Reviewerly/exHarmony huggingface dataset:
A hybrid Fifth Generation based approaches on extracting and analyzing customer requirement through online mode in healthcare industry

The proposed hybrid fifth Generation-based approaches are employed to analyze and investigate the feedback of 37,855 patients from 65 hospitals worldwide and gathered through various online modes. The collected data was analyzed with various cloud-computing technologies such as Artificial Intelligence (AI) based algorithms like natural language pre-processing (NLP), text mining (TM), and sentiment analysis (SA). This work discusses the new opportunities in the healthcare sector and the capabilities that 5G influences can bring to the healthcare industry. According to a recent investigation, the overall assessment ranking agrees with the personal view (sentiment) rating analysis for the title and full element of the web-based feedback obtained from the patients of different hospitals worldwide through an online survey. There are five main categories in the healthcare sector for which patients give priority and expect good services. They are treatment, physical facilities, dependability, timely committed service, and responsiveness. This research makes theoretical and practical contributions to the interdisciplinary areas comprising computer science, data management, and healthcare services by utilizing recent analytic techniques to attain optimized results.
A Deep Learning Approach for Detecting Face Mask Using an Improved Yolo-V2 With Squeezenet

Face mask detection has become a critical issue in security and Covid-19 prevention. In this regard, the YOLO V2 network has demonstrated outstanding performance. The YOLO V2 on the other hand, employed Darknet as a feature extractor. However, as compared to Darknet, SqueezeNet allows us to reduce model size while reaching or surpassing the highest accuracy score. SqueezeNet is designed to have lower parameters that can be more readily stored in computer memory and transferred across a computer network. As a result, in this study, we recommended enhancing the YOLO network by replacing Darknet with Squeezenet. Compared to other existing face mask recognition systems that use the standard YOLO V2 algorithm, this improves overall performance in terms of model size and accuracy. As a result, this study proposed a rapid face mask detection model by improving the existing YOLO V2 network architecture by employing logistic classifiers and SqueezeNet for multi-label classification using FMD and MMD face-masked dataset. The model was evaluated on MATLAB 2021 against state-of-the-art approaches. The proposed model outperforms previous algorithms by attaining a good accuracy value of 81% and a recall value of 99.99%.
Categorical Diversity-Aware Inner Product Search

The problem of maximum inner product search (MIPS) is one of the most important components in machine learning systems.However, this problem does not care about diversity, although result diversification can improve user satisfaction.This paper hence considers a new problem, namely the categorical diversity-aware IPS problem, in which users can select preferable categories.Exactly solving this problem needs O(n) time, where n is the number of vectors, and is not efficient for large n.We hence propose an approximation algorithm that has a probabilistic success guarantee and runs in sub-linear time to n.We conduct extensive experiments on real datasets, and the results demonstrate the superior performance of our algorithm to that of a baseline using an existing MIPS technique.
Soft Tissue Monitoring of the Surgical Field: Detection and Tracking of Breast Surface Deformations

Deformable object tracking is common in the computer vision field, with applications typically focusing on nonrigid shape detection and usually not requiring specific three-dimensional point localization. In surgical guidance however, accurate navigation is intrinsically linked to precise correspondence of tissue structure. This work presents a contactless, automated fiducial acquisition method using stereo video of the operating field to provide reliable three-dimensional fiducial localization for an image guidance framework in breast conserving surgery.On n = 8 breasts from healthy volunteers, the breast surface was measured throughout the full range of arm motion in a supine mock-surgical position. Using hand-drawn inked fiducials, adaptive thresholding, and KAZE feature matching, precise three-dimensional fiducial locations were detected and tracked through tool interference, partial and complete marker occlusions, significant displacements and nonrigid shape distortions.Compared to digitization with a conventional optically tracked stylus, fiducials were automatically localized with 1.6 ± 0.5 mm accuracy and the two measurement methods did not significantly differ. The algorithm provided an average false discovery rate <0.1% with all cases' rates below 0.2%. On average, 85.6 ± 5.9% of visible fiducials were automatically detected and tracked, and 99.1 ± 1.1% of frames provided only true positive fiducial measurements, which indicates the algorithm achieves a data stream that can be used for reliable on-line registration.Tracking is robust to occlusions, displacements, and most shape distortions.This work-flow friendly data collection method provides highly accurate and precise three-dimensional surface data to drive an image guidance system for breast conserving surgery.
SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework

Aiming at balancing data privacy and availability, Google introduces the concept of federated learning, which can construct global machine learning models over multiple participants while keeping their raw data localized. However, the exchanged parameters in traditional federated learning may still reveal the data information. Meanwhile, the training data are usually partitioned vertically in real-world scenes, which causes difficulties in model construction. To tackle these problems, in this paper, we propose an efficient and privacy-preserving vertical federated tree boosting framework, namely SGBoost, where multiple participants can collaboratively perform model training and query without staying online all the time. Specifically, we first design secure bucket sharing and best split finding algorithms, with which the global tree model can be constructed over vertically partitioned data; meanwhile, the privacy of training data can be well guaranteed. Then, we design an oblivious query algorithm to utilize the trained model without leaking any query data or results. Moreover, SGBoost does not require multi-round interactions between participants, significantly improving the system efficiency. Detailed security analysis shows that SGBoost can well guarantee the privacy of raw data, weights, buckets, and split information. Extensive experiments demonstrate that SGBoost can achieve high accuracy comparable to centralized training and efficient performance.
RawFormer: An Efficient Vision Transformer for Low-Light RAW Image Enhancement

Low-light image enhancement plays a central role in various downstream computer vision tasks. Vision Transformers (ViTs) have recently been adapted for low-level image processing and have achieved a promising performance. However, ViTs process images in a window- or patch-based manner, compromising their computational efficiency and long-range dependency. Additionally, existing ViTs process RGB images instead of RAW data from sensors, which is sub-optimal when it comes to utilizing the rich information from RAW data. We propose a fully endto-end Conv-Transformer-based model, RawFormer, to directly utilize RAW data for low-light image enhancement. RawFormer has a structure similar to that of U-Net, but it is integrated with a thoughtfully designed Conv-Transformer Fusing (CTF) block. The CTF block combines local attention and transposed selfattention mechanisms in one module and reduces the computational overhead by adopting a transposed self-attention operation. Experiments demonstrate that RawFormer outperforms state-ofthe-art models by a significant margin on low-light RAW image enhancement tasks.
Tangible Explainable AI - an Initial Conceptual Framework

Artificial Intelligence (AI) solutions are becoming prevalent in almost all aspects of human life. However, their acceptance may be limited by a lack of transparency of how the AI works. Explainable AI (XAI) aims to provide the users of AI systems with an understanding of why decisions are made, increasing trust in the system. To date, research into XAI has focused on the use of graphical user interfaces, presenting numerical, textual or graphical explanations. However, AI is increasingly being used in systems that include physical devices, and hence the need for explainability in physical or tangible user interfaces (TUI) is also increasing. We present an initial conceptual framework for tangible explainable AI (TangXAI), which identifies the potential approaches of communicating XAI through physical artifacts, using the concepts of data physicalization and tangible interaction. The framework provides a basis into which ongoing research of tangible explainable AI can be mapped and related research gaps identified.
The Impact of Data Mining Techniques on Information Quality: Insurance Companies as Case

This study aimed to show the impact of data mining techniques on achieving the quality of the information in insurance companies listed on the Amman Stock Exchange to achieve these goals the study authors used the descriptive and analytical approach based on the questionnaire distributed to the members of the study community by (130) employees in the areas of information technology, accounting and insurance risk management processes and distributed to (25) insurance companies representing the entire study community. The study found that the ranking of the areas seen when investigating the effect of data mining methods on achieving the quality of the data in insurance companies, was according to their importance and level of importance in them among the members of the study community, as follows: chances to strengthen knowledge systems with the growth of the environment of study systems and data retrieval This variable obtained the highest average and the arithmetic average of this variable was equal to (4.17), followed by the variable of discovery, distribution and participation of knowledge systems” and the arithmetic average of this variable was equal to (4.01). In addition, there is a statistically significant effect of data mining methods (enhancing knowledge systems with the growth of the environment of study and data retrieval systems, discovery, distribution and sharing of knowledge systems) on achieving the quality of the data in insurance companies listed on the Amman Stock Exchange. One of the main recommendations of the study is the need for continuous development and improvement of the use of data mining techniques, necessary to improve knowledge systems with the development of the environment of research and data retrieval systems, to achieve the quality of information, and in a way that helps achieve the goals sought by these techniques to increase the efficiency and effectiveness of the production of appropriate information, in addition to the need to employ data mining techniques to facilitate the innovation of new ideas in an organized and appropriate framework to produce information to increase the predictive capabilities of policymakers at all levels.
A transformer-based IDE plugin for vulnerability detection

Automatic vulnerability detection is of paramount importance to promote the security of an application and should be exercised at the earliest stages within the software development life cycle (SDLC) to reduce the risk of exposure. Despite the advancements with state-of-the-art deep learning techniques in software vulnerability detection, the development environments are not yet leveraging their performance. In this work, we integrate the Transformers architecture, one of the main highlights of advances in deep learning for Natural Language Processing, within a developer-friendly tool for code security. We introduce VDet for Java, a transformer-based VS Code extension that enables one to discover vulnerabilities in Java files. Our preliminary model evaluation presents an accuracy of 98.9% for multi-label classification and can detect up to 21 vulnerability types. The demonstration of our tool can be found at https://youtu.be/OjiUBQ6TdqE, and source code and datasets are available at https://github.com/TQRG/VDET-for-Java.
Biogeochemical prospecting for gold at the Yellowknife City Gold Project, Northwest Territories, Canada: Part 2 - Robust statistical analysis

The Yellowknife City Gold Project (YCGP) is a promising Au district located in proximity to the city of Yellowknife, Northwest Territories, Canada. Positive results of a short biogeochemical survey in 2015 over the Crestaurum and Barney shears (Part 1) resulted in a broad survey over the early and advanced Au targets across the YCGP (Part 2). This survey uses a systematic two-phase statistical approach, including process discovery and process validation, to evaluate the multi-element biogeochemical dataset and identify the geochemical process controlling elemental occurrence and distribution in black spruce needles. To achieve these objectives, 2788 black spruce needle samples were collected across the Northbelt and Eastbelt of the YCGP and analyzed by ICP-MS using a multi-element determinations package for unashed vegetation. Following that, a biogeochemical dataset including Au, As, Ag, As, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Ni, P, Pb, S, Sb, Se, Tl, and Zn were prepared and analyzed using univariate and multivariate statistical analyses, including principal component analysis (PCA) and inverse distance weighted (IDW) interpolation method. The univariate statistical analysis showed that the average Au value in the black spruce needle is higher than the background level in all targets. Contiguous anomalous Au values were identified in needle samples collected proximal to the Ptarmigan & Tom mines (∼10 ppb) and Ryan Lake target (3.8 ppb). According to the robust RQ mode-PCA, PC1 and PC2 control the distribution pattern of elements in black spruce needles. PC1 includes two sets of elements indicating geochemical/mineralization factor (Au, As, Sb, Fe, Pb, and Mo) and physiological factor (Cu, Ca, Zn, K, S, P, and Mg). On the other hand, PC2 differentiates these two factors with more emphasis on the influence of the geochemical/mineralization factor. The IDW interpolation method indicates zones of Au enrichments at the YCGP are associated with different sets of pathfinder elements based on the bedrock composition and mineralization style. According to the IDW, elevated Au values associated with shear zones hosted within mafic/ultramafic bodies are accompanied by As, Sb, Pb, and Cu, while those located proximal to late felsic bodies are accompanied by Ag, As, Se, Hg, and Tl. Zones of Au enrichment located along the faulted contacts between the felsic-intermediate metavolcanics and sulphide metasediments are accompanied by Bi, Se, Hg, and Zn. The results of this study attest to the robustness of multivariate statistical analysis in detecting zones of Au enrichment using biogeochemical exploration.
Counterfactual explanation generation with minimal feature boundary

The complex and opaque decision-making process of machine learning models restrains the interpretability of predictions and makes them cannot mine results outside of learning experiences. The causality between features and the target variable can be traced by injecting counterfactual explanations into the prediction model and generating counterfactual instances using adjusted features to reverse the prediction results. Existing algorithms, such as Diverse Counterfactual Explanations (DiCE) and Counterfactual Explanations Guided by Prototypes (Proto), can generate multiple/single counterfactual(s) for a data point by global optimization in the range of all-out features to gain on a local decision range. However, these methods cannot clearly identify which features are the key causes. Moreover, a Random Forest Optimal Counterfactual Set Extractor (RF-OCSE) extracts counterfactual sets from a random forest and needs to manipulate all the internal nodes of the tree, restricting it to only tree ensemble models. To address the above shortcomings, we proposed a Counterfactual Explanation Generation method with the Minimal Feature Boundary (MFB), named (CEGMFB). The proposed CEGMFB algorithm consists of two stages: 1) mining the MFB, which can reverse the prediction results to restrain the generation range of counterfactual instances, and 2) constructing a counterfactual generative method for generating counterfactual instances within the MFB to realize the minimum reversing cost. To evaluate its performance, we compared the proposed CEGMFB algorithm with six baseline algorithms on 16 datasets and conducted a case study in a real scenario. The results indicate that the proposed CEGMFB algorithm outperforms the compared algorithms.
Feature Masking on Non-Overlapping Regions for Detecting Dense Cells in Blood Smear Image

Detecting cells in blood smear images is of great significance for automatic diagnosis of blood diseases. However, this task is rather challenging, mainly because there are dense cells that are often overlapping, making some of the occluded boundary parts invisible. In this paper, we propose a generic and effective detection framework that exploits non-overlapping regions (NOR) for providing discriminative and confident information to compensate the intensity deficiency. In particular, we propose a feature masking (FM) to exploit the NOR mask generated from the original annotation information, which can guide the network to extract NOR features as supplementary information. Furthermore, we exploit NOR features to directly predict the NOR bounding boxes (NOR BBoxes). NOR BBoxes are combined with the original BBoxes for generating one-to-one corresponding BBox-pairs that are used for further improving the detection performance. Different from the non-maximum suppression (NMS), our proposed non-overlapping regions NMS (NOR-NMS) uses the NOR BBoxes in the BBox-pairs to calculate intersection over union (IoU) for suppressing redundant BBoxes, and consequently retains the corresponding original BBoxes, circumventing the dilemma of NMS. We conducted extensive experiments on two publicly available datasets, with positive results demonstrating the effectiveness of the proposed method against existing methods.
Heterogeneous Mission Planning for Multiple UAV Formations via Metaheuristic Algorithms

Unmanned aerial vehicle (UAV) formation has been widely applied in various aspects, both for military and civil purposes. In this article, multiple UAV formations are deployed to perform different types of tasks, and the heterogeneous mission planning problem is studied. The problem is divided into two subproblems, i.e., task assignment and path planning. First, the task assignment is formulated into a combinatorial optimization problem. Different from the existing articles, appropriate UAVs need to be selected to build the formation and perform specific types of tasks. To obtain the task assignment scheme, a hybrid genetic and simulated annealing (HGSA) algorithm is proposed. In this algorithm, a feasible solution is guaranteed by the designed task-based strategy, and the diversity of solutions is ensured by the group-based solution remaining approach. In the path planning problem, an obstacle avoidance-enabled consensus (OAEC) algorithm is developed to form the UAV formation, which extends the application of the standard consensus algorithm. A multistep particle swarm optimization (MPSO) algorithm is combined with the consensus algorithm to generate a path for the UAV formation to reach each task point. The conflict among multiple UAV formations is resolved by adjusting their departure time. Furthermore, the task assignment scheme is modified by utilizing real path information on the flying distance between the UAVs and the task points. The simulation results demonstrate that the HGSA algorithm can build the UAV formations and assign the tasks to them while satisfying all the complicated constraints. The advantages of the OAEC algorithm and the MPSO algorithm in path planning are verified by comparison with relevant algorithms.
Approaches to Overpower Proof-of-Work Blockchains Despite Minority

Blockchain (BC) technology has been established in 2009 by Nakamoto, using the Proof-of-Work (PoW) to reach consensus in public permissionless networks (Praveen et al., 2020). Since then, several consensus algorithms were proposed to provide equal (or higher) levels of security, democracy, and scalability, yet with lower levels of energy consumption. However, Nakamoto’s model (a.k.a. Bitcoin) still dominates as the most trusted model in the described sittings since alternative solutions might provide lower energy consumption and higher scalability, but they would always require deviating the system towards unrecommended centralization or lower levels of security. That is, Nakamoto’s model claims to tolerate (up to) < 50% of the network being controlled by a dishonest party (minority), which cannot be realized in alternative solutions without sacrificing the full decentralization property. In this paper, we investigate this tolerance claim, and we review several approaches that can be used to undermine/overpower PoW-based BCs, even with minority. We discuss those BCs taking Bitcoin as a representative application, where needed. However, the presented approaches can be applied in any PoW-based BC. Specifically, we technically discuss how a dishonest miner in minority, can take over the network using improved Brute-forcing, AI-assisted mining, Quantum Computing, Sharding, Partial Pre-imaging, Selfish mining, among other approaches. Our review serves as a needed collective technical reference (concluding more than 100 references), for practitioners and researchers, who either seek a reliable security implementation of PoW-based BC applications, or seek a comparison of PoW-based, against other BCs, in terms of adversary tolerance.
Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

Malaria, a life-threatening disease worldwide, can be diagnosed using antigen tests and microscopy tests. However, both of them are erroneous and time-consuming. Therefore, a trustworthy and fast early malaria prognosis infrastructure is required. In this age of machine learning (ML), there are several ML-based methods to do the task. This paper proposes an unorthodox method for malaria prognosis based on an extreme learning machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden layer (DELM) have been used as classifiers. A CNN model has been used as a feature extractor and also as a classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified dataset where ambiguous samples have been removed. The parasite inflator acts as the shape increaser of the small, darker malaria parasites in the RBC images in order to detect malaria easily. CNN-DELM has achieved a sanguine result on every performance standard compared to CNN and CNN-ELM. The proposed CNN-DELM method has achieved 97.79% and 99.66% accuracy for the original version and the modified version, respectively. Hence, the proposed CNN-DELM model has also produced either comparable or better results when compared to other methods proposed in the literature, showing its robustness in detecting malaria.