Reviewerly/exHarmony huggingface dataset:
Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light

A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.
FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades. In this work, we claim that SMOTE has an important issue when defining the neighborhood in order to create new minority samples: the use of the Euclidean distance may not be suitable in high-dimensional settings. Our hypothesis is that the use of a weighted metric that does not assume that all features are equally important could improve performance in the presence of noisy/redundant variables. In this line, we present a novel SMOTE-like method that uses the weighted Minkowski distance for defining the neighborhood for each example of the minority class. This methodology leads to a better definition of the neighborhood since it prioritizes those features that are more relevant for the classification task. A complementary advantage of the proposal is performing feature selection since attributes can be discarded when their corresponding weights are below a given threshold. Our experiments on 42 class-imbalance datasets show the virtues of the proposed SMOTE variant, achieving the best predictive performance when compared with the traditional SMOTE approach and other recent variants on low- and high-dimensional settings, handling issues such as class overlap and hubness adequately without increasing the complexity of the method.
Part of speech tagging: a systematic review of deep learning and machine learning approaches

Abstract Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.
Underwater Target Detection Algorithm Based on Improved YOLOv5

Underwater target detection plays an important role in ocean exploration, to which the improvement of relevant technology is of much practical significance. Although existing target detection algorithms have achieved excellent performance on land, they often fail to achieve satisfactory outcome of detection when in the underwater environment. In this paper, one of the most advanced target detection algorithms, YOLOv5 (You Only Look Once), was first applied in the underwater environment before being improved by combining it with some methods characteristic of the underwater environment. To be specific, the Swin Transformer was treated as the basic backbone network of YOLOv5, which makes the network suitable for those underwater images with blurred targets. It is possible for the network to focus on fusing the relatively important resolution features by improving the method of path aggregation network (PANet) for multi-scale feature fusion. The confidence loss function was improved on the basis of different detection layers, with the network biased to learn high-quality positive anchor boxes and make the network more capable of detecting the target. As suggested by the experimental results, the improved network model is effective in detecting underwater targets, with the mean average precision (mAP) reaching 87.2%, which makes it advantageous over general target detection models and fit for use in the complex underwater environment.
Blockchain technology in food supply chains: Review and bibliometric analysis

Increase in global population growth and the consequent rise in the demand for food while ensuring quality, preventing wastage, avoiding deforestation, and carbon footprinting has put tremendous pressure on the global food supply chain (FSC). Due to fast computing facilities and availability of data, blockchain technology has emerged as a potential system that may help in developing a safe and secure FSC. Blockchain has gained significant attention among researchers worldwide, which is evident from the extensive literature generated on this subject within a span of a few years. Through this paper, the authors attempt to showcase the immense scope and significance of blockchain in FSCs by examining and analyzing the existing studies. In addition to providing an overview of the application of blockchain in FSCs, this study also discusses challenges such as scalability, interoperability, and high cost and suggests potential solutions for the existing difficulties in adopting blockchain in FSCs. Additionally, a bibliometric analysis is provided to acquaint researchers and practitioners with the structure and research trends in this area. The study found that researchers have mostly focused on the applicability of blockchain for product authentication, finance, and logistics. Further, blockchain is likely to become a dominant technology for enhancing transparency and traceability, reducing risk and, most importantly, enhancing trust among different stakeholders in the area of FSC.
Pyramid particle swarm optimization with novel strategies of competition and cooperation

Particle swarm optimization (PSO) has shown its advantages in various optimization problems. Topology and updating strategies are among its key concepts and have significant impacts on optimization ability. This paper proposes a pyramid PSO (PPSO) with novel competitive and cooperative strategies to update particles’ information. PPSO builds a pyramid and assigns each particle to a specific layer according to its fitness. The particles at the same layer will make a pairwise comparison to determine the winners and the losers. The losers will cooperate with their corresponding winners, while the winners will cooperate with the particles at the upper layer and those at the top layer. Each particle in PPSO has its own learning behavior, having more than one exemplar rather than the only global best to learn from. The diversity of the swarm is enhanced and it positively affects the performance of PSO. Extensive experiments demonstrate that the PPSO has superior performance in terms of accuracy, Wilcoxon signed-rank test and convergence speed, yet achieves comparable running time in most cases, when compared with the canonical PSO and eight state-of-the-art PSO variants. Furthermore, we analyze the influence of parameters for the PPSO. All these illustrate that the PPSO is promising for numerical optimization.
Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine

With the advancement of wind energy, solar energy, and other new energy industries, the demand for energy storage systems are worth increasing. Supercapacitors gradually stand out among many energy storage components due to their advantages of high power density, fast charging and discharging speed, and long life. Predicting the capacity of supercapacitors from historical data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimized forecasting model-an extreme learning machine (ELM) model coupled with the heuristic Kalman filter (HKF) algorithm to forecast the capacity of supercapacitors. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient capacity forecasting in real-time. Our HKF-ELM model performed significantly better than other data-driven models models that are commonly used in forecasting life of supercapacitors. The performance of the proposed HKF-ELM model was also compared with traditional ELM, Kalman filtering model, ELM optimized by the particle swarm optimization (PSO-ELM) and Kalman filter extreme learning machine models (KA-ELM). Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Square Error (MSE) and R2 determination coefficient were used for the comparison of the selected models. The aging life of supercapacitors in different environments were also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional data-driven models in terms of prediction aging life of supercapacitors and it can be applied in real-time to predict state of health (SOH) based on the previous charge and discharge data of supercapacitors. In particular, considering RMSE of forecasting, the proposed HKF-ELM model performed 77.62% better than the traditional ELM model, 77.46% better than the PSO-ELM model, 87.40% better than the traditional Kalman filter model, 82.51% better than the KA-ELM model in forecasting aging life of supercapacitors. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HKF. Fewer setting parameters, lower time cost and higher prediction accuracy have been need in our methodology compared to available models. Our work presents an original way of performing aging life of supercapacitors forecasting in real-time in industry with highly accurate results which are much better than pre-existing life forecasting models.
An Entangled Pedagogy: Looking Beyond the Pedagogy—Technology Dichotomy

Abstract ‘Pedagogy first’ has become a mantra for educators, supported by the metaphor of the ‘pedagogical horse’ driving the ‘technological cart’. Yet putting technology first or last separates it from pedagogy, making us susceptible to technological or pedagogical determinism (i.e. where technology is seen either as the driving force of change or as a set of neutral tools). In this paper, I present a model of entangled pedagogy that encapsulates the mutual shaping of technology, teaching methods, purposes, values and context. Entangled pedagogy is collective, and agency is negotiated between teachers, students and other stakeholders. Outcomes are contingent on complex relations and cannot be determined in advance. I then outline an aspirational view of how teachers, students and others can collaborate whilst embracing uncertainty, imperfection, openness and honesty, and developing pedagogical knowledge that is collective, responsive and ethical. Finally, I discuss implications for evaluation and research, arguing that we must look beyond isolated ideas of technologies or teaching methods, to the situated, entangled combinations of diverse elements involved in educational activity.
Heterogeneous Computation and Resource Allocation for Wireless Powered Federated Edge Learning Systems

Federated learning (FL) is a popular edge learning approach that utilizes local data and computing resources of network edge devices to train machine learning (ML) models while preserving users’ privacy. Nevertheless, performing efficient learning tasks on the devices and achieving longer battery life are primary challenges faced by federated learning. In this paper, we are the first to study the application of heterogeneous computing (HC) and wireless power transfer (WPT) to federated learning to address these challenges. Especially, we propose a heterogeneous computation and resource allocation framework based on a heterogeneous mobile architecture to achieve effective implementation of FL. To minimize the energy consumption of smart devices and maximize their harvesting energy simultaneously, we formulate an optimization problem featuring multidimensional control, which jointly considers time splitting for WPT, dataset size allocation, transmit power allocation and subcarrier assignment during communications, and processor frequency of processing units (central processing unit (CPU) and graphics processing unit (GPU)). However, the major obstacle is how to design a proper algorithm to solve this optimization problem efficiently. For this purpose, we decouple the optimization variables so as to achieve high efficiency in deriving its solution. Particularly, we first compute the optimal processor frequency and dataset size allocation via employing the Lagrangian dual method, followed by finding the closed-form solution to the optimal time splitting allocation, and finally attain the optimal subcarrier assignment as well as transmit power for transmissions through an iteration algorithm. To evaluate the performance of our proposed scheme, we set up four baseline schemes as comparison, and simulation results show that the proposed scheme converges quite fast and better enhance the energy efficiency of the wireless powered FL system compared with the baseline schemes.
Status Quo in Requirements Engineering

Requirements Engineering (RE) has established itself as a software engineering discipline during the past decades. While researchers have been investigating the RE discipline with a plethora of empirical studies, attempts to systematically derive an empirically-based theory in context of the RE discipline have just recently been started. However, such a theory is needed if we are to define and motivate guidance in performing high quality RE research and practice. We aim at providing an empirical and valid foundation for a theory of RE, which helps software engineers establish effective and efficient RE processes. We designed a survey instrument and theory that has now been replicated in 10 countries world-wide. We evaluate the propositions of the theory with bootstrapped confidence intervals and derive potential explanations for the propositions. We report on the underlying theory and the full results obtained from the replication studies with participants from 228 organisations. Our results represent a substantial step forward towards developing an empirically-based theory of RE giving insights into current practices with RE processes. The results reveal, for example, that there are no strong differences between organisations in different countries and regions, that interviews, facilitated meetings and prototyping are the most used elicitation techniques, that requirements are often documented textually, that traces between requirements and code or design documents is common, requirements specifications themselves are rarely changed and that requirements engineering (process) improvement endeavours are mostly intrinsically motivated. Our study establishes a theory that can be used as starting point for many further studies for more detailed investigations. Practitioners can use the results as theory-supported guidance on selecting suitable RE methods and techniques.
6 Gbps real-time optical quantum random number generator based on vacuum fluctuation

We demonstrate a 6 Gbps real-time optical quantum random number generator by measuring vacuum fluctuation. To address the common problem that speed gap exists between fast randomness generation and slow randomness extraction in most high-speed real-time quantum random number generator systems, we present an optimized extraction algorithm based on parallel implementation of Toeplitz hashing to reduce the influence of classical noise due to the imperfection of devices. Notably, the real-time rate of randomness extraction we have achieved reaches the highest speed of 12 Gbps by occupying less computing resources and the algorithm has the ability to support hundreds of Gbps randomness extraction. By assuming that the eavesdropper with complete knowledge of the classical noise, our generator has a randomness generation speed of 6.83 Gbps and this supports the generation of 6 Gbps information-theoretically provable quantum random numbers, which are output in real-time through peripheral component interconnect express interface.
Fast Multi-Instance Multi-Label Learning

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.
Variational Quantum Factoring

Integer factorization has been one of the cornerstone applications of the field of quantum computing since the discovery of an efficient algorithm for factoring by Peter Shor. Unfortunately, factoring via Shor's algorithm is well beyond the capabilities of today's noisy intermediate-scale quantum (NISQ) devices. In this work, we revisit the problem of factoring, developing an alternative to Shor's algorithm, which employs established techniques to map the factoring problem to the ground state of an Ising Hamiltonian. The proposed variational quantum factoring (VQF) algorithm starts by simplifying equations over Boolean variables in a preprocessing step to reduce the number of qubits needed for the Hamiltonian. Then, it seeks an approximate ground state of the resulting Ising Hamiltonian by training variational circuits using the quantum approximate optimization algorithm (QAOA). We benchmark the VQF algorithm on various instances of factoring and present numerical results on its performance.
The ParallelEye Dataset: A Large Collection of Virtual Images for Traffic Vision Research

Dataset plays an essential role in the training and testing of traffic vision algorithms. However, the collection and annotation of images from the real world is time-consuming, labor-intensive, and error-prone. Therefore, more and more researchers have begun to explore the virtual dataset, to overcome the disadvantages of real datasets. In this paper, we propose a systematic method to construct large-scale artificial scenes and collect a new virtual dataset (named “ParallelEye”) for the traffic vision research. The Unity3D rendering software is used to simulate environmental changes in the artificial scenes and generate ground-truth labels automatically, including semantic/instance segmentation, object bounding boxes, and so on. In addition, we utilize ParallelEye in combination with real datasets to conduct experiments. The experimental results show the inclusion of virtual data helps to enhance the per-class accuracy in object detection and semantic segmentation. Meanwhile, it is also illustrated that the virtual data with controllable imaging conditions can be used to design evaluation experiments flexibly.