Reviewerly/exHarmony huggingface dataset:
Remote sensing image super-resolution via cross-scale hierarchical transformer

Global and local modeling is essential for image super-resolution tasks. However, current efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth observation scenarios, resulting in suboptimal single-scale representations in global and local modeling. The key motivation of this work is inspired by two observations: 1) There exists hierarchical features at the local and global regions in remote sensing images, and 2) they exhibit scale variation of similar ground objects (e.g. cross-scale similarity). In light of these, this paper presents an effective method to grasp the global and local image hierarchies by systematically exploring the cross-scale correlation. Specifically, we developed a Cross-scale Self-Attention (CSA) to model the global features, which introduces an auxiliary token space to calculate cross-scale self-attention matrices, thus exploring global dependency from diverse token scales. To extract the cross-scale localities, a Cross-scale Channel Attention (CCA) is devised, where multi-scale features are explored and progressively incorporated into an enriched feature. Moreover, by hierarchically deploying CSA and CCA into transformer groups, the proposed Cross-scale Hierarchical Transformer (CHT) can effectively explore cross-scale representations in remote sensing images, leading to a favorable reconstruction performance. Comprehensive experiments and analysis on four remote sensing datasets have demonstrated the superiority of CHT in both simulated and real-world remote sensing scenes. In particular, our CHT outperforms the state-of-the-art approach (TransENet) in terms of PSNR by 0.11 dB on average, but only accounts for 54.8% of its parameters.
Enhancing source code retrieval with joint Bi-LSTM-GNN architecture: A comparative study with ChatGPT-LLM

Retrieving relevant source code from large repositories is a significant and ongoing challenge in the field of software engineering, primarily due to the vast and ever-expanding amount of available code. Existing deep learning methods, although effective to some extent, exhibit limitations in capturing the intricate and complex structural information embedded within source code, which hinders their ability to provide highly accurate retrieval results. This study endeavors to tackle this prominent issue by introducing a novel and innovative approach known as the Joint Bi-directional LSTM and Graph Neural Networks (JBLG) model for source code retrieval. The central aim is to harness the combined strengths and capabilities of Bi-directional Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the model’s capacity to capture and interpret the complex structural characteristics intrinsic to source code. The proposed JBLG model employs a unique fusion of Bi-directional LSTM, which excels in capturing sequential and temporal dependencies within code, and GNN, which is adept at modeling the intricate graph structure of the code. By leveraging this hybrid architecture, the model aims to provide a comprehensive and highly effective solution for source code retrieval tasks. To assess the efficacy of the JBLG model, extensive experiments are conducted, and the model’s performance is evaluated against well-established benchmarks, including LSTM, GNN, and ChatGPT, using two diverse datasets: CodeSearchNet and CosBench datasets. These evaluations span multiple programming languages, ensuring a comprehensive and robust assessment of the model’s capabilities. The experimental results indicate that the JBLG model consistently outperforms its counterparts, including Bi-LSTM, GNN, ChatGPT, and DGMS, across various evaluation metrics. the JBLG model showcases an exceptional ability to handle and extract the intricate structural information inherent in source code, resulting in significantly enhanced retrieval accuracy. The JBLG model emerges as a highly promising solution for real-world source code retrieval applications, with the potential to revolutionize the field. The success of this model underscores the importance of combining deep learning techniques like Bi-directional LSTM and GNNs for tackling complex software engineering challenges. Furthermore, future research directions could involve exploring advanced techniques such as attention mechanisms and extending the model’s applicability to other software engineering tasks like code summarization and code completion. The findings of this study are expected to have a lasting impact on the advancement of source code retrieval methodologies.
Unusual agreement targets in Ripano

Abstract The Italo-Romance variety of Ripano, spoken in the southern Marche region of Italy, exhibits a wide range of unusual agreement targets, including interrogative words, conjunctions, and even nouns. Yet despite agreement morphology being attested on items belonging to nearly every part of speech in Ripano, there is no class (other than verbs) where agreement is found on all lexical items. The typologically rare phenomenon of agreement between arguments is attested in the speech of some individuals, but the acceptability of such structures is subject to intra-speaker variation, being more widespread in older speakers and speakers of rural varieties. In this comprehensive overview, the controllers, targets, domains, features, and values involved in the extensive agreement system of Ripano are examined, drawing on qualitative and quantitative analyses of data collected during interviews with ten native speakers of urban and rural Ripano.
Preface

Extract This book originated as part of a lecture course given at Oxford. The idea for the course was to give a general introduction to topological quantum ideas, including topological quantum field theories, topological quantum memories, topological quantum computing, topological quantum matter, and topological quantum order. After the better part of a decade the course finally became a book. Topological quantum is vast. Given enough time I could easily have written a book three times as long. In order to finish in a finite amount of time I focused on only certain parts of the field. Emphasis is given to the structure of topological theories, diagrammatic reasoning, and the examples of toric code, loop gases, and string-nets. The book is aimed at a physics audience (in particular I try to avoid the language of category theory), although some mathematicians may also find the perspectives presented here to be useful. While the ideas are very deep, in principle, an advanced undergraduate should be able to follow most of the material. The only prerequisite is a good background in basic quantum mechanics.
Grand Challenges in Super-resolution Image Reconstruction

Image resolution reflects the richness of detail information contained in the image. Compared with low-resolution images, high-resolution images usually contain greater pixel density, richer texture details and higher reliability. In reality, constrained by many factors such as acquisition equipment and environment, image degradation model itself and so on, ideal high-resolution images cannot be directly collected, and low-resolution images need to be processed with software and algorithms. Therefore, the research of image super-resolution reconstruction technology has become a hot research topic in the field of image processing and computer vision. Image super-resolution reconstruction is to use algorithms to process low-resolution images to generate clear high-resolution images. With the development of artificial intelligence technology, deep learning technology began to be applied in the field of image and vision. With convolution neural network (CNN) as the main modeling method, the research of super-resolution reconstruction based on deep learning has made a qualitative leap in super-resolution technology. Based on the algorithm of deep learning, this study uses convolution neural network (CNN), generating confrontation network, residual network and other models to reconstruct the image with super-resolution. This study uses CelebA data set as experimental data. After a series of data preprocessing, such as data size adjustment, data loading, data enhancement, and combined with pixel shuffle for up-sampling, the reconstruction is realized by constructing a GAN-based hybrid model of CNN, GAN, and ResNet as a super-resolution model. Finally, the experimental results show that good image effect can be obtained after 60 iterations of training.
HMCAT: Hierachical Multi-task and Commonsense-Aware Transformer for Emotion Recognition in Conversation

Understanding emotion is a prerequisite to expressing emotion. Emotion recognition in Conversation (ERC) has attracted more and more attention because of its significance in developing empathetic dialogue systems. Recently, many approaches have focused on using neural networks to model semantics in the context. However, these methods are inadequate in understanding emotions due to a lack of ability to extract and integrate emotion cues. In this paper, we propose a Hierarchical Multi-task and Commonsense-Aware Transformer (HMCAT) to handle the challenges. Based on the introduction of commonsense knowledge, we explore the interaction patterns between cognitive factors and emotional factors in the process of dialogue advancement so as to fully understand the emotional cues in the context of dialogue. The model has been experimented on two datasets in ERC and another two auxiliary tasks, demonstrating its superiority empirically over existing strong baselines. The experimental results show that HMCAT also has excellent ability in dialogue action recognition and emotion prediction.
The Effect of Computerized Accounting Information Systems, Big Data Anaylsis, and Internal Audit in Accounting Fraud Detection

Accounting fraud is a very important issue of particular concern to the government and society because it results in substantial losses for the state. Therefore, finding an effective method for detecting accounting fraud is important to minimize fraud cases. This research was conducted to analyze the influence of computerized accounting information systems, big data analysis, and internal audits in detecting accounting fraud. This research uses quantitative methods. The data collection technique used in this study was carried out by distributing questionnaires and obtaining 47 respondents, and we can use the survey results for the research. Data processing techniques were carried out using the SPSS version 29 program, and the analytical methods used in this study were descriptive analysis and Multiple Linear Regression. The results of the study show that the accounting information system has no significant effect in detecting accounting fraud, big data analysis has a significant influence in detecting accounting fraud, and internal audit has a significant influence in accounting fraud detection.
Persuasive-oriented Explanation Generation and Evaluation of Personalized Recommendation

Persuasion is essential to explainable recommendation systems in making users accept the recommended items. However, current explainable recommendation models do not specifically strengthen the persuasion and lack an effective method to evaluate the persuasion. By defining the pre-review set, this work clearly defines the persuasion and designs an off-line evaluation method of persuasion without manual annotation, and constructs persuasion-enhanced data sets. Furthermore, this work develops a persuasion enhancement explainable recommendation model based on persuasion-enhanced data sets and cross-attention networks. This model not only improves the recommendation performance but also generates a high-quality natural language explanation. Extensive experiments verify the advantages of the proposed model in recommendation and explanation compared with the SOTA models.
Keyword-driven Conversational Question Generation in Human-like Dialog Agent

In daily human discourse, it is often observed that individuals disregard the correct grammatical structure while articulating their ideas. Instead, they tend to focus on brevity and providing only the essential information. Similarly, when posing questions, individuals often refrain from sharing all the relevant details of the honest answer. To address this issue, this paper introduces the "KdCQG" model. This model takes the "keywords" present in the answer as input rather than the entire answer sentence to generate conversational questions. KdCQG follows a sequence-to-sequence framework and relies on a language constraint mechanism and a coreference alignment mechanism to formulate questions based on the "keywords" present in the answer. Our analysis on the CoQA dataset shows that the KdCQG model effectively generates high-quality conversational questions even when only keywords are provided as input.
An asymptotically efficient closed‐form estimator for the Dirichlet distribution

Abstract Maximum likelihood estimator (MLE) of the Dirichlet distribution is usually obtained by using the Newton–Raphson algorithm. However, in some cases, the computational costs can be burdensome, for example, in real‐time processes. Therefore, it is beneficial to develop a closed‐form estimator that is as efficient as the MLE for large sample. Here, we suggest asymptotically efficient closed‐form estimator based on the classical large sample theory.
Automatically Score Tissue Images Like a Pathologist by Transfer Learning

Cancer is the second leading cause of death in the world. Diagnosing cancer early on can save many lives. Pathologists have to look at tissue microarray (TMA) images manually to identify tumors, which can be time-consuming, inconsistent and subjective. Existing automatic algorithms either have not achieved the accuracy level of a pathologist or require substantial human involvements. A major challenge is that TMA images with different shapes, sizes, and locations can have the same score. Learning staining patterns in TMA images requires a huge number of images, which are severely limited due to privacy and regulation concerns in medical organizations. TMA images from different cancer types may share certain common characteristics, but combining them directly harms the accuracy due to heterogeneity in their staining patterns. Transfer learning is an emerging learning paradigm that allows borrowing strength from similar problems. However, existing approaches typically require a large sample from similar learning problems, while TMA images of different cancer types are often available in small sample size and further existing algorithms are limited to transfer learning from one similar problem. We propose a new transfer learning algorithm that could learn from multiple related problems, where each problem has a small sample and can have a substantially different distribution from the original one. The proposed algorithm has made it possible to break the critical accuracy barrier (the 75% accuracy level of pathologists), with a reported accuracy of 75.9% on breast cancer TMA images from the Stanford Tissue Microarray Database. It is supported by recent developments in transfer learning theory and empirical evidence in clustering technology. This will allow pathologists to confidently adopt automatic algorithms in recognizing tumors consistently with a higher accuracy in real time.
Blockchain Transaction Censorship: (In)secure and (In)efficient?

The ecosystem around blockchain and Decentralized Finance (DeFi) is seeing more and more interest from centralized regulators. For instance, recently, the US government placed sanctions on the largest DeFi mixer, Tornado.Cash (TC). To our knowledge, this is the first time that centralized regulators sanction a decentralized and open-source blockchain application. It has led various blockchain participants, e.g., miners/validators and DeFi platforms, to censor TC-related transactions. The blockchain community has extensively discussed that censoring transactions could affect users’ privacy. In this work, we analyze the efficiency and possible security implications of censorship on the different steps during the life cycle of a blockchain transaction, i.e., generation, propagation, and validation. We reveal that fine-grained censorship will reduce the security of block validators and centralized transaction propagation services, and can potentially cause Denial of Service (DoS) attacks. We also find that DeFi platforms adopt centralized third-party services to censor user addresses at the frontend level, which blockchain users could easily bypass. Moreover, we present a tainting attack whereby an adversary can prevent users from interacting normally with DeFi platforms by sending TC-related transactions.
Incentive Schemes for Rollup Validators

We design and analyze attention games that incentivize validators to check computation results. We show that no pure strategy Nash equilibrium of the game without outside parties exists by a simple argument. We then proceed to calculate the security of the system in the mixed Nash equilibrium, as a function of the number of validators and their stake sizes. Our results provide lower and upper bounds on the optimal number of validators. More concretely, a minimal feasible number of validators minimizes the probability of failure. The framework also allows to calculate optimum stake sizes, depending on the target function. In the end, we discuss optimal design of rewards by the protocol for validators.
Deep Reinforcement Learning-Based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks

Payment channel networks (PCNs) are a layer-2 blockchain scalability solution, with its main entity, the payment channel, enabling transactions between pairs of nodes “off-chain,” thus reducing the burden on the layer-1 network. Nodes with multiple channels can serve as relays for multihop payments by providing their liquidity and withholding part of the payment amount as a fee. Relay nodes might after a while end up with one or more unbalanced channels, and thus need to trigger a rebalancing operation. In this paper, we study how a relay node can maximize its profits from fees by using the rebalancing method of submarine swaps. We introduce a stochastic model to capture the dynamics of a relay node observing random transaction arrivals and performing occasional rebalancing operations, and express the system evolution as a Markov Decision Process. We formulate the problem of the maximization of the node’s fortune over time over all rebalancing policies, and approximate the optimal solution by designing a Deep Reinforcement Learning (DRL)-based rebalancing policy. We build a discrete event simulator of the system and use it to demonstrate the DRL policy’s superior performance under most conditions by conducting a comparative study of different policies and parameterizations. Our work is the first to introduce DRL for liquidity management in the complex world of PCNs.
Oracle Counterpoint: Relationships Between On-Chain and Off-Chain Market Data

We investigate the theoretical and empirical relationships between activity in on-chain markets and pricing in off-chain cryptocurrency markets (e.g., ETH/USD prices). The motivation is to develop methods for proxying off-chain market data using data and computation that is in principle verifiable on-chain and could provide an alternative approach to blockchain price oracles. We explore relationships in PoW mining, PoS validation, block space markets, network decentralization, usage and monetary velocity, and on-chain Automated Market Makers (AMMs). We select key features from these markets, which we analyze through graphical models, mutual information, and ensemble machine learning models to explore the degree to which off-chain pricing information can be recovered entirely on-chain. We find that a large amount of pricing information is contained in on-chain data, but that it is generally hard to recover precise prices except on short time scales of retraining the model. We discuss how even noisy information recovered from on-chain data could help to detect anomalies in oracle-reported prices on-chain.
Robustness Analysis of Machine Learning Models Using Domain-Specific Test Data Perturbation

This study examines how perturbations in image, audio, and text inputs affect the performance of different classification models. Various perturbators were applied to three seed datasets at different intensities to produce noisy test data. Then, the models’ performance was evaluated on the generated test data. Our findings indicate that there is a consistent relationship between larger perturbations and lower model performance across perturbators, models, and domains. However, this relationship varies depending on the characteristics of the specific model, dataset, and perturbator.
Symbolic Versus Deep Learning Techniques for Explainable Sentiment Analysis

Deep learning approaches have become popular in many different areas, including sentiment analysis (SA), because of their competitive performance. However, the downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. In contrast, previous approaches that used sentiment lexicons can do that, but their performance is normally not high. To leverage the strengths of both approaches, we present a neuro-symbolic approach that combines deep learning (DL) and symbolic methods for SA tasks. The DL approach uses a pre-trained language model (PLM) to construct sentiment lexicon. The symbolic approach exploits the constructed sentiment lexicon and manually constructed shifter patterns to determine the sentiment of a sentence. Our experimental results show that the proposed approach leads to promising results with the additional advantage that sentiment predictions can be accompanied by understandable explanations.
DyPrune: Dynamic Pruning Rates for Neural Networks

Neural networks have achieved remarkable success in various applications such as image classification, speech recognition, and natural language processing. However, the growing size of neural networks poses significant challenges in terms of memory usage, computational cost, and deployment on resource-constrained devices. Pruning is a popular technique to reduce the complexity of neural networks by removing unnecessary connections, neurons, or filters. In this paper, we present novel pruning algorithms that can reduce the number of parameters in neural networks by up to 98% without sacrificing accuracy. This is done by scaling the pruning rate of the models to the size of the model and scheduling the pruning to execute throughout the training of the model. Code related to this work is openly available.
A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics

As the frontier of machine learning applications moves further into human interaction, multiple concerns arise regarding automated decision-making. Two of the most critical issues are fairness and data privacy. On the one hand, one must guarantee that automated decisions are not biased against certain groups, especially those unprotected or marginalized. On the other hand, one must ensure that the use of personal information fully abides by privacy regulations and that user identities are kept safe. The balance between privacy, fairness, and predictive performance is complex. However, despite their potential societal impact, we still demonstrate a poor understanding of the dynamics between these optimization vectors. In this paper, we study this three-way tension and how the optimization of each vector impacts others, aiming to inform the future development of safe applications. In light of claims that predictive performance and fairness can be jointly optimized, we find this is only possible at the expense of data privacy. Overall, experimental results show that one of the vectors will be penalized regardless of which of the three we optimize. Nonetheless, we find promising avenues for future work in joint optimization solutions, where smaller trade-offs are observed between the three vectors.