Reviewerly/exHarmony huggingface dataset:
Web Services Clustering via Exploring Unified Content and Structural Semantic Representation

Clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.
Explaining Intrusion Detection-Based Convolutional Neural Networks Using Shapley Additive Explanations (SHAP)

Artificial intelligence (AI) and machine learning (ML) models have become essential tools used in many critical systems to make significant decisions; the decisions taken by these models need to be trusted and explained on many occasions. On the other hand, the performance of different ML and AI models varies with the same used dataset. Sometimes, developers have tried to use multiple models before deciding which model should be used without understanding the reasons behind this variance in performance. Explainable artificial intelligence (XAI) models have presented an explanation for the models’ performance based on highlighting the features that the model considered necessary while making the decision. This work presents an analytical approach to studying the density functions for intrusion detection dataset features. The study explains how and why these features are essential during the XAI process. We aim, in this study, to explain XAI behavior to add an extra layer of explainability. The density function analysis presented in this paper adds a deeper understanding of the importance of features in different AI models. Specifically, we present a method to explain the results of SHAP (Shapley additive explanations) for different machine learning models based on the feature data’s KDE (kernel density estimation) plots. We also survey the specifications of dataset features that can perform better for convolutional neural networks (CNN) based models.
Breast cancer detection using machine learning approaches: a comparative study

&lt;p&gt;&lt;span&gt;As the cause of the breast cancer disease has not yet clearly identified and a method to prevent its occurrence has not yet been developed, its early detection has a significant role in enhancing survival rate. In fact, artificial intelligent approaches have been playing an important role to enhance the diagnosis process of breast cancer. This work has selected eight classification models that are mostly used to predict breast cancer to be under investigation. These classifiers include single and ensemble classifiers. A trusted dataset has been enhanced by applying five different feature selection methods to pick up only weighted features and to neglect others. Accordingly, a dataset of only 17 features has been developed. Based on our experimental work, three classifiers, multi-layer perceptron (MLP), support vector machine (SVM) and stack are competing with each other by attaining high classification accuracy compared to others. However, SVM is ranked on the top by obtaining an accuracy of 97.7% with classification errors of 0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is noteworthy to mention that SVM is the best classifier and it outperforms even the stack classier.&lt;/span&gt;&lt;/p&gt;
Exponential time-scaling of estimation precision by reaching a quantum critical point

Quantum metrology refers to the use of quantum resources in parameter-estimation protocols, aiming at enhancing its precision. The quantum Fisher information is a key quantity in this context, setting the ultimate achievable precision with respect to available resources, such as the total time of the protocol. In this work, we report a scheme where the quantum Fisher information features an exponential scaling with the protocol duration. This is achieved by performing a periodic modulation of the coupling of a quantum critical system close to the its critical value. This modulation leads to an exponential growth of the excitation number in time. Relying on the precision bound derived by Garbe et al. [L. Garbe et al., Quantum Sci. Technol. 7, 035010 (2022)], we show that the quantum Fisher information inherits this exponential time scaling, which is corroborated by numerical simulations. Finally, the impact of dissipation and finite-size effects are analyzed, showing that the exponential time scaling is robust to dissipation, although its exponent decreases for larger values of the dissipation rate. Therefore, our work illustrates the novel metrological opportunities that quantum critical systems can offer.
Comparison of machine learning models for breast cancer diagnosis

&lt;p&gt;&lt;span lang="EN-US"&gt;Breast cancer is the most common cause of death among women worldwide. Breast cancer can be detected early, and the death rate can be reduced. Machine learning techniques are a hot topic for study and have proved influential in cancer prediction and early diagnosis. This study's objective is to predict and diagnose breast cancer using machine learning models and evaluate the most effective based on six criteria: specificity, sensitivity, precision, accuracy, F1-score and receiver operating characteristic curve. All work is done in the anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries, and pandas and matplotlib. This study used the Wisconsin diagnostic breast cancer dataset to test ten machine learning algorithms: decision tree, linear discriminant analysis, forests of randomized trees, gradient boosting, passive aggressive, logistic regression, naïve Bayes, nearest centroid, support vector machine, and perceptron. After collecting the findings, we performed a performance evaluation and compared these various classification techniques. Gradient boosting model outperformed all other algorithms, scoring 96.77% on the F1-score.&lt;/span&gt;&lt;/p&gt;
Comparative analysis of permissioned blockchain frameworks for industrial applications

Blockchain is a technology that creates trust among non-trusting parties without relying on any intermediaries. Consequently, it has attracted the interest of companies operating in a multitude of sectors. However, due to the number of different blockchain solutions that have emerged in the last few years and their rapid changes, it is challenging for such companies to orient their technological decisions. This paper presents a comparative analysis of the key dimensions—namely, governance, maturity, support, latency, privacy, interoperability, flexibility, efficiency, resiliency, and scalability—of some of the most-used permissioned blockchain platforms. Moreover, we present the results of a performance evaluation considering the following frameworks: Hyperledger Fabric 2.2, Hyperledger Sawtooth 1.2, and ConsenSys Quorum 21.1 (with both the GoQuorum client and the Hyperledger Besu client). The platforms were tested under similar conditions, and official releases were used, such that our findings provide a reference for companies establishing their technological orientation.
AUGER: automatically generating review comments with pre-training models

Code review is one of the best practices as a powerful safeguard for software quality. In practice, senior or highly skilled reviewers inspect source code and provide constructive comments, consider- ing what authors may ignore, for example, some special cases. The collaborative validation between contributors results in code being highly qualified and less chance of bugs. However, since personal knowledge is limited and varies, the efficiency and effectiveness of code review practice are worthy of further improvement. In fact, it still takes a colossal and time-consuming effort to deliver useful review comments. This paper explores a synergy of multiple practical review comments to enhance code review and proposes AUGER (AUtomatically GEnerating Review comments): a review comments generator with pre-training models. We first collect empirical review data from 11 notable Java projects and construct a dataset of 10,882 code changes. By leveraging Text-to-Text Transfer Transformer (T5) models, the framework synthesizes valuable knowledge in the training stage and effectively outperforms baselines by 37.38% in ROUGE-L. 29% of our automatic review comments are considered useful according to prior studies. The inference generates just in 20 seconds and is also open to training further. Moreover, the performance also gets improved when thoroughly analyzed in case study.
Image Classification Using Multiple Convolutional Neural Networks on the Fashion-MNIST Dataset

As the elderly population grows, there is a need for caregivers, which may become unsustainable for society. In this situation, the demand for automated help increases. One of the solutions is service robotics, in which robots have automation and show significant promise in working with people. In particular, household settings and aged people’s homes will need these robots to perform daily activities. Clothing manipulation is a daily activity and represents a challenging area for a robot. The detection and classification are key points for the manipulation of clothes. For this reason, in this paper, we proposed to study fashion image classification with four different neural network models to improve apparel image classification accuracy on the Fashion-MNIST dataset. The network models are tested with the highest accuracy with a Fashion-Product dataset and a customized dataset. The results show that one of our models, the Multiple Convolutional Neural Network including 15 convolutional layers (MCNN15), boosted the state of art accuracy, and it obtained a classification accuracy of 94.04% on the Fashion-MNIST dataset with respect to the literature. Moreover, MCNN15, with the Fashion-Product dataset and the household dataset, obtained 60% and 40% accuracy, respectively.
FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction

The increasing availability of video recordings made by multiple cameras has offered new means for mitigating occlusion and depth ambiguities in pose and motion reconstruction methods. Yet, multi-view algorithms strongly depend on camera parameters, particularly on relative transformations between the cameras. Such a dependency becomes a hurdle once shifting to dynamic capture in uncontrolled settings. We introduce FLEX (Free muLti-view rEconstruXion), an end-to-end extrinsic parameter-free multi-view model. FLEX is extrinsic parameter-free (dubbed ep-free) in the sense that it does not require extrinsic camera parameters. Our key idea is that the 3D angles between skeletal parts, as well as bone lengths, are invariant to the camera position. Hence, learning 3D rotations and bone lengths rather than locations allows for predicting common values for all camera views. Our network takes multiple video streams, learns fused deep features through a novel multi-view fusion layer, and reconstructs a single consistent skeleton with temporally coherent joint rotations. We demonstrate quantitative and qualitative results on three public data sets, and on multi-person synthetic video streams captured by dynamic cameras. We compare our model to state-of-the-art methods that are not ep-free and show that in the absence of camera parameters, we outperform them by a large margin while obtaining comparable results when camera parameters are available. Code, trained models, and other materials are available on https://briang13.github.io/FLEX .
SCENE: Reasoning About Traffic Scenes Using Heterogeneous Graph Neural Networks

Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains.
Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records

Background: Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers. Methods: This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset. Results: The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively). Conclusions: A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications.
Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.
Counterdiabatic Optimized Local Driving

A hybrid approach to achieving coherent control, dubbed COLD, promises substantial improvement when applied to annealing protocols, state preparation schemes, entanglement generation, and population transfer on a lattice.
Quantum phase detection generalization from marginal quantum neural network models

Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising Hamiltonian, which possesses a ferromagnetic, paramagnetic, and antiphase, showing that the whole phase diagram can be reproduced.
Go Closer to See Better: Camouflaged Object Detection via Object Area Amplification and Figure-Ground Conversion

Camouflaged Object Detection (COD) aims to detect objects well hidden in the environment. The main challenges of COD come from the high degree of texture and color overlapping between the objects and their surroundings. Inspired by that humans tend to go closer to the object and magnify it to recognize ambiguous objects more clearly, we propose a novel three-stage architecture called Search-Amplify-Recognize and design a network SARNet to address the challenges. Specifically, In the Search part, we utilize an attention-based backbone to locate the object. In the Amplify part, to obtain rich searched features and fine segmentation, we design Object Area Amplification modules (OAA) to perform cross-level and adjacent-level feature fusion and amplifying operations on feature maps. Besides, the OAA can be regarded as a simple and effective plug-in module to integrate and amplify the feature maps. The main components of the Recognize part are the Figure-Ground Conversion modules (FGC). The FGC modules alternately pay attention to the foreground and background to precisely separate the highly similar foreground and background. Extensive experiments on benchmark datasets show that our model outperforms other SOTA methods not only on COD tasks but also in COD downstream tasks, such as polyp segmentation and video camouflaged object detection. Source codes will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Haozhe-Xing/SARNet</uri> .
The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions.
Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min–Max Neural Network for Cervical Cancer Diagnosis

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min–max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min–max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min–max neural network classifiers mentioned in the literature.