Reviewerly/exHarmony huggingface dataset:
Innovating the humanities, reimagining the future: empirical evidence from virtual reality practice in China

Authors:
raw_author_name: Yu Shan
raw_affiliation_string: School of Humanities, Tongji University, Shanghai, China

Abstract: The influence of digital technologies on culture and the liberal arts is a global phenomenon. To contribute a non-Western perspective to such debates, this article considers the uptake of virtual reality technology in Chinese humanities education via the policy lens of a higher education reform called New Humanities Education, which was issued by the Ministry of Education of the People’s Republic of China in 2018. The paper draws on the concept of a ‘techno-cultural imaginary’ to show how the humanities and STEM fields have merged and have contributed to the task of national rejuvenation in the past decade. Drawing on interviews with virtual reality practitioners in universities and industry, the results indicate two ideological shifts in humanities education in China: The first is a shift from praising scholar-officials (shi) to praising academic entrepreneurs; and the second is from valuing knowledge to valuing power. The article argues that those being cultivated and nurtured in the contemporary Chinese humanities education system are expected to become pragmatic problem-solvers rather than critical thinkers.
Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

Authors:
raw_author_name: Maksym Diachuk
raw_affiliation_string: Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B2K3, Canada
raw_author_name: Said M. Easa
raw_affiliation_string: Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B2K3, Canada

Abstract: The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian N-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE’s nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line’s tangent. The trajectory shape, then, depends only on the final position of the AV’s mass center and the final reference’s curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated.
A Comparison of Head Movement Classification Methods

Authors:
raw_author_name: Chloe Callahan-Flintoft
raw_affiliation_string: U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory, Aberdeen, MD 21005, USA
raw_author_name: Emily Jensen
raw_affiliation_string: Department of Computer Science, University of Colorado Boulder, Boulder, CO 80303, USA
raw_author_name: Anna M. Madison
raw_affiliation_string: U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory, Aberdeen, MD 21005, USA; Warfighter Effectiveness Research Center, United States Air Force Academy, Colorado Springs, CO 80840, USA
raw_author_name: Anthony J. Ries
raw_affiliation_string: U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory, Aberdeen, MD 21005, USA; Warfighter Effectiveness Research Center, United States Air Force Academy, Colorado Springs, CO 80840, USA

Abstract: To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed. One such data stream is that of head position and rotation tracking, now made easily available from head-mounted systems. The current study presents five candidate algorithms of varying complexity for classifying head movements. Each algorithm is compared against human rater classifications and graded based on the overall agreement as well as biases in metrics such as movement onset/offset time and movement amplitude. Finally, we conclude this article by offering recommendations for the best practices and considerations for VR researchers looking to incorporate head movement analysis in their future studies.
Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Authors:
raw_author_name: Saeid Homayouni
raw_affiliation_string: Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada
raw_author_name: Amin Beiranvand Pour
raw_affiliation_string: Institute of Oceanography and Environment (INOS), Higher Institution Center of Excellence (HICoE) in Marine Science, Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia
raw_author_name: Abbas Bahroudi
raw_affiliation_string: School of Mining Engineering, College of Engineering, University of Tehran, Tehran 1417935840, Iran

Abstract: Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.
Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor

Authors:
raw_author_name: Yunyoung Nam
raw_affiliation_string: Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea

Abstract: The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.
Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

Authors:
raw_author_name: Pravesh Kumar
raw_affiliation_string: ASH (Mathematics) Department, REC Bijnor, Chandpur 246725, UP, India
raw_author_name: Musrrat Ali
raw_affiliation_string: Department of Basic Sciences, Preparatory Year, King Faisal University, Al Ahsa 31982, Saudi Arabia

Abstract: The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as "Improved DE with Best and Worst positions (IDEBW)", offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.
Hybrid Quantum-Classical Search Algorithms

Authors:
raw_author_name: Ansis Rosmanis
raw_affiliation_string: Graduate School of Mathematics, Nagoya University, Nagoya, Japan

Abstract: Search is one of the most commonly used primitives in quantum algorithm design. It is known that quadratic speedups provided by Grover’s algorithm are optimal, and no faster quantum algorithms for Search exist. While it is known that at least some quantum computation is required to achieve these speedups, the existing bounds do not rule out the possibility of an equally fast hybrid quantum-classical algorithm where most of the computation is classical. In this work, we study such hybrid algorithms and we show that classical computation, unless it by itself can solve the Search problem, cannot assist quantum computation. In addition, we generalize this result to algorithms with subconstant success probabilities.
GNNCL: A Graph Neural Network Recommendation Model Based on Contrastive Learning

Authors:
raw_author_name: Jinguang Chen
raw_affiliation_string: School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China
raw_author_name: Jiahe Zhou
raw_affiliation_string: School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China
raw_author_name: Lili Ma
raw_affiliation_string: School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China

Abstract: Abstract In the field of recommendation algorithms, the representation learning for users and items has evolved from using single IDs or historical interactions to utilizing higher-order neighbors. This can be achieved by modeling the user–item interaction graph to capture user preferences for items. Despite the promising results achieved by these algorithms, they still suffer from the issue of data sparsity. In order to mitigate the impact of data sparsity, contrastive learning has been adopted in graph collaborative filtering to enhance performance. However, current recommendation algorithms using contrastive learning yield uneven representations after data augmentation and do not consider the potential relationships among users (or items). To address these challenges, we propose a graph neural network-based recommendation model that integrates contrastive learning (GNNCL). This model combines data augmentation with added noise and the exploration of semantic neighbors for nodes. For the structural neighbors on the interaction graph, we introduce a novel and straightforward contrastive learning approach, abandoning previous graph augmentation methods, and introducing uniform noise into the embedding space to create contrastive views. To unearth potential semantic neighbor relationships in the semantic space, we assume that users with similar representations possess semantic neighbor relationships and merge these semantic neighbors into the prototype contrastive learning. We utilize a clustering algorithm to obtain prototypes for users and items and employ the EM algorithm for prototype contrastive learning. Experimental results validate the effectiveness of our approach. Particularly, on the Yelp2018 and Amazon-book datasets, our method exhibits significant performance improvements compared to basic graph collaborative filtering models.
A Modular Framework for Domain-Specific Conversational Systems Powered by Never-Ending Learning

Authors:
raw_author_name: Felipe Coelho de Abreu Pinna
raw_affiliation_string: Polytechnic School (EPUSP), Universidade de São Paulo, São Paulo 05508-010, Brazil
raw_author_name: Victor Takashi Hayashi
raw_affiliation_string: Polytechnic School (EPUSP), Universidade de São Paulo, São Paulo 05508-010, Brazil
raw_author_name: João Carlos Néto
raw_affiliation_string: Polytechnic School (EPUSP), Universidade de São Paulo, São Paulo 05508-010, Brazil
raw_author_name: Rosangela de Fátima Pereira Marquesone
raw_affiliation_string: Polytechnic School (EPUSP), Universidade de São Paulo, São Paulo 05508-010, Brazil
raw_author_name: Wilson Vicente Ruggiero
raw_affiliation_string: Polytechnic School (EPUSP), Universidade de São Paulo, São Paulo 05508-010, Brazil

Abstract: Complex and long interactions (e.g., a change of topic during a conversation) justify the use of dialog systems to develop task-oriented chatbots and intelligent virtual assistants. The development of dialog systems requires considerable effort and takes more time to deliver when compared to regular BotBuilder tools because of time-consuming tasks such as training machine learning models and low module reusability. We propose a framework for building scalable dialog systems for specific domains using the semi-automatic methods of corpus, ontology, and code development. By separating the dialog application logic from domain knowledge in the form of an ontology, we were able to create a dialog system for the banking domain in the Portuguese language and quickly change the domain of the conversation by changing the ontology. Moreover, by using the principles of never-ending learning, unsupported operations or unanswered questions create triggers for system knowledge demand that can be gathered from external sources and added to the ontology, augmenting the system’s ability to respond to more questions over time.
Unmasking the Power of Play Through TUI Designs

Authors:
raw_author_name: A. Nonnis
raw_affiliation_string: University of Arts London, London, UK
raw_author_name: N. Bryan-Kinns
raw_affiliation_string: University of Arts London, London, UK

Abstract: Research on the potential benefits of technology for autistic children is an emergent field in Human-Computer Interaction (HCI), especially within the Child-Computer Interaction Community. At the same time, there are concerns about what these interventions and technologies are for and who benefits. We present a research and design approach for Tangible User Interfaces (TUIs) for minimally verbal to nonverbal autistic children following a neurodiversity narrative through three field studies developed and evaluated with three groups of children within a semi-structured scholastic environment between 2018 and 2021 in the UK. We discuss our insights for research and TUI designs in the context of social play for nonverbal autistic children and critically reflect on the methods and approaches we used. We do this to disrupt the normalisation agenda that subtly permeates the field of HCI and to direct designers’ attention toward supporting autistic ways of being in the world.
The aesthetics of deconstruction: neural synthesis of transformation matrices using GANs on multichannel polyphonic MIDI data

Authors:
raw_author_name: Philon Nguyen
raw_affiliation_string: Department of Music, Faculty of Fine Arts, Concordia University, Montreal, QC, Canada
raw_author_name: Eldad Tsabary
raw_affiliation_string: Department of Music, Faculty of Fine Arts, Concordia University, Montreal, QC, Canada

Abstract: Deconstruction, a concept that originated from French philosopher Jacques Derrida's post-structuralist theories, has significantly influenced architecture and graphic design. It has evolved into a rhizomatic network of styles and concepts ranging from new architectural geometry to AI. Despite its limited explicit application in music, this paper explores deconstruction within musical aesthetics, especially in the post-1945 serial school. Computer-Aided Design (CAD) methods have become an important part in the deconstructivist architectural design process; similarly, in music, Computer-Aided Composition (CAC) techniques have become important from generative algorithms to AI techniques. In graphic design, such systems (i.e. GANs, VAEs, CNNs, RNNs, Stable Diffusion, Transformers, etc.) have been experimented with. Google's Imagen or OpenAI's DALL-E 2, which automatically generate images from text prompts given by users, use, for example, diffusion models. Also, Google LM is a music equivalent to these. This paper delves into the novel aesthetics brought about by the application of GANs on multichannel polyphonic MIDI data in music, discussing the conceptual grounding of such usage and overcoming the challenges associated with it. Effectively, using our concept of distance matrices rather than, for example, Hidden Markov Models, is more suitable for GAN generation.
ReFID: Reciprocal Frequency-aware Generalizable Person Re-identification via Decomposition and Filtering

Authors:
raw_author_name: Jinjia Peng
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, China
raw_author_name: Pengpeng Song
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, China
raw_author_name: Hui Li
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, China
raw_author_name: Huibing Wang
raw_affiliation_string: College of Information Science and Technology, Dalian Maritime University, Dalian, China

Abstract: Domain generalization (DG) of person re-identification (ReID) aims to conduct testing across domains that have not been previously encountered, without utilizing target domain data during the training stage. As the number of source domains increases, the relationships between training samples become more complex. This can lead to domain-invariant features that include certain instance-level spurious correlations, which can impact the model’s ability to generalize further. To overcome this limitation, the Reciprocal Frequency-aware Generalizable Person Re-identification (ReFID) method is proposed in this paper, which aims to utilize spectral feature correlation learning to transmit frequency component information and generate more discriminative hybrid features. A module called Bilateral Frequency Component-guided Attention (BFCA) is developed to help the network understand high-level semantic and texture information from various frequency features. Furthermore, to reduce the impact of noise from the frequency domain, this paper proposes an innovative module called Fourier Noise Masquerade Filtering (FNMF). This module enhances the portability of frequency domain components while simultaneously suppressing elements that do not contribute to generalization. Extensive experimental results on various datasets demonstrate that our method is effective and superior to the state-of-the-art methods.
Origins, Theoretical Foundations, and Economic Implications of Cryptocurrencies 

Authors:
raw_author_name: Jérôme Verny
raw_author_name: Eric Lambourdière

Abstract: The theoretical foundations and indeed implications related to the emergence of digital assets, particularly cryptocurrencies, remain a scarcely explored area in recent academic literature. This chapter aims to analyze the presumed influences of various economic schools of thought on the genesis of Bitcoin, the monetary policies underlying several digital assets, and the evolution of their market capitalization since 2009. The study also addresses the case of several cryptocurrency exchange platforms, commonly known as crypto exchanges, and focuses on industrial entities, such as Ledger and Bitmain. An overview of use cases involving “smart contracts” and Blockchain technology is provided. This chapter seeks to revisit the emergence of different approaches to national regulation on a global scale and to establish international comparisons. The examples of Japan, long trapped in a deflationary spiral, and Argentina, facing hyperinflation, are particularly enlightening for understanding the legal treatment of digital assets and their perception by local authorities. Two central questions guide this exploration: what are the economic and ideological foundations underlying the conception of Bitcoin, and to what extent does the current development of the digital asset ecosystem remain true to the initial ideas of Satoshi Nakamoto?