Reviewerly/exHarmony huggingface dataset:
Validation of full-remote reporting for cervicovaginal cytology: the Caltagirone-Acireale distributed lab

Cervical cancer is the fourth most common cancer in women, and its prevention is based on vaccination and screening. Screening consists of molecular human papillomavirus (HPV) testing and cytologic analysis of cervical smears, which require expensive equipment and the interaction of numerous professionals such as biologists, cytologists, laboratory technicians, and pathologists.We centralize the cervical samples from more than 51 clinics in 1 main laboratory, where automated HPV testing is performed. HPV-positive cases are collected and used to prepare a liquid-based cytology slide, which is stained and immediately scanned. The resulting whole-slide images (WSIs) are immediately available in a remote laboratory where they are examined by experienced cytologists using virtual microscopy. This setup was validated by making each of the 3 readers independently diagnose 506 specimens in random order, using both conventional light microscopy (CLM) and WSIs, with a minimum wash-out period of 3 weeks and with a final discussion for all cases.Intraobserver agreement among CLM and WSI ranged from 0.71 to 0.79, and interobserver agreement for the 3 readers compared with the consensus diagnosis was similar for the 2 modes of assessment. Readers subjectively felt confident in their WSI diagnosis for inadequate and negative cases, but less so in other cases. The perceived difficulty was slightly higher in WSI readings.Interobserver agreement in cervicovaginal cytology is moderate and does not vary if the slides are examined conventionally or digitally. Despite higher reported subjective difficulty and lower confidence in the WSI diagnosis, we did not observe a deterioration in diagnostic performance using WSI compared with CLM.
DRL-Based Adaptive Sharding for Blockchain-Based Federated Learning

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart decisions, improving vehicular services and enhancing the driving experience through a secure and privacy-preserving manner in Intelligent Transportation Systems (ITS). Many existing works exploit two-layer blockchain-based FL frameworks consisting of a mainchain and subchains for data interactions among intelligent vehicles, which resolve the limited throughput issue of single blockchain-based vehicular networks. However, the existing two-layer frameworks still suffer from a) strong dependency on predetermined and fixed parameters of vehicular blockchains which limit blockchain throughput and reliability; and b) high communication costs incurred by interactions among intelligent vehicles between the mainchain and subchains. To address the above challenges, we first design an adaptive blockchain-enabled FL framework for ITS based on blockchain sharding to facilitate decentralized vehicular data flows among intelligent vehicles. A streamline-based shard transmission mechanism is proposed to ensure communication efficiency almost without compromising the FL accuracy. We further formulate the proposed framework and propose an adaptive sharding mechanism using Deep Reinforcement Learning to automate the selection of parameters of vehicular shards. Numerical results clearly show that the proposed framework and mechanisms achieve adaptive, communication-efficient, credible, and scalable data interactions among intelligent vehicles.
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution

Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially render biased predictions towards certain demographic subgroups. Understanding how the bias in predictions arises is critical, as it guides the design of GNN debiasing mechanisms. However, most existing works overwhelmingly focus on GNN debiasing, but fall short on explaining how such bias is induced. In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes. Specifically, we propose a novel strategy named Probabilistic Distribution Disparity (PDD) to measure the bias exhibited in GNNs, and develop an algorithm to efficiently estimate the influence of each training node on such bias. We verify the validity of PDD and the effectiveness of influence estimation through experiments on real-world datasets. Finally, we also demonstrate how the proposed framework could be used for debiasing GNNs. Open-source code can be found at https://github.com/yushundong/BIND.
Independent Operation of Two Waveguide-Integrated Quantum Emitters

Scaling up single-photon sources directly on a chip is a fundamental requirement for realizing quantum information protocols that demand scalable, integrated photonic qubits. Integrating multiple quantum emitters in a circuit has encountered major roadblocks in wavelength disparity and the lack of a compact excitation method. The authors demonstrate simultaneous excitation of two quantum dots in the same circuit and independent control of the emitters' wavelengths, enabling two-photon quantum interference. Spectral diffusion is not as detrimental to the interference process as anticipated. These results address a longstanding challenge in quantum photonics.
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers

As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across all views? ii) How to exploit and utilize category correlations of multi-label to guide inference? iii) How to avoid the negative impact resulting from the incompleteness of views or labels? To cope with these problems, we propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper. First, we design two transformer-style based modules for cross-view features aggregation and multi-label classification, respectively. The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance. Second, considering the imbalance of expressive power among views, an adaptively weighted view fusion module is proposed to obtain view-consistent embedding features. Third, we impose a label manifold constraint in sample-level representation learning to maximize the utilization of supervised information. Last but not least, all the modules are designed under the premise of incomplete views and labels, which makes our method adaptable to arbitrary multi-view and multi-label data. Extensive experiments on five datasets confirm that our method has clear advantages over other state-of-the-art methods.
Determinants of E-Commerce, Artificial Intelligence, and Agile Methods in Small- and Medium-Sized Enterprises

Small- and medium-sized enterprises (SMEs) are constrained by scarce resources, yet they are under strong pressure to maintain a competitive position in global markets, especially during crises such as the coronavirus disease-19 (COVID-19) pandemic. In this context, electronic commerce (hereafter e-commerce) platforms combining artificial intelligence (AI) and agile methods have been thriving. On the one hand, AI is a better way to process the data essential to platform customization and optimization. On the other hand, agile adoption ensures iterative software delivery as well as improved management practices that reduce uncertainty and allow SMEs to adapt more easily to new market requirements. A limited number of studies have covered all three topics, especially in relation to these companies. This research focused on creating an innovative model to identify determinants that favor or inhibit SME development of e-commerce, AI, and agile method projects. The model was developed using cognitive mapping—based on strategic options development and analysis—and decision-making trial and evaluation laboratory technique. Recommendations were generated to help SMEs use these tools to acquire a stronger competitive position in global markets.
Developing central bank digital currencies: a reality check during cryptocurrency euphoria

Using estimated sentiment indices based on CBDC-related social media posts, and testing for the effects of regulatory-related announcements upon blockchain and cryptocurrency-related funds, this research presents two key findings: first, the continued evolution of the pricing structures of digital finance products to respond to such perceived threats constitutes a further evolutionary point in the product's life-cycle. However, secondly, the very fact that returns fall while volatility increases, indicates a largely negative market response to the threat of potential external regulation of cryptocurrencies in the future. The nature of this negative response validates concerns that anonymity continues to be a central attractive feature for cryptocurrency stakeholders, further verifying the necessity for third-party oversight.
On Nonlinear Learned String Indexing

We investigate the potential of several artificial neural network architectures to be used as an index on a sorted set of strings, namely, as a mapping from a query string to (an estimate of) its lexicographic rank in the set, which allows solving some interesting string-search operations such as range and prefix searches.Our evaluation on a variety of real and synthetic datasets shows that learned models can beat the space vs error trade-off of the classic (possibly compressed) trie-based solutions for relatively dense datasets only, while being slower to be trained and queried.This leads us to conclude that learned models are not yet competitive with classic trie-based solutions, and thus cannot completely replace them, but possibly only integrate them.Although our study does not settle the question conclusively, it highlights appropriate methods, provides a baseline for comparison, and introduces several open problems, thereby serving as a starting point for future research.
CCTEST: Testing and Repairing Code Completion Systems

Code completion, a highly valuable topic in the software development domain, has been increasingly promoted for use by recent advances in large language models (LLMs). To date, visible LLM-based code completion frameworks such as GitHub Copilot and GPT are trained using deep learning over vast quantities of unstructured text and open source code. As the paramount component and the cornerstone in daily programming tasks, code completion has largely boosted professionals' efficiency in building real-world software systems. In contrast to this flourishing market, we find that code completion systems often output suspicious results, and to date, an automated testing and enhancement framework for code completion systems is not available. This research proposes CCTEST, a framework to test and repair code completion systems in black-box settings. CCTEST features a set of novel mutation strategies, namely program structure-consistent (PSC) mutations, to generate mutated code completion inputs. Then, it detects inconsistent outputs, representing possibly erroneous cases, from all the completed code cases. Moreover, CCTEST repairs the code completion outputs by selecting the output that mostly reflects the “average” appearance of all output cases, as the final output of the code completion systems. With around 18K test inputs, we detected 33,540 inputs that can trigger erroneous cases (with a true positive rate of 86%) from eight popular LLM-based code completion systems. With repairing, we show that the accuracy of code completion systems is notably increased by 40% and 67% with respect to BLEU score and Levenshtein edit similarity.
QEBVerif: Quantization Error Bound Verification of Neural Networks

Abstract To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resource requirements for computational power and storage space by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers, resulting in quantized neural networks (QNNs). While it has been empirically shown to introduce minor accuracy loss, critical verified properties of a DNN might become invalid once quantized. Existing verification methods focus on either individual neural networks (DNNs or QNNs) or quantization error bound for partial quantization. In this work, we propose a quantization error bound verification method, named , where both weights and activation tensors are quantized. consists of two parts, i.e., a differential reachability analysis (DRA) and a mixed-integer linear programming (MILP) based verification method. DRA performs difference analysis between the DNN and its quantized counterpart layer-by-layer to compute a tight quantization error interval efficiently. If DRA fails to prove the error bound, then we encode the verification problem into an equivalent MILP problem which can be solved by off-the-shelf solvers. Thus, is sound, complete, and reasonably efficient. We implement and conduct extensive experiments, showing its effectiveness and efficiency.
Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology—Current Affairs and Perspectives

In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.
The novel thinking exploration model for Blockchain Technology Financial Sharing Services

This paper will explore the novel thinking for blockchain technology financial sharing services in China. It will explain the formation of a shared service center and the key success factors. Then will discuss the financial decision-making for payment business process in financial sharing services, the financial sharing dangers, and solutions as well as the notions to solve the hidden danger of financial sharing, and try to carry out model design and performance evaluation. In this chapter, the blockchain thinking mode of the financial sharing service concept involves consensus thinking, the coexistence of distribution, and concentration with code as a priority. The blockchain method for financial sharing services is to identify participants on the blockchain and establish a consensus mechanism, use code to effectively control the business transactions, and zone storage mode to realize the effective connection between business transactions and financial sharing and financial sharing service based on alliance chain network. The benefits are useful for Technical & Vocational Education and Training (TVET).
Robust echo state network with Cauchy loss function and hybrid regularization for noisy time series prediction

Noisy time series prediction is a hot research topic in practical applications. Echo state networks (ESNs) have superior performance on time series prediction. However, the ill-posed problem may weaken the generalization performance of ESNs when dealing with real data, because of the noise and the overlarge reservoir. To solve this problem, this paper proposes a robust ESN with a Cauchy loss function and hybrid regularizations for noisy time series prediction. First, the Cauchy loss function is used to suppress the large noise mixed in the real data. Then, an improved L1 regularization and the L2 regularization are combined to shrink the larger output weights and produce the sparse solution. Particularly, a simple iterative algorithm is proposed to calculate the output weights of the network. Finally, simulation results on both synthetic and real-world data sets show the super performance of the proposed model.
Feature Fusion Convolution-Aided Transformer for Automatic Modulation Recognition

Automatic Modulation Recognition (AMR) is becoming increasingly important due to its key role in wireless communications. In order to enrich the feature information and reduce computational complexity, a novel feature fusion convolution-aided transformer (FCAformer) approach is proposed in this paper. To enhance the differences between samples, we adopt Markov Transformation Field (MTF), which can extract potential correlation features between signal sequences and combine them with I/Q sequences to generate more information rich data as input for the transformer. We propose a novel patchify module that reduces invalid features and computational complexity by performing feature extraction. We also propose a convolution-aided encoder module consisting of an improved attention mechanism and a convolutional residual connection to improve performance through local-global feature fusion and further reduce the parameters. Experimental results on RadioRML2016.10a show that the proposed AMR method has both higher recognition accuracy and lower parameters than other contrastive methods.
Feature fusion network for long-tailed visual recognition

Deep learning has achieved remarkable success in recent years; however, deep learning methods face significant challenges on long-tailed datasets, which are prevalent in real-world scenarios. In a long-tailed dataset, there are many more samples in the head classes than in the tail classes, and this class imbalance makes it difficult to learn a good feature representation for both head and tail classes simultaneously, particularly when using a single-stage method. Although the existing two-stage methods can alleviate the problem of single-stage methods not performing well on the tail classes by classifier retraining in the second stage, this does not resolve the problem of insufficient learning of head and tail features. Thus, in this paper, we propose a two-stage feature fusion network (FFN). The proposed FFN addresses this issue using one network for the head classes and another network for the tail classes, each of which is trained with a different loss function. This allows the feature learning module to effectively distinguish between the head and tail classes in the embedding space. The classifier learning module fuses the features obtained from the feature learning module, and the classifier is fine-tuned to classify the input images. Different from traditional two-stage methods, the proposed utilizes different loss functions for the head and tail classes; thus, the classifier can achieve balanced results between the head and tail classes. We conduct extensive experiments on three benchmark datasets comparing the proposed FFN with six state-of-the-art methods including two baseline methods, the experimental results demonstrate that the FFN achieves significant improvement on all three benchmark datasets. The code is publicly available at https://github.com/zxsong999/Feature-Fusion-Network.pytorch.
Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta

Jakarta is the capital city of Indonesia, which has a high population density, and is an area that is frequently hit by floods. This study aims to determine the classification of flood-affected areas in Jakarta between severe, moderate, and low. Design/method/approach: The study was conducted using the elbow, Silhouette, Davidson-Bouldin, and Calinski-Harabasz methods on the K-means algorithm, as well as the Rand method. index for evaluation. Grouping with 3 and 6 groups is the best grouping value based on Calinski-Harabasz. By using the davies bouldin index from the observations, the K value with a value of 6 has the smallest Davies-Bouldin value with a value of 0.2737. By using sillhoute, the experimental results obtained the best values sequentially, namely K=2, K=3, and K=6 with silhouette values of 0.866, 0.854, and 0.803. In this experiment, based on the elbow method, it was found that the best K value was K=3. This was obtained because it was based on observations on the appearance of the SSE data compared to the value of K. In the graph above, it can be seen that the largest decrease in data occurred at K=3 and after this decrease, the decline began to slope. The rand index is a method used to compare several cluster methods. If the value is &gt;= 90 it is a very good result, if the value is in the range 80 to 90 it identifies a good index, whereas if it is below 80 it indicates a bad index. The results show that cluster three is verified as the best cluster with a value of 1, followed by a second alternative with cluster 2 of 0.9182. From several validation and evaluation methods it can be concluded that the best grouping can be done using 3 clusters. The results of the study yielded a value of 75.4% in low areas, 21.1% in moderate areas, and 3.5% in severe areas.
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models.Recently, in-context learning has gained popularity over fine-tuning due to its simplicity and improved out-of-domain generalization, and because extensive evidence shows that fine-tuned models pick up on spurious correlations.Unfortunately, previous comparisons of the two approaches were done using models of different sizes.This raises the question of whether the observed weaker out-of-domain generalization of finetuned models is an inherent property of finetuning or a limitation of the experimental setup.In this paper, we compare the generalization of few-shot fine-tuning and in-context learning to challenge datasets, while controlling for the models used, the number of examples, and the number of parameters, ranging from 125M to 30B.Our results show that fine-tuned language models can in fact generalize well outof-domain.We find that both approaches generalize similarly; they exhibit large variation and depend on properties such as model size and the number of examples, highlighting that robust task adaptation remains a challenge. 1