Reviewerly/exHarmony huggingface dataset:
Mapping uncertainty in precision medicine: A systematic scoping review

Although precision medicine is seen by many as one of the most promising advances in the field of medicine, it has also raised critical questions at various levels. Many of these concerns revolve around an observation described by Kimmelman and Tannock as the 'paradox of precision medicine': somewhat surprisingly, uncertainty seems to be a key characteristic of precision medicine in practice.To better understand this concept and the underlying issues, a scoping review was undertaken to search for factors stated in the literature as contributing to or being aspects of uncertainty in precision medicine.A systematic search of the literature was conducted in three databases (Pubmed, Web of Science, and Jstor) and complemented with a systematic hand-search. The initial search provided 1.252 items of which 51 articles for selected as eligible for further analysis. These articles were coded with MAXQDA and categorized into four main themes (a-d) of uncertainty. The main results were summarized and discussed with a view to the interrelations between different aspects and implications for precision medicine in practice.The mapping of different aspects and sources of uncertainty leads to the key result that 'uncertainty' should be understood as a cluster concept. Uncertainties are identified in many different respects and situated at different levels: Most complexity-related issues (theme a) can best be understood as ontological ('world-sided') aspects of the uncertainty paradox. Conceptual (theme b) and evidence-related uncertainties (theme c) are situated on an epistemological or methodological level, addressing foundational and normative challenges related to knowledge production in precision medicine. Finally, theme (d) targets issues on the level of material precision medicine practices. These levels are helpful to understand the different dimensions of the uncertainty paradox.Uncertainty may not merely be a transient effect of the novelty of the precision medicine paradigm. Rather, it should be seen as a consequence of the ontological, epistemological and practical complexity of precision medicine, implying that uncertainty will not necessarily be reduced by more research. This finding encourages further investigations to better understand the interactions among various factors and aspects of uncertainty in precision medicine and the resulting implications for research and medical practice.
Robust quantum walk search without knowing the number of marked vertices

There has been a very large body of research on searching a marked vertex on a graph based on quantum walks, and Grover's algorithm can be regarded as a quantum walk-based search algorithm on a special graph. However, the existing quantum walk-based search algorithms suffer severely from the souffl\'{e} problem which mainly means that the success probability of finding a marked vertex could shrink dramatically even to zero when the number of search steps is greater than the right one, thus heavily reducing the robustness and practicability of the algorithm. Surprisingly, while the souffl\'{e} problem of Grover's algorithm has attracted enough attention, how to address this problem for general quantum walk-based search algorithms is missing in the literature. Here we initiate the study of overcoming the souffl\'{e} problem for quantum walk-based search algorithms by presenting a new quantum walk-based search framework that achieves robustness without sacrificing the quantum speedup. In this framework, for any adjustable parameter $\epsilon$, the quantum algorithm can find a marked vertex on an $N$-vertex {\it complete bipartite graph} with probability at least $ 1-\epsilon$, whenever the number of search steps $h$ satisfies $h \geq \ln(\frac{2}{\sqrt{\epsilon}})\sqrt{N} + 1$. Note that the algorithm need not know the exact number of marked vertices. Consequently, we obtain quantum search algorithms with stronger robustness and practicability.
Understanding host interconnect congestion

We present evidence and characterization of host congestion in production clusters: adoption of high-bandwidth access links leading to emergence of bottlenecks within the host interconnect (NIC-to-CPU data path). We demonstrate that contention on existing IO memory management units and/or the memory subsystem can significantly reduce the available NIC-to-CPU bandwidth, resulting in hundreds of microseconds of queueing delays and eventual packet drops at hosts (even when running a state-of-the-art congestion control protocol that accounts for CPU-induced host congestion). We also discuss implications of host interconnect congestion to design of future host architecture, network stacks and network protocols.
Numerical security proof for the decoy-state BB84 protocol and measurement-device-independent quantum key distribution resistant against large basis misalignment

In this work, we incorporate decoy-state analysis into a well-established numerical framework for key rate calculation, and we apply the numerical framework to decoy-state BB84 and measurement-device-independent (MDI) QKD protocols as examples. Additionally, we combine with these decoy-state protocols what is called ``fine-grained statistics,'' which is a variation of existing QKD protocols that makes use of originally discarded data to get a better key rate. We show that such variations can grant protocols resilience against any unknown and slowly changing rotation along one axis, similar to reference-frame-independent QKD, but without the need for encoding physically in an additional rotation-invariant basis. Such an analysis can easily be applied to existing systems, or even data already recorded in previous experiments, to gain a significantly higher key rate when considerable misalignment is present, extending the maximum distance for BB84 and MDI-QKD and reducing the need for manual alignment in an experiment.
Research trends in corpus linguistics

Abstract This paper uses a bibliometric analysis to map the field of Corpus Linguistics (CL) research in arts and humanities over the last 20 years, tracking changes in popular CL research topics, outlets, highly cited authors, and geographical origins based on the metadata of 5,829 CL-related articles from 429 Scopus-indexed journals. Results reveal an increase in corpus-assisted discourse studies, lexical bundles and academic writing, alongside newer topics including multilingualism and social media. CL studies span 193 languages/dialects with a significant rise in Chinese, Russian, Spanish, and Italian CL research over the past decade. Clusters of highly cited CL researchers are identified spanning (inter)disciplinary research areas. An increase of CL researchers in China, Poland, South Korea, Japan, and more is evidence of the now global reach of CL research. These findings mirror diachronic socio-cultural developments in applied linguistics and society more generally and provide insights into what CL research might come next.
A brain-inspired computational model for extremely few reference image quality assessment

In the study of brain science, the free energy principle and attention perception mechanism have been the two of the most critical findings during the past few decades, arousing a wide range of attention and valuable applications from the research fields of image and video processing, computer vision, etc. Motivated by the aforementioned two important findings, we in this paper develop a brain-inspired computational model for extremely few reference image quality assessment (IQA), dubbed as BCM. The proposed BCM implements with the two main steps. First, we combine free energy principle and sparse perception mechanism to achieve the goal of only using extremely few reference for assessing the image quality. Second, we further introduce the attention perception mechanism to boost the assessment performance by improving the sparse perception mechanism mentioned above. Based on the most commonly used image quality database, it was found that our proposed model has derived higher performance than the peer extremely few reference IQA models and competitive performance as compared with the benchmark full reference IQA models.
The context effect for blind image quality assessment

Image quality assessment (IQA) is a process of visuo-cognitive, which is an essential stage in human interaction with the environment. The study of the context effect (Brown and Daniel, 1987) also shows that the evaluation results made by the human vision system (HVS) is related to the contrast between the distorted image and the background environment. However, the existing IQA methods carry out the quality evaluation that only depends on the distorted image itself and ignores the impact of environment to human perception. In this paper, we propose a novel blind image quality assessment(BIQA) based on the context effect. At first, we use a graphical model to describe how the context effect influences human perception of image quality. Based on the established graph, we construct the context relation between the distorted image and the background environment by the X. Han et al. (2015). Then the context features are extracted from the constructed relation, and the quality-related features are extracted by the fine-tuned neural network from the distorted image in pixel-wise. Finally, these features are concatenated to quantify image quality degradations and then regress to quality scores. In addition, the proposed method is adaptive to various deep neural networks. Experimental results show that the proposed method not only has the state-of-art performance on the synthetic distorted images, but also has a great improvement on the authentic distorted images.
Incremental concept-cognitive learning approach for concept classification oriented to weighted fuzzy concepts

Fuzzy formal concept analysis (FFCA) is generally used to describe the processes of concept-cognitive learning (CCL). However, for fuzzy formal contexts, each attribute has the same weight (i.e, the same degree of importance) before constructing fuzzy concepts, which limits mining interesting knowledge and affects its application promotion. On the other hand, this model is hard to resist the influence of noise hidden in data, which results in poor classification learning. Moreover, the existing incremental CCL algorithms still face some challenges that the previously acquired knowledge is not fully utilized to improve the classification accuracies for dynamic data. To address these issues, we introduce different weights into fuzzy formal contexts and propose a novel incremental CCL mechanism in dynamic environment. Firstly, weight values of attributes from different decisions based on fuzzy entropy are established to measure the significant degree of attributes. Then, to comprehensively explicate the hierarchical relationships of fuzzy concepts, we construct the weighted fuzzy concept lattice and the weighted fuzzy concept space. Secondly, we design an algorithm to update the weighted fuzzy concepts for facilitating concept classification. To overcome the individual cognitive limitation, we put forward the progressive weighted fuzzy concept to remove repeated information. Furthermore, the classification prediction label and dynamic updating mechanism after adding objects are systematically discussed. Finally, we perform an experimental evaluation on ten data sets which explicate the feasibility and efficiency of our proposed approach.
The quantum computer for accelerating image processing and strengthening the security of information systems

Many researchers and laboratories have been involved for many years in the development of the quantum computer. In 2019, Google has announced that they have reached quantum supremacy with their quantum computer Sycamore using a 53 qubits processor. On November 2021, IBM also announced that they have built a quantum computer with the highest number of 127 qubits and revealed their ambitious goal of building a 1000 qubits processor by 2023. According to these two giants of the quantum computing field, such a machine can perform an astronomical quantity of calculations significantly faster than any other conventional computer. This technology could be a real revolution in many fields such as computing, artificial intelligence, medicine, chemistry, banking, and experimental techniques, …etc. Since this technology is still in its infancy in hardware and software, its progress may be slow. However, many working in the field predict that 2,000 to 5,000 quantum computers of a first generation will be operational by 2030, but quantum computers needed to deal with more complex problems may not exist until 2035 or beyond. In this paper, we will go over some of the basic aspects of a quantum computer, in particular the concept of the qubit or the quantum bit and the quantum computer itself. Then we will see how a quantum computer can effectively speed up the processing of large images, reduce the amount of memory needed for computation and storage and even, strengthen the security of information systems. Lastly, we will present and discuss a real hardware implementation of the well-known quantum edge detection, as well as a spy hunter simulation.
Qubit noise deconvolution

We present a noise deconvolution technique to remove a wide class of noises when performing arbitrary measurements on qubit systems. In particular, we derive the inverse map of the most common single qubit noisy channels and exploit it at the data processing step to obtain noise-free estimates of observables evaluated on a qubit system subject to known noise. We illustrate a self-consistency check to ensure that the noise characterization is accurate providing simulation results for the deconvolution of a generic Pauli channel, as well as experimental evidence of the deconvolution of decoherence noise occurring on Rigetti quantum hardware.
Ensemble-Based Semi-Supervised Learning for Milling Chatter Detection

Chatter is one of the most deleterious phenomena during the machining process, and leads to a low quality of workpiece surface, a noisy workplace, and decreases in tool and machine life. In order to overcome these limitations and improve the machining performance, various effective methods have been developed for chatter detection. The main shortcoming of such methods is that they require all the data to be labeled. However, the labeled data that accurately reflect the chatter states are hard to collect in practical application. This paper proposes a semi-supervised method to classify chatter states with a small quantity of labeled data and large quantity of unlabeled ones. In order to improve the classification accuracy and generalization ability, ensemble learning is combined with the semi-supervised method, and an EB-SSL model is proposed in this paper. Take the non-stationarity and multiple scaling behaviors of chatter data into consideration, multifractal detrended fluctuation analysis (MF-DFA) is utilized to extract distinguished features from raw chatter detection signals. Experimental results show that this method can identify the chatter states more accurately. The performance analysis indicates that the proposed method is applicable in different milling conditions.
DeepDetection: Privacy-Enhanced Deep Voice Detection and User Authentication for Preventing Voice Phishing

The deep voice detection technology currently being researched causes personal information leakage because the input voice data are stored in the detection server. To overcome this problem, in this paper, we propose a novel system (i.e., DeepDetection) that can detect deep voices and authenticate users without exposing voice data to the server. Voice phishing prevention is achieved in two-way approaches by performing primary verification through deep voice detection and secondary verification of whether the sender is the correct sender through user authentication. Since voice preprocessing is performed on the user local device, voice data are not stored on the detection server. Thus, we can overcome the security vulnerabilities of the existing detection research. We used ASVspoof 2019 and achieved an F1-score of 100% in deep voice detection and an F1 score of 99.05% in user authentication. Additionally, the average EER for user authentication achieved was 0.15. Therefore, this work can be effectively used to prevent deep voice-based phishing.
A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN

Currently, data-driven based machine learning is considered one of the best choices in clinical pathology analysis, and its success is subject to the sufficiency of digitized slides, particularly those with deep annotations. Although centralized training on a large data set may be more reliable and more generalized, the slides to the examination are more often than not collected from many distributed medical institutes. This brings its own challenges, and the most important is the assurance of privacy and security of incoming data samples. In the discipline of histopathology image, the universal stain-variation issue adds to the difficulty of an automatic system as different clinical institutions provide distinct stain styles. To address these two important challenges in AI-based histopathology diagnoses, this work proposes a novel conditional Generative Adversarial Network (GAN) with one orchestration generator and multiple distributed discriminators, to cope with multiple-client based stain-style normalization. Implemented within a Federated Learning (FL) paradigm, this framework well preserves data privacy and security. Additionally, the training consistency and stability of the distributed system are further enhanced by a novel temporal self-distillation regularization scheme. Empirically, on large cohorts of histopathology datasets as a benchmark, the proposed model matches the performance of conventional centralized learning very closely. It also outperforms state-of-the-art stain-style transfer methods on the downstream Federated Learning image classification task, with an accuracy increase of over 20.0% in comparison to the baseline classification model.
Patch Similarity Aware Data-Free Quantization for Vision Transformers

Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constrained devices. Quantization is an effective approach to reduce model complexity, and data-free quantization, which can address data privacy and security concerns during model deployment, has received widespread interest. Unfortunately, all existing methods, such as BN regularization, were designed for convolutional neural networks and cannot be applied to vision transformers with significantly different model architectures. In this paper, we propose PSAQ-ViT, a Patch Similarity Aware data-free Quantization framework for Vision Transformers, to enable the generation of “realistic" samples based on the vision transformer’s unique properties for calibrating the quantization parameters. Specifically, we analyze the self-attention module’s properties and reveal a general difference (patch similarity) in its processing of Gaussian noise and real images. The above insights guide us to design a relative value metric to optimize the Gaussian noise to approximate the real images, which are then utilized to calibrate the quantization parameters. Extensive experiments and ablation studies are conducted on various benchmarks to validate the effectiveness of PSAQ-ViT, which can even outperform the real-data-driven methods. Code is available at: https://github.com/zkkli/PSAQ-ViT .
Spelling Checking with Deep Learning Model in Analysis of Tweet Data for Word Classification Process

The process of preprocessing produces data that still has writing and spelling errors that refer to Indonesian words so there are problems in the next process, namely in classifying formal and non-formal words. if the preprocessed tweet data is corrected manually, it will take a lot of time with 17,378 data. The solution to this problem in this context will be to improve spelling starting from the preprocessing results and then use a dictionary of slang words and the big Indonesian dictionary which in the process data from the Big Indonesian Dictionary and the slang word dictionary will be studied for patterns with models from deep learning which is used later in the process is converted into vector embedding then from the vector vectors formed the cosine similarity technique is used which measures the similarity between two text data. In this study, the model of deep learning is the Bi-LSTM method. The results obtained show that the spell checker produces better data with an accuracy of up to 82,5% for words that have irregular sentence structures.
A new contrastive learning framework for reducing the effect of hard negatives

Contrastive learning as a self-supervised method has achieved great success. Although it is an instance-level discriminative method, the model can eventually learn latent semantic class information. Its core idea is pulling different views of the same instance closer but pushing out different instances. However, treating an instance as a class hinders the model from learning true latent semantic classes, which is caused by instances (called hard negatives) that are similar to the anchor but do not belong to the same semantic class. In this paper, we propose a new contrastive learning framework based on the Student-t distribution with a neighbor consistency constraint (TNCC) to reduce the effect of hard negatives. In this framework, we propose to use the loss based on the Student-t distribution as the instance-level discriminative loss to keep hard negatives far away. Furthermore, we add a new neighbor consistency constraint to maintain consistency within the semantic classes. Finally, we compare TNCC with recent state-of-the-art contrastive learning methods on five benchmark datasets to verify the effectiveness of the proposed framework.
BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords

Sentiment analysis of online Chinese buzzwords (OCBs) is important for healthy development of platforms, such as games and social networking, which can avoid transmission of negative emotions through prediction of users’ sentiment tendencies. Buzzwords have the characteristics of varying text length, irregular wording, ignoring syntactic and grammatical requirements, no complete semantic structure, and no obvious sentiment features. This results in interference and challenges to the sentiment analysis of such texts. Sentiment analysis also requires capturing effective sentiment features from deeper contextual information. To solve the above problems, we propose a deep learning model combining BERT and BiLSTM. The goal is to generate dynamic representations of OCB vectors in downstream tasks by fine-tuning the BERT model and to capture the rich information of the text at the embedding layer to solve the problem of static representations of word vectors. The generated word vectors are then transferred to the BiLSTM model for feature extraction to obtain the local and global semantic features of the text while highlighting the text sentiment polarity for sentiment classification. The experimental results show that the model works well in terms of the comprehensive evaluation index F1. Our model also has important significance and research value for sentiment analysis of irregular texts, such as OCBs.