Reviewerly/exHarmony huggingface dataset:
Using the Concept of Generative Adversarial Network (GAN) to Strengthen the Classification Performance for X-ray Images of Small Samples

Because the medical technology is quickly developed, how to implement an accurate diagnosis and adopts timely treatment that will seriously influence the patient safety. With the advancement of artificial intelligence (AI), the medical diagnosis support system (MDSS) also plays key role and to increase the diagnostic performance. However, there is a problem that needs to be noted and solved for the MDSS in machine and deep learning. That is, when the data exhibits an imbalance situation and the lack of sufficient medical diagnosis data that will induce an inferior recognition result. Based on such a problem, the main purpose of this study is to construct a classification system so that the abnormal images of chest x-ray in small samples can be distinguished smoothly for pneumothorax. Firstly, the classified system uses the cycle generative adversarial network (CycleGAN) to learn the image features, and then forms the virtual pneumothorax images so as to raise the abnormal samples. When the virtual samples are generated completely, the system will apply the convolutional neural network (CNN) model to execute the training and discriminating tasks. Using the expanded sample cluster, the system can perform the training procedure and the real testing data is also used to evaluate the classified accuracy. The experiment studies showed that the expanded classification system is able to obtain a superior performance than the simple CNN classifier. At last, an experimental case for pneumothorax is used to illustrate the proposed system for verifying the feasibility of using our analysis procedure.
Implement Seven Patient-Oriented Data Security Metrics using Unclonable Health Insurance Cards

The healthcare system typically relies on health insurance cards. However, they can still be copied because they are not unique, like fingerprints. By combining unclonable health insurance cards with an algorithmic process, patients can achieve maximum performance in seven major data security metrics: storage security, confidentiality, integrity, availability, non-repudiation, privacy/ID verification, and access control. This study presents a set of algorithms that illustrate and validate how using an unclonable health insurance card can achieve these seven patient-oriented security key metrics. The unclonable health insurance card is like a personalized fingerprint card, which offers enhanced database and storage security through system algorithm authentication and data encryption for managing and querying patient information security.
Identification of Acute Lymphoblastic Leukemia Subtypes on a Microscopic Image of Multicellular Blood Using Object Detection Model with Swin Transformer

Detecting subtypes of acute lymphoblastic leukemia (ALL) in multicellular microscopic images is crucial for early diagnosis and treatment. In the previous study, ALL subtype detection has often employed conventional methods requiring multiple phases, including WBC segmentation, touch cell separation, feature extraction, and classification. We compare object detection algorithms that require only a single learning framework and no additional steps. The performance of the YOLO, Mask R-CNN, and Mask R-CNN with Swin Transformer models for detecting ALL subtypes are compared. The aim of model comparison is to evaluate the performance in detecting the subtype of ALL with the best mAP value. In the detection of ALL subtypes, the Mask R-CNN with Swin Transformer surpasses all other models. The Mask R-CNN model with Swin Transformer produced the best global test results for the L1, L2 and L3 detection process, with mAP(0.5) and mAP(0.95) values of 94.5% and 68%, respectively.
The Impact of Feature Normalization on Different Feature Types of Medical Datasets

To obtain quality data mining results, data pre-processing is usually performed in the knowledge discovery in databases (KDD) process. Particularly, feature normalization or scaling is one important step in data pre-processing. This is because many datasets usually contain some features that have broad ranges of values, and feature normalization is applied to normalize or rescale each feature value to a fixed range, usually between 0 and 1. For the medical domain datasets, they usually contain three different kinds of data including categorical, numerical, and the mixed data type, this paper examines the effect of performing feature normalization on the three different types of medical datasets. Our experimental results indicate that for the categorical and some mixed types of datasets performing feature normalization does not necessarily make the k-NN and SVM classifiers perform better than the ones without feature normalization. On the other hand, for the numerical type of datasets k-NN and SVM by feature normalization perform better than the baseline classifiers.
A Predictive Model for Clinical Health Risk Using Multimodal Electronic Health Record Data

To increase the availability of ICU resources and the quality of clinical monitoring, we develop a deep learning model to predict in-hospital mortality and decompensation risk using time series data and clinical notes from electronic health records (EHR). The proposed model integrates the time series prediction and text feature extraction modules. Demographics, laboratory tests, and vital signs data were extracted from EHR. Moreover, clinical notes were obtained to give additional information for risk prediction. A retrospective study was conducted using patient records from the open-source ICU database MIMIC-III. The performance was evaluated using the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). In comparison to the baseline model, the suggested model achieved the greatest metric value for two clinical tasks. It is anticipated that our model will assist medical personnel in identifying high-risk patients, enhancing the quality of nursing care, and facilitating more fair scheduling of medical resources.
Healthcare Material Review with Online Chatbot and Care-receiver Sentiment Analysis Information System

With the rapid increase in demand for telemedicine, it is imperative to use innovative enabling technologies combined with digital sovereignty for online healthcare education courses and telehealth consultation. In this paper, we develop a "healthcare material review with online chatbot and care-receiver sentiment analysis information system" to improve the effectiveness of remote training material review process and provide care measures for remote care-receivers. In the system, review process will screen the materials that are uploaded to the online health education database for words and phrases that may cause discomfort or anxiety to the users and affect the effectiveness of distance education and training. The system also reviews materials that are duplicated in content and also uses Latent Dirichlet Allocation (LDA) modelling to help categorize the materials by topics. In order to understand the situation of remote health consultation, the system uses machine vision, image processing and artificial intelligence recognition technologies to capture facial physiological features and characteristics for identifying the internal emotional state of the care-receivers. The system also develops a chatbot to offer assistance with appropriate health information.
Ensuring FHIR Authentication and Data Integrity by Smart Contract and Blockchain Enabled NFT

Department of Computer Science and Information Engineering, National Dong Hwa University, Taiwan
A Novel Prediction Approach for Effective Medical Data Mining

Data mining techniques have been employed for solving many medical problems, especially for disease prediction. For instance, given a dataset containing normal and cancerous patients, the goal is to develop a model to predict whether a new (unknown) patient belongs to the normal or cancerous class. In general, the model is constructed based on some machine learning technique over a collected training set. However, the quality of the training set can affect the final prediction performance of the model. That is, if the training set contains some certain amount of noisy data (or outliers), then the model's performance could be degraded. In literature, instance selection is performed over a given training set in order to filter out some noisy data and the reduced training set containing non-noisy data is used for developing the prediction model. In this paper, we present a novel approach where instance selection is performed to divide a given training set into noisy and non-noisy subsets. Then, they are used to train two models respectively. During prediction, the instance selection step is also executed over the testing set, in which the noisy and non-noisy subsets are used to test their corresponding models respectively. The experimental results based on various medical domain datasets show that our proposed approach performs better than the baseline, which is based on the conventional instance selection approach.
TransUNet-Lite: A Robust Approach to Cell Nuclei Segmentation

Deep convolutional neural networks have demonstrated superior performance in a variety of vision tasks. For biomedical applications, these methods suffer from problems such as predicting reliable segmentation masks for variable size input images, insufficient data and imbalanced datasets. This paper introduces an efficient and lightweight TransUNet, termed as TransUNet-Lite, that exploits rich feature representations produced by the convolution-based feature extractor, an external attention module instead of conventional self-attention, a fast token selector module, and skip connections from the feature extractor to the decoder to provide lost rich contextual information. The proposed network takes patches as input rather than resized images that fail to care for the original aspect ratio. For the nuclei segmentation task on the 2018 Science Bowl dataset, our TransUNet-Lite outperformed other SOTA networks, with the highest DSC of 93.08% and IoU of 87.95%. The results of our experiments provide insight into the impact of certain network design decisions. By configuring a transformer in a simplistic and efficient manner, it is possible to achieve segmentation quality that is at least equal to SOTA network architectures.
Multi-behavioral recommendation model based on dual neural networks and contrast learning

In order to capture the complex dependencies between users and items in a recommender system and to alleviate the smoothing problem caused by the aggregation of multi-layer neighborhood information, a multi-behavior recommendation model (DNCLR) based on dual neural networks and contrast learning is proposed. In this paper, the complex dependencies between behaviors are divided into feature correlation and temporal correlation. First, we set up a personalized behavior vector for users and use a graph-convolution network to learn the features of users and items under different behaviors, and we then combine the features of self-attention mechanism to learn the correlation between behaviors. The multi-behavior interaction sequence of the user is input into the recurrent neural network, and the temporal correlation between the behaviors is captured by combining the attention mechanism. The contrast learning is introduced based on the double neural network. In the graph convolution network layer, the distances between users and similar users and between users and their preference items are shortened, and the distance between users and their short-term preference is shortened in the circular neural network layer. Finally, the personalized behavior vector is integrated into the prediction layer to obtain more accurate user, behavior and item characteristics. Compared with the sub-optimal model, the HR@10 on Yelp, ML20M and Tmall real datasets are improved by 2.5%, 0.3% and 4%, respectively. The experimental results show that the proposed model can effectively improve the recommendation accuracy compared with the existing methods.
Better Predicates and Heuristics for Improved Commutativity Synthesis

Code commutativity has increasingly many applications including proof methodologies for concurrency, reductions, automated parallelization, distributed systems and blockchain smart contracts. While there has been some work on automatically generating commutativity conditions through abstraction refinement, the performance of such refinement algorithms critically depends on (i) the universe of predicates and (ii) the choice of the next predicate during search, and thus far this has not been examined in detail. In this paper, we improve commutativity synthesis by addressing these under-explored requirements. We prune the universe of predicates through a combination of better predicate generation, new a priori syntactic filtering, and through dynamic reduction of the search space. We also present new predicate selection heuristics: one based on look-ahead, and one that utilizes model counting to greedily cover the search space. Our work is embodied in the new commutativity synthesis tool Servois2, a generational improvement over the state-of-the-art tool Servois. Servois2 is implemented in a faster language and has support for CVC5 and Z3. We contribute new, non-trivial commutativity benchmarks. All of the new features in Servois2 are shown to either increase performance (geomean $$3.58 \times $$ speedup) or simplify the conditions generated, when compared against Servois. We also show that our look-ahead heuristic leads to better scaling with respect to the number of predicates.
Minimally Comparing Relational Abstract Domains

Value-based static analysis techniques express computed program invariants as logical formula over program variables. Researchers and practitioners use these invariants to aid in software engineering and verification tasks. When selecting abstract domains, practitioners weigh the cost of a domain against its expressiveness. However, an abstract domain’s expressiveness tends to be stated in absolute terms; either mathematically via the sub-polyhedra the domain is capable of describing, empirically using a set of known properties to verify, or empirically via logical entailment using the entire invariant of the domain at each program point. Due to carry-over effects, however, the last technique can be problematic because it tends to provide simplistic and imprecise comparisons. We address these limitations of comparing, in general, abstract domains via logical entailment in this work. We provide a fixed-point algorithm for including the minimally necessary variables from each domain into the compared formula. Furthermore, we empirically evaluate our algorithm, comparing different techniques of widening over the Zones domain and comparing Zones to an incomparable Relational Predicates domain. Our empirical evaluation of our technique shows an improved granularity of comparison. It lowered the number of more precise invariants when comparing analysis techniques, thus, limiting the prevalent carry-over effects. Moreover, it removed undecidable invariants and lowered the number of incomparable invariants when comparing two incomparable relational abstract domain.
Fast Equivalence Checking of Quantum Circuits of Clifford Gates

Checking whether two quantum circuits are equivalent is important for the design and optimization of quantum-computer applications with real-world devices. We consider quantum circuits consisting of Clifford gates, a practically-relevant subset of all quantum operations which is large enough to exhibit quantum features such as entanglement and forms the basis of, for example, quantum-error correction and many quantum-network applications. We present a deterministic algorithm that is based on a folklore mathematical result and demonstrate that it is capable of outperforming previously considered state-of-the-art method. In particular, given two Clifford circuits as sequences of single- and two-qubit Clifford gates, the algorithm checks their equivalence in $$O(n \cdot m)$$ time in the number of qubits n and number of elementary Clifford gates m. Using the performant Stim simulator as backend, our implementation checks equivalence of quantum circuits with 1000 qubits (and a circuit depth of 10.000 gates) in $$\sim $$ 22 s and circuits with 100.000 qubits (depth 10) in $$\sim $$ 15 min, outperforming the existing SAT-based and path-integral based approaches by orders of magnitude. This approach shows that the correctness of application-relevant subsets of quantum operations can be verified up to large circuits in practice.
The optimal probability of the risk for finite horizon partially observable Markov decision processes

&lt;abstract&gt;&lt;p&gt;This paper investigates the optimality of the risk probability for finite horizon partially observable discrete-time Markov decision processes (POMDPs). The probability of the risk is optimized based on the criterion of total rewards not exceeding the preset goal value, which is different from the optimal problem of expected rewards. Based on the Bayes operator and the filter equations, the optimization problem of risk probability can be equivalently reformulated as filtered Markov decision processes. As an advantage of developing the value iteration technique, the optimality equation satisfied by the value function is established and the existence of the risk probability optimal policy is proven. Finally, an example is given to illustrate the effectiveness of using the value iteration algorithm to compute the value function and optimal policy.&lt;/p&gt;&lt;/abstract&gt;
SWAP-test algorithm implemented on the photonic platform for Quantum Machine Learning

We design an integrated photonic chip to implement the SWAP-test algorithm: using single-particle entanglement as a resource, the circuit can be used to estimate the scalar product of two qubits for quantum kernel methods.
Fast Optical Look-up Operations on Integrated Photonic Memory Arrays

We experimentally present the first complete all-optical look-up memory operation at 10 Gb/s, utilizing two identical InP monolithically integrated PICs of multi-bit photonic-memory arrays, being able to operate as a 2-bit all-optical RAM or as 2-bit optical CAM tables.
Experimental Investigation of Time-Stretch-Based Reservoir Computing with an Optical Input Mask

In this paper, we experimentally demonstrated a novel all-optical reservoir computer with an all optical input mask. The combination of the binary random masks and the time-stretched ultrashort pulses has increased the system’s classification performance. Compared with the traditional digital masks, this method shows superior classification performance in spoken-digit classification tasks and eliminates the need for high-speed modulation for digital masks.
Minimal physical model of the cristal Baschet

The cristal Baschet is a musical instrument created during the 1950’s by Bernard and Francois Baschet. It is composed of a large number of glass rods arranged in a chromatic scale. The sound produced results of vibrations induced by friction between wet fingers and the glass rods. Each glass rod is connected to an assembly of threaded shafts and a mass. Mechanical properties of this assembly determine the pitch of the note. Then vibrations are transmitted to large metal panels or cones that act as radiating elements. The manufacturing and tuning of this instrument is based on empirical knowledge and involves many parameters whose effects are not clearly understood. One of the encountered problems is the difficulty to produce sound in the high register of the instrument. In an attempt to understand the influences of these parameters on playability, a minimal physical model of the cristal Baschet is developed. It focuses on the interaction between the finger and the isolated resonator. The dynamic behavior is described by a set of modes obtained from a finite element model or from experimental modal analysis. The musician’s gesture is described by two control parameters: the velocity of the finger along the glass rod and the normal force applied by the finger on the rod. To describe the interaction between the finger and the resonator, a friction law is implemented. The influence of different parameters is studied by means of linear stability analysis and time-domain simulations. Specific criteria are developed to highlight the role of design parameters on playability.
Time-domain image processing using photonic reservoir computing

Photonic computing has attracted much attention due to its great potential to accelerate artificial neural network operations. However, the processing of a large amount of data, such as image data, basically requires large-scale photonic circuits and is still challenging due to its low scalability of the photonic integration. Here, we propose a scalable image processing approach, which uses a temporal degree of freedom of photons. In the proposed approach, the spatial information of a target object is compressively transformed to a time-domain signal using a gigahertz-rate random pattern projection technique. The time-domain signal is optically acquired at a single-input channel and processed with a microcavity-based photonic reservoir computer. We experimentally demonstrate that this photonic approach is capable of image recognition at gigahertz rates.
Message recovery attack on NTRU using a lattice independent from the public key

In the present paper, we introduce a new attack on NTRU-HPS cryptosystem using lattice theory and Babai's Nearest Plane Algorithm. This attack generalizes the classic CVP attack on NTRU. We present numerical data in support of the validity of our result.
Comparative analysis of digital noise generated by additive Fibonacci generators

Noise generators and pseudorandom number generators (PRNGs) are widely used in the field of information technology, including cybersecurity, for modeling, authorization key generation, and technical protection of information. It has been found that the characteristics of digital noise directly depend on the chosen PRNG algorithm. To determine the quality of the generated noise, special tests are performed, which are primarily applied to the sequence generated by the PRNG. The results of digital noise generated by an PRNG based on four different algorithms of additive Fibonacci generators (AFG) are investigated. The choice of generators of the same type allowed us to analyze the effect of different modifications on the final result of the generated sequences to determine their advantages and disadvantages. Digital signal processing techniques such as frequency, autocorrelation and visual analysis, signal-to-noise ratio, and statistical tests of the NIST package were used to test the noise and generated sequences. Functions for interpreting the obtained data were developed using the MATLAB (DSP System Toolbox) application package and the C programming language for automating NIST tests. It has been found that for effective testing, specific stages and their sequence should be determined: determination of the PRNG period, statistical tests of the NIST package, calculation of the autocorrelation function, and other methods of digital signal processing. It was found that modification of one AFG by using a carry bit (MAFG2) does not improve the results of the generated sequence, unlike the PIKE algorithm, which consists of three AFGs. The MAFG algorithm showed better results during the period testing and at the same time passed NIST tests, unlike the unmodified version. The dependence between the order of the generated sequences and the results of their autocorrelation function was revealed. It is proposed that, in addition to general statistical tests, applied tests should be carried out when choosing or developing a new generator, its effectiveness should be checked under the conditions required by existing standards and requirements. The compliance of the generated digital noise with the requirements for devices for technical protection of information, namely the protection of speech information, has been established.