Reviewerly/exHarmony huggingface dataset:
DETECTING SPAM EMAIL WITH MACHINE LEARNING OPTIMIZED WITH BIO-INSPIRED METAHEURISTIC ALGORITHMS

Groups and individuals used email regularly, for sending and receiving messages. When spammers get to know any valid email address, then they can exploit it easily by sending unsolicited emails, which will directly reach our mail inbox. Detecting spam using the machine learning technique is a known way, but we want to improve the accuracy, so the ML algorithms were optimized with bio-inspired methods to achieve better accuracy. The research was done to implement ML models using many algorithms, such as Naive Bayes, Random Forest, Support Vector Machine, and Decision Tree on a particular email dataset, along with feature extraction and pre-processing. The techniques occurring in nature can be used to achieve better accuracy, viz. Genetic Algorithm and Particle Swarm Optimization.
Design of dangerous driving behavior warning system based on emotion recognition

In recent years, China's vehicle ownership has been increasing year by year, followed by more and more frequent traffic accidents.According to the statistics of traffic accidents at home and abroad, the main cause of traffic accidents is the dangerous driving behavior of drivers, which not only troubles other drivers, but also endangers the safety of pedestrians around the road.Therefore, this paper aimed at the three elements of human, vehicle and road, carried on the design of the early warning system.The purpose of the early warning system is to improve the driver's perception ability, so that the driver can make the right decision in the face of various road conditions during driving, so as to reduce the influence of anxiety on the driver's driving behavior.
Sets in the Normalized Spaces Completion

The article investigates the set closure in the normalized space completion. The adherent point of a set criterion is given and proved.
Creation of an Integrated Data Model of the Heterogeneous Sources Containing Digital Footprints

The article revealed the creation of an integrated data models based on ontology. The prototype that implements the integration of data by several heterogeneous sources and performs a logical reasoning required by the scoring anti-fraud system is presented.
Some notes on intuitionistic fuzzy equivalence relations and their use on real data

The research presented in this paper is motivated by possibilities of using fuzzy equivalence relations to classify the data into the specific classes. We try to improve these results with the use of intuitionistic fuzzy equivalence relations. We define the basic structures and their properties, which are used in the paper. Then we present the data which we decided to classify and the methods of processing these data. At the end of the paper we discus obtained results and problems which occurred during the processing of the selected data.
Quantifying individual scientific output in terms of a new intuitionistic fuzzy sets based author-level metrics (IFALM)

The present paper proposes the idea of formulating a new author-level citation metrics, quantifying the individual’s scientific output, which uses the concept of intuitionistic fuzzy sets. This new metrics gives more visibility to the proportion of direct self-citations and hidden (co-author or collaborative) self-citations in the form of an intuitionistic fuzzy pair. Examples are given retrieving the necessary information from Scopus, one of the largest databases of peer-reviewed literature, which algorithmically enables retrieval of one’s citations including and excluding their own or their co-authors’ self-citations, as well as the calculation of three different values of one’s h-index with and without these respectively. Linear and triangular graphical representations are given, as well as comparisons with the concept proposed in 2005 by Jorge E. Hirsch, the now famous h-index, and ideas for future elaboration of the concept of the new intuitionistic fuzzy sets based author-level metrics, or shortly IFALM.
An application of the InterCriteria Analysis and clusterization approach over a burnout dataset

In this investigation the level of burnout among the medical employees was analyzed. Тhe InterCriteria Analysis (ICA) approach is used to find the dependences between different parameters characterizing the 139 medical employees from 6 medical centers. The aim is to analyze the correlations between the health indicators, by surveying with a developed questionnaire. The obtained data from the InterCriteria Analysis were clustered using an adaptive neural network.
Prelims

Citation (2022), "Prelims", Barnett, W.A. and Sergi, B.S. (Ed.) Quantitative Analysis of Social and Financial Market Development (International Symposia in Economic Theory and Econometrics, Vol. 30), Emerald Publishing Limited, Bingley, pp. i-xii. https://doi.org/10.1108/S1571-038620220000030011
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control

Reinforcement learning (RL) and trajectory opti-mization (TO) present strong complementary advantages. On one hand, RL approaches are able to learn global control policies directly from data, but generally require large sample sizes to properly converge towards feasible policies. On the other hand, TO methods are able to exploit gradient-based information extracted from simulators to quickly converge towards a locally optimal control trajectory which is only valid within the vicinity of the solution. Over the past decade, several approaches have aimed to adequately combine the two classes of methods in order to obtain the best of both worlds. Following on from this line of research, we propose several improvements on top of these approaches to learn global control policies quicker, notably by leveraging sensitivity information stemming from TO methods via Sobolev learning, and Augmented Lagrangian (AL) techniques to enforce the consensus between TO and policy learning. We evaluate the benefits of these improvements on various classical tasks in robotics through comparison with existing approaches in the literature.
The impact of leadershift on cultural agility policy: the search for effective way of mindfulness

To achieve an agile business culture, leaders must match styles with situations (leadershift). Such a goal can be acquired by utilizing special abilities and skills, such as Mindfulness. We tested such a framework of research to identify the role of Mindfulness as a personality trait that any leader should effectively utilize to achieve an agile organizational culture successfully. The variance model was applied by a cross-sectional survey on a sample from the Egyptian Post Authority to evaluate their success using the proposed tools in their latest era of significant changes. Findings reveal that Mindfulness is a compelling personality trait that facilitates leadershift, identified as shifting leadership styles with high flexibility to fit the situation. Such a trait helped the management of the Egyptian Post Office to achieve a thriving agile culture as a state-owned enterprise. Moreover, further implications were highlighted for practitioners and academic researchers to investigate the relationship profoundly.
Determinants of the Profitability of the Domestic Commercial Banking Industry

본 연구는 4차 산업 혁명으로 인한 사회 경제적 변화가 국내상업은행 수익성에 어떤 영향을 주는지를 알아보고자 하는데 초점을 두고 있다. 이를 위해 기존에 사용되던 변수 외에 4차 산업으로 인한 소득 불평등, 비대면영업을 위한 연구개발비등과 은행의 자기자본이익률과의 관계를 2010년~ 2020년 은행 자료를 바탕으로 동태적 방법론인 Panel VAR 모형을 적용하여 실증분석하고 있다. 분석 결과, 정보통신기술 변화에 따른 은행의 비대면 업무 증가, 이를 위한 투자의 대용변수인 연구개발비의 증가는 은행 수익성에 큰 영향을 주지 못하는 것으로 나타나고 있다. 하지만, 소득 불평등과 은행 수익성은 유의미한 정(+)의 값을 보여 소득 불평등 심화가 은행 수익성을 증가시키는 것으로 분석되고 있다. 이는 일자리와 소득의 양극화로 인한 자산관리 필요성이 증가 된 것으로 추측해 볼 수 있다. 은행 대출 비중의 증가는 은행 수익성에 정(+)의 영향을 미치는 것은 어느 정도 예측이 되었으나, 지점수 증가는 은행 수익성 개선에 긍정적인 영향을 주지 못하는 것으로 분석되는 것은 본 논문의 특징적인 메시지로 보인다. This study focuses on how the socio-economic changes caused by the 4th industrial revolution affect the profitability of domestic commercial banks. To do this purpose, the relationship between income inequality caused by the 4th industrial revolution, R&D expenditures for non-face-to-face business, and the bank"s return on equity is empirically analyzed by applying the dynamic panel vector autoregression(VAR) method. As a result of the analysis, it appears that the increase in non-face-to-face businesses of banks due to changes in information and communication technology and the increase in R&D expenses, which is a proxy variable for investment for this purpose, do not significantly affect bank profitability yet. However, income inequality and bank profitability appear to have a statistically significant relationship, which is analyzed to indicate that deepening income inequality increases bank profitability. It can be speculated that the need for asset management has increased due to the polarization of jobs and income. On the other hand, while it is very usual to see that an increase in the proportion of bank loans has a positive (+) effect on bank profitability, it is very unusual to see that an increase in the number of branches does not have a positive effect on improving bank profitability.
A Hierarchy of Multipartite Correlations Based on Concentratable Entanglement

Multipartite entanglement is one of the hallmarks of quantum mechanics and is central to quantum information processing. In this work we show that Concentratable Entanglement (CE), an operationally motivated entanglement measure, induces a hierarchy upon pure states from which different entanglement structures can be certified. In particular, we find that nearly all genuine multipartite entangled states can be verified through CE. In the process we find the exact maximal value of CE and corresponding states for up to 18 qubits and show that these correspond to extremal quantum error correcting codes. The latter allows us to unravel a deep connection between CE and coding theory. Finally, our results also offer an alternative proof, on up to 31 qubits, that absolutely maximally entangled states do not exist.
Theoretical Insight into Batch Normalization: Data Dependant Auto-Tuning of Regularization Rate

Batch normalization is widely used in deep learning to normalize intermediate activations. Deep networks suffer from notoriously increased training complexity, mandating careful initialization of weights, requiring lower learning rates, etc. These issues have been addressed by Batch Normalization (\textbf{BN}), by normalizing the inputs of activations to zero mean and unit standard deviation. Making this batch normalization part of the training process dramatically accelerates the training process of very deep networks. A new field of research has been going on to examine the exact theoretical explanation behind the success of \textbf{BN}. Most of these theoretical insights attempt to explain the benefits of \textbf{BN} by placing them on its influence on optimization, weight scale invariance, and regularization. Despite \textbf{BN} undeniable success in accelerating generalization, the gap of analytically relating the effect of \textbf{BN} to the regularization parameter is still missing. This paper aims to bring out the data-dependent auto-tuning of the regularization parameter by \textbf{BN} with analytical proofs. We have posed \textbf{BN} as a constrained optimization imposed on non-\textbf{BN} weights through which we demonstrate its data statistics dependant auto-tuning of regularization parameter. We have also given analytical proof for its behavior under a noisy input scenario, which reveals the signal vs. noise tuning of the regularization parameter. We have also substantiated our claim with empirical results from the MNIST dataset experiments.
On the Edges of Characteristic Imset Polytopes

The edges of the characteristic imset polytope, $\operatorname{CIM}_p$, were recently shown to have strong connections to causal discovery as many algorithms could be interpreted as greedy restricted edge-walks, even though only a strict subset of the edges are known. To better understand the general edge structure of the polytope we describe the edge structure of faces with a clear combinatorial interpretation: for any undirected graph $G$ we have the face $\operatorname{CIM}_G$, the convex hull of the characteristic imsets of DAGs with skeleton $G$. We give a full edge-description of $\operatorname{CIM}_G$ when $G$ is a tree, leading to interesting connections to other polytopes. In particular the well-studied stable set polytope can be recovered as a face of $\operatorname{CIM}_G$ when $G$ is a tree. Building on this connection we are also able to give a description of all edges of $\operatorname{CIM}_G$ when $G$ is a cycle, suggesting possible inroads for generalization. We then introduce an algorithm for learning directed trees from data, utilizing our newly discovered edges, that outperforms classical methods on simulated Gaussian data.
Human-level Atari 200x faster

The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subject of research of a large body of work, with performance frequently measured by observing scores over the wide range of environments contained in the Atari 57 benchmark. Agent57 was the first agent to surpass the human benchmark on all 57 games, but this came at the cost of poor data-efficiency, requiring nearly 80 billion frames of experience to achieve. Taking Agent57 as a starting point, we employ a diverse set of strategies to achieve a 200-fold reduction of experience needed to out perform the human baseline. We investigate a range of instabilities and bottlenecks we encountered while reducing the data regime, and propose effective solutions to build a more robust and efficient agent. We also demonstrate competitive performance with high-performing methods such as Muesli and MuZero. The four key components to our approach are (1) an approximate trust region method which enables stable bootstrapping from the online network, (2) a normalisation scheme for the loss and priorities which improves robustness when learning a set of value functions with a wide range of scales, (3) an improved architecture employing techniques from NFNets in order to leverage deeper networks without the need for normalization layers, and (4) a policy distillation method which serves to smooth out the instantaneous greedy policy overtime.
Improving Robust Fairness via Balance Adversarial Training

Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem. Previously proposed Fair Robust Learning (FRL) adaptively reweights different classes to improve fairness. However, the performance of the better-performed classes decreases, leading to a strong performance drop. In this paper, we observed two unfair phenomena during adversarial training: different difficulties in generating adversarial examples from each class (source-class fairness) and disparate target class tendencies when generating adversarial examples (target-class fairness). From the observations, we propose Balance Adversarial Training (BAT) to address the robust fairness problem. Regarding source-class fairness, we adjust the attack strength and difficulties of each class to generate samples near the decision boundary for easier and fairer model learning; considering target-class fairness, by introducing a uniform distribution constraint, we encourage the adversarial example generation process for each class with a fair tendency. Extensive experiments conducted on multiple datasets (CIFAR-10, CIFAR-100, and ImageNette) demonstrate that our method can significantly outperform other baselines in mitigating the robust fairness problem (+5-10\% on the worst class accuracy)
On the Soft-Subnetwork for Few-shot Class Incremental Learning

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks (SoftNet)}. Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets.
Learning to Constrain Policy Optimization with Virtual Trust Region

We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update. In addition to using the proximity of one single old policy as the normal trust region, we propose forming a second trust region through another virtual policy representing a wide range of past policies. We then enforce the new policy to stay closer to the virtual policy, which is beneficial if the old policy performs poorly. More importantly, we propose a mechanism to automatically build the virtual policy from a memory of past policies, providing a new capability for dynamically learning appropriate virtual trust regions during the optimization process. Our proposed method, dubbed Memory-Constrained Policy Optimization (MCPO), is examined in diverse environments, including robotic locomotion control, navigation with sparse rewards and Atari games, consistently demonstrating competitive performance against recent on-policy constrained policy gradient methods.
A scalable quantum gate-based implementation for causal hypothesis testing

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [\textit{Nat Commun} 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence.
Analysis of current video databases for quality assessment

The popularity of video streaming has grown significantly over the past few years. Video quality prediction metrics can be used to perform extensive video codec analysis and customize high-quality assurance. Video databases with subjective ratings form an important basis for training video quality metrics, and codecs based on machine learning algorithms. More than three dozen subjective video databases are now available. In this article, modern video databases are presented, analyzed current database and findings methods for improving. For analysis, performance criteria are proposed based on subjective assessments when creating a database of video sequences. At this stage of development, subjective assessments are the most difficult part of creating a database of video sequences, since these assessments are expensive and time-consuming. In addition, subjective experimentation is further complicated by many factors, including viewing distance, a display device, lighting conditions, vision, and mood of the subjects. This information will allow researchers to have a more detailed understanding of the video databases, a new method for collecting subjective data, and can also help in planning future experiments.
Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data

An issue documents discussions around required changes in issue-tracking systems, while a commit contains the change itself in the version control systems. Recovering links between issues and commits can facilitate many software evolution tasks such as bug localization, and software documentation. A previous study on over half a million issues from GitHub reports only about 42.2% of issues are manually linked by developers to their pertinent commits. Automating the linking of commit-issue pairs can contribute to the improvement of the said tasks. By far, current state-of-the-art approaches for automated commit-issue linking suffer from low precision, leading to unreliable results, sometimes to the point that imposes human supervision on the predicted links. The low performance gets even more severe when there is a lack of textual information in either commits or issues. Current approaches are also proven computationally expensive. We propose Hybrid-Linker to overcome such limitations by exploiting two information channels; (1) a non-textual-based component that operates on non-textual, automatically recorded information of the commit-issue pairs to predict a link, and (2) a textual-based one which does the same using textual information of the commit-issue pairs. Then, combining the results from the two classifiers, Hybrid-Linker makes the final prediction. Thus, every time one component falls short in predicting a link, the other component fills the gap and improves the results. We evaluate Hybrid-Linker against competing approaches, namely FRLink and DeepLink on a dataset of 12 projects. Hybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and F-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and 41.3%, regarding the F-measure. Moreover, Hybrid-Linker exhibits extensive improvements in terms of performance as well.
PTab: Using the Pre-trained Language Model for Modeling Tabular Data

Tabular data is the foundation of the information age and has been extensively studied. Recent studies show that neural-based models are effective in learning contextual representation for tabular data. The learning of an effective contextual representation requires meaningful features and a large amount of data. However, current methods often fail to properly learn a contextual representation from the features without semantic information. In addition, it's intractable to enlarge the training set through mixed tabular datasets due to the difference between datasets. To address these problems, we propose a novel framework PTab, using the Pre-trained language model to model Tabular data. PTab learns a contextual representation of tabular data through a three-stage processing: Modality Transformation(MT), Masked-Language Fine-tuning(MF), and Classification Fine-tuning(CF). We initialize our model with a pre-trained Model (PTM) which contains semantic information learned from the large-scale language data. Consequently, contextual representation can be learned effectively during the fine-tuning stages. In addition, we can naturally mix the textualized tabular data to enlarge the training set to further improve representation learning. We evaluate PTab on eight popular tabular classification datasets. Experimental results show that our method has achieved a better average AUC score in supervised settings compared to the state-of-the-art baselines(e.g. XGBoost), and outperforms counterpart methods under semi-supervised settings. We present visualization results that show PTab has well instance-based interpretability.