Reviewerly/exHarmony huggingface dataset:
On-Board Crowd Counting and Density Estimation Using Low Altitude Unmanned Aerial Vehicles—Looking beyond Beating the Benchmark

Recent advances in deep learning-based image processing have enabled significant improvements in multiple computer vision fields, with crowd counting being no exception. Crowd counting is still attracting research interest due to its potential usefulness for traffic and pedestrian stream monitoring and analysis. This study considered a specific case of crowd counting, namely, counting based on low-altitude aerial images collected by an unmanned aerial vehicle. We evaluated a range of neural network architectures to find ones appropriate for on-board image processing using edge computing devices while minimising the loss in performance. Through experiments on a range of neural network architectures, we also showed that the input image resolution significantly impacts the prediction quality and should be considered an important factor before going for a more complex neural network model to improve accuracy. Moreover, by extending a state-of-the-art benchmark with more in-depth testing, we showed that larger models might be prone to overfitting because of the relative scarcity of training data.
A mapping review of literature on Blockchain usage by libraries: Challenges and opportunities

The Library and Information Science (LIS) community has started discussing some possible uses of Blockchain (BC) technologies in solving library-related problems and increasing the overall efficiency of libraries. This study aimed to systematically collect and review the relevant literature to comprehend the scope of BC for libraries, its benefits, as well as the challenges, and implications related to its use. The authors explored six reputed databases (Web of Science, Scopus, LISTA (Library, Information Science and Technology Abstracts), LISA (Library and Information Science Abstracts), IEEE (Institute of Electrical and Electronics Engineers), and Google Scholar) to conduct this review. This study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After the final data extraction, 21 documents were considered eligible for the systematic review. A systematic review of the selected works indicated that the usage of BC in libraries ranged from record-keeping to processing payments and ensuring security and transparency. Some of the opportunities that can be hunted from BC were the elimination of corruption, enhanced security, improved efficiency of services, and better time management. Literature also indicated that a lack of awareness of technology, unskilled staff, and financial constraints could impede the adoption of BC by libraries. It is hoped that this study would provide a holistic overview of BC technologies for libraries, thus improving the effectiveness of the decision-makers. This study is first that collected (systematically) and reviewed the literature on BC usage in libraries. The review will help educational institutions and library professionals understand the usage, challenges, and benefits of BC for libraries.
Concept drift estimation with graphical models

This paper deals with the issue of concept-drift in machine learning in the context of high dimensional problems. In contrast to previous concept drift detection methods, this application does not depend on the machine learning model in use for a specific target variable, but rather, it attempts to assess the concept drift as an independent characteristic of the evolution of a dataset. This major achievement enables data to be tested for the presence of drift, independently of the specific problem at hand. This is extremely useful when the same dataset is utilized for different classifications simultaneously, as it is often the case in a business environment. Moreover, unlike previous approaches, this method does not require the re-testing of each new model; a strategy which could prove expensive in computational terms. The fundamental intention of this work is to make use of graphical models to elicit the visible structure of data and represent it as a network. Specifically, we investigate how a graphical model evolves by looking at the creation of new links, and the disappearance of existing ones, in different time periods. We perform this task in four steps. We compute the adjacency matrix of a graph in each period, we apply a function that maps each possible state of the adjacency matrix over time into a transition matrix. We use the information in the transition matrix to produce a metric to estimate the presence of a drift in the data. Eventually, we evaluate this method with both three real-world datasets and a synthetic one.
Explaining Artificial Intelligence with Care

Abstract In the recent past, several popular failures of black box AI systems and regulatory requirements have increased the research interest in explainable and interpretable machine learning. Among the different available approaches of model explanation, partial dependence plots (PDP) represent one of the most famous methods for model-agnostic assessment of a feature’s effect on the model response. Although PDPs are commonly used and easy to apply they only provide a simplified view on the model and thus risk to be misleading. Relying on a model interpretation given by a PDP can be of dramatic consequences in an application area such as forensics where decisions may directly affect people’s life. For this reason in this paper the degree of model explainability is investigated on a popular real-world data set from the field of forensics: the glass identification database. By means of this example the paper aims to illustrate two important aspects of machine learning model development from the practical point of view in the context of forensics: (1) the importance of a proper process for model selection, hyperparameter tuning and validation as well as (2) the careful used of explainable artificial intelligence. For this purpose, the concept of explainability is extended to multiclass classification problems as e.g. given by the glass data.
A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.
A comparison of automated classification techniques for image processing in video internet of things

The counts of various types of white blood cells give vital information for identifying a variety of ailments utilizing the video internet of things (VIoT). This technique needs to be automated to save time and minimize counting errors. This paper aims to apply recent image processing techniques to identify and categorize white blood cells (WBCs) found in peripheral blood and to develop a system that would automatically detect and evaluate WBCs. The proposed method consists of steps such as segmentation scanning, feature extraction, and blood cell categorization. First, we segment the cell pictures, which entails grouping white blood cells into clusters. The second stage consists of scanning each input image and preparing the dataset. The third stage involves determining the texture and contour of a scanned picture. Finally, different machine learning algorithms are used to classify the results based on these criteria.
Institutional entrepreneurial orientation: Beyond setting the rules of the game for blockchain technology

Regulations keep evolving on currency-based blockchain technology, making it difficult for entrepreneurs within this realm to find a home. Going beyond temporal regulations and traditional metrics to help entrepreneurs evaluate formal institutional environments, we apply and extend the theoretical framework of entrepreneurial orientation (EO) to the institutional level (termed institutional EO) to index an institution's innovativeness, proactiveness, and risk-taking towards blockchain technology. Evaluating a country's institutional EO allows entrepreneurs to understand how embedded the institution is within a certain technology (in this case blockchain technology), which in turn, signals to entrepreneurs longer-term havens in which to locate to develop and commercialize the technology.
An Ensemble Clustering Framework Based on Hierarchical Clustering Ensemble Selection and Clusters Clustering

Ensemble clustering combines the results of multiple individual clustering methods for better results. Basically, all available clustering methods can be combined to produce final clusters. However, selecting a subset of optimal methods can reduce the complexity and increase the efficiency of ensemble clustering methods. This article examines the problem of selecting individual clustering methods to produce an ensemble hierarchical clustering method. Hierarchical clustering is a technique for grouping data at different scales by creating dendrograms. The aim is to select a subset of individual hierarchical clustering methods considering diversity and quality that can create an ensemble clustering method with minimal complexity. The proposed method consists of three main phases. The selection of a subset of individual hierarchical clustering methods is done in the first phase. In the second phase, the results of the selected clusters are re-clustered to create super-clusters. Super-clusters can combine clustering knowledge of different methods into one clustering form. Finally, the final clusters are formed by assigning each sample to a super-cluster with the shortest distance in the third phase. Experimental results on several datasets from the University of California Irvine (UCI) repository show that the proposed method performs better than the state-of-the-art algorithms.
A Blockchain Solution for Water and Electricity Management

Information is one of the essential assets in any business. Due to the rapid technological changes, maintaining and protecting the information of individuals and organizations has become more imperative. Blockchain is a sequence of blocks that store information as a hashed value chained together as a ledger of records. This paper proposes a blockchain-based solution to manage water and electricity services. The solution keeps track of water and electricity consumption by consumers, allows them to view and pay bills, and secures their online transactions. It functions as a shared ledger between Abu Dhabi Distribution Company, Bin Moosa & Daly water and electricity supplement store, and Fazaa discount card. In addition, the proposed solution offers transparency and full trust between the different entities, controls the blockchain network, and validates transactions. Finally, the proposed application supports the UAE sustainability initiative by reducing paper usage.
Contrastive author-aware text clustering

In the era of User Generated Content (UGC), authors (IDs) of texts widely exist and play a key role in determining the topic categories of texts. Existing text clustering efforts are mainly attributed to utilizing textual information, but the effect of authors on text clustering remains largely underexplored. To mitigate this issue, we propose a novel Contrastive Author-aware Text clustering approach, dubbed as CAT. CAT injects author information not only in characterizing texts through representations but also in pushing or pulling text representations of different authors through contrastive learning, which is rarely adopted by text clustering. Specifically, the developed contrastive learning method conducts both cluster-instance contrast by the text representation augmentation and instance-instance contrast by the multi-view representations. We perform comprehensive experiments on three public datasets, demonstrating that CAT largely outperforms strong competitive text clustering baselines and validating the effectiveness of the CAT's main components.
Identifying humanitarian information for emergency response by modeling the correlation and independence between text and images

Information residing in multiple modalities (e.g., text, image) of social media posts can jointly provide more comprehensive and clearer insights into an ongoing emergency. To identify information valuable for humanitarian aid from noisy multimodal data, we first clarify the categories of humanitarian information, and define a multi-label multimodal humanitarian information identification task, which can adapt to the label inconsistency issue caused by modality independence while maintaining the correlation between modalities. We proposed a Multimodal Humanitarian Information Identification Model that simultaneously captures the Correlation and Independence between modalities (CIMHIM). A tailor-made dataset containing 4,383 annotated text-image pairs was built to evaluate the effectiveness of our model. The experimental results show that CIMHIM outperforms both unimodal and multimodal baseline methods by at least 0.019 in macro-F1 and 0.022 in accuracy. The combination of OCR text, object-level features, and the decision rule based on label correlations enhances the overall performance of CIMHIM. Additional experiments on a similar dataset (CrisisMMD) also demonstrate the robustness of CIMHIM. The task, model, and dataset proposed in this study contribute to the practice of leveraging multimodal social media resources to support effective emergency response.
Reversible data hiding in encrypted DICOM images using sorted binary sequences of pixels

In this paper, a novel reversible data hiding method for encrypted DICOM images is proposed. The method utilizes binary decomposition of the input data paired with a sorting process of the obtained binary sequences to ensure efficient data embedding in each predefined data block for specific most significant bit (MSB) planes while exploiting the properties of run-length encoding. The proposed scheme is lossless, and based on the receiver's privileges, allows separate or simultaneous recovery of the original and embedded data. The proposed method was originally designed for 16-bit DICOM images of monochrome photometric interpretation, however it can be used with any type of data while providing satisfactory results. A computer-based analysis has been carried out and simulation results for DICOM and non-DICOM images are shown at the end of this paper.
Gaussian Processes for Signal Processing and Representation in Control Engineering

The Gaussian process is an increasingly well-known type of stochastic process, which is a generalization of the Gaussian probability distribution. It allows us to model complex functions thanks to its flexibility, which would not be possible with the use of other tools. Gaussian processes also have a couple of other features that are used in various branches of automation with positive results, ranging from industrial processes to image processing. There are also many ways of setting up the Gaussian processes, which required knowledge on the topic and depend on the presented problem. Considerations on these topics lead to the conclusion that the current state of practical usefulness of Gaussian processes increases significantly, therefore the deepening of knowledge about the ways of its use is highly suggested. In this review, we present selected technical applications of Gaussian Processes allowing an understanding of their broad applicability.
Adaptive feature extraction and fine‐grained modulation recognition of multi‐function radar under small sample conditions

Multi-function radars (MFRs) are sophisticated sensors with fine-grained modes, which modify their modulation types and parameters range generating various signals to fulfil different tasks, such as surveillance and tracking. In electromagnetic reconnaissance, recognition of MFR fine-grained modes can provide a basis for analysing strategies and reaction. With the limit of real applications, it is hard to obtain a large number of labelled samples for existing methods to learn the difference between categories. Therefore, it is essential to develop new methods to extract general knowledge of MFRs and identify modes with only a few samples. This paper proposes a few-shot learning (FSL) framework based on efficient neural architecture search (ENAS) with high robustness and portability, which designs a suitable network structure automated and quickly adapts to new environments. The experimental results show that the proposed method can still achieve excellent fine-grained modulation recognition performance (92.6%) under the condition of -6 dB signal-to-noise ratio (SNR), even when each class only provides one fixed-duration signal sample. The robustness is also verified under different conditions.
Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN Intelligent Controllers

Recently, Federated Learning (FL) has been applied in various research domains specially because of its privacy preserving and decentralized approach of model training. However, very few FL applications have been developed for the Radio Access Network (RAN) due to the lack of efficient deployment models. Open RAN (O-RAN) promises a high standard of meeting 5G services through its disaggregated, hierarchical, and distributed network function processing framework. Moreover, it comes with built-in intelligent controllers to instill smart decision making ability into RAN. In this paper, we propose a framework named O-RANFed to deploy and optimize FL tasks in O-RAN to provide 5G slicing services. To improve the performance of FL we formulate a joint mathematical optimization model of local learners selection and resource allocation to perform model training in every iteration. We solve this non-convex problem using the decomposition method. First, we propose a slicing based and deadline aware client selection algorithm. Then, we solve the reduced resource allocation problem by using successive convex approximation (SCA) method. Our simulation results show the proposed model outperforms the state-of-the-art FL methods such as FedAvg and FedProx in terms of convergence, learning time, and resource costs.
Efficient Federated Matrix Factorization Against Inference Attacks

Recommender systems typically require the revelation of users’ ratings to the recommender server, which will subsequently use these ratings to provide personalized services. However, such revelations make users vulnerable to a broader set of inference attacks, allowing the recommender server to learn users’ private attributes, e.g., age and gender. Therefore, in this paper, we propose an efficient federated matrix factorization method that protects users against inference attacks. The key idea is that we obfuscate one user’s rating to another such that the private attribute leakage is minimized under the given distortion budget, which bounds the recommending loss and overhead of system efficiency. During the obfuscation, we apply differential privacy to control the information leakage between the users. We also adopt homomorphic encryption to protect the intermediate results during training. Our framework is implemented and tested on real-world datasets. The result shows that our method can reduce up to 16.7% of inference attack accuracy compared to using no privacy protections.
M3SPCANet: A simple and effective ConvNets with unsupervised predefined filters for face recognition

The principal component analysis network (PCANet) with predefined filters has recognized as a lightweight convolutional neural networks (CNN) baseline. However, there are three shortages in PCANet: (i) single-scale convolution leads to insufficient feature learning, (ii) the spatial layout information and relationship between channel features are neglected, (iii) lack of nonlinearity because there is no activation function. Therefore, we devise a PCANet alternative dubbed Multi-Scale Spatial pyramid Second-order pooling Principle Component Analysis Network (M3SPCANet) in the paper. First, multi-scale PCA filters are learned and applied to obtain feature maps via convolution. Second, the feature maps are activated via nonlinear function Tanh and stacked together. Third, second-order pooling is exploited to extract correlation information between any two channels of the feature maps, moreover, spatial pyramid strategy is employed to learn local and holistic features simultaneously. Finally, whitening PCA based fully connected layer is constructed to remove redundancy information. We utilize cosine similarity based nearest neighbor classifier for face matching. Extensive experiments on four homogeneous and one heterogeneous face databases indicate that our method can achieve superior identification rates, and yield competitive verification performance. Cross database evaluations demonstrate that the multi-scale filters of our method have strong generalization ability. Our implementation code will be available at https://github.com/dxtyut/facerecognition.
ICT-enabled informal learning in EFL writing

With the advancement and popularity of information and communication technologies (ICT), English-as-a-foreign-language (EFL) students do have now access and opportunities to interact with a wide range of learning sources and resources in online informal spaces to develop their writing beyond the classroom. Yet, research addressing the potential of ICT-enabled informal learning has attracted insufficient attention in EFL writing. This study explores undergraduate students’ online informal writing practices and the extent to which they are adopted for formal writing purposes. A survey with both closed-ended and open-ended questions and semi-structured interviews were employed to collect data from English-major students (N = 118) at an Omani university. The results indicate that students resorted to various digital technologies not only to help them communicate effectively on online informal platforms but also to navigate the requirements of classroom-based writing. Furthermore, translation and proofreading applications were rated as the most frequently used and useful tools in both contexts, suggesting EFL students’ concern with linguistic aspects of their writing. However, while students relied mostly on synchronous platforms to engage in informal writing activities, they consulted asynchronous tools to resolve formal writing problems. The paper ends with a discussion of pedagogical implications and directions for future research.