Reviewerly/exHarmony huggingface dataset:
Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data

Contrary to national guidelines, women with ovarian cancer often receive treatment at the end of life, potentially due to the difficulty in accurately estimating prognosis. We trained machine learning algorithms to guide prognosis by predicting 180-day mortality for women with ovarian cancer using patient-reported outcomes (PRO) data. We collected data from a single academic cancer institution in the United States. Women completed biopsychosocial PRO measures every 90 days. We randomly partitioned our dataset into training and testing samples. We used synthetic minority oversampling to reduce class imbalance in the training dataset. We fitted training data to six machine learning algorithms and combined their classifications on the testing dataset into an unweighted voting ensemble. We assessed each algorithm's accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) using testing data. We recruited 245 patients who completed 1319 PRO assessments. The final voting ensemble produced state-of-the-art results on the task of predicting 180-day mortality for ovarian cancer paitents (Accuracy = 0.79, Sensitivity = 0.71, Specificity = 0.80, AUROC = 0.76). The algorithm correctly identified 25 of the 35 women in the testing dataset who died within 180 days of assessment. Machine learning algorithms trained using PRO data offer encouraging performance in predicting whether a woman with ovarian cancer will die within 180 days. This model could be used to drive data-driven end-of-life care and address current shortcomings in care delivery. Our model demonstrates the potential of biopsychosocial PROM information to make substantial contributions to oncology prediction modeling. This model could inform clinical decision-making Future research is needed to validate these findings in a larger, more diverse sample.
Guidance and control for autonomous emergency landing of the rotorcraft using the incremental backstepping controller in 3-dimensional terrain environments

This paper treats the approach to safe emergency landing for an autonomous rotorcraft in 3-dimensional terrain environments. The autorotation flight that the rotorcraft flies without the power supply is dangerous and needs the high-skilled control ability of the pilot. Ordinary, this flight is divided into three phases: Entry, Steady-Descent, and Flare phase. The emergency landing trajectory is generated by imitating each phase. The Trajectory optimization method using the high-fidelity math model is applied to the entry and flare phase to consider the complex dynamics of the rotorcraft. To generate a steady-descent phase trajectory without obstacle collisions, one of the path planning methods, the improved Rapidly-exploring Random Tree algorithm is used. The Incremental Backstepping controller is adopted as the trajectory tracking controller. Simulation is conducted in the One Engine Inoperative condition. Through the simulation, it is shown that the method proposed in this paper guarantees the safe landing of the rotorcraft in an emergency like engine failure.
Device‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning

Abstract The incorporation of high‐performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive matrix multiplication operations in machine learning (ML) algorithms. However, the conventional designs of individual devices and system are largely disconnected, and the system optimization is limited to the manual exploration of a small design space. Here, a device‐system end‐to‐end design methodology is reported to optimize a free‐space optical general matrix multiplication (GEMM) hardware accelerator by engineering a spatially reconfigurable array made from chalcogenide phase change materials. With a highly parallelized integrated hardware emulator with experimental information, the design of unit device to directly optimize GEMM calculation accuracy is achieved by exploring a large parameter space through reinforcement learning algorithms, including deep Q‐learning neural network, Bayesian optimization, and their cascaded approach. The algorithm‐generated physical quantities show a clear correlation between system performance metrics and device specifications. Furthermore, physics‐aware training approaches are employed to deploy optimized hardware to the tasks of image classification, materials discovery, and a closed‐loop design of optical ML accelerators. The demonstrated framework offers insights into the end‐to‐end and co‐design of optoelectronic devices and systems with reduced human supervision and domain knowledge barriers.
A corpus-based pragmatic analysis of Jordanians Facebook status updates during COVID-19

This study investigates the communicative functions of status updates on Facebook during COVID-19. For this purpose, a corpus of 500 status updates was collected from 100 Facebook users for 90 consecutive days. Subsequently, the data were characterized into five speech acts drawing heavily on Searle's speech act framework, prominent among which are expressives and assertives. Data analysis revealed that status updates could be considered a substantial medium for understanding intended human communication. Various types of speech acts were used with different frequencies and percentages, although people were inclined mostly to use expressive speech acts. The sociocultural variations in conjunction with forming and constructing identities were reflected in the status updates manifested in the current situation of the pandemic, which makes Jordanians appear more humorous than before. This research is significant because studying aspects of a language helps in understanding the hidden motivations, beliefs, ideas, attitudes and identities along with the social, cultural, and political factors, which in turn provides logical solutions for certain problems.
A Prototype Network Enhanced Relation Semantic Representation for Few-shot Relation Extraction

Abstract Few-shot relation extraction is one of the current research focuses. The key to this research is to fully extract the relation semantic information through very little training data. Intuitively, raising the relation semantics awareness in sentences can improve the efficiency of the model to extract relation features to alleviate the overfitting problem in few-shot learning. Therefore, we propose an enhanced relation semantic feature model based on prototype network to extract relations from few-shot texts. Firstly, we design a multi-level embedding encoder with position information and Transformer, which uses local information in the text to enhance the relation semantics representation. Secondly, the encoded relation features are fed into the novel prototype network, which designs a method that utilizes query prototype-level attention to guide the extraction of supporting prototypes, thereby enhancing the prototypes representation to better classify the relations in query sentences. Finally, through experimental comparison and discussion, we prove and analyze the effectiveness of the proposed multi-level embedding encoder, and prototype-level attention can enhance the stability of the model. Furthermore, our model has substantial improvements over baseline methods.
Network measurement methods for locating and examining censorship devices

Advances in networking and firewall technology have led to the emergence of network censorship devices that can perform large-scale, highly-performant content blocking. While such devices have proliferated, techniques to locate, identify, and understand them are still limited, require cumbersome manual effort, and are developed on a case-by-case basis.
Development of oculomotor control throughout childhood: A multicenter and multiethnic study

Although steady fixation is a key aspect of a proper visual function, it is only subjectively assessed in young and uncooperative children. In the present study, we characterize the development of fixational behavior throughout childhood in a large group of healthy children 5 months of age and up, recruited in five geographically diverse sites. In order to do it, we examined 802 healthy children from April 2019 to February 2020. Their oculomotor behavior was analyzed by means of an automated digital system, based on eye-tracking technology. Oculomotor outcomes were gaze stability, fixation stability and duration of fixations (for both long and short fixational tasks), and saccadic reaction time. Ninety-nine percent of all recruited children were successfully examined. Fixational and saccadic performance improved with age throughout childhood, with more pronounced changes during the first 2 years of life. Gaze and fixation tended to be more stable with age (p < 0.001 for most the outcomes), and saccades tended to be faster. In a multivariate analysis, including age and ethnicity as independent variables and adjusting by data quality, age was related with most fixational outcomes. Our automated digital system and eye-tracking data allow us to quantitatively describe the development of oculomotor control during childhood, assess visual fixation and saccadic performance in children 5 months of age and up, and provide a normative reference of fixational outcomes for clinical practice.
A Systematic Literature Review on Cybersecurity Threats of Virtual Reality (VR) and Augmented Reality (AR)

In light of the scope, scale, and sensitivity of the information that can be collected by AR and VR devices, new privacy concerns arise. In this paper, we provide a systematic literature review of potential security, safety, and privacy (SSP) threats from virtual reality (VR) and augmented reality (AR) environments, which have become extremely popular. We introduce a detailed comparison of current risks that become much more dangerous in the environment of AR and VR, such as the security and privacy challenges of AR platforms. We then discuss expected mitigation techniques for security and privacy threats proposed by previous literature studies, such as creating three AR visual output models that balance OS control with application flexibility. The purpose of this article is to raise awareness of potential security and privacy vulnerabilities caused by seemingly safe VR and AR technologies.
Intra-list similarity and human diversity perceptions of recommendations: the details matter

Abstract The diversity of the generated item suggestions can be an important quality factor of a recommender system. In offline experiments, diversity is commonly assessed with the help of the intra-list similarity (ILS) measure, which is defined as the average pairwise similarity of the items in a list. The similarity of each pair of items is often determined based on domain-specific meta-data, e.g., movie genres. While this approach is common in the literature, it in most cases remains open if a particular implementation of the ILS measure is actually a valid proxy for the human diversity perception in a given application. With this work, we address this research gap and investigate the correlation of different ILS implementations with human perceptions in the domains of movie and recipe recommendation. We conducted several user studies involving over 500 participants. Our results indicate that the particularities of the ILS metric implementation matter. While we found that the ILS metric can be a good proxy for human perceptions, it turns out that it is important to individually validate the used ILS metric implementation for a given application. On a more general level, our work points to a certain level of oversimplification in recommender systems research when it comes to the design of computational proxies for human quality perceptions and thus calls for more research regarding the validation of the corresponding metrics.
Dissipative quantum computation and quantum state preparation based on BEC qubits

Among environmental effects, dissipation allows the stabilization of quantum resources, a fact that may accomplish key advantages over unitary quantum state manipulation. In this paper, we study an open quantum system composed of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math> two-mode Bose–Einstein condensates (BECs), namely, BEC qubits, interacting with their environments. Usually, the coupling of a system with its environment results in a dissipation process that washes out the quantum effects, but the environment may also play exactly the opposite role. Here, we show that the coupling of a BEC qubit with its local environments can be exploited to establish dissipative state engineering and dissipative quantum computation. Also, we discuss how to generate entanglement and transfer it using such macroscopic BEC qubits. Generally, the couplings can be manipulated such that the system is driven to a desired steady state. We demonstrate that the steady state of a BEC qubit just depends on the tunneling strength between its two modes and the features of the local environments. By preparing the proper physical conditions and adjusting the parameters involved in the model, it can be shown that the steady state of the system may coincide with its initial state. Also, the interaction between the BEC qubits and local environments can be engineered to provide physically controlled single- and two-qubit operations, which are necessary for performing dissipative universal quantum computation. With the prosperous development of quantum computing, such a system can be employed to implement quantum computers based on non-unitary operations.
Ensemble Clustering in GPS Velocities: A Case Study of Turkey

Block modeling is an effective way to understand Earth’s crustal deformation. However, the choice of block boundaries and the number of blocks affect the model results. Therefore, the subjectivity of this analysis should be avoided. Clustering analysis can be used to define the blocks of GPS (Global Positioning System) velocity fields without a priori information. Unfortunately, clustering methods also have unique solutions and differ with various algorithms. Ensemble methods could be an answer to enhance the clustering results for GPS velocities. In this study, we use ensemble clustering to identify block boundaries before block modeling without a priori information about the data. The ensemble clustering method is used for the first time in the clustering of GPS velocities and the case of Turkey is discussed. The published horizontal GPS velocities were first clustered with five different clustering methods and the optimum classes were determined using ensemble clustering methods. It is proven that the Meta-CLustering Algorithm can be used in terms of ensemble clustering for this region.
Kernel reconstruction learning

This paper proposes a class of kernel interpolation-based methods, called kernel reconstruction learning, for solving machine learning problems. Kernel reconstruction learning uses kernel interpolators to reconstruct the unknown functions, which are needed to estimate in the problem, with estimated function values at selected knots. It can be applied to any learning problem that involves function estimation. We prove a reconstruction representer theorem, which indicates that conventional kernel methods, including kernel ridge regression, kernel support vector machine, and kernel logistic regression, can be viewed as special cases of kernel reconstruction learning. Furthermore, kernel reconstruction learning provides new algorithms for large datasets. The kernel reconstruction vector machine, kernel reconstruction logistic regression, and kernel reconstruction density estimation are discussed in detail. With appropriate implementations, they are shown to have higher prediction/estimation accuracy and/or less computational cost than popular kernel methods.
Graph representation learning based on deep generative gaussian mixture models

Graph representation learning is an effective tool for facilitating graph analysis with machine learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph Recurrent Neural Networks (GRNN), and Graph Auto-Encoders (GAE), employ vectors to represent nodes in a deterministic way without exploiting the uncertainty in hidden variables. Deep generative models are combined with GAE in the Variational Graph Auto-Encoder (VGAE) framework to address this issue. While traditional VGAE-based methods can capture hidden and hierarchical dependencies in latent spaces, they are limited by the data’s multimodality. Here, we propose the Gaussian Mixture Model (GMM) to model the prior distribution in VGAE. Furthermore, an adversarial regularization is incorporated into the proposed approach to ensure the fruitful impact of the latent representations on the results. We demonstrate the performance of the proposed method on clustering and link prediction tasks. Our experimental results on real datasets show remarkable performance compared to state-of-the-art methods.
Graph neural topic model with commonsense knowledge

Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents' topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods.
An Improved Crystal Structure Algorithm for Engineering Optimization Problems

Crystal Structure Algorithm (CryStAl) is a new meta-heuristic algorithm, and it has been studied by many scholars because of its wide adaptability and the fact that there is no need to set parameters in advance. An improved crystal structure algorithm (GLCryStAl) based on golden sine operator and Levy flight is designed in this paper. The algorithm makes good use of the relationship between the golden sine operator and the unit circle to make the algorithm exploration space more comprehensive, and then gradually narrows the search space in the iterative process, which can effectively speed up the convergence rate of the algorithm. At the same time, a Levy operator is introduced to help the algorithm effectively get rid of the attraction of local optimal value. To evaluate the performance of GLCryStAl, 12 classic benchmark functions and eight CEC2017 test functions were selected to design a series of comparative experiments. In addition, the experimental data of these algorithms are analyzed using the Wilcoxon and Friedman tests. Through these two tests, it can be found that GLCryStAl has significant advantages over other algorithms. Finally, this paper further tests the optimization performance of GLCryStAl in engineering design. GLCryStAl was applied to optimize pressure vessel design problems and tension/compression spring design problems. The optimization results show that GLCryStAl is feasible and effective in optimizing engineering design.
Optimal Underwater Acoustic Warfare Strategy Based on a Three-Layer GA-BP Neural Network

A defense platform is usually based on two methods to make underwater acoustic warfare strategy decisions. One is through Monte-Carlo method online simulation, which is slow. The other is by typical empirical (database) and typical back-propagation (BP) neural network algorithms based on genetic algorithm (GA) optimization, which is less accurate and less robust. Therefore, this paper proposes a method to build an optimal underwater acoustic warfare feedback system using a three-layer GA-BP neural network and dropout processing of the neural network to prevent overfitting, so that the three-layer GA-BP neural network has adequate memory capability while still having suitable generalization capability. This method improves the accuracy and stability of the defense platform in making underwater acoustic warfare strategy decisions, thus increasing the survival probability of the defense platform in the face of incoming torpedoes. This paper uses the optimal underwater acoustic warfare strategies corresponding to incoming torpedoes with different postures as the sample set. Additionally, it uses a three-layer GA-BP neural network with an overfitting treatment for training. The prediction results have less error than the typical single-layer GA-BP neural network, and the survival probability of the defense platform improves by 6.15%. This defense platform underwater acoustic warfare strategy prediction method addresses the impact on the survival probability of the defense platform due to the decision speed and accuracy.
Better Together: Attaining the Triad of Byzantine-robust Federated Learning via Local Update Amplification

Manipulation of local training data and local updates, i.e., the Byzantine poisoning attack, is the main threat arising from the collaborative nature of the federated learning (FL) paradigm. Many Byzantine-robust aggregation algorithms (AGRs) have been proposed to filter out or moderate suspicious local updates uploaded by Byzantine participants at the central aggregator. However, they largely suffer from model quality degradation due to the over-removal of local updates or/and the inefficiency caused by the expensive analysis of the high-dimensional local updates.
Learning from Failures: Secure and Fault-Tolerant Aggregation for Federated Learning

Federated learning allows multiple parties to collaboratively train a global machine learning (ML) model without sharing their private datasets. To make sure that these local datasets are not leaked, existing works propose to rely on a secure aggregation scheme that allows parties to encrypt their model updates before sending them to the central server that aggregates the encrypted inputs. In this work, we design and evaluate a new secure and fault-tolerant aggregation scheme for federated learning that is robust against client failures. We first develop a threshold-variant of the secure aggregation scheme proposed by Joye and Libert. Using this new building block together with a dedicated decentralized key management scheme and an input encoding solution, we design a privacy-preserving federated learning protocol that, when executed among n clients, can recover from up to failures. Our solution is secure against a malicious aggregator who can manipulate messages to learn clients' individual inputs. We show that our solution outperforms the state-of-the-art fault-tolerant secure aggregation schemes in terms of computation cost on the client. For example, with an ML model of 100,000 parameters, trained with 600 clients, our protocol is 5.5x faster (1.6x faster in case of 180 clients drop).