Reviewerly/exHarmony huggingface dataset:
Understanding Deep Learning via Decision Boundary

This article discovers that the neural network (NN) with lower decision boundary (DB) variability has better generalizability. Two new notions, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">algorithm DB variability</i> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\epsilon, \eta)$</tex-math> </inline-formula> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data DB variability</i> , are proposed to measure the DB variability from the algorithm and data perspectives. Extensive experiments show significant negative correlations between the DB variability and the generalizability. From the theoretical view, two lower bounds based on algorithm DB variability are proposed and do not explicitly depend on the sample size. We also prove an upper bound of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{O}\left(({1}/{\sqrt{m}})+\epsilon+\eta\log({1}/{\eta})\right)$</tex-math> </inline-formula> based on data DB variability. The bound is convenient to estimate without the requirement of labels and does not explicitly depend on the network size which is usually prohibitively large in deep learning.
Comparing neural sentence encoders for topic segmentation across domains: not your typical text similarity task

Neural sentence encoders (NSE) are effective in many NLP tasks, including topic segmentation. However, no systematic comparison of their performance in topic segmentation has been performed. Here, we present such a comparison, using supervised and unsupervised segmentation models based on NSEs. We first compare results with baselines, showing that the use of NSEs does often provide improvements, except for specific domains such as news shows. We then compare over three different datasets a range of existing NSEs and a new NSE based on ad hoc pre-training strategy. We show that existing literature documenting general performance gains of NSEs does not always conform to the results obtained by the same NSEs in topic segmentation. If Transformers-based encoders do improve over previous approaches, fine-tuning in sentence similarity tasks or even on the same topic segmentation task we aim to solve does not always equate to better performance, as results vary across method being used and domains of application. We aim to explain this phenomenon and the relative poor performance of NSEs in news shows by considering how well different NSEs encode the underlying lexical cohesion of same-topic segments; to do so, we introduce a new metric, ARP. The results from this study suggest that good topic segmentation results do not always rely on good cohesion modelling on behalf of the segmenter and that is dependent upon what kind of text we are trying to segment. Also, it appears evident that traditional sentence encoders fail to create topically cohesive clusters of segments when used on conversational data. Overall, this work advances our understanding of the use of NSEs in topic segmentation and of the general factors determining the success (or failure) of a topic segmentation system. The new proposed metric can quantify the lexical cohesion of a multi-topic document under different sentence encoders and, as such, might have many different uses in future research, some of which we suggest in our conclusions.
High-fidelity quantum teleportation through noisy channels via weak measurement and environment-assisted measurement

A perfect teleportation protocol requires pure maximally shared entangled states. While in reality the shared entanglement is drastically degraded due to the inevitable interaction with the noisy environment. Here, we propose a teleportation protocol to teleport an unknown qubit through amplitude damping channels with a fidelity up to one with a single copy of the entangled state. Our proposed teleportation protocol, while illustrated using the Bell and W entangled states as examples, can be utilized with any type of entangled states. In our protocol, we utilize environment-assisted measurement (EAM) during the entanglement distribution, and further modify the original teleportation protocol to apply weak measurement in the last step of teleportation. We find a balance between teleportation fidelity and success probability by varying the strength of the weak measurement. Furthermore, we investigate the protection of third-party assisted teleportation protocols, where all the qubits of the entangled state pass through the amplitude damping channel. In particular, for the third-party assisted teleportation with the W state, the decoherence of the shared entanglement can be totally suppressed by using EAM, hence no weak measurement is required to achieve an average teleportation fidelity of unity. The numerical simulation results reveal that our proposed teleportation protocol outperforms both the weak measurement based probabilistic teleportation protocol and the original teleportation protocols without protection.
Transforming Learning Environments with Information Technology: Trends and Best Practices

&#x0D; This research was conducted as a way to examine contextual factors in the effective application of information technology to help the learning environment which can help carry out transformation in the field of education as a new learning method. The research method used is a qualitative descriptive method by prioritizing library or literature studies in the research process. By referring to the design components and also the implementation of initiatives in the field of education as a way of educational transformation, this research can describe what happens with the advancement of information technology to transformation in the world of education and learning which is also related to the integration of technology, policy recommendations, and also resources. sustainable power. The transformation of the education system of course supports national competitiveness as one of the long-term efforts that receives continuous support and also visionary leadership in providing input for the world of education with the help of advanced information technology.&#x0D; Keywords: Learning Environments, Information Technology, Effective Application
A Schematic Review of Knowledge Reasoning Approaches Based on the Knowledge Graph

In the contemporary world, the Internet technology and its implementation mode are advancing at a swift pace, leading to an exponential growth in the scale of Internet data. This data contains a significant amount of valuable knowledge. The effective organization and articulation of knowledge, as well as the ability to conduct thorough calculations and analyses, have garnered significant attention and developments within a particular environmental context. The utilization of knowledge graphs for knowledge reasoning has emerged as a prominent area of focus within the realm of knowledge graph research. It holds substantial significance in the realm of vertical search, intelligent answering, and various other applications. This article will be centered on fundamental principles of reasoning. The approach of knowledge reasoning oriented towards knowledge graphs is focused on the derivation of novel knowledge or the detection of erroneous knowledge through the utilization of pre-existing knowledge. In contrast to conventional knowledge reasoning approaches, the knowledge reasoning technique employed in knowledge graphs is characterized by greater diversity, owing to the succinct, adaptable, and flexible representation of knowledge.
All-Day Vehicle Detection From Surveillance Videos Based on Illumination-Adjustable Generative Adversarial Network

Vehicle detection from surveillance videos is of great significance for various Intelligent Transportation System (ITS) applications. However, existing deep learning methods oftentimes fail under nighttime conditions on account of the lack of sufficient labeled nighttime data. To fill this gap, this paper proposes a novel framework for all-day vehicle detection, by introducing an illumination-adjustable GAN (IA-GAN). The IA-GAN transforms labeled daytime images into multiple nighttime images with diverse illumination, using an adjustable illumination vector as input. Notably, we utilize gray histogram distributions to automatically generate illumination labels, by which IA-GAN gains the knowledge of simulating lights. Following that, we construct a large dataset containing both labeled daytime images and all generated synthetic nighttime images with bounding box labels. Finally, a detector named Day-Night Balanced EfficientDet (DNBED) is developed for all-day vehicle detection. The experiments show that the proposed framework yields promising performance for all-day vehicle detection and competitive results for nighttime vehicle detection compared to existing GANs, indicating the effectiveness of proposed framework. The privacy-sanitized image data and its corresponding labels will be made publicly available at https://github.com/vvgoder/SEU_PML_Dataset.
A Unified Approach to Hierarchical Random Measures

Abstract Hierarchical models enjoy great popularity due to their ability to handle heterogeneous groups of observations by leveraging on their underlying common structure. In a Bayesian nonparametric framework, the hierarchy is introduced at the level of group-specific random measures, and then translated to the observations’ level via suitable transformations. In this work, we propose a new strategy to derive closed-form expressions for the marginal and posterior distributions of each group. Indeed, by directly inserting a suitable set of latent variables into the generative model for the data, we unravel a common core shared by the different hierarchical constructions proposed in the Bayesian nonparametric literature. Specifically, we identify a key identity that underlies these models and highlight its role in the derivation of quantities of interest.
Beating the Fault-Tolerance Bound and Security Loopholes for Byzantine Agreement with a Quantum Solution

Byzantine agreement, the underlying core of blockchain, aims to make every node in a decentralized network reach consensus. Classical Byzantine agreements unavoidably face two major problems. One is 1/3 fault-tolerance bound, which means that the system to tolerate f malicious players requires at least 3 f + 1 players. The other is the security loopholes from its classical cryptography methods. Here, we propose a Byzantine agreement framework with unconditional security to break this bound with nearly 1/2 fault tolerance due to multiparty correlation provided by quantum digital signatures. It is intriguing that quantum entanglement is not necessary to break the 1/3 fault-tolerance bound, and we show that weaker correlation, such as asymmetric relationship of quantum digital signature, can also work. Our work strictly obeys two Byzantine conditions and can be extended to any number of players without requirements for multiparticle entanglement. We experimentally demonstrate three-party and five-party consensus for a digital ledger. Our work indicates the quantum advantage in terms of consensus problems and suggests an important avenue for quantum blockchain and quantum consensus networks.
Exploring the power of telepresence: enhancing education through telepresence robots

Purpose This study aims to enhance the understanding of the current research landscape regarding the utilisation of telepresence robots (TPRs) in education. Design/methodology/approach The bibliometric and thematic analysis of research publications on TPRs was conducted using papers in the Scopus database up to 2023. The final analysis focused on 53 papers that adhered to the selection criteria. A qualitative analysis was performed on this set of papers. Findings The analysis found a rising trend in TPR publications, mostly from the USA as conference papers and journal articles. However, these publications lacked technology integration frameworks, acceptance models and specific learning design models. TPRs have proven effective in various learning environments, fostering accessible education, better communication, engagement and social presence. TPRs can bridge geographical gaps, facilitate knowledge sharing and promote collaboration. Obstacles to implementation include technical, physical, social and emotional challenges. Publications were grouped into four thematic categories: didactic methods of using TPRs, TPRs for educational inclusivity, TPR as a teacher mediator and challenges in using TPRs. Despite the significant potential of TPRs, their broader adoption in education is still facing challenges. Research limitations/implications This research solely analysed research papers in the Scopus database, limiting TPR publications with the keywords “telepresence robots”, “learning”, “teaching” and “education”, excluding studies with different other keywords. Originality/value This study enhances understanding of TPR research in education, highlighting its pedagogical implications. It identifies a gap in the inclusion of technology integration frameworks, acceptance models and learning design models, indicating a need for further research and development.
Parallel Construction of Knowledge Graphs from Relational Databases

Knowledge graphs have recently seen a wide range of applications in various domains. In many such applications data stored in relational databases constitutes an important source for the construction of knowledge graphs. R2RML is a mapping language that can be used to specify mappings from relational to RDF data, and so it naturally suits the purpose of knowledge graph construction from relational data. In this paper, we present Fingr, a concurrent dictionary aided parallel R2RML engine that achieves fine-grained parallelization at the database tuple level. Our experiments show that our prototypical system parallelizes well, and it yields better performance than existing R2RML engines.
Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study

Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2-4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008-2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.
Differential Privacy and Blockchain-Empowered Decentralized Graph Federated Learning Enabled UAVs for Disaster Response

Natural disasters such as earthquakes can cause damage to critical infrastructures and limit access to vital information, making it difficult for disaster response teams to respond effectively. Unmanned aerial vehicles (UAVs) have the potential to aid and provide real-time information for disaster response teams, however, the need to process distributed learning for huge amounts of interconnected nodes in a graph network poses several challenges. First, distributed learning in graph networks for UAVs is still an open issue, making it difficult to train and share models on such networks. Second, such a network can leak privacy-sensitive information, making it harder to ensure data security. To address these challenges, we propose, in this paper, a novel privacy and blockchain-empowered UAVs-enabled decentralized graph federated learning (DPBE-DGFL) framework for disaster response. The framework includes three phases: (i) local model training utilizing stochastic gradient descent with differential privacy, (ii) model weights integrity authentication using blockchain to ensure secure and efficient sharing of model weights, and (iii) final validator selection and model weights aggregation using a dedicated proof-of-stake, (DPoS), consensus mechanism to ensure efficient and decentralized consensus while maintaining security and integrity. Our DPBE-DGFL framework was evaluated using extensive simulations on EMNIST and real-world disaster datasets from Tonga. The results show that it offers a promising solution for privacy-preserving federated learning in graph networks, balancing privacy protection and model accuracy while maintaining latency, communication, and computational efficiency.
Reconhecimento e Tradução de Sinais de Libras para Língua Portuguesa Escrita usando Redes Neurais Profundas

The Brazilian Sign Language (Libras) enables deaf people to understand and interact with others in order to facilitate their access to culture, knowledge and social integration. However, there are only few solutions to reduce the communication barrier between deaf and hearing people. In this work, we propose a solution based on deep neural networks for sign language recognition. This is an exploratory study in which signs in Libras (“Hello”, “Good morning”, and “Thank you”) are used in training, recognition and classification in continuous and real-time mode. We compared two machine learning models that were trained on the LSTM and BiLSTM neural network architectures. The results point to superior assertiveness of the LSTM model, with an accuracy of 84.71% compared to the 77.07% achieved by the BiLSTM model. Therefore, the LSTM architecture is more suitable for classifying the signals investigated in this study. Besides, its use in sign image recognition systems in Libras proves to be viable.
AI-Generated Image Detection using a Cross-Attention Enhanced Dual-Stream Network

With the rapid evolution of AI Generated Content (AIGC), forged images produced through this technology are inherently more deceptive and require less human intervention compared to traditional Computer-generated Graphics (CG). However, owing to the disparities between CG and AIGC, conventional CG detection methods tend to be inadequate in identifying AIGC-produced images. To address this issue, our research concentrates on the text-to-image generation process in AIGC. Initially, we first assemble two text-to-image databases utilizing two distinct AI systems, DALL•E2 and DreamStudio. Aiming to holistically capture the inherent anomalies produced by AIGC, we develope a robust dual-stream network comprised of a residual stream and a content stream. The former employs the Spatial Rich Model (SRM) to meticulously extract various texture information from images, while the latter seeks to capture additional forged traces in low frequency, thereby extracting complementary information that the residual stream may overlook. To enhance the information exchange between these two streams, we incorporate a cross multi-head attention mechanism. Numerous comparative experiments are performed on both databases, and the results show that our detection method consistently outperforms traditional CG detection techniques across a range of image resolutions. Moreover, our method exhibits superior performance through a series of robustness tests and cross-database experiments. When applied to widely recognized traditional CG benchmarks such as SPL2018 and DsTok, our approach significantly exceeds the capabilities of other existing methods in the field of CG detection.
AI-Generated Review Detection

Online reviews of e-commerce platforms have long been recognized as a major factor that influences a consumer’s purchasing decisions. However, the emergence of generative artificial intelligence (AI) has accelerated the proliferation of fake online reviews, which can significantly reduce consumer trust in these platforms. This study proposes a novel supervised learning approach to help platforms effectively detect AI-generated reviews. In the approach, we first construct three types of variables to distinguish between human-written reviews and AI-generated reviews. Then, we introduce an outlier detection method based on cumulative probability density to calculate the probability that a review generated by AI. Finally, we train several well-known classification models using the cumulative probability density values of reviews computed above to obtain classifiers that can accurately detect AI-generated reviews. Numerical experiments demonstrate that the proposed method can produce more accurate detections of AI-generated review than several state-of-the-art baseline methods. We contribute to the related literature by the exploitation of the statistical theory, which posits that outliers, as small probability events, are typically located at the tails of feature distributions, a principle effectively employed in detecting AI-generated reviews.
PET-Exchange: A Privacy Enhanced Trading Exchange using Homomorphic Encryption

The underlying trading mechanisms of electronic securities exchanges have mostly stayed the same over the years with some additions and improvements. However, over the recent decade, high-frequency traders using algorithmic trading have shifted the field using practices that many consider unfair or unethical. In addition, insider trading continues to cause trust issues on certain trading platforms. In this paper, we present PET-Exchange, a privacy-preserving framework for trading securities on an electronic stock exchange. By using homomorphic encryption, PET-Exchange prevents information disclosures and unfair advantages in the trading processes. By matching and trading encrypted orders, we study the performance under various volumes and timing constraints, and compare this to the unencrypted counterparts. Our analysis of PET-Exchange using market trade data shows the privacy and cryptographic tradeoffs, demonstrating it to be suitable for small-scale trading and privacy-preserving auctions. Finally, we discuss the potential impact on transparency, fairness, and opportunities for financial crime in an electronic securities exchange. The insights we provide take us one step closer to a privacy-aware and fair public securities exchange.
Certifying Semantic Robustness of Deep Neural Networks

Since the discovery of adversarial examples, the local robustness of deep neural networks (DNNs) has received much attention. Moreover, researchers find that DNNs are also sensitive to semantic perturbations like fog, contrast and Gaussian noise. Due to the complexity of semantic perturbations, existing works only focus on local robustness towards some specific perturbations such as brightness and rotation. In this paper, we propose a statistics-based method to certify DNN’s local robustness towards general semantic perturbations. First, we give the formal definitions of semantic perturbations and local semantic robustness. Our definitions are general enough to cover almost all perturbations of concern. Then we develop a statistical certification algorithm. Our evaluations on CIFAR-10 and ImageNet show that compared with the state-of-the-art statistical certification algorithm, our method can provide the same theoretical guarantees using only 3.32%-6.55% of running time.