Reviewerly/exHarmony huggingface dataset:
Multi-Scale Attentive Feature Fusion Network for Single Image Dehazing

Single image dehazing is a challenging ill-posed problem due to the severe degeneration of image information, which hinders the practical application of images. Due to its ill-posed quality, it faces great challenges especially in dealing with dense haze. In this paper, we propose a Multi-Scale Attentive Feature Fusion Network (MAFFNet) based on the U-Net structure to process dense haze regions from the hazy images while accurately preserving the image details. Further, we introduce the Boost Information Connection (BIC) module, Dense Residual (DR) module, and Feature Fusion (FF) module in MAFFNet to guide the feature extraction and feature fusion and eventually construct the dehazing image. Since the connection of shallow and deep information is helpful for network feature extraction, we use BIC to perform phase and phase subtraction operations on the feature maps of the front and back blocks. DR is used to identify dense haze regions and color channels. In addition, FF aims to accelerate convergence by aggregating features at several layers. The approach is thoroughly evaluated on the SOTS and the NH-HAZE datasets. The result indicates that our MAFFNet surpasses the current state-of-the-art single image dehazing approaches in improving the dehazing performance and maintaining image details.
BigCloneBench Considered Harmful for Machine Learning

BigCloneBench is a well-known large-scale dataset of clones mainly targeted at the evaluation of recall of clone detection tools. It has been beneficial for research on clone detection and evaluating the performance of clone detection tools, for which it has become standard. It has also been used in machine learning approaches to clone detection or code similarity detection. However, the way BigCloneBench has been constructed makes it problematic to use as ground truth for learning code similarity. This paper highlights the features of BigCloneBench that affect the ground truth quality and discusses common misperceptions about the benchmark. For example, extending or replacing the ground truth without understanding the properties of BigCloneBench often leads to wrong assumptions which can lead to invalid results. Also, a manual investigation of a sample of Weak-Type-3/Type-4 clone pairs revealed 86% of pairs to be false positives, threatening the results of machine learning approaches using BigCloneBench. We call for a halt in using BigCloneBench as the ground truth for learning code similarity.
Relevant gene selection using ANOVA-ant colony optimisation approach for malaria vector data classification

Recent progress in gene expression data research makes it possible to quantify and identify several thousand genes' expressions simultaneously. For malaria infection and transmission, gene expression data classification using dimensionality reduction is a standard approach in gene expression data analysis and proposed for this study. A major problem occurs in the reduction of high dimensional data, it plays a significant role in improving the precision of classification, allowing biologists and clinicians to correctly predict infections in humans by choosing a limited subclass of appropriate genes and deleting redundant, and noisy genes. The combination of a novel analysis of variance (ANOVA) with ant colony optimisation (ACO) approach as a hybrid feature selection to select relevant genes is suggested in this study to minimise the redundancy between genes, and SVM is used for classification. The proposed method's efficacy was shown by the experimental outcomes based on the high-dimensional of gene expression data.
Rethinking the Rule and Role of Law in Decentralized Finance

Decentralized Finance (DeFi) has presented itself as the next building block in a market-based crypto economy. While the jury is still out on its (potential) benefits for society at large, it has become apparent that DeFi is qualitatively different from earlier blockchain applications because of its governance model based on Decentralized Autonomous Organizations (DAOs). This decentralized governance model has enabled the evolution from peer-to-peer to peer-to-network applications, allowing more sophisticated financial transactions to take place on permissionless blockchains. This paper analyzes the political economy of decentralized governance, the means by which the law attempts to govern DeFi, and alternative roles that the law could occupy in the governance of DeFi. It is found that as applications grow more complex, the need for politicized decision-making increases, but that enforcing regulatory perimeters becomes even more challenging. It is therefore suggested that the law could occupy a more facilitative, incentive-based role in the governance of DeFi as opposed to an enforcement-oriented role. By way of illustration, a state-organized DAO serving as forum to reach rough consensus on network-to-network matters is proposed. It is concluded that rethinking the rule and role of law in DeFi serves purpose as it provides state actors with additional tools and policy options in a transnational environment hostile to formal state intervention.
Research on knowledge tree growth model for intelligent English teaching system based on hypertext structure

At present, the knowledge management model of intelligent English teaching system has some problems, such as weak relevance of knowledge points and poor judgment accuracy of precursor knowledge points. This paper proposes a new knowledge tree growth model for intelligent English teaching system based on hypertext structure. The knowledge representation of intelligent English teaching resources is completed through representation of tree of knowledge map, description of knowledge attribute and knowledge relation, and construction of a knowledge tree structure. On this basis, the hypertext structure is used to construct the content model of English teaching resources, package the metadata and content, and thus complete the organisation of English teaching resources. The experimental results show that the proposed knowledge tree growth model greatly improves the relevance of knowledge points and the accuracy of judgment of precursor knowledge points, improves the knowledge base of intelligent English, and improves the efficiency and quality of English teaching.
Deep In-Loop Filtering via Multi-Domain Correlation Learning and Partition Constraint for Multiview Video Coding

The deep learning-based in-loop filtering methods have greatly improved the coding efficiency for High Efficiency Video Coding (HEVC). However, directly applying these HEVC-orientated in-loop filtering methods to multiview video coding may not obtain satisfactory performance due to the characteristics of multiview video. In this paper, a deep in-loop filtering method based on multi-domain correlation learning and partition constraint network (MDP-Net) is proposed to boost the multiview video coding performance. To the best of our knowledge, this work is the first attempt at deep in-loop filtering for multiview video coding. Specifically, a multi-domain correlation learning module is presented to restore the high-frequency details of the distorted frame by exploring the multi-domain correlations. Besides, based on the block partition information generated in video coding, a partition-constrained reconstruction module is proposed to better attenuate the compression artifacts by designing a partition loss. Finally, the proposed MDP-Net is integrated into 3D-HEVC reference software, and the experimental results demonstrate that the proposed method achieves considerable performance improvement compared with 3D-HEVC.
Implementation and Analysis of Quantum Homomorphic Encryption

Growing interest in the field of quantum computing is fueled by quantum computers projected ”quantum supremacy” in speed and security. The potential for ultra-high speeds may produce a dramatic change in data science, machine learning, analytics, and information processing. This research study will focus on encryption algorithms where quantum computing may affect protocols and deciphering codes. Specifically, homomorphic encryption (HE) enables mathematical operations to be performed on encrypted data without having to decrypt the data in the process. Quantum homomorphic encryption (QHE) enables quantum circuits to be performed on encrypted qubits. In this research experience for undergraduates (REU) study, we design quantum circuits to implement QHE on a quantum teleportation circuit. The teleportation algorithm is profiled in terms of performance and complexity and comparative results are provided for encoded versus unencoded circuits. This work serves as a building block for encrypting more complex quantum algorithms such as Quantum Neural Networks (QNN).
Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets into a common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.
Data Mining Analysis of Online Drug Reviews

Data mining methods like sentiment analysis provide useful information. This paper examines drug online user reviews. This research predicts user satisfaction with sentiments and applied drugs on effectiveness and side effects using sentiment analysis based on classification and analyzes model transfer across data sources like Emzor and May & Baker data. Online medication review data. Web crawlers was used to collect the ratings and comments of forum members. Emzor Pharmaceutical Company had 463 reviews and May & Baker Pharmaceutical Company had 421 reviews. Data was split 70% for training and 30% for testing. We used sentiment analysis to predict user ratings on overall satisfaction, side effects, and drug efficacy. Emzor data performs better 89.1% in-domain sentiment analysis, while May & Baker data accuracy is 86.90% overall. In cross-data sentiment analysis, the Emzor and May & Baker data performed well when the trained model was applied to side effects. This study acquired data by trawling an internet drug review forum. This study shows that transfer learning can leverage cross-domain similarities to analyze cross-domain sentiment.
A Novel Action Saliency and Context-Aware Network for Weakly-Supervised Temporal Action Localization

Temporal action localization is a challenging task in computer vision, and it tries to find the start time and the end time of the actions and predict their categories. However, compared to temporal action localization, weakly supervised temporal action localization (WTAL) is a more challenging task due to its poor annotations. With only video-level annotation, some background frames, similar to actions, would be classified as actions and produce inaccurate results. In addition, the two-stream fusion problem, ignored previously, also needs to be further considered. To resolve these issues, we propose a novel action saliency and context-aware network (ASCN) for weakly supervised temporal action localization tasks. Specifically, the temporal saliency and context module is designed to enhance the global saliency and context information of the RGB and the flow features to suppress the backgrounds and enhance the actions. In addition, a hybrid attention mechanism using frame differences and two-stream attention is designed to model the local action context information and further enlarge the scores of the potential action regions and suppress the background regions. Finally, to obtain two-stream consistency and solve the fusion problem, we use the similarity loss and a channel self-attention module to adaptively fuse the enhanced RGB and flow features. Extensive experiments demonstrate that ASCN can outperform all of the SOTA WTAL methods on the THUMOS14 dataset and the ActivityNet1.3 dataset with an average mAP that can reach 37.2% on the THUMOS14 dataset and attains an average mAP of 26.3% on the ActivityNet1.3 dataset. On the ActivityNet1.2 dataset, ASCN can also obtain comparable results. Compared with AdapNet (TNNLS20), MMSD (TIP22), and FTCL (CVPR22) on the THUMOS14 dataset, ASCN can outperform them by 13.5%, 2.9%, and 2.8%, respectively.
Frequency Combs with Parity-Protected Cross-Correlations and Entanglement from Dynamically Modulated Qubit Arrays

We develop a general theoretical framework to dynamically engineer quantum correlations in the frequency-comb emission from an array of superconducting qubits in a waveguide, rigorously accounting for the temporal modulation of the qubit resonance frequencies. We demonstrate, that when the resonance frequencies of the two qubits are periodically modulated with a $\pi$ phase shift, it is possible to realize simultaneous bunching and antibunching in cross-correlations of the scattered photons from different sidebands. Our approach, based on the dynamical conversion between the quantum excitations with different parity symmetry, is quite universal. It can be used to control two-particle correlations in generic dynamically modulated dissipative quantum systems.
Anytime Informed Multi-Path Replanning Strategy for Complex Environments

In many real-world applications (e.g., human-robot collaboration), the environment changes rapidly, and the intended path may become invalid due to moving obstacles. In these situations, the robot should quickly find a new path to reach the goal, possibly without stopping. Planning from scratch or repairing the current graph can be too expensive and time-consuming. This paper proposes MARS, a sampling-based Multi-pAth Replanning Strategy that enables a robot to move in dynamic environments with unpredictable obstacles. The novelty of the method is the exploitation of a set of precomputed paths to compute a new solution in a few hundred milliseconds when an obstacle obstructs the robot’s path. The algorithm enhances the search speed by using informed sampling, builds a directed graph to reuse results from previous replanning iterations, and improves the current solution in an anytime fashion to make the robot responsive to environmental changes. In addition, the paper presents a multithread architecture, applicable to several replanning algorithms, to handle the execution of the robot’s trajectory with continuous replanning and the collision checking of the traversed path. The paper compares state-of-the-art sampling-based path-replanning algorithms in complex and high-dimensional scenarios, showing that MARS is superior in terms of success rate and quality of solutions found. An open-source ROS-compatible implementation of the algorithm is also provided.
Intelligent digital twin for federated learning in AIoT networks

Federated Learning (FL) promises to solve the data privacy problem by training the local model on each node and sharing the model parameters instead of the data itself. Next, the FL server applies model aggregation techniques to aggregate the received models and broadcast the resulting model to the connected clients. This study proposes a Digital Twin-based Federated Learning (DT-FL) framework to virtually monitor and controls the remotely deployed physical clients and their training process. The connection-oriented protocol, Open Connectivity Foundation (OCF) Iotivity, connects the FL clients with the FL server to ensure packet delivery. OCF Iotivity sends/receives the models’ weights to/from the server, and Hyper Text Transfer Protocol (HTTP) is used to monitor clients’ local training. After receiving partially trained models from clients, the server performs the optimal model selection using the normal distribution method by considering the performance of the model. Finally, the best-selected models are aggregated, and the final model is broadcasted to the clients. The framework utilizes Raspberrypi 4 devices as clients with limited computational capabilities, due to which the experiments are conducted with structured energy consumption data. The dataset comprises of 8 multistory residential buildings located in different geographical locations of the Republic of Korea. Each residential building is treated as an FL client and registered on DT using the IP address and port number. The DT-FL framework can be used with classification and regression datasets, and the model architecture for that data can be designed on the DT platform. The experiments are conducted with the partial and full participation of clients. The results show the minimum delay time in physical and virtual object synchronization and better performance and generalization of the global model for each client. The source code of the proposed DT-FL framework is available on GitHub.
IndeGx: A model and a framework for indexing RDF knowledge graphs with SPARQL-based test suits

In recent years, a large number of RDF datasets have been built and published on the Web in fields as diverse as linguistics or life sciences, as well as general datasets such as DBpedia or Wikidata. The joint exploitation of these datasets requires specific knowledge about their content, access points, and commonalities. However, not all datasets contain a self-description, and not all access points can handle the complex queries used to generate such a description. In this article, we provide a standard-based approach to generate the description of a dataset. The generated descriptions as well as the process of their computation are expressed using standard vocabularies and languages. We implemented our approach into a framework, called IndeGx, where each indexing feature and its computation is collaboratively and declaratively defined in a GitHub repository. We have experimented IndeGx on a set of 339 RDF datasets with endpoints listed in public catalogs, over 8 months. The results show that we can collect, as much as possible, important characteristics of the datasets depending on their availability and capacities. The resulting index captures the commonalities, variety and disparity in the offered content and services and it provides an important support to any application designed to query RDF datasets.
DNA Code from Cyclic and Skew Cyclic Codes over F4[v]/⟨v3⟩

The main motivation of this work is to study and obtain some reversible and DNA codes of length n with better parameters. Here, we first investigate the structure of cyclic and skew cyclic codes over the chain ring R:=F4[v]/⟨v3⟩. We show an association between the codons and the elements of R using a Gray map. Under this Gray map, we study reversible and DNA codes of length n. Finally, several new DNA codes are obtained that have improved parameters than previously known codes. We also determine the Hamming and the Edit distances of these codes.
Comparing bound entanglement of bell diagonal pairs of qutrits and ququarts

We compare the classification as entangled or separable of Bell diagonal bipartite qudits with positive partial transposition (PPT) and their properties for different dimensions. For dimension [Formula: see text], a form of entanglement exists that is hard to detect and called bound entanglement due to the fact that such entangled states cannot be used for entanglement distillation. Up to this date, no efficient solution is known to differentiate bound entangled from separable states. We address and compare this problem named separability problem for a family of bipartite Bell diagonal qudits with special algebraic and geometric structures and applications in quantum information processing tasks in different dimensions. Extending analytical and numerical methods and results for Bell diagonal qutrits ([Formula: see text]), we successfully classify more than [Formula: see text] of representative Bell diagonal PPT states for [Formula: see text]. Via those representative states we are able to estimate the volumes of separable and bound entangled states among PPT ququarts ([Formula: see text]). We find that at least [Formula: see text] of all PPT states are separable, [Formula: see text] bound entangled and for [Formula: see text] it remains unclear whether they are separable or bound entangled. Comparing the structure of bound entangled states and their detectors, we find considerable differences in the detection capabilities for different dimensions and relate those to differences of the Euclidean geometry for qutrits ([Formula: see text]) and ququarts ([Formula: see text]). Finally, using a detailed visual analysis of the set of separable and bound entangled Bell diagonal states in both dimensions, qualitative observations are made that allow to better distinguish bound entangled from separable states.
Exploring teachers’ perceptions of critical digital literacies and how these are manifested in their teaching practices

Digital systems are increasingly becoming central to the running of contemporary schools. A range of digital tools are also adopted by teachers to facilitate face to face teaching and learning and more recently to accommodate remote schooling. Similarly, digital technologies lie at the heart of how students support their learning but also interact with peers. These digital practices raise questions in relation to teachers’ own critical digital literacies as well as their role in developing students’ critical digital literacies. This paper presents the findings of a qualitative study that aimed to develop an understanding of teachers’ current experiences and future needs relating to critical digital literacies within school contexts. Drawing on empirical data collected during focus group interviews with primary and secondary school teachers in Finland, Italy, Spain and the UK this paper looks at teachers’ perceptions of critical digital literacies and explores whether and how these are manifested in their practices. Findings revealed that different dimensions of critical digital literacies were more prevalent for each national group and highlighted the disjuncture between how Critical digital literacies (CDL) is defined and perceived in academic research with a stronger emphasis on the “critical” and between the more “twenty-first century skills” oriented policy agendas and curricula which inform teachers’ practice. The paper goes on to discuss the implications of these findings and identifies gaps in relation to teachers’ understandings of critical digital literacies. Last, it offers original insights for future policymaking, research and practice regarding the challenges of supporting teachers with developing critical digital literacies.