Reviewerly/exHarmony huggingface dataset:
Multimodal adversarial representation learning for breast cancer prognosis prediction

With the increasing incidence of breast cancer, accurate prognosis prediction of breast cancer patients is a key issue in current cancer research, and it is also of great significance for patients’ psychological rehabilitation and assisting clinical decision-making. Many studies that integrate data from different heterogeneous modalities such as gene expression profile, clinical data, and copy number alteration, have achieved greater success than those with only one modality in prognostic prediction. However, many of these approaches that exist fail to dramatically reduce the modality gap by aligning multimodal distributions. Therefore, it is crucial to develop a method that fully considers a modality-invariant embedding space to effectively integrate multimodal data. In this study, to reduce the modality gap, we propose a multimodal data adversarial representation framework (MDAR) to reduce the modal heterogeneity by translating source modalities into distributions for the target modality. Additionally, we apply reconstruction and classification losses to embedding space to further constrain it. Then, we design a multi-scale bilinear convolutional neural network (MS-B-CNN) for uni-modality to improve the feature expression ability. In addition, the embedding space generates predictions as stacked feature inputs to the extremely randomized trees classifier. With 10-fold cross-validation, our results show that the proposed adversarial representation learning improves prognostic performance. A comparative study of this method and other existing methods on the METABRIC (1980 patients) dataset showed that Matthews correlation coefficient (Mcc) was significantly enhanced by 7.4% in the prognosis prediction of breast cancer patients.
Early detection of cognitive decline in Alzheimer’s disease using eye tracking

Background Patients with Alzheimer’s disease (AD) are known to exhibit visuospatial processing impairment, as reflected in eye movements from the early stages of the disease. We investigated whether the pattern of gaze exploration during visual tasks could be useful for detecting cognitive decline at the earliest stage. Methods Sixteen AD patients (age: 79.1 ± 7.9 years, Mini Mental State Examination [MMSE] score: 17.7 ± 5.3, mean ± standard deviation) and 16 control subjects (age: 79.4 ± 4.6, MMSE score: 26.9 ± 2.4) participated. In the visual memory task, subjects memorized presented line drawings for later recall. In the visual search tasks, they searched for a target Landolt ring of specific orientation (serial search task) or color (pop-out task) embedded among arrays of distractors. Using video-oculography, saccade parameters, patterns of gaze exploration, and pupil size change during task performance were recorded and compared between AD and control subjects. Results In the visual memory task, the number of informative regions of interest (ROIs) fixated was significantly reduced in AD patients compared to control subjects. In the visual search task, AD patients took a significantly longer time and more saccades to detect the target in the serial but not in pop-out search. In both tasks, there was no significant difference in the saccade frequency and amplitude between groups. On-task pupil modulation during the serial search task was decreased in AD. The number of ROIs fixated in the visual memory task and search time and saccade numbers in the serial search task differentiated both groups of subjects with high sensitivity, whereas saccade parameters of pupil size modulation were effective in confirming normal cognition from cognitive decline with high specificity. Discussion Reduced fixation on informative ROIs reflected impaired attentional allocation. Increased search time and saccade numbers in the visual search task indicated inefficient visual processing. Decreased on-task pupil size during visual search suggested decreased pupil modulation with cognitive load in AD patients, reflecting impaired function of the locus coeruleus. When patients perform the combination of these tasks to visualize multiple aspects of visuospatial processing, cognitive decline can be detected at an early stage with high sensitivity and specificity and its progression be evaluated.
Exploring Effective Interactive Text-Based Video Search in vitrivr

vitrivr is a general purpose retrieval system that supports a wide range of query modalities. In this paper, we briefly introduce the system and describe the changes and adjustments made for the 2023 iteration of the video browser showdown. These focus primarily on text-based retrieval schemes and corresponding user-feedback mechanisms.
Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

Summary Cloud computing (CC) refers to the on‐demand availability of network resources, particularly data storage and processing power, without requiring special or direct administration by users. CC, which just made its debut as a collection of public and private data centers, provides clients with a unified platform throughout the Internet. Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Due to the exponential rise of cloud computing, systems based on cloud computing now require an effective data security mechanism. Comprehensive security policies, corporate security culture, and cloud security solutions are used to ensure the level of cloud data security. Many techniques exist to protect data communication in the cloud environment, including encryption. Encryption algorithms play an important role in information security systems and various cloud computing‐based systems. Current researchers have focused on lightweight cryptography, genetics‐based cryptography, and machine learning (ML) algorithms for security in CC. This review study analyses CC security threats, problems, and solutions that use one or more algorithms. The work discusses several lightweight cryptographies, genetics‐based cryptography and different ML algorithms that are used to overcome cloud security issues, including supervised, unsupervised, semi‐supervised, and reinforcement learning. Moreover, we enlist future research directions to secure CC models.
Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networks

Abstract High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution (HR) image sensors or through image super-resolution (SR) algorithms. Currently, SR algorithms are applied to either single low-resolution (LR) images or multiple LR image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate HR images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitative evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.
Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients

Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural network, especially Generative Adversarial Network (GAN), is able to reproduce the original data from model parameters, i.e. gradient leakage problem. To solve this problem, SL provides a framework for secure aggregation using blockchain methods. In this paper, we consider the scenario of compromised and malicious participants in the SL environment, where a participant can manipulate the privacy of other participant in collaborative training. We propose a method, Swarm-FHE, Swarm Learning with Fully Homomorphic Encryption (FHE), to encrypt the model parameters before sharing with the participants which are registered and authenticated by blockchain technology. Each participant shares the encrypted parameters (i.e. ciphertexts) with other participants in SL training. We evaluate our method with training of the convolutional neural networks on the CIFAR-10 and MNIST datasets. On the basis of a considerable number of experiments and results with different hyperparameter settings, our method performs better as compared to other existing methods.
The role of uncertainty and affect in decision-making on the adoption of AI-based contact-tracing technology during the COVID-19 pandemic

Objective This study explores how negative affect, perceived net equity, and uncertainty influence the public's privacy decision-making regarding the adoption of contact-tracing technology based on artificial intelligence (AI) during the COVID-19 pandemic. Methods Four hundred and eighteen adults in the US participated in the study via Amazon Mechanical Turk in August 2020. Statistical analyses were performed using the PROCESS macro. Indirect effects and their significance were estimated using bias-corrected bootstrap confidence intervals (CIs) with resampling set to n = 5000. Results Perceived net equity was positively associated with low levels of perceived uncertainty regarding a COVID-19 contact-tracing application and intention to adopt it. Low levels of perceived uncertainty were positively associated with intentions to adopt such an application, thereby suggesting that a perceived level of uncertainty mediates the association between perceived net equity and adoption intentions. Anxieties regarding AI technology and COVID-19 risks both moderate the associations among perceived net equity, perceived level of uncertainty, and intentions to adopt the contact-tracing technology. Conclusions Our findings highlight how the differing sources of emotion influence the associations among rational judgment, perceptions, and decision-making about new contact-tracing technology. Overall, the results suggest that both rational judgments and affective reactions to risks are important influencers of individuals’ perceptions and privacy-related decision-making regarding a new health technology during the pandemic.
Accelerated quantum Monte Carlo with probabilistic computers

Abstract Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent of various special purpose devices and domain specific hardware, it has become increasingly important to establish clear benchmarks of what improvements these technologies offer compared to existing technologies. In this paper, we demonstrate 2 to 3 orders of magnitude acceleration of a standard QMC algorithm using a specially designed digital processor, and a further 2 to 3 orders of magnitude by mapping it to a clockless analog processor. Our demonstration provides a roadmap for 5 to 6 orders of magnitude acceleration for a transverse field Ising model (TFIM) and could possibly be extended to other QMC models as well. The clockless analog hardware can be viewed as the classical counterpart of the quantum annealer and provides performance within a factor of &lt; 10 of the latter. The convergence time for the clockless analog hardware scales with the number of qubits as ∼ N , improving the ∼ N 2 scaling for CPU implementations, but appears worse than that reported for quantum annealers by D-Wave.
Strong Quantum Metrological Limit from Many-Body Physics

Surpassing the standard quantum limit and even reaching the Heisenberg limit using quantum entanglement, represents the Holy Grail of quantum metrology. However, quantum entanglement is a valuable resource that does not come without a price. The exceptional time overhead for the preparation of large-scale entangled states raises disconcerting concerns about whether the Heisenberg limit is fundamentally achievable. Here, we find a universal speed limit set by the Lieb-Robinson light cone for the quantum Fisher information growth to characterize the metrological potential of quantum resource states during their preparation. Our main result establishes a strong precision limit of quantum metrology accounting for the complexity of many-body quantum resource state preparation and reveals a fundamental constraint for reaching the Heisenberg limit in a generic many-body lattice system with bounded one-site energy. It enables us to identify the essential features of quantum many-body systems that are crucial for achieving the quantum advantage of quantum metrology, and brings an interesting connection between many-body quantum dynamics and quantum metrology.
Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems

The use of machine learning (ML) inference for various applications is growing drastically. ML inference services engage with users directly, requiring fast and accurate responses. Moreover, these services face dynamic workloads of requests, imposing changes in their computing resources. Failing to right-size computing resources results in either latency service level objectives (SLOs) violations or wasted computing resources. Adapting to dynamic workloads considering all the pillars of accuracy, latency, and resource cost is challenging. In response to these challenges, we propose InfAdapter, which proactively selects a set of ML model variants with their resource allocations to meet latency SLO while maximizing an objective function composed of accuracy and cost. InfAdapter decreases SLO violation and costs up to 65% and 33%, respectively, compared to a popular industry autoscaler (Kubernetes Vertical Pod Autoscaler).
QSAM-Net: Rain Streak Removal by Quaternion Neural Network With Self-Attention Module

Real-world images captured in remote sensing, image or video retrieval, and outdoor surveillance are often degraded due to poor weather conditions, such as rain and mist. These conditions introduce artifacts that make visual analysis challenging and limit the performance of high-level computer vision methods. In time-critical applications, it is vital to develop algorithms that automatically remove rain without compromising the quality of the image contents. This article proposes a novel approach called QSAM-Net, a quaternion multi-stage multiscale neural network with a self-attention module. The algorithm requires significantly fewer parameters by a factor of 3.98 than the real-valued counterpart and state-of-the-art methods while improving the visual quality of the images. The extensive evaluation and benchmarking on synthetic and real-world rainy images demonstrate the effectiveness of QSAM-Net. This feature makes the network suitable for edge devices and applications requiring near real-time performance. Furthermore, the experiments show that the improved visual quality of images also leads to better object detection accuracy and training speed.
Evaluation Modelling of Asteroids’ Hazardousness using ChaosNet

Modern Computational Fields including Machine Learning, Artificial Intelligence, Data Science, Internet of Things, and many others have emerged in response to recent technological advancements. The human race benefits greatly from these fields. Artificial Intelligence led to the innovations of a lot of Artificial Neural Networks. In this work, we are making use of one such Artificial Neural Network, namely ChaosNet, which is build on the Chaotic Dynamics shown by the Human Brain. It is made of unidimensional Generalized Luroth Series. The work encompasses around predicting Hazardousness of Asteroids. Numerous Asteroids are present in the solar space. Many have their course of path towards our Earth. Though, very few of them are hazardous, while many are non hazardous. The non hazardous ones gets completely burned up as they enter our atmosphere. In the other hand, the hazardous ones pose a great threat to mankind. Making use of ChaosNet, we would predict the hazardousness of any Asteroid to a higher accuracy than the traditional Classifiers. To do so, we have trained a Machine Learning Model making use of a recent dataset of Asteroids from the National Aeronautics and Space Administration.
The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination

Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists' diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the influence of (incorrect) algorithmic suggestions.
Enabling Representation Learning in Ontology-Driven Conceptual Modeling Using Graph Neural Networks

Conceptual Models (CMs) are essential for information systems engineering since they provide explicit and detailed representations of the subject domains at hand. Ontology-driven conceptual modeling (ODCM) languages provide primitives for articulating these domain notions based on the ontological categories put forth by upper-level (or foundational) ontologies. Many existing CMs have been created using ontologically-neutral languages (e.g., UML, ER). Connecting these models to ontological categories would provide better support for meaning negotiation, semantic interoperability, and complexity management. However, given the sheer size of this legacy base, manual stereotyping is a prohibitive task. This paper addresses this problem by proposing an approach based on Graph Neural Networks towards automating the task of stereotyping UML class diagrams with the meta-classes offered by the ODCM language OntoUML. Since these meta-classes (stereotypes) represent ontological distinctions put forth by a foundational ontology, this task is equivalent to ontological category prediction for these classes. To enable this approach, we propose a strategy for representing CM vector embeddings that preserve the model elements' structure and ontological categorization. Finally, we present an evaluation that shows convincing learning of OntoUML model node embeddings used for OntoUML stereotype prediction.
Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction

Although time series prediction is widely used to estimate the future state of complex systems in various industries, accurate, interpretable and generalizable methods are still limited when used to make long-term nonstationary predictions. To this end, this article proposes deep attention fuzzy cognitive maps (DAFCM), which is composed of spatiotemporal fuzzy cognitive maps (STFCM), long short-term memory (LSTM) neural network, temporal fuzzy cognitive maps (TFCM) and residual structures. First, an improved attention mechanism is used to build spatiotemporal fuzzy cognitive maps that capture the spatial correlation in pairs of nodes and the temporal correlation of respective nodes. Second, the node state updated through the STFCM is input to the LSTM to capture the long-term trend of these series, and the TFCM with improved time attention is applied for the nonstationary problem in the time series. Finally, we add the state values of previous nodes into the DAFCM and build residual structures through linear transformation to prevent gradient explosion and gradient disappearance in long-term backpropagation. By combining the interpretability of fuzzy cognitive maps (FCM) and the high prediction accuracy of deep learning, the DAFCM can be used to accomplish tasks such as multivariate long-term nonstationary time series forecasting in multiple domains, and its efficiency is validated with 6 public datasets across 9 baselines.
The mediating effect of information technology on the cost of internal control systems and enhancing confidence in quality relationship on accounting information quality

This study aimed to investigate the mediating effect of information technology on the relationship between internal control system's costs and enhancing confidence in the quality of accounting information in banks working in Jordan. To achieve the objectives of the study, the researchers designed a 56-items questionnaire and 200 versions of this questionnaire were distributed on a random sample from the study population represented by all employees of the internal control departments in banks operating in Jordan, and 152 questionnaires were retrieved, and 4 questionnaires were excluded because they were not valid for analysis so, the valid questionnaires for analysis were 148 questionnaires. Effect was assessed using the Multiple Regression Analysis and after the appropriate statistical treatment, the study concluded with a group of results the most important among which that there was a mediating effect of information technology on the relationship between internal control system's costs and increasing confidence in accounting information in banks working in Jordan in terms of internal control environment costs, internal control activities' costs, internal risk assessment's costs, internal monitoring and follow-up costs, information and internal communication costs among banks working in Jordan that use internal control systems. In light of the results revealed, the researchers recommended the need for implementing internal control systems within certain cost limits in banks because of their significant role in increasing confidence in accounting information through the aforementioned variables.
SurroundNet: Towards effective low-light image enhancement

Although Convolution Neural Networks (CNNs) have made substantial progress in the low-light image enhancement task, one critical problem of CNNs is the paradox of model complexity and performance. This paper presents a novel SurroundNet that only involves less than 150K parameters (about 80–98 percent size reduction compared to SOTAs) and achieves very competitive performance. The proposed network comprises several Adaptive Retinex Blocks (ARBlock), which can be viewed as a novel extension of Single Scale Retinex in feature space. The core of our ARBlock is an efficient illumination estimation function called Adaptive Surround Function (ASF). It can be regarded as a general form of surround functions and be implemented by convolution layers. In addition, we also introduce a Low-Exposure Denoiser (LED) to smooth the low-light image before the enhancement. We evaluate the proposed method on two real-world low-light datasets. Experimental results demonstrate the superiority of our submitted SurroundNet in both performance and network parameters against State-of-the-Art low-light image enhancement methods. The code is available at https://github.com/ouc-ocean-group/SurroundNet.