Reviewerly/exHarmony huggingface dataset:
Surveillance Platform of cybersecurity maturity of micro and small enterprises

Cyberattacks are malicious and sophisticated events that attempt to exploit the principles of confidentiality, integrity and availability of organizations, causing financial losses and reputational damage, among others. In general, the number of threats and their complexity increases every day as well as the digital transformation of organizations. While it is necessary for each company to increase its digital index, it is also necessary to increase its maturity in cybersecurity. Micro and small enterprises represent a large percentage of companies in Chile, which do not always have the capital to hire qualified cybersecurity personnel. Therefore, this bridge project between the Academy and the Industry, seeks to provide tools so that micro and small companies can evaluate themselves, and potentially have the accompaniment for continuous improvement and ultimately to mature digitally using a successful proposal of the study of different cybersecurity maturity modelels reviewed in the literature.
Epistemic Argumentation Framework: Theory and Computation

The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an ω-epistemic labelling set, where ω is complete, stable, grounded, or preferred, which is a set of ω-labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation. Finally, the paper discusses complexity issues and computation using epistemic logic programming.
Methods and Trends of Machine Reading Comprehension in the Arabic Language

Machine Reading Comprehension (MRC) is an essential task in natural language understanding in which the goal is to teach machines to understand the text. Machine understanding can be evaluated through question answering techniques. Limited research has been done in Arabic reading comprehension. This study presents a survey on trends and methods of Machine Reading Comprehension (MRC) methods in Arabic. The survey summarizes recent advances in Arabic reading comprehension, primarily emphasizing on two aspects (i.e., available datasets and methods). The study provides a detailed analysis of MRC techniques and compares various datasets used to study Arabic reading compression.
Variants in the Infinitary Unification Wonderland

So far, results about variants, the finite variant property (FVP), and variant unification have been developed for equational theories $$E \cup B$$ where B is a set of axioms having a finitary unification algorithm, and the equations E, oriented as rewrite rules $$\vec {E}$$ , are convergent modulo B. The extension to the case when B has an infinitary unification algorithm, for example because of non-commutative symbols having associative axioms, seems undeveloped. This paper takes a first step in developing such an extension. In particular, the relationships between the FVP and the boundedness properties, the identification of conditions on $$E \cup B$$ ensuring FVP, and the effective computation of variants and variant unifiers are explored in detail. The extension from the finitary to the infinitary case includes both surprises and opportunities.
Information Retrieval and Social Media Mining

The large amount of digital content available through web sites, social networks, streaming services, and other distribution media, allows more and more people to access virtually unlimited sources of information, products, and services [...]
Optimization of PBKDF2-HMAC-SHA256 and PBKDF2-HMAC-LSH256 in CPU Environments

Password-Based Key-Derivation Function 2 (PBKDF2) is commonly employed to derive secure keys from a password in real life such as file encryption and implementation of authentication systems. Nevertheless, owing to the limited entropy of the password, the security of the generated keys is lower than that of the normally generated keys. To address this, issue increase the number of iterative operations during the PBKDF2 may increase. However, the higher the number of iterative operations, the more time it takes to generate the key. This paper presents various techniques for optimizing the performance of PBKDF2. The main idea of our proposed methods is to reduce redundant block operations and to optimize Pseudo Random Function (PRF) itself by combining operations and making full use of fixed values within PBKDF2. As the underlying hash function in PRF, we utilize two algorithms: Hash-based Message Authentication Code-Secure Hash Algorithm 256 (HMAC-SHA256) and HMAC-Lightweight Secure Hash 256 (HMAC-LSH256) (SHA256 is the most widely used hash function and LSH256 was recently developed hash function in South Korea). With the proposed techniques, the proposed implementation of PBKDF2-HMAC-SHA256 provides a performance enhancement of about 135.27% over the reference implementation provided by Korea Internet & Security Agency (KISA) and about 80.21% over OpenSSL. Concerning PBKDF2-HMAC-LSH256, the proposed implementation provides a huge performance enhancement of about 330.48% over the reference implementation provided by KISA. With the proposed implementation, more iteration operations can be possible for higher security. Furthermore, we can use the proposed techniques to optimize PBKDF2 performance on embedded MCUs.
Opponent Modeling in Poker Games

Texas Hold’em poker is a popular game worldwide and it attracts increasing attention from community of artificial intelligence as a typical decision-making problem in non-deterministic and incomplete information environment. One of the key tasks to deal with the poker game is the opponent modeling, which aims to exploit the opponent weakness based on history behaviors. In this paper, we study mixed-method opponent modeling, one is Bayesian probabilistic model, one is the neural network (NN)-based prediction model, and the last is opponent type identifying model. Then, we combine these three methods to generate an integrated agent for opponent modeling. The opponents are categorized into 4 types according to their risk preference of strategies. The main step in Bayesian method is calculating the posterior distribution over opponent’s strategy space and selecting the maximum probability. The main idea of NN-based method is using observation data to improve the prediction accuracy of opponent’s hand. The main idea of opponent type identifying model is building a classifier with two factors. Finally, we design a simplified poker game to conduct experiment and demonstrate the effectiveness of our methods.
Existence of primitive pairs with prescribed traces over finite fields

Let F=Fqm, m≥7, n a positive integer, and f=p1/p2 with p1, p2 co-prime irreducible polynomials in F[x] and deg(p1)+deg(p2)=n. We obtain a sufficient condition on (q, m), which guarantees, for any prescribed a, b in E=Fq, the existence of primitive pair (α,f(α)) in F such that TrF/E(α)=a and TrF/E(α−1)=b. Further, for every positive integer n, such a pair definitely exists for large enough m. The case n = 2 is dealt separately and proved that such a pair exists for all (q, m) apart from at most 64 choices.
Evaluation of University Students Motivation in Learning Kinematics Through M-Learning

M-learning is a pedagogical tool that involves the active and dominant presence of mobile technologies in the classroom, demonstrating its effectiveness in meaningful learning, which is why its implementation is increasingly common by university professors. The purpose of this study is to investigate the relationship between motivation and learning in university students through m-learning. The Google Science Journal mobile application was used by students to determine the magnitude and direction of the acceleration vector of a previously selected motor vehicle under the ARCS instructional model. The motivation of university students was measured with the Instructional Material Motivational Survey (IMMS) instrument, which was applied to a group of 15 students from the Civil Engineering career at the Universidad Católica del Maule. The reliability of the instrument was determined by Cronbach's alpha, giving an overall value of 0.89. The results suggest that the implementation of m-learning in university classrooms was positively valued by the majority of the students surveyed, as well as an increase in the percentage of students who achieved the expected learning compared to previous versions of the course, where the proposed methodology was not implemented. Teachers provide a validated measurement model, as well as solid scientific references that aim to stimulate the use of m-learning, as it has been shown that its implementation favorably stimulates students, as well as their interest in learning and confidence in themselves.
lpopt: A Rule Optimization Tool for Answer Set Programming

State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant handtuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.
Improving Arabic Sentiment Classification Using a Combined Approach

The aim of sentiment classification is to automatically extract and classify a textual review as expressing a positive or negative opinion. In this paper, we study the sentiment classification problem in the Arabic language. We propose a method that attempts to extract subjective parts of document reviews. In addition, a lexicon is used to find implicit opinions and sentiments in reviews. We combine the extracted opinions with the sentiment words returned by the lexical approach. Finally, a feature reduction technique is applied. To evaluate the proposed method, support vector machines (SVM) classifier is applied for the classification task. The results indicate that our proposed approach provides superior performance in terms of classification measures.
Hematology and Digital Image Processing: Watershed Transform-Based Methodology for Blood Cell Counting Using the WT-MO Algorithm

Background: Most diseases can be detected by routine examination, even if they are in the initial phase. Currently, one of the most requested medical laboratory tests is that which allows detecting from bacterial infections until leukemias. However, for less favored populations, this examination can be seen as having a high cost.&#x0D; Methods: Thus, this study introduces an algorithm of segmentation of images capable of detecting and counting red blood cells and leukocytes present in digital images of blood smear. The methodology was named by WT-MO, once it relies on the concepts of Watershed Transform and Morphological Operations. The experiments were conducted in the MATLAB software simulation environment, where 25 images were used in order to evaluate the accuracy, processing time, and execution time of the WT-MO algorithm.&#x0D; Results: The results show that the WT-MO methodology presents high accuracy, reaching 96% and 92% in the red blood cell and leukocyte counts, respectively; reliability and low processing time, reaching an average processing time and execution time, achieving from 0.74 to 2.17 seconds. Therefore, the WT-MO algorithm can be seen as the first step in making laboratory tests more accessible to populations in underdeveloped and developing countries.&#x0D; Conclusion: The WT-MO methodology helps not only disadvantaged populations gain access to low-cost, high-reliability tests but also has excellent potential for use in laboratories in developed countries.
Event-Guided Denoising for Multilingual Relation Learning

General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus – the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).
hinglishNorm - A Corpus of Hindi-English Code Mixed Sentences for Text Normalization

We present hinglishNorm - a human annotated corpus of Hindi-English code-mixed sentences for text normalization task. Each sentence in the corpus is aligned to its corresponding human annotated normalized form. To the best of our knowledge, there is no corpus of Hindi-English code-mixed sentences for text normalization task that is publicly available. Our work is the first attempt in this direction. The corpus contains 13494 segments annotated for text normalization. Further, we present baseline normalization results on this corpus. We obtain a Word Error Rate (WER) of 15.55, BiLingual Evaluation Understudy (BLEU) score of 71.2, and Metric for Evaluation of Translation with Explicit ORdering (METEOR) score of 0.50.
Fast matrix algebra for Bayesian model calibration

In Bayesian model calibration, evaluation of the likelihood function usually involves finding the inverse and determinant of a covariance matrix. When Markov Chain Monte Carlo (MCMC) methods are used to sample from the posterior, hundreds of thousands of likelihood evaluations may be required. In this paper, we demonstrate that the structure of the covariance matrix can be exploited, leading to substantial time savings in practice. We also derive two simple equations for approximating the inverse of the covariance matrix in this setting, which can be computed in near-quadratic time. The practical implications of these strategies are demonstrated using a simple numerical case study and the "quack" R package. For a covariance matrix with 1000 rows, application of these strategies for a million likelihood evaluations leads to a speedup of roughly 4000 compared to the naive implementation
Exploring Chinese Dynamic Sentiment/Emotion Analysis with Text Mining—Taiwanese Popular Movie Reviews Comment as a Case

With the rapid development of technology, viewers’ opinions are no longer passed on by word-of-mouth in the real world but expressed on the Internet. In the six categories of life, food, clothing, housing, travel, education, and entertainment, every part is inseparable from comments. The most common entertainment in the six majors is watching movie. When people are interested in a movie, they might collect information online firstly so they might spend a lot of time reading online reviews and ratings. In order to have a general understanding of the movie, they will check different reviews and ratings on different online platforms. Thus, it is very meaningful to sort out helpful reviews from a large number of reviews and comments from other people. In this paper, we apply text exploration technology to the comments of movie and the analysis will allow the viewers to clearly identify the information they need. This study collects movie reviews from the online platform (YAHOO film) for text exploration and analysis. Many comments don’t align with the ratings, so the primary goal of research is to verify whether the netizens’ comments are consistent with the ratings, then discuss the connection between movie date and the emotional words. The viewers can refer to these overall analysis and emotional time dynamics in the future when they want to check feedbacks from other people. The result shows that the time dynamic analysis has significant differences in emotional words in the third week so it would be better to the viewers referring to the third-week rating as a quick check before watching movies.
An Internet Medical Service Recommendation Method based on Collaborative Filtering

The recommendation system could mine the user's behavior operation data and provide different personalized recommendation services for different users. The problems of inaccurate and incomplete description of patients' needs in internet medical service have brought great challenges to the recommendation of internet medical service. This paper takes the medical service field as the research object to improve the collaborative filtering algorithm in the recommendation system. Firstly, the static evaluation model based on doctor entity and hospital entity is established by using Analytic Hierarchy Process (AHP) model to realize the initial distribution of weight. Then, based on the evaluation methods of doctors and hospitals, the user interest model is established and K-means clustering is carried out for users, and dynamic recommendation is carried out to users by combining collaborative filtering recommendation method. The experimental results show that the proposed collaborative filtering recommendation model based on user interest clustering has smaller error, better recommendation effect and more accurate recommendation.
Usability Assessment of the Visualization-Driven Approaches to the HVAC Data Exploration

Application of the Internet-connected operational devices in the heating, ventilation and conditioning (HVAC) systems has extended the cyber-attack surface by introducing different malicious scenarios. The analysis of the HVAC data may provide insight on typical patterns of the system operations. Implementation of the thoroughly elaborated visualization models may significantly increase the efficiency of the suspicious activity identification in the HVAC systems. In the paper we present the results of the laboratory usability testing of three visualization models used to analyze HVAC data – matrix-based visualization technique, non-linear multidimensional visualization technique RadViz and timeline chart. Matrix-based visualization and RadViz visualization are often used in anomaly detection process, while timeline charts are a traditional way to present operational HVAC data. We describe the experiment design and discuss the results obtained. The usability testing revealed advantages and limitations of these visualization techniques in behavior pattern and anomaly identification tasks. The results can further serve as guidelines for task-dependent selection of a visualization technique.
Control of maximum evolution speed of quantum states by two-mode entangled light field

We consider a two-mode entangled coherent light field, in which one mode of the light field is injected into the cavity and interacts with a two-level atom. According to the theory of cavity electrodynamics, the evolution state of atomic system is deduced. Aiming at the dynamic process from the initial state of an atomic system to the target evolution state, the quantum speed limit time is used to characterize the maximum evolution rate of the quantum state of an atomic system. The maximum evolution rate of an atomic system can be controlled by adjusting the coherent parameters of the two-mode entangled coherent light field. But up to now, which kind of quantum speed limit time can be better, universal and strict applicable to the quantum dynamics process of any system is still an unresolved important issue. Based on different methods of measuring the distance between two quantum states and different dynamic evolution channels, many definitions of the quantum speed limit time have been given. The quantum speed limit time proposed by Deffner and Lutz (Deffner S, Lutz E 2013 &lt;i&gt;Phys. Rev. Lett.&lt;/i&gt; &lt;b&gt;111&lt;/b&gt; 010402) and the other deduced by Campaioli et al. (Campaioli F, Pollock F A, Binder F C, Modi K 2018 &lt;i&gt;Phys. Rev. Lett.&lt;/i&gt; &lt;b&gt;120&lt;/b&gt; 060409; Campaioli F, Pollock F A, Modi K 2018 arXiv:1806.08742) have attracted extensive attention. In this paper, we first compare the advantages of two quantum speed limit times to characterize the maximum evolution rate of quantum states. We find that the quantum speed limit time expression given by Campaioli et al. (Campaioli F, Pollock F A, Modi K 2018 arXiv:1806.08742) in 2018 is a tighter bound when describing the boundary of the shortest time required for the quantum state evolution of a two-level atomic system. Therefore, this paper is mainly based on the quantum speed limit time given by Campaioli et al. to analyze how to achieve the maximum evolution rate of the quantum state of the atomic system by manipulating the parameters of a two-level entangled coherent light field. The results show that the maximum evolution speed of the quantum state of the atomic system can be significantly affected by the coherent parameters of the light field interacting with the atom under certain conditions. Furthermore, when the interacting light field parameters cannot well control the maximum evolution speed of the quantum state, the quantum correlation with the two-mode entangled coherent light field can realize the remote control of the maximum evolution speed of quantum state of atomic system by the light field parameters that are not involved in the atom-light interaction.
Neural Gibbs Sampling for Joint Event Argument Extraction

Event Argument Extraction (EAE) aims at predicting event argument roles of entities in text, which is a crucial subtask and bottleneck of event extraction. Existing EAE methods either extract each event argument roles independently or sequentially, which cannot adequately model the joint probability distribution among event arguments and their roles. In this paper, we propose a Bayesian model named Neural Gibbs Sampling (NGS) to jointly extract event arguments. Specifically, we train two neural networks to model the prior distribution and conditional distribution over event arguments respectively and then use Gibbs sampling to approximate the joint distribution with the learned distributions. For overcoming the shortcoming of the high complexity of the original Gibbs sampling algorithm, we further apply simulated annealing to efficiently estimate the joint probability distribution over event arguments and make predictions. We conduct experiments on the two widely-used benchmark datasets ACE 2005 and TAC KBP 2016. The Experimental results show that our NGS model can achieve comparable results to existing state-of-the-art EAE methods. The source code can be obtained from https://github.com/THU-KEG/NGS.