Reviewerly/exHarmony huggingface dataset:
Enhancing social and health care educators’ competence in digital pedagogy: A pilot study of educational intervention

The purpose of this pilot study was to explore connection of an educational intervention on the competence of health care educators and educator candidates (n=11) in digital pedagogy as a part of national TerOpe project. An educational intervention, Basics of Digital Pedagogy was developed by the TerOpe project’s experts. The participating educators and educator candidates of the educational intervention were recruited from the universities and university of applied sciences, which were involved in TerOpe project. All the participants of the educational interventions were invited to take part in this study. The educational intervention was conducted during spring 2019. Pre- and post-tests were implemented digitally by using an Educators’ and Educator Candidates’ Competence in Digital Pedagogy self-assessment instrument (OODI), which was developed for this study. The OODI includes 32 items divided in six digital competence areas professional engagement, digital resources, teaching and learning, assessment, empowering learners and facilitating learners’ digital competence. The data was analysed statistically. The self-assessed level of overall competence in digital pedagogy and competence in all competence areas of digital pedagogy increased statistically significantly during the intervention. The educational intervention used in this study seems to increase educators’ self-assessed competence in digital pedagogy. We recommend that all educators be encouraged to conduct continuous education on the basics of digital pedagogy.
Truly Low-Space Element Distinctness and Subset Sum via Pseudorandom Hash Functions

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Truly Low-Space Element Distinctness and Subset Sum via Pseudorandom Hash FunctionsLijie Chen, Ce Jin, R. Ryan Williams, and Hongxun WuLijie Chen, Ce Jin, R. Ryan Williams, and Hongxun Wupp.1661 - 1678Chapter DOI:https://doi.org/10.1137/1.9781611977073.67PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We consider low-space algorithms for the classic Element Distinctness problem: given an array of n input integers with O(log n) bit-length, decide whether or not all elements are pairwise distinct. Beame, Clifford, and Machmouchi [FOCS 2013] gave an Õ(n1.5)-time randomized algorithm for Element Distinctness using only O(log n) bits of working space. However, their algorithm assumes a random oracle (in particular, read-only random access to polynomially many random bits), and it was asked as an open question whether this assumption can be removed. In this paper, we positively answer this question by giving an Õ(n1.5)-time randomized algorithm using O(log3 n log log n) bits of space, with one-way access to random bits. As a corollary, we also obtain a poly(n)-space O∗(20.86n)-time randomized algorithm for the Subset Sum problem, removing the random oracles required in the algorithm of Bansal, Garg, Nederlof, and Vyas [STOC 2017]. The main technique underlying our results is a pseudorandom hash family based on iterative restrictions, which can fool the cycle-finding procedure in the algorithms of Beame et al. and Bansal et al. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach

Social media such as Instagram and Twitter have become important platforms for marketing and selling illicit drugs. Detection of online illicit drug trafficking has become critical to combat the online trade of illicit drugs. However, the legal status often varies spatially and temporally; even for the same drug, federal and state legislation can have different regulations about its legality. Meanwhile, more drug trafficking events are disguised as a novel form of advertising - commenting leading to information heterogeneity. Accordingly, accurate detection of illicit drug trafficking events (IDTEs) from social media has become even more challenging. In this work, we conduct the first systematic study on fine-grained detection of IDTEs on Instagram. We propose to take a deep multimodal multilabel learning (DMML) approach to detect IDTEs and demonstrate its effectiveness on a newly constructed dataset called multimodal IDTE (MM-IDTE). Specifically, our model takes text and image data as the input and combines multimodal information to predict multiple labels of illicit drugs. Inspired by the success of BERT, we have developed a self-supervised multimodal bidirectional transformer by jointly fine-tuning pretrained text and image encoders. We have constructed a large-scale dataset MM-IDTE with manually annotated multiple drug labels to support fine-grained detection of illicit drugs. Extensive experimental results on the MM-IDTE dataset show that the proposed DMML methodology can accurately detect IDTEs even in the presence of special characters and style changes attempting to evade detection.
Educational Response Method Based on Artificial Intelligence Perspective

Mobile Internet technology is constantly improving, and its impact on people's daily life style continues to deepen.The rapid development of artificial intelligence technology has brought new opportunities and challenges to mankind, especially having a profound impact on education.This article uses qualitative research methods to conduct research and finds that the advent of the artificial intelligence era has caused major changes in education.Artificial intelligence technology provides new ideas for the development of education and points out new directions.Education and artificial intelligence are new cores.The integrated development of technology can provide data support for the scientific management of education, optimize the relationship between teachers and students, and promote personalized learning.Grabbing valuable information, establishing an education and teaching information resource database, and establishing an education and teaching effect evaluation mechanism based on big data are the countermeasures for education in the era of artificial intelligence.
The Nexus between Cryptocurrencies, Currencies and Commodities: A Primer

This primer provides a pairwise comparison of cryptocurrency characteristics with those of fiat currency and hard commodities to shed light on the nexus between cryptocurrencies, fiat currency, and hard commodities. Then, it synthesizes methods and results from empirical research that investigate the nexus. The findings reveal that the existing literature has not reached a consensus on the nature of cryptocurrencies, in particular whether they should be categorized as a currency or a commodity which indicates the research area is not yet saturated.
Optical Hybrid Multiple Image Encryption

A method including diffractive encoding and intermodulation has been developed to both encode multiple images and use communication bandwidth efficiently. Experimental results show that 1500 images are sent to different users as single encoded image.
Understanding Class Representations : An Intrinsic Evaluation of Zero-Shot Text Classification

Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.
Philosophy Facing Cognitive Studies

The stage of world civilization, into which humankind entered and within which it is developing today, was called the information society, then the knowledge society. Today, more and more people are talking about digital civilization. Of course, there are differences between information and knowledge, and digitaliza­tion speaks not about the information itself but about the ways of encoding it. However, it must be admitted that all these names are essentially related to one phenomenon. The point is that the production, dissemination, and use of knowl­edge (and it is, of course, information, although not every information is knowl­edge) began to play a new and exclusive role in all forms of human life. The author has analyzed three types of relations between philosophical and sci­entific study of cognition and mind: 1. Philosophy ignores the results of cogni­tive disciplines, as it deals with meanings and norms of cognition in distinction from cognitive sciences which study the mechanisms of cognitive processes; 2. Philosophy is considered as a general part of cognitive science: “naturaliza­tion” of philosophy; 3. Philosophy and cognitive sciences are in a constant dia­logue which presupposes cross-fertilization and mutual criticism. Philosophy holds its normative role, as it analyzes the presuppositions of conceptions in cog­nitive sciences and evaluates them. The author argues the thesis that it is the third type of such relations that is the most fruitful.
Harari's World History: Evolution toward Intelligence without Consciousness?

Yuval Noah Harari's three books, Sapiens: A Brief History of Humankind, Homo Deus: A Brief History of Tomorrow, and 21 Lessons for the 21st Century, propose a sweeping world history that takes an externalist view of technology, minimizing the importance of cultural difference and individual free will. His deterministic narrative argues that a single world culture is emerging and that artificial intelligence inevitably will replace human consciousness. In this story, technology is increasingly powerful, while inventors, entrepreneurs, and consumers have little influence over events. A far more convincing popular overview based on a more contextual approach could be constructed based on research on technology and culture that Harari disregards.
Realtime Object Detection Using TensorFlow an application of ML

Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of deep learning techniques, the accuracy of object detection has increased drastically. The project aims to incorporate state-of-the-art techniques for object detection with the goal of achieving high accuracy with real-time performance. A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning-based approach, which leads to slow and non-optimal performance. In this project, we use a completely deep learning-based approach to solve the problem of object detection in an end-to-end fashion. The network is trained on the most challenging publicly available dataset (PASCAL VOC), on which an object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection.
Reconsidering the Past: Optimizing Hidden States in Language Models

We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time.Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters.We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution.We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.
From the Classroom to Digital Platforms—A Study with Teachers and Families

With the Covid-19 pandemic, classroom teaching activities were suspended and distance learning was seen as an alternative for students at all levels of education, to continue their learning. This study aims to know the perceptions of teachers and families regarding distance learning. The study had the participation of two teachers and two families, from two primary schools in the north of Portugal. Through a qualitative methodology using the interview, as an instrument of data collection, an attempt was made to emphasize the participants’ points of view and the meaning they have on the subject. The main results demonstrate that although distance learning allows flexible management of the timetable and greater autonomy for students, it can be unfavorable in terms of the development of socio-emotional skills. The difficulties felt point to the lack of monitoring by students, time management and the lack of technological skills. The investment in teacher training in this area was considered as a suggestion for improvement.
Unadversarial Examples: Designing Objects for Robust Vision

We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized. We develop a framework that leverages this capability to significantly improve vision models’ performance and robustness. This framework exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design “robust objects,” i.e., objects that are explicitly optimized to be confidently detected or classified. We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks, to (in-simulation) robotics, to real-world experiments. Our code can be found on GitHub.
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

The recent explosive interest on transformers has suggested their potential to become powerful ``universal models for computer vision tasks, such as classification, detection, and segmentation. While those attempts mainly study the discriminative models, we explore transformers on some more notoriously difficult vision tasks, e.g., generative adversarial networks (GANs). Our goal is to conduct the first pilot study in building a GAN completely free of convolutions, using only pure transformer-based architectures. Our vanilla GAN architecture, dubbed TransGAN, consists of a memory-friendly transformer-based generator that progressively increases feature resolution, and correspondingly a multi-scale discriminator to capture simultaneously semantic contexts and low-level textures. On top of them, we introduce the new module of grid self-attention for alleviating the memory bottleneck further, in order to scale up TransGAN to high-resolution generation. We also develop a unique training recipe including a series of techniques that can mitigate the training instability issues of TransGAN, such as data augmentation, modified normalization, and relative position encoding. Our best architecture achieves highly competitive performance compared to current state-of-the-art GANs using convolutional backbones. Specifically, TransGAN sets new state-of-the-art inception score of 10.43 and FID of 18.28 on STL-10, outperforming StyleGAN-V2. When it comes to higher-resolution (e.g. 256 x 256) generation tasks, such as on CelebA-HQ and LSUN-Church, TransGAN continues to produce diverse visual examples with high fidelity and impressive texture details. In addition, we dive deep into the transformer-based generation models to understand how their behaviors differ from convolutional ones, by visualizing training dynamics. The code is available at this https URL.
Intelligent Affect-Sensitive Tutoring Systems

With the integration of Artificial Intelligence techniques into the field of education and learning, various tools including intelligent tutoring systems for facilitating learning and improving learning experience have emerged. Affective tutoring systems (ATS) enable recognition of human emotions which could be employed in collaborative learning systems for monitoring and tracking learners. This chapter discusses the efficiency of an ATS – an electronic learning system in detecting and responding to the emotional states of learners. The tutoring system introduced in this chapter utilizes an existing generic affective application model to infer and appropriately respond to the learner’s affective state. This approach brings several advantages, notably the direct support for reuse and retrospective addition of affect sensing functionality into existing e-learning software. The evaluation of the effectiveness of this tutoring system has yielded positive findings and shows that measurable improvements in perceived learning may be obtained with a modest level of software development. The chapter also discusses different developments and implications of ATS and the future directions. It is hoped that these findings will demonstrate the viability of the approach and generate new avenues for real-world implementation and evaluation in the future.
Nested Counterfactual Identification from Arbitrary Surrogate Experiments

The Ladder of Causation describes three qualitatively different types of activities an agent may be interested in engaging in, namely, seeing (observational), doing (interventional), and imagining (counterfactual) (Pearl and Mackenzie, 2018). The inferential challenge imposed by the causal hierarchy is that data is collected by an agent observing or intervening in a system (layers 1 and 2), while its goal may be to understand what would have happened had it taken a different course of action, contrary to what factually ended up happening (layer 3). While there exists a solid understanding of the conditions under which cross-layer inferences are allowed from observations to interventions, the results are somewhat scarcer when targeting counterfactual quantities. In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, building on a more explicit definition of nested counterfactuals, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones. For instance, applications in mediation and fairness analysis usually evoke notions of direct, indirect, and spurious effects, which naturally require nesting. Second, we introduce a sufficient and necessary graphical condition for counterfactual identification from an arbitrary combination of observational and experimental distributions. Lastly, we develop an efficient and complete algorithm for identifying nested counterfactuals; failure of the algorithm returning an expression for a query implies it is not identifiable.
Distributed Multi-robot Collision Avoidance under Localization Uncertainty

Multi-robot collision avoidance is an avoidable problem in the actual application of robots. Whether in multi-robot collaborative work or human-machine cooperation, the problem of multi-robot collision avoidance must be solved. In this paper, we put forward a distributed multi-robot collision avoidance algorithm, which ensures a crowd of robots to avoid colliding to each other while they are navigating to the goals cooperatively. To achieve this goal, a variant of the Voronoi diagram, the buffered Voronoi diagram, is utilized to incorporate robot path planning with their localization errors to guarantee safety. In order to verify our algorithm, a series of simulation experiments have been carried out. According to the analysis, the algorithm is effective in avoiding robot collision, which interlocks to each other when they are moving in space simultaneously. These results shed light for the robots can avoid collisions with each other under the control of the algorithm, and this research can be extended in the aspect of unmanned driving technology.
Where Residual Risks Reside: A Comparative Approach to Art 9(4) of the European Union's Proposed AI Regulation

This paper explores the question of how to judge the acceptability of “residual risks” in the European Union’s Proposal for a Regulation laying down harmonized rules on Artificial Intelligence (the Proposal). The Proposal is a risk-based regulation that prohibits certain uses of AI and imposes several layers of risk controls upon ‘high-risk’ AI systems. Much of the commentary on the Proposal has focused on the issue of what AI-systems should be prohibited, and which should be classified as high risk. This paper bypasses this threshold question, engaging instead with a key issue of implementation. The Proposal imposes a wide range of requirements on providers of high-risk AI systems (among others) but acknowledges that certain AI systems would still carry a level of “residual risk” to health, safety and fundamental rights. Art 9(4) provides that, in order for high-risk systems to be put into use, risk management measures must be such that residual risks are judged “acceptable”. Participants in the AI supply chain need certainty about what degree of care and precaution in AI development, and in risk management specifically, will satisfy the requirements of Art 9(4).This paper advocates for a cost-benefit approach to art 9(4). It argues that art 9(4), read in context, calls for proportionality between precautions against risks posed by high-risk AI systems and the risks themselves, but leaves those responsible for implementing art 9(4) in the dark about how to achieve such proportionality. This paper identifies potentially applicable mid-level principles both in European laws (such as medical devices regulation) and in laws about the acceptability of precaution in relation to risky activities from common law countries (particularly negligence and workplace health and safety). It demonstrates how these principles would apply to different kinds of systems with different risk and benefit profiles, with hypothetical and real -world examples. And it sets out some difficult questions that arise in weighing the costs and benefits of precautions, calling on European policy-makers to provide more clarity to stakeholders on how they should answer those questions.
Formulas and algorithms for the length of a Farey sequence

This paper proves several novel formulas for the length of a Farey sequence of order n. The formulas use different trade-offs between iteration and recurrence and they range from simple to more complex. The paper also describes several iterative algorithms for computing the length of a Farey sequence based on these formulas. The algorithms are presented from the slowest to the fastest in order to explain the improvements in computational techniques from one version to another. The last algorithm in this progression runs in [Formula: see text] time and uses only [Formula: see text] memory, which makes it the most efficient algorithm for computing [Formula: see text] described to date. With this algorithm we were able to compute the length of the Farey sequence of order [Formula: see text].