Reviewerly/exHarmony huggingface dataset:
Optimal Control of Non-Holonomic Robotic Systems Based on Type-3 Fuzzy Model

The paper studies the control of wheeled land mobile robots (MRs) using nonlinear equations and non-holonomic dynamic constraints. Due to the complex and unpredictable nature of the environments in which these robots operate, designing a controller for them is a challenging task. Uncertainties in the system further compound the problem. To tackle these challenges, this paper proposes a novel approach based on type-3 (T3) fuzzy logic systems (FLSs) for system identification and parameter estimation. The T3-FLSs are used to create an online model of the MRs dynamics, which is then used to design a model-based control system. To account for the approximation error of T3-FLSs and the effect of un-modeled dynamics and constraints, an optimal supervisor is designed. The supervisor compensates for any error in the model and ensures that the control system remains stable under symmetrical constraints. A Lyapunov analysis is conducted to verify the stability of the system. The simulations demonstrate that the proposed controller yields excellent results even in the presence of non-holonomic constraints and fully unknown dynamics. The findings of this study offer significant insights into the challenges associated with controlling MRs and provide a promising solution to address these issues.
Towards a Formal Framework for Normative Requirements Elicitation

As software and cyber-physical systems interacting with humans become prevalent in domains such as healthcare, education and customer service, software engineers need to consider normative (i.e., social, legal, ethical, empathetic and cultural) requirements. However, their elicitation is challenging, as they must reflect the often conflicting or redundant views of stakeholders ranging from users and operators to lawyers, ethicists and regulators. To address this challenge, we introduce a tool-supported Formal framework for normaTive requirements elicitation (FormaTive). It allows specification of normative rules for a software system in an intuitive high-level language, and automates: (i) the mapping of the rules to an internal formal representation; (ii) their analysis to identify rule conflicts, redundancies, and concerns; and (iii) the synthesis of feedback enabling users to understand and resolve problems.
AGE OF NATALKA OROGENIC GOLD DEPOSIT (U-PB, 40AR/39AR, RE-OS CONSTRAIN)

Geological observations and U-Pb dating of zircons from intrusions of the Yana-Kolyma gold province constrain the age of orogenic gold deposits to the time interval between 150 ± 3 Ma and 108 ± 1 Ma. The age of mineralization at Natalka, the largest deposit of key importance in the gold province, is Valanzhinian (136 ± 1 to 132 ± 2 Ma) as we determined by muscovite 40Ar/39Ar dating and Re-Os dating of arsenopyrite and native gold. Since magmatic events of such ages are unknown in the province we conclude that the formation of gold-quartz mineralization is very likely related to metamorphism and metasomatism triggered by regional-scale shear deformation.
Your Memory Palace in the Metaverse with AI

The metaverse is recognised as an immersive environment where individuals congregate as avatars to perform activities in imaginary places, similar to how they would in reality. Although promising, it has been unsuccessful in retaining novelty with current renditions of the metaverse looking unappealing and devoid of human touch, lacking in genius loci. The objective of this paper is to construct a framework for the metaverse that is built directly from the human experience to become deeply personalized and meaningful, providing users with a unique and immersive experience. This paper proposes a framework for the metaverse that leverages wearable technology and artificial intelligence (AI) to generate virtual spaces based on the conscious and subconscious experiences of individuals.
Comprehension and production of Kinyarwanda verbs in the Discriminative Lexicon

Abstract The Discriminative Lexicon is a theory of the mental lexicon that brings together insights from various other theories: words are the relevant cognitive units in morphology, the meaning of a word is represented by its distribution in utterances, word forms and their meaning are learned by minimizing prediction errors, and fully connected networks successfully capture language learning. In this article we model comprehension and production of Kinyarwanda verb forms in the Discriminative Lexicon model. Kinyarwanda is a highly inflectional language, and therefore particularly interesting, because its paradigms are almost unlimited in size. Can knowledge of its enormous paradigms be modeled only on the basis of words? To answer this question we modeled a data set of 11,528 verb forms, hand-annotated for meaning and their grammatical functions, in the Linear Discriminative Learning (LDL), a two-layered, fully connected computational implementation of the Discriminative Lexicon model. We also extracted 573 verbs from our data set for which meanings are available that are based on empirical word embeddings obtained from large text corpora, and modeled them in LDL. Both comprehension and production is learned accurately: Kinyarwanda verb forms can be comprehended and produced relying on words as cognitive units, in a two-layered network, in which prediction errors are minimized.
Frontier Research on Low-Resource Speech Recognition Technology

With the development of continuous speech recognition technology, users have put forward higher requirements in terms of speech recognition accuracy. Low-resource speech recognition, as a typical speech recognition technology under restricted conditions, has become a research hotspot nowadays because of its low recognition rate and great application value. Under the premise of low-resource speech recognition technology, this paper reviews the research status of feature extraction and acoustic models, and conducts research on resource expansion. Especially in terms of the technical challenges faced by this technology, solutions are proposed, and future research directions are prospected.
A NOVEL DISCRETE RAT SWARM OPTIMIZATION ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

The quadratic assignment problem (QAP) is an NP-hard problem with a wide range of applications in many real-world applications. This study introduces a discrete rat swarm optimizer (DRSO)algorithm for the first time as a solution to the QAP and demonstrates its effectiveness in terms of solution quality and computational efficiency. To address the combinatorial nature of the QAP, a mapping strategy is introduced to convert real values into discrete values, and mathematical operators are redefined to make then suitable for combinatorial problems. Additionally, a solution quality improvement strategy based on local search heuristics such as 2-opt and 3-opt is proposed. Simulations with test instances from the QAPLIB test library validate the effectiveness of the DRSO algorithm, and statistical analysis using the Wilcoxon parametric test confirms its performance. Comparative analysis with other algorithms demonstrates the superior performance of DRSO in terms of solution quality, convergence speed, and deviation from the best-known values, making it a promising approach for solving the QAP.
PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

Abstract Background Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. Methods In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&amp;E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). Results We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. Conclusion The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.
Lexical Normalization Using Generative Transformer Model (LN-GTM)

Abstract Lexical Normalization (LN) aims to normalize a nonstandard text to a standard text. This problem is of extreme importance in natural language processing (NLP) when applying existing trained models to user-generated text on social media. Users of social media tend to use non-standard language. They heavily use abbreviations, phonetic substitutions, and colloquial language. Nevertheless, most existing NLP-based systems are often designed with the standard language in mind. However, they suffer from significant performance drops due to the many out-of-vocabulary words found in social media text. In this paper, we present a new (LN) technique by utilizing a transformer-based sequence-to-sequence (Seq2Seq) to build a multilingual characters-to-words machine translation model. Unlike the majority of current methods, the proposed model is capable of recognizing and generating previously unseen words. Also, it greatly reduces the difficulties involved in tokenizing and preprocessing the nonstandard text input and the standard text output. The proposed model outperforms the winning entry to the Multilingual Lexical Normalization (MultiLexNorm) shared task at W-NUT 2021 on both intrinsic and extrinsic evaluations.
Research on Aggregation of Federated Model for Software Defect Prediction Based on Dynamic Selection

With the continuous expansion of the application scope of computer software, the standards of software quality of various organizations are becoming more and more strict. As a testing method, software defect prediction can effectively improve software quality. Software defect prediction is to predict the potential defect modules in advance at the early stage of project development, so as to improve the software quality. The software defect prediction under Federal learning realized the data sharing of all project parties, but the problem of low-quality data holders was not considered in the aggregation phase. Therefore, the dynamically selected software defect prediction federated model aggregation method (DS-SDP). Firstly, a threshold is set on the server side based on the public dataset. To prevent malicious attacks, participants add Gaussian noise to the parameters after completing local training and transmit them to the server. Then, during the aggregation process, the server uses a dynamic selection method to filter participants who do not meet the threshold criteria, and weights the participants who meet the criteria. Finally, construct a convolutional neural network model. Experiments based on NASA software defect prediction dataset show that compared with the traditional federated aggregation method, the DS-SDP method proposed in this paper improves the model performance, greatly reduces the impact of low-quality data on the model, and also enhances the robustness of the model.
Impact of Leadership Incivility on Employee Leaving Intention and Job Insecurity: Mediating role of Workplace Ostracism

Purpose: Supervisors' Incivility is considered a key antecedent of workplace ostracism and it is one of the burning issues and has a direct relationship with Job Insecurity, similarly, Job Insecurity generates the intention to leave the organization among employees. Methodology: The targeted population was the employee of healthcare institutions working in Pakistan. The sample of 336 was collected using the purposive sampling technique and the quantitative approach was applied due to the explanatory nature of the study. A five-level Likert scale questionnaire was employed to collect the data from the employees of the healthcare institutions regardless of their role and designation. Data analysis was run in twos steps, first demographic &amp; descriptive by using Statistical Package for social science (SPSS 25.0), and in the second stage we used structural equation modeling to test the hypotheses, and confirmatory factor analysis (CFA) was used for convergent &amp; discriminant validities the Partial least squares (PLS) approach was adopted by using the smart PLS software for the analysis of data. Findings: The results revealed that there is a direct positive relationship between leadership incivility and employee leaving intention and job security. The mediating role of workplace ostracism is not established among the dependent and independent variables however job insecurity ignites and mediates the employee leaving intentions. Conclusion: The study in Pakistan revealed that the workplace environment contributes 32% to job performance and 23% to Employee Leaving Intention. The study aims to evaluate leadership incivility and its effects on employee leaving intention and job insecurity, and also moderate the relationship between workplace ostracism with job insecurity and employee leaving intentions.
Fuzzy Nonlinear Regression Modeling With Radial Basis Function Networks

In this paper, we extend the popular supervised learning technique Radial Basis Function Network (RBFN) for regression modeling based on fuzzy responses and exact predictors. For this purpose, we suggest a penalized squared error ridge-based method to estimate the model components including fuzzy parameters and exact tuning constants. The performance of the newly proposed model is examined via established goodness-of-fit criteria and the effectiveness is demonstrated within some numerical application examples. Following the obtained results it is indicative that the fuzzy RBFN regression model outperforms conventional fuzzy nonlinear and multiple regression models and provides more accurate results for nonlinear regression problems.
Latency of High-Tech Crimes: Concept, Structure and Methods of Assessing its Level

The detection and solving rates for high-tech crimes result in our insufficient knowledge about the true scale of high-tech crimes and hinders the organization of fighting them. The author discusses specific features and characteristics of high-tech crimes and offers an interpretation of the concept of their latency. Key factors influencing the latency of high-tech crimes are analyzed and main types of natural and artificial latency of such offences are identified. The author reveals and examines the factors that lead to a considerable increase in high-tech crime latency, including specific victimogenic factors. It is suggested that a coefficient reflecting the real contribution of hidden and concealed crimes to the actual criminality should be used for the quantitative assessment of latency rate for this group of crimes. The article presents a description of the specific features of methods and approaches used for the quantitative assessment of high-tech crime latency rate, including the me­thods of analogy, expert evaluation, and the information-comparison method. The difficulties of applying traditional methods of analyzing latency, specifically, methods of analyzing victimization, have been researched. The author presents the results of the expert evaluation method obtained by conducting a questionnaire for various groups of respondents. Special attention is paid to the necessity of developing new methods aimed at detecting high-tech crimes and providing a more accurate asses­sment of their latency. The Honeypot system is given as an example. It is concluded that high-tech crimes should be viewed as high latency crimes with an increasing negative trend for latency. The specific correlation between latency and public danger of these crimes is pointed out. The conducted research should contribute to organizing the counteraction to modern high-tech crimes. The obtained results could improve the effectiveness of criminalistic methods and recommendations used at the initial stage of investigating high-tech crimes and improve the rate of solving them.
Remote Sensing Image Detection Algorithm Based on GhostNetv2 Improved YOLOv5s Algorithm

The imaging characteristics of remote sensing images make the detection of many targets and different target sizes, resulting in the difficulty of small target detection and insufficient detection accuracy. In this paper, we propose an optimization model for remote sensing image detection based on YOLOv5s, using GhostNetv2 self-attention mechanism to capture global information, introducing GhostNetv2 module to improve YOLOv5s algorithm backbone network, and using EIOU border position regression loss function instead of CIOU loss function. Model training tests are conducted on the NWPU-VHR 10 public dataset, and the improvements improve the detection accuracy of remote sensing images, while making the model more lightweight. Compared with the YOLOv5s benchmark model, the map value of the algorithm in this paper is 94.9%, which is 2.2% better than YOLOv5s in terms of map, and the number of parameters and computation are reduced by 9.2% and 3.1%, respectively. It can effectively improve the accuracy and lighten the model at the same time.
Optimization of Deep Learning Parameters for Magneto-Impedance Sensor in Metal Detection and Classification

Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, we propose and analyze a deep learning model based on recurrent neural networks (RNNs) optimized for the MI sensor, such that it can detect and classify data that are relatively irregular and diverse compared to the EMG and acoustic signals. Our proposed method combines the long short-term memory (LSTM) and gated recurrent unit (GRU) models to detect and classify metal objects from signals acquired by an MI sensor. First, we configured various layers used in RNN with a basic model structure and tested the performance of each layer type. In addition, we succeeded in increasing the accuracy by processing the sequence length of the input data and performing additional work in the prediction process. An MI sensor acquires data in a non-contact mode; therefore, the proposed deep learning approach can be applied to drone control, electronic maps, geomagnetic measurement, autonomous driving, and foreign object detection.
DCAN:DenseNet with Channel Attention Network for Super-resolution of Wireless Capsule Endoscopy

Wireless Capsule Endoscopy (WCE) captures images of the gastrointestinal (GI) tract and transmits the images in a wireless manner. Due to the hardware limitations of the capsule and the varying imaging conditions within the GI tract, the recorded images can have a low spatial resolution with a high frame rate or a high spatial resolution with a low frame rate. Although it is generally common to have low spatial resolution to capture details of the GI tract, low spatial resolution limits the detection of minor anatomical features and abnormalities in the small intestine and other portions of the GI tract. Super-Resolution (SR) is a class of software-based techniques that are used to enhance the resolution of a Low-Resolution (LR) image. This work proposes a new model referred as DCAN-DenseNet with Channel Attention Network for Super-resolution of LR WCE images. The design of DCAN consists of multiple strategies adopted from state-of-the-art methods such as Channel Attention Network (CAN) from RCAN and short dense connections from DenseNet to extract details from LR observation. Additionally, to improve the accuracy of the SR images, we create a derivative dataset of 10,000 images from a publicly available WCE dataset. The proposed approach has been validated against multiple state-of-the-art methods by conducting quantitative evaluation using perceptual metrics. The analysis is complemented by statistical validation to demonstrate the consistency of the proposed method over the other models for the SR task.
Strong Call-by-Value and Multi Types

This paper provides foundations for strong (that is, possibly under abstraction) call-by-value evaluation for the $$\lambda $$ -calculus. Recently, Accattoli et al. proposed a form of call-by-value strong evaluation for the $$\lambda $$ -calculus, the external strategy, and proved it reasonable for time. Here, we study the external strategy using a semantical tool, namely Ehrhard’s call-by-value multi types, a variant of intersection types. We show that the external strategy terminates exactly when a term is typable with so-called shrinking multi types, mimicking similar results for strong call-by-name. Additionally, the external strategy is normalizing in the untyped setting, that is, it reaches the normal form whenever it exists. We also consider the call-by-extended-value approach to strong evaluation shown reasonable for time by Biernacka et al. The two approaches turn out to not be equivalent: terms may be externally divergent but terminating for call-by-extended-value.
Automatic Generating System of Information Security Policy

Information is indispensable in any organization, and its security must be properly guaranteed. At present, information security in an organization includes not only confidentiality but also integrity and availability, and means a balance between them. Establishing an information security policy is effective as a means for that purpose, but it is considered to be a high hurdle for organizations such as SMEs, which have neither personnel nor financial leeway, to tackle it. We thought that a system to help establish information security policies was necessary, so we proposed a framework and tried to implement it in application programs. At present, the creation process of the basic policy by presenting the template and the creation of the organizational profile are implemented. In this paper, we propose a method to reflect the characteristics obtained from the organization profile not only in the basic policy but also in the following countermeasure standards and implement it in the application program. Keywords: security policy, information asset, ontology, generation system, SMEs