Reviewerly/exHarmony huggingface dataset:
Inventory of ICTs for learning in engineering for emergency virtual teaching by COVID-19

Technologies in educational institutions during the pandemic have provided an effective response to a health emergency. Determining the economic and technical resources, the adaptability, the impact of the emerging mode of study and the effect of the initiatives with the use of ICTs, are the challenges that different investigations must be contrasted. This work contributes with an inventory of ICTs used in engineering careers at the Salesian Polytechnic University of Guayaquil in Ecuador. A descriptive analytical methodology with quantitative approach is developed using the random survey technique to a sample of 62 professors. The results with a confidence level of 95% and a margin of error of 9% have allowed us to have an x-ray of how the community of professors has faced the pandemic from a responsible management of the technological resources provided by the institution to the context occurred. Eleven categories of ICTs are evidenced for classroom work.
Human Interpretation of Saliency-based Explanation Over Text

While a lot of research in explainable AI focuses on producing effective explanations, less work is devoted to the question of how people understand and interpret the explanation. In this work, we focus on this question through a study of saliency-based explanations over textual data. Feature-attribution explanations of text models aim to communicate which parts of the input text were more influential than others towards the model decision. Many current explanation methods, such as gradient-based or Shapley value-based methods, provide measures of importance which are well-understood mathematically. But how does a person receiving the explanation (the explainee) comprehend it? And does their understanding match what the explanation attempted to communicate? We empirically investigate the effect of various factors of the input, the feature-attribution explanation, and visualization procedure, on laypeople's interpretation of the explanation. We query crowdworkers for their interpretation on tasks in English and German, and fit a GAMM model to their responses considering the factors of interest. We find that people often mis-interpret the explanations: superficial and unrelated factors, such as word length, influence the explainees' importance assignment despite the explanation communicating importance directly. We then show that some of this distortion can be attenuated: we propose a method to adjust saliencies based on model estimates of over- and under-perception, and explore bar charts as an alternative to heatmap saliency visualization. We find that both approaches can attenuate the distorting effect of specific factors, leading to better-calibrated understanding of the explanation.
Deep camera obscura: an image restoration pipeline for pinhole photography

Modern machine learning has enhanced the image quality for consumer and mobile photography through low-light denoising, high dynamic range (HDR) imaging, and improved demosaicing among other applications. While most of these advances have been made for normal lens-based cameras, there has been an emerging body of research for improved photography for lensless cameras using thin optics such as amplitude or phase masks, diffraction gratings, or diffusion layers. These lensless cameras are suited for size and cost-constrained applications such as tiny robotics and microscopy that prohibit the use of a large lens. However, the earliest and simplest camera design, the camera obscura or pinhole camera, has been relatively overlooked for machine learning pipelines with minimal research on enhancing pinhole camera images for everyday photography applications. In this paper, we develop an image restoration pipeline of the pinhole system to enhance the pinhole image quality through joint denoising and deblurring. Our pipeline integrates optics-based filtering and reblur losses for reconstructing high resolution still images (2600 × 1952) as well as temporal consistency for video reconstruction to enable practical exposure times (30 FPS) for high resolution video (1920 × 1080). We demonstrate high 2D image quality on real pinhole images that is on-par or slightly improved compared to other lensless cameras. This work opens up the potential of pinhole cameras to be used for photography in size-limited devices such as smartphones in the future.
Adaptation for Automated Drift Detection in Electromechanical Machine Monitoring

Practical machine learning applications for streaming data can involve concept drift (the change in statistical properties of data over time), one-shot or few-shot learning (starting with only one or a few examples for each class), a scarcity of representative training data, and extreme verification latency (only the initial dataset has ground-truth labels). This work presents a framework for organizing signal processing and machine learning techniques to provide adaptive classification and drift detection. Nonintrusive load monitoring (NILM) serves as an ideal case study, as modern sensing solutions provide a wellspring of electromechanical data sources. There is a lack of training datasets that generalize across different load and fault scenarios. Accordingly, training must be accomplished with a limited set of data when deploying a NILM to a new power system. Also, loads can exhibit concept drift over time either due to faults or normal variation. NILM field data is used as an illustrative case study to demonstrate the proposed framework for adaptation and drift tracking.
Learn to Predict How Humans Manipulate Large-Sized Objects From Interactive Motions

Understanding human intentions during interactions has been a long-lasting theme, that has applications in human-robot interaction, virtual reality and surveillance.In this study, we focus on full-body human interactions with large-sized daily objects and aim to predict the future states of objects and humans given a sequential observation of human-object interaction.As there is no such dataset dedicated to full-body human interactions with large-sized daily objects, we collected a largescale dataset containing thousands of interactions for training and evaluation purposes.We also observe that an object's intrinsic physical properties are useful for the object motion prediction, and thus design a set of object dynamic descriptors to encode such intrinsic properties.We treat the object dynamic descriptors as a new modality and propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task.We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.We also demonstrate the predicted results are useful for human-robot collaborations.
Comparing In-ear EOG for Eye-Movement Estimation With Eye-Tracking: Accuracy, Calibration, and Speech Comprehension

This presentation details and evaluates a method for estimating the attended speaker during a two-person conversation by means of in-ear electro-oculography (EOG). Twenty-five hearing-impaired participants were fitted with molds equipped with EOG electrodes (in-ear EOG) and wore eye-tracking glasses while watching a video of two life-size people in a dialog solving a Diapix task. The dialogue was directionally presented and together with background noise in the frontal hemisphere at 60 dB SPL. During three conditions of steering (none, in-ear EOG, conventional eye-tracking), participants' comprehension was periodically measured using multiple-choice questions. Based on eye movement detection by in-ear EOG or conventional eye-tracking, the estimated attended speaker was amplified by 6 dB. In the in-ear EOG condition, the estimate was based on one selected channel pair of electrodes out of 36 possible electrodes. A novel calibration procedure introducing three different metrics was used to select the measurement channel. The in-ear EOG attended speaker estimates were compared to those of the eye-tracker. Across participants, the mean accuracy of in-ear EOG estimation of the attended speaker was 68%, ranging from 50 to 89%. Based on offline simulation, it was established that higher scoring metrics obtained for a channel with the calibration procedure were significantly associated with better data quality. Results showed a statistically significant improvement in comprehension of about 10% in both steering conditions relative to the no-steering condition. Comprehension in the two steering conditions was not significantly different. Further, better comprehension obtained under the in-ear EOG condition was significantly correlated with more accurate estimation of the attended speaker. In conclusion, this study shows promising results in the use of in-ear EOG for visual attention estimation with potential for applicability in hearing assistive devices.
Innovative Research on English Teaching Model Based on Artificial Intelligence and Wireless Communication

It is a network system for teaching English through a wireless communication (WC) premised distance teaching system. This is a process of education that is capable of encouraging students’ concerns to acquire knowledge voluntarily. The paper is designed to develop and implement an online intelligent English training system using artificial intelligence (AI) that helps students improve their English learning efficiency in line with knowledge and personality. The system’s numerous sensor nodes may create a variety of topologies. The gathered information is transmitted over the global system for mobile communication (GSM) network to the user interface. The operator can manage the remote sensor node via the GSM network. Nevertheless, there are certain derivative aspects such as the absence of verbal judgment, the actual evaluation and signaling system, the interactive educational platform teachers and learners need. The paper is based on the above issues. It contains a whole talk-based system where teachers, students, and English teaching can be revised together — AIWC (ET-AIWC) systems are designed to improve and advance the genetic algorithm based on an encoding technique for dynamic parameter adjustment of the iterative process based on these problems. In combination with an AI expert system, suitable learning techniques were created to enable students to double the learning effect by half the amount of work. An online teaching assistant system was designed to monitor, regulate, and engage with students throughout the learning process and a modified scoring system that provides real-time evaluation of student speakers to improve students’ oral competence in English better and more efficiently, achieving 95.2%.
Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos

Growing evidence suggests that YouTube's recommendation algorithm plays a role in online radicalization via surfacing extreme content. Radical Islamist groups, in particular, have been profiting from the global appeal of YouTube to disseminate hate and jihadist propaganda. In this quantitative, data-driven study, we investigate the prevalence of religiously intolerant Arabic YouTube videos, the tendency of the platform to recommend such videos, and how these recommendations are affected by demographics and watch history. Based on our deep learning classifier developed to detect hateful videos and a large-scale dataset of over 350K videos, we find that Arabic videos targeting religious minorities are particularly prevalent in search results (30%) and first-level recommendations (21%), and that 15% of overall captured recommendations point to hateful videos. Our personalized audit experiments suggest that gender and religious identity can substantially affect the extent of exposure to hateful content. Our results contribute vital insights into the phenomenon of online radicalization and facilitate curbing online harmful content.
Modelling efficient BB84 with applications for medium-range, terrestrial free-space QKD

Abstract Terrestrial free-space (FS) quantum key distribution (QKD) is ideally suited for deployment in dense urban environments. The transition from laboratory to commercial deployment, however, raises a number of important engineering and deployment issues. Here, we investigate these issues for efficient BB84 using a weak coherent pulse-decoy state protocol. We calculate expected key lengths for different environmental conditions and when the scope for optimisation of protocol parameters is restricted due to practical considerations. In particular, we find that for a fixed receiver basis choice probability, it can be advantageous to allow the transmitter to have a different basis choice probability depending on varying channel loss and background light levels. Finally, we examine the effects of pulse intensity uncertainty finding that they can dramatically reduce the key length. These results can be used to determine the loss budget for the FS optics of a QKD systems and assist in their design.
Explainable Session-based Recommendation with Meta-path Guided Instances and Self-Attention Mechanism

Session-based recommendation (SR) gains increasing popularity because it helps greatly maintain users' privacy. Aside from its efficacy, explainability is also critical for developing a successful SR model, since it can improve the persuasiveness of the results, the users' satisfaction, and the debugging efficiency. However, the majority of current SR models are unexplainable and even those that claim to be interpretable cannot provide clear and convincing explanations of users' intentions and how they influence the models' decisions. To solve this problem, in this research, we propose a meta-path guided model which uses path instances to capture item dependencies, explicitly reveal the underlying motives, and illustrate the entire reasoning process. To begin with, our model explores meta-path guided instances and leverages the multi-head self-attention mechanism to disclose the hidden motivations beneath these path instances. To comprehensively model the user interest and interest shifting, we search paths in both adjacent and non-adjacent items. Then, we update item representations by incorporating the user-item interactions and meta-path-based context sequentially. Compared with recent strong baselines, our method is competent to the SOTA performance on three datasets and meanwhile provides sound and clear explanations.
A Novel Human-Centred Evaluation Approach and an Argument-Based Method for Explainable Artificial Intelligence

One of the aim of Explainable Artificial Intelligence (XAI) is to equip data-driven, machine-learned models with a high degree of explainability for humans. Understanding and explaining the inferences of a model can be seen as a defeasible reasoning process. This process is likely to be non-monotonic: a conclusion, linked to a set of premises, can be retracted when new information becomes available. In formal logic, computational argumentation is a method, within Artificial Intelligence (AI), focused on modeling defeasible reasoning. This research study focuses on the automatic formation of an argument-based representation for a machine-learned model in order to enhance its degree of explainability, by employing principles and techniques from computational argumentation. It also contributes to the body of knowledge by introducing a novel quantitative human-centred technique to evaluate such a novel representation, and potentially other XAI methods, in the form of a questionnaire for explainability. An experiment have been conducted with two groups of human participants, one interacting with the argument-based representation, and one with a decision trees, a representation deemed naturally transparent and comprehensible. Findings demonstrate that the explainability of the original argument-based representation is statistically similar to that associated to the decision-trees, as reported by humans via the novel questionnaire.
Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture

In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that simultaneously encode two images. The fusion of early encoder features with deeper decoder ones allows UNet models to produce finer-grained dense predictions. While proven in segmentation tasks, the network's benefits are down-weighted for dense regression tasks as these long-range skip connections additionally result in texture transfer artifacts. Specifically for depth estimation, this hurts smoothness and introduces false positive edges which are detrimental to the task due to the depth maps' piece-wise smooth nature. The proposed HybridSkip connections show improved performance in balancing the trade-off between edge preservation, and the minimization of texture transfer artifacts that hurt smoothness. This is achieved by the proper and balanced exchange of information that Hybrid-Skip connections offer between the high and low frequency, encoder and decoder features, respectively.
Sentiment Analysis of COVID-19 Tweets: Leveraging Stacked Word Embedding Representation for Identifying Distinct Classes Within a Sentiment

The pandemic that arose due to the novel Corona Virus Disease of 2019 (COVID) has become the biggest challenge of all time. The entire world’s population has stormed social media to express their opinions, emotions, and sentiments. This manuscript implements classical machine and deep learning approaches with static and stacked word embeddings to identify the sentiments of the COVID-19 tweets extracted from Twitter. The problem we have tackled in this manuscript is the multi-class classification problem for three and five classes, respectively. Our proposed deep learning model with stacked word embeddings has outperformed the individual static pre-trained embeddings representation, classical machine, and deep learning approaches altogether. The proposed model has proven useful in complex classification tasks such as identifying classes belonging to the same group of sentiments namely Extremely Negative and Negative, Extremely Positive and Positive. The experimental results also show the superior performance of stacked word embeddings for the peculiar contextual semantic comprehension from small tweets and dealing with the unbalancedness of the experimental dataset. We achieved the accuracy with stacked embeddings with accuracy being 73.01% and 84.25% for three and five classes, respectively.
A unifying causal framework for analyzing dataset shift-stable learning algorithms

Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability and accuracy. Taking a causal graphical view, we use a flexible graphical representation to express various types of dataset shifts. Given a known graph of the data generating process, we show that all invariant distributions correspond to a causal hierarchy of graphical operators which disable the edges in the graph that are responsible for the shifts. The hierarchy provides a common theoretical underpinning for understanding when and how stability to shifts can be achieved, and in what ways stable distributions can differ. We use it to establish conditions for minimax optimal performance across environments, and derive new algorithms that find optimal stable distributions. Using this new perspective, we empirically demonstrate that that there is a tradeoff between minimax and average performance.
Analysis of the Use, Knowledge and Problems of E-learning in a Distance Learning Master’s Programme

The pandemic caused by COVID-19 has led many more universities to increase the number of online courses they offer. Methodological processes and infrastructure have had to be adapted so as to make the shift from a classroom-based model to distance learning. The context of our research is the Master’s Degree in Research in Teacher Training and Information and Communication Technologies (ICT). Two research objectives were established: To determine the changes that take place between the beginning and end of the Master’s degree in relation to the students’ engagement with ICT and to ascertain if students’ perception of the problems encountered in the virtual teaching/learning process has changed. Twenty-one master’s students took part. The research was based on the qualitative paradigm, with a content analysis research design. A table of categories was developed and validated, from which the script for the structured interviews was prepared. The main findings highlight the lack of training and involvement of teachers and their failure to adapt to the online methodology, together with a decrease in student motivation, as being particularly problematic in the teaching and learning process.
Sample-Based and Feature-Based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA

Federated learning (FL) has become a hot research area in enabling the collaborative training of machine learning models among multiple clients that hold sensitive local data. Nevertheless, unconstrained federated optimization has been studied mainly using stochastic gradient descent (SGD), which may converge slowly, and constrained federated optimization, which is more challenging, has not been investigated so far. This paper investigates sample-based and feature-based federated optimization, respectively, and considers both unconstrained and constrained nonconvex problems for each of them. First, we propose FL algorithms using stochastic successive convex approximation (SSCA) and mini-batch techniques. These algorithms can adequately exploit the structures of the objective and constraint functions and incrementally utilize samples. We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively. Next, we provide algorithm examples with appealing computational complexity and communication load per communication round. We show that the proposed algorithm examples for unconstrained federated optimization are identical to FL algorithms via momentum SGD and provide an analytical connection between SSCA and momentum SGD. Finally, numerical experiments demonstrate the inherent advantages of the proposed algorithms in convergence speeds, communication and computation costs, and model specifications.