Reviewerly/exHarmony huggingface dataset:
The Role of the Social Sciences When Choosing University Studies: Motivations in Life Stories

The motivations that lead students to decide on a study modality or on a university degree provide relevant information to improve teaching–learning processes. This work addresses the question regarding the choice of study through analysis of four particular cases. They are special because the individuals chose their studies according to reasons different from the usual ones. To study these cases, interviews were carried out with the individuals. The results were subsequently analyzed according to a qualitative methodology using WebQDA software. The aim was to distinguish the incidence of internal and external motivations when choosing studies. The results obtained represent a contradiction with other pieces of research that analyzed the incidence of these types of motivations for choosing studies and allow for the assessment of various factors that may influence this process. The results obtained in this research indicate that the participants presented a high incidence of internal motivation. This allows for establishing some conclusions about the nature of those motivations when compared with the standard ones referred to in other works that dealt with a general population. The results reveal that in the subjects of this study’s sample, there is a higher incidence of internal motivational factors, such as vocation, while in other subjects, external factors predominate, such as socioeconomic position.
Development of Face Recognition Based Smart Door Lock

Nowadays, there is a growing interest in the smart home system using the Internet of Things.One of the important aspects of the smart home system is the security capability which can simply lock and unlock the door or the gate.This paper proposed a face recognition security system using Raspberry Pi which can be connected to the smart home system.The components that we will use for this solution are Raspberry pi, camera, Blynk app, and SD card.The output of the face recognition algorithm is connected to a relay circuit, in which it will lock or unlock the magnetic lock placed at the door.Results showed the effectiveness of our proposed system, in which we obtain around 90% face recognition accuracy.We also proposed a hierarchical image processing approach to reduce the training or testing time while improving the recognition accuracy.
Penerapan Media Webquest Berbasis Google Sites dalam Pembelajaran Masa Pandemi Covid-19 di MI Bilingual Roudlotul Jannah Sidoarjo

This research will discuss about the application of webquest media based on googe sites for online learning in low class classes as well as examining the strengths and weaknesses of implementing this media.. Qualitative research is the type of research chosen in conducting research on how the webquest media is applied. Data collection using interviews, observation, and documentation. Data analysis techniques start from data reduction, data presentation, and conclusions. The conclusion from the results of this study is the webquest media can be applied as an alternative to implementing distance learning by utilizing google sites. The advantage of the googe sites based webquest media is that it can increase interest in learning and develop students' scientific attitudes. The weakness of the googe sites based webquest media lies in the differences in the creativity of teachers in making media and the barriers for students who have less reading skills.
Principles of Natural Resource Economics for Bitcoin

To assess claims such as Bitcoin is 'digital gold' it makes sense to examine whether Bitcoin exhibits features common to other exhaustible natural resources that are the concern of natural resource economists.We therefore present some foundational ideas in the economics of exhaustible resources and examine their relevance to Bitcoin.There are several useful similarities but also some key differences, chiefly with respect to how miners manage inventories, or their 'inventory policy'.Therefore, to highlight this aspect, we use a simple model for any physical natural resource and introduce sensitivity to a capital-to-energy ratio.The resulting policy for Bitcoin miner over a halving cycle is not unlike a traditional miner in that optimal inventories are determined by optimal capital investments over the entire duration of the cycle.
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

Decision forests, in particular random forests and gradient boosting trees have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices. However, in structured data lying on a manifold—such as images and time-series—deep networks, specifically convolutional deep networks (ConvNets), tend to outperform forests. We conjecture that it is in part due to networks not simply analyzing feature magnitudes, but also their indices. In contrast, naïve forest implementations fail to explicitly consider feature indices. A recent approach demonstrates that forests, for each node, implicitly sample a random matrix from some specific distribution. These forests, like some networks, learn by partitioning the feature space into convex polytopes corresponding to linear functions. We build on that approach with Manifold Oblique Random Forests (Morf) that chooses distributions in a manifold-aware fashion to incorporate feature locality. Morf runs fast and maintains interpretability and theoretical justification. Morf also has excellent empirical classification performance on simulated data and real images and multivariate time-series. It outperforms non-neural network approaches that ignore feature space structure and challenges the performance of ConvNets in some cases.
DIGITAL COMPETENCE OF PEDAGOGICAL STAFF IN THE SYSTEM OF POSTGRADUATE PEDAGOGICAL EDUCATION

The main topic of the article is the issue of developing the digital competence of teachers in postgraduate education. It is noted that the development of the digital competence of the teacher is one of the key issues of education, which are related to the challenges of the current information society and the rapid technical and technological processes. The aim of the research is to single out the main problems of the development of digital competence of pedagogical workers in postgraduate pedagogical education. The main research method is a theoretical analysis of the framework and regulatory documents on standards and requirements for digital competence of teachers in the European Union and Ukraine; the best practices of the specialists of the conditions of formation of digital competence of teachers. The importance of digital competence is emphasized, which allows the teacher to participate in the information environment, to use the latest advances in technology in their professional activities. The definition of digital competence of a pedagogical worker is given. The conditions of formation of digital competence of teachers in the system of postgraduate pedagogical education are considered, such as the digitalization of education, the availability of legal documents on standards and requirements for digital competence of teachers, the need to create and implement criteria for the development of digital competence, the readiness of postgraduate education to ensure the formation of digital competence of teachers. Emphasis is placed on the challenges that hinder the effective use of digital technologies by teachers in the educational process. It is concluded that the formation of digital competence contributes to the comprehensive development of teachers, their self-improvement, the desire to learn throughout life, the ability to apply the acquired knowledge, which improves the quality of education. Changing the legal framework for standards of digital competencies, acquaintance with the achievements of the European community will help identify new approaches to building individual areas of development of digital competence of teachers.
Kahoot – A Game- Based Formative Assessment Tool During the Covid-19 Movement Control Order

The rapid development of technology has influenced the current system of education. The existence of various digital learning platforms online enables the teachers nowadays to implement them in teaching and learning as well as testing and evaluation. Kahoot is one of the digital learning platforms that mostly used by higher institutions students to assess their learning. Thus, a research conducted to investigate the usability of Kahoot as a formative assessment tool in ESL primary classroom during Covid 19 Movement Control Order period. The research employed mixed method approach whereby semi-structured interviews and survey questionnaire used as data instruments. There are 20 Year 4 pupils participated and responded to the questionnaire, while three of them and an English teacher responded to the interview. The findings show that most of the respondents coincided that the application of Kahoot enables them to learn English in a fun way, increase their English knowledge, capable to do the sharing of English knowledge to other people and increase their motivation and engagement during this pandemic period. In a classroom context, Kahoot has high potential in supporting and enhancing teaching and learning for both teacher and pupils with its outstanding features, especially during the pandemic period.
A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

Fall is one of the leading causes of accidental or unintentional injury deaths worldwide due to serious injuries such as head traumas and hip fractures. As life expectancy improved, the rapid increase in aging population implied the need for the development of vital sign detector such as fall detector to help elderly in seeking for medical attention. Immediate rescue could prevent victims from the risk of suspension trauma and reduce the mortality rate among elderly population due to fall accident effectively. This paper presents the development of FPGA-based fall detection algorithm using a threshold-based analytical method. The proposed algorithm is to minimize the rate of false positive fall detection proposed from other researchers by including the non-fall events in the data analysis. Based on the performance evaluation, the proposed algorithm successfully achieved a sensitivity of 97.45% and specificity of 97.38%. The proposed algorithm was able to differentiate fall events and non-fall events effectively, except for fast lying and fall that ending with sitting position. The proposed algorithm shows a good result and the performance of the proposed algorithm can be further improved by using an additional gyroscope to detect the posture of the lower body part.
What Changed? Interpretable Model Comparison

We consider the problem of distinguishing two machine learning (ML) models built for the same task in a human-interpretable way. As models can fail or succeed in different ways, classical accuracy metrics may mask crucial qualitative differences. This problem arises in a few contexts. In business applications with periodically retrained models, an updated model may deviate from its predecessor for some segments without a change in overall accuracy. In automated ML systems, where several ML pipelines are generated, the top pipelines have comparable accuracy but may have more subtle differences. We present a method for interpretable comparison of binary classification models by approximating them with Boolean decision rules. We introduce stabilization conditions that allow for the two rule sets to be more directly comparable. A method is proposed to compare two rule sets based on their statistical and semantic similarity by solving assignment problems and highlighting changes. An empirical evaluation on several benchmark datasets illustrates the insights that may be obtained and shows that artificially induced changes can be reliably recovered by our method.
Self-supervised Network Evolution for Few-shot Classification

Few-shot classification aims to recognize new classes by learning reliable models from very few available samples. It could be very challenging when there is no intersection between the alreadyknown classes (base set) and the novel set (new classes). To alleviate this problem, we propose to evolve the network (for the base set) via label propagation and self-supervision to shrink the distribution difference between the base set and the novel set. Our network evolution approach transfers the latent distribution from the already-known classes to the unknown (novel) classes by: (a) label propagation of the novel/new classes (novel set); and (b) design of dual-task to exploit a discriminative representation to effectively diminish the overfitting on the base set and enhance the generalization ability on the novel set. We conduct comprehensive experiments to examine our network evolution approach against numerous state-of-the-art ones, especially in a higher way setup and cross-dataset scenarios. Notably, our approach outperforms the second best state-of-the-art method by a large margin of 3.25% for one-shot evaluation over miniImageNet.
Learning with Generated Teammates to Achieve Type-Free Ad-Hoc Teamwork

In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type-based approach, which pre-trains the agent with a set of pre-prepared teammate types, then associates the unknown teammate with a particular type. Typically, these types are collected manually. This hampers previous works by both the availability and diversity of types they manage to obtain. To eliminate these limitations, this work addresses to achieve ad-hoc teamwork in a type-free approach. Specifically, we propose the model of Entropy-regularized Deep Recurrent Q-Network (EDRQN) to generate teammates automatically, meanwhile utilize them to pre-train our agent. These teammates are obtained from scratch and are designed to perform the task with various behaviors, therefore their availability and diversity are both ensured. We evaluate our model on several benchmark domains of ad-hoc teamwork. The result shows that even if our model has no access to any pre-prepared teammate types, it still achieves significant performance.
Increasing Information Entropy of Both Weights and Activations for the Binary Neural Networks

In terms of memory footprint requirement and computing speed, the binary neural networks (BNNs) have great advantages in power-aware deployment applications, such as AIoT edge terminals, wearable and portable devices, etc. However, the networks’ binarization process inevitably brings considerable information losses, and further leads to accuracy deterioration. To tackle these problems, we initiate analyzing from a perspective of the information theory, and manage to improve the networks information capacity. Based on the analyses, our work has two primary contributions: the first is a newly proposed median loss (ML) regularization technique. It improves the binary weights distribution more evenly, and consequently increases the information capacity of BNNs greatly. The second is the batch median of activations (BMA) method. It raises the entropy of activations by subtracting a median value, and simultaneously lowers the quantization error by computing separate scaling factors for the positive and negative activations procedure. Experiment results prove that the proposed methods utilized in ResNet-18 and ResNet-34 individually outperform the Bi-Real baseline by 1.3% and 0.9% Top-1 accuracy on the ImageNet 2012. Proposed ML and BMA for the storage cost and calculation complexity increments are minor and negligible. Additionally, comprehensive experiments also prove that our methods can be applicable and embedded into the present popular BNN networks with accuracy improvement and negligible overhead increment.
Towards an Explainer-agnostic Conversational XAI

Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overall performance of an XAI system can be augmented by considering the end-user as a human being, we are studying the ways we can improve the explanations by making them more informative and easier to use from one hand, and interactive and customisable from the other hand.
Optimal Transport for Deep Generative Models: State of the Art and Research Challenges

Optimal transport has a long history in mathematics which was proposed by Gaspard Monge in the eighteenth century (Monge, 1781). However, until recently, advances in optimal transport theory pave the way for its use in the AI community, particularly for formulating deep generative models. In this paper, we provide a comprehensive overview of the literature in the field of deep generative models using optimal transport theory with an aim of providing a systematic review as well as outstanding problems and more importantly, open research opportunities to use the tools from the established optimal transport theory in the deep generative model domain.
Covid-19 Hoax Detection Using KNN in Jaccard Space

Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief &amp; Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief &amp; Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.
Analysis and Detection of Errors in KECCAK Hardware Implementation

The third family Secure Hash Algorithm cryptographic function, named KECCAK, is implemented in cryptographic circuits to assure high security level to any system which necessitates hashing as the generation of random numbers and the data integrity checking. One of the most efficient physical attacks against KECCAK hardware implementation is the fault attacks which can extract the secret data. Until today, a few KECCAK fault detection schemes against the fault attacks have been presented. In this paper, in order to provide a high level of security against fault attacks, we perform a detailed fault analysis to estimate the impact of fault attacks against the KECCAK implementation. We then propose an efficient error detection scheme based on the KECCAK architecture modification. For this reason, the round of KECCAK is divided into two half rounds and a KECCAK pipeline register is implemented between them. The proposed scheme is independent of the method the KECCAK is implemented. Thus, it can be applied to both the pipeline and iterative architectures.To evaluate the KECCAK detection scheme robustness against faults injection attacks, we perform fault injection attacks and we determined the fault detection capability; it is about 99.997%. We have modeled the KECCAK detection scheme using the VHDL hardware language and through hardware FPGA implementation, the FPGA results demonstrate that our scheme can efficiently secure the KECCAK implementation against fault attacks. It can be simply implemented with low complexity. In addition, the FPGA implementation performances prove the low slice area overhead and the high working frequency for the proposed KECCAK detection scheme.
Subspace Differential Privacy

Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the formulation, implementation, and interpretation of privacy guarantees. We propose subspace differential privacy, to honestly characterize the dependence of the sanitized output on confidential aspects of the data. We discuss two design frameworks that convert well-known differentially private mechanisms, such as the Gaussian and the Laplace mechanisms, to subspace differentially private ones that respect the invariants specified by the curator. For linear queries, we discuss the design of near-optimal mechanisms that minimize the mean squared error. Subspace differentially private mechanisms rid the need for post-processing due to invariants, preserve transparency and statistical intelligibility of the output, and can be suitable for distributed implementation. We showcase the proposed mechanisms on the 2020 Census Disclosure Avoidance demonstration data, and a spatio-temporal dataset of mobile access point connections on a large university campus.
The Quantitative Easing Bursts Bitcoin Price

In this paper we analyze the existence of cointegrating relationships between Bitcoin, S&amp;P 500, and the quantity of money M2. We perform our analysis with and without applying time warping pre-processing. In all cases we find strong evidence that, in the period 2016-2021 the three time series show two cointegrating relationships and therefore share a common stochastic trend. In addition, a low correlation between Bitcoin and S&amp;P 500 is detected. These finding justify the increased interest of investors in Bitcoin as an alternative asset class. The economic interpretation is that the stock valuation is primarily determined by financial phenomena, in particular the availability of large quantity of money. Money supporting investment is due both to the actions of Quantitative Easing and to the exchange of creditor/debtor role that took place between households and firms. The price of both Bitcoin and stocks is increasingly influenced by the amount of money in circulation and follows the same stochastic trend.
Layer Information Similarity Concerned Network Embedding

Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while ignoring the similarity between different layers. In this paper, for multiplex network embedding, we propose a Layer Information Similarity Concerned Network Embedding (LISCNE) model considering the similarities between layers. Firstly, we introduce the common vector for each node shared by all layers and layer vectors for each layer where common vectors obtain the overall structure of the multiplex network and layer vectors learn semantics for each layer. We get the node embeddings in each layer by concatenating the common vectors and layer vectors with the consideration that the node embedding is related not only to the surrounding neighbors but also to the overall semantics. Furthermore, we define an index to formalize the similarity between different layers and the cross-network association. Constrained by layer similarity, the layer vectors with greater similarity are closer to each other and the aligned node embedding in these layers is also closer. To evaluate our proposed model, we conduct node classification and link prediction tasks to verify the effectiveness of our model, and the results show that LISCNE can achieve better or comparable performance compared to existing baseline methods.