Reviewerly/exHarmony huggingface dataset:
Universal equivalence and majority of probabilistic programs over finite fields

We study decidability problems for equivalence of probabilistic programs, for a core probabilistic programming language over finite fields of fixed characteristic. The programming language supports uniform sampling, addition, multiplication and conditionals and thus is sufficiently expressive to encode boolean and arithmetic circuits. We consider two variants of equivalence: the first one considers an interpretation over the finite field Fq, while the second one, which we call universal equivalence, verifies equivalence over all extensions Fqk of Fq. The universal variant typically arises in provable cryptography when one wishes to prove equivalence for any length of bitstrings, i.e., elements of F2k for any k. While the first problem is obviously decidable, we establish its exact complexity which lies in the counting hierarchy. To show decidability, and a doubly exponential upper bound, of the universal variant we rely on results from algorithmic number theory and the possibility to compare local zeta functions associated to given polynomials. Finally we study several variants of the equivalence problem, including a problem we call majority, motivated by differential privacy.
Publicly Verifiable M + 1st-Price Auction Fit for IoT with Minimum Storage

In an <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>M</mi> <mo>+</mo> <mn>1</mn> </math> st-price auction, all bidders submit their bids simultaneously, and the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>M</mi> </math> highest bidders purchase <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>M</mi> </math> identical goods at the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>M</mi> <mo>+</mo> <mn>1</mn> </math> st bidding price. Previous research is constructed based on trusted managers such as a trusted third party (TTP), trusted mix servers, and honest managers. All of the previous auctions are not fit for edge-assisted IoT since they need TTP. In this paper, we formalize a notion of commutative bi-homomorphic multiparty encryption and achieve no-TTP <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>M</mi> <mo>+</mo> <mn>1</mn> </math> -st auction based on blockchain with public verifiability. Our <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>M</mi> <mo>+</mo> <mn>1</mn> </math> st auction guarantees financial fairness, robustness, and correctness without TTP and is secure under a malicious model for the first time. Our <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>M</mi> <mo>+</mo> <mn>1</mn> </math> st auction can be executed over a distributed network and is thus fit for edge-assisted IoT. Furthermore, our formalized commutative bi-homomorphic multiparty encryption can be used in various applications for edge-assisted IoT, which needs to protect privacy and correctness.
Text Preprocessing and its Implications in a Digital Humanities Project

This paper focuses on data cleaning as part of a preprocessing procedure applied to text data retrieved from the web.Although the importance of this early stage in a project using NLP methods is often highlighted by researchers, the details, general principles and techniques are usually left out due to consideration of space.At best, they are dismissed with a comment "The usual data cleaning and preprocessing procedures were applied".More coverage is usually given to automatic text annotation such as lemmatisation, part-of-speech tagging and parsing, which is often included in preprocessing.In the literature, the term 'preprocessing' is used to refer to a wide range of procedures, from filtering and cleaning to data transformation such as stemming and numeric representation, which might create confusion.We argue that text preprocessing might skew original data distribution with regard to the metadata, such as types, locations and times of registered datapoints.In this paper we describe a systematic approach to cleaning text data mined by a data-providing company for a Digital Humanities (DH) project focused on cultural analytics.We reveal the types and amount of noise in the data coming from various web sources and estimate the changes in the size of the data associated with preprocessing.We also compare the results of a text classification experiment run on the raw and preprocessed data.We hope that our experience and approaches will help the DH community to diagnose the quality of textual data collected from the web and prepare it for further natural language processing.
Attraction Basins in Metaheuristics: A Systematic Mapping Study

Context: In this study, we report on a Systematic Mapping Study (SMS) for attraction basins in the domain of metaheuristics. Objective: To identify research trends, potential issues, and proposed solutions on attraction basins in the field of metaheuristics. Research goals were inspired by the previous paper, published in 2021, where attraction basins were used to measure exploration and exploitation. Method: We conducted the SMS in the following steps: Defining research questions, conducting the search in the ISI Web of Science and Scopus databases, full-text screening, iterative forward and backward snowballing (with ongoing full-text screening), classifying, and data extraction. Results: Attraction basins within discrete domains are understood far better than those within continuous domains. Attraction basins on dynamic problems have hardly been investigated. Multi-objective problems are investigated poorly in both domains, although slightly more often within a continuous domain. There is a lack of parallel and scalable algorithms to compute attraction basins and a general framework that would unite all different definitions/implementations used for attraction basins. Conclusions: Findings regarding attraction basins in the field of metaheuristics reveal that the concept alone is poorly exploited, as well as identify open issues where researchers may improve their research.
Fuzzy logic expert system for evaluating the activity of university teachers

Assessing the performance of academics at different levels is increasingly difficult to achieve using traditional methods based mainly on numerical scores in evaluating teaching and research activity. The indexing of academic performance in various international databases with impact indices at different scales has led to the need for advanced computer models, such as expert systems based on fuzzy logic, proposed in this research, which address the evaluation of teachers even in the face of imprecise information and under conditions of uncertainty. In this research, as a contribution and novelty, a fuzzy logic model was developed in which an algorithm was simulated and implemented in Matlab using the Mandami toolkit, which allows inference of the rules of fuzzy logic and visualization. 3D. The system implementation was done by software in Matlab environment, using systems with fuzzy Mandami logic. The result of this pilot study was to test and validate the proposed model through a graphical interface, giving the results according to minimum criteria and with additional explanations.
<i>What</i> and <i>Why</i>? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision

Fauxtography is a category of multi-modal posts that spread misleading information on various big data online social platforms that generate billions of posts on a daily basis (e.g., Facebook, Twitter, Reddit). A fauxtography post usually consists of an image, a text description and comments from its readers. In this paper, we focus on explaining fauxtography posts by identifying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what</i> specific component and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">why</i> that component of a post leads to the fauxtography (i.e., duo explanations). This problem is motivated by the limitations of current fauxtography detection solutions that only focus on the detection but ignore the important <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explanation</i> aspect of their results. Two critical challenges exist in solving our problem: i) it is difficult to accurately identify the “guilty” component of a fauxtography post given the fact that different components of the post and their associations could all lead to the fauxtography; ii) it is expensive and time-consuming to obtain a good training set with fine-grained labels of fauxtography posts in terms of explainability, making it challenging to develop fully supervised explainable solutions. To address the above challenges, we develop a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> uo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ex</b> plainable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> auxtography Detection Framework under a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onstrained Supervision (DExFC) to generate duo explanations from both content and comment parts of fauxtography posts. We evaluate the DExFC by creating real-world datasets from different online social media platforms (Twitter and Reddit). The results show that DExFC not only detects the fauxtography posts more accurately than the state-of-the-art solutions but also provides well-justified explanations to its results without the full supervision.
Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.
CARO: A Conflict-Aware Requirement Ordering Tool for DevOps

Requirement prioritization is an inherently important step in the DevOps framework. Unfortunately, the prioritization process often disregards the non-functional requirements and the possible conflicts among them. This implies that unresolved dependencies and conflicts would be identified at integration time only, which may lead to major refactoring issues. We introduce CARO a new tool that generates an ordering among the requirements based on conflicts and dependencies among the requirements. The tool provides a quantitative risk evaluation framework along with risk mitigation strategies based on conflicts and dependencies among the requirements.
Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.
A Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm Detection

Sarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media.Sarcasm identification is therefore essential for a good public opinion decision.There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis.However, once a sequence of terms is being tackled, the performance drops.This is because averaging the embedding of each term in a sentence to get the general embedding would discard the important embedding of some terms.LSTM showed significant improvement in terms of document embedding.However, within the classification LSTM requires adding additional information in order to precisely classify the document into sarcasm or not.This study aims to propose two technique based on LSTM and Auto-Encoder for improving the sarcasm detection.A benchmark dataset has been used in the experiments along with several pre-processing operations that have been applied.These include stop word removal, tokenization and special character removal with LSTM which can be represented by configuring the document embedding and using Auto-Encoder the classifier that was trained on the proposed LSTM.Results showed that the proposed LSTM with Auto-Encoder outperformed the baseline by achieving 84% of f-measure for the dataset.The main reason behind the superiority is that the proposed auto encoder is processing the document embedding as input and attempt to output the same embedding vector.This will enable the architecture to learn the interesting embedding that have significant impact on sarcasm polarity.
Handling non-unitaries in quantum circuit equivalence checking

Quantum computers are reaching a level where interactions between classical and quantum computations can happen in real-time. This marks the advent of a new, broader class of quantum circuits: dynamic quantum circuits. They offer a broader range of available computing primitives that lead to new challenges for design tasks such as simulation, compilation, and verification. Due to the non-unitary nature of dynamic circuit primitives, most existing techniques and tools for these tasks are no longer applicable in an out-of-the-box fashion. In this work, we discuss the resulting consequences for quantum circuit verification, specifically equivalence checking, and propose two different schemes that eventually allow to treat the involved circuits as if they did not contain non-unitaries at all. As a result, we demonstrate methodically, as well as, experimentally that existing techniques for verifying the equivalence of quantum circuits can be kept applicable for this broader class of circuits.
MELHISSA: a multilingual entity linking architecture for historical press articles

Abstract Digital libraries have a key role in cultural heritage as they provide access to our culture and history by indexing books and historical documents (newspapers and letters). Digital libraries use natural language processing (NLP) tools to process these documents and enrich them with meta-information, such as named entities. Despite recent advances in these NLP models, most of them are built for specific languages and contemporary documents that are not optimized for handling historical material that may for instance contain language variations and optical character recognition (OCR) errors. In this work, we focused on the entity linking (EL) task that is fundamental to the indexation of documents in digital libraries. We developed a Multilingual Entity Linking architecture for HIstorical preSS Articles that is composed of multilingual analysis, OCR correction, and filter analysis to alleviate the impact of historical documents in the EL task. The source code is publicly available. Experimentation has been done over two historical document corpora covering five European languages (English, Finnish, French, German, and Swedish). Results have shown that our system improved the global performance for all languages and datasets by achieving an F-score@1 of up to 0.681 and an F-score@5 of up to 0.787.
Graph-based knowledge tracing: Modeling student proficiency using graph neural networks

Recent advancements in computer-assisted learning systems have caused an increase in the research in knowledge tracing, wherein student performance is predicted over time. Student coursework can potentially be structured as a graph. Incorporating this graph-structured nature into a knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of graph neural networks (GNNs), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information.
Selective Federated Learning for Mobile Edge Intelligence

Federated learning enables distributed agents to train a common and shared model in a privacy-protected way. Due to the heterogeneity of computing and communication capabilities, these agents may produce different training effects and asynchronous model update in the learning process. To improve the learning efficiency, here emerges the challenge to optimize agents' selection and collaboration. Catering for this challenge, in this paper, we leverage Shapley Value to measure the learning contribution of each agent in the model training. Moreover, we propose joint optimization schemes of agent selection and resource scheduling that maximize learning energy efficiency. Numerical results demonstrate that our schemes outperform benchmark schemes in various data distribution scenarios.
Parameter Estimation of Poisson–Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping

Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.
The impact of marketing agility on crisis management stages at five and four-star hotels in Jordan

This paper aims to examine the impact of marketing agility on decision-making during crisis management stages in five-star and four-star hotels in Jordan. The study included 71 top management staff members from Jordan’s five- and four-star hotels. A questionnaire was designed and utilized to collect the data on marketing agility and crisis management at four and five-star hotels in Jordan. A total of 213 questionnaires were distributed, and 187 useable responses were returned, which resulted in an 86% response rate. Results show that marketing agility plays a significant role in decision-making during crisis management stages. The analysis revealed that marketing agility affected the five stages of crisis management with mixed levels. The findings show that accessibility, decisiveness, swiftness, and flexibility have an immense impact on crisis management stages, while the alertness dimension result shows a weak impact on crisis management stages except for containment. Hotel managers are recommended to emphasize enhancing coordination and integration internally with different managerial levels and units and with external partners to boost information exchange. In addition, it will help promote learning orientation amongst hotel staff to handle the fast-changing environment.
Deep fake videos identification using ANN and LSTM

With the demand for deep learning (DL) and artificial intelligence (AI) techniques, fake digital content has multiplied in the current days. Like fake movies, images, audio and video can be an unfavorable and dangerous phenomenon and can have the ability to alter trust in the truth by providing false reality content. But the explanations provided lack of ability to provide the historical tracing and origin of electronic media. In the current research, we try to provide a general solution using different methods like ANN, CNN and LSTM with smart contracts to copy and trace the root of origin and history of the computerized content to its unique source, regardless of whether the advanced substance is replicated on numerous occasions. The shrewd agreement utilizes the hash upsides of the interplanetary document framework used to store advanced substance and related metadata. Our answer is generally centered on video content.
Noise Reduction, Enhancement and Classification for Sonar Images

Ultrasound imaging has some problems with image properties output. These affects the specialist decision. Ultrasound noise type is the speckle noise which has a grainy pattern depending on the signal. There are two parts of this study. The first part is the enhancing of images with adaptive Weiner, Lee, Gamma and Frost filters with 3x3, 5x5, and 7x7 sliding windows. The evaluated process was achieved using signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and maximum difference (MD) criteria. The second part consists of simulating noise in a standard image (Lina image) by adding different percentage of speckle noise from 0.01 to 0.06. The supervised classification based minimum distance method is used to evaluate the results depending on selecting four blocks located at different places on the image. Speckle noise was added with different percentage from 0.01 to 0.06 to calculate the coherent noise within the image. The coherent noise was concluded from the slope of the standard deviation with the mean for each noise. The results showed that the additive noise increased with the slide window size, while multiplicative noise did not change with the sliding window nor with increasing noise ratio. Wiener filter has the best results in enhancing the noise.