Reviewerly/exHarmony huggingface dataset:
Nebula

With the rapid growth of data volume, data-driven machine learning models have become a necessary part of many industrial applications. Intuitively, the more high-quality data used for training leads to better model performance. However, in reality, data are usually scattered and isolated in different organizations or companies. Such a "data isolation" problem stimulates both academia and industry to explore the collaborative learning paradigm to build better models jointly with multiple data sources. Despite the potential performance gains, this learning paradigm inevitably faces privacy issues, especially for the Fintech domain where data are sensitive by nature. In this paper, we present a privacy-preserving collaborative learning system in Ant Financial, named Nebula. Our system aims to facilitate privacy-preserving collaborative model training for industrial-scale applications. Our system is built upon a ring-allreduce MPI based distributed framework. On top of that, with some optimization strategies and novel sharing scheme, our system is able to scale up to tens of millions of data samples with hundreds of thousands of features and achieve more than 100x speedup compared with the existing state-of-the-art implementations.
A Hybrid Recommender System: Uniqueness of Choices by Using Machine Learning Technique

The problem of recommending similar sets of items in the online business community is called item recommendation. An item recommendation aims to recommend a new item that matches the user’s interests. Universally, recommendation amenities have become significant due to their support in e-commerce applications like online shopping, digital promotions, and various research domains. The collaborative approach of the recommender engine filters out the k-nearest neighbours and then the similarity is compared between the neighbourhoods. In this paper, an algorithm is proposed, to recommend the items to the users with respect to the uniqueness of users’ choice. The result achieved is a mixture of both types of items, which are commonly and rarely bought by others. The proposed technique uses machine learning modules to learn actively and recommend.
Towards the Modeling of the Hot Rolling Industrial Process. Preliminary Results

In the paper we describe the industrial process of hot rolling of steel. In cooperation with ArcelorMittal Poland we consider a specific fully automated production line. While it is equipped with a number of industrial sensors, the acquired data has only been analyzed on a basic statistical level, mainly for reporting. In the paper we outline opportunities for the use of AI methods in order to improve the process and possibly the quality of the resulting product. We report on preliminary results using selected methods of eXplainable AI.
Construction and Exploration of University Software Engineering Teaching System Based on CDIO Educational Concept

In recent years, with the increasing call for reform of China's software engineering education model, the CDIO engineering education model has gradually been widely respected by domestic engineering colleges. The purpose of this article is to discuss the software engineering education in colleges and universities under the CDIO engineering education model in order to improve the quality of engineering talents in China. This article mainly adopts literature analysis, case analysis, and questionnaire survey to carry out research. Taking the software engineering major of University A as an example, the questionnaire survey is conducted on the major students, from the aspects of course teaching, course design and ability development. analysis. It is concluded that project-based curriculum design methods and problem-driven teaching methods have a very good effect on improving students' abilities. Among them, 82. 80% of the students are satisfied with the problem-driven theory teaching method; 96. 0% of students think that the curriculum design has greatly improved the comprehensive programming ability. This paper is based on the CDIO software engineering teaching training method and model is feasible, and it has a certain reference value to promote the implementation of the CDIO engineering education model in Chinese universities.
The Fundamental doctrine of the criminalistics study of computer tools and systems as part of the theory of information and computer support for criminalistics activities

The development of digitalization constantly generates new forms of crime, changes in the mechanisms of how they are committed, and concealment. The problem of combating such crimes is transnational in nature. This article considers the difficulties of creating the criminalistics study of computer tools and systems doctrine as part of the theory of information and computer support of criminalistics activities. The authors’ negative stance on the issue is indicated as well as the addition of definitions to the name of criminalistics that are related to the emergence of new objects of expertise: “electronic criminalistics”, “linguistic criminalistics”, “digital criminalistics”, and others. The unity of criminalistics as a science having its own subject, system, tasks, and objects is declared. The article substantiates the subject of the theory of criminalistics research of computer tools and systems as a system for gathering significant computer information on the basis of the knowledge of which technical and criminalistics methods, tools, techniques, and methods for identifying, fixing, and seizing evidence and orienting criminalistics information about computer tools are developed. In addition, the theory is based on systems in legal proceedings in criminal, civil, and administrative cases. The objects of this theory are computer tools and systems as well as the search and criminalistics evidence of information contained in them. Based on the doctrine of the criminalistics investigation of computer tools and systems, the foundations of a new area of criminalistics techniques and technology, as a system of scientific principles, are provided. This area centers around recommendations, tools, techniques, and methods designed to collect and study information about computer tools and systems. Digital footprints and the processes of their occurrence, transmission, and transformation in order to obtain evidence in legal proceedings, disclosure, and prevention of crimes, are included.
HANDLING AVIATION OOV WORDS FOR MACHINE TRANSLATION AND CORPUS CREATION

The Indian aviation industry has been one of the fastest growing in the world since the last decade.Aviation experts anticipate it to be the largest in the world by 2030.The accelerated growth in passenger number has been influential enough to encourage more job opportunities in the industry.The anticipated exponential growth also envisions increase in the usage of aviation OOV words and phrases.For communicating the various proceedings and news through the mainstream and social media, these aviation words have become more frequent nowadays.The aviation domain is heavily dependent on the use of OOV words, which, in turn, are structured words molded to fit the aviation domain.This causes a huge problem during the conversion from English to other languages using machine translation (MT).This paper explores the problems encountered during translation of such sentences and suggests how these sentences can be pre-processed before being fed into the MT software for better translations.This paper also investigates how these structured and OOV words can be handled in the English language itself, before being converted to any other language and surveys the prospects of maneuvering this tool to create a bilingual corpus for Machine Translation applications.
Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels

The classification accuracy of pattern recognition is determined by the extracted features and the utilized classifiers. Many efforts have been conducted to obtain the best features either by introducing a new feature or proposing a new projection method to increase class separability. Recently, spectral regression extreme learning machine (SRELM) has been introduced to improve the class separability of the features. However, the evaluation of SRELM was only focused on the myoelectric or electromyography pattern recognition from many EMG channels. In practical application, the user is more convenient with less number of channels. Then, the problem is whether the SRELM would be able to work efficiently for less EMG channels. The objective of this paper is to examine the performance of SRELM for bio-signal pattern recognition using two EMG channels. The EMG electrodes were located on flexor policies lounges and flexor digitorium superficial muscles of ten healthy subjects. Various time domain features were involved with various sizes. SRELM will project these features to more recognize features before being feed to multiple classifiers. Those classifiers are randomized Variable Translation Wavelet Neural Networks (RVT-WNN), extreme learning machine(ELM), support vector machine (SVM), and linear discriminant analysis (LDA). The performance of SREM was compared to other feature methods, such as LDA, uncorrelated LDA (ULDA), orthogonal fuzzy neighborhood dimensionality reduction (OFNDA), and spectral regression discriminant analysis (SRDA). The experimental results show that SRELM performed well when dealing with different class numbers by classification accuracy of around 95.67% for ten class movements and performed better than SRDA.
An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers

Ensembles of classifiers are a very popular type of method for performing classification, due to their usually high predictive accuracy. However, ensembles have two drawbacks. First, ensembles are usually considered a ‘black box’, non-interpretable type of classification model, mainly because typically there are a very large number of classifiers in the ensemble (and often each classifier in the ensemble is a black-box classifier by itself). This lack of interpretability is an important limitation in application domains where a model’s predictions should be carefully interpreted by users, like medicine, law, etc. Second, ensemble methods typically involve many hyper-parameters, and it is difficult for users to select the best settings for those hyper-parameters. In this work we propose an Evolutionary Algorithm (an Estimation of Distribution Algorithm) that addresses both these drawbacks. This algorithm optimizes the hyper-parameter settings of a small ensemble of 5 interpretable classifiers, which allows users to interpret each classifier. In our experiments, the ensembles learned by the proposed Evolutionary Algorithm achieved the same level of predictive accuracy as a well-known Random Forest ensemble, but with the benefit of learning interpretable models (unlike Random Forests).
A Case-Based Approach to Assess Employees’ Satisfaction with Work Guidelines in Times of the Pandemic

The actual pandemic crisis posed new challenges for organizations that had to adapt new working procedures to prevent infection. In this context, monitoring employee satisfaction is paramount, but a very difficult task. To respond to this challenge, a workable problem-solving methodology was developed and tested. It examines the dynamics between Logic Programming and the Laws of Thermodynamics for Knowledge Representation and Reasoning. Such formalisms are consistent with a computer-based approach based on Case-Based Reasoning, the ultimate goal of which is to assess employee satisfaction in water analysis laboratories. The model was trained and tested with real data, collected through questionnaires, exhibiting an overall accuracy of 85.9%.
Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.&#x0D;
An Ontology for Formal Models of Kinship

The near ubiquity of family relationship ontologies in the Semantic Web has brought on the question of whether any formal analysis has been done in this domain. This paper examines kinship relationships that are normally overlooked in formal analyses of domain-specific ontologies: how are such ontologies verified and validated? We draw inspiration from existing work done in anthropology, where attempts have been made to formally model kinship as atemporal algebraic models. Based on these algebraic models, we provide an ontology for kinship written in first-order logic and demonstrate how the ontology can be used to validate definitions found in Canadian legal laws and data collection documentation.
How Incremental is the Processing of Perfective and Imperfective Aspect in Polish? An Exploratory Event-Related Potential Study

The present paper reports two ERP experiments in Polish that examined the processing of mismatches between perfective and imperfective verbs and temporal modifiers, which preceded the VP (Experiment 1) and followed it (Experiment 2). The mismatch between perfective verb and a preceding durative adverbial elicited an N400 on the object. No ERP effect was found for the analogous mismatch between imperfective verbs and a preceding time-span adverbial. The mismatching temporal adverbial elicited an early positivity (potentially an early P600) when it followed a perfective VP and a LAN when it followed an imperfective VP. The results suggest that: (i) the domain of aspectual interpretation in Polish is a VP; (ii) mismatches with perfective and imperfective verbs are resolved differently depending on the degree of their semantic specificity (only semantically underspecified imperfective verbs can be easily adjusted to the requirements of the preceding context); (iii) the position of the temporal adverbial plays a role in that a preverbal adverbial sets up a frame within which the eventuality should be interpreted and the aspectual value computed on AspP can be potentially adjusted to it (semantic integration reflected in N400), whereas a post-verbal adverbial must agree with the aspectual value already computed on AspP (syntactic integration reflected in an early positivity or a LAN).
Deepnets do not need category supervision to predict visual system responses to objects

Substantial progress has been made understanding the nature of the representations along the visual hierarchy by focusing on the goal of core object recognition. As evidence, many deep neural networks trained only to do object categorization develop internal representations that are closely aligned with primate cortical responses. However, these recognition-trained deepnets receive extensive supervision with millions of object category labels, unlike natural visual experience. Here we test the hypothesis that visual system responses may be better captured under a different, unsupervised goal—that is, to remember and uniquely represent everything it sees. To examine this possibility, we trained several models with a common base architecture (alexnet, resnet18, and corrnet), but with different tasks. Supervised models were trained on 1000-way object categorization. Unsupervised models were trained to maximize instance-level discrimination within a fixed size memory-bank (leveraging and extending Wu et al., 2018). The size of the memory bank was varied in different models to have 128, 256, or 1000 dimensions. Human brain responses were measured with functional magnetic resonance imaging (n=10), to a set of 72 inanimate object images, and we calculated the similarity structure evident in both the responses of object-selective cortex and all deepnet model layers. Overall we observed that human object-selective cortex responses were equal to or better captured by the unsupervised instance-level deep nets than the supervised categorization deep nets, particularly in the later representational stages. These results were robust across memory-bank sizes and base network architectures. These data provide the first evidence to our knowledge that unsupervised networks can fit brains better than matched supervised networks. Further, these data provide computational support for the broader hypothesis that the visual system’s goal may be better conceptualized as providing unique, compressed descriptions of the visual world.
Unpaired PET/CT image synthesis of liver region using CycleGAN

Cross-modality synthesis represent nowadays a promising application in medical image processing to manage the problem of paired data scarcity. In this work we designed and trained a CycleGAN model to generate PET/CT data from 2D slices collected from the liver body region of twelve patients. The results obtained from the six test patients show how our model can outperform baseline CycleGAN framework and effectively be used for synthesizing artificial images to be used for data augmentation or dataset completion.
EFL learners’ phonemic awareness: A correlation between English phoneme identification skill toward word processing

This research aims to find out the correlation between English phoneme identification skills and word processing. It applies the quantitative approach with correlation design. The participants are 100 of 3rd- semester students in English Language Education Program. The correlational result reveals that it has correlation with .382 degrees in phoneme identification skill toward blending skill with the significance level .000, and .359 degrees in phoneme identification skill toward segmentation skill with the significance level .000. Then, the correlation result of English phoneme identification skill toward word processing is .462 degree with its significance .000. By the result, awareness to identify phoneme by initial, medial, and final sound correlates to the blending and segmenting skills which influence the comprehension of word. The more the students are able to identify phoneme based on its sound, the more the students will be able to blend and segment phoneme. Lastly, the ability to identify English phonemes is proven to be a skill that supports EFL learners on their productive and receptive skills. Then being able to identify its phonemes will assist on recognizing and processing English words appropriately so that English language teaching can be associated with the use of phoneme-based instruction on its teaching process.&#x0D; Highlights :&#x0D; &#x0D; Ability to identify English phonemes is proven to be a skill that supports EFL learners on their English productive and receptive skills.&#x0D; English phoneme identification skill contributes to blending and segmentation skill since phonemic awareness provides both decoding and encoding skill.&#x0D;
An Axiomatic Role Similarity Measure Based on Graph Topology

RoleSim and SimRank are popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. Our experimental results on real and synthetic datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while retaining comparable computational complexity bounds of RoleSim.
Tangible VR: Traversing Space in XR to Grow a Virtual Butterfly

Immersive reality technologies have been widely utilized in the area of cultural heritage, also known as Virtual Heritage. We present a tangible Virtual Reality (VR) interaction demo that allows users to freely walk in the physical space while engaging with digital and tangible objects in a “learning area”. The space setup includes stations that are used symbiotically in the virtual and physical environments, such setup defines consistency throughout the experience. With this method, we enhance the immersive learning experience by mapping the large virtual space into a smaller physical place with a seamless transition.
GENERATING TRAINING DATA FOR WORD SENSE DISAMBIGUATION IN RUSSIAN

The best approaches in Word Sense Disambiguation (WSD) are supervised and rely on large amounts of hand-labelled data, which is not always available and costly to create. For the Russian language there is no sense-tagged resource of the size sufficient to train supervised word sense disambiguation algorithms. In our work we describe an approach that is used to create an automatically labelled collection based on the monosemous relatives (related unambiguous entries). The main contribution of our work is that we extracted monosemous relatives that can be located at relatively long distances from a target ambiguous word and ranked them according to the similarity measure to the target sense. The selected candidates are then used to extract training samples from the news corpus. We evaluated word sense disambiguation models based on a nearest neighbor classification on BERT and ELMo embeddings. Our work relies on the Russian wordnet RuWordNet.