Reviewerly/exHarmony huggingface dataset:
Computational Complexity Evaluation of Neural Network Applications in Signal Processing

In this paper, we provide a systematic approach for assessing and comparing the computational complexity of neural network layers in digital signal processing. We provide and link four software-to-hardware complexity measures, defining how the different complexity metrics relate to the layers' hyper-parameters. This paper explains how to compute these four metrics for feed-forward and recurrent layers, and defines in which case we ought to use a particular metric depending on whether we characterize a more soft- or hardware-oriented application. One of the four metrics, called `the number of additions and bit shifts (NABS)', is newly introduced for heterogeneous quantization. NABS characterizes the impact of not only the bitwidth used in the operation but also the type of quantization used in the arithmetical operations. We intend this work to serve as a baseline for the different levels (purposes) of complexity estimation related to the neural networks' application in real-time digital signal processing, aiming at unifying the computational complexity estimation.
A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.
GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing

The security of passwords depends on a thorough understanding of the strategies used by attackers. Unfortunately, real-world adversaries use pragmatic guessing tactics like dictionary attacks, which are difficult to simulate in password security research. Dictionary attacks must be carefully configured and modified to represent an actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper reviews various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. It also introduces GNPassGAN, a password guessing tool built on generative adversarial networks for trawling offline attacks. In comparison to the state-of-the-art PassGAN model, GNPassGAN is capable of guessing 88.03\% more passwords and generating 31.69\% fewer duplicates.
A generating model for Finnish nominal inflection using distributional semantics

Finnish nouns are characterized by rich inflectional variation, with obligatory marking of case and number, with optional possessive suffixes and with the possibility of further cliticization. We present a model for the conceptualization of Finnish inflected nouns, using pre-compiled fasttext embeddings (300- dimensional semantic vectors that approximate words’ meanings). Instead of deriving the semantic vector of an inflected word from another word in its paradigm, we propose that an inflected word is conceptualized by means of summation of latent vectors representing the meanings of its lexeme and its inflectional features. We tested this model on the 2,000 most frequent Finnish nouns and their inflected word forms from a corpus of Finnish (84 million tokens). Visualization of the semantic space of Finnish using t-SNE clarified that a ‘main effects’ additive model does not do justice to the semantics of inflection. In Finnish, how number is realized turns out to vary substantially with case. Further interactions emerged with the possessive suffixes and the clitics. By taking these interactions into account, the accuracy of our model, evaluated with the fasttext embeddings as gold standard, improved from 76% to 89%. Analyses of the errors made by the model clarified that 15% of errors are due to overabundance (and hence not true errors), and that 25% of the errors involved exchanges of semantically highly similar stems (lexemes). Our results indicate, first, that the semantics of Finnish noun inflection are more intricate than assumed thus far, and second, that these intricacies can be captured with surprisingly high accuracy by a simple generating model based on imputed semantic vectors for lexemes, inflectional features, and interactions of inflectional features.
Mathematical comparison of defuzzification of fuzzy logic controller for smart washing machine

This is the age of modern science where different kinds of gadgets are used for the betterment of humanity. Various electronic, intelligence systems are invented via modern technology where FLC (Fuzzy Logic Controller) is used to develop their intelligence systems. Nowadays, FLC is widely used in uncertainty and robust development systems. Two inputs and one output have been used in this paper. Also, various defuzzification methods are used to determine the washing time of a smart washing machine. Finally, the performance of the various defuzzification methods has been compared mathematically. This work has been simulated by using the MATLAB toolbox. J. Bangladesh Acad. Sci. 46(1); 1-8: June 2022
Corrections and Comments on “An Efficient Algorithm for Maneuvering Target Tracking” [Corrections and Comments]

Presents corrections to the above named article.
Lexical Richness of Abstracts in Scientific Papers in Anglophone and Non-Anglophone Journals

Journal research article abstracts, considered as the most read section of the entire paper, have been the focus of multi-dimensional research studies. In the genre of abstracts, vocabulary richness is the basis for the construction of sentences, paragraphs, and complete texts; it contributes to non-native (and even native) English speakers of the language in the production and comprehension of written texts. To explore its importance, the present study examines the lexical richness in abstracts of scientific papers; it consists of three distinct measurement dimensions: lexical density, lexical variation, and lexical sophistication. The comparative-descriptive analysis is based on a corpus of abstracts in English published in Anglophone and non-Anglophone contexts. The written corpora were subjected to a software-driven text analysis using the complete lextutor vocab-profile available online at https://www.lextutor.ca/vp/eng/; the output texts from the software analyser were mined using SPSS statistics. Results show that although abstracts in both publication contexts use varied and extensive vocabulary throughout the two English sub-corpora, Anglophone texts, unlike non-Anglophone ones, produce more content and off-list words. This study announces valuable insights, particularly for inexperienced and novice writers, on using automatic online tools, such as vocab-profile, to gauge the type of vocabulary used in their written compositions. Keywords: abstract; lexical richness; publication context; text analyser; written corpora
Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion

The so-called multimodal information refers to the information from different information sources on different or the same side of the same description target. These pieces of information are different in terms of storage structure, representation, semantic connotation, credibility, and emphasis, but there is a certain inevitable connection between them. This paper aims to study how to analyze and study the low carbon of enterprises with the help of multimodal information fusion based on the background of big data and construct the evolutionary neural network of the improved adaptive genetic algorithm. This paper puts forward the problem of low carbon in enterprises, which is based on multimodal information fusion, and then elaborates on the concept and related algorithms of multimodal information fusion. Information fusion has carried out case design and analysis. Through the research and analysis of enterprise low carbon and self-adaptive algorithm, it can be obtained that the neural network has reached the threshold of 3.95 after iterating for nearly 60 generations, and stopped iterating to obtain the best individual. The evolutionary neural network in this paper reaches stability after a small number of iterations and can basically achieve a certain low carbonization.
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising

Despite substantial advances in network architecture performance, the susceptibility of adversarial attacks makes deep learning challenging to implement in safety-critical applications. This paper proposes a data-centric approach to addressing this problem. A nonlocal denoising method with different luminance values has been used to generate adversarial examples from the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research (CIFAR-10) data sets. Under perturbation, the method provided absolute accuracy improvements of up to 9.3% in the MNIST data set and 13% in the CIFAR-10 data set. Training using transformed images with higher luminance values increases the robustness of the classifier. We have shown that transfer learning is disadvantageous for adversarial machine learning. The results indicate that simple adversarial examples can improve resilience and make deep learning easier to apply in various applications.
Voice Recognition Security Reliability Analysis Using Deep Learning Convolutional Neural Network Algorithm

This study discusses the reliability analysis of voice recognition security using the deep learning convolutional neural network (CNN) algorithm. The CNN algorithm has learning advantages in that it is safer, faster, and more accurate. CNN also can solve user identification problems in large amounts of data. The measured voice input is ten types of user's voice with the number of iterations of 6000, 12000, and 15000 sound files. Furthermore, voice extraction features are performed to recognize conversations and retain information that is very much needed. After that, the voice file iteration data is trained to register the user's voice so that a trained model is obtained. These results measure performance (confusion matrix) to analyze the actual value compared to the predicted value in the CNN algorithm. The results obtained are that the best accuracy is obtained at 15000 sound file iterations, 96.87%, 12000 sound file iterations get 96.30%, and 6000 sound file iterations get 95.77%. CNN's performance data shows that 15000 iterations of voice files produce high accuracy. Voice recognition security helps provide high security and maintain the privacy of one's identity.
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective

Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based pretrained models have achieved superb performances in both areas. However, most accent classification tasks focused on classifying different kinds of English accents and little attention was paid to geographically-proximate accent classification, especially under a low-resource setting where forensic speech science tasks usually encounter. In this paper, we explored three main accent modelling methods combined with two different classifiers based on 105 speaker recordings retrieved from five urban varieties in Northern England. Although speech representations generated from pretrained models generally have better performances in downstream classification, traditional methods like Mel Frequency Cepstral Coefficients (MFCCs) and formant measurements are equipped with specific strengths. These results suggest that in forensic phonetics scenario where data are relatively scarce, a simple modelling method and classifier could be competitive with state-of-the-art pretrained speech models as feature extractors, which could enhance a sooner estimation for the accent information in practices. Besides, our findings also cross-validated a new methodology in quantifying sociophonetic changes.
Breast cancer histological images nuclei segmentation and optimized classification with deep learning

Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
Machine Learning Model for Sentiment Analysis of COVID-19 Tweets

COVID-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide.The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions.Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel.During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions.This research aims to analyze the public sentiment related to COVID-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks.This goal was achieved by building a machine learning model using a dataset of COVID-19 related English tweets.Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications.A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%.This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases.
Pedagogical conditions for developing cognitive skills of elementary school children in project activities

Introduction. For the successful functioning of modern educational technologies based on the principles of a system-activity approach, in the conditions of the Federal State Educational Standard, it is necessary to create a set of pedagogical conditions. The key pedagogical conditions that affect the success of developing cognitive skills of elementary school children in project activities are currently insufficiently discussed in the scientific literature. The purpose of the study is to determine the necessary and sufficient pedagogical conditions for the effective formation of cognitive skills of younger students in project activities. Methodology and methods: the study was carried out on the basis of the analysis of scientific literature, using the method of categorical-system methodology “Method of triadic category decoding”. Results of the study: 1) the key elements of pedagogical conditions that make up their essence are identified: a) the position of the teacher organizing project activities; b) stimulating the formation of motivation for project activities; c) monitoring cognitive skills; 2) a two-level triadic decoding of the corresponding category was made; 3) a set of pedagogical conditions was obtained, necessary and sufficient, for the effective formation of cognitive skills of younger students in project activities. Conclusions. The study made it possible to identify and reveal the essence of the key categories of pedagogical conditions and a complex of pedagogical conditions and create a detailed model that gives a complete picture of the qualitative characteristics of the pedagogical conditions for the formation of cognitive skills of younger students in project activities.
Keyness, Context, and Cultural Specificity in Indirect Translation

The translation of references specific to a given source culture has long been a prominent, and often problematic aspect of translation practice and research.In indirect translation, or the translation of already translated material, linguistic and cultural differences accumulate, meaning that the omission of cultural references (CRs) or culture-specific items (CSIs) might be a generally expected outcome.Yet before such hypotheses can be tested, research methods are needed that can account for broad patterns across whole texts, and preferably, across semantic categories, genres, time periods, and languages.A 'textual' approach, focused on the linguistic context in which CRs are likely to occur, should complement the currently dominant 'cultural' approach, which mainly relies on predefined categories and intuition for the selection of objects of study.This article illustrates that corpus research, and particularly keyness analysis, can aid in uncovering recurrent structural patterns and textual functions in which CRs are expected to pose translation difficulties.In this regard, it focuses on expressions of enumeration, or lists, and indicators of identification, or voice.Based on a trilingual (English, French, and Italian) corpus-assisted study of Jonathan Swift's Gulliver's Travels (1726) and John Cary's An Essay on the State of England (1695), the article accentuates the productive complementarity of numerical operations and context-sensitive readings.
Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection occurring in treatment settings. To find relevant literature, PubMed and Google Scholar were searched. Out of 183 retrieved studies, 31 met all inclusion criteria. This review found that AI has been used to detect different types of fraud such as identify theft and kickbacks in healthcare. Additionally, this review discusses how AI techniques used in network mapping fraud can detect and visualize the hacker's network. A proper system must be implemented in healthcare settings for successful fraud detection, which may overall improve the healthcare system.
Aspect Oriented Programming: Trends and Applications

The competitive advantage of aspect oriented programming (AOP) is that it improves the maintainability and understandability of software systems by modularizing crosscutting concerns. However, some concerns, such as logging or debugging, may be overlooked and should be entangled and distributed across the code base. AOP is a software development paradigm that enables developers to capture crosscutting concerns in split-aspect modes. Additionally, it is a novel notion that has the potential to improve the quality of software programs by removing the complexity involved with the production of code tangles via the usage of separation of concerns. As a result, it provides more modularity. Throughout its early development, some believed that AOP was easier to build and maintain than other implementations since it was based on an existing one. The statements are predicated on the premise that local improvements are easier to implement. Additionally, without appropriate visualization tools for both static and dynamic structures, cross-cutting challenges may be difficult for developers and researchers to appreciate. In recent years, AspectJ has begun to enable the depiction of crosscutting concerns via the release of IDE plugins. This article explains aspect oriented programming and how it may be used to improve the readability and maintainability of software projects. Additionally, it will evaluate the challenges it presents to application developers and academics.
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization (EM) over latent master's distribution and clients' parameters. We also introduce formal differential privacy (DP) guarantees compatibly with our EM optimization scheme. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners, even when local parameters perturbation is included to provide DP guarantees. Moreover, the variability of data, views and centers can be quantified in an interpretable manner, while guaranteeing high-quality data reconstruction as compared to state-of-the-art autoencoding models and federated learning schemes. The code is available at https://gitlab.inria.fr/epione/federated-multi-views-ppca.