Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Hybridized Genetic Algorithm in Predictive Models of Breast Cancer Tumors

Advancement in computer-aided tools towards accurate breast cancer early prediction models has proven to be advantageous, which in turn helps to reduce the mortality rate associated with this cancer. From literature, random forest predictor has been observed to have high accuracy in comparison to other machine learning regressors, also genetic algorithm has been observed to be a good feature selection method in data pre-processing. In a bid to improve the accuracy of breast cancer predictive models, several studies have developed hybridized genetic algorithm models for feature selection, however, the order of hybridization may not have been taken into considered, as this can have an impact on the hybridized model performance. Therefore, this paper aims to propose several high-performing predictive models using hybridized genetic algorithm, based on other learning models, while taking into consideration the placement order of the feature selection algorithms in the hybridized models. The Wisconsin Breast Cancer dataset was used as the test bench, while filter, wrapper and embedded feature selection algorithms were used in the proposed hybridized models. The performances of proposed hybridized models were compared with those of the individual learning models, considered in this work. These models include Fisher_Score, Mutual Information Gain, Correlation Chi-square test, Coefficient, Variance, Genetic Algorithm, Lasso and Linear Regressors with L1 regularization, Ridge Regressor with L2 regularization, Tree-based methods. From the performance evaluation results, the proposed hybridized Genetic Algorithm with Fisher_Score (GA + Fisher_Score) model showed promising results, as it had an accuracy score of 99.12%, thereby out-performing other proposed hybridized genetic algorithm models considered.
A Simple Optimization Strategy via Contrastive Loss for Recognizing Human Activity Using Wearable Sensors

The key toward sensor-based human activity recognition (SHAR) recently lies in how to learn more contextual representation from complex activities. In this work, we present a simple-yet-effective optimization strategy based on mutual information maximization to directly optimize the distance or similarity between samples and thus control the shape of the decision boundary for recognizing complex activities. Specifically, our optimization introduces contrastive loss into the HAR task to generalize the performance. The contrastive loss can maximize the mutual information between similar samples, where the positive selection is significant for learning decision boundaries. To this end, we also propose a positive selection strategy to avoid the inconsistency of the amplified in time and space when augmentation, which leverages the different representations of the same samples as positives and the corresponding contradiction pairs as negatives. Extensive experiments conducted on three benchmark SHAR datasets, i.e., PAMAP2, USC-HAD, and UNIMIB, demonstrate the superiority of our optimization strategy. It is worth mentioning that by applying our strategy, excellent results can be achieved just with a shallow ResNet encoder.
A Secure and Disambiguating Approach for Generative Linguistic Steganography

Segmentation ambiguity in generative linguistic steganography could induce decoding errors. One existing dis-ambiguating way is removing the tokens whose mapping words are the prefixes of others in each candidate pool. However, it neglects probability distribution of candidates and degrades imperceptibility. To enhance steganographic security, meanwhile addressing segmentation ambiguity, we propose a secure and disambiguating approach for linguistic steganography. In this letter, we focus on two questions: (1) Which candidate pools should be modified? (2) Which tokens should be retained? Firstly, we propose a secure token-selection principle that the sum of selected tokens' probabilities is positively correlated to statisti-cal imperceptibility. To meet both disambiguation and optimal security, we present a lightweight disambiguating approach that is finding out a maximum weight independent set (MWIS) in one candidate graph only when candidate-level ambiguity occurs. Experiments show that our approach outperforms the existing method in various security metrics, improving 25.7% statistical imperceptibility and 11.2% anti-steganalysis capacity averagely.
DSF-Net: Dual-Stream Fused Network for Video Frame Interpolation

Video frame interpolation aims to improve the video quality by increasing the frame rate. Existing methods adopt the cascaded architecture. They first estimate intermediate flow maps and then refine the synthesized intermediate frames with contextual features separately. However, the separated flow estimation and refined module ignore the mutual facilitation of them in frame interpolation. Following this issue, we propose a Dual-Stream Fused Network (DSF-Net) to joint flow estimation and refinement module for frame interpolation. Specifically, it first extracts the contextual features from input frames by a contextual feature extractor module, and then jointly refines the intermediate flow maps with the extracted features through a coarse-to-fine frame synthesis module. DSF-Net allows the intermediate flow and the contextual features to benefit each other while generating sharper moving objects and capturing better textual details. Experimental results demonstrate that DSF-Net performs consistently better than existing SOTA methods.
Multi-Scale Density-Aware Network for Single Image Dehazing

Dehazing based on deep learning has attracted a lot of attention recently. Most dehazing networks seldom consider two critical features of real outdoor-scene haze, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , depth and haze density, resulting in degraded performance on real hazy images compared with synthetic hazy images. Moreover, the uncertainty problem is crucial in the image restoration field, but it is often ignored. In this letter, we propose a novel multi-scale density-aware network (MSDAN) for single image dehazing, where a key dual feedback module (DFB) is proposed and embedded in the decoder part of MSDAN. Furthermore, the DFB includes a feedforward mechanism and two feedback mechanisms: feature feedback (FF) and transmission feedback (TF). Specifically, the feedforward mechanism predicts a low-scale transmission map ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -map), while FF and TF aim to enhance confident features to reduce model uncertainty in the training process and correct features by introducing depth and density information. In addition, two novel modules: confident feature attention module (CFA) and transmission adjustment module (TADJ) are proposed as cores for confident features estimation of FF and TF, respectively. Extensive quantitative and qualitative experiments are conducted on several public datasets, which demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms.
Preliminaries on Advanced Calculus

In this book we use the approach of the intrinsic differential calculus, which frequently saves time, space and gives clarity to the presentation. Therefore, we recall some facts from Advanced Calculus. For more details the reader can consult any text on Advanced Calculus, for instance the books by Goffman, Loomis-Sternberg or Rudin.
Monetary policy in time of cryptocurrencies

First generation cryptocurrencies have indicated practical options for rethinking our monetary system and our way to conceive monetary policies. Central Banks have for many years relinquished the task of affecting the behavior of cryptocurrency holders. Instead, it has been state regulation that has sought to discourage the increase in cryptocurrency demand through specific normative interventions. Since 2018, regulators have begun to pay close attention to the phenomenon, taking more seriously the potential threats to financial stability that could arise from an uncontrolled proliferation of cryptocurrencies. Monetary authorities are considering the possibility of creating digital currencies issued directly by Central Banks. This could change the scope of monetary policy as we know it today.
Leveraging Summary Guidance on Medical Report Summarization

This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the created datasets with pre-trained encoder-decoder language models, including BERT2BERT, BERTShare, RoBERTaShare, Pegasus, ProphetNet, T5-large, BART and GSUM. Further, based on the BART model, we leverage the sampled summaries from the training set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method.
LSD: Adversarial Examples Detection Based on Label Sequences Discrepancy

Deep neural network (DNN) models have been widely used in many tasks due to their superior performance. However, DNN models are usually vulnerable to adversarial example attacks, which limits their applications in many safety-critic scenarios. How to effectively detect adversarial examples to enhance the robustness of DNN models has attracted much attention in recent years. Most adversarial example detection methods require modifying or retraining the model, which is impractical and reduces the classification accuracy of normal examples. In this paper, we propose an adversarial example detection approach that does not require modification of the DNN models and meanwhile retains the classification accuracy of normal examples. The key observation is that when we transform the input example with some operations (e.g., masking a pixel with a reference value), feed the transformed example to the target model, and use the output of the intermediate layers to predict the label of the example, the generated label sequences of adversarial examples will be extremely discrepant but the label sequences of normal examples keep nearly unchanged. Motivated by this observation, we design an approach to detect adversarial examples based on the label sequence discrepancy (LSD) of the given examples. The experimental results against five mainstream adversarial attacks on three benchmark datasets demonstrate that LSD outperforms the state-of-the-art solutions in the detection rate of adversarial examples. Moreover, LSD performs well at various confidence levels and exhibits good generalizability between different attacks.
Animation2API: API Recommendation for the Implementation of Android UI Animations

UI animations, such as card movement and menu slide in/out, provide appealing user experience and enhance the usability of mobile applications. In the process of UI animation implementation, it is difficult for developers to identify suitable APIs for the animation to be implemented from a large number of APIs. Fortunately, the huge app market contains millions of apps, and they can provide valuable data resources for solving this problem. By summarizing the API usage for the same or similar animations in apps, reusable knowledge can be mined for the API recommendation. In this paper, we propose a novel method Animation2API, which mines the knowledge about APIs from existing apps and recommends APIs for UI animations. Different from existing text-based API recommendation approaches, Animation2API takes the UI animation in GIF/video format as query input. Firstly, we construct a database containing mappings between UI animations and APIs by analyzing a broad set of apps. Then, we build a UI animation feature extractor, which can be used to gain temporal-spatial feature vectors of UI animations. By comparing the temporal-spatial feature vectors between UI animations, we identify animations that are similar to the query animation from the database. Finally, we summarize the APIs used for implementing these animations and recommend a list of APIs for developers. The empirical evaluation results show that our method can achieve 82.66% <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Success rate</i></b> and outperform the baseline Guru by 230.77% and 184.95% in terms of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Precision</i></b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Recall</i></b> when considering twenty APIs. In the user study, we take the scenarios of using web search and ChatGPT to implement animations as baselines, and the results show that participants can complete animations faster (14.54%) after using Animation2API. Furthermore, participants’ positive feedbacks on the questionnaire indicate the usefulness of Animation2API.
Conditional Stochastic Normalizing Flows for Blind Super-Resolution of Remote Sensing Images

Remote sensing images (RSIs) in real scenes may be disturbed by multiple factors, such as optical blur, undersampling, and additional noise, resulting in complex and diverse degradation models. At present, mainstream super-resolution (SR) algorithms only consider a single and fixed degradation (such as bicubic interpolation) and cannot flexibly handle complex degradations in real scenes. Therefore, designing an SR model that can deal with various degradations has gradually attracted researchers’ attention. Some early studies estimate degradation kernels and then perform degradation-adaptive SR but face the problems of estimation error amplification and insufficient high-frequency details in the results. Although blind SR algorithms based on generative adversarial networks (GANs) have greatly improved visual quality, they still suffer from pseudo-texture, mode collapse, and poor training stability. This article proposes a novel blind SR framework based on the stochastic normalizing flow (BlindSRSNF) to address the above problems. BlindSRSNF learns the conditional probability distribution over the high-resolution image space given a low-resolution (LR) image by explicitly optimizing the variational bound on the likelihood. BlindSRSNF is easy to train and can generate photorealistic SR results that outperform GAN-based models. In addition, we introduce a degradation representation strategy based on contrastive learning to avoid the error amplification problem caused by explicit degradation estimation. Comprehensive experiments show that the proposed algorithm can obtain SR results with excellent visual perception quality on both simulated LR and real-world RSIs. The code is available at https://github.com/hanlinwu/BlindSRSNF.
Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset

Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.
Trajectory Optimization for the Nonholonomic Space Rover in Cluttered Environments Using Safe Convex Corridors

Due to the limitation of space rover onboard computing resources and energy, there is an urgent need for high-quality drive trajectories in complex environments, which can be provided by delicately designed motion optimization methods. The nonconvexity of the collision avoidance constraints poses a significant challenge to the optimization-based motion planning of nonholonomic vehicles, especially in unstructured cluttered environments. In this paper, a novel obstacle decomposition approach, which swiftly decomposes nonconvex obstacles into their constituent convex substructures while concurrently minimizing the proliferation of resultant subobstacles, is proposed. A safe convex corridor construction method is introduced to formulate the collision avoidance constraints. The numerical approximation methods are applied to transfer the resulting continuous motion optimization problem to a nonlinear programming problem (NLP). Simulation experiments are conducted to illustrate the feasibility and superiority of the proposed methods over the rectangle safe corridor method and the area method.
The Arabic Citation Index: Toward a better understanding of Arab scientific literature

Abstract The Arabic Citation Index (ARCI) was launched in 2020. This article provides an overview of the scientific literature contained in this new database and explores its possible usage in research evaluation. As of May 2022, ARCI had indexed 138,283 scientific publications published between 2015 and 2020. ARCI’s coverage is characterized by using the metadata available in scientific publications. First, I investigate the distributions of the indexed literature at various levels (research domains, countries, languages, open access). Articles make up nearly all the documents indexed with a share of 99% of ARCI. The Arts &amp; Humanities and Social Sciences fields have the highest concentration of publications. Most indexed journals are published in Egypt, Algeria, Iraq, Jordan, and Saudi Arabia. About 8% of publications in ARCI are published in languages other than Arabic. Second, I use an unsupervised machine learning model, Latent Dirichlet Allocation, and the text mining algorithm of VOSviewer to uncover the main topics in ARCI. These methods provide a better understanding of ARCI’s thematic structure. Next, I discuss how ARCI can complement global standards in the context of a more inclusive research evaluation. Finally, I suggest a few research opportunities after discussing the findings of this study.
Extraction of Hidden Authentication Factors from Possessive Information

Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones from unauthorized access. Numerous authentication techniques have been developed; however, several authentication variables exhibit instability in the face of external influences or physical impairments. The potential failure of the authentication system might be attributed to several unpredictable circumstances. This research suggests that the use of distinctive and consistent elements over an individual’s lifespan may be employed to develop an authentication classification model. This model would be based on prevalent personal behavioral biometrics and could be readily implemented in security authentication systems. The biological biometrics acquired from an individual’s typing abilities during data entry include their name, surname, email, and phone number. Therefore, it is possible to establish and use a biometrics-based security system that can be sustained and employed during an individual’s lifetime without the explicit dependance on the functionality of the smartphone devices. The experimental findings demonstrate that the use of a mobile touchscreen as the foundation for the proposed verification mechanism has promise as a high-precision authentication solution.
GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization

Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smaller ones and for preserving most knowledge, its capability in treating different node subgroups fairly during compression remains unexplored. In this paper, we investigate current graph condensation techniques from a perspective of fairness, and show that they bear severe disparate impact toward node subgroups. Specifically, GNNs trained on condensed graphs become more biased than those trained on original graphs. Since the condensed graphs comprise synthetic nodes, which are absent of explicit group IDs, the current algorithms used to train fair GNNs fail in this case. To address this issue, we propose Graph Condensation with Adversarial Regularization (GCARe), which is a method that directly regularizes the condensation process to distill the knowledge of different subgroups fairly into resulting graphs. A comprehensive series of experiments substantiated that our method enhances the fairness in condensed graphs without compromising accuracy, thus yielding more equitable GNN models. Additionally, our discoveries underscore the significance of incorporating fairness considerations in data condensation, and offer invaluable guidance for future inquiries in this domain.
Bespoke Virtual Machine Orchestrator: An Approach for Constructing and Reconfiguring Bespoke Virtual Machine in Private Cloud Environment

A cloud-computing company or user must create a virtual machine to build and operate a cloud environment. With the growth of cloud computing, it is necessary to build virtual machines that reflect the needs of both companies and users. In this study, we propose a bespoke virtual machine orchestrator (BVMO) as a method for constructing a virtual machine. The BVMO builds resource volumes as core assets to meet user requirements and builds virtual machines by reusing and combining these resource volumes. This can increase the reusability and flexibility of virtual-machine construction. A case study was conducted to build a virtual machine by applying the proposed BVMO to an actual OpenStack cloud platform, and it was confirmed that the construction time of the virtual machine was reduced compared with that of the existing method.