Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Evaluation of Multisource Soil Moisture Products in a Managed Agricultural Region: An Integrated Hydrologic Modeling Approach

Soil moisture (SM) is important in understanding Earth's hydrologic cycles. However, an overall performance of multisource SM products is still unclear due to a lack of comprehensive validation. Using SM simulated by hydrologic models as a reference to perform validation is a promising alternative since SM simulation is not restricted by its coverage or scale. In this study, an integrated hydrologic model (ParFlow-CLM) forced by hydrometeorological and agricultural water use reanalysis data is built in the middle reaches of the Heihe River Basin (HRB), a typical managed agricultural region in Northwestern China. Using the ParFlow-CLM simulated SM data as the validating reference, ten SM products, including four single-source RS SM, three merged SM, and three assimilated SM products are systematically assessed by a comprehensive evaluation framework composed of fifteen statistical performance indicators. For validation, the results show that ParFlow-CLM SM simulations agree with the time domain reflectometry probe and cosmic ray neutron probes observations. The merged SM and assimilated SM outperform the single-source RS SM products if compared with ParFlow-CLM simulated data. Results from the intercomparison demonstrate that hydraulic conductivity and leaf area index are the dominant factors in SM spatial variations based on the generalized additive model. The statistical linkage indicates that mean absolute deviation, uncertainty at 95% (U95), Nash–Sutcliffe's efficiency, and combined performance index serve as substitutes for quantifying the relative uncertainty of SM. This study paves a way for model-data intercomparison of SM products in the HRB as well as in other arid and semiarid basins.
Leadership and Service in the Police Context_A Qualitative Study

This research aims to explore the leadership and service in the Indonesian National Police. This research is very significant for improving police policy due at the ontological level and sociological level. The problem is very interesting to be explored by conducting qualitative research method. Data were collected through in-depth interview, documentation and observation. Data were analyzed by using data reduction, data display and data verification and supported by triangulation. The results are categorized into several themes. Based on these results as per ontological level and sociological level are useful for improving police policy and practice and providing information to stakeholders related as inputs for making better regulation on police policy as well as for public officials and practitioners.
IS2N: Intent-Driven Security Software-Defined Network with Blockchain

Software-defined network (SDN) is characterized by its programmability, flexibility, and the separation of control and data planes. However, SDN still have many challenges, particularly concerning the security of network information synchronization and network element registration. Blockchain and intent-driven networks are recent technologies to establish secure and intelligent SDN. This article investigates the blockchain-based architecture and intent-driven mechanisms to implement intent-driven security software-defined networks (IS2N). Specifically, we propose a novel four-layer architecture of the IS2N with security capabilities. We integrate an intent-driven security management mechanism in the IS2N to achieve automate network security management. Finally, we develop an IS2N platform with blockchain middle-layer to achieve security capabilities and security store network-level snapshots, such as device registration and OpenFlow messages. Our simulations show that IS2N is more flexible than conventional strategies at resolving problems during network operations and has a minimal effect on the SDN.
STUDI MENGENAI INTENSI MEMBUANG SAMPAH DI SUNGAI CIKAPUNDUNG PADA IBU-IBU RW 15 KELURAHAN TAMANSARI BANDUNG

Permasalahan turunan yang terjadi akibat dari adanya permukiman warga Kelurahan Tamansari di sempadan Sungai Cikapundung adalah pencemaran air sungai yang disebabkan oleh pembuangan sampah maupun limbah rumah tangga ke dalam sungai tersebut. Pemukiman yang padat serta tercemarnya air sungai, menandakan kerusakan lingkungan Sungai Cikapundung oleh faktor manusia. RW 15 Kelurahan Tamansari merupakan lingkungan rumah yang padat penduduk dengan dikelilingi sungai Cikapundung. Adanya tempat pembuangan sampah sementara di wilayah tersebut tidak membuat warga memilih membuang sampah pada tempatnya melainkan tetap membuang sampah di sungai. Tujuan penelitian ini untuk memperoleh gambaran setiap determinan intensi membuang sampah ke sungai cikapundung. Metode yang digunakan adalah metode penelitian deskriptif. Subjek penelitian sejumlah 132 orang yang merupakan sampel dari ibu-ibu RW 15 Tamansari Bandung. Pengumpulan data menggunakan kuesioner mengenai intensi, sikap, norma subjektif, persepsi terhadap ontrol perilaku dengan menggunakan teori intensi dari Icek Ajzen. Hasil perhitungan menunjukkan bahwa ibu-ibu memiliki intensi kuat untuk membuang sampah ke sungai sebesar 61,36%. Hasil ini merupakan hasil kontribusi dari ketiga determinan intensi dengan koefisien regresi; sikap sebesar 0,343, norma subjektif 0,241,dan persepsi terhadap perilaku 0,375. Ketiga determinan intensi secara bersama-sama memberikan kontribusi sebesar 86,4%. Determinan yang memberikan kontribusi paling besar yakni, persepsi terhadap ontrol perilaku sebesar 76,1% lalu diikuti dengan norma subjektif sebesar 72,9% dan sikap sebesar 63.6%. Artinya banyaknya kondisi yang memfasilitasi ibu-ibu untuk membuang sampah ke sungai cikapundung serta hambatan yang dapat diatasi oleh ibu-ibu memberikan kontribusi yang besar dalam memunculkan intensi membuang sampah ke sungai cikapundung.
RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images

In remote sensing image (RSI) object detection, the oriented bounding box (OBB) can accurately locate objects with arbitrary orientation and obtain orientation information. The detection based on OBB is still a challenging task. In RSI, the distribution of objects is extremely uneven, which causes aggregation to occur. Some researchers believe that the characteristic of dense distribution is a reason for the difficulty of object detection. However, there are no in-depth experimental studies on this. This paper proposes an OBB-based dense object determination method, which determines the dense objects in datasets by two conditions consisting of interclass distance, intraclass distance, minimum distance between objects, and minimum edge length of objects. The experimental results of dense and non-dense object detection concludes that the characteristics of dense distribution in RSI do not easily cause the objects to be more difficult to detect. To make full use of the object features, we propose a second-stage detection head named RoIF-Net, in which we extract region of interest (RoI) from the input image and fuse it with the RoI extracted from feature maps to add detail features, and construct a feature induction module based on self-attention mechanism to achieve position regression and category classification. This structure can be used in any two-stage network to enhance detection capabilities. Using our method on three credible and challenging datasets, DOTA, DIOR-R, and UCAS-AOD, we obtained 81.80%, 68.49%, and 90.25% mAP, respectively, reaching SOTA based on OBB detection, proving the effectiveness and advancement of our method.
Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks

The issue of animal attacks is increasingly concerning for rural populations and forestry workers. To track the movement of wild animals, surveillance cameras and drones are often employed. However, an efficient model is required to detect the animal type, monitor its locomotion and provide its location information. Alert messages can then be sent to ensure the safety of people and foresters. While computer vision and machine learning-based approaches are frequently used for animal detection, they are often expensive and complex, making it difficult to achieve satisfactory results. This paper presents a Hybrid Visual Geometry Group (VGG)−19+ Bidirectional Long Short-Term Memory (Bi-LSTM) network to detect animals and generate alerts based on their activity. These alerts are sent to the local forest office as a Short Message Service (SMS) to allow for immediate response. The proposed model exhibits great improvements in model performance, with an average classification accuracy of 98%, a mean Average Precision (mAP) of 77.2%, and a Frame Per Second (FPS) of 170. The model was tested both qualitatively and quantitatively using 40,000 images from three different benchmark datasets with 25 classes and achieved a mean accuracy and precision of above 98%. This model is a reliable solution for providing accurate animal-based information and protecting human lives.
Dual-Task Network for Road Extraction From High-Resolution Remote Sensing Images

In high-resolution remote sensing images, road scale diversity and occlusions caused by shadows, buildings, and vegetation often pose challenges for road extraction. Currently, end-to-end models constructed using deep convolutional neural networks are widely used in road extraction and have significantly improved the accuracy of this task. However, the connectivity and completeness of their results require improvement. This article proposes a dual task-driven deep convolutional neural network constructed by combining road shape patterns and scale differences. The mainline task is road-surface segmentation, the encoder of which employs residual convolution for feature extraction. The decoder comprises a multiscale and multidirection strip convolution module, the output of which is the final extraction result. The splitting task is road centerline extraction, the input features of which come from the coding layer of the road-surface segmentation branches. The intermediate features are incorporated into the decoder of the road-surface segmentation branches, to fully exploit the road centerline and thus improve the road-surface segmentation result connectivity. Implementation of the proposed method on the CHN6-CUG and DeepGlobe datasets reveals superior performance to comparative methods as regards quantitative evaluation metrics; evident advantages for road coverings, road intersections, and low-scale roads; greater model portability; and better small-sample learning capability.
Web Accessibility of the Cyprus Island Food Retailers' Websites

Accessibility is the process of making information and electronic communication environments meaningful and usable for most people including those with disabilities. It is not always an easy task to provide all user communities with different areas of interests and needs with exact styles and contents. It is a good practice to employ responsible designs and a certain degree of interaction that provide equitable conditions to suit the needs of the most user communities. It became evident that in the passed two years the world went thru a terrifying Coronavirus disease which made the accessibility of web sites of retailers even more challenging. Most of the food supplying companies introduced shop-to door deliveries of food products with web based applications. Although, technically speaking, these web sites accomplished the task for most of the people but with some accessibility issues. These issues can be identified and solved by following the international standards to gain world wide acceptance. This study covers the investigation of the accessibility of the Food Retailers' websites in the whole of Cyprus Island. Web Content Accessibility Guidelines 2.1 (WCAG 2.1) published by the World Wide Web Consortium (W3C) is followed in the process. Three testing software are used to determine the degree of compliance of the food retailers with the WCAG 2.1 in Cyprus Island. The aim of this study is to raise awareness about web accessibility among general public. The findings are not so promising and the sites examined are not in compliance with the standard of the guidelines defined in WCAG.
A Survey on Cyber–Physical Systems Security

Cyber–physical systems (CPSs) are new types of intelligent systems that integrate computing, control, and communication technologies, bridging the cyberspace and physical world. These systems enhance the capabilities of our critical infrastructure and are widely used in a variety of safety-critical systems. CPSs are susceptible to cyber attacks due to their vulnerabilities such that their security has become a critical issue. Therefore, it is important to classify and comprehensively investigate this issue. Most of the existing surveys on it are conducted from a single perspective. In this article, we present a comprehensive view of the security of CPSs from three perspectives: 1) the physical domain; 2) the cyber domain; and 3) the cyber–physical domain. In the physical domain, we review some attacks that directly damage the physical components of CPSs such as sensors and discuss corresponding defenses. We also review the attacks that CPSs in the cyber domain may face and study methods to detect and defend against them. In addition, we survey the intelligent attacks faced by CPSs and the corresponding defensive means. In the cyber–physical domain, we provide an overview of attacks that come from the cyber domain and eventually damage the physical parts, and discuss the corresponding detection and defense methods. Finally, we present the challenges and future research directions. Through this in-depth review, we attempt to summarize the current security threats to CPSs and the state-of-the-art security means to provide researchers with a comprehensive overview.
AIRFRAME - Fast prototyping framework for UAVs definition

Developing a new UAV platform is a long and iterative process that requires a lot of time and effort to be successful. The difficulty of performing a realistic evaluation of system performance during the development process represents a major drawback. As a matter of fact, in most contexts, the first proof of UAVs’ capabilities arrives only during the first flights of the real platform. This may lead, in case of possible issues detected in the platform, to a revaluation of the design, which is not optimal at the very last stage of platform development. To overcome this issue, we propose AIRFRAME, a framework for fast-developing UAV prototypes in simulation to allow for systematic evaluation and analysis of UAV performance during the development process. The developed prototype integrates software and hardware for a better evaluation of the system’s capability and performance at an early stage. The implementation of the framework has succeeded with Gazebo-ROS-Matlab in Docker Environment. It allows high integrability and fast evaluation of multiple UAV designs.
Noise-Reduced and PVT-Robust Double-Balanced CMOS Mixer Using Common-Mode Feedback

This paper presents a noise reduction technique using common-mode feedback in active CMOS mixers. The proposed technique decreases the common-mode noise, thus reducing the common-to-differential conversion noise arising from mismatch and process variations. In addition, the proposed technique reduces sensitivity to process, voltage, and temperature (PVT) variations due to negative feedback. A negative feedback theory is adopted to analyze the low-noise performance of the proposed technique. The theoretical analysis is validated by simulations and measurements. The conventional and proposed mixers are fabricated in a 65-nm CMOS process. Measurement results of the proposed mixer operating at an RF of 2.1 GHz show a conversion gain of 21.5 dB, the input-referred third-order intercept point (IIP3) of -16.2 dBm, and a flicker noise figure of 8.7 dB at 10 kHz while it dissipates 3.45 mW from a 1.5 V supply voltage. Measurements also show common-mode noise reduction of 10.1 dB at 10 kHz, without degradation of other characteristics. It is demonstrated that the proposed mixer can be applied to decrease phase noise of a self-oscillating mixer (SOM) due to the low common-mode flicker noise mixer.
Arbitrary Triangle Structure Adaptive Mean PCA and Image Recognition

Currently, many robust principal component analysis (PCA) methods with low-dimensional representations have been proposed to improve the overall performance of image recognition. However, most existing methods are still sensitive to noise in the selection of structure information. To overcome this problem, we propose a novel structure for PCA, called arbitrary triangle structure adaptive mean PCA (ATAM-PCA). On the basis of ensuring that the variance, reconstruction error and input data with the flexible ${l_{2,p}}$ -norm can establish a triangle structure, ATAM-PCA maximizes the summation of the difference between the variance and reconstruction error of each projection data sample, which improves the universality and robustness of the overall structure, but also successfully protects the rotational invariance and geometry of the data. Moreover, ATAM-PCA applies the adaptive mean strategy to perform the data centralization task, which further effectively reduces the negative impact of noise. Finally, we design a fast iterative algorithm for solving ATAM-PCA. Extensive experimental results based on several image databases show the effectiveness and advantages of our method.
Intellectual Capital and Chaos of Innovation: Antagonist Coalition in Organizations

As a critical resource of human capital, employees might be the leading supporters of innovation management considering the integrated intellectual capital. In addition to the formal organizational structure, employees in an organization usually group informally within the institution. They conflict by taking an opposing role to other informal groups. When a manager/leader comes from outside or "someone from within the organization" takes a new position, conflicting groups quickly take a stand against the newcomer and even come together spontaneously with other groups. Hence, opposition groups’ unity against this newcomer within the organization has been defined as an "Antagonist Coalition in Organizations". The antagonist action structure that is the subject of this article plays a role in innovation management and negatively affects the process. This research examines the chaotic effect of antagonist coalitions on innovation. The semi-structured observation questionnaire was used as a data collection tool in the research. The tables containing the frequency values were used to analyze the survey data. The answers to the open-ended question were analyzed by qualitative data analysis. As a result of the research, most of the participants expressed a positive opinion on an antagonist coalition in organizations and that this would drive innovation into chaos. Study findings indicate a significant relationship between the antagonist coalition in organizations and the chaos of innovation.
FABS: A Framework for Addressing the Business Process Change Challenges for Smart City Development

Over the past decade, extensive research has focused on the conceptualization and implementation of smart cities, considering aspects such as “City as a system of systems,” “city as an enterprise,” and “city systems integration” as necessary for Smart City Development (SCD). However, despite these considerations, including our existing works, numerous challenges remain in the design and implementation of SCD roadmaps. One critical element for successful SCD is city systems integration, which necessitates cross-sectoral Business Process Change (BPC). However, limited attention has been given to addressing these challenges. To address this gap, our research builds upon and extends our prior works, utilising established principles and research findings to develop and validate a Framework for Addressing the BPC challenges for SCD (FABS). By undertaking a rigorous theoretical adaptation process and incorporating success factors, tools, techniques, and approaches from diverse domains, including enterprise systems, as well as conducting interviews and document analysis, our study contributes to the development of FABS. This empirically supported framework offers a systematic approach to effectively tackle BPC challenges in SCD, enabling smart city developers to assess their current SCD status and readiness for BPC, formulate their SCD roadmap, and assist solution providers in devising specialized tools and techniques tailored for SCD. Thus, our previous works have played a pivotal role in building the foundational components of this research’s outcome, namely the FABS framework, which has the potential to advance the field of smart city development.
Conversational Contextual Bandits: The Generalized Linear Case

Conversational Contextual Bandits (CCBs) have been widely used in recommender systems to accelerate the learning speed of users' preferences by incorporating conversational feedback on key terms. In this paper, we propose a general framework for CCB in the Generalized Linear Model (GLM) setting, which can handle non-Gaussian reward distributions. We design two algorithms, ConGLM and ConGLM-explore, with different strategies for selecting key-terms. The proposed algorithms can be promising in various applications when the received reward from the user is more complex. We provide theoretical guarantees on the regret upper bounds of ConGLM and conduct extensive experiments on synthetic datasets and two real-world datasets-Last.FM and Movielens. Our results show that the proposed algorithms outperform other baselines in terms of cumulative regret.
Control Ore Processing Quality Based on Xgboost Machine Learning Algorithm

In this paper, we study the problem of quality control of ore processing, using xgboost machine learning algorithm of sklearn module in python, sample interpolation method, 011 minimization loss model and confusion matrix to build xgboost regression prediction model and classification prediction model and error value evaluation model. Firstly, data processing is performed to normalize the data, then xgboost regression prediction is performed for product quality results, and finally confusion matrix is established for model testing, and the parameters are cyclically modified to reach the optimum to obtain various indexes. On this basis, the production and processing data and process data are added, and the data are processed by the spline interpolation method, and then the xgboost machine learning algorithm is used to calculate the prediction results of the pass rate can be obtained from various indicators.
Automatic breast density classification using neural network

According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network.