Reviewerly/exHarmony huggingface dataset:
Secure Remote Attestation with Strong Key Insulation Guarantees

Secure processors with hardware-enforced isolation are crucial for secure cloud computation. However, commercial secure processors have underestimated the capabilities of attackers and failed to provide secure execution environments capable of protecting sensitive information against side-channel attacks. Remote Attestation protocols based on traditional signature schemes are not secure under side-channel attacks anymore since their secret keys can be leaked. Previously, Key-Insulated Schemes (KIS) have been introduced to mitigate the damage caused by secret key exposure in cryptosystems by breaking the lifetime of secret keys into independent sessions. KIS protect the security of all other sessions if any session keys are compromised, however, provide no security guarantees for a compromised session. We introduce a new cryptographic primitive called One-Time Signature with Secret Key Exposure (OTS-SKE), which ensures no one can forge a valid signature of a new message or nonce even if all secret session keys are leaked. OTS-SKE enables us to sign attestation reports securely under a powerful adversary who can observe all digital states in secure enclaves through side-channel attacks. We also minimize the trusted computing base by introducing a secure co-processor that is only responsible for key generation into the system. Our experiments show that the signing of OTS-SKE is faster than KIS as well as Elliptic Curve Digital Signature Algorithm (ECDSA) used in Intel SGX.
Examining the validity and fidelity of a virtual reality simulator for basic life support training

Abstract Background Virtual reality (VR) offers an immersive and practical method for training medical skills, especially in emergency healthcare settings. However, it is unclear whether learning in VR will translate into real-world performance benefits. To explore these potential transfer effects, we examined the validity and fidelity of a bespoke VR environment for Basic Life Support (BLS) training, a generic skill in medical training programmes. Methods Twenty-two medical trainees performed standardised BLS procedures within two simulation conditions: one in VR, using a Pico Neo 3 standalone system; the other in a real-world synthetic environment, which included a physical mannequin and resuscitation equipment. Patterns of task behaviour, workload, sense of presence, and visual attention were derived from user self-report questionnaires, video recordings, and eye-tracking data. Results Data showed that the VR training environment was sufficiently high in face validity to immerse the participants, and that trainees were displaying realistic task behaviours and procedural actions. However, the fidelity of user interactions and movements in VR proved atypical, which seemed to disrupt participants’ attentional and motor responses. Conclusions Results suggest that VR may have limitations for improving physical skills in the context of BLS training, yet be potentially valuable for developing task procedures and/or perceptual abilities.
The Ngoko Javanese Stemmer uses the Enhanced Confix Stripping Stemmer Method

Stemming is vital in text processing. The stemming that is most often encountered is Indonesian and English stemming. This is because more articles are processed in text processing in English and Indonesian. Indonesia has several regional languages, especially local school content, often used in learning. Therefore, research is needed to process Javanese language texts to make it easier for education practitioners, especially in Ngoko Javanese. Ngoko Javanese stemming, which still uses the affix removal stemmers method (rule-based approach) in previous research. Has a problem, namely the lack of success of this method when returning the root words of Ngoko Javanese, so it is necessary to check the Ngoko Javanese dictionary so that the results of the root words obtained are maximized. This study aims to conduct stemmer research on Ngoko Javanese using the Enhanced Confix Stripping (ECS) method. This stemmer is designed to do word splitting according to the Enhanced Confix Stripping algorithm and through checking the dictionary adapted to the Ngoko Javanese language. The results of this study are the ability to extract essential words in Javanese Ngoko to achieve a level of truth in returning root words reaching 97 percent.
GCL: Contrastive learning instead of graph convolution for node classification

Contrastive learning as an effective representation learning technique has attracted tremendous attention due to its general success in downstream tasks. However, the theoretical explanations and quantitative experimental analyses of its generalization ability are still limited. These issues are pivotal yet challenging for improving both the interpretability and performance of contrastive learning. To address these issues, we first re-examine the least squares bias-variance decomposition and successfully derive GCL, a novel bias-variance decomposition with two optional generalized biases and one generalized variance. GCL is shown to be extendable to common contrastive learning models so that it can be utilized as a unified contrastive learning framework. Meanwhile, a surprising finding that the gradient descent of contrastive loss concerning feature representation is closely related to the message passing mechanism (graph convolution) of Graph Neural Networks (GNNs). The contrastive learning model called GCP is then proposed as a convincing implementation of GCL. GCP has a pure MLP-based structure and employs a conventional cross-entropy to reduce the bias between predictions and ground truth labels, two optional contrastive losses to optimize the variance of the model. Finally, extensive experiments demonstrate that the two biases proposed by GCL have their own merits; GCP achieves comparable or even better performance than GNNs in a more efficient and robust manner, its bias and variance meet the bias-variance tradeoff to some extent.
The Smart Classroom: Learning Challenges in the Digital Ecosystem

Digital devices are causing the emergence of new learning ecosystems, including the smart classroom. This model changes the pedagogical intermediation paradigm, redefining educational values and principles. The present paper delves into this scenario through the results of a doctoral thesis funded by the Ministry of Science, Innovation and Universities of the Spanish Government. The research is based on a qualitative approach, involving a field study in four schools in the Valencian Community. The information is collected through the study of the school documentation, in-depth interviews with 23 teachers and participant observation sessions. This information is analysed under the postulates of discourse analysis. The results show that teachers see the smart classroom as an innovative paradigm that facilitates active learning. However, the focus tends to be on technologies, which are assumed to optimise learning. Teachers opt for the implementation of technology-mediated methodologies, such as the flipped classroom, PBL or gamification, but these often operate more at the formal level than at the applied level. Despite this, a learning ecosystem is emerging that is significantly different from the traditional one, involving a change of logic in which digital technologies adopt the centrality formerly held by the textbook or the teacher.
Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators

In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation of neural networks for deblurring is their sensitivity to noise and other perturbations, which can lead to instability and produce poor reconstructions. In addition, networks do not necessarily take into account the numerical formulation of the underlying imaging problem when trained end-to-end. In this paper, we propose some strategies to improve stability without losing too much accuracy to deblur images with deep-learning-based methods. First, we suggest a very small neural architecture, which reduces the execution time for training, satisfying a green AI need, and does not extremely amplify noise in the computed image. Second, we introduce a unified framework where a pre-processing step balances the lack of stability of the following neural-network-based step. Two different pre-processors are presented. The former implements a strong parameter-free denoiser, and the latter is a variational-model-based regularized formulation of the latent imaging problem. This framework is also formally characterized by mathematical analysis. Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise. In particular, the model-based framework represents the most reliable trade-off between visual precision and robustness.
METHODS OF THE FRACTAL APPROACH IN SCIENCE EDUCATION: INNOVATIVE TECHNOLOGY AND CONCEPTS OF COMPUTER MODELING

Formulation of the problem. At the present stage of the development of science education and information technology, their integration, complementarity, and implementation are essential. Therefore, the search for methods of teaching natural sciences, based on the principles of self-organization and computer modeling, corresponds to the immediate tasks of the present. Materials and methods. Methods of comparative analysis, computer modeling, and generalization strategy are used. The study is based on the physics course content and the use of the programming language. Results. An innovative fractal approach to the teaching of physical and mathematical disciplines is proposed as a method of improving independent and creative computer modeling of natural phenomena. The fundamental principles of object-oriented programming (encapsulation, inheritance, polymorphism) have proven to be influential in shaping the physical and mathematical aspects of the information architecture of the perception of educational disciplines. The possibility of using this approach in other sections of physics is demonstrated. The developed iterations of the fractal structure are presented in the example of the study of the "Geometric Optics" and "Wave Optics" sections of physics. It is shown that each iteration is characterized by synergy: the addition of a new iteration provides a high-quality and in-depth perception of new information. Conclusions. The formation of the specified integrated fractal structure conditions the integrity of information perception and its formation happens intuitively. The analysis of the conducted studies confirmed the innovativeness and effectiveness of the fractal approach. This approach can be used to develop systems for the processing and transmission of information, intelligent information materials, and artificial intelligence.
PENERAPAN MODEL PEMBELAJARAN PBL MENGGUNAKAN PENDEKATAN TaRL

This classroom action research was conducted on class 1 students at SD Negeri 1 Bajang, Mlarak sub-district, Ponorogo district, for the 2022/2023 academic year due to a problem, namely the low interest of students in learning. Through the PBL learning model with the TaRL approach it is expected to overcome these problems. The aims of this research is to increase students' interest in learning mathematics. This research is a class action research with 8 students as the subject of class 1 SDN 1 Bajang research. This research was conducted in 2 cycles, each cycle 1 meeting. Each meeting consisted of 4 stages, namely planning, implementation, observation, reflection. In the pre-cycle it was found that the learning interest of grade 1 students was 4.8 which were in the "low" category. In cycle 1 it was found an increase in student learning interest of 8.6 in the "medium" category. And in cycle 2 there was a significant increase in students' learning interest, namely 11 in the "high" category. Thus it can be concluded that the PBL learning model using the TaRL approach can increase students' interest in learning mathematics.
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With the widespread use of Internet applications, ensuring the quality and reliability of online services has become increasingly important. Therefore, anomaly detection methods play a critical role in identifying potential anomalies in the data streams of infrastructure systems and service applications. However, most of known detection methods have an underlying assumption that the data streams are continuous. In practice, we learn that many real-world data streams can be sporadic. It incurs particular challenges for the task of anomaly detection, for which the common preprocessing of downsampling on sporadic data can omit potential anomalies and delay alarms. In this paper, we propose an ensemble learning-based anomaly detection method on sporadic data streams named AD2S. It consists of two modules: a monitor module to continuously and adaptively determine the measure windows for observations, and a detection module that utilizes an isolation partition strategy to estimate the anomaly degree of each incoming observation. Based on experimental results on eight synthetic and public real-world datasets, our method outperforms other state-of-the-art methods with an average AUC score of 0.923. Additionally, our analysis demonstrates that the proposed method has constant amortized time and space complexity, enabling once detection within an average of 9.9 ms and maximum memory usage of 26.14 KB. The code of AD2S is open-sourced for further research.
Fixing Plans for PDDL+ Problems: Theoretical and Practical Implications

The plan, execution, and replan framework has proven to be extremely valuable in complex real-world applications, where the dynamics of the environment cannot be fully encoded in the domain model. However, this comes at the cost of regenerating plans from scratch, which can be expensive when expressive formalisms like PDDL+ are used. Given the complexity of generating PDDL+ plans, it would be ideal to reuse as much as possible of an existing plan, rather than generating a new one from scratch every time. To support more effective exploitation of the plan, execution, and replan framework in PDDL+, in this paper, we introduce the problem of discretized PDDL+ plan fixing, which allows one to fix existing plans according to some defined constraints. We demonstrate the theoretical implications of the introduced notion and introduce reformulations to address the problem using domain-independent planning engines. Our results show that such reformulations can outperform replanning from scratch and unlock planning engines to solve more problems with fine-grained discretizations.
A Study on the Appropriate Size of the Mongolian General Corpus

This study aims to determine the appropriate size of the Mongolian general corpus. This study used the Heaps’ function and Type-Token Ratio (TTR) to determine the appropriate size of the Mongolian general corpus. This study’s sample corpus of 906,064 tokens comprised texts from 10 domains of newspaper politics, economy, society, culture, sports, world articles and laws, middle and high school literature textbooks, interview articles, and podcast transcripts. First, we estimated the Heaps’ function with this sample corpus. Next, we observed changes in the number of types and TTR values while increasing the number of tokens by one million using the estimated Heaps’ function. As a result of observation, we found that the TTR value hardly changed when the number of tokens exceeded 39~42 million. Thus, we conclude that an appropriate size for a Mongolian general corpus is 39-42 million tokens.
SOCIAL ASPECTS OF MANAGING RISKS AND OPPORTUNITIES FOR ADVANCED DEVELOPMENT OF NEURAL NETWORKS

Artificial intelligence is increasingly becoming the object and analytical field of the problems of modern sociology. The transition to generative neural networks means a change in the global paradigm, a transition to industry 4.0 - to a new economy, new types of social communications and social relations. Modern artificial intelligence is already revolutionizing medicine, education, marketing, military affairs, and has a great impact on the youth generation. There is a new terminology in the description of algocracy; requests for state regulation of the use and deep learning of neural networks are formulated in different countries. Opportunities and limitations in the use of artificial intelligence cannot be considered without attention to the social aspects of risk management and opportunities for advanced development of neural networks. In the proposed article, this task is analytically considered in the projection on modern higher education.
The Effectiveness of Using Digital Books on the Problem-Solving Ability of High School Students in Physics Learning

Problem-solving ability is one of the essential skills needed in the 21st century. Therefore, there has to be learning media that supports students' problem-solving abilities in physics lessons. One teaching material that is very suitable for the digital era is a digital book, commonly called an ebook. This study aims to determine the effectiveness of digital books in improving problem-solving over the past five years, 2018-2022 taken. This study uses a literature study method that includes reputable international and national journals. From the study results, it was found that during the last five years of the 30 journals we have reviewed, 98% that use of digital books was effective in improving problem-solving skills. This digital book can also improve some of the skills needed in the 21st century. Digital books have many advantages, including lots of pictures, graphs, phenomena, and animations that are relevant to learning physics, making learning physics more fun, and developing students' creativity in exploring and learning physics. Besides that, the drawback of digital books is that they can tire your eyes because you have to linger in front of your cell phone or laptop.
AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion

Despite the impressive results achieved by many existing Structure from Motion (SfM) approaches, there is still a need to improve the robustness, accuracy, and efficiency on large-scale scenes with many outlier matches and sparse view graphs. In this paper, we propose AdaSfM: a coarse-to-fine adaptive SfM approach that is scalable to large-scale and challenging datasets. Our approach first does a coarse global SfM which improves the reliability of the view graph by leveraging measurements from low-cost sensors such as Inertial Measurement Units (IMUs) and wheel encoders. Subsequently, the view graph is divided into sub-scenes that are refined in parallel by a fine local incremental SfM regularised by the result from the coarse global SfM to improve the camera registration accuracy and alleviate scene drifts. Finally, our approach uses a threshold-adaptive strategy to align all local reconstructions to the coordinate frame of global SfM. Extensive experiments on large-scale benchmark datasets show that our approach achieves state-of-the-art accuracy and efficiency. [Project Page]
Field test of quantum key distribution over aerial fiber based on simple and stable modulation

We have developed a simple time-bin phase encoding quantum key distribution system, using the optical injection locking technique. This setup incorporates both the merits of simplicity and stability in encoding, and immunity to channel disturbance. We have demonstrated the field implementation of quantum key distribution over long-distance deployed aerial fiber automatically. During the 70-day field test, we achieved approximately a 1.0 kbps secure key rate with stable performance. Our work takes an important step toward widespread implementation of QKD systems in diverse and complex real-life scenarios.
EgoHMR: Egocentric Human Mesh Recovery via Hierarchical Latent Diffusion Model

Egocentric vision has gained increasing popularity in social robotics, demonstrating great potentials for personal assistance and human-centric behavior analysis. Holistic per-ception of human body itself is a prerequisite for downstream applications, including action recognition and anticipation. Extensive research has been performed for human mesh recovery from the exocentric images captured from a third-person view, but limited studies are conducted for heavily distorted yet occluded egocentric images. In this paper, we propose Egocentric Human Mesh Recovery (EgoHMR), a novel hierarchical network based on latent diffusion models. Our method takes a single egocentric frame as the input and it can be trained in an end-to-end manner without supervision of 2D pose. The network is built upon the latent diffusion model by incorporating both global and local features in a hierarchical structure. To train the proposed network, we generate weak labels from synchronized exocentric images. The proposed method can perform human mesh recovery directly from egocentric images and detailed quantitative and qualitative experiments have been conducted to demonstrate the effectiveness of the proposed EgoHMR method.
The Effects of the Physical Environment, Promotion, and Educational Costs on Parents' Choices Regarding Education at Al-Hikmah Vocational High School, Jakarta, Indonesia

It has an effect on the number of new students entering Al-Hikmah Vocational School for the last 3 (three) years starting the 2018 school year fluctuating decreased. The physical environment, promotion, and educational costs are one of the main factors influencing parents in choosing a school for their children. This study aims to: &#x0D; &#x0D; Identify and analyze the impact of the physical environment on parents' trust in Al-Hikmah Vocational High School's decision-making regarding education, &#x0D; Identify and evaluate the impact of sales promotion on parents' trust in Al-Hikmah Vocational High School's decision-making regarding education. &#x0D; To determine and assess the impact of education prices on parents' trust in Al-Hikmah Vocational High School's decision-making, &#x0D; To determine and examine the impact of the physical environment on parents' choices of schooling at Al-Hikmah Vocational School, &#x0D; To determine and examine the influence of promotion on parents' choices of education at Al-Hikmah Vocational School, &#x0D; To determine and examine the influence of education costs on parents' choices of education at Al-Hikmah Vocational School, &#x0D; To learn about and analyze the role that trust plays in parents' decisions regarding their children's education at Al-Hikmah Vocational School; &#x0D; To learn about and evaluate the role that the physical environment plays in decisions made through trust; &#x0D; To learn about and evaluate the role that promotion plays in decisions made through trust; and &#x0D; To learn about and evaluate the role that education costs play in decisions made through trust. &#x0D; &#x0D; Descriptive quantitative, associative, and quantitative research methods are used along with SmartPLS software for data analysis. The study's results indicate that the Al-Hikmah Vocational School's physical environment, promotional strategies, and educational prices impact parents' decisions on which school to send their children to.