Reviewerly/exHarmony huggingface dataset:
A Multi-modal CBIR Framework with Image Segregation using Autoencoders and Deep Learning-based Pseudo-labeling

Various modulated techniques of Content-Based Image Retrieval (CBIR) using deep learning provide better search outputs even though they are computationally challenging. These methods can be enhanced further, if the search key can be tagged effectively and directed towards target images. In this paper, we have developed a new multilevel aggregation technique along with autoencoders, to be implemented on image features for precise feature selection and accurate search output. Locally significant datasets and generic ones are dealt separately for obtaining better hit rates along with the process of query expansion. The concept of pseudo-labelling by deep learning is introduced to classify images into positive and negative classes, where positive class consists of the images that are similar to the query image. The feature spaces of query images are compared with the images in the search pool based on their assigned weights. The target images are finally ranked and selected based on an adaptive threshold level. The methodology for this enhanced CBIR technique is tested on public datasets and its results are collated with recent approaches proposed in the literature. The proposed method has attained significant improvements in precision, recall and computation and thereby the use of images from connected devices has been proven to be significant. Given the importance of more accurate image retrieval in CBIR, this method can be experimented for other similar applications as well.
Remediating data drifts and re-establishing ML models

Machine learning models are significantly used in organizations for critical business processes and decision-making. The inputs and predictions from these models might drift over time due to several reasons, causing unanticipated behaviour and performance issues. COVID-19 has a significant impact on business resulting in huge dispersions of the data with unstable characteristics and irregular spikes at various time steps. When the data set experiences significant drifts due to the changes in their statistical properties, continuing with existing models based on historical data will lead to poor decision outcomes. It's hard to anticipate all significant future events and create an ML model which can withstand black swan events. Therefore, it's imperative to have an automated process for continuous monitoring in the ML lifecycle to identify the potential issues using drift detection methodologies with proper actions to avoid false predictions in silence. This study is focused on time series where data stream is represented as observations at regular intervals. Many dynamic real-world processes can be modelled using time series where data can flow quickly and change over time which is detrimental to time series analysis and forecasting. The objective of this study is to compare and evaluate different approaches for data drifts in time series models to retain its optimum performance and proposing an integrated approach for handling those drifts in real-time pipelines using timely alerts and re-establishing models.
Imbalanced aspect categorization using bidirectional encoder representation from transformers

Sentiment analysis (also called opinion mining) is one of the widely used research fields of natural language processing. E-commerce service providers use this technique to analyze the sentiment of a product or a service in texts, posts, and comments. In particular, the service providers and users want to understand the sentiment on product aspect categories rather than the overall sentiment of a product. These aspect categories encounter the class imbalance problem. Therefore, the BERT (Bidirectional Encoder Representation from Transformers) based fine-tuning model is presented to deal with the imbalanced aspect categorization task. Specifically, this paper studies various data sampling techniques such as stratified random sampling (SRS), random undersampling (RUS), and random oversampling (ROS) for reducing the class imbalance problem. Empirically, the results show that the proposed BERT fine-tuning model with the SRS technique achieves better results. In particular, the model achieves 96.21% for the validation and 96.47% for testing using the news aggregator data. Similarly, the SMS spam collection data achieves 99.20% for the validation and 99.10% for testing.
Integration of Global and Local Descriptors for Mass Characterization in Mammograms

Breast cancer is the most common cancer among women and is associated with high morbidity and mortality. Early detection has proven successful in effective treatment and increased survival rate. Mammography is the most common modality for screening breast cancer; nevertheless, it is associated with low sensitivity and specificity due to subtlety of abnormalities in early stages. A mass which is the most common and important indicator of breast cancer, is especially challenging to analyse and interpret. Computer aided diagnosis (CAD) systems can improve the diagnostic performance by aiding radiologists in providing an objective assessment. The proposed work aims to develop a CAD system to evaluate mammographic masses and characterize them into benign and malignant categories. The system employs an integrated approach based on fusion of global descriptors extracted from the segmented mass regions and local descriptors from a ribbon of pixels surrounding the mass boundary, to effectively characterize masses. The proposed approach is validated using the Digital Database for Screening Mammograms (DDSM) database, yielding a classification accuracy as high as 94.6% for mass characterization.
Short text classification applied to item description: Some methods evaluation

The increasing demand for information classification based on content in the age of social media and e-commerce has led to the need for automated product classification using their descriptions. This study aims to evaluate various techniques for this task, with a focus on descriptions written in Portuguese. A pipeline is implemented to preprocess the data, including lowercasing, accent removal, and unigram tokenization. The bag of words method is then used to convert text into numerical data, and five classification techniques are applied: argmaxtf, argmaxtfnorm, argmaxtfidf from information retrieval, and two machine learning methods logistic regression and support vector machines. The performance of each technique is evaluated using simple accuracy via thirty-fold cross validation. The results show that logistic regression achieves the highest mean accuracy among the evaluated techniques.
SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder

Text summarization models are often trained to produce summaries that meet human quality requirements. However, the existing evaluation metrics for summary text are only rough proxies for summary quality, suffering from low correlation with human scoring and inhibition of summary diversity. To solve these problems, we propose SummScore, a comprehensive metric for summary quality evaluation based on Cross-Encoder. Firstly, by adopting the original-summary measurement mode and comparing the semantics of the original text, SummScore gets rid of the inhibition of summary diversity. With the help of the text-matching pre-training Cross-Encoder, SummScore can effectively capture the subtle differences between the semantics of summaries. Secondly, to improve the comprehensiveness and interpretability, SummScore consists of four fine-grained submodels, which measure Coherence, Consistency, Fluency, and Relevance separately. We use semi-supervised multi-rounds of training to improve the performance of our model on extremely limited annotated data. Extensive experiments show that SummScore significantly outperforms existing evaluation metrics in the above four dimensions in correlation with human scoring. We also provide the quality evaluation results of SummScore on 16 mainstream summarization models for later research.
VarCorC: Developing Object-Oriented Software Product Lines Using Correctness-by-Construction

Functional correctness is an important concern, especially in the field of safety-critical systems. Correctness-by-Construction (CbC) is an incremental software development technique to create functionally correct programs guided by a formal specification. The specification is defined first, and then the program is incrementally created using a small set of refinement rules that define side conditions preserving the correctness. CbC is mostly used to create small algorithms. However, software in-field is often larger and more complex to meet the requirements of today’s life. Therefore, our vision is to scale the applicability of CbC to larger scale software systems, like software product lines (SPLs). SPLs are one way to implement a whole product family by managed reuse. Advanced implementation techniques for SPLs rely on object-orientation and variability realization mechanisms on the source code level. In this tool paper, we present our tool VarCorC which supports the development of correct SPLs using CbC including object-orientation and feature-oriented programming. We describe VarCorC from user-perspective and explain how it works internally. Additionally, we provide a feasibility evaluation of VarCorC on three case studies that are used as benchmarks in the field of product line verification.
Two Shaky Pillars of Quantum Computing

Superposition and entanglement are two theoretical pillars quantum computing rests upon.In the g-qubit theory quantum wave functions are identified by points on the surface of three-dimensional sphere 3 .That gives differ- ent, more physically feasible explanation of what superposition and entanglement are.The core of quantum computing scheme should be in manipulation and transferring of wave functions on 3  as operators acting on obser- vables and formulated in terms of geometrical algebra.In this way quantum computer will be a kind of analog computer keeping and processing information by sets of objects possessing infinite number of degrees of freedom, contrary to the two value bits or two-dimensional Hilbert space elements, qubits.
Learning Stochastic Graph Neural Networks With Constrained Variance

Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular output realizations around the optimal expectation. To overcome this issue, we propose a variance-constrained optimization problem for SGNNs, balancing the expected performance and the stochastic deviation. An alternating primal-dual learning procedure is undertaken that solves the problem by updating the SGNN parameters with gradient descent and the dual variable with gradient ascent. To characterize the explicit effect of the variance-constrained learning, we conduct a theoretical analysis on the variance of the SGNN output and identify a trade-off between the stochastic robustness and the discrimination power. We further analyze the duality gap of the variance-constrained optimization problem and the converging behavior of the primal-dual learning procedure. The former indicates the optimality loss induced by the dual transformation and the latter characterizes the limiting error of the iterative algorithm, both of which guarantee the performance of the variance-constrained learning. Through numerical simulations, we corroborate our theoretical findings and observe a strong expected performance with a controllable standard deviation.
Decentralized Moving Horizon Estimation for Large-Scale Networks of Interconnected Unconstrained Linear Systems

This paper addresses the problem of designing a decentralized state estimation solution for a large-scale network of interconnected unconstrained linear time invariant (LTI) systems. The problem is tackled in a novel moving horizon estimation (MHE) framework, while taking into account the limited communication capabilities and the restricted computational power and memory, which are distributed across the network. The proposed design is motivated by the fact that, in a decentralized setting, a Luenberguer-based framework is unable to leverage the full potential of the available local information. A method is derived to solve a relaxed version of the resulting optimization problem. It can be synthesized offline and its stability can be assessed prior to deployment. It is shown that the proposed approach allows for significant improvement on the performance of recent Luenberger-based filters. Furthermore, we show that a state-of-the-art distributed MHE solution with comparable requirements underperforms in comparison to the proposed solution.
Building a digital geological knowledge management system to support scientific research

The paper describes new approaches to collecting data on scientific publications from open access systems with the subject of Earth Science. Based on the developed and adapted approaches, an archive of scientific publications (repository) and a set of programs for accessing scientific publications for collecting, searching, filtering, cataloging and managing publications and their&#x0D; metadata have been created. In order to improve the availability of publications and other related data on the websites of the SGM RAS, the Wiki – Geology of Russia system has been developed. This system is a thematic rubric in the direction of "Mineral deposits of Russia", with an additional topics "Mineralogy" and "Geological Structures". All articles must have a link to the source of information from the archive of scientific publications and, optionally, additional links on similar topics. Wiki – Geology of Russia is the first step in creating a knowledge base on mineral deposits.
Neuro-fuzzy Control of a Mobile Robot

This paper is aimed at looking in to solve the robot navigation problem. This problem is treated using two techniques. It has been found that the fuzzy logic system is able to navigate the autonomous mobile robot in an unknown environment. In the same way, the neuro-fuzzy is used to adjust the consequences parameters of fuzzy controller in order to drive the khepera IV robot to the target, with an optimal and safe trajectory. A comparative study is made to show the performance of the Adaptative Neuro-Fuzzy Inference System (ANFIS) against fuzzy logic controller (FLC). Simulation results are given to demonstrate the satisfactory of the developed techniques.
Real-Time Intentional Eye Blink Detection Using Rhombus Identification

Eye blinking is an indicator for various applications such as face recognition, drowsiness detection, phone unlocking, etc. Existing work on blink recognition typically considers the normal expressions that the two eyes are unintentionally opened and closed simultaneously. There is no literature on detecting intentional eye blink with simultaneous one eye open and one eye closed. Such intentional eye blinks can serve as a customized signal for various applications, such as activating an intelligent security system by a member in danger. Further, the existing work uses the outline of an eye (called the Eye Aspect Ratio (EAR) method) to judge whether to blink or not. However, the sizes of people's eyes could be different, and this method is thus prone to misjudgment. To remedy these loopholes, we propose the following two methods: (1) An intentional eye blinking mechanism with simultaneous one eye open and one eye closed as a signal to activate an intelligent security system; (2) A novel, fast, and accurate Rhombus Identification Method (RIM), considering the eye contour and normalized area, which can accurately identify expressions with eyes open and closed simultaneously, as a signal to activate security alarms. Compared with the EAR method, our RIM has significantly higher accuracy rates under different situations. Based on machine learning, we incorporate our RIM into an intelligent security system and justify the practicability of our RIM.
Category of Tonality Explicated in the Texts of Virtual Genre

The research dealt with the category of tonality explicated in the texts of virtual communication. It featured the official community of students, graduates, academics, and employees of the Ural State Law University in the Vkontakte social network. The research objective was to identify the specifics of tonality in the communicative and pragmatic aspects of the virtual genre. The method of categorical-textual analysis revealed the types of tonality that made up the tonal field of virtual communication, as well as their linguistic explication means at the lexical, morphological, syntactic, and graphical levels. A specific transformation of tonal varieties proved to be a characteristic feature of the hybrid virtual genre: it combined a monologue text and a reflective commentary on it. The neutral and benevolent tonalities of the monologue tonal core were complemented by peripheral varieties of the commentary text with pleading and disapproving tonalities. In the commentary texts, the tonality of benevolence remained in the core, while the neutral tonality became peripheral. The communicative and pragmatic solution was evaluated as positive when the tonality in the tonal core of monologue texts and comments was benevolent, or when the neutral tonality of the monologue text turned benevolent in the commentary. These tactics yielded a positive perception of the information presented by the sender of the message.
The Analysis and Development of an XAI Process on Feature Contribution Explanation

Explainable Artificial Intelligence (XAI) research focuses on effective explanation techniques to understand and build AI models with trust, reliability, safety, and fairness. Feature importance explanation summarizes feature contributions for end-users to make model decisions. However, XAI methods may produce varied summaries that lead to further analysis to evaluate the consistency across multiple XAI methods on the same model and data set. This paper defines metrics to measure the consistency of feature contribution explanation summaries under feature importance order and saliency map. Driven by these consistency metrics, we develop an XAI process oriented on the XAI criterion of feature importance, which performs a systematical selection of XAI techniques and evaluation of explanation consistency. We demonstrate the process development involving twelve XAI methods on three topics, including a search ranking system, code vulnerability detection and image classification. Our contribution is a practical and systematic process with defined consistency metrics to produce rigorous feature contribution explanations.
Transition-Aware Multi-Activity Knowledge Tracing

Accurate modeling of student knowledge is essential for large-scale online learning systems that are increasingly used for student training. Knowledge tracing aims to model student knowledge state given the student’s sequence of learning activities. Modern Knowledge tracing (KT) is usually formulated as a supervised sequence learning problem to predict students’ future practice performance according to their past observed practice scores by summarizing student knowledge state as a set of evolving hidden variables. Because of this formulation, many current KT solutions are not fit f or modeling student learning from non-assessed learning activities with no explicit feedback or score observation (e.g., watching video lectures that are not graded). Additionally, these models cannot explicitly represent the dynamics of knowledge transfer among different learning activities, particularly between the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning activities. In this paper, we propose Transition-Aware Multi-activity Knowledge Tracing (TAMKOT), which models knowledge transfer between learning materials, in addition to student knowledge, when students transition between and within assessed and non-assessed learning materials. TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities. Accordingly, our model allows for representing each material type in a different yet transferrable latent space while maintaining student knowledge in a shared space. We evaluate our model on three real-world publicly available datasets and demonstrate TAMKOT’s capability in predicting student performance and modeling knowledge transfer.
Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy

Spontaneous reporting systems (SRS) have been developed to collect adverse event records that contain personal demographics and sensitive information like drug indications and adverse reactions. The release of SRS data may disclose the privacy of the data provider. Unlike other microdata, very few anonymyization methods have been proposed to protect individual privacy while publishing SRS data. MS(k, θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> )-bounding is the first privacy model for SRS data that considers multiple individual records, mutli-valued sensitive attributes, and rare events. PPMS(k, θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> )-bounding then is proposed for solving cross-release attacks caused by the follow-up cases in the periodical SRS releasing scenario. A recent trend of microdata anonymization combines the traditional syntactic model and differential privacy, fusing the advantages of both models to yield a better privacy protection method. This paper proposes the PPMS-DP(k, θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , ε) framework, an enhancement of PPMS(k, θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> )-bounding that embraces differential privacy to improve privacy protection of periodically released SRS data. We propose two anonymization algorithms conforming to the PPMS-DP(k, θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , ε) framework, PPMS-DP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">num</inf> and PPMS-DP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</inf> . Experimental results on the FAERS datasets show that both PPMS-DP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">num</inf> and PPMS-DP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</inf> provide significantly better privacy protection than PPMS-(k, θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> )-bounding without sacrificing data distortion and data utility.