Reviewerly/exHarmony huggingface dataset:
The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer

This study aimed to develop a machine learning model to retrospectively study and predict the recurrence risk of breast cancer patients after surgery by extracting the clinicopathological features of tumors from unstructured clinical electronic health record (EHR) data.This retrospective cohort included 1,841 breast cancer patients who underwent surgical treatment. To extract the principal features associated with recurrence risk, the clinical notes and histopathology reports of patients were collected and feature engineering was used. Predictive models were next conducted based on this important information. All algorithms were implemented using Python software. The accuracy of prediction models was further verified in the test cohort. The area under the curve (AUC), precision, recall, and F1 score were adopted to evaluate the performance of each model.A training cohort with 1,289 patients and a test cohort with 552 patients were recruited. From 2011 to 2019, a total of 1,841 textual reports were included. For the prediction of recurrence risk, both LSTM, XGBoost, and SVM had favorable accuracies of 0.89, 0.86, and 0.78. The AUC values of the micro-average ROC curve corresponding to LSTM, XGBoost, and SVM were 0.98 ± 0.01, 0.97 ± 0.03, and 0.92 ± 0.06. Especially the LSTM model achieved superior execution than other models. The accuracy, F1 score, macro-avg F1 score (0.87), and weighted-avg F1 score (0.89) of the LSTM model produced higher values. All P values were statistically significant. Patients in the high-risk group predicted by our model performed more resistant to DNA damage and microtubule targeting drugs than those in the intermediate-risk group. The predicted low-risk patients were not statistically significant compared with intermediate- or high-risk patients due to the small sample size (188 low-risk patients were predicted via our model, and only two of them were administered chemotherapy alone after surgery). The prognosis of patients predicted by our model was consistent with the actual follow-up records.The constructed model accurately predicted the recurrence risk of breast cancer patients from EHR data and certainly evaluated the chemoresistance and prognosis of patients. Therefore, our model can help clinicians to formulate the individualized management of breast cancer patients.
Socio-inspired evolutionary algorithms: a unified framework and survey

It is well recognized that human societies and their problem-solving capabilities have evolved much faster than biological evolution. The inspiration from human behaviors, knowledge exchange, and transformation has given rise to a new evolutionary computation paradigm. Multiple research endeavors have been reported in the literature inspired by the diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in piles of algorithms, worded differently but with more or less similar underlying mechanisms causing immense confusion for a new reader. This paper presents a generalized framework for these socio-inspired evolutionary algorithms (SIEAs) or socio-inspired meta-heuristic algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description and the comparison of each SIEA with the general framework enable a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. This paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.
Case Histories of Orogenic Gold Deposits

This review compares genetic parameters of 12 orogenic gold deposits. The set of examples is considered to represent largely the variability of orogenic gold deposit (OGD) characteristics. The data are presented in tables and include following definitive parameters: regional geologic settings, nature of hosts rocks and mineralization, ore controlling structures, ages of host rocks and mineralization and timing of mineralization relative to metamorphism, hydrothermal alteration mineralogy and ore mineral assemblages, isotopic signatures, physical conditions of ore formation and proposed origin of ore fluids aa well as gold reserves, production, and grades. This allows comparison of deposits from different geologic terrains having different ages and formed under different P-T conditions. The data are further discussed before the background of the orogenic gold system and the crustal metamorphic models that provide different scenarios to explain the source of ore fluids. The orogenic gold system model advocates a metal and fluid source external to the terrain in which mineralization occurred, but the model applies only for 3 of the 12 deposits studied. All other deposits formed most likely from a crustal source, which would favor the crustal metamorphic model. However, the formation of hypozonal OGDs cannot be accounted for by the crustal metamorphic model or by the metamorphic devolatilization model. The data identify a set of coherent signatures in OGDs, but there seem to be no unified model for all possible environmental conditions and facets of ore formation and fluid sources, tectonic and lithologic setting, and scale of gold endowment.
Face Completion Based on Symmetry Awareness with Conditional GAN

Face completion is an important topic in the field of computer vision and image processing. Its core task is to restore image information, so that the generated completion results are as consistent as possible with the ground-truth results. In existing methods, there is no strong constraint on the consistency between the completion result and the true value, and the symmetry characteristics of the face are ignored, which makes it impossible to generate a natural and consistent completion result for any position and symmetrical position of the face. In response to these problems, we propose a novel method called face completion generative adversarial network (FC GAN). Our generator uses a u-net-like structure, and the discriminator uses a combination of global discriminator and local discriminator. We use a new perceptual loss based on VGG-19 to constrain the consistency of the completion result and the true value. We use symmetry awareness in our method and it takes full advantage of face symmetry features to optimize face completion. For irregular mask image completion, our method produces visually realistic and semantically correct results. We evaluate our model on the CelebA dataset and use FID and SSIM as the indicators. Compared with existing methods, the face completion method in this paper has a certain improvement in visual effects and evaluation indicators.
Comparison of metaheuristic optimization algorithms for numerical solutions of optimal control problems

Summary The objective in optimal control problems is to determine the control rule to be applied according to the objective function determined for a given system. Theoretically, different solution methods have been developed starting from calculus of variation such as Pontriagin, Hamilton, or Riccati. In this study, apart from analytical methods, it is proposed to apply algorithms inspired by nature to the same problem. Among these algorithms, genetic algorithm, firefly algorithm, Harris Hawk's optimization algorithm, and differential evolution optimization algorithm have been applied to given optimal control problems and their successes were compared in terms of statistical analysis such as minimum, maximum, average, Wilcoxon, and Friedman analysis. The originality of this study is to control the system with discrete control rule by partitioning the control signal, to be applied to the system which is wanted to be optimally controlled. This will allow the control of different techniques, such as MPC or NMPC, which will contribute to the determination of the global optimum control rule, especially in the solution of nonlinear systems. In addition, three different applications have been performed to demonstrate. As a result of these experiments, it has been shown that these algorithms can be applied successfully to such problems.
Differential Effects of Macroprudential Policy

We explore the differential effects of lender-based macroprudential policies on new mortgage borrowing for households of different income using a comprehensive dataset that links macroprudential policy actions with household survey data for European Union countries.The main results suggest that higher-income households on average experience a larger reduction in mortgage loan size than lower-income households when regulation targeting total lenders' assets tightens.In contrast, lower-income households on average experience a larger reduction in mortgage loan size than higher-income households when regulation targeting lenders' capital requirements tightens.We also provide evidence of the different channels through which the differential effects operate.
Certificateless multi-source elliptic curve ring signcryption for cloud computing

Network coding can encode and forward the received data packets to save the network resources and improve the bandwidth utilization in cloud computing environment. To resist the pollution attacks and improve the computational efficiency in network coding, we construct certificateless multi-source elliptic curve ring signcryption (CL-MSECRSC) scheme suitable for cloud computing, its security relies on the hardness of elliptic curve discrete logarithm (ECDL) and elliptic curve computation Diffie-Hellman (ECCDH) problems. CL-MSECRSC has the properties of anti-forgery, anti-pollution, confidentiality and unconditional anonymity; also, scalar multiplication operations greatly save its computation overhead.
ASIC Design and Implementation of the Real-Time Collision Detection for Machine Tool Automation

Collision detection of two objects plays an essential role for the machine tool automation. Although the collision detection of two objects has been studied in applications like virtual reality, the collision detection for the machine tool requires high precision to avoid overcut damage to the high-cost machine tools. The current collision detection for machine tools is under the soft-ware based computer numerical control (CNC), the low computation capability of which refrains the CNC based approach from real-time collision detection. In this paper, we consider the design of application specific integrated circuit (ASIC) to enhance the collision detection for machine tool automation. Because the bounded objects are represented by meshed triangles, we consider the separating axis theorem (SAT) based detection algorithm. Furthermore, by considering high precision required by machine tool applications, the proposed algorithm includes collision detection of either non-coplanar or coplanar triangles. Following the collision detection algorithm, we design hardware architecture with parallel processing to provide higher throughput rate over the architecture reported in our conference paper. The VLSI implementation results under the TSMC TN40G (45nm) CMOS technology reveal that our architecture requires 1,212K gates and provides detection throughput 38.46M per second for collision detection of two triangles, while operating at 500 MHz. For two objects represented by 400 and 400 meshed triangles, respectively, our hardware architecture can provide collision detection in 0.96 ms, which is smaller than the 1 ms required for real-time processing of collision detection of two objects.
A Comprehensive Empirical Study of Heterogeneity in Federated Learning

Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private data sets owned by nontrusting entities. FL has seen successful deployment in production environments, and it has been adopted in services, such as virtual keyboards, auto-completion, item recommendation, and several IoT applications. However, FL comes with the challenge of performing training over largely heterogeneous data sets, devices, and networks that are out of the control of the centralized FL server. Motivated by this inherent challenge, we aim to empirically characterize the impact of device and behavioral heterogeneity on the trained model. We conduct an extensive empirical study spanning nearly 1.5K unique configurations on five popular FL benchmarks. Our analysis shows that these sources of heterogeneity have a major impact on both model quality and fairness, causing up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.6\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula> degradation in the quality and fairness, respectively, thus shedding light on the importance of considering heterogeneity in FL system design.
Scaling Stratified Stochastic Gradient Descent for Distributed Matrix Completion

Stratified SGD (SSGD) is the primary approach for achieving serializable parallel SGD for matrix completion. State-of-the-art parallelizations of SSGD fail to scale due to large communication overhead. During an SGD epoch, these methods send data proportional to one of the dimensions of the rating matrix. We propose a framework for scalable SSGD through significantly reducing the communication overhead via exchanging point-to-point messages utilizing the sparsity of the rating matrix. We provide formulas to represent the essential communication for correctly performing parallel SSGD and we propose a dynamic programming algorithm for efficiently computing them to establish the point-to-point message schedules. This scheme, however, significantly increases the number of messages sent by a processor per epoch from <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(K)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(K^{2})$</tex-math></inline-formula> for a <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -processor system which might limit the scalability. To remedy this, we propose a Hold-and-Combine strategy to limit the upper-bound on the number of messages sent per processor to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(K\lg \!K)$</tex-math></inline-formula> . We also propose a hypergraph partitioning model that correctly encapsulates reducing the communication volume. Experimental results show that the framework successfully achieves a scalable distributed SSGD through significantly reducing the communication overhead. Our code is publicly available at: github.com/nfabubaker/CESSGD
Assessing Cyber-Security Readiness of Nigeria to Industry 4.0

The fourth industrial revolution (4IR), widely known as Industry 4.0, is a revolution many authors believe has come to stay. It is a revolution that has been fast blurring the line between physical, digital and biological technologies. These disruptive and innovative technologies largely rely on high-speed internet connectivity, cloud computing, augmented reality, additive manufacturing, data science and artificial intelligence. Most developed economies have embraced the revolution, while the developing economies are yet to do so probably because of the lack of enabling facilities, e.g. requisite skills, knowledge and technologies. Thus, this study investigates Nigeria, one of the developing economies, to understand its readiness for 4IR adoption, and preparedness to mitigate cyber threats associated with 4IR. Our investigation adopts a quantitative research approach, utilises an online questionnaire survey for its data collation from a sample population comprising academia, industry and government stakeholders. A total of 116 respondents were analysed with descriptive statistical tools in SPSS. Results show that 60% of the respondents believe that Nigeria is not yet ready to adopt 4IR as evidenced by the lack of basic prerequisites, such as 4IR-relevant laws, strong institutional frameworks and policies. The country lacks essential facilities to mitigate risks associated with 4IR. So far at the university level, 52% of the courses offered at the undergraduate and 42% at the postgraduate levels are relevant in the development of skills required for 4IR.
Block Cipher Identification Scheme Based on Hamming Weight Distribution

In order to solve the problem of worse recognition performance under the multi-classification scenarios of existing cryptosystem identification, this paper proposes a block cryptosystem recognition scheme based on Hamming weight distribution. A feature extraction method based on Hamming weight distribution is designed to extract the cipher text features, and the cryptosystem is further described. XGBoost-RFE feature selection algorithm is proposed to filter invalid features. In order to better adapt to the characteristics of complex ciphertext data and difficult fitting in multi classification scenarios, XGB-LGBM ensemble learning model with multi-layer fusion structure is designed to further improve the accuracy and generalization of recognition. The experiment carried out mixed identification of 10 common block cipher algorithm, and the overall recognition accuracy reached 89.65%.
Knowledge-Enhanced Relation Extraction in Chinese EMRs

Electronic Medical Records (EMRs) is one of the important data sources of clinical information. Relation extraction is a key step to extract rich medical knowledge from EMRs, which has been studied by many scholars. However, there are some problems in EMRs corpus, such as entity nesting and relation overlapping, which make it difficult to achieve ideal results in EMRs relation extraction task. Previous studies rarely considered the fusion of knowledge graph containing rich and valuable structured knowledge, which leads to semantic ambiguity and other issues. Aiming at the above problems, Relation Extraction model based on Knowledge Graph and Chinese character Radical information(RE-KGR) model is proposed in this paper to study the relation extraction of EMRs in diabetic patients. Firstly, knowledge information is extracted from knowledge graph and embedded by GCN. At the same time, the corresponding radical features of Chinese characters are fused to enhance the semantic information of the input text. Compared with other baseline models, the DEMRC and DiaKG experiments of EMRs datasets of diabetic patients were improved by 1.32% and 2.19%.
EINE:Relation Classification by Enhancing the Impact of Non-Entity words

Information extraction from unstructured text is an important task in natural language processing field, since the performance of relation extraction models directly affects the accuracy of information extraction, especially when joint information extraction is not suitable. However, recent approaches do not pay enough attention to the non-entity words in sentences. Existing works investigate entity words in text sequences, others by examining the semantics of the whole text sequence. In this paper, we propose a relation classification model called EINE, which stands for Enhancing the Impact of Non-Entity words, integrating relation information into word-level semantics and reasonably enhancing the impact of non-entity words. Experimental results show that relation classification not only relies on the representation of entity words in sentences, but also needs to use the non-entity words to avoid over-relying on the entity words. Meanwhile, the integration of relation information significantly improves the classification effect. Finally, our model obtains superior results on two datasets, SemEval-2010 Task 8 and WebNLG.
Causal Pattern Representation Learning for Extracting Causality from Literature

Extracting causality from literature has become an important task due to the essential role of causality. Traditional methods use pattern matching to extract causality, requiring domain knowledge and extensive human effort. Recent researches focus on utilizing pre-trained language models due to their success in Natural Language Processing (NLP). However, long sentences in literature hinders the performance of causality extraction. In this paper, we propose to focus on the representation of causal virtual pattern <head_entity, causal_virtual_trigger, tail_entity> and design a Causal Pattern Representation Learning (CPRL) method to tackle this challenge. For the causal_virtual_trigger representation, CPRL applies the attention mechanism on the shortest dependency path between entities to filter irrelevant information. For the head_entity and tail_entity representation, CPRL applies graph convolution networks to encode word dependency on entities. By crawling health-related literature abstracts, we create a new causality extraction dataset, namely HealthCE, with a size of 3479. Experiments on HealthCE demonstrate the effectiveness of our approach over existing causality extraction and general relation extraction baselines on the task of causality extraction.
A Novel Fall Detection System using Mediapipe

The society values the elderly, yet because of their deteriorating health as they age, they are more susceptible to serious injury from falls. This study describes a mediapipe-based vision-based fall detection system that notifies caretakers by chatbot when a fall is detected. The camera is placed so that the area in which the elderly spend most of their time receives the most coverage. The Mediapipe architecture is taught to identify a fall, and the Random Forest approach is used to categorize diverse circumstances. The model has a 90% accuracy rate and is designed to identify a single person at a time.
The Analysis of Attacks Against Port 80 Webserver with SIEM Wazuh Using Detection and OSCAR Methods

The existence of the internet in the company leads data and information exchange activities easier. The ease to obtain company data and information which is not accompanied by information security awareness results in the emergence of data leaks and lateral movements. Companies are required to ensure reliable and secure network security to protect information technology assets from hacking. SIEM assists the companies and security officers to monitor attacks, find vulnerabilities and analyze attacks. This study applies a network forensic approach with OSCAR and detection methods to determine the effectiveness of SIEM wazuh performance against port 80 attacks on the web server. The attack testing stages of port scanning and http directory scanning, the web server service still looks normal or no 404 error message is found in the browser. The deviation of attack detection in this study is 1.402 seconds. The existence of SIEM Wazuh is able to assist security officers in monitoring company data security and securing company IT assets.