Reviewerly/exHarmony huggingface dataset:
Digitalisation – ‘The Invisible Hand’ of Digital economy

This paper explores the way in which digitalisation might be considered the metaphor of ‘The Invisible Hand’ for the current economic environment, in which the paradigm of economics has been shifted towards a Digital economy and the transition has been accelerated by the crisis determined from the recent COVID-19 pandemic. Therefore, the mainstream of Classical economics has been replaced by digital activities based on the development of digital technologies, both as goods and services, production processes, and infrastructure. As a fast solution for recovering after the difficult period, many businesses have implemented digital technologies in their business models and strategies. Hence, the present paper collects information available from the scientific literature, creating a brief overview of digitalisation, the Digital economy, and the role of digitalisation in developing the Digital economy. Moreover, the metaphor is explained as digitalisation represents the driver for business development, achieving sustainable business strategies, implementing innovative business models for higher revenues, and reshaping the economic environment. The methodology used by the authors is the review of scientific literature relevant to the research, approaching the main ideas exposed, and exploring them for conclusions and further discussions.
Image Inpainting with Multi-scale Consistent Features

Deep neural networks based on multi-scale features have achieved great success in image inpainting. Existing methods usually learn features at different scale levels to fill holes gradually. However, they ignore the relationship between these features, which results in inconsistency. A new image inpainting model based on the U-Net structure is proposed in this paper, which considers the consistency between multi-scale features. With the proposed Ladderlike Feature Consistent Module (LFCM), we ensure the consistency of multi-scale features, which is achieved by referring to other-scale features when filling specific-scale features. An attention mechanism called the Multi-level Attention Mechanism (MAM) is proposed to focus on feature relationships at different scale levels, further refining the filled features. Furthermore, the High-Frequency Residual Discriminator (HFRD) is proposed to improve high-frequency texture details. And the Attention Reusing Module (ARM) is also introduced in every encoder feature to pre-fill the missing regions. Experiments on human faces (CelebA-HQ), street view (Paris StreetView), and natural landscapes (Places2) datasets demonstrate that our proposed inpainting model generates higher quality results in comparison to the existing state-of-the-art methods.
Faktor-Faktor Penghambat Orang Tua Siswa dalam Pembelajaran Berbasis Online di Masa Pandemi Covid 19

The purpose of this study was to 1) How was the online learning process during the Covid-19 Pandemic in the social studies subject for junior high school students in RT 18 RW 04 Kelurahan Jalan Gedang, Gading Cempaka District, Bengkulu City. In an online-based learning effort during the Covid-19 Pandemic, students can carry out learning through a zoom application that uses a laptop or cellphone, junior high school students do not always learn to use the zoom application because within a day there are junior high school students working on assignments given by the teacher at the school then sends the assignment via e-mail or whatshapp message. 2) The factors inhibiting the parents of these students often occur in RT 18 RW 04 Kelurahan Jalan Gedang, Gading Cempaka District, Bengkulu City and also experienced by many other parents in other areas especially parents of students who live in areas with difficult signals. to be reached and under economic conditions. Because there are several obstacles in the learning process such as: a) The difficulty of managing time to supervise children and doing work in the office, b) There are many needs that must be fulfilled such as laptops, quotas, cell phones, c ) The limitations of parents who are teachers at home and d) Children tend to be lazy about learning.&#x0D; Keywords: Parents, Online Learning, Social Studies.
Generating Diverse Clustering Datasets with Targeted Characteristics

When evaluating clustering algorithms, it is important to assess their performance in retrieving clusters of datasets with known structures. Nonetheless, generating and choosing diverse datasets to compose such test benchmarks is non-trivial. The datasets must present a large variety of structures and characteristics so that the algorithms can be challenged and their strengths and weaknesses can be revealed. The use of generators currently available in the literature relies on trial and error procedures that can be quite costly and inaccurate. Taking advantage of an Instance Space Analysis of popular clustering benchmarks, where datasets are projected into a 2-D embedding with linear trends according to different characteristics, we use a genetic algorithm to produce new datasets at targeted locations in the instance space. This is a natural extension of the Instance Space Analysis framework, and as a result, we are able to produce diverse datasets for composing test benchmarks for clustering.
Upgrading Speaking Ability Through Peer Teaching Technique

This research focus in eighth grade is VIII A and VIII B. the researcher used Peer teaching technique to prove that students taught to use Peer teaching technique better and positively affect students' speaking ability than students taught by using Ordinary way in the eighth grade at SMP 1 Teluk Bayur, the specifics problems discussed in the following formulated questions; (1) What is the students’ performance of pretest and posttest between control and experimental groups?, (2) Is there any significant difference between pretest and posttest performance of the control and experimental groups? This research used Experimental method. This research was conducted from February 22th 2019 to 25th March 2019 in second grade at SMPN 1 Teluk Bayur. The research using Peer teaching technique to improve students’ speaking ability. The Instrument’s research is test, in the test there are Pre-test, treatment and Post-test. Data analysis was scoring, normality, homogeneity, finding mean, and demonstrated T-Test.
The Kerdroya Postmortem: Navigating the Labyrinth of Co-creative Design and Collective Vision

In this paper, the authors explore the development process for Tregeagle's Kerdroya, an augmented reality app designed to immerse players in the exploration of a real physical labyrinth on Bodmin Moor. The authors discuss the lineage of this game in previous projects from Falmouth University and WAVE, contextualizing Kerdroya within a larger set of themes involving co-creation and educational game design. The authors further unpack Kerdroya's complex design and development process, situating it within the context of debates surrounding collaborative authorship, co-design, and co-creativity. The authors then explore the specific design and development process of Kerdroya with emphasis on the strengths and weaknesses associated with collaboratively developing an experimental app whose aesthetic sensibilities run counter to many of the assumptions of commercial apps regarding usability and polish. The authors conclude that the financial and time constraints of the process allowed them to make a virtue of necessity, finding opportunities for avant-garde creativity amid scarce resources and limited time.
Hierarchical Architecture for Multi-Agent Reinforcement Learning in Intelligent Game

The intelligent game has made great achievements while also posing grand challenges to reinforcement learning such as multi-agent coordination, long time horizons, complex action control, sparse rewards, etc. In this paper, we propose a hierarchical architecture learning paradigm that methodologically combines the multi-agent algorithm and single-agent algorithm in multi-agent environments. With learning hierarchical policy and the independence of each level in the model, macro-operation is introduced to reduce the original action space, while skillfully mitigating the scalability issue. Besides, the PER-QMIX algorithm, based on the QMIX, uses the prioritized experience replay mechanism to sample important memories more frequently and thus learn more efficiently. We implement the hierarchical structure with the PER-QMIX algorithm and macro-operation in the complex wargame with extremely sparse rewards. We experimentally validate the effectiveness of the proposed methods in intelligent wargame by demonstrating that our approaches can significantly facilitate and accelerate learning compared with baselines.
BEFSR: A Multiple Attention-Based Model Considering Bidirectional Entity Information Flows and Few-Shot Relations

The traditional knowledge representation learning (KRL) models treat each triplet in a knowledge base independently, so they can not make full use of the neighborhood information across triplets. KRL models based on graph attention networks (GAT) can not only capture feature interactions across triplets, but also further distinguish the importance of neighbor entities. Recently, we find that there are two flaws in GAT-based KRL models: (1) Ignoring the bidirectionality of information flows leads to insufficient utilization of entity neighborhood information. When encapsulating the neighborhood information, only the forward information flows flowing into the target entity are considered, but the backward information flows flowing out are neglected. (2) The unified update process for all relations causes the useful information related the few-shot relations to be diluted. We propose a multiple attention-based model considering bidirectional entity information flows and few-shot relations (BEFSR). In our model, a GAT-based attention framework is used to integrate forward information flows and backward information flows of each triplet respectively to capture feature interactions across triplets, and an LSTM-based attention framework is adopted to gradually aggregate the entity-pair information for few-shot relations’ updating. In BEFSR, entities and relations can be updated more appropriately. Experiments demonstrate that BEFSR outperforms state-of-the-art KRL models in knowledge base completion task.
Local planning methods for autonomous navigation on sidewalks: a comparative survey

This paper presents a comparative survey of autonomous navigation systems for mobile robots on sidewalks. The kinds of systems mentioned above endow robots with the capability of estimating an optimal trajectory that once followed, allows the robot to move autonomously from a current pose to a target pose on a sidewalk. In addition, they allow avoiding both static and dynamic obstacles reliably and efficiently. An autonomous navigation system functions on data from multiple sensors and a global representation of the environment in which it is located, and allows the robot to perform its motion task satisfactorily without leaving the sidewalk on which it is moving.It is important to mention that an intense search of the state of the art was made around the existing autonomous navigation algorithms. Finally, different tools are mentioned such as ROS2 middleware, the Gazebo simulation program and the Rviz2 visualization program.
Decoupled Contrastive Learning for Intra-Camera Supervised Person Re-identification

Intra-camera supervised (ICS) person re-identification (Re-ID) assumes that people are annotated independently in each camera, which is a newly proposed setting to reduce the cost of manual annotation. Most existing methods are developed on generating global pseudo labels to learn camera-agnostic features by classification loss. However, they do not utilize intra-camera labels well for cross-camera association. In this paper, we propose a Decoupled Contrastive Learning (DCL) strategy to tackle the issue. Concretely, we first conduct an intra-camera pre-train stage to reduce the intra-identity variance. Then an inter-identity association and an inter-camera learning step are alternatively iterated to improve the feature representation gradually. We propose a decoupled identity contrastive loss in the inter-camera learning stage to make the training more effective. In order to improve the compactness of the associated cluster, we further adopt a hard-aware contrastive loss. Extensive experiments on two large-scale Re-ID datasets demonstrate that the proposed method outperforms most ICS methods and performs comparably to fully supervised methods.
Meta-Learning Amidst Heterogeneity and Ambiguity

Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tasks to be similar such that a single meta-learner is sufficient to aggregate the variations in all aspects. In addition, there has been less consideration of uncertainty when limited information is given as context. In this paper, we devise a novel meta-learning framework, called Meta-learning Amidst Heterogeneity and Ambiguity (MAHA), that outperforms previous works in prediction based on its ability to task identification. By extensively conducting several experiments in regression and classification, we demonstrate the validity of our model, which turns out to generalize to both task heterogeneity and ambiguity.
Improving Reinforcement Learning with Ensembles of Different Learners

Different reinforcement learning (RL) methods exist to address the problem of combining multiple different learners to generate a superior learner. These existing methods usually assume that each learner uses the same algorithm and/or state representation. We propose an ensemble learner that combines a set of base learners and leverages the strengths of the different base learners online. We demonstrate the proposed ensemble learner’s ability to combine the strengths of multiple base learners and adapt to changes in base learner performance on various domains, including the Atari Breakout domain.
Development of future teachers’ competencies in formation of financial literacy among schoolchildren

The formation of functional literacy of the younger students and its one of the leading areas – financial literacy is important. The leading role in the formation of financial literacy belongs to the teacher, which determines the quality and new direction of professional training of educators. Based on the analysis of the concepts of didactic design of pedagogical education and the practice of developing general professional competencies applicable to the field of financial literacy, the data of the conducted questionnaires and self-assessment of students in Derzhavin Tambov State University (direction 44.03.01 Pedagogical education) we consider it is necessary to update the topics of term papers and graduation papers, research tasks of academic disciplines, tasks on pedagogical practice, the inclusion of new topics in disciplines. The research presents a designed block on the formation of the foundations of financial literacy of schoolchildren within the discipline “Methods of Educational Work”, digital resources and tasks for independent work of students. The proposed design-didactic approach is useful in the development of small elective courses, innovative blocks of disciplines in the development of dynamic functional competencies using digital resources, mixed educational technologies and activation of independent work of students.
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization

Robust quantization improves the tolerance of networks for various implementations, allowing reliable output in different bit-widths or fragmented low-precision arithmetic. In this work, we perform extensive analyses to identify the sources of quantization error and present three insights to robustify a network against quantization: reduction of error propagation, range clamping for error minimization, and inherited robustness against quantization. Based on these insights, we propose two novel methods called symmetry regularization (SymReg) and saturating nonlinearity (SatNL). Applying the proposed methods during training can enhance the robustness of arbitrary neural networks against quantization on existing post-training quantization (PTQ) and quantization-aware training (QAT) algorithms and enables us to obtain a single weight flexible enough to maintain the output quality under various conditions. We conduct extensive studies on CIFAR and ImageNet datasets and validate the effectiveness of the proposed methods.
A Brief Survey of Detecting Spam Techniques: Twitter and Email

In this era of digital living, everything is moving from offline to virtual online world. This virtual world had introduced us to a society known as “social media platform”. The influence of this mobile world is reflected everywhere. With the development of these new emerging technologies it had esteemed the life of people in several ways and a lot of data is generated from these platforms. But, it has also introduced spiteful data and activities that need to be abolished known as “Spam”. In this work, we reviewed the evolution of spam from email to multimedia spam and its detection techniques in last 5 years.
Sentiment Analysis from Student Feedbacks Using Supervised Machine Learning Approaches

Human life experiences a radical change due to the availability of all kinds of information on the World Wide Web. This phenomenon makes the life of each individual easier and better than before. This flooding of information also contains huge amount of people’s opinions about their used products or services. The massive flow of information in terms of opinions and views need to be analyzed to know the efficacy of the products and/or services. The subjective expressions, which are known as opinions describes the peoples’ feelings or sentiments towards the service or product. The opinion can be either positive or negative, depending on the evocation that the product or service has created. Similarly, in the realms of education, initiatives are being taken by Govt. Organizations as well as private organizations to know the effectiveness of the contemporary teaching-learning process. The present undertaking of the investigation is to evaluate the academic transaction by eliciting the students’ subjective expressions and analyzing the same as their feedback. The scope of this paper is to analyze the student’s feedback, which covers the teaching quality, style of delivery by the faculty, the components of the course, and the overall ambiance in which the academic activities are conducted. The present study investigates the applicability of different supervised machine learning approaches for sentiment analysis from students’ subjective feedback. The chosen supervised machine learning approaches are Naive Bayes (NB), N-gram, Support Vector Machine (SVM), and Maximum Entropy (ME). These four approaches are applied only for binary sentiment classifications and to obtain a comparative analysis that would help to build a superior teaching and learning process.
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees

The Chow–Liu algorithm (IEEE Trans. Inform. Theory, 1968) has been a mainstay for the learning of tree-structured graphical models from i.i.d. sampled data vectors. Its theoretical properties have been well-studied and are well-understood. In this paper, we focus on the class of trees that are arguably even more fundamental, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homogeneous</i> trees in which each pair of nodes that forms an edge has the same correlation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> . We ask whether we are able to further reduce the error probability of learning the structure of the homogeneous tree model when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active learning</i> is allowed. Our figure of merit is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">error exponent</i> , which quantifies the exponential rate of decay of the error probability with an increasing number of data samples. We design and analyze an algorithm <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Active</u> Learning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> lgorithm for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> rees with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u> omogeneous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> dges (ACTIVE-LATHE), which surprisingly boosts the error exponent by at least 40% when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> is at least 0.8. For all other values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> , we also observe commensurate, but more modest, improvements in the error exponent. Our analysis hinges on judiciously exploiting the minute but detectable statistical variation of the samples to allocate more data to parts of the graph in which we are less confident of being correct.
A Comprehensive Study of Current and Future Trends in Cloud Forensics

Organizations are increasingly turning to cloud computing, which offers convenience and provides services based on virtualization technology. Some benefits of cloud computing include accessibility, availability, flexibility, vast storage capacity, speed, flexibility, and on-demand network connectivity. Significant potential security dangers are associated with this new technology, and digital forensics cannot keep up with the rapid adoption of cloud computing solutions. This study gives an overview of cloud forensics to offer better prospects, highlighting the existing problems and difficulties. It also suggests actions that can be taken to address these difficulties.
Attacking Malware Detection using Adversarial Machine Learning

Machine Learning has been successfully used for many different cyber threat classification tasks including malware detection and distinguishing between malicious and nonmalicious programs. In cybersecurity, we have an adversary who is working to make the detection technologies fail. Although artificial neural networks perform very well on these cyber threat classification tasks, they are also vulnerable to adversarial examples. An adversarial example is a sample that has modifications made to it so that the neural networks misclassify it. Many techniques have been proposed, both for crafting adversarial examples and for hardening neural networks against them. However, most previous work has been done in the computer vision domain especially with images. We explain the challenges and findings of using Adversarial Machine Learning (AML) for attacking a Portable Executable (PE) Malware Classification System. We demonstrate how a Black-Box attack can be made against a PE Malware Classification System using AML Intelligence and evaluate popular defense techniques and their effectiveness in the malware domain.