Reviewerly/exHarmony huggingface dataset:
Mental models of sustainable groundwater management among farmers in semi-arid regions of Maharashtra, India

Mental models are the dynamic, internal cognitive representations of people's interaction with the world. Such models can be used to gain insights into how humans structure their beliefs and actions about environmental issues. This research paper aims to understand the mental models of sustainable groundwater management among farmers in semi-arid regions of Maharashtra, India. Using a mixed method approach of qualitative interviews and systems mapping, we assess how past experiences with drought and water scarcity have influenced farmers' beliefs, perceptions, and actions and develop mental models that highlight the dynamic processes that guide farmer actions regarding groundwater use and management. We identify policy triggers that can nudge farmers toward sustainable groundwater management in the future. Our results reveal three key insights: i) Farmers who experience higher water scarcity have a higher desire for groundwater conservation and higher consciousness towards future groundwater sustainability, ii) Farmers' actions towards either increased groundwater extraction or conservation are shaped by complex social, environmental, and institutional dynamics rather than self-interested individual will, and iii) Supply-driven water policies and initiatives can lead to maladaptive outcomes such as an increase in groundwater extraction in the long run. Current water policies need a transformative shift from focusing on short-term groundwater supply to those that facilitate long-term sustainable groundwater management by influencing the norms, values, and behavior toward groundwater conservation. Future interventions that allow and encourage collective mobilization, enhanced ownership and participation, adequate training, financial resources, and decentralized management structures with enhanced accountability are likely to be more effective in developing long-term solutions for sustainable groundwater management.
An Enhanced Static Taint Analysis Approach to Detect Input Validation Vulnerability

The detection of feasible paths helps to minimize the false positive rate. However, the previous works did not consider the feasibility of the program paths during the analysis detection of input validation vulnerabilities, which led to false positive results. They also needed to validate the usage of the proper sanitization functions for each context of the user input. Therefore, we proposed an enhanced static taint analysis approach to analyse the source code and track the tainted inputs in the program. It started by examining the source code to find the feasibility of each path in the program. The tainted variables were tracked through the analysis until the sink statement, which executes the tainted variables. An algorithm was built to enhance the static analyzer to handle the variables handling functions in PHP. The proposed approach was evaluated with SARD datasets and large-scale PHP programs. The results indicated that the precision in detecting XSS vulnerability was approximately 44% better than WAP and 26% better than RIPS, and its precision in detecting SQL injection was about 10% better than WAP and 19% better than RIPS. Furthermore, the proposed approach outperformed previous symbolic execution studies regarding the number of detected vulnerabilities.
What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

With the rise of social media platforms, sharing reviews has become a social norm in today's modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald's, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.
Redactable Blockchain: Comprehensive Review, Mechanisms, Challenges, Open Issues and Future Research Directions

The continuous advancements of blockchain applications impose constant improvements on their technical features. Particularly immutability, a highly secure blockchain attribute forbidding unauthorized or illicit data editing or deletion, which functions as crucial blockchain security. Nonetheless, the security function is currently being challenged due to improper data stored, such as child pornography, copyright violation, and lately the enaction of the “Right to be Forgotten (RtbF)” principle disseminated by the General Data Protection Regulation (GDPR), where it requires blockchain data to be redacted to suit current applications’ urgent demands, and even compliance with the regulation is a challenge and an unfeasible practice for various blockchain technology providers owing to the immutability characteristic. To overcome this challenge, mutable blockchain is highly demanded to solve previously mentioned issues, where controlled and supervised amendments to certain content within constrained privileges granted are suggested by several researchers through numerous blockchain redaction mechanisms using chameleon and non-chameleon hashing function approaches, and methods were proposed to achieve reasonable policies while ensuring high blockchain security levels. Accordingly, the current study seeks to thoroughly define redaction implementation challenges and security properties criteria. The analysis performed has mapped these criteria with chameleon-based research methodologies, technical approaches, and the latest cryptographic techniques implemented to resolve the challenge posed by the policy in which comparisons paved current open issues, leading to shaping future research directions in the scoped field.
Adaptation of a genetic operator and a dynamic S-box for chaotic encryption of medical and color images

In this article, we will propose a new method for encrypting of arbitrary sizes and formats. Based on chaos, this new technique starts with the construction of an S-box of size 16×16, improved by a dynamic behavior and attached to new substitution and diffusion functions to overcome the problem of differential and statistical attacks. A passage in hexadecimal notation is carried out on the values of the pixels of the original image, followed by a subdivision of the vector obtained into sub-blocks of size 1×6, for a future submission to two rounds of genetic mutation adapted to ciphers of color images and controlled by chaotic vectors. The outgoing vector will be subjected to substitutions accompanied by an improved operating mode (CBC), to increase the impact of the avalanche effect and protect the system against any known attack. Tests carried out on many images of different sizes and formats have proven the robustness of our approach.
A Comprehensive Study on Machine Learning in Breast Cancer Detection and Classification

Breast cancer is one of the diseases that led to a huge number of deaths in the recent decades. One of the major issues that affect the recovery procedure is the early detection of the disease. Thus, in this paper, several machine learning algorithms that support the early detection process, along with the impact on combining these algorithms with hyperparameter tuning optimization techniques will be presented. Moreover, we conducted a comparison among proposed techniques to figure out which classifier model can achieve better detection accuracy of the disease.
Eco-FL: Adaptive Federated Learning with Efficient Edge Collaborative Pipeline Training

Federated Learning (FL) has been a promising paradigm in distributed machine learning that enables in-situ model training and global model aggregation. While it can well preserve private data for end users, to apply it efficiently on IoT devices yet suffer from their inherent variants: their available computing resources are typically constrained, heterogeneous, and changing dynamically. Existing works deploy FL on IoT devices by pruning a sparse model or adopting a tiny counterpart, which alleviates the workload but may have negative impacts on model accuracy. To address these issues, we propose Eco-FL, a novel Edge Collaborative pipeline based Federated Learning framework. On the client side, each IoT device collaborates with trusted available devices in proximity to perform pipeline training, enabling local training acceleration with efficient augmented resource orchestration. On the server side, Eco-FL adopts a novel grouping-based hierarchical architecture that combines synchronous intra-group aggregation and asynchronous inter-group aggregation, where a heterogeneity-aware dynamic grouping strategy that jointly considers response latency and data distribution is developed. To tackle the resource fluctuation during the runtime, Eco-FL further applies an adaptive scheduling policy to judiciously adjust workload allocation and client grouping at different levels. Extensive experimental results using both prototype and simulation show that, compared to state-of-the-art methods, Eco-FL can upgrade the training accuracy by up to 26.3%, reduce the local training time by up to 61.5%, and improve the local training throughput by up to 2.6 ×.
Enhancement of Steady Quantum Entanglement and Directional Controllability of Quantum Steering in Cavity Magnetic Hybrid Systems

Abstract Quantum entanglement (QE) and quantum steering (QS) are of importance for quantum information processing and computation. Though there are several schemes proposed for their realization, how to increase their degrees encounters a great challenge. In the present manuscript, it is proposed to enhance steady QE and control Gaussian QS for two magnons using a two‐photon field acting on either magnon. The cavity‐magnetic hybrid system consists of a microwave cavity in which two identical Yttrium‐iron‐garnet spheres are placed and the cavity is driven by a Josephson parametric amplifier (JPA) so as to generate steady QE and control Gaussian QS for the two Kittel modes. Besides, a two‐photon driving field acting on either magnon in order to enhance the degree of QE and the ability of QS. The best condition to maximum entanglement and steering degrees for the two magnon modes at MHz and is found via the competitive relationship between the two‐photon driving and JPA. Furthermore, it is revealed that steering direction for the two magnons can be controlled not only by the ratio between the two photon‐magnon coupling parameters but also by the two‐photon driving field, making controllable steering direction become easier.
Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia

Customer satisfaction and loyalty are essential for every business. Feedback prediction and social media classification are crucial and play a key role in accurately identifying customer satisfaction. This paper presents sentiment analysis-based customer feedback prediction based on Twitter Arabic datasets of telecommunications companies in Saudi Arabia. The human brain, which contains billions of neurons, provides feedback based on the current and past experience provided by the services and other related stakeholders. Artificial Intelligent (AI) based methods, parallel to human brain processing methods such as Deep Learning (DL) algorithms, are famous for classifying and analyzing such datasets. Comparing the Arabic Dataset to English, it is pretty challenging for typical methods to outperform in the classification or prediction tasks. Therefore, the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model was used and analyzed with various parameters such as activation functions and topologies and simulated customer satisfaction prediction takes using Arabic Twitter datasets. The prediction results were compared with two famous DL algorithms: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results show that these methods have been successfully applied and obtained highly accurate classification results. AraBERT achieved the best prediction accuracy among the three ML methods, especially with Mobily and STC datasets.
A Serious Mixed-Reality Game for Training Police Officers in Tagging Crime Scenes

Recognizing and collecting evidence at a crime scene are essential tasks for gathering information about perpetrators and/or the dynamics of a criminal event. Hence, the success of a crime investigation is strongly based on the ability of forensic investigators to perform these tasks. Recent studies observing and comparing the performance of experts and novices have highlighted the importance of experience and training for search and recovery strategies at crime scenes. Therefore, relevant training programs in evidence-recovery techniques should be attended by novices to improve their skills. However, the knowledge transfer between skills acquired in the classroom and their practical application in the field is a challenging task. In order to relieve this problem, this paper proposes a serious mixed-reality game, which is called TraceGame, aiming to support the training activities of novice forensic investigators by improving their skills related to the search and recovery of evidence at crime scenes. The purpose of the game is to identify the greatest number of useful traces present in a crime scene that is physically reconstructed at the training site as quickly as possible. As shown in an experimental session, TraceGame is a promising tool for supporting the training of novice forensic investigators.
A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user's semantic features for these three factors. In addition, we improve the algorithm's adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment.
PLOT: a 3D point cloud object detection network for autonomous driving

Abstract 3D object detection using point cloud is an essential task for autonomous driving. With the development of infrastructures, roadside perception can extend the view range of the autonomous vehicles through communication technology. Computation time and power consumption are two main concerns when deploying object detection tasks, and a light-weighted detection model applied in an embedded system is a convenient solution for both roadside and vehicleside. In this study, a 3D Point cLoud Object deTection (PLOT) network is proposed to reduce heavy computing and ensure real-time object detection performance in an embedded system. First, a bird’s eye view representation of the point cloud is calculated using pillar-based encoding method. Then a cross-stage partial network-based backbone and a feature pyramid network-based neck are implemented to generate the high-dimensional feature map. Finally, a multioutput head using a shared convolutional layer is attached to predict classes, bounding boxes, and the orientations of the objects at the same time. Extensive experiments using the Waymo Open Dataset and our own dataset are conducted to demonstrate the accuracy and efficiency of the proposed method.
Blockchain, Quo Vadis? Recent Changes in Perspectives on the Application of Technology in Agribusiness

Information technologies such as blockchain are developing fast, overcoming bottlenecks, and quickly taking advantage of their application. The present study analyzes recent changes concerning the benefits, disadvantages, challenges, and opportunities of blockchain applications in agribusiness. Interviews were conducted with and a questionnaire was applied to professionals working in the development and application of blockchain technology in agribusiness, to compare their perception of the recent advances. The results showed that the importance of blockchain technology to improve governance and information flow along supply chains has increased, and this is the main perceived benefit. The main disadvantages were removing intermediaries and the high cost of implementing the technology. The absence of a widely accepted platform in blockchain operations is the leading and growing challenge, while patterns for blockchain technology seem to be being overcome. The integration of blockchain with new technologies, and the competitiveness provided by the technology, are seen as the main and growing opportunities. Despite the study limitations, we conclude that the benefits and opportunities associated with blockchain application in agribusiness outweigh the challenges and disadvantages in number and importance, and are becoming more relevant.
Blind Hyperspectral Image Denoising with Degradation Information Learning

Although existing hyperspectral image (HSI) denoising methods have exhibited promising performance in synthetic noise removal, they are seriously restricted in real-world scenarios with complicated noises. The major reason is that model-based methods largely rely on the noise type assumption and parameter setting, and learning-based methods perform poorly in generalizability due to the scarcity of real-world clean–noisy data pairs. To overcome this long-standing challenge, we propose a novel denoising method with degradation information learning (termed DIBD), which attempts to approximate the joint distribution of the clean–noisy HSI pairs in a Bayesian framework. Specifically, our framework learns the mappings of noisy-to-clean and clean-to-noisy in a priority dual regression scheme. We develop more comprehensive auxiliary information to simplify the joint distribution approximation process instead of only estimating noise intensity. Our method can leverage both labeled synthetic and unlabeled real data for learning. Extensive experiments show that the proposed DIBD achieves state-of-the-art performance on synthetic datasets and has better generalization to real-world HSIs. The source code will be available to the public.
Content‐based image retrieval using a fusion of global and local features

Abstract Color, texture, and shape act as important information for images in human recognition. For content‐based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content‐based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.
Image Compression Network Structure Based on Multiscale Region of Interest Attention Network

In this study, we proposed a region of interest (ROI) compression algorithm under the deep learning self-encoder framework to improve the reconstruction performance of the image and reduce the distortion of the ROI. First, we adopted a remote sensing image cloud detection algorithm for detecting important targets in images, that is, separating the remote sensing background from important regions in remote sensing images and then determining the target regions because most traditional ROI-based image compression algorithms utilize the manual labeling of the ROI to achieve region separation in images. We designed a multiscale ROI self-coding network from coarse to fine with a hierarchical super priority layer to synthesize images to reduce the spatial redundancy more effectively, thus greatly improving the distortion rate performance of image compression. By using a spatial attention mechanism for the ROI in the image compression network, we achieved better compression performance.