Reviewerly/exHarmony huggingface dataset:
On weighted k-mer dictionaries

Abstract We consider the problem of representing a set of $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mers and their abundance counts, or weights, in compressed space so that assessing membership and retrieving the weight of a $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mer is efficient. The representation is called a weighted dictionary of $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mers and finds application in numerous tasks in Bioinformatics that usually count $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mers as a pre-processing step. In fact, $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mer counting tools produce very large outputs that may result in a severe bottleneck for subsequent processing. In this work we extend the recently introduced SSHash dictionary (Pibiri in Bioinformatics 38:185–194, 2022) to also store compactly the weights of the $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mers. From a technical perspective, we exploit the order of the $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mers represented in SSHash to encode runs of weights, hence allowing much better compression than the empirical entropy of the weights. We study the problem of reducing the number of runs in the weights to improve compression even further and give an optimal algorithm for this problem. Lastly, we corroborate our findings with experiments on real-world datasets and comparison with competitive alternatives. Up to date, SSHash is the only $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -mer dictionary that is exact, weighted, associative, fast, and small.
Causal Feature Selection in the Presence of Sample Selection Bias

Almost all existing causal feature selection methods are proposed without considering the problem of sample selection bias. However, in practice, as data-gathering process cannot be fully controlled, sample selection bias often occurs, leading to spurious correlations between features and the class variable, which seriously deteriorates the performance of those existing methods. In this article, we study the problem of causal feature selection under sample selection bias and propose a novel Progressive Causal Feature Selection (PCFS) algorithm which has three phases. First, PCFS learns the sample weights to balance the treated group and control group distributions corresponding to each feature for removing spurious correlations. Second, based on the sample weights, PCFS uses a weighted cross-entropy model to estimate the causal effect of each feature and removes some irrelevant features from the confounder set. Third, PCFS progressively repeats the first two phases to remove more irrelevant features and finally obtains a causal feature set. Using synthetic and real-world datasets, the experiments have validated the effectiveness of PCFS, in comparison with several state-of-the-art classical and causal feature selection methods.
Implementation of Active Cybersecurity Education in Ukrainian Higher School

Cybersecurity as a part of information technology requires constant professional development for teachers. Therefore it is indicative of the study of the implementation of active learning methods. The experience of higher technical educational institutions in the countries of the European Union (Germany, France, Sweden, etc.) shows that the introduction of active learning elements and Conceive-Design-Implement-Operate (CDIO) methods dramatically increases student engagement and improves their learning outcomes. The article considers the stages of formation of the process of training specialists in cybersecurity. In addition, the experience of introducing active learning methods into the educational process is presented, and its results are analyzed. The technology of implementation of active learning and the results obtained have been presented concerning training professionals of the 2nd (Master’s) degree for the specialty 125 “Cybersecurity” in Borys Grinchenko Kyiv University. These actions are confirmed by the study results of the average score of graduate students, which has increased by three points from 76.3 to 79.3.
Recommendation Methods for Information Technology Support of Lifelong Learning Situations

In a rapidly changing world, learning through life becomes an important part of personal and professional development in a formal, informal, or non-formal environment. A variety of e-learning content and activities are available to meet the learning demands of individuals. However, lifelong learners need advice or guidance in their search for learning experiences corresponding to their needs and preferences. Recommendation techniques are applied to facilitate a choice of a product by a user in different contexts, including education. They rely on the available information about users' preferences and similarity measures for users and products and thus operate mostly within an isolated recommendation system. This research is aimed at identification of appropriate recommendation methods for intelligent e-learning support of the lifelong learner. A study reveals four types of lifelong learning situations with different primary recommendation methods in each case. A generalized schema for providing recommendations in lifelong e-learning is suggested.
Learning latent representations to co-adapt to humans

When robots interact with humans in homes, roads, or factories the human’s behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners: actions the robot has learned to coordinate with the original human may fail after the human adapts to the robot. In this paper we introduce an algorithmic formalism that enables robots (i.e., ego agents) to co-adapt alongside dynamic humans (i.e., other agents) using only the robot’s low-level states, actions, and rewards. A core challenge is that humans not only react to the robot’s behavior, but the way in which humans react inevitably changes both over time and between users. To deal with this challenge, our insight is that—instead of building an exact model of the human–robots can learn and reason over high-level representations of the human’s policy and policy dynamics. Applying this insight we develop RILI: Robustly Influencing Latent Intent. RILI first embeds low-level robot observations into predictions of the human’s latent strategy and strategy dynamics. Next, RILI harnesses these predictions to select actions that influence the adaptive human towards advantageous, high reward behaviors over repeated interactions. We demonstrate that—given RILI’s measured performance with users sampled from an underlying distribution—we can probabilistically bound RILI’s expected performance across new humans sampled from the same distribution. Our simulated experiments compare RILI to state-of-the-art representation and reinforcement learning baselines, and show that RILI better learns to coordinate with imperfect, noisy, and time-varying agents. Finally, we conduct two user studies where RILI co-adapts alongside actual humans in a game of tag and a tower-building task. See videos of our user studies here: https://youtu.be/WYGO5amDXbQ
FUNDAMENTAL AND BASIC COGNITIVE SKILLS REQUIRED FOR TEACHERS TO EFFECTIVELY USE CHATBOTS IN EDUCATION

With the rapid advancement of technology, education is undergoing a transformational change. Chatbots have become increasingly popular in recent years and are being utilized as teaching assistants to support teachers and students in various ways. However, little research has been done on the skills required by teachers to prepare curriculum content using chatbots. The research aims to identify the skills teachers need to prepare curriculum content with chatbots. It examines the fundamental and cognitive skills individuals need to interpret content generated by chatbots and explores the difference between self-assessment and evaluator-based assessment. Fifty-eight third-year students, pre-service teachers, in the Elementary education program attempted to write a lesson plan using ChatGPT and completed a questionnaire to assess the skills required. Their communication with the chatbot as well as their prepared lesson plans were reviewed by an evaluator who rated the skills of the participating pre-service teachers. Results indicate that pre-service teachers tend to overestimate their skills required to interpret chatbot-generated content compared to the evaluator's ratings. Such discrepancies could lead to inaccurate or incomplete assessments of their skills, which could hinder their potential for growth and development. Keywords: artificial intelligence, chatbots in education, cognitive skills, fundamental skills, lessons plan
Passion Projects: Creating Space for Adolescents to Ask Real Questions

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Quantum Algorithms for Trust-Based AI Applications

Quantum computing is a rapidly growing field of computing that leverages the principles of quantum mechanics to significantly speed up computations that are beyond the capabilities of classical computing. This type of computing can revolutionize the field of trustworthy artificial intelligence, where decision-making is data-driven, complex, and time-consuming. Different trust-based AI systems have been proposed for different AI applications. In this paper, we have reviewed different trust-based AI systems and summarized their alternative quantum algorithms. This review provides an overview of quantum algorithms for three trust-based AI applications: fake user detection in social networks, medical diagnostic system, and finding the shortest path used in social network trust aggregation.
A Novel Hybrid Model Based on CNN and Bi-LSTM for Arabic Multi-domain Sentiment Analysis

Since Web 2.0 and the freedom to share information, perspectives, and opinions on global events, services, goods, etc., most of the content on social media platforms comes from users. Social media data includes user sentiment-related subjects. Due to its complexity, ambiguity, dialects, shortage of resources, and morphological diversity, little work has been done on Arabic sentiment analysis. This study introduces a novel word embedding model, AraWord2Vec, based on Wor2Vec trained on Wikipedia and the Sentiment Benchmark dataset. We also examined how Wor2Vec embedding models improved classification tasks. Second, a novel CM_BiLSTM deep learning model called Convolutional Max Pooling and Bidirectional LSTM is presented to enhance Arabic sentiment analysis. Our experimental findings revealed the performance of all models on diverse datasets with an accuracy of 98.47% in binary classification; in multi-classes classification, we obtained a high performance of 98.92%.
Deep recurrent Q-learning for energy-constrained coverage with a mobile robot

In this paper, we study the problem of coverage of an environment with an energy-constrained robot in the presence of multiple charging stations. As the robot’s on-board power supply is limited, it might not have enough energy to cover all the points in the environment with a single charge. Instead, it will need to stop at one or more charging stations to recharge its battery intermittently. The robot cannot violate the energy constraint, i.e., visit a location with negative available energy. To solve this problem, we propose a deep Q-learning framework that produces a policy to maximize the coverage and minimize the budget violations. Our proposed framework also leverages the memory of a recurrent neural network (RNN) to better suit this multi-objective optimization problem. We have tested the presented framework within a $$16 \times 16$$ grid environment having different charging station layouts and various obstacle configurations. Results show that our proposed method finds feasible solutions while performing favorably against two comparable techniques.
Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based Analysis

Hate speech is a challenging problem, and its dissemination can cause potential harm to individuals and society by creating a sense of general unwelcoming to the marginalized groups, which usually are targeted. Therefore, it is essential to understand this issue and which techniques are useful for automatic detection. This paper presents a survey on automatic hate speech detection on social media, providing a structured overview of theoretical aspects and practical resources. Thus, we review different definitions of the term “hate speech” from social network platforms and the scientific community. We also present an overview of the methodologies used for hate speech detection, and we describe the main approaches currently explored in this context, including popular features, datasets, and algorithms. Furthermore, we discuss some challenges and opportunities for better solving this issue.
Transformation of the University in the Age of Artificial Intelligence: Models and Competences

Abstract The article considers the possible concepts of the university of the near future considering the real emergence of special artificial intelligence applicable in the field of higher education. The authors propose a new university model (5th generation university) based on rethinking the use of the concept of competence and applied results in the field of cognitive sciences. It is proposed to revise the existing classical set of professional and social competencies, which will consider the redistribution of responsibilities in society between a person and intellectual systems. The technical readiness of society is assessed to significantly expand the scope of the use of IT technologies and free up human resources for creative activity.
Generative AI Futures: A Speculative Design Exploration

What generative AI futures do we want—and what futures do we not want? To imagine what might exist in the future, we apply speculative design to explore plausible scenarios for generative AI and human coexistence. In this paper, we present gAIrden and Onion AI: two in-progress speculative concepts of future generative AI tools, their use cases, and the systems in which they exist. We analyze the designs through lenses of Environment, Data Privacy, Embodiment, and Play. This trip into the future is driven by the research question: how might generative AI tools change how we produce creativity and culture? When we return to the present, we ask ourselves, how might generative AI support positive outcomes for individuals and communities? Can we predict (and potentially mitigate) negative consequences of generative AI tools? The speculative designs purposefully engage viewers in futures thinking to reclaim conversation around the future of technology.
Explainable AI for the Arts: XAIxArts

This first workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a core concern of Human-Centred AI and relies heavily on HCI techniques to explore how complex and difficult to understand AI models such as deep learning techniques can be made more understandable to people. However, XAI research has primarily focused on work-oriented and task-oriented explanations of AI and there has been little research on XAI for creative domains such as the Arts. This workshop will: i) build an XAIxArts research community; ii) map out the current and future possible landscapes of XAIxArts; iii) critically reflect on the potential of XAI for the Arts, forming the basis for an edited book on XAIxArts and an international network of researchers.
An attention-erasing stripe pyramid network for face forgery detection

Face forgery detection aims to distinguish between real and fake facial images or videos by identifying manipulated or forged visual media. The main challenge in face forgery detection is achieving high model generalization ability, i.e., satisfactory performance under cross-database scenarios where the training and testing datasets are from different forgery methods. To achieve this goal, this paper presents an attention-erasing stripe pyramid network (ASPNet) to utilize high-frequency noises and exploit both the RGB and fine-grained frequency clues. First, since separately extracting features from different scales and granularities will ignore their complementarity, we employ a stripe pyramid block (SPB) to learn multi-scale and multi-granularity features simultaneously. Second, to make the model focus on useful information and suppress noise, a two-stage attention block (TSAB) is introduced by combining spatial attention and channel attention to filter out the pixel-wise and channel-wise noise in the learned feature maps. Finally, to dynamically guide the model to pay attention to different areas of the human face, an attention erasing (AE) scheme is adopted by randomly erasing units in attention maps. Sufficient experiments demonstrate that ASPNet has superior performance than $$F^{3}$$ -Net on the FaceForensics++ dataset. The area under the receiver operating characteristic curve (AUC) and the accuracy (ACC) of our model reach 77.4% and 70.85%, respectively, which are improved by 0.83% and 1.28% compared with $$F^{3}$$ -Net. Our code is available at: https://github.com/NWPU-Zwu .
Aspect-Based Sentiment Analysis for Arabic Food Delivery Reviews

Business customers and consumers share their reviews online on social platforms such as Twitter. Therefore, Twitter data sentiment analysis is extremely useful for both research and commercial purposes. Manually analyzing reviews takes a long time and effort, hence, automatic sentiment analysis is required. In this article, we address aspect-based sentiment analysis for Arabic food delivery reviews using several deep learning approaches. In particular, we propose to use Transformer-based models (GigaBERT and AraBERT), Bi-LSTM-CRF, and LSTM, as well as a classical machine learning algorithm (SVM). We also present our dataset of food delivery service reviews, which we collected from Twitter. We annotated them and used them for training and evaluating our approaches. The experiments show that both GigaBERT and AraBERT outperformed the other models in all the tasks. The Transformer-based models received F1-scores of 77% in the aspect terms detection task, 82% in the Aspect category detection task, and 81% in the aspect polarity detection task, gaining 2%, 4%, and 4% over Bi-LSTM-CRF and LSTM in the first, second, and third tasks, respectively.
YA Author: Embracing the Digital Backchannel

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Research on Intelligent Wheelchair Attitude-Based Adjustment Method Based on Action Intention Recognition

At present, research on intelligent wheelchairs mostly focuses on motion control, while research on attitude-based adjustment is relatively insufficient. The existing methods for adjusting wheelchair posture generally lack collaborative control and good human-machine collaboration. This article proposes an intelligent wheelchair posture-adjustment method based on action intention recognition by studying the relationship between the force changes on the contact surface between the human body and the wheelchair and the action intention. This method is applied to a multi-part adjustable electric wheelchair, which is equipped with multiple force sensors to collect pressure information from various parts of the passenger's body. The upper level of the system converts the pressure data into the form of a pressure distribution map, extracts the shape features using the VIT deep learning model, identifies and classifies them, and ultimately identifies the action intentions of the passengers. Based on different action intentions, the electric actuator is controlled to adjust the wheelchair posture. After testing, this method can effectively collect the body pressure data of passengers, with an accuracy of over 95% for the three common intentions of lying down, sitting up, and standing up. The wheelchair can adjust its posture based on the recognition results. By adjusting the wheelchair posture through this method, users do not need to wear additional equipment and are less affected by the external environment. The target function can be achieved with simple learning, which has good human-machine collaboration and can solve the problem of some people having difficulty adjusting the wheelchair posture independently during wheelchair use.
A semi-independent policies training method with shared representation for heterogeneous multi-agents reinforcement learning

Humans do not learn everything from the scratch but can connect and associate the upcoming information with the exchanged experience and known knowledge. Such an idea can be extended to cooperated multi-reinforcement learning and has achieved its success on homogeneous agents by means of parameter sharing. However, it is difficult to straightforwardly apply parameter sharing when dealing with heterogeneous agents thanks to their individual forms of input/output and their diverse functions and targets. Neuroscience has provided evidence that our brain creates several levels of experience and knowledge-sharing mechanisms that not only exchange similar experiences but also allow for sharing of abstract concepts to handle unfamiliar situations that others have already encountered. Inspired by such a brain's functions, we propose a semi-independent training policy method that can well tackle the conflict between parameter sharing and specialized training for heterogeneous agents. It employs a shared common representation for both observation and action, enabling the integration of various input and output sources. Additionally, a shared latent space is utilized to maintain a balanced relationship between the upstream policy and downstream functions, benefiting each individual agent's target. From the experiments, it can approve that our proposed method outperforms the current mainstream algorithms, especially when handling heterogeneous agents. Empirically, our proposed method can also be improved as a more general and fundamental heterogeneous agents' reinforcement learning structure for curriculum learning and representation transfer. All our code is open and released on https://gitlab.com/reinforcement/ntype.