Reviewerly/exHarmony huggingface dataset:
Parametric Model Manipulation in Virtual Reality

Algorithmic design (AD) uses algorithms to describe architectural designs, producing results that are visual by nature and greatly benefit from immersive visualization.Having this in mind, several approaches have been developed that allow architects to access and change their AD programs in virtual reality (VR).However, programming in VR introduces a new level of complexity that hinders creative exploration.Solutions based in visual programming offer limited parameter manipulation and do not scale well, particularly when used in a remote collaboration environment, while those based in textual programming struggle to find adequate interaction mechanisms to efficiently modify existing programs in VR.This research proposes to ease the programming task for architects who wish to develop and experiment with collaborative textual-based AD in VR, by bringing together the user-friendly features of visual programming and the flexibility and scalability of textual programming.We introduce an interface for the most common parametric changes that automatically generates the corresponding code in the AD program, and a hybrid programming solution that allows participants in an immersive collaborative design experience to combine textual programming with this new visual alternative for the parametric manipulation of the design.The proposed workflow aims to foster remote collaborative work in architecture studios, offering professionals of different backgrounds the opportunity to parametrically interact with textual-based AD projects while immersed in them.
Phased Feature Extraction Network for Vehicle Search Tasks Based on Cross-Camera for Vehicle–Road Collaborative Perception

The objective of vehicle search is to locate and identify vehicles in uncropped, real-world images, which is the combination of two tasks: vehicle detection and re-identification (Re-ID). As an emerging research topic, vehicle search plays a significant role in the perception of cooperative autonomous vehicles and road driving in the distant future and has become a trend in the future development of intelligent driving. However, there is no suitable dataset for this study. The Tsinghua University DAIR-V2X dataset is utilized to create the first cross-camera vehicle search dataset, DAIR-V2XSearch, which combines the cameras at both ends of the vehicle and the road in real-world scenes. The primary purpose of the current search network is to address the pedestrian issue. Due to varying task scenarios, it is necessary to re-establish the network in order to resolve the problem of vast differences in different perspectives caused by vehicle searches. A phased feature extraction network (PFE-Net) is proposed as a solution to the cross-camera vehicle search problem. Initially, the anchor-free YOLOX framework is selected as the backbone network, which not only improves the network’s performance but also eliminates the fuzzy situation in which multiple anchor boxes correspond to a single vehicle ID in the Re-ID branch. Second, for the vehicle Re-ID branch, a camera grouping module is proposed to effectively address issues such as sudden changes in perspective and disparities in shooting under different cameras. Finally, a cross-level feature fusion module is designed to enhance the model’s ability to extract subtle vehicle features and the Re-ID’s precision. Experiments demonstrate that our proposed PFE-Net achieves the highest precision in the DAIR-V2XSearch dataset.
<scp>Chinese‐Tibetan</scp> Bilingual Knowledge Organization in the Cultural Heritage Domain: A Practice for Traditional Tibetan Festivals

ABSTRACT The trend of multilingualism in the web environment has put new demands on the digitization of cultural heritage. However, minority languages in cultural heritage have received little attention in China. This study selects traditional Tibetan festivals, a representative minority cultural heritage in China. Based on constructing a Chinese‐Tibetan bilingual ontology, the cultural and tourism data of Tibet are linked. By further constructing a knowledge graph, a Chinese‐Tibetan Bilingual Tibetan cultural service platform with traditional Tibetan festivals as the core is established.
Improving Cardiology-Rehospitalization Prediction Through the Synergy of Process Mining and Deep Learning: An Innovative Approach

Nowadays, hospitals are facing the need for an accurate prediction of rehospitalizations. Rehospitalizations, indeed, represent both a high financial burden for the hospital and a proxy measure of care quality. The current work aims to address such a problem with an innovative approach, by building a Process Mining-Deep Learning model for the prediction of 6-months rehospitalization of patients hospitalized in a Cardiology specialty at San Raffaele Hospital, starting from their medical history contained in the Patients Hospital Records, with the double purpose of supporting resource planning and identifying at-risk patients.
Enhancing Speaking Skill By Utilizing Podcast Material for Teenager Learners

Speaking is a form of practicing how to communicate ideas. The students can convey their intentions by speaking. Many high school students struggled to articulate their thoughts. Their low vocabulary, pronunciation, and grammar skills are what cause it. The teacher employed podcast resources in the teaching-learning process to address these issues. The instructor went over the basic grammar to put it into practice. In order to make it stick in their minds, the teacher instructed the student to look for the corresponding grammar in the transcript. Then, in order to expand their vocabulary, the student translate it. To improve the students' listening abilities, an audio recording was employed. The transcript was then read aloud. The students' newly acquired information is put to use in the text and audio descriptions of the images. The students took a pre-test before beginning this treatment, and they finished a post-test at the conclusion of it. A mixed-method approach was used to analyze the data that was found. It may be inferred that using podcast content improved the students' speaking proficiency. This kind of research can be used byteachers to teach relevant subjects. It also motivates other scholars to conduct deeper investigation.
Korea Armed Forces Nursing Academy Cadets' Experience with Virtual Reality(VR) Core Nursing Skills Training Program

Purpose: This study was conducted to describe the lived experience of the VR core nursing skills training program from the learner's perspective among cadets in Korea Armed Forces Nursing Academy. Methods: The VR training program using a head-mounted device and hand motion tracking technology. Twenty cadets from third- and fourth-year were asked to use the program and respond to semi-structured questions using a reflection journal. The data were analyzed using content analysis techniques. Results: Among 339 statements, 249 meaningful sentences were derived by integrating similar contents. Seven categories of 'Experiencing immersion and fun with new technology', 'Considering the training as thinking practice, not memorization procedure', 'Feeling psychological comfort from virtual reality', 'Feeling physical effects of wearing equipment', 'Practicing skills repetitively without additional cost', 'Experiencing limits of untouchable virtual reality technology', and 'Expecting the future progress' were identified. Conclusion: The VR core nursing skills training program enables independent practice at the learner's own pace and is useful for learning step-by-step theoretical evidence. It is necessary to develop a core nursing skills training program in the future which may complement the advantages and disadvantages of current VR training nursing programs.
EFL Teachers’ English Language Use During the Pandemics in the Indonesian Senior High School Context

In response to the significant upheaval caused by the global pandemic, education systems worldwide have shifted from traditional classroom settings to remote teaching methods. This drastic transition has presented numerous challenges, particularly in the realm of English as a Foreign Language (EFL) education in non-English speaking countries, such as Indonesia. This study embarks on an in-depth exploration of how the teaching methodologies and language functions utilized by EFL teachers at an Indonesian senior high school, MAN 2 Parepare, have adapted to these new learning environments. The research adopts a descriptive qualitative research design, providing a comprehensive view of the phenomena under investigation. The in-depth investigation focuses on the practices of two English teachers at MAN 2 Parepare, analyzing the way they employ different language functions during their online classes. The analysis seeks to elucidate the interaction between instructional language and the online learning environment, how it affects student comprehension, and the potential for instructional refinement. Findings indicate a broad range of language functions used by both teachers in their instructional strategies, demonstrating the adaptive potential of EFL instruction under the pressures of pandemic-induced remote teaching. Notably, the study reveals that the use of students' native language and the target language - English - plays a crucial role in aiding students' comprehension, highlighting the importance of maintaining a balance between the two. In sum, this paper offers valuable insights into the language function dynamics at play in online EFL classrooms during the pandemic. The findings have implications for informing instructional best practices and contribute to the broader understanding of language education in a rapidly changing global context.
Source Engineering for Quantum Key Distribution with Noisy Photon-Added Squeezed States

Quantum key distribution (QKD) is a key enabler toward unconditionally secure communications. The imperfections exhibited by non-ideal sources degrade the QKD performance. This raises the problem of engineering the employed quantum states to mitigate the impairments caused by such imperfections. This paper proposes to employ noisy photon-added squeezed states (PASSs) as QKD sources for the decoy-state protocol. First, noisy PASSs are characterized in the Fock space. Then, noisy PASSs are engineered for the decoy-state protocol. Finally, the performance of the decoy-state protocol with engineered noisy PASSs are quantified in a variety of settings.
An Autonomous Player Agent for Game Balance Insight on an Educational Video Game

This paper explores the use of Reinforcement Learning (RL) to address the challenge of game balance in Educational Video Games (EVG). We adapt Eco City, a city management simulator EVG where the player enacts city policies to maintain the city for as long as possible, as the environment for study. Our RL agent, based on a Deep Q-Network (DQN), is trained to maximize its duration of survival. We analyze the behavior patterns of the agent during both training and evaluation to gain insights into the game dynamics and player strategies. The results show that the agent outperforms random play and exhibits a dominant strategy that effectively ignores half of the possible actions in the game. The analysis of the agent's behavior provides valuable information for game balancing efforts, such as adjusting policy effects and introducing new policies. We demonstrate the potential and the implications of our RL approach to augment the game balance process for developers, particularly in the educational gaming domain.
FBA-SDN: A Federated Byzantine Approach for Blockchain-Based Collaborative Intrusion Detection in Edge SDN

This paper presents FBA-SDN, a novel Stellar Consensus Protocol (SCP)-based Federated Byzantine Agreement System (FBAS) approach to trustworthy Collaborative Intrusion Detection (CIDS) in Software-Defined Network (SDN) environments. The proposed approach employs the robustness of Byzantine Fault Tolerance (BFT) consensus mechanisms and the decentralized nature of blockchain ledgers to coordinate the Intrusion Detection System (IDS) operation securely. The federated architecture adopted in FBA-SDN facilitates collaborative analysis of low-confidence alert data, reaching system-wide consensus on potential intrusions. Additionally, the Quorum-based nature of the approach reduces the risk of a single point of failure (SPoF) while simultaneously improving upon the scalability offered by existing blockchain-based approaches. Through simulation, we demonstrate promising results concerning the efficacy of reaching rapid and reliable consensus on both binary and multi-class simulated intrusion data compared with the existing approaches.
Mobile E-Health On-Demand Knowledge Sharing with Copyright Protection: Joint Blockchain and NFT Approach

Nowadays, empowered by mobile networks, electronic health (E-health) can bring more advanced medical services. Electronic health records (EHRs) with rich medical knowledge have gained increasing popularity for supporting E-health. Blockchain-based secure storage and access are novel trends for EHRs, but there are still following unresolved problems in this area. On one hand, specific medical tasks (e.g., COVID-19 diagnosis) require corresponding EHRs, but it lacks the medicine knowledge graph to help doctors or researchers find the EHRs on-demand in the massive distributed and encrypted medical data. On the other hand, as the documents include knowledge, EHRs with digital copyrights shared by medical institutions or patients need to be protected during sharing. To address these challenges, this paper proposes a blockchain and Non-Fungible Token (NFT) empowered on-demand medicine knowledge sharing architecture for EHRs. First, we propose a medicine knowledge graph construction scheme based on smart contracts and medical task knowledge relationships. Second, to provide on-demand EHRs sharing, we design the EHRs matching and clustering algorithms, regarding the dynamic importance and similarity of graph nodes. Third, we establish the interplanetary file address driven NFT minting mechanism for EHRs to protect digital copyrights. Finally, we conduct experiments in Ethereum using real medical datasets, which demonstrates the feasibility and efficiency of the proposed architecture. To our best knowledge, this work is the first to realize the medicine knowledge graph with copyright protection for EHRs.
Exquisite Feature Selection for Machine Learning Powered Probing Attack Detection

Network attacks have been intensively studied by recent research. Probing attacks, however, seem not receiving as much attention as others, because they do not explicitly impact the network operations. Nevertheless, probing attacks may monitor network behaviors, extract web-sensitive information, and gather topology information of a target network, which opens a door for other attacks. It is critically important to understand the traffic patterns of network probing attacks and prevent suspicious probing activities from attackers. In this work, we present a novel user selection tool to build the optimal feature set that can characterize probing attacks. It consists of three modules: 1) feature correlation analyzer to remove highly correlated features for training efficiency; 2) coarse-grain feature selection to select key features that can describe the traffic patterns of probing attacks; 3) fine-grain feature refinement to understand temporal/spatial correlations among multiple packets to further improve the detection rate. In addition, we propose a fast hybrid training architecture that allows simultaneous training for both feature selection and attack detection to improve the overall training efficiency. In the experiment, we build a real-world network testbed to validate our design. The results show that the detection model can achieve a detection rate of up to 99.74% with the proposed fine-grain feature selection tool.
Deep Extreme Learning Machine With its Application to Body-Conducted-Sound-Based Handwork Recognition

Technology that can digitalize handwork in detail in real time should be developed to introduce collaborative robots, improve production efficiency, and take over skilled manufacturing and maintenance work in factories. For this purpose, we focus on body-conducted sounds because they are robust to visual occlusions and surrounding noise interference, can be acquired using wrist sensors, and may help recognize hand gestures as well as hand-contact objects. In this paper, we propose a novel modeling method for handwork recognition using body-conducted sounds. This method adopts deep residual learning with dilated causal convolution extreme learning machines (DRLDCC-ELM). The DRLDCC-ELM was compared with a transformer baseline model to identify 13 types of handwork. The experimental results confirmed that DRLDCC-ELM outperformed the transformer baseline model and stably obtained models with almost the same F1 scores despite the fact that the training dataset was not large.
g-BERT: Enabling Green BERT Deployment on FPGA via Hardware-Aware Hybrid Pruning

Transformer-based models suffer from large num-ber of parameters and high inference latency, whose deployment are not green due to the potential environmental damage caused by high inference energy consumption. In addition, it is difficult to deploy such models on devices, especially on resource constrained devices such as FPGA. Various model pruning methods are proposed to shrink the model size and resource consumption, so as to fit the models on hardware. However, such methods often introduce floating point of operations (FLOPs) as an agent of hardware performance, which is not accurate. Furthermore, structural pruning methods are always in a single head-wise or layer-wise pattern, which fails to compress the models to the extreme. To resolve the above issues, we propose a green BERT deployment method on FPGA via hardware-aware and hybrid pruning, named g-BERT. Specifically, two hardware-aware metrics are introduced by High Level Synthesis (HLS) to evaluate the latency and power consumption of inference on FPGA, which can be optimized directly while pruning. Moreover, we simultaneously consider pruning of heads and full encoder layers. To efficiently find the optimal structure, g-BERT applies differentiable neural architecture search (NAS) with a special 0–1 loss function. Compared with the BERT-base, g-BERT achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.1\times$</tex> speedup, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.9\times$</tex> power consumption reduction and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.8\times$</tex> model size reduction with comparable accuracy, on par with the state-of-the-art methods.
Edge Learning as a Hedonic Game in LoRaWAN

Federated learning provides access to more data which is paramount for constrained LoRaWAN devices with limited memory storage. Learning on a larger data set will reduce the variance of the learned model, hence reducing its error. However, federating the learning process incurs a communication cost among learning devices that must be taken into account. In this paper, we formulate a Cooperative Hedonic game and introduce a new cost function that captures both the learning error and communication cost. LoRaWAN devices engage in the devised game by identifying if they should keep their learning local or federate with other devices in order to reduce both their learning error and communication cost. We compute the optimal size of formed coalitions and assess their stability. Then, we show through extensive simulations that devices have incentive to form learning coalitions depending on the data characteristics at hand and the communication cost in LoRaWAN.
Basic human requirements of physical and virtual spaces and their implications

This chapter begins with an elaboration of the basic human requirements of physical space, fixity, and mobility, and the human needs that they satiate. This discussion will be followed by assessments of the relationships between fixity and mobility. We then will move to a similar interpretation of virtual space, attempting to expose the two basic human requirements of virtual space, namely access and connectivity, as well as the relationships between these two basic requirements. These expositions will be followed by an evaluation of the relationships between the human requirements of physical and virtual spaces, and their implications for human activity. Finally, a joint interpretation of human spatial requirements and human activities will be suggested, through the notion of human spatial pulses.
Proximity, interaction, and distance in physical and virtual personal mobilities

Social-spatial interaction constitutes basic human mobility. During modern pre-Internet times, the ability to maintain instant and online remote social ties was restricted by the petrifying powers of space and distance, both being unchallenged at the time by communications technologies, other than the rather limited fixed-line telephony, as of the twentieth century. Thus, it was for face-to-face communications to constitute the leading mode of social communication at the time, implying fewer albeit relatively rich exchanges. The introduction and adoption of the Internet has enabled individuals to enjoy the optionality of distance in the sense that the Internet has permitted the establishment of close and remote virtual ties with the same ease. However, individuals have still tended to establish and maintain spatially closer ties over remote ones, alongside the use of virtual communications. Hence, they revealed their continued preference for proximity to each other. The human need for physical proximity was obviously there already during pre-Internet times, as well, but it was hidden by the inexistence at the time of the alternative option of virtual communications, which also facilitate remote social ties.
Revisiting the Boomerang Attack from a Perspective of 3-differential

In this paper, inspired by the work of Beyne and Rijmen at CRYPTO 2022, we explore the accurate probability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -differential in the fixed-key model. The theoretical foundations of our method are based on a special matrix - quasi- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -differential transition matrix, which is a natural extension of the quasidifferential transition matrix. The role of quasi- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -differential transition matrices in polytopic cryptananlysis is analogous to that of correlation matrices in linear cryptanalysis. Therefore, the fixed-key probability of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -differential can be exactly expressed as the sum of the correlations of its quasi- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -differential trails. Then we revisit the boomerang attack from a perspective of 3-differential. Different from previous works, the probability of a boomerang distinguisher can be exactly expressed as the sum of the correlations of its quasi-3-differential trails without any assumptions in our work. In order to illustrate our theory, we apply it to the lightweight block cipher GIFT. It is interesting to find the probability of every optimal 3-differential characteristic of an existing 2-round boomerang is zero, which can be seen as an evidence that the security of block ciphers adopting half-round key XOR might be overestimated previously to some extent in differential-like attacks.