Reviewerly/exHarmony huggingface dataset:
Upper bounds for the clock speeds of fault-tolerant distributed quantum computation using satellites to supply entangled photon pairs

Despite recent advances in quantum repeater networks, entanglement distribution on a continental scale remains prohibitively difficult and resource intensive. Using satellites to distribute maximally entangled photons (Bell pairs) between distant stations is an intriguing alternative. Quantum satellite networks are known to be viable for quantum key distribution, but the question of if such a network is feasible for fault tolerant distributed quantum computation (FTDQC) has so far been unaddressed. In this paper we determine a closed form expression for the rate at which logical Bell pairs can be produced between distant surface code encoded qubits using a satellite network to supply imperfect physical Bell pairs. With generous parameter assumptions, our results show that FTDQC with satellite networks over statewide distances (500--999 km) is possible up to a collective clock rate on the order of 1 MHz, while continental (1000--4999 km) and transcontinental ($5000+$ km) distances run on the order of 10 kHz and 100 Hz, respectively.
Boosting Deep Reinforcement Learning Agents with Generative Data Augmentation

Data augmentation is a promising technique in improving exploration and convergence speed in deep reinforcement learning methodologies. In this work, we propose a data augmentation framework based on generative models for creating completely novel states and increasing diversity. For this purpose, a diffusion model is used to generate artificial states (learning the distribution of original, collected states), while an additional model is trained to predict the action executed between two consecutive states. These models are combined to create synthetic data for cases of high and low immediate rewards, which are encountered less frequently during the agent’s interaction with the environment. During the training process, the synthetic samples are mixed with the actually observed data in order to speed up agent learning. The proposed methodology is tested on the Atari 2600 framework, producing realistic and diverse synthetic data which improve training in most cases. Specifically, the agent is evaluated on three heterogeneous games, achieving a reward increase of up to 31%, although the results indicate performance variance among the different environments. The augmentation models are independent of the learning process and can be integrated to different algorithms, as well as different environments, with slight adaptations.
Interpolation and Quantifiers in Ortholattices

We study quantifiers and interpolation properties in orthologic, a non-distributive weakening of classical logic that is sound for formula validity with respect to classical logic, yet has a quadratic-time decision procedure. We present a sequent-based proof system for quantified orthologic, which we prove sound and complete for the class of all complete ortholattices. We show that orthologic does not admit quantifier elimination in general. Despite that, we show that interpolants always exist in orthologic. We give an algorithm to compute interpolants efficiently. We expect our result to be useful to quickly establish unreachability as a component of verification algorithms.
A Parallel Multimodal Integration Framework and Application for Cake Shopping

Multimodal interaction systems can provide users with natural and compelling interactive experiences. Despite the availability of various sensing devices, only some commercial multimodal applications are available. One reason may be the need for a more efficient framework for fusing heterogeneous data and addressing resource pressure. This paper presents a parallel multimodal integration framework that ensures that the errors and external damages of integrated devices remain uncorrelated. The proposed relative weighted fusion method and modality delay strategy process the heterogeneous data at the decision level. The parallel modality operation flow allows each device to operate across multiple terminals, reducing resource demands on a single computer. The universal fusion methods and independent devices further remove constraints on the integrated modality number, providing the framework with extensibility. Based on the framework, we develop a multimodal virtual shopping system, integrating five input modalities and three output modalities. The objective experiments show that the system can accurately fuse heterogeneous data and understand interaction intent. User studies indicate the immersive and entertaining of multimodal shopping. Our framework proposes a development paradigm for multimodal systems, fostering multimodal applications across various domains.
Characteristics and Evaluation of Wayland High Education Thought and Practice

Branch field of educational thought, and also an important part of people's educational thought. In terms of land, the higher education thought should be said is between the two levels, his higher education thought is not scattered, but based on the American higher education deep thinking and long-term practice exploration gradually formed on the basis of the rational understanding of higher education, on the other hand, his higher education thought is not systematic, is not constitute a theory or theory. Still, Waylander's higher education philosophy was very prominent in the United States in the 19th century.
Between Self-Tracking and Alternative Medicine: Biomimetic Imaginary in Contemporary Biohacking

While anthropological and social studies of the body have extensively explored self-tracking cultures, they have so far overlooked the phenomenon of biohacking, which represents a distinct though overlapping mode of contemporary techno-asceticism with its own set of norms and frameworks for bodily self-use. This article seeks to address this gap by examining biohacking within the context of self-tracking cultures and its simultaneous alignment with alternative health cultures. By analysing a substantial collection of recorded Biohacker Summit presentations, the study argues that biohacking reintroduces a dualistic biomimetic imaginary while simultaneously striving to transcend such dichotomies through a univocal emphasis on information processing. In particular, this is evident in the biomimetic impulse to align their interventions with the principles found in nature and to perceive technologies as subordinate to ‘natural’ biological processes, the privileging of the human sensorium as a bridge between science and nature, and the ultimate inclination to discard technology in favour of intuition and embodiment.
Evaluating Embeddings from Pre-Trained Language Models and Knowledge Graphs for Educational Content Recommendation

Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in order to recommend textbook content based on exercises. Such methods are typically based on semantic textual similarity (STS) and the use of embeddings for text representation. However, it remains unclear what types of embeddings should be used for this task. In this study, we carry out an extensive empirical evaluation of embeddings derived from three different types of models: (i) static embeddings trained using a concept-based knowledge graph, (ii) contextual embeddings from a pre-trained language model, and (iii) contextual embeddings from a large language model (LLM). In addition to evaluating the models individually, various ensembles are explored based on different strategies for combining two models in an early vs. late fusion fashion. The evaluation is carried out using digital textbooks in Swedish for three different subjects and two types of exercises. The results show that using contextual embeddings from an LLM leads to superior performance compared to the other models, and that there is no significant improvement when combining these with static embeddings trained using a knowledge graph. When using embeddings derived from a smaller language model, however, it helps to combine them with knowledge graph embeddings. The performance of the best-performing model is high for both types of exercises, resulting in a mean Recall@3 of 0.96 and 0.95 and a mean MRR of 0.87 and 0.86 for quizzes and study questions, respectively, demonstrating the feasibility of using STS based on text embeddings for educational content recommendation. The ability to link digital learning materials in an unsupervised manner—relying only on readily available pre-trained models—facilitates the development of AI-enhanced learning.
Developing a multifunctional wearable system consisting of a helmet and clothing for two-wheeled vehicle users' safety assistance

Purpose This study aims to develop a safe, wearable clothing system that combines visibility-enhancing and emergency–accident-responding functions for two-wheeled vehicle (TWV) users' safety assistance. Design/methodology/approach First, the wearable system (WS) allowing users to control turn signals, brake lights and emergency flasher only with head movements was developed. Second, multiconnected systems were developed between WSs and a smartphone application (AS), providing accident occurrence recognition, driving photo capture–storage and emergency notification functions. Third, usability testing in each function was performed to assess the operability of the systems. Findings The intuitive interface, which uses head movement as gesture commands, was effectively operated for controlling turn signals, brake lights and emergency flasher when driving, despite differences in user physique and boarding structure among TWVs. In addition, using Bluetooth low energy and Wi-Fi protocols simultaneously can establish automatic accident recognition–notification and driving photo capture–storage–display functions by linking two WSs with one AS. Research limitations/implications This study presents a case using relatively accessible technologies within the fashion industry to improve users' safety and provide fundamental data for convergence education for smart fashion products, highlighting the significance of this study in this convergence era. Originality/value The WSs and the AS of a TWV user visually evoke the attention of other drivers and pedestrians, reducing the risk of accidents; social contribution regarding public safety will be possible by allowing the system to autonomously inform emergencies and receive emergency medical treatment quickly when the accident occurred.
Automatic and Incremental Repair for Speculative Information Leaks

We present CureSpec, the first model-checking based framework for automatic repair of programs with respect to information leaks in the presence of side-channels and speculative execution. CureSpec is based on formal models of attacker capabilities, including observable side channels, inspired by the Spectre-PHT attacks. For a given attacker model, CureSpec is able to either prove that the program is secure, or detect potential side-channel vulnerabilities and automatically insert mitigations such that the resulting code is provably secure. Moreover, CureSpec can provide a certificate for the security of the program that can be independently checked. We have implemented CureSpec in the SeaHorn framework and show that it can effectively repair security-critical code, for example the AES encryption from the OpenSSL library.
Enhancing Hybrid Eye Typing Interfaces with Word and Letter Prediction: A Comprehensive Evaluation

Eye typing interfaces enable a person to enter text into an interface using only their own eyes. But despite the inherent advantages of touchless operation and intuitive design, such eye-typing interfaces often suffer from slow typing speeds, resulting in slow words per minute (WPM) counts. In this study, we add word and letter prediction to the eye-typing interface and investigate users’ typing performance as well as their subjective experience while using the interface. In experiment 1, we compared three typing interfaces with letter prediction (LP), letter + word prediction (L + WP), and no prediction (NoP), respectively. We found that the interface with L + WP achieved the highest average text entry speed (5.48 WPM), followed by the interface with LP (3.42 WPM), and the interface with NoP (3.39 WPM). Participants were able to quickly understand the procedural design for word prediction and perceived this function as very helpful. Compared to LP and NoP, participants needed more time to familiarize themselves with L + WP in order to reach a plateau regarding text entry speed. Experiment 2 explored training effects in L + WP interfaces. Two moving speeds were implemented: slow (6.4°/s same speed as in experiment 1) and fast (10°/s). The study employed a mixed experimental design, incorporating moving speeds as a between-subjects factor, to evaluate its influence on typing performance throughout 10 consecutive training sessions. The results showed that the typing speed reached 6.17 WPM for the slow group and 7.35 WPM for the fast group after practice. Overall, the two experiments show that adding letter and word prediction to eye-typing interfaces increases typing speeds. We also find that more extended training is required to achieve these high typing speeds.
Secured tracing for group signatures from attribute‐based encryption

Summary This article presents a tracing mechanism for group signatures answering the security threats of malicious authorities and users' forgeries. The proposal weakens the high trust placed on the centralized tracing party in previous group signatures by decentralizing tracing power using a multiple tracer setting and limiting the tracers' access using attribute‐based encryption and the requirement of the group manager's agreement. We allow the group manager to control tracers identifying his group users. Instead of a centralized tracer, our setting has multiple tracers possessing attribute sets. Thus, after getting the group manager's permission, a tracer should satisfy the access policy in a given signature to identify the signer. On the other hand, our group signature scheme decentralizes the tracing key generation and removes the group manager's tracing ability. Thus, it ensures that only the attribute‐satisfying and permitted tracers can identify the signer. Moreover, this article delivers security against malicious users. It presents a verification process of access policy of the signatures to prevent users from utilizing invalid attributes for signing. In addition, the article delivers a collaborative tracing mechanism to satisfy attribute sets that a tracer fails to fulfill alone for identifying a signer. Thus, our tracing mechanism ensures security against malicious authorities and group users in group signatures. The article gives the general construction of the scheme and discusses the security.
MSGeN: Multimodal Selective Generation Network for Grounded Explanations

Modern models have shown impressive capabilities in visual reasoning tasks. However, the interpretability of their decision-making processes remains a challenge, causing uncertainty in their reliability. In response, we present the Multimodal Selective Generation Network (MSGeN), a novel approach to enhancing interpretability and transparency in visual reasoning. MSGeN can generate explanations that seamlessly integrate diverse modal information, providing a comprehensive and intuitive understanding of its decisions. The model consists of five collaborative components: (1) the Multimodal Encoder, which encodes and fuses input data; (2) the Reasoner, which is responsible for generating stepwise inference states; (3) the Selector, which is utilized for selecting the modality for each step’s explanation; (4) the Speaker, which generates natural language descriptions; and (5) the Pointer, which produces visual cues. These components work harmoniously to generate explanations enriched with natural language context and visual cues. Our extensive experimentation demonstrates that MSGeN surpasses existing multimodal explanation generation models across various metrics, including BLEU, METEOR, ROUGE, CIDEr, SPICE, and Grounding. We also show detailed visual examples highlighting MSGeN’s ability to generate comprehensive and coherent explanations, showcasing its effectiveness through practical case studies.
Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems.
Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate defects and their impact on a complex antique wall painting of Giotto. The interferometry data is acquired with a portable off-axis interferometer set-up with a phase-shifted reference beam coupled with the object beam in front of the digital photosensitive medium. A digital holographic speckle pattern interferometry (DHSPI) system is used to register digital recordings of interferogram sequences over time. The surface is monitored for as long as it deforms prior to returning to its initial reference equilibrium state prior to excitation. The attempt to separate the whole vs. local defect complex amplitudes from the interferometric data is presented. The main aim is to achieve isolation and visualization of each defect’s impact amplitude in order to obtain detailed documentation of each defect and its structural impact on the surface for structural diagnosis purposes.
Abstract Interpretation-Based Feature Importance for Support Vector Machines

We study how a symbolic representation for support vector machines (SVMs) specified by means of abstract interpretation can be exploited for: (1) enhancing the interpretability of SVMs through a novel feature importance measure, called abstract feature importance (AFI), that does not depend in any way on a given dataset or the accuracy of the SVM and is very fast to compute; and (2) certifying individual fairness of SVMs and producing concrete counterexamples when this verification fails. We implemented our methodology and we empirically showed its effectiveness on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels. Our experimental results prove that, independently of the accuracy of the SVM, our AFI measure correlates much strongly with stability of the SVM to feature perturbations than major feature importance measures available in machine learning software such as permutation feature importance, therefore providing better insight into the trustworthiness of SVMs.
Light “You Only Look Once”: An Improved Lightweight Vehicle-Detection Model for Intelligent Vehicles under Dark Conditions

Vehicle detection is crucial for traffic surveillance and assisted driving. To overcome the loss of efficiency, accuracy, and stability in low-light conditions, we propose a lightweight “You Only Look Once” (YOLO) detection model. A polarized self-attention-enhanced aggregation feature pyramid network is used to improve feature extraction and fusion in low-light scenarios, and enhanced “Swift” spatial pyramid pooling is used to reduce model parameters and enhance real-time nighttime detection. To address imbalanced low-light samples, we integrate an anchor mechanism with a focal loss to improve network stability and accuracy. Ablation experiments show the superior accuracy and real-time performance of our Light-YOLO model. Compared with EfficientNetv2-YOLOv5, Light-YOLO boosts mAP@0.5 and mAP@0.5:0.95 by 4.03 and 2.36%, respectively, cuts parameters by 44.37%, and increases recognition speed by 20.42%. Light-YOLO competes effectively with advanced lightweight networks and offers a solution for efficient nighttime vehicle-detection.
Financial deepening on income inequality: A quantitative meta-analysis study

Abstract There exists a vast empirical literature on Financial Sector Development (FSD) and the income inequality nexus; however, it lacks consensus. To study this, 24 studies with 87 regression estimates on financial institution depth and income inequality were collected. This paper used the most common method of economic meta-analysis, the Partial Correlation Coefficient (PCC), to answer the question: What is the magnitude and impact, if any, of financial institution depth on income inequality? In addition, a multivariate meta-regression model was used to find moderator variables that produced mixed results in the literature. The results show that the global average comovement of financial institution depth (domestic credit) on income inequality is very small but positive; suggesting that growth in domestic credit may widen income inequality. The positive correlation between domestic credit and income inequality highlights how financial institutions use household income and collateral as a signal when deciding on credit applications. Finally, the multivariate regression results suggest that the present heterogeneity within the literature stems from different methodologies and control variables included in the econometric models, and panel studies that mix countries with heterogeneous characteristics. These suggest that different components of FSD may impact income inequality differently.
Decoupling: Image occlusion removal based on image segmentation and image inpainting

Abstract In daily photography, there are often occlusions in the picture, which affect the overall image perception, so we hope to achieve object removal through image processing. At present, this research field is relatively new, has not yet formed a completed implementation system, and most of the implementation effect is rough, this study plans to achieve object removal by combining semantic segmentation and image inpainting. This paper also compares our model qualitatively or quantitatively with existing methods and conduct ablation experiments to fully illustrate our ideas. Extensive experiments show that the method obtains fairly good object removal performance.