Reviewerly/exHarmony huggingface dataset:
An ML-Powered Risk Assessment System for Predicting Prospective Mass Shooting

The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, domestic violence, firearms access, and aggressive social media posts. With the advancement of machine learning (ML), it is more possible than ever to predict mass shootings before they occur by studying the behavior of prospective mass shooters. This paper presents an ML-based system that uses various unsupervised ML models to warn about a balanced progressive tendency of a person to commit a mass shooting. Our system used two models, namely local outlier factor and K-means clustering, to learn both the psychological factors and social media activities of previous shooters to provide a probabilistic similarity of a new observation to an existing shooter. The developed system can show the similarity between a new record for a prospective shooter and one or more records from our dataset via a GUI-friendly interface. It enables users to select some social and criminal observations about the prospective shooter. Then, the webpage creates a new record, classifies it, and displays the similarity results. Furthermore, we developed a feed-in module, which allows new observations to be added to our dataset and retrains the ML models. Finally, we evaluated our system using various performance metrics.
Machine Learning in Mechatronics and Robotics and Its Application in Face-Related Projects

Implementing non-verbal information is one of the ways to create communication between people. Using this type of information can improve the interaction process in human–robot interaction. One of the aspects of people’s non-verbal information is facial images, which can play an essential role in the development of mechatronic and robotic systems. This chapter discusses some uses of facial images, such as facial recognition, and facial expression recognition. Using these images and applications, authors explored and developed mechatronic and robotic systems which were based on special access for different persons and changes in facial expressions. As mentioned earlier, in chapter nine, different aspects of face-related projects are explored, apart from that, some ideas that can be applied to create new approaches are discussed. Since these projects perform based on a procedure, the flow of the facial projects from face detection to facial expression recognition and other applications are discussed. This chapter helps the readers get acquainted with using facial images in human–robot interaction.
Is, Ought, and Cut

Abstract In this paper we use proof-theoretic methods, specifically sequent calculi, admissibility of cut within them and the resultant subformula property, to examine a range of philosophically-motivated deontic logics. We show that for all of those logics it is a (meta)theorem that the Special Hume Thesis holds, namely that no purely normative conclusion follows non-trivially from purely descriptive premises (nor vice versa). In addition to its interest on its own, this also illustrates one way in which proof theory sheds light on philosophically substantial questions.
The Development of Mobile AR-Based Module for Teaching and Learning Pneumatic System: A Needs Analysis

Practical skills and theoretical knowledge are vital competencies that technical and vocational trainees need to master before joining the industrial workforce to perform tasks efficiently. Therefore, comprehensive instructional materials and training equipment are essential to ensure that the skills and knowledge are delivered effectively to trainees. This study aimed to identify the need to develop a mobile augmented reality module to teach and learn pneumatic systems in Malaysian vocational colleges. Descriptive research was conducted to determine the module’s need, involving 13 subject-matter experts from various vocational colleges. A set of questionnaires consisting of three main themes: the need for the module, the module’s user interface design, and the module’s usability criteria with 49 items was used. The data collected were analysed using the Fuzzy Delphi Method. All three constructs showed high levels of expert consensus of more than 75% and a threshold value of ≤ 0.2. Therefore, experts agreed that there was a need to develop a mobile AR-based module for teaching and to learn pneumatic systems with the stated user interface designs and usability criteria.
Secret sharing on regular bipartite access structures

Abstract Bipartite secret sharing schemes realize access structures in which the participants are divided into two parts, and all the participants in the same part play an equivalent role. Such a bipartite structure can be described by the collection of its minimal points. The complexity of a scheme is the ratio between the maximum share size given to the participants and the secret size, and the Shannon complexity of a structure is the best lower bound provided by the entropy method. Within this work, we compute the Shannon complexity of regular bipartite structures and provide optimal constructions for some bipartite structures defined by 2 and 3 points.
Vectorial Boolean functions with the maximum number of bent components beyond the Nyberg’s bound

Abstract Recently, several interesting constructions of vectorial Boolean functions with the maximum number of bent components (MNBC functions, for short) were proposed. However, many of them have component functions from the completed Maiorana-McFarland class $${\mathcal {M}}^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mo>#</mml:mo> </mml:msup> </mml:math> . Moreover, no examples of MNBC functions containing component functions provably outside $${\mathcal {M}}^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mo>#</mml:mo> </mml:msup> </mml:math> are known. In this paper, we classify all MNBC functions in six variables. Based on the analysis of the obtained equivalence classes, we propose several infinite families of MNBC functions with component functions outside the $${\mathcal {M}}^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mo>#</mml:mo> </mml:msup> </mml:math> class. In particular, two of our new constructions are solutions to the open problem [Bapić et al (eds) Proceedings of the twelfth international workshop on coding and cryptography, 2022, Item 1., p. 9].
Bias characterization, assessment, and mitigation in location-based recommender systems

Abstract Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizations.
A semantics-aware approach for multilingual natural language inference

This paper introduces a semantics-aware approach to natural language inference which allows neural network models to perform better on natural language inference benchmarks. We propose to incorporate explicit lexical and concept-level semantics from knowledge bases to improve inference accuracy. We conduct an extensive evaluation of four models using different sentence encoders, including continuous bag-of-words, convolutional neural network, recurrent neural network, and the transformer model. Experimental results demonstrate that semantics-aware neural models give better accuracy than those without semantics information. On average of the three strong models, our semantic-aware approach improves natural language inference in different languages.
Quasi optimal anticodes: structure and invariants

Abstract It is well-known that the dimension of optimal anticodes in the rank-metric is divisible by the maximum m between the number of rows and columns of the matrices. Moreover, for a fixed k divisible by m , optimal rank-metric anticodes are the codes with least maximum rank, among those of dimension k . In this paper, we study the family of rank-metric codes whose dimension is not divisible by m and whose maximum rank is the least possible for codes of that dimension, according to the Anticode bound. As these are not optimal anticodes, we call them quasi optimal anticodes (qOACs). In addition, we call dually qOAC a qOAC whose dual is also a qOAC. We describe explicitly the structure of dually qOACs and compute their weight distributions, generalized weights, and associated q -polymatroids.
Optimizing Structure and Internal Unit Weights of Echo State Network for an Efficient LMS-Based Online Training

Echo state network (ESN) is a special type of recurrent neural networks (RNN) wherein a dynamic reservoir is used in the hidden layer, the weight of internal units of ESN is kept fix during training process, and output weights are the only trainable weights. Therefore, network training in an offline mode can be changed into a linear regression equation which is simply solved, although it is required to use online training of ESN in some applied problems. The least mean square (LMS) algorithm can provide an easy and constant method for online training of ESN; however, the huge eigenvalue spreads of the correlation matrix of internal network states reduce the speed of the algorithm convergence. In this study, harmony search algorithm (HSA) is used to optimally produce the structure and weight of internal network units. It is possible to significantly reduce the eigenvalue spreads of the correlation matrix of network states by means of this algorithm. Thereafter, the LMS algorithm is used for the online training of ESN built with the help of HSA. Already-obtained simulation results show that the eigenvalue spreads of the correlation matrix are reduced millions of times, and the LMS algorithm increases the online training speed of the network several times with an acceptable precision of training.
Evaluating Deep Learning Techniques for Natural Language Inference

Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called the premise and hypothesis. If the premise entails the hypothesis, the pair is labeled as an “entailment”. If the hypothesis contradicts the premise, the pair is labeled a “contradiction”, and if there is not enough information to infer a relationship, the pair is labeled as “neutral”. In this paper, we present experimentation results of using modern deep learning (DL) models, such as the pre-trained transformer BERT, as well as additional models that relay on LSTM networks, for the NLI task. We compare five DL models (and variations of them) on eight widely used NLI datasets. We trained and fine-tuned the hyperparameters for each model to achieve the best performance for each dataset, where we achieved some state-of-the-art results. Next, we examined the inference ability of the models on the BreakingNLI dataset, which evaluates the model’s ability to recognize lexical inferences. Finally, we tested the generalization power of our models across all the NLI datasets. The results of the study are quite interesting. In the first part of our experimentation, the results indicate the performance advantage of the pre-trained transformers BERT, RoBERTa, and ALBERT over other deep learning models. This became more evident when they were tested on the BreakingNLI dataset. We also see a pattern of improved performance when the larger models are used. However, ALBERT, given that it has 18 times fewer parameters, achieved quite remarkable performance.
Гадаад хэлний сурах бичгийн дасгал даалгаврын шинжилгээний асуудалд

Due to the development of modern information technology, the structure of foreign language textbooks is becoming more complex. On the other hand, the learning needs of modern learners are wider than they were before. The rationale of this study is the need for a close analysis of textbook exercises and assignments, which are key units in the implementation of language training programs. Inthe framework of this article, we aim at analyzing the English textbook exercises of General comprehensive school of Mongolia and calculate the results. In order to fulfill the purpose of the research, the objectives include to study the theoretical basis of the analysis of foreign language textbook exercises, carry out a close analysis in “English 3” textbook exercises, and to calculate the results. This model of the close analysis of foreign language textbook exercises which was suggested by Littlejohn in 1998 comprises the following components: 1. Students activities 2. Communication in the classroom 3. Content of exercises and tasks. This model reveals the information about the role of students, classroom communication and curriculum content. Thus, our study has shown that this model of the close analysis of foreign language textbook exercises can be used for selecting appropriate textbooks and for improving the textbook design in teaching practice.
The implementation of mobile learning for geometry and trigonometry students at CECyT 16 "Hidalgo"

This paper presents the work of qualitative cut research as a result of the analysis of the implementation of Mobile Learning for students of the upper middle level in the Centrodef Estudios Científicos y Tecnológicos No. 16 Hidalgo, Mexico; being a support strategy for the teaching and learning process, where ICT is easy to transfer and viable, improving and speeding up its implementation; since having an Internet connection is having access to countless open resources and the barrier of distances do not exist. In the academic context, Internet revolutionized the way of presenting information, innovating the way in which it is learned and how it is taught, becoming playful, dynamic and fun; Currently, young people are used to new technologies, mobile devices such as cell phones and tablets used inside and outside the classroom, become a vital tool for mobile education taking advantage of the portability and ease of being able to connect anywhere and at any time.
Lao Qida-ийн хятад эхийн хэлний онцлог

In this paper the objective is to show the peculiarities of the Chinese language of Lao Qida (or Nogeldai), the book was compiled during the period of the Mongolian Yuan Dynasty. The book was used as a textbook for teaching of foreign languages in Korea during Koryo Dynasty (1392-1910). Later on the Mongolian, Manchurian and other versions of the book have appeared. The main distinctive feature of the language of the book is the use of the colloquial Chinese language. This article describes some phonological, lexical and grammatical peculiarities of the Chinese language of Lao Qida. Our team of philologists is conducting a comparative study the Chinese, Mongolian and Manchurian versions of Lao Qida and the results of the study will be published within the framework of the project.
Aнгли хэлний нэрийн бүлгийн орчуулгын алдаанд хийсэн дүн шинжилгээ (ШУС, гадаад хэлний орчуулга хөтөлбөрийн оюутны жишээгээр)

The purpose of the present research study was to carry out an error analysis on the English noun phrase translation. In this respect, we investigated the errors in a corpus of 8 versions of mini tests translated by 16 intermediate students of the School of Arts and Sciences at NUM. The instrument used for this study was students’ noun phrase translations in the English language. All the errors related to the noun phrase in this article were identified, described, explained and evaluated according to Stephen Pit Corder’s taxonomy. Specifically, we aimed to identify the source of the errors. The findings demonstrated that the source of students' errors could be explained by interlingual and intralingual transfers. Furthermore, we wrote a recommendation for the students to refine their language proficiency.
Provability multilattice logic

In this paper, we introduce provability multilattice logic PMLn and multilattice arithmetic MPAn which extends first-order multilattice logic with equality by multilattice versions of Peano axioms. We show that PMLn has the provability interpretation with respect to MPAn and prove the arithmetic completeness theorem for it. We formulate PMLn in the form of a nested sequent calculus and show that cut is admissible in it. We introduce the notion of a provability multilattice and develop algebraic semantics for PMLn on its basis, by the method of Lindenbaum-Tarski algebras we prove the algebraic completeness theorem. We present Kripke semantics for PMLn and establish the Kripke completeness theorem via syntactical and semantic embeddings from PMLn into GL and vice versa. Last but not least, the decidability of PMLn is shown.
Hur FAIR är svensk digitiserad kulturarvsdata idag?

Historical research is increasingly dependent on digital methods to find materials and information online. This requires a very different form of source criticism in order to understand what is available or not through different services. In this paper we present an overview of how digitalised heritage data from Swedish museums and archives are made publicly available on various platforms today, and highlight the often hidden challenges researchers face when trying to make use of them. The study is grounded in the FAIR data principles: to which extent is digitalised heritage data findable, accessible, interoperable and reusable? Content available through aggregated web services by the National Heritage Board, the National Archives and the Royal Library, among several others, are examined and compared. The published collections data of the Nordic Museum are used to highlight the limits of FAIR. We identify areas where lack of national strategies and good digital practices causes problems, and suggest ways forward. Digitalisation is much more than digital versions of analogue practices and materials. Trusted vocabularies combined with linked data and persistent identifiers will enable a richer and more multivocal heritage that is not restricted in space or to the information that fits onto a catalogue card. Researchers and heritage institutions need deeper collaboration to ensure digitalisation results in usable data, and they need to embrace good digital practices.
From virtual to physical environments when judging action opportunities: are diagnostics and trainings transferable?

Abstract The proper evaluation of whether our given bodily capabilities and environmental properties allow particular actions is indispensable for pertinent decisions, so-called affordance judgments. These can be impacted by older age or brain damage. Virtual Environments (VEs) may provide an efficient opportunity to offer trainings. But do people make affordance judgments in VEs in the same way that they do in Physical Environments (PEs)? And are these decisions trainable by use of VEs? We investigated 24 healthy young adults’ judgment performance of whether or not they could fit their hand into a given aperture. They were presented with a set of opening-increments and indicated their judgments by pressing a yes- or no-button. The stimuli were presented in PE using an aperture apparatus and in VE displayed by use of Oculus Rift goggles. Our results demonstrated the level of equivalence to be specific to the variable: While we found equivalence between VE and PE for the accuracy parameter, results were uncertain or non-equivalent for perceptual sensitivity and for judgment tendency, respectively. When applying training in VE, judgment accuracy improved significantly when tested subsequently within VE. Improvement appeared detectable in PE only on a descriptive level. Furthermore, equivalence testing post-training revealed that perceptual sensitivity performance in VE approached a PE-level. Promisingly, the VE training approach appeared applicable and efficacious within the VE. Future studies need to specify factors that enhance equivalence for detection theory variables and that facilitate transfer from VEs to PEs when judging action opportunities.
Supposition and (Statistical) Models

Abstract In a recent paper, Sprenger advances what he calls a “suppositional” answer to the question of why a Bayesian agent’s degrees of belief should align with the probabilities found in statistical models. I show that Sprenger’s account trades on an ambiguity between hypothetical and subjunctive suppositions and cannot succeed once we distinguish between the two.
Automatic Detection of Leukemia through Convolutional Neural Network

Leukemia is a fatal cancer disease that develops in blood-forming tissue by the excessive development of white blood cells (WBCs) in the human body. However, a bone marrow test is recommended by the pathologist to diagnose leukemia and further types of leukemia. Leukemia has two classes i.e., acute and chronic leukemia. Therefore, early leukemias detection enables preventative actions to be taken to avoid any harm to human life. In addition, several manual and automatic methods have been proposed, however, they possess some drawbacks and are inefficient for the precise detection of leukemia. This research proposes a deep learning-based framework for precise and automatic leukemia identification using microscopic images. The proposed framework comprises four stages which are pre-processing, data augmentation, segmentation, and the classification of leukemia. Moreover, pre-processing is utilized to clean the dataset images and eliminate the noise. Following that, data augmentation approaches have been employed to increase the number of images, and remove the class imbalance, and overfitting problems. The modified Convolutional Neural Network (CNN) based model is employed to segment the leukemia images. A well-known pre-trained AlexNet architecture has been used for classification. Besides that, a publicly available dataset Acute Lymphoblastic Leukemia Image DataBase (ALL-IDB) has been utilized to train and test the proposed model. The proposed model yielded 98.05% accuracy, a specificity of 97.59%, 100% of recall, and a 99.06% of F1-score. The experimentation results demonstrate that this model is effective and reliable for leukemia identification using the ALL-IDB dataset and suitable for deployment in clinical applications.