Reviewerly/exHarmony huggingface dataset:
Bilevel Online Deep Learning in Non-stationary Environment

Recent years have witnessed enormous progress of online learning. However, a major challenge on the road to artificial agents is concept drift, that is, the data probability distribution would change where the data instance arrives sequentially in a stream fashion, which would lead to catastrophic forgetting and degrade the performance of the model. In this paper, we proposed a new Bilevel Online Deep Learning (BODL) framework, which combine bilevel optimization strategy and online ensemble classifier. In BODL algorithm, we use an ensemble classifier, which use the output of different hidden layers in deep neural network to build multiple base classifiers, the important weights of the base classifiers are updated according to exponential gradient descent method in an online manner. Besides, we apply the similar constraint to overcome the convergence problem of online ensemble framework. Then an effective concept drift detection mechanism utilizing the error rate of classifier is designed to monitor the change of the data probability distribution. When the concept drift is detected, our BODL algorithm can adaptively update the model parameters via bilevel optimization and then circumvent the large drift and encourage positive transfer. Finally, the extensive experiments and ablation studies are conducted on various datasets and the competitive numerical results illustrate that our BODL algorithm is a promising approach.
A Survey of Scheduling Tasks in Big Data: Apache Spark

Today is the age of big data in which data is increased exponentially. Since the traditional computing systems unable to process these massive data, big data processing frameworks were developed. Apache Spark considers as one of the most relevant cluster computing frameworks for scalable data processing. The issue of task scheduling has been an active field of research in computing systems since its inception, and now in the age of big data, it is considered as one of the most important research fields. The main goal of this paper is to provide a comprehensive overview of the researches undertaken in the field of scheduling tasks in Apache Spark, and therefore, this study can be used as a starting point in the field of scheduling tasks in Apache Spark and as a benchmark to propose a novel improvement of job scheduling for Apache Spark.
Predicting Election Results Via Social Media: A Case Study for 2018 Turkish Presidential Election

Social media platforms provide massive amounts of data that can be used to analyze social issues and forecast events in the future. However, it is a challenging task due to the biased and noisy nature of the data. In this work, we propose a method to predict election results via Twitter. In particular, we first detect the stance of social media accounts using their retweets. Subsequently, we develop four different counting methods for our prediction task. In the simple user counting (SC) method, we count labeled users without taking any further steps to reduce bias. In the city-based weighted counting (CBWC) method, we apply a weighted counting based on the number of electorate in each city. The closest-city-based prediction (CCBP) method utilizes sociological similarity between cities to predict results for cities with limited sample sizes. The using former election results (UFERs) method compares predictions for each city against former election results to detect data bias and uses them accordingly. We evaluate our proposed methods with the data collected for the presidential election of Turkey held in 2018. In our extensive evaluation, we show that utilizing domain-specific information and location-based weighted counting is effective in reducing bias. CBWC, CCBP, and UFER methods outperform tweet-counting-based baseline methods. Furthermore, UFER and CCBP outperform almost all traditional polls, suggesting that social media platforms are alternative mediums for conducting election polls.
Approximate Pattern Matching Using Search Schemes and In-Text Verification

Search schemes enable the efficient identification of all approximate occurrences of a search pattern in a text. Using a bidirectional FM-index, search schemes describe how to explore the search space in such a way that runtime is minimized. Even though in-index matching has an optimal time complexity, relatively expensive random memory access is required for elementary operations on the FM-index. We analyze to what extent in-index matching can be complemented with in-text verification where a candidate occurrence is directly validated in the text using a bit-parallel, pairwise alignment procedure. We find that hybrid in-index/in-text matching can reduce the running time by more than a factor of two, compared to pure in-index matching. We present Columba 1.1, an open-source (AGPL-3.0 license) software tool written in C++ that efficiently implements these ideas. Using a single CPU core, Columba 1.1 can identify, within a maximum edit distance of four, all occurrences of 100 000 Illumina reads (150 bp) in the human reference genome in roughly half a minute. This significantly outperforms existing, state-of-the-art tools.
METHODOLOGICAL APPROACHES TO THE STUDY OF THE PEDAGOGICAL CONCEPT OF EDUCATION OF FUTURE TEACHERS IN THE CONDITIONS OF DIGITALIZATION OF EDUCATION

The article analyzes the scientific and pedagogical publications and highlights the methodological approaches to the study of education of future teachers in the context of its digitalization. The author's understanding of the concept "research methodology in the context of digitalization of education" is highlighted. The following specific scientific approaches are defined: competence, frame, activity, personality-oriented. The specific scientific level of methodology directs the researcher to appeal to the generally accepted concepts of outstanding scientists in the field of the researched issue. Competence approach may help future teachers to navigate in interconnected and interdependent areas of their activity; will contribute to the effective and high-quality performance of professional duties. Frame approach provides the formation of skills to structure and organize information in the form of tables, diagrams, chains, etc., in particular with the help of innovation platforms. Activity approach is one of the main approaches, which is a part of the professionally-oriented component of the conceptual model of research process, the effective interaction of all participants in the educational process. Application of this approach involves using appropriate technologies, methods, forms and tools, designed primarily for independent (extracurricular), educational work, and thus for self-education and self-development. The personality-oriented approach assumes the orientation of education of future teachers on formation of creative, comprehensively developed personality of future teachers who are motivated to introduce innovations in their educational process in the conditions of digitalization of education. It was found out that in order to create a pedagogical concept of education of future teachers in the context of digitalization of education, it is necessary to take into account the unity of these specific scientific approaches. After all, during the education of future teachers in the conditions of digitalization it is necessary to develop general and professional competencies, develop skills to systematize information, improve skills of transferring theoretical knowledge into practice, taking into account the needs of self-development and self-improvement, ability to work with innovative technologies, electronic platforms, online services etc. We see the prospect of research in the development of the author's pedagogical concept of education of future teachers in the context of digitalization of education.
A training methodology based on virtual reality to promote the learning-by-doing approach

Virtual reality (VR) training allows companies to train their workforce thanks to virtually simulated environments, leveraging the skills of people before the system production with the final aim to reduce the downtime of productive equipment and improve the global factory efficiency. However, the use of VR immersive training is still limited in industry due to the lack of structured methodologies to effectively implement these simulations. This paper deals with the application of VR technologies to create virtual training simulations addressing assembly or maintenance tasks. It suggests a methodology to create an interactive virtual space in which operators can perform predefined tasks in a realistic way, having dedicated instructions to support the learn-by-doing, based on key training features (KTFs). This methodology was applied to an industrial case study concerning some specific tractor assembly phases. Results show that operators generally appreciate this new training process, enabling faster and more intuitive learning.
Tangible ‘Design Non-Proposals’ for Relationship Building in Community-Based Co-Design Projects

We present tangible design non-proposals as a technique for establishing and growing relationships in community-based co-design projects, particularly with Australian Aboriginal and Torres Strait Islander communities. Prior tangible design products from our research group were used to introduce our capabilities and interests and as tangible provocations to understand potential community interests for co-design. We describe how the non-proposals supported initial conversations and imagination, forming relationships, formalising an official relationship, understanding the scope and scale of community aspirations, artful appropriation, and how they morphed into a research prototype aligned to the community's needs and vision. We describe the ways that tangibles supported a mutual sensitisation process and realising project limits, bounds and misalignments. We contribute a framework for design non-proposals with both general characteristics and dimensions salient to our collaborative work with Aboriginal communities. We evaluate our own design non-proposals using the framework to present implications for seeding co-design processes.
Why are we here? A reflexive story of decolonising

This paper grapples with complicities of decolonising as we attempt to unsettle Dominant conventions in Design, which ossify through repetition, such as reoccurring conferences. Such conventions are troubled through questions around labour, privilege and ethics of being present and situated when we come together to share our practices on unceded Indigenous lands. The paper is co-authored by hosts of a conference that leveraged the organising mechanisms to surface implicit agendas, interrogate normalised approaches and confront tensions and paradoxes of decolonising our own practices. Instead of obeying disciplinary expectations to propose methodologies or ‘alternative’ conference engagements, we reflexively share unsettling complicities in responding to a penetrating question by an Indigenous Elder, ‘why are you here and what is your purpose?’ Rather than discussing decolonising through arms-length theory, we share our endeavours through practice and ontologies of our feeling-thinking (sentipensar), to offer learnings and thoughts towards what is yet still to do.
Gym Trainer Application using Machine Learning.

Abstract: Human pose estimation localizes body key points to accurately recognizing the postures of individuals given an image. This step is a crucial prerequisite to multiple tasks of computer vision which include human action recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. Fitness exercises are very beneficial to personal health and fitness; however, they can also be ineffective and potentially dangerous if performed incorrectly by the user. Exercise mistakes are made when the user does not use the proper form, or pose. In our work, we introduce Pose Trainer, an application that detects the user’s exercise pose and provides personalized, detailed recommendations on how the user can improve their form. Pose Trainer uses the state of the art in pose estimation to detect a user’s pose, then evaluates the vector geometry of the pose through an exercise to provide useful feedback. We record a dataset of over 100 exercise videos of correct and incorrect form, based on personal training guidelines, and build geometric heuristic and machine learning algorithms for evaluation. Pose Trainer works on four common exercises and supports any Windows or Linux computer with a GPU
Blockchain, a transformation of new financing and management progress of megaprojects in the context of smart contracts with the usage of metaverse and oracle

Today the emergence of many technologies has transformed the financial and commercial sectors.Blockchain is considered one of the emerging technologies in various industries.Blockchain financing allows investors to choose projects they trust and participate in without the need for third-party intermediaries.The main features of blockchain are security, transparency, and immutability; hence, using blockchain in crowdfunding makes this process more reliable.The current study presents a model based on a smart contract on the Ethereum blockchain network and integrates the (ICO) and (IPO) models.This study proposes an integrated ecosystem for attracting and monitoring project progress by stakeholders based on a smart contract, where the smart contract's data are connected via Oracle to an external database.As a result, the shareholders can track the project's progress via its corresponding Digital Twins in its respective Metaverse.
Exploring Finnic written oral folk poetry through string similarity

Abstract Suomen Kansan Vanhat Runot (Old Poems of the Finnish People) is a collection of nearly 90,000 oral folk poems written down between 1564 and the early 20th century. It is characterized by frequent reoccurrence of similar pieces of text on various levels (from entire poems, through passages to single verses and collocations). However, finding these similarities is challenging due to a high degree of orthographical, morphological, and compositional variation. In this article, we propose a method for automatically identifying equivalent verses, i.e. verses conveying the same meaning with the same words, using a clustering based on cosine similarity of character bigram vectors. The method achieves around 81% F-score and has been successfully used for identifying similarities across the entire SKVR corpus on the level of verse, passage, and poem. The results can be browsed through a Web interface.
The Relationship between Machine Translation and Human Translation under the Influence of Artificial Intelligence Machine Translation

Machine translation based on artificial intelligence has many commercial applications, such as Google translation, Baidu translation, and Youdao translation. More artificial intelligence and its translation are still used in all aspects of life. Therefore, we should reexamine its impact on the relationship between human translation and machine translation. Therefore, based on this background, this paper discusses the impact of the development of artificial intelligence machine translation on the relationship between human translation and machine translation. Although the translation accuracy and overall situation of machine translation based on artificial intelligence are similar to that of human translation, the basic algorithm of machine translation is still a program that judges right and wrong through computer code. It cannot simulate the “faithfulness, expressiveness, and elegance” of human translation in combination with social background and human culture. However, for some mechanical operations, such as business translation, scenes with low requirements, such as common vocabulary in daily tourism, can still meet the needs. Therefore, under the influence of artificial intelligence machine translation, the relationship between the two is that machine translation can replace human translation in some aspects, but it cannot replace human translation.
Coaching as a Tool for Adaptive Personnel Management of Modern Companies

The purpose of the investigation is to reveal the content and characteristics of coaching as a modern tool for professional staff training, given the need to use adaptive management in the practice of domestic companies. The research is based on the dialectical method of cognition of processes and phenomena, in particular, the monographic method (in identifying current trends and research of scientific achievements of domestic and foreign science and practice of personnel training), comparative (in assessing costs and forms of personnel training), abstract-logical method (theoretical and methodological generalizations, substantiation of conclusions), which were used in determining the essential features and role of coaching in the HR-management system. The essence and characteristic features of coaching are studied; the size and forms of training of the personnel of the companies of the USA and Ukraine are analyzed. It was found that in foreign practice coaching is used as an effective tool for adaptive management of company personnel and a tool for gaining strategic competitive advantages of companies in the market. The types of coaching, the set of socio-economic and psychological factors on which modern coaching technologies are based are studied, the set of professional competencies of coaches is determined. The author’s vision of the key principles of coaching training is substantiated, a number of rules of effective coaching are recommended in order to increase the effectiveness of adaptive personnel management of the company. The author’s model of implementation of coaching on conditions of adaptive management of the company is suggested. The obtained results of the research allow improving the personnel policy of domestic companies on the basis of using the forms, principles and advantages of coaching as a promising method of staff training.
Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams

Abstract Today’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.
Visual Object Tracking Based on Deep Neural Network

Computer vision systems cannot function without visual target tracking. Intelligent video monitoring, medical treatment, human-computer interaction, and traffic management all stand to benefit greatly from this technology. Although many new algorithms and methods emerge every year, the reality is complex. Targets are often disturbed by factors such as occlusion, illumination changes, deformation, and rapid motion. Solving these problems has also become the main task of visual target tracking researchers. As with the development for deep neural networks and attention mechanisms, object-tracking methods with deep learning show great research potential. This paper analyzes the abovementioned difficult factors, uses the tracking framework based on deep learning, and combines the attention mechanism model to accurately model the target, aiming to improve tracking algorithm. In this work, twin network tracking strategy with dual self-attention is designed. A dual self-attention mechanism is used to enhance feature representation of the target from the standpoint of space and channel, with the goal of addressing target deformation and other problems. In addition, adaptive weights and residual connections are used to enable adaptive attention feature selection. A Siamese tracking network is used in conjunction with the proposed dual self-attention technique. Massive experimental results show our proposed method improves tracking performance, and tracking strategy achieves an excellent tracking effect.
State Machines and Hypergroups

State machines are a type of mathematical modeling tool that is commonly used to investigate how a system interacts with its surroundings. The system is thought to be made up of discrete states that change in response to external inputs. The state machines whose environment is a two-element magma are investigated in this study, focusing on the case when the magma is a group or a hypergroup. It is shown that state machines in any two-element magma can only have up to three states. In particular, the quasi-automata and quasi-multiautomata state machines are described and enumerated.
Mitigating adversarial evasion attacks by deep active learning for medical image classification

Abstract In the Internet of Medical Things (IoMT), collaboration among institutes can help complex medical and clinical analysis of disease. Deep neural networks (DNN) require training models on large, diverse patients to achieve expert clinician-level performance. Clinical studies do not contain diverse patient populations for analysis due to limited availability and scale. DNN models trained on limited datasets are thereby constraining their clinical performance upon deployment at a new hospital. Therefore, there is significant value in increasing the availability of diverse training data. This research proposes institutional data collaboration alongside an adversarial evasion method to keep the data secure. The model uses a federated learning approach to share model weights and gradients. The local model first studies the unlabeled samples classifying them as adversarial or normal. The method then uses a centroid-based clustering technique to cluster the sample images. After that, the model predicts the output of the selected images, and active learning methods are implemented to choose the sub-sample of the human annotation task. The expert within the domain takes the input and confidence score and validates the samples for the model’s training. The model re-trains on the new samples and sends the updated weights across the network for collaboration purposes. We use the InceptionV3 and VGG16 model under fabricated inputs for simulating Fast Gradient Signed Method (FGSM) attacks. The model was able to evade attacks and achieve a high accuracy rating of 95%.