Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.     

Non Deterministic Classical Logic: The λμ++ ‐calculus

In this paper, we present an extension of λμ‐calculus called λμ⁺⁺‐calculus which has the following properties: subject reduction, strong normalization, unicity of the representation of data and thus confluence only on data types. This calculus allows also to program the parallel‐or.     

Über Bildung und Reaktionen der CH2Li‐Derivate von tBu2P–P=P(CH3)tBu2 und (Me3Si)tBuP–P=P(CH3)tBu2

Formation and Reactions of the CH₂Li‐Derivatives of ^tBu₂P–P=P(CH₃)^tBu₂ and (Me₃Si)^tBuP–P=P(CH₃)^tBu₂ With ⁿBuLi, (Me₃Si)^tBuP–P=P(CH₃)^tBu₂ ( 1 ) and ^tBu₂P–P=P(CH₃)^tBu₂ ( 2 ) yield (Me₃Si)^tBuP–P=P(CH₂Li)^tBu₂ ( 3 ) and ^tBu₂P–P=P(CH₂Li)^tBu₂ ( 4 ), wich react with Me₃SiCl to give (Me₃Si)^tBuP–P=P(CH₂–SiMe₃)^tBu₂ ( 5 ) and ^tBu₂P–P=P(CH₂–SiMe₃)^tBu₂ ( 6 ), respectively. With ^tBu₂P–P(SiMe₃)–P^tBuCl ( 7 ), compound 3 forms 5 as well as the cyclic products [H₂C–P(^tBu)₂=P–P(^tBu)–P^tBu] ( 8 ) and [H₂C–P(^tBu)₂=P–P(P^tBu₂)–P(^tBu)] ( 9 ). Also 3 forms 8 with ^tBuPCl₂. The cleavage of the Me₃Si–P‐bond in 1 by means of C₂Cl₆ or N‐bromo‐succinimide yields (Cl)^tBuP–P=P(CH₃)^tBu₂ ( 10 ) or (Br)^tBuP–P=P(CH₃)^tBu₂ ( 11 ), resp. With LiP(SiMe₃)₂, 10 forms (Me₃Si)₂P–P(^tBu)–P=P(CH₃)^tBu₂ ( 12 ), and Et₂P–P(^tBu)–P=P(CH₃)^tBu₂ ( 13 ) with LiPEt₂. All compounds are characterized by ³¹P NMR Data and mass spectra; the ylide 5 and the THF adduct of 4 additionally by X‐ray structure analyses.     

Muffin‐tin potentials in EXAFS analysis

Muffin‐tin potentials are the standard tool for calculating the potential surface of a cluster of atoms for use in the analysis of extended X‐ray absorption fine‐structure (EXAFS) data. The set of Cartesian coordinates used to define the positions of atoms in the cluster and to calculate the muffin‐tin potentials is commonly also used to enumerate the scattering paths used in the EXAFS data analysis. In this paper, it is shown that these muffin‐tin potentials are sufficiently robust to be used to examine quantitatively contributions to the EXAFS data from scattering geometries not represented in the original cluster.     

A data mining and ab initio study of the interaction between the aromatic and backbone amide groups in proteins

Interactions between aromatic groups and backbone amide groups in protein environments are characterized both through data mining analyses of X‐ray protein structures and through ab initio molecular orbital calculations on a model complex. The data mining analyses of 1029 X‐ray protein structures elucidate the configurational characteristics of the interaction as well as the positions of the interacting moieties involved. On a statistical average, more than seven such interactions occur in a typical protein structure. The configurations of these interactions are restricted with the face‐to‐face orientation as the preferred arrangement. The interaction occurs mainly within a single peptide chain. Both α‐helix and β‐strand secondary structures provide an almost equal number of backbone amides to participate within this interaction. The interaction energy was evaluated with the supermolecular ab initio method at the MP2 level. It is shown that aromatic–amide(backbone) interactions identified in proteins can achieve a stabilization energy of 3.3 kcal/mol. The interaction involves the entirety of the backbone amide rather than only its amine portion. This study concludes that the interaction between aromatic and backbone amide groups is of general significance to protein structure due to its strength and common occurrence. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 80: 44–60, 2000     

Multiresolutional decomposition and modeling with an application to joint probabilistic data association

A multiresolutional approach for measurement decomposition and system modeling is presented in this paper. The decomposition is performed in both spatial and time domains and provides an excellent platform for developing computationally efficient algorithms. Using multiresolutional decomposition and modeling, a multiresolutional joint probabilistic data association (MR-JPDA) algorithm is developed for multiple target tracking. Monte Carlo simulations demonstrate that the computation of the MRJPDA algorithm is much less than the traditional joint probabilistic data association (JPDA) algorithm with a comparable performance.     

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.     

Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.     

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.     

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examine such systems and consider a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples are used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically prove the optimality of our algorithm, bound its complexity, and complement it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets.