Reviewerly/exHarmony huggingface dataset:
Breast cancer survival analysis agents for clinical decision support

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.
Sputum smears quality inspection using an ensemble feature extraction approach

The diagnosis of tuberculosis (TB) is extremely important. Sputum smear microscopy is thought to be the best method available in terms of accessibility and ease of use in resource-constrained countries. In this paper, research was conducted to evaluate the effectiveness of tuberculosis diagnosis by examining, among other things, the underlying causes of sputum smear quality for Ethiopian states such as Tigray, Amahira, and Oromia. However, because it is done manually, it has its limitations. This study proposes a model for sputum smear quality inspection using an ensemble feature extraction approach. The dataset used was recorded and labeled by experts in a regional lab in Bahir Dar, near Felege Hiwot Hospital after being collected from Gabi Hospital, Felege Hiwot Hospital, Adit Clinic and Gondar Hospital, as well as Kidanemihret Clinic in Gondar. We used a controlled environment to reduce environmental influences and eliminate variation. All the data was collected using a smartphone (the standard 15) with a jpg file extension and a pixel resolution of 1,728 × 3,840. Prior to feature extraction, bicubic resizing, and ROI extraction using thresholding was performed. In addition, sequential Gaussian and Gabor filters were used for noise reduction, augmentation, and CLAHE was used for enhancement. For feature extraction, GLCM from the gray label and CNN from the color image were both chosen. Ultimately, when CNN, SVM, and KNN classifiers were used to test both CNN and GLCM features, KNN outperformed them all with scores of 87, 93, and 94% for GLCM, CNN, and a hybrid of CNN and GLCM, respectively. CNN with GLCM outperformed other methods by 0.7 and 0.1% for GLCM and CNN feature extractors using the same classifier, respectively. In addition, the KNN classifier with the combination of CNN and GLCM as feature extractors performed better than existing methods by 1.48%.
Siamese Neural Pointnet: 3D Face Verification under Pose Interference and Partial Occlusion

Face verification based on ordinary 2D RGB images has been widely used in daily life. However, the quality of ordinary 2D RGB images is limited by illumination, and they lack stereoscopic features, which makes it difficult to apply them in poor lighting conditions and means they are susceptible to interference from head pose and partial occlusions. Considering point clouds are not affected by illumination and can easily represent geometric information, this paper constructs a novel Siamese network for 3D face verification based on Pointnet. In order to reduce the influence of the self-generated point clouds, the chamfer distance is adopted to constrain the original point clouds and explore a new energy function to distinguish features. The experimental results with the Pandora and Curtin Faces datasets show that the accuracy of the proposed method is improved by 0.6% compared with the latest methods; in large pose interference and partial occlusion, the accuracy is improved by 4% and 5%. The results verify that our method outperforms the latest methods and can be applied to a variety of complex scenarios while maintaining real-time performance.
CARES: A Corpus for classification of Spanish Radiological reports

This paper presents a new corpus of radiology medical reports written in Spanish and labeled with ICD-10. CARES (Corpus of Anonymised Radiological Evidences in Spanish) is a high-quality corpus manually labeled and reviewed by radiologists that is freely available for the research community on HuggingFace. These types of resources are essential for developing automatic text classification tools as they are necessary for training and tuning computational systems. However, in the medical domain these are very difficult to obtain for different reasons including privacy and data protection issues or the involvement of medical specialists in the generation of these resources. We present a corpus labeled and reviewed by radiologists in their daily practice that is available for research purposes. In addition, after describing the corpus and explaining how it has been generated, a first experimental approach is carried out using several machine learning algorithms based on transformer language models such as BioBERT and RoBERTa to test the validity of this linguistic resource. The best performing classifier achieved 0.8676 micro and 0.8328 macro f1-score and these results encourage us to continue working in this research line.
Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability.
Multiphoton Resonance Band and Bloch–Siegert Shift in a Bichromatically Driven Qubit

Abstract The resonance and dynamics of a qubit exposed to a strong aperiodic bichromatic field are studied by using a periodic counter‐rotating hybridized rotating wave (CHRW) Hamiltonian, which is derived from the original Hamiltonian with the unitary transformations under a reasonable approximation and enables the application of the Floquet theory. It is found that the consistency between the CHRW results and numerically exact generalized‐Floquet‐theory (GFT) results in the valid regime of the former while the widely used rotating‐wave approximation breaks down. It is illustrated that the resonance exhibits band structure and the Bloch–Siegert shifts induced by the counter‐rotating couplings of the bichromatic field become notable at the multiphoton resonance band. In addition, the CHRW method is found to have a great advantage of efficiency over the GFT approach particularly in the low beat‐frequency case where the latter converges very slowly. The present CHRW method provides a highly efficient way to calculate the resonance frequency incorporating the Bloch–Siegert shift and provides insights into the effects of the counter‐rotating couplings of the bichromatic field in the strong‐driving regimes.
Transformer-Based Subject-Sensitive Hashing for Integrity Authentication of High-Resolution Remote Sensing (HRRS) Images

The implicit prerequisite for using HRRS images is that the images can be trusted. Otherwise, their value would be greatly reduced. As a new data security technology, subject-sensitive hashing overcomes the shortcomings of existing integrity authentication methods and could realize subject-sensitive authentication of HRRS images. However, shortcomings of the existing algorithm, in terms of robustness, limit its application. For example, the lack of robustness against JPEG compression makes existing algorithms more passive in some applications. To enhance the robustness, we proposed a Transformer-based subject-sensitive hashing algorithm. In this paper, first, we designed a Transformer-based HRRS image feature extraction network by improving Swin-Unet. Next, subject-sensitive features of HRRS images were extracted by this improved Swin-Unet. Then, the hash sequence was generated through a feature coding method that combined mapping mechanisms with principal component analysis (PCA). Our experimental results showed that the robustness of the proposed algorithm was greatly improved in comparison with existing algorithms, especially the robustness against JPEG compression.
Control-enhanced quantum metrology under Markovian noise

Quantum metrology is supposed to significantly improve the precision of parameter estimation by utilizing suitable quantum resources. However, the predicted precision can be severely distorted by realistic noises. Here, we propose a control-enhanced quantum metrology scheme to defend against these noises to improve the metrology performance. Our scheme can automatically alter the parameter-encoding dynamics with adjustable controls, thus leading to optimal resultant states that are less sensitive to the noises under consideration. As a demonstration, we numerically apply it to the problem of frequency estimation under several typical Markovian noise channels. By comparing our control-enhanced scheme with the standard scheme and the ancilla-assisted scheme, we show that our scheme performs better and can improve the estimation precision up to around one order of magnitude. Furthermore, we conduct a proof-of-principle experiment in a nuclear magnetic resonance system to verify the effectiveness of the proposed scheme. The research here is helpful for current quantum platforms to harness the power of quantum metrology in realistic noise environments.
Decolorization based on the weighted combination of image entropy and canny edge retention ratio

Image decolorization is used to realize the color-to-gray conversion, which is widely used in black-and-white printing, digital-ink display, photo stylization, etc. Image contrast fidelity preservation is the key issue during this conversion process. We present a parametric model combining image entropy and the Canny edge retention ratio (CERR). Maximizing the image entropy keeps the global image contrast content, while maximizing the CERR enhances the local image contrast, mainly along the edges. The weighting parameter λ can be robustly estimated. Experimental results on Cadik’s and the COLOR250 datasets demonstrate that the model can achieve significant performance improvement over state-of-the-art methods both qualitatively and quantitatively.
Generation of large-scale continuous-variable cluster states multiplexed both in time and frequency domains

A large-scale continuous variable (CV) cluster state is necessary in quantum information processing based on measurement-based quantum computing (MBQC). Specially, generating a large-scale CV cluster state multiplexed in a time domain is easier to implement and has strong scalability in experiment. Here one-dimensional (1D) large-scale dual-rail CV cluster states multiplexed both in time and frequency domains are parallelly generated, which can be further extended to a three-dimensional (3D) CV cluster state by combining two time-delay nondegenerate optical parametric amplification systems with beam-splitters. It is shown that the number of parallel arrays depends on the corresponding frequency comb lines, the partite number of each array can be very large (million), and the scale of the 3D cluster state can be ultra-large. In addition, the concrete quantum computing schemes of applying the generated 1D and 3D cluster states are also demonstrated. Our schemes may pave the way for fault-tolerant and topologically protected MBQC in hybrid domains, by further combining with efficient coding and quantum error correction.
Analysis and Handling of Dynamic Problem Changes in Open-Ended Optimization

Changes in dynamic optimization problems entail updates to the problem model, which in turn can result in changes to the problem’s fitness landscape and even its solution encoding. In order to yield valid solutions that are applicable to the current problem state, optimization algorithms must be able to cope with such dynamic problem updates. Furthermore, depending on the optimization use case, changes occurring in real-world environments require an optimizer to adapt to changing process conditions and yield updated, valid solutions within a short time frame. In this paper, dynamic problem changes and their effects on an optimizer’s algorithmic behavior are studied in the context of crane scheduling. Three open-ended versions of RAPGA, a relevant alleles preserving genetic algorithm, are evaluated, some of which include self-adaption and a special treatment of certain events that require domain knowledge to be recognized. The proposed extensions affect the algorithm behavior as desired. On the one hand, the algorithms converge faster after a loss in solution quality is detected. On the other hand, new genetic material is introduced, making it possible to reach high quality areas of the search space again.
Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation

During the COVID-19 epidemic, the online prescription pattern of Internet healthcare provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. Online drug recommendation system can effectively assist doctors by analysing the electronic medical records (EMR) of patients. Unlike commercial recommendations, the accuracy of drug recommendations should be very high due to their relevance to patient health. Besides, concept drift may occur in the drug treatment data streams, handling drift and location drift causes is critical to the accuracy and reliability of the recommended results. This paper proposes a multi-model fusion online drug recommendation system based on the association of drug and pathological features with online-nearline-offline architecture. The system transforms drug recommendation into pattern classification and adopts interpretable concept drift detection and adaptive ensemble classification algorithms. We apply the system to the Percutaneous Coronary Intervention (PCI) treatment process. The experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%
Autonomous UAV with obstacle management using AIoT: a case study on healthcare application

With the simultaneously connected 26.66 billion devices worldwide, the Internet of Things (IoT) is becoming a vast field of research and helping hand to every individual. However, when IoT and Artificial Intelligence (AI) and machine learning (ML) consolidate, it results in smart applications and future revolutions that are known as Artificial Intelligent of Things (AIoT). Similarly, the unmanned aerial vehicle (UAV) domain is also developing daily, helping many unrest people in the healthcare industry. One step towards developing the healthcare industry is the use of UAV devices like drones embedded with AIoT to work autonomously in the healthcare industry. This can help the healthcare industry in many ways. This chapter proposes an algorithm to recast these UAV drones to autonomous UAV drones and use them as intelligent or smart for various healthcare purposes like COVID-19. The proposed autonomous UAV drone uses Raspberry Pi 3, a Hubney, and a bearing formula to automatically determine the direction of the UAV movement, making it work without any controller. Also, the comparative study presented in this chapter highlighted the benefits of this proposed algorithm with others present in the literature.
DualFlow: Generating imperceptible adversarial examples by flow field and normalize flow-based model

Recent adversarial attack research reveals the vulnerability of learning-based deep learning models (DNN) against well-designed perturbations. However, most existing attack methods have inherent limitations in image quality as they rely on a relatively loose noise budget, i.e., limit the perturbations by L p -norm. Resulting that the perturbations generated by these methods can be easily detected by defense mechanisms and are easily perceptible to the human visual system (HVS). To circumvent the former problem, we propose a novel framework, called DualFlow , to craft adversarial examples by disturbing the image's latent representations with spatial transform techniques. In this way, we are able to fool classifiers with human imperceptible adversarial examples and step forward in exploring the existing DNN's fragility. For imperceptibility, we introduce the flow-based model and spatial transform strategy to ensure the calculated adversarial examples are perceptually distinguishable from the original clean images. Extensive experiments on three computer vision benchmark datasets (CIFAR-10, CIFAR-100 and ImageNet) indicate that our method can yield superior attack performance in most situations. Additionally, the visualization results and quantitative performance (in terms of six different metrics) show that the proposed method can generate more imperceptible adversarial examples than the existing imperceptible attack methods.
Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification

The majority of recent state-of-the-art speaker verification architectures adopt multi-scale processing and frequency-channel attention mechanisms. Convolutional layers of these models typically have a fixed kernel size, e.g., 3 or 5. In this study, we further contribute to this line of research utilising a selective kernel attention (SKA) mechanism. The SKA mechanism allows each convolutional layer to adaptively select the kernel size in a data-driven fashion. It is based on an attention mechanism which exploits both frequency and channel domain. We first apply existing SKA module to our baseline. Then we propose two SKA variants where the first variant is applied in front of the ECAPA-TDNN model and the other is combined with the Res2net backbone block. Through extensive experiments, we demonstrate that our two proposed SKA variants consistently improves the performance and are complementary when tested on three different evaluation protocols.
DIR: A Large-Scale Dialogue Rewrite Dataset for Cross-Domain Conversational Text-to-SQL

Semantic co-reference and ellipsis always lead to information deficiency when parsing natural language utterances with SQL in a multi-turn dialogue (i.e., conversational text-to-SQL task). The methodology of dividing a dialogue understanding task into dialogue utterance rewriting and language understanding is feasible to tackle this problem. To this end, we present a two-stage framework to complete conversational text-to-SQL tasks. To construct an efficient rewriting model in the first stage, we provide a large-scale dialogue rewrite dataset (DIR), which is extended from two cross-domain conversational text-to-SQL datasets, SParC and CoSQL. The dataset contains 5908 dialogues involving 160 domains. Therefore, it not only focuses on conversational text-to-SQL tasks, but is also a valuable corpus for dialogue rewrite study. In experiments, we validate the efficiency of our annotations with a popular text-to-SQL parser, RAT-SQL. The experiment results illustrate 11.81 and 27.17 QEM accuracy improvement on SParC and CoSQL, respectively, when we eliminate the semantic incomplete representations problem by directly parsing the golden rewrite utterances. The experiment results of evaluating the performance of the two-stage frameworks using different rewrite models show that the efficiency of rewrite models is important and still needs improvement. Additionally, as a new benchmark of the dialogue rewrite task, we also report the performance results of different baselines for related studies. Our dataset will be publicly available once this paper is accepted.
Traffic Pattern Plot: Video Identification in Encrypted Network Traffic

Most of the Internet traffic is encrypted, and the challenge is its ability to recognize the streaming videos from the Internet traffic. In this paper, we present a methodology named traffic pattern plot (TPP) to identify video streams in encrypted network traffic. The proposed methodology plots the video traffic flows and uses a convolutional neural network (CNN) to detect the videos. The results show that the traffic pattern plot generated from 120 s of sniffing network traffic is enough to identify the video even in the encrypted network traffic with 94% accuracy.
Position-Based Visual Servo Control of Dual Robotic Arms With Unknown Kinematic Models: A Cerebellum- Inspired Approach

The inverse kinematics problem of robotic arms can be solved effectively based on the kinematic model. However, the control of robotic arms with unknown kinematic models remains challenging. This article proposes a position-based visual servo (PBVS) control system for dual robotic arms with unknown kinematic models. First, a unified PBVS control method based on dual gradient neural dynamic (DGND) models is developed to achieve the position and orientation control of a single robotic arm with unknown kinematic model. Next, a cerebellum-inspired DGND (CIDGND) control system is devised to achieve higher accuracy. More importantly, a control system for dual robotic arms is proposed based on the CIDGND method to expand the working space of the primary robotic arm, by autonomously adjusting the camera pose with another robotic arm. Finally, simulations and experiments are performed to verify the efficacy of the proposed control systems. The results validate that the DGND method can achieve the PBVS control of robotic arms in the absence of kinematic models and the CIDGND method is able to reduce the tracking error significantly. Comparative results reveal that the CIDGND method is effectively and robustly better than existing methods.