Reviewerly/exHarmony huggingface dataset:
Atomic-coherence-assisted multipartite entanglement generation with dressing-energy-level-cascaded four-wave mixing

Multipartite entanglement plays an important role in quantum information processing and quantum metrology. Here, the dressing-energy-level-cascaded four-wave mixing (FWM) processes are proposed to generate all-optical controlled multipartite entanglement within a single device. The entanglement characteristics of the produced states of light are characterized by applying the Duan criterion and the positivity under partial transposition criterion. Moreover, by using an internal dressing field to modulate atomic coherence, multiple quantum coherent channels of FWM are simultaneously constructed, which result in a great extension of entanglement mode number and quantum information capacity. We find that the violation of the entanglement criteria inequalities is coherent-channel dependent, and the produced states can be directly modulated via atomic coherence. Our system can integrate the generation and modulation of the entangled states in one process. It may help provide a compact method for realizing large-scale quantum networks.
Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning

The frequent variations of XSS (cross-site scripting) payloads make static and dynamic analysis difficult to detect effectively. In this paper, we proposed a fusion verification method that combines traffic detection with XSS payload detection, using machine learning to detect XSS attacks. In addition, we also proposed seven new payload features to improve detection efficiency. In order to verify the effectiveness of our method, we simulated and tested 20 public CVE (Common Vulnerabilities and Exposures) XSS attacks. The experimental results show that our proposed method has better accuracy than the single traffic detection model. Among them, the recall rate increased by an average of 48%, the F1 score increased by an average of 27.94%, the accuracy rate increased by 9.29%, and the accuracy rate increased by 3.81%. Moreover, the seven new features proposed in this paper account for 34.12% of the total contribution rate of the classifier.
A Study of the Information Embedding Method into Raster Image Based on Interpolation

This article is devoted to the study of the improved neighbor mean interpolation (INMI) steganographic method. To date, no steganalysis of such a method of information embedding has been carried out. We implemented the INMI method of embedding messages in raster files and conducted a stegoanalysis on a set of 800 images of 225 × 225 size. Experimentally, we found that with this embedding method, the maximum container capacity is 21% and that it depends on the contents of the container. It is established that only 60 files out of 800 actually have the maximum capacity. We presented the calculation of the Type I error and the percentage of information detection in the obtained containers by the regular–singular (RS) method. The results showed that the considered steganographic algorithm is resistant to RS steganalysis and is comparable to the stegosystem of the permutation method investigated in one of our previous articles.
Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts

Abstract Aims Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address. We evaluated the performance of a novel deep learning model, BEHRT, under data shifts and compared it with several ML-based and established risk models. Methods and results Using linked electronic health records of 1.1 million patients across England aged at least 35 years between 1985 and 2015, we replicated three established statistical models for predicting 5-year risk of incident heart failure, stroke, and coronary heart disease. The results were compared with a widely accepted machine learning model (random forests), and a novel deep learning model (BEHRT). In addition to internal validation, we investigated how data shifts affect model discrimination and calibration. To this end, we tested the models on cohorts from (i) distinct geographical regions; (ii) different periods. Using internal validation, the deep learning models substantially outperformed the best statistical models by 6%, 8%, and 11% in heart failure, stroke, and coronary heart disease, respectively, in terms of the area under the receiver operating characteristic curve. Conclusion The performance of all models declined as a result of data shifts; despite this, the deep learning models maintained the best performance in all risk prediction tasks. Updating the model with the latest information can improve discrimination but if the prior distribution changes, the model may remain miscalibrated.
Job Insecurity and Intention to Quit: The Role of Psychological Distress and Resistance to Change in the UAE Hotel Industry

Hotel organizations today are in a state of constant change due to high competition, the emergence of pandemics, and cyclical economic crises. Hospitality employees are currently affected by job insecurity. The purpose of this research was to investigate the effect of job insecurity on intention to quit among hospitality workers, integrating the mediating effect of psychological distress and resistance to change and their mutual relationship. A total of 312 surveys were completed in four four- and five-star hotels in the UAE (Dubai and Sharjah). The SmartPLS 4 software was used to test the hypotheses in a mediation model with the bootstrapping method. The results showed that all of the direct links were positive and significant, and mediating relationships were confirmed. This study found that job insecurity predicts intention to quit through psychological distress and resistance to change acting as mediators, and these factors themselves also impact significantly on intention to quit. Resistance to change is impacted significantly by job insecurity and psychological distress, which suggests that a deeper approach to employees' resistance to change should be taken, especially when conducting performance appraisals in the hotel industry, by searching for its roots and aiming to minimize employees' intention to quit.
Orthogonal autoencoder regression for image classification

Least squares regression (LSR) and its extended methods are widely used for image classification. However, these LSR-based methods don’t consider the importance of global information and ignore the connection between feature learning and regression representation. To address the above problems, we propose a novel regression learning method called orthogonal autoencoder regression (OAR). Specifically, the proposed model considers the global information by combining the feature learning part with the regression representation part. In addition, in order to enhance the model’s feature learning ability, we introduce the orthogonal autoencoder model to learn more effective data. For promoting the model’s regression representation ability, we also add weight constraint on the model and make OAR more discriminative. An iterative algorithm with ADMM is proposed to solve the model. The experimental results on several databases show the effectiveness of OAR.
A silicon source of frequency-bin entangled photons

We demonstrate an integrated source of frequency-entangled photon pairs on a silicon photonics chip. The emitter has a coincidence-to-accidental ratio exceeding 103. We prove entanglement by showing two-photon frequency interference with a visibility of 94.6% ± 1.1%. This result opens the possibility of on-chip integration of frequency-bin sources with modulators and the other active and passive devices available in the silicon photonics platform.
AQUA: Scalable Rowhammer Mitigation by Quarantining Aggressor Rows at Runtime

Rowhammer allows an attacker to induce bit flips in a row by rapidly accessing neighboring rows. Rowhammer is a severe security threat as it can be used to escalate privilege or break confidentiality. Moreover, the threshold of activations needed to induce Rowhammer continues to reduce and new attacks like Half-Double break existing solutions that refresh victim rows. The recently proposed Randomized Row-Swap (RRS) scheme is resilient to Half-Double as it provides mitigation by swapping an aggressor row with a random row. However, to ensure security, the threshold for triggering a row-swap must be set much lower than the Rowhammer threshold, leading to a significant performance loss of 20% on average, at a Rowhammer threshold of 1K. Furthermore, the SRAM overhead for storing the indirection table of RRS becomes prohibitively large – 2.4MB per rank at a Rowhammer threshold of 1K. Our goal is to develop a scalable Rowhammer mitigation that incurs negligible performance and storage overheads.To this end, we propose AQUA, a Rowhammer mitigation that breaks the spatial correlation between aggressor and victim rows by dynamically quarantining the aggressor row in a dedicated region of memory. AQUA allows for an effective row migration threshold much higher than in RRS, leading to an order of magnitude less slowdown and SRAM. As the security of AQUA is not reliant on keeping the destination row a secret, we further reduce the SRAM overheads of the indirection table by storing it in DRAM, and accessing it on-demand. We derive the size of the quarantine region required to ensure security for AQUA and show that reserving about 1% of DRAM is sufficient to mitigate Rowhammer at a threshold of 1K. Our evaluations show that AQUA incurs an average slowdown of 2% and an SRAM overhead (for mapping and migration) of only 41KB per rank at a Rowhammer threshold of 1K.
Quantized Semantic Segmentation Deep Architecture for Deployment on an Edge Computing Device for Image Segmentation

In the field of computer vision technology, deep learning of image processing has become an emerging research area. The semantic segmentation of an image is among the utmost essential and significant tasks in image-processing research, offering a wide range of application fields such as autonomous driving systems, medical diagnosis, surveillance security, etc. Thus far, many studies have suggested and developed neural network modules in deep learning. To the best of our knowledge, all existing neural networks for semantic segmentation have large parameter sizes and it is therefore unfeasible to implement those architectures in low-power and memory-limited embedded platforms such as FPGAs. Building an embedded platform with that architecture is possible after reducing the parameter size without affecting the module’s architecture. The quantization technique lowers the precision of the neural network parameters while mostly keeping the accuracy. In this paper, we propose a quantization algorithm for a semantic segmentation deep learning architecture, which reduces the parameter size by four to eight times with a negligible accuracy abatement. As long as the parameter size is reduced, the deep learning architecture is improved in terms of required storage, computational speed, and power efficiency.
Blockchain Platforms in Energy Markets—A Critical Assessment

Compared to other applications of distributed ledger technologies, for example, in decentralized finance, non-fungible tokens, and logistics, Blockchain applications in the energy industry have not found widespread dissemination and fell short of market expectations during the Blockchain hype in the late 2010s. In semi-structured qualitative interviews with leading providers in the energy industry, conducted from 2019 to 2021, hurdles in energy applications are compared with a control group of additional interviews with representatives of companies operating in IT and FinTech. The analysis uses a framework covering technical feasibility, desirability, and economic viability, as well as the role of regulatory frameworks. The interviews reveal that the first Blockchain applications suffered from a combination of technological constraints and inter-platform competition. Due to the permissionless configuration of the early energy Blockchains, they were slow in terms of transaction speed compared to existing platforms and prices per transaction were high, in addition to high degrees of complexity related to requirements from both critical-infrastructure systems and financial market regulation. The analysis further points to the slow adoption of Blockchain applications in the energy sector being related to business models rather focusing on products and platforms as well as on transactional rather than procedural use cases, with a high degree of standardization of the offering and low levels of inclusiveness concerning processes. The move from transaction platforms to innovation platforms and the emergence of Blockchain as a service provider—plus technical advances with regards to high-frequency transactions combined with the increasing importance of use cases, such as proof of origin for fuels or e-charging—may induce a shift from pilot applications to commercialization within the larger innovation ecosystem. While the involvement of Blockchain solutions in energy markets increases with pilot projects and with this, the acceptance of players and stakeholders in the energy ecosystem, a big hurdle for innovation remains the regulation of energy markets to allow for peer-to-peer trading, a usage-driven distribution of network costs, and bottom-up pricing markets.
Design and Development of a Heterogeneous Active Assisted Living Solution for Monitoring and Following Up with Chronic Heart Failure Patients in Spain

Heart failure is the most common disease among elderly people, and the risk increases with age. The use of smart Internet of Things (IoT) systems for monitoring patients with chronic heart failure (CHF) in a non-intrusive manner can result in better control of the disease, improving proactive healthcare through real-time and historical patient's data, promoting self-care in patients, reducing unneeded interaction between patients and doctors, reducing the number of hospitalizations and saving healthcare costs. This work presents an active assisted living (AAL) solution based on the IoT to provide a tele-assistance platform for CHF patients from the public health service of the region of Murcia in Spain, with formal and informal caregivers and health professionals also as key actors. In this article, we have detailed the methodology, results, and conclusions of the prevalidation phase for the set of IoT technologies to be integrated in the AAL platform, the first mandatory step before the deployment of a large-scale pilot that will lead to improving the innovation of the system from its current technology readiness level to the market. The work presented, in the framework of the H2020 Pharaon project, aims to serve as inspiration to the R&D community for the design, development, and deployment of AAL solutions based on heterogeneous IoT technologies, or similar approaches, for smart healthcare solutions in real healthcare institutions.
Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficiency, slow convergence speed, and even inability to converge, which seriously reduces the performance and practicability of it. To solve these problems, this paper proposes an Improved Dyna-Q algorithm for AGV path planning in large complex dynamic environments. First, to solve the problem of the large search space, this paper proposes a global path guidance mechanism based on heuristic graph, which can effectively reduce the path search space and, thus, improve the efficiency of obtaining the optimal path. Second, to solve the problem of the sparse reward function in Dyna-Q, this paper proposes a novel dynamic reward function and an action selection method based on the heuristic graph, which can provide more intensive feedback and more efficient action decision for AGV path planning, effectively improving the convergence of the algorithm. We evaluated our approach in scenarios with static obstacles and dynamic obstacles. The experimental results show that the proposed algorithm can obtain better paths more efficiently than other reinforcement-learning-based methods including the classical Q-Learning and the Dyna-Q algorithms.
Feature reconstruction and metric based network for few-shot object detection

In the object detection task, deep learning-based methods always need a large amount of annotated training data. However, annotating a large number of images is labor-intensive. In order to reduce the dependency of expensive annotations, we propose a novel end-to-end feature reconstruction and metric based network for few-shot object detection (FM-FSOD). FM-FSOD integrates metric learning and meta-learning to tackle the few-shot object detection task. FM-FSOD is a class-agnostic detection model that can accurately recognize novel categories without fine-tuning on novel categories. Specifically, to quickly learn the characteristics of novel categories, we propose a meta-representation module (MR module) to learn from intra-class mean prototypes and acquire the ability to reconstruct high-level features with the meta-learning method. To further conduct the similarity of features between support prototypes and query ROI features, we propose Pearson metric module (PR module), which serves as a classifier. Compared with the previous standard cosine distance module, the PR module enables the model to acquire robust ability for large bias features. We have conducted extensive experiments on benchmark datasets FSOD, MS COCO, and PASCAL VOC to demonstrate the feasibility and efficiency of our model. Comparing with the previous methods, FM-FSOD obtains comparable results.
A Proposed System for Multi-UAVs in Remote Sensing Operations

This paper proposes the design of the communications, control systems, and navigation algorithms of a multi-UAV system focused on remote sensing operations. A new controller based on a compensator and a nominal controller is designed to dynamically regulate the UAVs' attitude. The navigation system addresses the multi-region coverage trajectory planning task using a new approach to solve the TSP-CPP problem. The navigation algorithms were tested theoretically, and the combination of the proposed navigation techniques and control strategy was simulated through the Matlab SimScape platform to optimize the controller's parameters over several iterations. The results reveal the robustness of the controller and optimal performance of the route planner.
Lung cancer histopathological image classification using wavelets and AlexNet

Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.
Graph Flow: Cross-Layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation

With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, high-performance convolutional neural networks always mean numerous parameters and high computation costs, which will hinder the applications in resource-limited medical scenarios. Meanwhile, the scarceness of large-scale annotated medical image datasets further impedes the application of high-performance networks. To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network-efficiency and annotation-efficiency medical image segmentation. Specifically, the Graph Flow Distillation transfers the essence of cross-layer variations from a well-trained cumbersome teacher network to a non-trained compact student network. In addition, an unsupervised Paraphraser Module is integrated to purify the knowledge of the teacher, which is also beneficial for the training stabilization. Furthermore, we build a unified distillation framework by integrating the adversarial distillation and the vanilla logits distillation, which can further refine the final predictions of the compact network. With different teacher networks (traditional convolutional architecture or prevalent transformer architecture) and student networks, we conduct extensive experiments on four medical image datasets with different modalities (Gastric Cancer, Synapse, BUSI, and CVC-ClinicDB). We demonstrate the prominent ability of our method on these datasets, which achieves competitive performances. Moreover, we demonstrate the effectiveness of our Graph Flow through a novel semi-supervised paradigm for dual efficient medical image segmentation. Our code will be available at Graph Flow.