Reviewerly/exHarmony huggingface dataset:
Validation of deep learning-based markerless 3D pose estimation

Deep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that under specific circumstances deep learning-based approaches can match more traditional motion tracking methods. Although more work needs to be done to determine their specific performance characteristics and limitations, this study should help build confidence within the research community using these new tools.
Birkhoff's Completeness Theorem for Multi-Sorted Algebras Formalized in Agda

This document provides a formal proof of Birkhoff's completeness theorem for multi-sorted algebras which states that any equational entailment valid in all models is also provable in the equational theory. More precisely, if a certain equation is valid in all models that validate a fixed set of equations, then this equation is derivable from that set using the proof rules for a congruence. The proof has been formalized in Agda version 2.6.2 with the Agda Standard Library version 1.7 and this document reproduces the commented Agda code.
Transformer-Based Multimodal Infusion Dialogue Systems

The recent advancements in multimodal dialogue systems have been gaining importance in several domains such as retail, travel, fashion, among others. Several existing works have improved the understanding and generation of multimodal dialogues. However, there still exists considerable space to improve the quality of output textual responses due to insufficient information infusion between the visual and textual semantics. Moreover, the existing dialogue systems often generate defective knowledge-aware responses for tasks such as providing product attributes and celebrity endorsements. To address the aforementioned issues, we present a Transformer-based Multimodal Infusion Dialogue (TMID) system that extracts the visual and textual information from dialogues via a transformer-based multimodal context encoder and employs a cross-attention mechanism to achieve information infusion between images and texts for each utterance. Furthermore, TMID uses adaptive decoders to generate appropriate multimodal responses based on the user intentions it has determined using a state classifier and enriches the output responses by incorporating domain knowledge into the decoders. The results of extensive experiments on a multimodal dialogue dataset demonstrate that TMID has achieved a state-of-the-art performance by improving the BLUE-4 score by 13.03, NIST by 2.77, image selection Recall@1 by 1.84%.
Implementasi Metode Cantol Roudhoh Berbasis Modul Dalam Mengembangkan Kemampuan Membaca Permulaan Anak Usia Dini

This study aims to describe the Implementation of the Cantol Roudhoh method based on the module on developing early childhood reading skills at PAUD Kuncup Harapan. The method used in this study was a qualitative descriptive method with data collection techniques in the form of observation, interviews, and documentation. This study used source triangulation to make the data valid. The results showed that the Cantol Roudhoh method could affect reading ability in early childhood. It means the Cantol Roudhoh method in education can build children's excellence in learning to read early and can frame an exciting and fun learning environment.
Key knowledge and skills university students need in a digital environment

The era of digital transformation implies fundamental changes in society , educational processes , professional activities and everyday life of people . This allows building economic relations in a new way , establishing cultural ties , planning , making and implementing decisions . Digital technologies are becoming an important tool for educational transformation and they affect the value of the digital system positively . The purpose of the article is to evaluate the introduction of digital technologies into the system of teaching students at the university . The empirical methods such as questioning , analysis , study and generalization are used in the research . The paper concludes that undergraduate students consider the active introduction of digital transformation into the educational process as a necessary component for acquiring modern digital competencies . The perspectives for the study lie in the introduction of modern digital technologies focused on the widespread use of active teaching methods that contribute to the deepening of the professional specialization of the educational process in the era of the digital economy . The scientific novelty of the paper bases on the fact that the authors examine the impact of digital technologies used in the process of teaching at a university on the degree of students ’ readiness to enter a new educational space and to participate in the educational process based on the use of information , communication and digital technologies .
Deep Cross-Dimensional Attention Hashing for Image Retrieval

Nowadays, people’s lives are filled with a huge amount of picture information, and image retrieval tasks are widely needed. Deep hashing methods are extensively used to manage such demands due to their retrieval rate and memory consumption. The problem with conventional deep hashing image retrieval techniques, however, is that high dimensional semantic content in the image cannot be effectively articulated due to insufficient and unbalanced feature extraction. This paper offers the deep cross-dimensional attention hashing (DCDAH) method considering the flaws in feature extraction, and the important points of this paper are as follows. This paper proposes a cross-dimensional attention (CDA) module embedded in ResNet18; the module can capture the cross-dimension interaction of feature maps to calculate the attention weight effectively because of its special branch. For a feature map acquired by a convolutional neural network (CNN), each branch takes different rotation measurements and residual transformations to process it. To prevent the DCDAH model from becoming too complex, the CDA module is designed to have the characteristics of low computational overhead. This paper introduces a scheme to reduce the dimension of tensors, which can reduce computation and retain abundant representation. For a dimension of a feature map, the Maxpool and Avgpool are performed, respectively, and the two results are connected. The DCDAH method significantly enhances image retrieval performance, according to studies on the CIFAR10 and NUS-WIDE data sets.
PENERAPAN E-LEARNING DALAM INOVASI PENDIDIKAN UNTUK MENINGKATKAN KEMAMPUAN LITERASI DIGITAL SISWA

Our research aims to be able to analyze e-learning learning systems in an effort to improve digital literacy skills for students in the 21st century. Given that technology has penetrated into many human activities, including teaching and learning activities, it needs to be used optimally so that learning is not only a process of knowledge transfer but also as an increase in literacy skills for students. This research uses literature study method. Where will be obtained a conclusion from the findings of previous research that is focused on certain data sources. The results show that the application of e-learning is related to digital literacy. E-learning can make students become independent in finding information which can also train students' critical thinking patterns towards information that is obtained or received. The background for choosing this research is because we see from the phenomena that occur around us that today's technology is sometimes not optimized, therefore it is necessary to integrate basic things such as from the world of education. Where with education there will be a learning process and training skills which are certainly useful in the 21st century. The conclusion from our research is that the application of e-learning has a good impact on increasing students' digital literacy. Moreover, currently the application of e-learning has not been fully implemented in Indonesia, so this has become an innovation that needs to be promoted in all schools in every educational unit. Because as students adapt to the 21st century, we are required to have good literacy such as digital literacy as a way of receiving information.
Cooperative sUAV Swarm Navigation for Urban Infrastructure Monitoring

This paper focusses on one challenge of operating small Unmanned Aircraft Systems swarms in urban environments, namely advanced absolute and relative navigation methods for swarms and swarm members. The paper discusses various absolute and relative pose and velocity estimation approach that extends conventional integrated navigation methods to include principles of cognition (i.e., appropriate, and adapted actions based on perception and knowledge) and collaboration (i.e., improved ability to reason and interact based on information exchange and spatial distribution) between the swarm members. This paper outlines the underlying methodology, first simulation results, and the platform hardware components (three multi-copters and one ground station). It furthermore analyses and discusses the navigation performance using sUAS flight test data collected in an open-sky and simulations in an urban environment.
Correlation-Preserving Mean Plausible Values as a Basis for Prediction in the Context of Bayesian Structural Equation Modeling

Mean plausible values can be computed when Bayesian structural equation modeling (BSEM) is performed. As mean plausible values do not preserve the factor inter-correlations, they yield path coefficients that are different from the estimated path coefficients of the model. As it might be of interest to perform exactly the same predictions on the level of mean plausible values that have been estimated by BSEM, correlation-preserving mean plausible values were proposed. An example for the computation of the correlation preserving mean plausible values is given and the corresponding syntax can be found in the Appendix.
Lore

The workflow scheduling problem is a critical challenge in clouds. Meticulously designed heuristics have attempted to address the intricate decision problem at a high cost. A more general approach is expected to handle different types of workflows and resource configurations. In this paper, a deep reinforcement Learning based apprOach for woRkflow schEduling (Lore) in clouds has been proposed to minimize the completion time of workflows. Moreover, Monte Carlo Tree Search and graph convolutional network are applied to improve performance further. Experimental results show that Lore outperforms the baselines, reducing average makespan by 2--10%, and enabling resource utilization increase by up to 20%.
ARIMA and Facebook Prophet Model in Google Stock Price Prediction

We use the Autoregressive Integrated Moving Average (ARIMA) model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’ predictions. We first examine the stationary of the dataset and use ARIMA(0,1,1) to make predictions about the stock price during the pandemic, then we train the Prophet model using the stock price before January 1, 2021, and predict the stock price after January 1, 2021, to present. We also make a comparison of the prediction graphs of the two models. The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mixes all these roles, obscuring the problem definitions and research goals of the security role that aims to reveal the practical concerns of NLP models. In this paper, we rethink the research paradigm of textual adversarial samples in security scenarios. We discuss the deficiencies in previous work and propose our suggestions that the research on the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their methods on security tasks to demonstrate the real-world concerns; (2) consider real-world attackers' goals, instead of developing impractical methods. To this end, we first collect, process, and release a security datasets collection Advbench. Then, we reformalize the task and adjust the emphasis on different goals in SoadNLP. Next, we propose a simple method based on heuristic rules that can easily fulfill the actual adversarial goals to simulate real-world attack methods. We conduct experiments on both the attack and the defense sides on Advbench. Experimental results show that our method has higher practical value, indicating that the research paradigm in SoadNLP may start from our new benchmark. All the code and data of Advbench can be obtained at \url{https://github.com/thunlp/Advbench}.
Perceptual Multi-Exposure Fusion

As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are too computationally expensive to be deployed on mobile devices. This paper presents a perceptual multi-exposure fusion method that not just ensures fine shadow/highlight details but with lower complexity than detailenhanced methods. We analyze the potential defects of three classical exposure measures in lieu of using detail-enhancement component and improve two of them, namely adaptive Wellexposedness (AWE) and the gradient of color images (3-D gradient). AWE designed in YCbCr color space considers the difference between varying exposure images. 3-D gradient is employed to extract fine details. We build a large-scale multiexposure benchmark dataset suitable for static scenes, which contains 167 image sequences all told. Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value. Moreover, our approach can achieve a better improvement for current image enhancement techniques, ensuring fine detail in bright light.
Saving Brian's privacy

Given the importance of privacy, many Internet protocols are nowadays designed with privacy in mind (e.g., using TLS for confidentiality). Foreseeing all privacy issues at the time of protocol design is, however, challenging and may become near impossible when interaction out of protocol bounds occurs. One demonstrably not well understood interaction occurs when DHCP exchanges are accompanied by automated changes to the global DNS (e.g., to dynamically add hostnames for allocated IP addresses). As we will substantiate, this is a privacy risk: one may be able to infer device presence and network dynamics from virtually anywhere on the Internet --- and even identify and track individuals --- even if other mechanisms to limit tracking by outsiders (e.g., blocking pings) are in place.
Penetration of renewable sources through solar systems: a case study Kosovo

This paper analyzes the potential of solar energy and the efficiency of a solar system for real measurements. During the analysis, one-year radiation measurements for the city of Pristina were used. Analyzes show a high potential for exploitation. What affects the increase of efficiency of such systems is the possibility of use throughout the year. For each respective month is shown the potential of thermal energy that can be achieved and efficiency. The paper highlights the role of solar energy in maximizing the penetration of renewable sources. The analysis shows that in August the coverage of the needs for hot sanitary water from the proposed solar system is 99% and minimally in January by 16%. Taking into account the seasonal changes that apply to the country, I consider that a possible combination of central heating systems with those of sanitary water - solar system would be an adequate solution.
Exploring Augmented Reality Affordances for Media Literacy in the EFL Context

The ever-evolving information and communication technologies (ICTs) have affected the learners’ preferences for on-demand and on-the-spot access to information that challenge the traditional classroom practices and call for a reconstruction of curricula. New educational approaches are to be encompassed so as to align with the tech-savvy Generation Z needs and the contemporary digitized world that demands competences and skills for successful and well-informed personal and professional choices. Nevertheless, consumption and creation of new knowledge in the plethora of the new crowdsourced information ecosystem have to be critically accessed, analyzed, evaluated, and leveraged so as to lead to creativity and innovation. This paper is a report of the design of an educational intervention with the intent to enhance students’ media literacy skills in the English as a Foreign Language (EFL) secondary education context leveraging immersive technologies. In the first part there is a review of related work leveraging AR affordances in the EFL context. The second part explores the instructional design and pedagogical framework that AR assets can enrich a course material on media literacy for inclusive education practices taking into consideration students’ preferences on their learning process.
The Benefits and Disadvantages of e-Learning During Covid-19

The theme of the research is topical, there are changes in the e-learning during Covid-19 which changes set new tasks in the study process at the universities, as well as new communication links and the changes of the culture of pedagogical technologies. The aim of the research is to define the benefits and disadvantages of the e-learning in the study process at the universities and their correspondence with the needs of the students and academic staff during Covid-19. There are the following tasks to achieve the aim: to specify the functions of the meaning of the e-learning based on the changes in the e-environment during Covid-19; to investigate the adequacy of the contents of the university studies with the demand of e-learning nowadays; to find out the pedagogical instruments in the cognitive process which functions as a pedagogical form of the professional development and manifests itself in the benefits and disadvantages to the university education in the e-environment. In order to define the benefits and disadvantages of e-learning of the university education the authors take into account the theories of the science of pedagogy and education management. The following methods are used in the paper: abstract analysis, content analysis, systems analysis, interviews, surveys, questionnaires, economic experiment. The base of the research are the Universities of Latvia. The research is done in the period of 2019 - 2022.&#x0D; &#x0D;
A Personalized Approach to Learning Across Time and Space

In a higher education environment massively disrupted by the pandemic, the importance of exploring the efficacy of purposeful, flexible, online learning is essential. Research shows that blended approaches to learning can be active, flexible, and student-centred. However, this research suggests that without human intervention or a bespoke context, there can also be very poor engagement. As such, this study presents the successes and challenges of introducing a personalised mobile-learning resource in a supported, personalized environment. The resource was designed and developed in 2019 and integrated in 2020 and 2021 into a large (n=200+) communication design class at an Australian university. This paper evaluates the effectiveness of the app using measures drawn from Abdullah &amp; Ward’s (2016) take on the Technology Acceptance Model. Their model, GETAMEL (general extended technology acceptance model for e-learning) measures technology acceptance factors in eLearning such as enthusiasm, self-efficacy, and student enjoyment of technology in relation to contextual purpose. This paper also considers the value of self-directed learning and co-curricular activities in the context of additional learning to support core curriculum. This paper presents an evaluation of student experience drawn from a class-wide survey and analysis of usage data. We conclude that the use of a personalised app fosters an autonomously supportive experience that leads to intrinsic motivation and improves overall learning (Baker &amp; Goodboy, 2018). The app provides additional support for students unable to attend tutorials and enabled students to remain engaged and abreast of the general topics each week and provided some additional prompts about self-directed learning activities they could engage in, that they wouldn’t have otherwise had. Enjoyment, as one of the key factors of the GETAMEL model, was explicitly mentioned by students. While a personalised tool is labour intensive and expensive in cost and time, this study concludes that the rewards regarding student experience make it worthwhile.
Mixed and constrained input mutation for effective fuzzing of deep learning systems

Adversarial examples cause misclassifications of deep learning (DL) systems. It isn’t easy to debug misclassifications due to the intrinsic complexity of DL architecture. Thus, applying coverage-guided fuzzing, a widely used technique to find crashes in complex software, is promising. However, mutation strategies of DL fuzzers, such as DeepHunter, have a limitation. They restrict multiple transformations and so penalize generating diverse inputs. Otherwise, they cause significant distortion, rendering invalid inputs, such as unrecognizable or ambiguous to humans. However, multiple transformations are critical in mutation-based fuzzing. To address this problem, we propose the mixed and constrained mutation (MCM) for DL fuzzers. Human perception-based constraints of MCM avoid significant distortion in a single transformation and the aggregation of multiple transformations. We verify transformation parameters through a survey with 15 participants on each MNIST, STL-10, and ImageNet dataset to implement such constraints, followed by statistical tests. MCM returns valid inputs in almost every fuzzing iteration. Furthermore, MCM improved the fuzzing performance on various DL architectures on MNIST, STL-10, and ImageNet compared to DeepHunter: MCM discovered 17.6% more seeds showing new coverage and 132% more adversarial examples on average. These adversarial examples correspond to more than double the incorrect classes for each original image than DeepHunter.
Optical character recognition for ancient non-alphabetic scripts

Optical character recognition for ancient non-alphabetic scripts Cuneiform is one of the earliest writing systems in the world, invented at the end of the fourth millennium BCE. It is usually written by pressing a stylus on moist clay tablets, creating a three-dimensional script. The script is logo-syllabic, like the Chinese or Japanese writing systems, meaning the same sign can be read logographically, as a word, as syllables, or as determinatives (ie semantic classifiers). The correct reading depends on the context. There are close to a thousand cuneiform signs, not all of which were used simultaneously; usually about 200-300 signs were used at once. This article shows Shai Gordin, Senior Lecturer at Digital Pasts Lab in Ariel University, look at the deciphering of ancient non-alphabetic scripts, and the technology we use to understand it.