Reviewerly/exHarmony huggingface dataset:
Functional Encryption with Secure Key Leasing

Secure software leasing is a quantum cryptographic primitive that enables us to lease software to a user by encoding it into a quantum state. Secure software leasing has a mechanism that verifies whether a returned software is valid or not. The security notion guarantees that once a user returns a software in a valid form, the user no longer uses the software. In this work, we introduce the notion of secret-key functional encryption (SKFE) with secure key leasing, where a decryption key can be securely leased in the sense of secure software leasing. We also instantiate it with standard cryptographic assumptions. More specifically, our contribution is as follows. Some previous secure software leasing schemes capture only pirate software that runs on an honest evaluation algorithm (on a legitimate platform). However, our secure key leasing notion captures arbitrary attack strategies and does not have such a limitation. As an additional contribution, we introduce the notion of single-decryptor FE (SDFE), where each functional decryption key is copy-protected. Since copy-protection is a stronger primitive than secure software leasing, this notion can be seen as a stronger cryptographic primitive than FE with secure key leasing. More specifically, our additional contribution is as follows.
Patient-level performance evaluation of a smartphone-based malaria diagnostic application

Abstract Background Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis. Methods A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net. Results Malaria Screener reached 74.1% (95% CI 63.5–83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0–81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8–96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0–88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6–86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development. Conclusion Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.
Comparison of a VR Stylus with a Controller, Hand Tracking, and a Mouse for Object Manipulation and Medical Marking Tasks in Virtual Reality

In medical surgery planning, virtual reality (VR) provides a working environment, where 3D images of the operation area can be utilized. VR allows 3D imaging data to be viewed in a more realistic 3D environment, reducing perceptual problems and increasing spatial understanding. In the present experiment, we compared a mouse, hand tracking, and a combination of a VR stylus and a grab-enabled VR controller as interaction methods in VR. The purpose was to investigate the suitability of the methods in VR for object manipulation and marking tasks in medical surgery planning. The tasks required interaction with 3D objects and high accuracy in the creation of landmarks. The combination of stylus and controller was the most preferred interaction method. According to subjective results, it was considered as the most appropriate because it allows the manipulation of objects in a way that is similar to the use of bare hands. In the objective results, the mouse interaction method was the most accurate.
Few-shot image classification algorithm based on attention mechanism and weight fusion

Abstract Aiming at the existing problems of metric-based methods, there are problems such as inadequate feature extraction, inaccurate class feature representation, and single similarity measurement. A new model based on attention mechanism and weight fusion strategy is proposed in this paper. Firstly, the image is passed through the conv4 network with channel attention mechanism and space attention mechanism to obtain the feature map of the image. On this basis, the fusion strategy is used to extract class-level feature representations according to the difference in contributions of different samples to class-level feature representations. Finally, the similarity scores of query set samples are calculated through the network to predict the classification. Experimental results on the miniImageNet dataset and the omniglot dataset demonstrate the effectiveness of the proposed method.
Simulating scalar field theories on quantum computers with limited resources

We present a quantum algorithm for implementing ${\ensuremath{\phi}}^{4}$ lattice scalar field theory on qubit computers. The field is represented in the discretized field amplitude basis. The number of qubits and elementary gates required by the implementation of the evolution operator is proportional to the lattice size. The algorithm allows efficient ${\ensuremath{\phi}}^{4}$ state preparation for a large range of input parameters in both the normal and broken-symmetry phases. The states are prepared using a combination of variational and adiabatic evolution methods. First, the ground state of a local Hamiltonian, which includes the ${\ensuremath{\phi}}^{4}$ self-interaction, is prepared using short variational circuits. Next, this state is evolved by switching on the coupling between the lattice sites adiabatically. The parameters defining the local Hamiltonian are adjustable and constitute the input of our algorithm. We present a method to optimize these parameters in order to reduce the adiabatic time required for state preparation. For preparing broken-symmetry states, the adiabatic evolution problems caused by crossing the phase transition critical line and by the degeneracy of the broken-symmetry ground state can be addressed using an auxiliary external field which gradually turns off during the adiabatic process. We show that the time dependence of the external field during the adiabatic evolution is important for addressing the broken-symmetry ground state degeneracy. The adiabatic time dependence on the inverse error tolerance can be reduced from quadratic to linear by using a field strength that decreases exponentially in time relative to one that decreases linearly.
Non-Gaussian quantum state generation by multi-photon subtraction at the telecommunication wavelength

In the field of continuous-variable quantum information processing, non-Gaussian states with negative values of the Wigner function are crucial for the development of a fault-tolerant universal quantum computer. While several non-Gaussian states have been generated experimentally, none have been created using ultrashort optical wave packets, which are necessary for high-speed quantum computation, in the telecommunication wavelength band where mature optical communication technology is available. In this paper, we present the generation of non-Gaussian states on wave packets with a short 8-ps duration in the 1545.32 nm telecommunication wavelength band using photon subtraction up to three photons. We used a low-loss, quasi-single spatial mode waveguide optical parametric amplifier, a superconducting transition edge sensor, and a phase-locked pulsed homodyne measurement system to observe negative values of the Wigner function without loss correction up to three-photon subtraction. These results can be extended to the generation of more complicated non-Gaussian states and are a key technology in the pursuit of high-speed optical quantum computation.
An enhanced vision transformer with wavelet position embedding for histopathological image classification

Histopathological image classification is a fundamental task in pathological diagnosis workflow. It remains a huge challenge due to the complexity of histopathological images. Recently, hybrid methods combining convolutional neural networks(CNN) with vision transformers(ViT) are proposed to this field. These methods can well represent the global and local contextual information and achieve excellent classification performances. However, the downsampling operation like max-pooling which ignores the sampling theorem transmits the jagged artifacts into transformer, which would lead to an aliasing phenomenon. It makes the subsequent feature maps focus on the incorrect regions and influences the final classification results. In this work, we propose an enhanced vision transformer with wavelet position embedding to tackle this challenge. In particular, a wavelet position embedding module, which introduces the wave transform into position embedding, is employed to enhance the smoothness of discontinuous feature information by decomposing sequences into amplitude and phase in pathological feature maps. In addition, an external multi-head attention is proposed to replace self-attention in the transformer block with two linear layers. It reduces the cost of computation and excavates potential correlations between different samples. We evaluate the proposed method on three public histopathological classification challenging datasets, and perform a quantitative comparison with previous state-of-the-art methods. The results empirically demonstrate that our method achieves the best accuracy. Furthermore, it has the least parameters and a very low FLOPs. In conclusion, the enhanced vision transformer shows high classification performances and demonstrates significant potential for assisting pathologists in pathological diagnosis.
Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

Lung and colon cancer are among humanity's most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.
ALPS: Achieving accuracy-aware location privacy service via assisted regions

Location search services (LSSs) provide convenience and enhanced functionality of ubiquitous computing, but they lead to a new risk of private location data leakage. The existing approaches generally use obfuscation to protect location privacy by perturbing the user’s location. However, this strategy inevitably degrades the accuracy of LSSs and affects the quality of service. In this paper, we propose an achieving accuracy-aware location privacy service via assisted regions, named ALPS, to protect user location privacy while ensuring the accuracy of services. Specifically, we devise two novel mechanisms (assisted regions mechanism and query obfuscation mechanism) to protect the user’s location privacy and ensure the accuracy of LSSs based on trilateration. The assisted regions mechanism prevents the attackers from getting the exact location of the user by protecting the user’s location within the overlapping region formed by the assisted regions. The query obfuscation mechanism obscures the user’s query content to prevent the attacker from predicting the user’s location based on the query content; the distance between the user and the query contents is calculated locally based on trilateration and the user’s location to ensure the accuracy of the LSSs. Theoretical analysis and experimental evaluation demonstrate that our scheme can protect location privacy without compromising the accuracy of LSSs.
An immersive virtual reality learning environment with CFD simulations: Unveiling the Virtual Garage concept

Virtual reality has become a significant asset to diversify the existing toolkit supporting engineering education and training. The cognitive and behavioral advantages of virtual reality (VR) can help lecturers reduce entry barriers to concepts that students struggle with. Computational fluid dynamics (CFD) simulations are imperative tools intensively utilized in the design and analysis of chemical engineering problems. Although CFD simulation tools can be directly applied in engineering education, they bring several challenges in the implementation and operation for both students and lecturers. In this study, we develop the “Virtual Garage” as a task-centered educational VR application with CFD simulations to tackle these challenges. The Virtual Garage is composed of a holistic immersive virtual reality experience to educate students with a real-life engineering problem solved by CFD simulation data. The prototype is tested by graduate students (n = 24) assessing usability, user experience, task load and simulator sickness via standardized questionnaires together with self-reported metrics and a semi-structured interview. Results show that the Virtual Garage is well-received by participants. We identify features that can further leverage the quality of the VR experience with CFD simulations. Implications are incorporated throughout the study to provide practical guidance for developers and practitioners.
Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review

Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient's activity monitoring models. Then, improving such systems' performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area.
Multi-unit Discrete Hopfield Neural Network for higher order supervised learning through logic mining: Optimal performance design and attribute selection

In the perspective of logic mining, the attribute selection, and the objective function of the best logic is the two main factors that identifies the effectiveness of our proposed logic mining model. The non-significant attributes selected will cause the Discrete Hopfield Neural Network to learned and obtain wrong synaptic weight. Thus, this will result to suboptimal solution. Although we might select the correct attributes, the conventional objective function of the best logic limits the search space to obtained more induced logic during the retrieval phase of Discrete Hopfield Neural Network. Therefore, this paper proposes a novel logic mining by integrating statistical analysis in the pre-processing phase to ensure that only optimal attributes will be selected. Supervised learning approach via correlation analysis is implemented for the purpose of attribute selection. Additionally, permutation operator serves to enhance the probability of the higher order satisfiability logical rule to be satisfied by having finite arrangement of attributes. During the learning phase, we proposed multi-unit Discrete Hopfield Neural Network to enhance the search space which leads to optimal solution. The efficiency of the proposed model is tested on 15 real-life datasets by comparing the performance of the model with existing works in logic mining using five performance metrics including accuracy, sensitivity, precision, Matthews Correlation Coefficient (MCC) and F1 Score. According to the results, the proposed model has its own strength by dominating most of the average rank of the performance metrics. This demonstrates that the proposed model can differentiate across all domains in the confusion matrix. Additionally, the p-value obtained based on the five-performance metrics indicate that there is a significantly difference between the proposed model and all existing works since the value obtained for accuracy (0.000), sensitivity (0.001), precision (0.000), F1 score (0.000) and MCC (0.000) are less than 0.05. This finding statistically prove that the proposed model is more effective compared with existing works in logic mining.
Synthetic electronic health records generated with variational graph autoencoders

Data-driven medical care delivery must always respect patient privacy-a requirement that is not easily met. This issue has impeded improvements to healthcare software and has delayed the long-predicted prevalence of artificial intelligence in healthcare. Until now, it has been very difficult to share data between healthcare organizations, resulting in poor statistical models due to unrepresentative patient cohorts. Synthetic data, i.e., artificial but realistic electronic health records, could overcome the drought that is troubling the healthcare sector. Deep neural network architectures, in particular, have shown an incredible ability to learn from complex data sets and generate large amounts of unseen data points with the same statistical properties as the training data. Here, we present a generative neural network model that can create synthetic health records with realistic timelines. These clinical trajectories are generated on a per-patient basis and are represented as linear-sequence graphs of clinical events over time. We use a variational graph autoencoder (VGAE) to generate synthetic samples from real-world electronic health records. Our approach generates health records not seen in the training data. We show that these artificial patient trajectories are realistic and preserve patient privacy and can therefore support the safe sharing of data across organizations.
Incomplete multi-view clustering by simultaneously learning robust representations and optimal graph structures

Incomplete multi-view clustering aims to assign data samples into cohesive groups with partially available information from multiple views. In this paper, we propose a novel generalized incomplete multi-view clustering method that integrates latent representation learning, spectral embedding as well as optimal graph clustering into a unified framework. Specifically, our method first learns a set of view-specific latent representations via low-rank subspace recovery. To bridge the gap brought by the missing samples from each view, we then unify the dimensions of the latent representations and obtain the spectral embeddings by preserving the local geometric structures. Moreover, our model seeks a consensus similarity matrix with optimal cluster structures by simultaneously approximating the affinity graphs reconstructed from multiple views. As a result, both intrinsic subspace structures and the global cluster structure could be mutually optimized by exploiting the consistent information shared among view-specific available samples. Compared with seven competing techniques, the experimental results confirm the superiority of our method on two different types of incomplete multi-view clustering tasks.
Monogamy of entanglement for tripartite systems

We study the monogamy of arbitrary quantum entanglement measures $E$ for tripartite quantum systems. Both the sufficient and necessary conditions for $E$ to be monogamous in terms of the $\ensuremath{\alpha}\mathrm{th}$ power of $E$ are explicitly derived. It is shown that such monogamy of an entanglement measure $E$ only depends on the boundedness of the solution set of certain equations. Moreover, the monogamy conditions are also obtained with respect to certain subsets of quantum states for a given quantum correlation. Detailed examples are given to illustrate our results.