Reviewerly/exHarmony huggingface dataset:
Hammering Floating-Point Arithmetic

Abstract Sledgehammer, a component of the interactive proof assistant Isabelle/HOL, aims to increase proof automation by automatically discharging proof goals with the help of external provers. Among these provers are a group of satisfiability modulo theories (SMT) solvers with support for the SMT-LIB input language. Despite existing formalizations of IEEE floating-point arithmetic in both Isabelle/HOL and SMT-LIB, Sledgehammer employs an abstract translation of floating-point types and constants, depriving the SMT solvers of the opportunity to make use of their dedicated decision procedures for floating-point arithmetic. We show that, by extending Sledgehammer’s translation from the language of Isabelle/HOL into SMT-LIB with an interpretation of floating-point types and constants, floating-point reasoning in SMT solvers can be made available to Isabelle/HOL. Our main contribution is a description and implementation of such an extension. An evaluation of the extended translation shows a significant increase of Sledgehammer’s success rate on proof goals involving floating-point arithmetic.
Translating SUMO-K to Higher-Order Set Theory

Abstract We describe a translation from a fragment of SUMO (SUMO-K) into higher-order set theory. The translation provides a formal semantics for portions of SUMO which are beyond first-order and which have previously only had an informal interpretation. It also for the first time embeds a large common-sense ontology into an interactive theorem proving system. We further extend our previous work in finding contradictions in SUMO from first-order constructs to include a portion of SUMO’s higher-order constructs. Finally, using the translation, we can create problems that can be proven using higher-order interactive and automated theorem provers. This is tested in several systems and used to form a corpus of higher-order common-sense reasoning problems.
Jajapy: A Learning Library for Stochastic Models

We present Jajapy, a Python library that implements a number of methods to aid the modelling process of Markov models from a set of partially-observable executions of the system. Currently, Jajapy supports different types of Markov models such as discrete and continuous-time Markov chains, as well as Markov decision processes. Jajapy can be used both to learn the model from scratch or to estimate parameter values of a given model so that it fits the observed data the best. To this end, the tool offers different learning techniques, either based on expectation-maximization or state-merging methods, each adapted to different types of Markov models. One key feature of Jajapy consists in its compatibility with the model checkers Storm and Prism. The paper briefly presents Jajapy’s functionalities and reports an empirical evaluation of their performance and accuracy. We conclude with an experimental comparison of Jajapy against AALpy, which is the current state-of-the-art Python library for learning automata. Jajapy and AALpy complement each other, and the choice of the library should be determined by the specific context in which it will be used.
Consumer Risk Perception Towards Cybercrimes and E-Commerce

The concept of this chapter is to review the literature for a research gap on risk perception among online consumers and the intention for online purchases in Malaysia. Online platforms and digital payments make online shopping easier. During the year 2020 - 2021, online shopping had become increasingly active with the spike of the novel coronavirus pandemic. Online users who share their personal information online are most likely getting into the scammer's trap. Malaysia executed the national cyber security policy to defend the country's critical national information infrastructure. Financial awareness among online users plays a crucial role in fighting cybercrime. Digital guardianship should exist to prevent irresponsible parties from taking advantage. The routine activity theory has been proposed to explain the phenomenon whereby the offender makes the selection to commit a crime based on an online shopper as his target and the guardianship.
Electronic Banking Frauds

Electronic banking (e-banking) has played an important role in the development and economic growth of developing countries. E-Banking also acts as a catalyst for the development of the financial sector, capital markets, and digital economy. India's rapidly growing banking sector paved the way for development and fraud in the e-banking regime. The present chapter seeks to assess the techniques of detection of fraud from a global perspective and will also validate the boom of E-Banking with a thought of precaution and awareness. Secondary data has been collected through bank statements, journals, and the internet. The results will help banks and regulators to better understand online banking frauds and to strengthen the existing fraud prevention systems. It is recommended that the government should provide adequate infrastructural facilities in the telecommunications and energy sectors, coupled with encouraging the use of information and communication technologies among the masses for beneficial impact.
An Abstract CNF-to-d-DNNF Compiler Based on Chronological CDCL

Abstract We present Abstract CNF2dDNNF , a calculus describing an approach for compiling a formula in conjunctive normal form (CNF) into deterministic negation normal form (d-DNNF). It combines component-based reasoning with a model enumeration approach based on conflict-driven clause learning (CDCL) with chronological backtracking. Its properties, such as soundness and termination, carry over to implementations which can be modeled by it. We provide a correctness proof and a detailed example. The main conceptual differences to currently available tools targeting d-DNNF compilation are discussed and future research directions presented. The aim of this work is to lay the theoretical foundation for a novel method for d-DNNF compilation. To the best of our knowledge, our approach is the first knowledge compilation method using CDCL with chronological backtracking.
Weighted Path Orders Are Semantic Path Orders

Abstract We explore the relationship between weighted path orders and (monotonic) semantic path orders. Our findings reveal that weighted path orders can be considered instances of a variant of semantic path orders that comprise order pairs. This observation leads to a generalization of weighted path orders that does not impose simplicity on their underlying algebras. As a result, the generalized version is capable of proving termination of term rewrite systems beyond the realm of simple termination. In order to assess practicality we provide experimental data comparing generalized weighted path orders with the original ones as well as other well-known classes of reduction orders.
How Confident Are High Schoolers to Speak English: A Report on Speaking Self-Efficacy Survey

Speaking in English is one of the toughest aspects to master in acquiring the English language. This is reflected in the broad job vacancies that require English as a mandatory skill. It means that it is important to equip students with adequate English skills. However, several reports have found that many students still lack confidence in speaking. On the other hand, a well-known theory suggests that one of the prominent factors influencing learning achievement is confidence, specifically called efficacy. Students' self-efficacy level seems to be one of the prominent factors that could influence their speaking achievement. As the pandemic has transformed the learning mode back into traditional face-to-face learning, this current study was conducted to investigate students' self-efficacy level in public speaking in the post-pandemic era. The study utilized a quantitative method and employed a questionnaire adapted from Paradewari's article. The questionnaire was converted into two types, online and offline conditions, and distributed to 182 students. An independent t-test method was used to compare whether there were differences in students' self-efficacy levels in public speaking between online and offline learning. The study revealed a significant difference in the overall students' self-efficacy level (as indicated by the T-Test Result score) and the factors influencing it. It also found that offline learning had a more positive effect on students' self-efficacy in public speaking compared to online learning.
Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers' health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers' health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.
PERBANDINGAN DIKSI DAN METAFORA DALAM KUMPULAN PUISI PERCINTAAN DAN 365 HARI

A collection of Love Poems will be published by Penari Pena Publisher in 2022 as well as a book Collection of Poetry 365 Days. The two titles of these books were produced by students and lecturers of the Indonesian Language and Literature Education Study Program, Faculty of Language and Literature, PGRI Kanjuruhan University, Malang through the Merdeka Learning Campus Curriculum as a product of the Literature Practice course in the odd semester of the 2021/2022 school year based on a project-based lecture model. . This poem is the first work produced by students, so it becomes the interest of researchers to further examine the diction and metaphors in the poem.&#x0D; Based on the problems found, the researcher wants to compare the diction and metaphors between the Love Poetry Collection and 365 Days. This study uses a qualitative approach and is examined based on descriptive methods. Research Location at PGRI University Kanjuruhan Malang. The data of this study are in the form of the entire text, metaphorical sub-text, words/diction and a collection of words in the Collection of Love Poems and 365 Days. While the data source for this research is the text of Collection of Love Poems and 365 Days published by Penari Pena Publisher (2022). Data Collection and Information Analysis was carried out based on observation and interview techniques. Data analysis was carried out based on interactional data analysis techniques and hermeneutical interpretation.&#x0D; Based on the results of the study it was concluded that the works in the collection of poems contain many hidden messages and different sense values. In addition to connotative sentences, denotative sentences are also found, namely sentences with true meaning. Meanwhile, there are also metaphorical figures of speech, although this figure of speech does not dominate. It is recommended for poets to pay attention to the diction and figure of speech used in creating a poem so that the poetry created has variations in every word and sentence. In addition, the element of beauty and taste in every choice of words will add beauty to everyone who reads it. As for researchers or teachers, this research can be used to develop theories related to literary practice, especially for the creation of poetry and its learning aspects when applied based on a project-based learning model. In addition, as a form of implementation of the implementation of the MBKM curriculum which is oriented towards learning literature and modeling as a form of good practice in designing creative economy-based courses and developing productivity in collaboration with the study program with industry and the book market and is useful in supporting literacy programs that now developed nationally.
Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems

Challenging combinatorial optimization problems are ubiquitous in science and engineering. Several quantum methods for optimization have recently been developed, in different settings including both exact and approximate solvers. Addressing this field of research, this manuscript has three distinct purposes. First, we present an intuitive method for synthesizing and analyzing discrete (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>i</mml:mi><mml:mo>.</mml:mo><mml:mi>e</mml:mi><mml:mo>.</mml:mo><mml:mo>,</mml:mo></mml:math> integer-based) optimization problems, wherein the problem and corresponding algorithmic primitives are expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent. This compact representation often allows for more efficient problem compilation, automated analyses of different encoding choices, easier interpretability, more complex runtime procedures, and richer programmability, as compared to previous approaches, which we demonstrate with a number of examples. Second, we perform numerical studies comparing several qubit encodings; the results exhibit a number of preliminary trends that help guide the choice of encoding for a particular set of hardware and a particular problem and algorithm. Our study includes problems related to graph coloring, the traveling salesperson problem, factory/machine scheduling, financial portfolio rebalancing, and integer linear programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables, demonstrating that compact (binary) encodings are more amenable to QAOA than previously understood. We expect this toolkit of programming abstractions and low-level building blocks to aid in designing quantum algorithms for discrete combinatorial problems.
Large-scale Video Analytics with Cloud–Edge Collaborative Continuous Learning

Deep learning–based video analytics demands high network bandwidth to ferry the large volume of data when deployed on the cloud. When incorporated at the edge side, only lightweight deep neural network (DNN) models are affordable due to computational constraint. In this article, a cloud–edge collaborative architecture is proposed combining edge-based inference with cloud-assisted continuous learning. Lightweight DNN models are maintained at the edge servers and continuously retrained with a more comprehensive model on the cloud to achieve high video analytics performance while reducing the amount of data transmitted between edge servers and the cloud. The proposed design faces the challenge of constraints of both computation resources at the edge servers and network bandwidth of the edge–cloud links. An accuracy gradient-based resource allocation algorithm is proposed to allocate the limited computation and network resources across different video streams to achieve the maximum overall performance. A prototype system is implemented and experiment results demonstrate the effectiveness of our system with up to 28.6% absolute mean average precision gain compared with alternative designs.
Study of violence against women and its characteristics through the application of text mining techniques

The Internet provides a wide variety of information that can be collected and studied, creating a massive data repository. Among the data available on the Internet, we can find articles about Violence against Women (VAW) published in the digital press, which are of great societal interest. In this work, we utilized Web scraping techniques to gather VAW-related news from the internet. Applying Text Mining techniques, we conducted a study on VAW and its characteristics. Our work comprises an exploratory analysis and the application of Topic Modelling to VAW events to identify latent topics and their semantic structures. We employed classification algorithms on a set of VAW press articles to determine the type of violence they refer to, namely physical, psychological, sexual, or a combination of them. We proposed two methodologies to target the data: the first one is based on dictionaries of VAW types, while the second approach extends the former by using the predominant violence to identify other associated types. Furthermore, we implemented two feature selection techniques: TF-IDF and $${Chi}^{2}$$ . Then, we applied Support Vector Machine, Decision Tree, Bayesian Networks, XGBoost Classifier, Random Forest, and Artificial Neural Networks. The results obtained showed that the classifiers achieved better performance when using $${Chi}^{2}$$ . The Boost Classifier demonstrated the best performance, followed by Random Forest.
A New Study of AI Artists for Changing the Movie Industries

Due to the rise of artificial intelligence (AI) in the arts, this paper aims to explore the use of AI for reducing film production costs through the creation of realistic images. Additionally, we investigate whether AI can recreate the same character at the same age. Without needing to replace the original actor, qualitative data collection tools were employed to study three distinct population groups within the film industry: film industry professionals, moviegoers, and technologists. Our research reveals that AI, or AI artists in film production, still face limitations in significantly reducing production costs. Furthermore, it is crucial to engage a text expert in the image production process for films who possesses a comprehensive understanding of film principles in order to achieve images that align with the project's requirements. Moreover, the introduction of the AI artist technique allows for the recreation of a character at the same age portrayed by the same actor, even if that actor may have passed away. Consequently, obtaining consent from the relatives of the actor or actress becomes a necessary step. Furthermore, the aspect of audience acceptance does not hold significant interest, as it demands a greater level of realism in both the image and the actors, surpassing what AI can provide. Therefore, this paper underscores the increasing influence of AI in the arts, particularly within film production, and examines its potential to reduce costs and recreate characters.
Diagnostic concordance between traditional and digital workflows. A study on 1427 prostate biopsies

To evaluate intra-observer diagnostic reproducibility using traditional slides (TS) versus whole slide images (WSI).TS and WSI of 1427 prostatic biopsies (107 consecutive patients) were evaluated by a single pathologist. Agreement between readings was evaluated with Gwet's Agreement coefficient (AC) and Landis and Koch benchmark scale.The positive/negative agreement between the readings was almost perfect (AC1= 0.962; 95% CI[0.949,0.974]), with method independent distribution of discrepancies. Among positive biopsies, 212 had identical Gleason score (GS) on TS and WSI and discordant GS in 69 cases (AC2 = 0.932; 95% CI[0.907, 0.956]). Concordant negative and positive patient classification was observed in 39 and 64 cases, respectively; two cases were assigned to the positive group on TS and 2 on WSI configuring an almost perfect agreement (AC1=0.929; 95% C1[0.860, 0.998]). ISUP Grade group (ISUP GG) agreement was evaluated in the 60 concordantly positive cases: in 45 cases it was identical on TS and WSI; in 10 biopsies the discrepancy implied a modification of the assigned ISUP GG of ≤ 1 class and in 5 the discrepancy implied a modification of 2 classes. Gwet's agreement coefficient was (95% CI [0.834, 0.962]), i.e.: almost perfect agreement.Our data show almost perfect agreement between digital and traditional diagnostic activity in a routine setting, confirming that digital pathology can be safely introduced into routine workflows.
Enhancing yes/no question answering with weak supervision via extractive question answering

Abstract The effectiveness of natural language processing models relies on various factors, including the architecture, number of parameters, data used during training, and the tasks they were trained on. Recent studies indicate that models pre-trained on large corpora and fine-tuned on task-specific datasets, covering multiple tasks, can generate remarkable results across various benchmarks. We propose a new approach based on a straightforward hypothesis: improving model performance on a target task by considering other artificial tasks defined on the same training dataset. By doing so, the model can gain further insights into the training dataset and attain a greater understanding, improving efficiency on the target task. This approach differs from others that consider multiple pre-existing tasks on different datasets. We validate this hypothesis by focusing on the problem of answering yes/no questions and introducing a multi-task model that outputs a span of the reference text, serving as evidence for answering the question. The task of span extraction is an artificial one, designed to benefit the performance of the model answering yes/no questions. We acquire weak supervision for these spans, by using a pre-trained extractive question answering model, dispensing the need for costly human annotation. Our experiments, using modern transformer-based language models, demonstrate that this method outperforms the standard approach of training models to answer yes/no questions. Although the primary objective was to enhance the performance of the model in answering yes/no questions, it was discovered that span texts are a significant source of information. These spans, derived from the question reference texts, provided valuable insights for the users to better comprehend the answers to the questions. The model’s improved accuracy in answering yes/no questions, coupled with the supplementary information provided by the span texts, led to a more comprehensive and informative user experience.
Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter.