Reviewerly/exHarmony huggingface dataset:
EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS

Writing a well-structured scientific documents, such as articles and theses, is vital for comprehending the document's argumentation and understanding its messages. Furthermore, it has an impact on the efficiency and time required for studying the document. Proper document segmentation also yields better results when employing automated Natural Language Processing (NLP) manipulation algorithms, including summarization and other information retrieval and analysis functions. Unfortunately, inexperienced writers, such as young researchers and graduate students, often struggle to produce well-structured professional documents. Their writing frequently exhibits improper segmentations or lacks semantically coherent segments, a phenomenon referred to as "mal-segmentation." Examples of mal-segmentation include improper paragraph or section divisions and unsmooth transitions between sentences and paragraphs. This research addresses the issue of mal-segmentation in scientific writing by introducing an automated method for detecting mal-segmentations, and utilizing Sentence Bidirectional Encoder Representations from Transformers (sBERT) as an encoding mechanism. The experimental results section shows a promising results for the detection of mal-segmentation using the sBERT technique.
MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK

Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy of face recognition is greatly reduced due to the obscuring of objects, such as masks and sunglasses. Wearing masks in public has been a crucial approach to preventing illness, especially since the Covid-19 outbreak. This poses challenges to applications such as face recognition. Therefore, the removal of masks via image inpainting has become a hot topic in the field of computer vision. Deep learning-based image inpainting techniques have taken observable results, but the restored images still have problems such as blurring and inconsistency. To address such problems, this paper proposes an improved inpainting model based on generative adversarial network: the model adds attention mechanisms to the sampling module based on pix2pix network; the residual module is improved by adding convolutional branches. The improved inpainting model can not only effectively restore faces obscured by face masks, but also realize the inpainting of randomly obscured images of human faces. To further validate the generality of the inpainting model, tests are conducted on the datasets of CelebA, Paris Street and Place2, and the experimental results show that both SSIM and PSNR have improved significantly.
PDL on Steroids: on Expressive Extensions of PDL with Intersection and Converse

We introduce CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , a family of expressive logics rooted in Propositional Dynamic Logic (PDL). In terms of expressive power, CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> strictly contains PDL extended with intersection and converse (a.k.a. ICPDL) as well as Conjunctive Queries (CQ), Conjunctive Regular Path Queries (CRPQ), or some known extensions thereof (Regular Queries and CQPDL). We investigate the expressive power, indistinguishability via bisimulations, satisfiability, and model checking for CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> .We argue that natural subclasses of CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> can be defined in terms of the tree-width of the underlying graphs of the formulas. We show that the class of CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> formulas of tree-width 2 is equivalent to ICPDL, and that it also coincides with CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> formulas of tree-width 1. However, beyond tree-width 2, incrementing the tree-width strictly increases the expressive power. We characterize the expressive power for every class of fixed tree-width formulas in terms of a bisimulation game with pebbles. Based on this characterization, we show that CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> has a tree-like model property. We prove that the satisfiability problem is decidable in 2EXPTIME on fixed tree-width formulas, coinciding with the complexity of ICPDL. We also exhibit classes for which satisfiability is reduced to EXPTIME. Finally, we establish that the model checking problem for fixed tree-width formulas is in PTIME, contrary to the full class CPDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> .
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs

We introduce a new setting, the category of ωPAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages. Our semantics is general enough to assign meanings to most practical probabilistic and differentiable programs, including those that use general recursion, higher-order functions, discontinuous primitives, and discrete and continuous sampling. But crucially, it is also specific enough to exclude many pathological denotations, enabling us to establish new results about differentiable and probabilistic programs. In the differentiable setting, we prove general correctness theorems for automatic differentiation and its use within gradient descent. In the probabilistic setting, we establish the almost-everywhere differentiability of probabilistic programs’ trace density functions, and the existence of convenient base measures for density computation in Monte Carlo inference. In some cases these results were previously known, but required detailed proofs of an operational flavor; by contrast, all our proofs work directly with programs’ denotations.
The Logic of Prefixes and Suffixes is Elementary under Homogeneity<sup>*</sup>

In this paper, we study the finite satisfiability problem for the logic BE under the homogeneity assumption. BE is the cornerstone of Halpern and Shoham’s interval temporal logic, and features modal operators corresponding to the prefix (a.k.a. "Begins") and suffix (a.k.a. "Ends") relations on intervals. In terms of complexity, BE lies in between the "Chop" logic C, whose satisfiability problem is known to be non-elementary, and the PSpace-complete interval logic D of the sub-interval (a.k.a. "During") relation. BE was shown to be ExpSpace-hard, and the only known satisfiability procedure is primitive recursive, but not elementary. Our contribution consists of tightening the complexity bounds of the satisfiability problem for BE, by proving it to be ExpSpace-complete. We do so by devising an equi-satisfiable normal form with boundedly many nested modalities. The normalization technique resembles Scott’s quantifier elimination, but it turns out to be much more involved due to the limitations enforced by the homogeneity assumption.
Allegories of Symbolic Manipulations

Moving from the mathematical theory of (abstract) syntax, we develop a general relational theory of symbolic manipulation parametric with respect to, and accounting for, general notions of syntax. We model syntax relying on categorical notions, such as free algebras and monads, and show that a general theory of symbolic manipulation in the style of rewriting systems can be obtained by extending such notions to an allegorical setting. This way, we obtain an augmented calculus of relations accounting for syntax-based rewriting. We witness the effectiveness of the relational approach by generalising and unifying milestones results in rewriting, such as the parallel moves and the Tait-Martin-Löf techniques.
Fixpoint operators for 2-categorical structures

Fixpoint operators are tools to reason on recursive programs and data types obtained by induction (e.g. lists, trees) or coinduction (e.g. streams). They were given a categorical treatment with the notion of categories with fixpoints. A theorem by Plotkin and Simpson characterizes existence and uniqueness of fixpoint operators for categories satisfying some conditions on bifree algebras and recovers the standard examples of the category Cppo (ω-complete pointed partial orders and continuous functions) in domain theory and the relational model in linear logic.We present a categorification of this result and develop the theory of 2-categorical fixpoint operators where the 2-dimensional framework allows to model the execution steps for languages with (co)inductive principles. We recover the standard categorical constructions of initial algebras and final coalgebras for endofunctors as well as fixpoints of generalized species and polynomial functors.
Automatic Amortized Resource Analysis with Regular Recursive Types

The goal of automatic resource bound analysis is to statically infer symbolic bounds on the resource consumption of the evaluation of a program. A longstanding challenge for automatic resource analysis is the inference of bounds that are functions of complex custom data structures. This article builds on type-based automatic amortized resource analysis (AARA) to address this challenge. AARA is based on the potential method of amortized analysis and reduces bound inference to standard type inference with additional linear constraint solving, even when deriving non-linear bounds. Such bounds come from resource functions, which are linear combinations of basic functions of data structure sizes that fulfill certain closure properties.Previous work on AARA defined resource functions for many data structures such as lists of lists, but left open whether such functions exist for arbitrary data structures. This work answers this question positively by uniformly constructing resource polynomials for algebraic data structures defined by regular recursive types. These functions are a generalization of all previously proposed polynomial resource functions and can be seen as a general notion of polynomials for values of a given recursive type. A resource type system for FPC, a core language with recursive types, demonstrates how resource polynomials can be integrated with AARA while preserving all benefits of past techniques. The article also proposes the use of new techniques useful for stating the rules of this type system succinctly and proving it sound against a small-step cost semantics. First, multivariate potential annotations are stated in terms of free semimodules, substantially abstracting details of the presentation of annotations and the proofs of their properties. Second, a logical relation giving semantic meaning to resource types enables a proof of soundness by a single induction on typing derivations.
A categorical account of composition methods in logic

We present a categorical theory of the composition methods in finite model theory – a key technique enabling modular reasoning about complex structures by building them out of simpler components. The crucial results required by the composition methods are Feferman–Vaught–Mostowski (FVM) type theorems, which characterize how logical equivalence be-haves under composition and transformation of models.Our results are developed by extending the recently introduced game comonad semantics for model comparison games. This level of abstraction allow us to give conditions yielding FVM type results in a uniform way. Our theorems are parametric in the classes of models, logics and operations involved. Furthermore, they naturally account for the positive existential fragment, and extensions with counting quantifiers of these logics. We also reveal surprising connections between FVM type theorems, and classical concepts in the theory of monads.We illustrate our methods by recovering many classical theorems of practical interest, including a refinement of a previous result by Dawar, Severini, and Zapata concerning the 3-variable counting logic and cospectrality. To highlight the importance of our techniques being parametric in the logic of interest, we prove a family of FVM theorems for products of structures, uniformly in the logic in question, which cannot be done using specific game arguments.
Operational Algorithmic Game Semantics

We consider a simply-typed call-by-push-value calculus with state, and provide a fully abstract trace model via a labelled transition system (LTS) in the spirit of operational game semantics. By examining the shape of configurations and performing a series of natural optimisation steps based on name recycling, we identify a fragment for which the LTS can be recast as a deterministic visibly pushdown automaton. This implies decidability of contextual equivalence for the fragment identified and solvability in exponential time for terms in canonical form. We also identify a fragment for which these automata are finite-state machines.Further, we use the trace model to prove that translations of prototypical call-by-name (IA) and call-by-value (RML) languages into our call-by-push-value language are fully abstract. This allows our decidability results to be seen as subsuming several results from the literature for IA and RML. We regard our operational approach as a simpler and more intuitive way of deriving such results. The techniques we rely on draw upon simple intuitions from operational semantics and the resultant automata retain operational style, capturing the dynamics of the underlying language.
A Secure Deep Autoencoder-based 6G Channel Estimation to Detect/Mitigate Adversarial Attacks

Channel estimation (CE) is critical in wireless communications. However, it is vulnerable to adversarial attacks (AA) that are associated with the incorporated artificial intelligence (AI) functionality in 6G wireless communication systems/networks. The hazardous threat can compromise communications’ confidentiality and integrity due to the expected infrastructure, features, and AI models of the 6G paradigm. This paper proposed a deep autoencoder (DAE)-based 6G CE model to detect and prevent AA. It was trained using a dataset generated from the MATLAB toolbox for AA and incorporated a secure transmission protocol. Simulations were conducted to evaluate the model’s performance under different parameters (i.e., CE and DAE) with maximal epsilon values range (0.5-3.0). The results proved the model’s sufficiency of accuracy and security to detect AA compared to existing CE techniques. The proposal provided a promising solution for a secure 6G DAE-based CE and showed robustness against AA. Additionally, it offered a feasible solution for the deep learning training data required and avoids overfitting. Overall, the proposed model provides a valuable contribution towards enhancing the security of 6G networks, and its performance should be further validated in real-world scenarios.
Optimizing Solar Power Generation: Real-time IoT Monitoring and ANN-Based Production Forecasting

This paper describes an IOT-based energy meter for photovoltaic system (PV), with forecasting energy production in an industrial location in Morocco, A typical PV system installed in Benguerir city monitors values with the proposed device based on ESP-32 card that receives energy consumption data from the sub-meter and sends data to the cloud. The proposed system enhances a PV system management. The method includes an analysis of the solar resource available at the location in Benguerir (Morocco), as well as analysis, evaluation, and selection of the components of the solar station using simulation software such as the PVSYST tool, in addition, the development of a datalogger used for monitoring energy production by solar panels, and the storage of data in a cloud with the display of results on a web interface. Finally, the use of the ANN algorithm to predict future production.
Bridging the Gap: Intelligent Environments with Smart Materials

Smart Materials (SMat) promise to open new opportunities in the area of Intelligent Environments (IE), whether as part of dedicated smart devices or as the fabric constituting everyday appliances and building infrastructure. Through the use of ontologies both IE engineers and the IEs themselves can be aware of, and predict, how novel configurable and changing materials react under different conditions. In contrast to conventional Smart Objects, however, as computational software/hardware-systems, lending themselves to the object-oriented perspective of conventional ontology specification languages, SMat and IE in the wider sense require a perspective focussing on extended spaces and numerical domains. Both are known to be problematic in terms of usability and computational complexity for the traditional object-oriented languages, with even very basic notions already leading into undecidability. Context Logic (CL), in contrast, is a formalism specialized for these domains. This paper demonstrates how terminology from this area involving extended spaces and numerical domains can be modeled in CL.
Air-Writing Segmentation using a single IMU-based system

This paper presents a novel and generic method to employ deep neural networks for segmenting in-air performed gestures to detect writing activity. We consider various factors such as temporal, geometric, and frequency constraints to define the parameters and fine-tune the deep-learning methods. The proposed method is benchmarked on air-gesture data from 50 participants, which included air-writing gestures followed and preceded by non-writing gestures. The reported results establish the potential of deep-learning methods to segment air-writing activity. The proposed novel approach provides a foundation to develop sophisticated systems for recognizing air gestures to enhance interaction in virtual and augmented reality environments.
Face Restoration Via Generative Adversarial Networks

The objective of face restoration or de-occlusion is to eliminate occluded regions in facial images that are caused by objects or disguises. This study proposes an innovative approach to address the challenge of facial image de-occlusion. Specifically, the proposed method employs a Deep Convolutional Generative Adversarial Network (DCGAN) to restore blocked regions of facial images, resulting in enhanced visual clarity and completeness. We utilize GANs which are trained on both occlusion-free and occluded images, enabling the restoration of corrupted facial regions. Furthermore, we achieve effective model training through mixed precision training. Our method shows promising results on testing images, successfully restoring facial images with up to 50% blocked regions.
Text Extraction &amp; Recognition from Visiting Cards

The increasing demand for automation in business card data entry has led to the development of various tools and techniques for optical character recognition (OCR). In this paper, we propose a flask-based Python application that processes input images of visiting or business cards, detects text regions, and recognizes text to extract important information such as name, address, contact number, email, and website. Our proposed system uses a combination of image pre-processing techniques, machine learning algorithms, and text analysis methods to achieve high accuracy in recognizing the text from the input images. The extracted information is then stored in an Excel table for further use. We present the experimental results of our system on a dataset of business card images and show that it achieves high accuracy in recognizing the text and extracting the relevant information. Our proposed system provides an efficient and effective solution for business card data entry, which can be applied in various industries such as marketing, sales, and customer relationship management.
Micro-Moment Classification for Anomalous Power Consumption Detection using 1D CNN

Identifying anomalous power consumption is essential in improving energy efficiency in buildings. With the help of sensors and other intelligent systems installed in buildings (including smart homes), identifying anomalous power consumption becomes easy. In this work, 1 Dimensional Convolutional Neural Network (1D CNN)-based classification model is proposed to classify the micro-moments to identify the anomalous power consumption in the presence and absence of the consumer. The SimDataset values are normalized, and each instance with ten features is given as input to the 1D CNN. The robustness of the proposed model is defined by experimenting with varying the hyperparameter to obtain the best performance in the standard performance evaluation metrics. The results depicted that the suggested model outperformed the state-of-the-art, producing an accuracy of 96.4% and a weighted average F1-score of 0.962.
Transaction fee forecasting in post EIP-1559 Ethereum using 1-D Convolutional Neural Network

Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a “fee” besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models.
Software Cost Estimation: A Literature Review and Current Trends

Software cost estimation is a challenging and complex task during software development. It directs project managers and developers to analyze and predict costs at the beginning of the software development life cycle. The most important job for developing software projects is correctly estimating cost, time duration, and needed effort. This paper aims to review different models used for software cost estimation, including algorithmic, non-algorithmic, and learning-oriented models, which have been published over the last ten years (i.e., from 2011 to 2022). The comparison is done based on the methods, selected datasets, and metrics used in different techniques. We observed that machine learning-based models perform better compared to other existing methods. However, to the best of our knowledge, analysis, and literature survey, we found that no technique in the literature provides a fit-all solution.
Copy-Move Forgery Localization Using DCT With LoG Filter

Images have been used as evidence for a long time. With the introduction of digital images, there was a rise in manipulation of these images. This was a serious issue as morphed images cause misinterpretation of information available in the image. To counter image forgeries, many methodologies were developed. While some of them were computational intensive, others suffered with less accuracy. This research paper presents a novel approach to the above problem by using a LoG (Laplacian of Gaussian) filter and DCT (Discrete CosineTransform) on the image to detect a type of forgery known as Copy-Move Forgery.