Reviewerly/exHarmony huggingface dataset:
A Hierarchical Approach to Population Training for Human-AI Collaboration

A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase. This is specifically worsened by an increased variance in action responses when the DRL agents collaborate with human partners due to the lack of consistency in human behaviors. Recent work have shown that training a single agent as the best response to a diverse population of training partners significantly increases an agent's robustness to novel partners. We further enhance the population-based training approach by introducing a Hierarchical Reinforcement Learning (HRL) based method for Human-AI Collaboration. Our agent is able to learn multiple best-response policies as its low-level policy while at the same time, it learns a high-level policy that acts as a manager which allows the agent to dynamically switch between the low-level best-response policies based on its current partner. We demonstrate that our method is able to dynamically adapt to novel partners of different play styles and skill levels in the 2-player collaborative Overcooked game environment. We also conducted a human study in the same environment to test the effectiveness of our method when partnering with real human subjects. Code is available at https://gitlab.com/marvl-hipt/hipt.
Transferable Curricula through Difficulty Conditioned Generators

Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial ``Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in curricula generation focuses on training RL agents efficiently, yet such methods rely on surrogate measures to track student progress, and are not suited for training robots in the real world (or more ambitiously humans). In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents in parameterized environments. Inspired by Item Response Theory, PERM seeks to model difficulty of environments and ability of RL agents directly. Given that RL agents and humans are trained more efficiently under the ``zone of proximal development", our method generates a curriculum by matching the difficulty of an environment to the current ability of the student. In addition, PERM can be trained offline and does not employ non-stationary measures of student ability, making it suitable for transfer between students. We demonstrate PERM's ability to represent the environment parameter space, and training with RL agents with PERM produces a strong performance in deterministic environments. Lastly, we show that our method is transferable between students, without any sacrifice in training quality.
Fast and Differentially Private Fair Clustering

This study presents the first differentially private and fair clustering method, built on the recently proposed density-based fair clustering approach. The method addresses the limitations of fair clustering algorithms that necessitate the use of sensitive personal information during training or inference phases. Two novel solutions, the Gaussian mixture density function and Voronoi cell, are proposed to enhance the method's performance in terms of privacy, fairness, and utility compared to previous methods. The experimental results on both synthetic and real-world data confirm the compatibility of the proposed method with differential privacy, achieving a better fairness-utility trade-off than existing methods when privacy is not considered. Moreover, the proposed method requires significantly less computation time, being at least 3.7 times faster than the state-of-the-art.
Seeing is Behaving? The Role of Social Norms in the Relationship Between Witnessing and Perpetrating Online Hate

AbstractPeople are frequently exposed to hateful content online, as online hate is a globally prevalent communication phenomenon. However, existing studies have mainly emphasized a bystander intervention perspective, focusing on the observers’ role in online hate, despite theories and empirical evidence suggesting that the observation of a particular behavior can lead to the performance of that behavior. Thus, this study examined how witnessing online hate is associated with the perpetration of it, and the mediating role of social norms in this process. Findings from a cross-sectional survey showed that the observation and perpetration of online hate were significantly positively associated, and that perceived prevalence and social approval of online hate separately mediated this relationship. Theoretical and practical implications are discussed.Keywords: Descriptive normsdigital hateinjunctive normsonline hatesocial norms Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 As an illustration, the survey asked, “Although I was not involved, I have seen other people attacking someone because of the person’s gender, such as the use of derogatory terms like ‘gimchinyeo’ and ‘hannam.’” These slangs are deemed offensive in Korea, where the former is used to belittle Korean women who exploit men, while the latter is used to disparage Korean men who display sexist behavior. Instances of age-based hate were demonstrated by the utilization of expressions such as 'teulttak' and 'kkondae,' both of which are employed to demean and ridicule elderly individuals who are considered out of touch with the times and belong to the boomer generation.Additional informationFundingThe work was supported by the Ministry of Education [NRF-2021S1A5C2A02088387]; National Research Foundation of Korea [NRF-2021S1A5C2A02088387].
MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection

Abstract Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning methods have achieved reasonable accuracy at this task. However, they lack practicality for real-time pedestrian lane detection due to non-optimal accuracy, speed, and model size trade-off. Hence, an optimized deep neural network (DNN) for pedestrian lane detection is required. Designing a DNN from scratch is a laborious task that requires significant experience and time. This paper proposes a novel neural architecture search (NAS) algorithm, named MSD-NAS, to automate this laborious task. The proposed method designs an optimized deep network with multi-scale input branches, allowing the derived network to utilize local and global contexts for predictions. The search is also performed in a large and generic space that includes many existing hand-designed network architectures as candidates. To further boost performance, we propose a Short-term Visual Memory mechanism to improve information facilitation within the derived networks. Evaluated on the PLVP3 dataset of 10,000 images, the DNN designed by MSD-NAS achieves state-of-the-art accuracy (0.9781) and mIoU (0.9542), while being 20.16 times faster and 2.56 times smaller than the current best deep learning model.
From Information Extraction to Remaining Data—The Production Mystery of Digital Capitalism

Capitalism has shifted to the stage of digital capitalism with the use of digital technology, bringing new changes such as the spread of digital labor, the prominence of data value, the construction of digital platforms, and the formation of digital capital, but this transformation of capitalism in the digital era is only in the form of peripheral expression, and the nature of the capitalist private system and the pursuit of surplus value have not changed. The way in which capitalism gains profit in the digital era is mainly information extraction, converting self-made information into surplus data to make profit. The self-made information generated by the subject becomes a tool for capital to make profit. In order to promote the healthy development of digital productivity, it is necessary to use technological development for the people, so that digital technology can be used again to meet people’s needs for a better life and promote human liberation.
Integrating Large Language Models into Higher Education: Guidelines for Effective Implementation

The emergence of large language models (LLMs), such as OpenAI's GPT-4, introduces transformative opportunities for higher education across various disciplines.While the integration of LLMs into higher education has sparked significant debate regarding whether to fully incorporate these systems into curricula or restrict their use, this paper contends that there has been an inadequate focus on the process of establishing suitable guidelines for their usage.Given the importance of stakeholder buy in, especially in terms of perceiving the final decision as legitimate, this paper advocates for transparent and inclusive procedures that involve faculty, administration, and students during the integration process.Once a decision is made, clear justifications for LLM guidelines should be provided, paired with an effective implementation strategy, to ensure widespread acceptance and adherence.
New Concept of Information Science

With the development of information science and technology, people’s cognition of information concept is gradually changing. The author puts forward a new concept of information science. It is believed that the universe is composed of two parallel worlds: the material world and the information world. In the material world, there are two main elements: matter and energy. In the information world, there are also two main elements: information and intelligence. To a certain extent, material science, energy science, information science, and intelligence science can be considered as four independent disciplines. This report explains the meaning, basic properties, patterns of change, mutual differences, and relationships of the four major elements of material, energy, information, and intelligence.
Practical Decoy-State Memory-Assisted Measurement-Device-Independent Quantum Key Distribution

The measurement-device-independent quantum key distribution (MDI QKD) can resist all attacks against measurement devices. However, its practical performance is still limited due to very low coincidence counting rates in Bell-state measurements. In this paper, we propose a practical memory-assisted MDI QKD protocol by cooperating the heralded single-photon sources and an all-optical quantum memory. By making use of the heralding characteristic of heralded single-photon sources, the earlier photon will be stored in the Sagnac optical quantum memory, and then released till another photon's arrival. Moreover, by combining with the decoy-state method, the present work exhibits significantly improved key rates and transmission distances compared with all existing MDI QKD protocols without using quantum memories. Therefore, this work can provide valuable references for practical implementation of long-distance quantum communications.
A Hybrid Binary Dragonfly Algorithm with an Adaptive Directed Differential Operator for Feature Selection

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality.
Design and Implementation of Aspect-Based Sentiment Analysis Task

This paper presents the design and implementation of a deep-learning-based aspect-level sentiment analysis model for the aspect term extraction and sentiment polarity classification of Chinese text comments. The model utilizes two layers of BERT models and fully connected neural networks for feature extraction and classification. It also incorporates a context feature dynamic weighting strategy to focus on aspect words and enhance the model’s performance. Experimental results demonstrate that the proposed model performs well in aspect-level sentiment analysis tasks and effectively extracts sentiment information from the text. Additionally, to facilitate user interaction, a lightweight system is built, which enables model invocation and the visualization of analysis results, offering practical value.
Eye-tracking detection of the area of interest in data visualization

This study examines the features of forming in data visualization. To do this, the authors hypothesize that there are special areas of interest on the charts. The user pays attention to them in order to decode data encrypted with graphics. The presence of such areas, and in some cases, points, are intuitively determined during the formation of the design rules of information graphics. To verify them, the study used the eye-tracking method and the cluster analysis method. The application of interdisciplinary principles and rules of infographics design has been studied on the example of horizontal and vertical bar charts, pie, pictorial and flow charts. The result of experiments with various types of charts showed the presence of additional areas of interest not previously indicated by data visualization specialists. This makes it possible to clarify the features of graphic forms and the formation of diagrams, allows you to verify the use of design rules formulated by the efforts of domestic and foreign specialists in the late XIX — early XX centuries. Such verification can be done using a methodology that includes statistical methods and the tracking method, which allows us to take into account the valuable experience of the past in modern information design. The presented procedure can be extended to other types of charts, diagrams and thematic maps, and have practical application in the analysis of big data visualization.
The Effectiveness of a Proposed Model for a Blended Learning Environment Based on Design Thinking in Developing Motivation for Achievement

This study aimed to investigate the effectiveness of a proposed model of a blended learning environment based on design thinking in developing motivation for achievement, and the researchers used the semi-experimental approach as a study methodology by employing design thinking in a blended learning environment and measuring its impact on developing motivation for achievement. The sample consisted of 60 students of the digital technology course 1-3 in the secondary stage, who were divided into two groups with 30 students in each. The study tool was a measure of motivation for achievement. The results of the study revealed that there are statistically significant differences between the experimental group and the control group in the post-measurement in favor of the experimental group. The study came with many recommendations, the most important is the need to integrate technological innovations into the educational process to keep pace with the requirements of the digital age, reorganize course content, and prepare educational tasks that suit the stages of design thinking to help students perform their tasks.
Epilogue

A book on predictive policingPredictive policing cannot have a conclusion. We seem to have just started with AI systems less than a decade ago and already we are in the thick of it. There are talks of AI taking over the world. Predictive policingPredictive policing itself has produced a number of iterations since the fieldwork for this book was conducted in 2017–2019. I hope that this book is not just a study of policingPolicing but answers questions for a larger study of AI systems as well. This book needs to make aware of the assumptions we carry within ourselves about AI and in general about machines. One of the goals of the book is to make it clear that machines are very much situated in the social and it is the social from where they draw their energy.
Quantum Computer on Nvidia GPU

Geometric Algebra formalism opens the door to developing a theory deeper than conventional quantum mechanics.Generalizations, stemming from implementation of complex numbers as geometrically feasible objects in three dimensions, unambiguous definition of states, observables, measurements, Maxwell equations solution in those terms, bring into reality a kind of physical fields spreading through the whole three-dimensional space and values of the time parameter.The fields can be modified instantly in all points of space and time values, thus eliminating the concept of cause and effect, and perceiving of one-directional time.In the suggested theory all measured observable values get available all together, not through looking one by one.In this way quantum computer appeared to be a kind of analog computer keeping and instantly processing information by and on sets of objects possessing an infinite number of degrees of freedom.As practical implementation, the multithread GPUs bearing the CUDA language functionality allow to simultaneously calculate observable measurement values at a number of space/time discrete points only restricted by the GPU threads capacity.
Development of an information system for organizing and managing the educational process based on smart technologies

This article discusses the concept of business processes that are widely used in the educational process -the structure of the learning process management system (SMART LMS) and the implementation of part of the system in SMART LMS. When developing an LMS information system, it is necessary to formulate the concepts and principles of the learning process management system. The LMS concept demonstrates the strategic solutions that need to be addressed in higher education. Through the principles of LMS, the concept of LMS is defined using modern educational technologies. Through SMART LMS, all business processes in higher education activities are carried out transparently and SMART LMS has become a decision-making tool. A single management system is formed through the principle of mutual integration of LMS integrated systems. According to this principle, additional and other management systems of the university information environment should be integrated with existing systems and mutual information exchange should be organized.
Geological data for mineral exploration

Geological data provide information necessary for discovering mineral resources. Geological mapping is deemed a prerequisite for most exploratory surveys and constitutes many stages, including observing lithological and structural features in the field and recording these features on a map. The information collected in the field must be factual and thorough, gathered by many methods in traversing. In addition, since a preliminary knowledge of structural mapping, petrography, mineralogy, and stratigraphy is an asset for geological mapping, these issues will be thoroughly covered in this chapter.
Application of the project method using modern IT technologies

This article is devoted to the application of project methodology using IT technologies in the process of studying the subject “Foreign language” and “Foreign language in professional sphere” in the conditions of professional education among students of non-language high school. The article also analyzes the project methodology used as one of the best methods of teaching a foreign language. The article deals with the main theoretical and practical foundations of using project methodology in the educational process. Particular attention is paid to the consideration of the working phases of the project, as well as shown methodological techniques used by the teacher at different stages of the project. Besides, the article has covered the results of the experiment held by the “Foreign language in the sphere of Engineering and Technologies” department among the students of the Faculty of Industrial and Civil Engineering of the Don State Technical University, in connection with which the conclusion is made that the project method application has a considerable influence on the increase of the level of motivation of students to learn a foreign language and the effectiveness of teaching.
The pipeline processing of NLP

The problem of NLP should be divided into several small parts and solved step by step. In this article, where NLP is necessary at every stage of solving the problem, all forms of text processing are considered. The step-by-step text processing is called a pipeline process in NLP. When creating any NLP model, the pipeline process is a sequence of steps that must be carried out. The planning and development of the text processing is considered as the starting point for the creation of any NLP project. This article discusses the steps involved in implementing a pipeline process and their role in solving NLP tasks. This article analyzed the most common preliminary processing steps on the NLP processing pipeline. All processing stages are pre-trained in various NLP libraries, identified as usable models. If necessary, additional, modified preprocessing steps can be developed depending on the given problem condition. One can determine how a particular initial processing stage serves a given NLP problem by many experimentations.
Machine learning for analysis of geo-exploration data

This chapter covers the basic concepts of applying artificial intelligence, including machine learning and deep learning, in the geospatial toolbox of mineral exploration. We will use examples of using open-access tools, such as libraries available for Python and R, compatible with publically available software packages such as QGIS for addressing the mineral exploration problems.
Exploration and practice of teaching methods of basic specialized courses for postgraduate majors based on the characteristics of “Three Teaching Processes”

The ability of engineering graduate students to use their knowledge and independently engage in scientific research is one of the important indicators to measure the quality of postgraduate training. This paper takes the comprehensive improvement of graduate students’ ability to engage in scientific research independently as the starting point, and improves the teaching quality of basic courses of graduate majors in an all-round way by constructing teaching methods of basic knowledge intensive teaching, knowledge application and case process teaching, so that students can deeply grasp the course knowledge system and flexibly apply the knowledge learned in the course learning process. Finally, it lays a good foundation for students to engage in scientific research independently.