGAs, a critical need for embedded applications that require parallelism. Here, we present a methodology and open-source tools for implementing CNNs quantized with TFLite on FPGAs. We developed a customizable accelerator for AXI-Lite-based systems on chips (SoCs), and we tested it on a Digilent Zybo-Z7 board featuring the XC7Z020 FPGA and an ARM processor at 667 MHz. Moreover, we evaluated this approach by employing CNNs trained to identify handwritten characters using the MNIST dataset and facial expressions with the JAFFE database. We validated the accelerator results with TFLite running on a laptop with an AMD 16-thread CPU running at 4.2 GHz and 16 GB RAM. The accelerator’s power consumption was 11× lower than the laptop while keeping a reasonable execution time.
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems

In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse modality features into item representations is still debated. We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities. MMSR represents each user's history as a graph, where the modality features of each item in a user's history sequence are denoted by cross-linked nodes. The edges between homogeneous nodes represent intra-modality sequential relationships, and the ones between heterogeneous nodes represent inter-modality interdependence relationships. During graph propagation, MMSR incorporates dual attention, differentiating homogeneous and heterogeneous neighbors. To adaptively assign nodes with distinct fusion orders, MMSR allows each node's representation to be asynchronously updated through an update gate. In scenarios where modalities exhibit stronger sequential relationships, the update gate prioritizes updates among homogeneous nodes. Conversely, when the interdependent relationships between modalities are more pronounced, the update gate prioritizes updates among heterogeneous nodes. Consequently, MMSR establishes a fusion order that spans a spectrum from early to late modality fusion. In experiments across six datasets, MMSR consistently outperforms state-of-the-art models, and our graph propagation methods surpass other graph neural networks. Additionally, MMSR naturally manages missing modalities. The code is available at: https://github.com/HoldenHu/MMSR.
XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters

Recently, with the popularity of ChatGPT, large-scale language models have experienced rapid development. However, there is a scarcity of open-sourced chat models specifically designed for the Chinese language, especially in the field of Chinese finance, at the scale of hundreds of billions. To address this gap, we introduce XuanYuan 2.0, the largest Chinese chat model to date, built upon the BLOOM-176B architecture. Additionally, we propose a novel training method called hybrid-tuning to mitigate catastrophic forgetting. By integrating general and domain-specific knowledge, as well as combining the stages of pre-training and fine-tuning, XuanYuan 2.0 is capable of providing accurate and contextually appropriate responses in the Chinese financial domain.
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems

In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or mitigating disparate impacts in recommendation utility. None of them has leveraged explainability techniques to inform unfairness mitigation. In this paper, we propose an approach that relies on counterfactual explanations to augment the set of user-item interactions, such that using them while inferring recommendations leads to fairer outcomes. Modeling user-item interactions as a bipartite graph, our approach augments the latter by identifying new user-item edges that not only can explain the original unfairness by design, but can also mitigate it. Experiments on two public data sets show that our approach effectively leads to a better trade-off between fairness and recommendation utility compared with state-of-the-art mitigation procedures. We further analyze the characteristics of added edges to highlight key unfairness patterns. Source code available at https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023.