Reviewerly/exHarmony huggingface dataset:
Development paradigms of cyberspace endogenous safety and security

This paper tries to systematically explain the thinking perspectives and methodologies for cyberspace security developments from the perspective of scientific research paradigm, and points out the intrinsic reasons for the corresponding development paradigm evolution. Aiming at the cyberspace endogenous security common problems and the “unknown-unknown" security threats based on these common problems, this study proposes a new theory, a new methodology, and related practice norms to solve these problems. These aim to contribute to a replicable, successful model for the cyberspace endogenous safety and security area and a new generation ofinformation technologies and related industries using the new development paradigm. Based on a brief introduction of the concept and theory of the paradigm, this paper reviews the main development periods of cyberspace endogenous safety and security and insurmountable challenges in terms of the paradigm world outlook and methodology, summarizes cyberspace endogenous security ubiquitous problems, proposes a new development paradigm for cyberspace endogenous safety and security, and elaborates on the theoretical basis, practical norms, mechanism, and methods of the new paradigm.
Identification of glomerulosclerosis using IBM Watson and shallow neural networks

Advanced stages of different renal diseases feature glomerular sclerosis at a histological level which is observed by light microscopy on tissue samples obtained by performing a kidney biopsy. Computer-aided diagnosis (CAD) systems leverage the potential of artificial intelligence (AI) in healthcare to support physicians in the diagnostic process. We propose a novel CAD system that processes histological images and discriminates between sclerotic and non-sclerotic glomeruli. To this goal, we designed, tested, and compared two artificial neural network (ANN) classifiers. The former implements a shallow ANN classifying hand-crafted features extracted from Regions of Interest (ROIs) by means of image-processing procedures. The latter, instead, employs the IBM Watson Visual Recognition System, which uses a deep artificial neural network making decisions taking the images as input, without the need to design any procedure for describing images with features. The input dataset consisted of 428 sclerotic glomeruli and 2344 non-sclerotic glomeruli derived from images of kidney biopsies scanned by the Aperio ScanScope System. Both AI approaches allowed to very accurately distinguish (mean MCC 0.95 and mean Accuracy 0.99) between sclerotic and non-sclerotic glomeruli. Although the systems may seem interchangeable, the approach based on feature extraction and classification would allow clinicians to gain information on the most discriminating features. In fact, further procedures could explain the classifier’s decision by analysing which subset of features impacted the most on the final decision. We developed a customizable support system that can facilitate the work of renal pathologists both in clinical and research settings.
Why Is Everyone Training Very Deep Neural Network With Skip Connections?

Recent deep neural networks (DNNs) with several layers of feature representations rely on some form of skip connections to simultaneously circumnavigate optimization problems and improve generalization performance. However, the operations of these models are still not clearly understood, especially in comparison to DNNs without skip connections referred to as plain networks (PlainNets) that are absolutely untrainable beyond some depth. As such, the exposition of this article is the theoretical analysis of the role of skip connections in training very DNNs using concepts from linear algebra and random matrix theory. In comparison with PlainNets, the results of our investigation directly unravel the following: 1) why DNNs with skip connections are easier to optimize and 2) why DNNs with skip connections exhibit improved generalization. Our investigation results concretely show that the hidden representations of PlainNets progressively suffer from information loss via singularity problems with depth increase, thus making their optimization difficult. In contrast, as model depth increases, the hidden representations of DNNs with skip connections circumnavigate singularity problems to retain full information that reflects in improved optimization and generalization. For theoretical analysis, this article studies in relation to PlainNets two popular skip connection-based DNNs that are residual networks (ResNets) and residual network with aggregated features (ResNeXt).
Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data

Background The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. Objective The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. Methods We developed a named entity recognition model to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. To identify effects that are symptoms of opioid withdrawal and substances that are potential remedies for these symptoms, we deduplicated substances and effects by using clustering and manual review, then built a network of substance and effect co-occurrence. For each of the 16 effects identified as symptoms of opioid withdrawal in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, we identified the 10 most strongly associated substances. We classified these pairs as follows: substance is a Food and Drug Administration–approved or commonly used treatment for the symptom, substance is not often used to treat the symptom but could be potentially useful given its pharmacological profile, substance is a home or natural remedy for the symptom, substance can cause the symptom, or other or unclear. We developed the Withdrawal Remedy Explorer application to facilitate the further exploration of the data. Results Our named entity recognition model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on hold-out data. We identified 458 unique substances and 235 unique effects. Of the 130 potential remedies strongly associated with withdrawal symptoms, 54 (41.5%) were Food and Drug Administration–approved or commonly used treatments for the symptom, 17 (13.1%) were not often used to treat the symptom but could be potentially useful given their pharmacological profile, 13 (10%) were natural or home remedies, 7 (5.4%) were causes of the symptom, and 39 (30%) were other or unclear. We identified both potentially promising remedies (eg, gabapentin for body aches) and potentially common but harmful remedies (eg, antihistamines for restless leg syndrome). Conclusions Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of a web-based community of people who use opioids. Our Withdrawal Remedy Explorer application provides a platform for using these data for pharmacovigilance, the identification of new treatments, and the better understanding of the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states for which people self-manage their symptoms and discuss their experiences on the web.
Joint Range-Doppler Estimation Based on Multipulse Processing of Composite Hyperbolic Frequency Modulated Waveforms

We herein investigate the joint estimation of range and Doppler based on multipulse processing of composite hyperbolic frequency-modulated (HFM) waveforms. The composite HFM waveform solves the range bias problem in the HFM waveform by using the mapping relationship between the radial velocity and the peak-to-peak interval in the outputs of matched filters. However, the result may be inaccurate in the presence of multiple false peaks. In this letter, we fit matched filter outputs into a hidden Markov model by utilizing the kinetic model of the target. We can subsequently solve the peak-pair selection problem and perform the joint range-Doppler estimation by using the Viterbi algorithm. The proposed method, by multipulse processing, lowers the signal-to-noise ratio (SNR) requirement, and exact detection of the target in each return pulse is no longer necessary. We prove the effectiveness of the proposed method via numerical simulations, and the simulation results demonstrate that this method outperforms existing methods in low SNR environments.
Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment

Histological changes in tissue are of primary importance in pathological research and diagnosis. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue. Conventional histopathological assessments are performed from individual tissue sections, leading to the loss of three-dimensional context of the tissue. Yet, the tissue context and spatial determinants are critical in several pathologies, such as in understanding growth patterns of cancer in its local environment. Here, we develop computational methods for visualization and quantitative assessment of histopathological alterations in three dimensions. First, we reconstruct the 3D representation of the whole organ from serial sectioned tissue. Then, we proceed to analyze the histological characteristics and regions of interest in 3D. As our example cases, we use whole slide images representing hematoxylin-eosin stained whole mouse prostates in a Pten+/- mouse prostate tumor model. We show that quantitative assessment of tumor sizes, shapes, and separation between spatial locations within the organ enable characterizing and grouping tumors. Further, we show that 3D visualization of tissue with computationally quantified features provides an intuitive way to observe tissue pathology. Our results underline the heterogeneity in composition and cellular organization within individual tumors. As an example, we show how prostate tumors have nuclear density gradients indicating areas of tumor growth directions and reflecting varying pressure from the surrounding tissue. The methods presented here are applicable to any tissue and different types of pathologies. This work provides a proof-of-principle for gaining a comprehensive view from histology by studying it quantitatively in 3D.
Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+

BACKGROUND: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effective and accurate method for early detection. Due to the poor contrast, noise and artifacts which results in difficulty for radiologists to diagnose, Computer-Aided Diagnosis (CAD) systems are hence developed. The extraction of breast region is a fundamental and crucial preparation step for further development of CAD systems. OBJECTIVE: The proposed method aims to extract breast region accurately from mammographic images where noise is suppressed, contrast is enhanced and pectoral muscle region is removed. METHODS: This paper presents a new deep learning-based breast region extraction method that combines pre-processing methods containing noise suppression using median filter, contrast enhancement using CLAHE and semantic segmentation using Deeplab v3+ model. RESULTS: The method is trained and evaluated on mini-MIAS dataset. It has also been evaluated on INbreast dataset. The results outperform those generated by other recent researches and are indicative of the capacity of the model to retain its accuracy and runtime advantage across different databases with different image resolutions. CONCLUSIONS: The proposed method shows state-of-the-art performance at extracting breast region from mammographic images. Wide range of evaluation on two commonly used mammography datasets proves the ability and adaptability of the method.
Incentive-Aware Autonomous Client Participation in Federated Learning

Federated learning (FL) emerges as a promising paradigm to enable a federation of clients to train a machine learning model in a privacy-preserving manner. Most existing works assumed that the central parameter server (PS) determines the participation of clients implying that clients cannot make autonomous participation decisions. The above assumption is unrealistic because the participation in FL training may incur various cost and clients also have strong desire to be rewarded for participation. To address this problem, we design a novel autonomous client participation scheme to incentivize clients. Specifically, the PS provides a certain reward shared among participating clients for each training round. Clients decide whether to participate each FL training round or not based on their own utilities (i.e., reward minus cost). The process can be modeled as a minority game (MG) with incomplete information and clients end up in the minority side win after each training round because the reward of each participating client may not cover its cost if too many clients participate and vice verse. The challenge of autonomous participation schemes lies in lowering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">volatility</i> of participating clients in each round due to the lack of coordination among clients. Through solid analysis, we prove that: 1) The volatility of participating clients in each round is very high under the standard MG scheme. 2) The volatility of participating clients can be reduced significantly under the stochastic MG scheme. 3) A coalition based MG is proposed, which can further reduce the volatility in each round. By conducting extensive experiments in real settings, we demonstrate that the stochastic MG-based scheme outperforms other state-of-the-art algorithms in terms of utility and volatility, and the coalition MG-based client participation scheme can further boost the utility by 39%-48% and reduce the volatility by 51%–100%. Moreover, our algorithms can achieve almost the same model accuracy as that obtained by centralized client participation algorithms.
BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices

Inference with trained machine learning models is now possible with small computing devices while only a few years ago it was run mostly in the cloud only.The recent technique of Federated Learning offers now a way to do also the training of the machine learning models on small devices by distributing the computing effort needed for the training over many distributed machines.But, the training on these low-capacity devices takes a long time and often consumes all the available CPU resource of the device.Therefore, for Federated Learning to be done by low-capacity devices in practical environments, the training process must not only target for the highest accuracy, but also on reducing the training time and the resource consumption.In this paper, we present an approach which uses a dynamic epoch parameter in the model training.We propose the BePOCH (Best Epoch) algorithm to identify what is the best number of epochs per training round in Federated Learning.We show in experiments with medical datasets how with the BePOCH suggested number of epochs, the training time and resource consumption decreases while keeping the level of accuracy.Thus, BePOCH makes machine learning model training on lowcapacity devices more feasible and furthermore, decreases the overall resource consumption of the training process, which is an important aspect towards greener machine learning techniques.
Image-Captioning Model Compression

Image captioning is a very important task, which is on the edge between natural language processing (NLP) and computer vision (CV). The current quality of the captioning models allows them to be used for practical tasks, but they require both large computational power and considerable storage space. Despite the practical importance of the image-captioning problem, only a few papers have investigated model size compression in order to prepare them for use on mobile devices. Furthermore, these works usually only investigate decoder compression in a typical encoder–decoder architecture, while the encoder traditionally occupies most of the space. We applied the most efficient model-compression techniques such as architectural changes, pruning and quantization to several state-of-the-art image-captioning architectures. As a result, all of these models were compressed by no less than 91% in terms of memory (including encoder), but lost no more than 2% and 4.5% in metrics such as CIDEr and SPICE, respectively. At the same time, the best model showed results of 127.4 CIDEr and 21.4 SPICE, with a size equal to only 34.8 MB, which sets a strong baseline for compression problems for image-captioning models, and could be used for practical applications.
The relationship between big data analytics and green supply chain management by looking at the role of environmental orientation: Evidence from emerging economy

Academics and practitioners have become more interested in big data analytics (BDA) in recent years. There have been few empirical studies on the relationship between BDA and green supply chain management (GSCM), as well as the importance of environmental orientation (EO). A total of 128 responses from Jordanian industrial businesses were evaluated using the structural equation modeling method. The BDA, EO has a favorable and significant relationship with external and internal GSCM, according to the findings of this study. Furthermore, EO serves as a mediator between BDA and the external, and internal GSCM. The findings provide managerial insight into how to use BDA to establish a proactive environmental policy that covers all GSCM activities.
Deep learning model improves radiologists’ performance in detection and classification of breast lesions

Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve.The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively.The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.
Black-Box Audio Adversarial Attack Using Particle Swarm Optimization

The development of artificial neural networks and artificial intelligence has helped to address problems and improve services in various fields, such as autonomous driving, image classification, medical diagnosis, and speech recognition.However, this technology has raised security threats that are different from existing ones.Recent studies have shown that artificial neural networks can easily malfunction by adversarial examples.The adversarial examples operate the neural network model as intended by the adversary.In particular, adversarial examples targeting speech recognition models is an area that has been actively studied in recent years.Existing studies have focused more on white-box methods.However, most speech recognition services are provided online and involve black-box, making it difficult or impossible for adversaries to attack.Black-box attacks have several challenges.Typically, they have a low success rate and a high risk of detection.In particular, previously proposed genetic algorithm (GA)-based attacks are at a high risk of detection because they require numerous queries.Therefore, we propose an adversarial attack system using particle swarm optimization (PSO) algorithms to address these problems.The proposed system uses adversarial candidates as particles to obtain adversarial examples through iterative optimization.PSO-based adversarial attacks are more efficient in queries and have a higher attack success rate than the adversarial methods using GAs.In particular, our key function is that temporary particle generation maximizes query efficiency to reduce detection risk and prevent wastage of system resources.On average, our system exhibits 96% attack success rates with 1416.17 queries, indicating that is 71.41% and 8% better in terms of query and success rates than existing GA-based attacks, respectively.
Is the key to phishing training persistence?: Developing a novel persistent intervention.

Most previous phishing interventions have employed discrete training approaches, such as brief instructions aimed at improving phishing detection. However, these discrete interventions have demonstrated limited success. The present studies focused on developing an alternative to discrete training by providing college-age adults with a persistent classification aid that guided them on what characteristics a phishing email might include. Experiment 1 determined if this classification aid improved email categorization performance relative to feedback and control. Experiment 2 continued the evaluation of the classification aid to determine whether performance improvements were due to increased systematic processing of emails. Experiment 3 explored whether the classification aid would be more effective when embedded directly into the email interface. The results suggested three major findings. (a) Persistent interventions can improve phishing email detection. (b) Performance improvements were largest when the classification aid was embedded into the task. (c) These benefits were likely driven by an improved systematic processing of the emails. This novel phishing classification aid serves as a promising persistent intervention that can be adaptable to specific email environments and individuals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).