Reviewerly/exHarmony huggingface dataset:
CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech

Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation model, they might not include the appropriate properties required to counter a particular hate speech instance. In this paper, we propose CounterGeDi - an ensemble of generative discriminators (GeDi) to guide the generation of a DialoGPT model toward more polite, detoxified, and emotionally laden counterspeech. We generate counterspeech using three datasets and observe significant improvement across different attribute scores. The politeness and detoxification scores increased by around 15% and 6% respectively, while the emotion in the counterspeech increased by at least 10% across all the datasets. We also experiment with triple-attribute control and observe significant improvement over single attribute results when combining complementing attributes, e.g., politeness, joyfulness and detoxification. In all these experiments, the relevancy of the generated text does not deteriorate due to the application of these controls
Evaluating the Fairness Impact of Differentially Private Synthetic Data

Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information. Due to the suppression of underrepresented classes that is often required to achieve privacy, however, it may be in conflict with fairness. We evaluate four DP synthesizers and present empirical results indicating that three of these models frequently degrade fairness outcomes on downstream binary classification tasks. We draw a connection between fairness and the proportion of minority groups present in the generated synthetic data, and find that training synthesizers on data that are pre-processed via a multi-label undersampling method can promote more fair outcomes without degrading accuracy.
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning

In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding invariances from raw pixels. Nevertheless, we empirically find that not all samples are equally important and hence simply injecting more augmented inputs may instead cause instability in Q-learning. In this paper, we approach this problem systematically by developing a model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF), which can fully exploit sample importance and improve learning efficiency in a self-supervised manner. Facilitated by the proposed contrastive curiosity, CCLF is capable of prioritizing the experience replay, selecting the most informative augmented inputs, and more importantly regularizing the Q-function as well as the encoder to concentrate more on under-learned data. Moreover, it encourages the agent to explore with a curiosity-based reward. As a result, the agent can focus on more informative samples and learn representation invariances more efficiently, with significantly reduced augmented inputs. We apply CCLF to several base RL algorithms and evaluate on the DeepMind Control Suite, Atari, and MiniGrid benchmarks, where our approach demonstrates superior sample efficiency and learning performances compared with other state-of-the-art methods.
Implementation of an efficient, portable and platform-agnostic cryptocurrency mining algorithm for Internet of Things devices

Recently, there has been a remarkable amount of research being done in both, the fields of Blockchain and Internet of Things (IoT). Blockchain technology synergises well with IoT, solving key problems such as privacy, concerns with interoperability and security. However, the consensus mechanisms that allows trustless parties to maintain an agreement, the same algorithms that underpins cryptocurrency mining, are usually extremely computationally expensive, making implementation on low-power IoT devices difficult. More importantly, mining requires downloading and synchronizing hundred of gigabytes worth of blocks which is far beyond the capabilities of most IoT devices. In this paper, we present an efficient, portable and platform-agnostic cryptocurrency mining algorithm using the Stratum protocol to avoid downloading the entire blockchain. We implement the algorithm in four different platforms- PC, ESP32, an emulator and an old PlayStation Portable (PSP) to demonstrate that it is indeed possible for any device to mine cryptocurrencies with no assumptions except the ability to connect to the internet. To make sure of ease of portability on any platform and for reproducibility of the reported results we make the implementation publicly available with detailed instructions at: https://anonymous.4open.science/r/cryptominer.
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation

Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). In this paper, we study efficient architecture design for real-time multi-person pose estimation on edge. We reveal that HRNet's high-resolution branches are redundant for models at the low-computation region via our gradual shrinking experiments. Removing them improves both efficiency and performance. Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs. Fusion Deconv Head removes the redundancy in high-resolution branches, allowing scale-aware feature fusion with low overhead. Large Kernel Convs significantly improve the model's capacity and receptive field while maintaining a low computational cost. With only 25% computation increment, 7x7 kernels achieve +14.0 mAP better than 3x3 kernels on the CrowdPose dataset. On mobile platforms, LitePose reduces the latency by up to 5.0x without sacrificing performance, compared with prior state-of-the-art efficient pose estimation models, pushing the frontier of real-time multi-person pose estimation on edge. Our code and pre-trained models are released at https://github.com/mit-han-lab/litepose.
Simpler is Better: off-the-shelf Continual Learning Through Pretrained Backbones

In this short paper, we propose a baseline (off-the-shelf) for Continual Learning of Computer Vision problems, by leveraging the power of pretrained models. By doing so, we devise a simple approach achieving strong performance for most of the common benchmarks. Our approach is fast since requires no parameters updates and has minimal memory requirements (order of KBytes). In particular, the "training" phase reorders data and exploit the power of pretrained models to compute a class prototype and fill a memory bank. At inference time we match the closest prototype through a knn-like approach, providing us the prediction. We will see how this naive solution can act as an off-the-shelf continual learning system. In order to better consolidate our results, we compare the devised pipeline with common CNN models and show the superiority of Vision Transformers, suggesting that such architectures have the ability to produce features of higher quality. Moreover, this simple pipeline, raises the same questions raised by previous works \cite{gdumb} on the effective progresses made by the CL community especially in the dataset considered and the usage of pretrained models. Code is live at https://github.com/francesco-p/off-the-shelf-cl
Microrollers Flow Uphill as Granular Media

Pour sand into a container and only the grains near the top surface move. The collective motion associated with the translational and rotational energy of the grains in a thin flowing layer is quickly dissipated as friction through multibody interactions. Alternatively, consider what will happen to a bed of particles if one applies a torque to each individual particle. In this paper, we demonstrate an experimental system where torque is applied at the constituent level through a rotating magnetic field in a dense bed of microrollers. The net result is the grains roll uphill, forming a heap with a negative angle of repose. Two different regimes have been identified related to the degree of mobility or fluidization of the particles in the bulk. Velocimetry of the near surface flowing layer reveals the collective motion of these responsive particles scales in a similar way to flowing bulk granular flows. A simple granular model that includes cohesion accurately predicts the apparent negative coefficient of friction. In contrast to the response of active or responsive particles that mimic thermodynamic principles, this system results in macroscopic collective behavior that has the kinematics of a purely dissipative granular system.
Adaptive Behavioral Model Learning for Software Product Lines

Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims at constructing behavioral models for software systems in some form of a finite state machine. Due to the commonalities among the products of an SPL, it is possible to reuse the previously learned models during the model learning process. In this paper, an adaptive approach (the $\text{PL}^*$ method) for learning the product models of an SPL is presented based on the well-known $L^*$ algorithm. In this method, after model learning of each product, the sequences in the final observation table are stored in a repository which will be used to initialize the observation table of the remaining products to be learned. The proposed algorithm is evaluated on two open-source SPLs and the total learning cost is measured in terms of the number of rounds, the total number of resets and input symbols. The results show that for complex SPLs, the total learning cost for the $\text{PL}^*$ method is significantly lower than that of the non-adaptive learning method in terms of all three metrics. Furthermore, it is observed that the order in which the products are learned affects the efficiency of the $\text{PL}^*$ method. Based on this observation, we introduced a heuristic to determine an ordering which reduces the total cost of adaptive learning in both case studies.
Classification and Generation of real-world data with an Associative Memory Model

Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biological memory is equipped with an impressive compression algorithm that can store the essential, and then infer the details to match perception. The Willshaw Memory is a simple abstract model for cortical computations which implements mechanisms of biological memories. Using our recently proposed sparse coding prescription for visual patterns, this model can store and retrieve an impressive amount of real-world data in a fault-tolerant manner. In this paper, we extend the capabilities of the basic Associative Memory Model by using a Multiple-Modality framework. In this setting, the memory stores several modalities (e.g., visual, or textual) of each pattern simultaneously. After training, the memory can be used to infer missing modalities when just a subset is perceived. Using a simple encoder-memory-decoder architecture, and a newly proposed iterative retrieval algorithm for the Willshaw Model, we perform experiments on the MNIST dataset. By storing both the images and labels as modalities, a single Memory can be used not only to retrieve and complete patterns but also to classify and generate new ones. We further discuss how this model could be used for other learning tasks, thus serving as a biologically-inspired framework for learning.
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN

The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques. The recently proposed StyleGAN architectures achieve state-of-the-art generation ability but the original StyleGAN is not friendly for sketch-based creation due to its unconditional generation nature. To address this issue, we propose a direct conditioning strategy to better preserve the spatial information under the StyleGAN framework. Specifically, we introduce Spatially Conditioned StyleGAN (SC-StyleGAN for short), which explicitly injects spatial constraints to the original StyleGAN generation process. We explore two input modalities, sketches and semantic maps, which together allow users to express desired generation results more precisely and easily. Based on SC-StyleGAN, we present DrawingInStyles, a novel drawing interface for non-professional users to easily produce high-quality, photo-realistic face images with precise control, either from scratch or editing existing ones. Qualitative and quantitative evaluations show the superior generation ability of our method to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.
Competencias digitales docentes en el proceso de enseñanza-aprendizaje en tiempos de covid-19

In the current context, the growing expectation of knowledge and application of new technologies and their applications produce important repercussions in all spheres of society, even more so during the pandemic caused by covid-19, therefore it is essential to reflect on their importance and implications. This study presents a theoretical overview of some meanings, concepts and models of digital competencies, socializes data that allow to demonstrate the level of development and usability in the Faculty of Philosophy, Letters and Educational Sciences of the Central University of Ecuador, finally aspects to deepen the subject and its relevance in the educational process are exposed.
Nonlinear-Mapping Optimization for Effective Nonlinear- Distortion Compensation through Artificial Neural Network

The optimization procedure on the artificial-neural-network (ANN) has been carried out through selecting the nonlinear-mapping functions. The power threshold of larger than -5.5dBm was obtained for QAM-16 signals when the optimized ANN equalizer applied.
Non-null interferometric surface figure testing beyond the detector pixel MTF cutoff spatial frequency limit

We report a non-null interferometric surface figure measurement of a spherical mirror with slope departure angles mapping to interferogram spatial frequencies larger than the cutoff spatial frequency limit of a full-fill detector with 4.6 µm-pixels.
DEVELOPING AN INTERCULTURAL CURRICULUM - AN OVERVIEW OF THE POSSIBILITIES OF ITS APPLICATION IN PRIMARY SCHOOL’S DAILY LIFE

Intercultural education should promote and develop a better understanding of cultures in modern society, an increased ability to communicate between people of different cultures, a much more flexible attitude towards cultural diversity in society, a greater willingness of people to engage actively in social interaction with people from another cultural origin as well as the recognition of the basic features of human nature as something in common. Thus, the basic goal of intercultural education will be to promote and develop the capacities of those who learn about mutual interaction and communication with the world around them, even when differences are present. To achieve all this through teaching in schools, concrete measures should be taken such as: learning to be based on cultural and linguistic diversity, to include a range of different cultural knowledge, experiences and perspectives in the curriculum, to celebrate, to to value and learn the history and life of different cultures and indigenous peoples, visits to culturally important places and intercultural experiences. Institutions must also ensure education that is historically accurate, impartial, culturally appropriate.
RESISTANCE MANAGEMENT IN EDUCATION - TEACHERS' RESISTANCE TO THE USE OF ELECTRONIC BOARDS (SMART)

Modern and innovative management in education requires full commitment to work, responsibility and efficiency in performing tasks that ultimately leads to the success or failure of the manager. When the managerial function is performed in the educational process, in scientific research or in the educational activity, then success is related to a number of other factors that produce the character of the institution, such as the specifics of their business, the wider the importance of these institutions for development. general country, qualification structure, etc. Referring to these segments of the characteristics of a successful principal manager in the school and starting from the fact that the managerial skills of a principal in the education system is evaluated in managing the innovations or changes that occur in the school and knowing the fact that these changes show resistance tendencies from both teachers and other school staff, the technical, administrative and pedagogical, this theoretical-empirical work includes a concise analysis of a resistance of teachers that was presented within several years of education (2013-2019) to the use of the Whiteboard in the teaching process in high school. It is worth mentioning that with the increase of the number of Electronic Boards in the school, as an innovation, the number of processors that show resistance to their use also increased and in the last two years it is worth mentioning that with the measures and mechanisms undertaken by the principal (organizing trainings in the school for the use of Electronic Boards and rewards for teachers who achieve concrete success with the use of Electronic Boards) so, in the last two years this resistance is exhausted and decreases. KEYWORDS: Management; Change management in education; Educational methods; ICT in education; Smart electronic tables
Isomorphism as an interdisciplinary-psychological matrix of analysis of the attitude to nature and health in organizations (on the example of the Baikal region)

The appearance in organizations in order to protect the environment and improve human health of natural algorithms of activity and nature-like technologies actualizes the methodological role of analysis of such strategies on the basis of isomorphisma. A multidisciplinary approach to isomorphism, as a matrix for bringing human beings closer to the social, animal and plant world on the basis of natural resources, is particularly important in regions that are monuments to the naturaland cultural heritage of mankind, which Baikal has been advocating since 1996. The algorithms of such analysis will primarily affect the interrelated isomorphic parameters of personality and organization, which are the most significant in their interaction with nature. For a systematic consideration of the problem, it is advisable to combine the main analysis with the search (use) of developments in natural sciences that can function as specific sources of human and organization communication with the natural world; in our case, the provisions of biomimicry: nature as a model, as a measure and as amentor. The analysis also touches on the significant “mechanisms” of adaptation of organizations and people to the implementation of innovative tasks of ensuring natural life and environmental safety — imitative, normative, and forced isomorphism. The core of the interdisciplinary psychological analysisis the socio-psychological concept of one of the authors of the article on the relationship of isomorphic parameters of the community (organization) and personality, modified for environmental activities. It (the relationship) concerns four aspects: firstly, the genetic memory and mental attitudes of people about “oikos” — home, homeland, as the main place of their being; secondly, the unity of the targets of organizations with the motivation of their members to protect nature and promote health; thirdly, ensuring environmental friendliness of actions when implementation of social roles and proactive behavior in formal and informal organizations; fourth, achieving an understanding by members oforganizations and the public of the importance of feedback not only for determining development prospects, but also for assessing the level of activity of subjects in overcoming environmental problems.
A DEEP CONVOLUTIONAL NEURAL NETWORK TRAINING TECHNIQUE FOR REMOTE SENSING ROBUST OBJECT LOCALIZATION

Intoduction: At present, aerial photographic object detection systems using deep convolutional neural networks are heavily used. However, in the case of remote sensing of the Earth, the quality of images can be extremely low due to a variety of negative phenomena. The most common distortion in the images is the Gaussian blur, which leads to a reduction in the clarity of the objects in these images, which in turn greatly reduces the effectiveness of the object detection system. Purpose: the purpose of the paper is to improve robustness (distortion performance) of the convoluted neural network in the system of localization of objects on blurred aerial photographs without the use of additional pre-processing or post-processing units, which results in unnecessary computing costs. Methods: to achieve the main goal, using training data augmentation by including blurred images with various levels of distortion is proposed. Results: The simulation has shown that, as the fraction of distorted images increases and the level of distortion raises, the training dataset increases the metric of the average recall of the vehicle localization system on distorted test images. At the same time, however, there is a decline in the metric of intersection of the predicted regions of interest with the true objects in the same distorted test images. The study has shown the existence of an optimal level of image distortion in the training dataset to produce the most stable (robust) deep convolutional neural networks. Problem is solved on the example of localization of vehicles on aerial photographs of the Earth. Practical relevance: the proposed method makes it possible to increase the efficiency of the system for localizing objects on distort! ed aerial images without increasing the computational complexity. Discussion: Further detailed studies of deep convolutional neural networks behavior in the real world, namely, under various image distortions, will allow to increase robustness in a wide variety of situations.