Reviewerly/exHarmony huggingface dataset:
A Survey on Blockchain Technology Supported Approaches for Healthcare System, Open Issues and Challenges

In the last few years, technological development has multiplied. New technologies are affecting almost every industry. One of these which noticed the abrupt change in their ecosystem is the healthcare industry. As this industry has a direct impact on the human lives so adoption of new technology is not so easy. Proper testing and trials are performed before amalgamating any new technology with the healthcare system. Emerging technologies are being employed to deliver better health-related services to patients. Different communication and information collection technologies are used to maintain and access patients' health data to authorized users. But with the employment of technology, health data security and privacy-related issues are increasing. Maintaining trust among multiple entities of the healthcare system is a challenging task. One emerging technology, distributed ledger technology (DLT), is creating some hope for resolving the fears mentioned above of the healthcare ecosystem. Attributes like Security, privacy and authentication can be enhanced by integrating this technology with the healthcare ecosystem. This study examines the role of distributed ledger technology in the healthcare system while highlighting how some aspects of this disruptive technology can completely alter current business practices. To achieve this, the theoretical foundations of numerous research papers published in prestigious scientific journals and several reports from industries are included in this survey. This helps to streamline our analysis and fully capture the constantly expanding blockchain domain. This survey also identifies several research holes and prospective areas for further study using these findings as a starting point. Both academics and practitioners should find great value in these discoveries. This survey presents a comprehensive evaluation of current DLT- supported approaches in the healthcare system. and provides benefits and limitations of the recent work. This survey also emphasizes the essential security needs for a healthcare ecosystem and discusses the security flaws with existing methods.
Metaverse!

This study aimed to synthesize the literature on Metaverse to highlight its current research, opportunities, and applications in e-healthcare and education to reduce inequalities and for delivering fair and equal opportunities and solutions. The authors employed preferred reporting items for systematic reviews and meta-analyses (PRISMA) to rapidly map the field of a metaverse in health and education. Two major application domains emerged in the literature from the 88 research publications, which include (1) Metaverse in healthcare and (2) Metaverse in education. This study will act as a road map to help academics who desire to continue their research work in the Metaverse for various healthcare and educational services. However, its implementation is required in the future to improve mental healthcare and the effectiveness of mental health services, particularly in low and medium-income (LMIC) and conflict-affected areas.
An intelligent vision recognition method based on deep learning for pointer meters

Abstract Nowadays, pointer instruments remain the main state monitoring devices in the power industry, because they have strong mechanical stability to resist electromagnetic interferences compared with digital instruments. Although the object detection algorithms based on deep learning have widely been used in the field of instrument detection, the meter recognition process still relies on threshold segmentation to recognize object points and on Hough transform to extract the meter pointer. An intelligent vision recognition method based on YOLOv5 and U 2 -Net network (YLU 2 -Net) is proposed to improve the accuracy and efficiency of meter recognition in a complex environment. Firstly, the pointer meter is located in the instrument images by using the YOLOv5 network as a region of interest (RoI). Then, the instrument RoI is processed by means of perspective transformation and image resizing. Thirdly, an improved U 2 -Net image segmentation method with the deep separable convolution and the focal loss function is devised to distinguish the pointers and scales from the background in the instrument RoI. Further, a dimension reduction reading method with the polar coordinate transformation is developed to calculate the meter reading accurately and efficiently. Finally, the ablation experiment is conducted to test the performance of each algorithm module in our method, and the competition experiment is completed to compare our method with other state-of-the-art ones. The experimental results verify the accuracy and efficiency of the YLU 2 -Net recognition method proposed.
Issue report classification using pre-trained language models

This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).
A level set based fractional order variational model for motion estimation in application oriented spectrum

In recent years, motion estimation has become prominent in the field of computer vision. This is due to its applications in various domains such as object detection, video surveillance, undersea navigation, fire damage control, particle image velocimetry etc. Therefore, in this paper, a nonlinear fractional order variational model is introduced for motion estimation in an image sequence (video). The motion estimation is performed in terms of optical flow. The objective of this work is to generalize the existing variational models from integer order to fractional order and provide the increased robustness against outliers, and furnish the dense and discontinuity preserving optical flow in various spectra such as synthetic, sintell, thermal, underwater, medical, fire and smoke and fluid image sequences. For this purpose, a level set segmentation based fractional order variational functional composed of a non-quadratic Charbonnier norm and a regularization term is propounded. This non-quadratic penalty provides an effective robustness against outliers, whereas the fractional derivative possesses a non-local character, and therefore is capable to deal with discontinuous information about texture and edges. The level set segmentation is performed on the flow field instead of images, which is a union of disjoint and independently moving regions such that each motion region contains objects of equal flow velocity. The numerical discretization of the fractional partial differential equations is employed with the help of Grünwald–Letnikov fractional derivative. The resulting nonlinear formulation is converted into a linear system and solved by an efficient numerical technique. The experimental results are carried out on 10 different application oriented spectra. The performance of the model is tested using different error measures and demonstrated against several outliers. The efficiency and accuracy of the proposed model is shown against recently published works. The graphical abstract for this algorithm is illustrated under the next section.
Reversible Data Hiding for JPEG Images With Adaptive Multiple Two-Dimensional Histogram and Mapping Generation

Reversible data hiding based on joint photographic experts group (JPEG) images has been extensively studied to enhance embedding performance in terms of visual quality and file size preservation at the desired payload. In this paper, an efficient adaptive RDH method for JPEG images with multiple two-dimensional (2D) histogram modification is proposed. Firstly, the proposed method proposes the block smoothness estimator and the band smoothness estimator, and then combines the two estimators to reduce the embedding distortion as much as possible at the desired payload. Instead of adopting a fixed 2D mapping or choosing one from several empirically-designed mappings for each 2D histogram, the proposed method designs an adaptive 2D mapping generation strategy to adaptively generate a large number of mappings with considering the local characteristics of histogram distribution. Since exhaustively searching for the optimal mapping achieving the highest embedding performance for each 2D histogram is time-consuming, an improved discrete particle swarm optimization is utilized in the proposed method to speed up the optimization process. Extensive experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality and file size increment of the stego image.
Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

State-of-the-art deep learning-based Head Pose Estimation (HPE) techniques have reached spectacular performance on High-Resolution (HR) face images. However, they still fail to achieve expected performance on low-resolution images at large scales. This work presents an end-to-end HPE framework assisted by a Face Super-Resolution (FSR) algorithm. The proposed FSR model is specifically guided to enhance the HPE performance rather than considering FSR as an independent task. To this end, we utilized a Multi-Stage Generative Adversarial Network (MSGAN) which benefit from a pose-aware adversarial loss and head pose estimation feedback to generate super-resolved images that are properly aligned for HPE. Also, we propose a degradation strategy rather than simple down-sampling approach to mimic the diverse properties of real-world Low-Resolution (LR) images. We evaluate the performance of our proposed method on both synthetic and real-world LR datasets and show the superiority of our approach in both visual and HPE metrics on the AFLW2000, BIWI, and WiderFace Datasets.
Geochemistry and geostatistics for the assessment of trace elements contamination in soil and stream sediments in abandoned artisanal small-scale gold mining (Bétaré-Oya, Cameroon)

Soil and stream sediment contamination from abandoned artisanal mines is of prime importance due to the associated potential for heavy metal and metalloid contamination. Indeed, mine wastes are an evident source of soil and stream sediment contamination. The aim of the current study is to propose a conceptual approach to assess the contamination degree and the spatial distribution of trace elements in soils and stream sediments around mine sites. The abandoned artisanal gold mine of Bétaré-Oya in Cameroon was considered as a case study. In order to separate anthropogenically contaminated soils and stream sediments from the un contaminated ones, representative soil, stream sediment and mine wastes samples were collected and analyzed for the pH, Electrical Conductivity (EC), mineralogical paragenesis, major elements and trace elements. Very acidic pH values and high EC values ranging from 3.2 to 5.8 and from 44 to 315 μs cm−1, respectively were obtained for the analyzed samples. Based on the enrichment factor (EF) calculation, four elements of interest (Co, Cd, Cu and As) were selected for soils and seven elements of interest (Co, Cd, Cu, As, Cr, Pb and Zn) were selected for stream sediments. The Geochemical Background (GB) of these elements was determined with the normal Q-Q plot, the Geo-accumulation index (Igeo) was calculated and the geochemical maps for soils and stream sediments were elaborated using geostatistics in Geographic Information System (GIS) environment. The obtained results show that Bétaré-Oya soils and stream sediments are contaminated with metals and metalloid that exceed the established GB values for soil (Co ≈ 17.73 mg/kg, Cd ≈ 0.36 mg/kg, Cu ≈ 146.81 mg/kg and As≈48.82 mg/kg) and GB values for stream sediments (Co ≈ 30.90 mg/kg, Cd ≈ 0.24 mg/kg, Cu ≈ 173 mg/kg, As≈51.59 mg/kg, Cr ≈ 137 mg/kg, Pb ≈ 7.56 mg/kg, and Zn ≈ 39.48 mg/kg). The Igeo calculation revealed that soils and stream sediments were highly influenced by anthropogenic activity. The maximum values of the Igeo, especially for Cd ≈ 4.59, Pb ≈ 2.83, and Zn ≈ 2.97, highlighted the highly contaminated samples and therefore quantify accurately the contamination degree. Finally, geochemical maps of soils and stream sediments show clearly that mine wastes are the main source of trace elements contamination in the study area.
Compound fault diagnosis for photovoltaic arrays based on multi-label learning considering multiple faults coupling

For photovoltaic (PV) systems with complex operating environment and long operation time, there are multiple faults coupled simultaneously. However, most of the existing fault diagnosis methods for PV systems can only diagnose single faults. In this paper, a composite fault diagnosis schema based on multi-label classification for PV systems with multi-fault coupling is proposed. In order to effectively distinguish between various faults, new effective features extracted from the preprocessed current–voltage (I–V) curves are used. Then, for realizing the diagnosis of compound faults, two different types of diagnostic models are developed, that is, k-Nearest Neighbor for multi-label learning (ML-KNN) combined with Random Forest (ML-RFKNN), and simply residual network for multi-label learning (ML-SResNet). Besides, a variety of simulations and experiments are performed to obtain enough PV fault datasets, and verify the performance of the compound fault diagnosis models, indicating that they have more excellent results at different irradiance and temperature levels compared with the existing models. Moreover, preprocessing of I–V curves and extraction of features are also analyzed and compared with the other literatures.
Machine learning for all! Benchmarking automated, explainable, and coding-free platforms on civil and environmental engineering problems

One of the key challenges in fully embracing machine learning (ML) in civil and environmental engineering revolves around the need for coding (or programming) experience and for acquiring ML-related infrastructure. This barrier can be overcome through the availability of various platforms that provide automated and coding-free ML services, as well as ML infrastructure (in the form of a cloud service or software). Thus, engineers can now adopt, create, and apply ML to tackle various problems with ease and little coding. From this view, this paper presents a comparison of five automated and coding-free ML platforms: BigML, Dataiku, DataRobot, Exploratory, and RapidMiner on civil and environmental engineering problems. This comparison shows that although these platforms differ in their setup, services, and provided ML algorithms, all platforms performed adequately and comparably well to each other and to coding-based ML analyses. These findings denote the usefulness of coding-free ML platforms, which can lead to a brighter future for ML integration into our domain.
SC-YOLO: A Object Detection Model for Small Traffic Signs

Automatic traffic sign detection has great potential for intelligent vehicles. In recent years, traffic sign detection has made significant progress with the rise of deep learning. Detecting small traffic signs in real-world scenarios is still a challenging problem due to the complex and variable traffic environment. In this paper, a model with a small number of parameters is proposed to improve the accuracy of small traffic sign detection. Firstly, the cross-stage attention network module is proposed to enhance the feature extraction capability of the network. Secondly, a dense neck structure is proposed to make the detail information and semantic information fully fused. Finally, for the model’s loss function, SIOU with direction information is introduced to optimize the model’s training process. Tests on the challenging public datasets TT100K, CCTSDB2021, and VOC show that our approach achieves significant performance improvement with the minimum number of parameters compared to existing algorithms.
Bayesian Optimization based Score Fusion of Linguistic Approaches for Improving Legal Document Summarization

Due to the lengthy and complex nature of legal documents, automatic summarization has very high applicability in this domain. Recently, several researchers have proposed extractive summarization based ideas for legal documents. Extraction of important sentences via appropriate sentence scoring is the main idea behind extractive summarization, for which several linguistic approaches are proposed in the text summarization literature. However, the field of legal document summarization needs proper exploration in terms of the interaction among these approaches. This work proposes a Bayesian Optimization (BO) based Score Fusion approach that performs an efficient weighted combination of several statistical and semantic sentence scoring techniques. Experimental evaluation on two publicly available datasets and one privately accessed dataset suggests that the efficient combination of heterogeneous linguistic features for sentence scoring can improve the summarization performance for ROUGE-1, ROUGE-2, and ROUGE-L metrics. Moreover, the proposed BO-based Score Fusion approach can effectively summarize lengthy legal documents outperforming the current state-of-the-art techniques.
FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation

Emotional Support Conversation (ESConv) aims to reduce help-seekers’ emotional distress with a supportive strategy and response. It is essential for the supporter to select an appropriate strategy according to the feedback of the help-seeker (e.g., emotion change during dialog turns, etc.) in ESConv. However, previous methods mainly rely on the dialog history to select the strategy and ignore the help-seeker’s feedback, causing wrong and user-irrelevant strategy predictions. Meanwhile, these methods only model the context-to-strategy flow but pay less attention to the strategy-to-context flow involving the strategy-related context for generating strategy-constrained responses. In this paper, a Feedback-Aware Double COntrolling Network (FADO) is proposed to make a strategy schedule and generate supportive responses. The core modules in FADO include a dual-level feedback strategy selector and a double control reader, where the former leverages the turn-level and conversation-level feedback to encourage or penalize strategies, and the latter constructs a novel strategy-to-context flow to generate strategy-constrain responses. Besides, a strategy dictionary is designed to enrich the semantic information of the strategy and improve the quality of the strategy-constrained response. Experimental results on ESConv indicate that the proposed FADO achieves SOTA performance in terms of strategy selection and response generation. Our code is available at https://github.com/Thedatababbler/FADO.
TextGuise: Adaptive adversarial example attacks on text classification model

Adversarial examples greatly compromise the security of deep learning models. The key to improving the robustness of a natural language processing (NLP) model is to study attacks and defenses involving adversarial text. However, the current adversarial attack methods still face problems, such as the low success rates of attacks on some datasets, and the existing defense methods can already successfully defend against some attack methods. As a result, such attacks are unable to dig deeper into the flaws of NLP models to inform further defense improvements. Hence, it is necessary to design an adversarial attack method with a wider attack range and stronger performance. Aiming at the advantages and disadvantages of existing methods, this paper proposes a new adaptive black-box text adversarial example generation scheme, TextGuise. First, we design a keyword selection method in which word scores are calculated by combining context semantics to select the appropriate keywords to modify. Second, to maintain semantics, new keyword substitution rules are designed in combination with the characteristics of text and popular text expressions. Finally, the best modification strategy is adaptively selected through a querying model to reduce the magnitudes of disturbances. TextGuise can automatically select replacement keywords and replacement strategies that efficiently generate adversarial examples with good readability for various text classification tasks. Attack experiments conducted with TextGuise on 5 datasets yield high attack success rates that can surpass 80% when the perturbation ratio does not exceed 0.2. In addition, we present and discuss experiments focusing on defense, text similarity, query times, time consumption, etc., to test the attack performance of TextGuise. The results show that our attack method can achieve a good balance among various metrics.
Adaptive iterated stochastic metaheuristic to optimize holes drilling path in manufacturing industry: The Adaptive-Dhouib-Matrix-3 (A-DM3)

Tool path optimization approaches are often used to reduce production time and cost, energy consumption, etc. on Computer Numerical Control Machines. Thus, several artificial intelligence algorithms based on Traveling Salesman Problem (TSP) have been implemented to optimize tool trajectory length in various production sectors mainly the holes drilling process. In this field, this article exhibits first a related works survey detailing the main used algorithms. Then, it suggests a novel approach for optimizing non-productive tool paths for drilling a hole series. The proposed approach entitled Adaptive-Dhouib-Matrix-3 (A-DM3) is based on combining the iterated stochastic Dhouib-Matrix-3 (DM3) with a tabu memory inspired by Tabu Search (TS) metaheuristic. To confirm its ability and stability to find the shortest drilling tool path, A-DM3 method is tested on six practical case studies of a rectangular matrix of holes. Further, it is compared to some commonly used algorithms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and some of its derivates, modified Shuffled Frog Leaping Algorithm (mSFLA), Cuckoo Search (CS), and hybrid Cuckoo Search Genetic Algorithm (CS-GA). Computational results proved the superiority of the proposed A-DM3 compared to these well-known metaheuristics in literature, particularly in a medium and large number of holes. Hence, A-DM3 provided a new shortest path length record with an improvement exceeding 100% in some cases compared to competing algorithms.
Quadratic pseudospectrum for identifying localized states

We examine the utility of the quadratic pseudospectrum in photonics and condensed matter. Specifically, the quadratic pseudospectrum represents a method for approaching systems with incompatible observables, as it both minimizes the "eigen-error" in the joint approximate spectrum of the incompatible observables and does not increase the system's computational complexity. Moreover, we derive an important estimate relating the Clifford and quadratic pseudospectra. Finally, we prove that the quadratic pseudospectrum is local, and derive the bounds on the errors that are incurred by truncating the system in the vicinity of where the pseudospectrum is being calculated.