Reviewerly/exHarmony huggingface dataset:
A Survey on Attribute-Based Encryption Schemes Suitable for the Internet of Things

The Internet of Things (IoT) is an information service paradigm based on the integration of smart objects, mobile devices, and computers via the Internet. IoT technologies are key enablers for a multitude of applications in diverse fields, such as digital health, smart city, industrial automation, and supply chain. This raises new security and privacy challenges that can be addressed by advanced cryptographic methods. One of the most prominent is attribute-based encryption (ABE), which allows one to encrypt data while enforcing fine-grained access control on it. ABE is advantageous in many IoT applications since it allows data to be safely stored on untrusted storage, such as third-party cloud servers, hackable publish-subscribe brokers, physically accessible sensors, etc. This article surveys the ABE literature proposing schemes and solutions that are best suited for IoT applications. To do so, it first identifies three performance indicators that are key in IoT, namely, the data producer CPU efficiency, the data producer bandwidth efficiency, and the key authority bandwidth efficiency. Then, it analyzes only those schemes that are promising from the point of view of one or more indicators and, therefore, more applicable in typical IoT applications. As a further contribution, this article selects a subset of representative schemes and assesses their efficiency by thorough simulations. Such simulations show that no scheme excels in all three performance indicators at once, but some simultaneously perform well in two indicators.
Class Semantics-based Attention for Action Detection

Action localization networks are often structured as a feature encoder sub-network and a localization sub-network, where the feature encoder learns to transform an input video to features that are useful for the localization sub-network to generate reliable action proposals. While some of the encoded features may be more useful for generating action proposals, prior action localization approaches do not include any attention mechanism that enables the localization sub-network to attend more to the more important features. In this paper, we propose a novel attention mechanism, the Class Semantics-based Attention (CSA), that learns from the temporal distribution of semantics of action classes present in an input video to find the importance scores of the encoded features, which are used to provide attention to the more useful encoded features. We demonstrate on two popular action detection datasets that incorporating our novel attention mechanism provides considerable performance gains on competitive action detection models (e.g., around 6.2% improvement over BMN action detection baseline to obtain 47.5% mAP on the THUMOS-14 dataset), and a new state-of-the-art of 36.25% mAP on the ActivityNet v1.3 dataset. Further, the CSA localization model family which includes BMN-CSA, was part of the second-placed submission at the 2021 ActivityNet action localization challenge. Our attention mechanism outperforms prior self-attention modules such as the squeeze-and-excitation in action detection task. We also observe that our attention mechanism is complementary to such self-attention modules in that performance improvements are seen when both are used together.
Securing e-Health application of cloud computing using hyperchaotic image encryption framework

Electronic health (e-Health) is one the latest technology that optimizes its societal benefits by implementing it in a cloud-based environment. However, the complete utilization of cloud-based e-Health facility is determined to be hampered by the challenges of security. Further, most of the data stored and transmitted as images are related to sensitive information of the patients. In this paper, a secure framework integrating e-Health and cloud using Hyper Chaos-based Image Encryption is proposed for potential implementation of security over sensitive image data transmitted over the clouds. This framework alternatively applies any one of the proposed chaos-based image encryptions and image scrambling schemes depending on the intensity and type of attack over the images shared in the cloud environment. Simulation experiments of the proposed framework are conducted to evaluate its potential in resisting known and unknown attacks that are possible under e-Health image sharing process.
Redactable Blockchain in Decentralized Setting

Immutability has been widely accepted as a fundamental property protecting the security of blockchain technology. However, this property impedes the development of blockchain because of the abuse of blockchain storage and legal obligations. To mitigate this issue, a novel construction of blockchain, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">redactable blockchain</i> , was introduced. It enables a central authority to issue the rewriting privilege to a particular party who can rewrite a registered object, e.g., a block or a transaction, in a controlled way. Unfortunately, the central authority must be fully trusted and is an obvious target suffering from various attacks. In this paper, we introduce a redactable blockchain controlled at a fine-grained level in a decentralized setting. In our solution, the rewriting privilege is issued by multiple authorities for reducing the vulnerability of the centralized setting. To formalize our solution, we introduce a novel cryptographic notion, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized policy-based chameleon hash</i> (DPCH), with the formal definition and security model. By applying several simple cryptographic tools, such as chameleon hash, digital signature, and multi-authority attribute-based encryption, we present the generic construction of DPCH along with rigorous security proofs. By applying RSA-based chameleon hash and BLS short signature, we give a practical instantiation of DPCH with performance evaluation. The comprehensive evaluation shows that our solution has superior performance than the state-of-the-art solution.
A deep neural network framework for real‐time on‐site estimation of acceleration response spectra of seismic ground motions

Abstract Various earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground‐shaking intensities, and/or potential consequences) about ongoing seismic events for real‐time/near real‐time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified empirical models to estimate the source parameters and intensity measures of interest. Given the recent boost in computational resources, data‐driven methods/models are now widely accepted as effective alternatives for EEW. This study introduces a highly accurate deep‐learning‐based computational framework named ROSERS (i.e., Real‐time On‐Site Estimation of Response Spectra) to estimate the acceleration response spectrum ( S a ( T )) of the expected on‐site ground‐motion waveforms using early non‐damage‐causing early p ‐waves and site characteristics. The framework is trained using a carefully selected extensive database of recorded ground motions. Due to the well‐known correlation of S a ( T ) with structures’ seismic response and resulting damage/losses, rapid and accurate knowledge of expected on‐site S a ( T ) values is highly beneficial to various end‐users to make well‐informed real‐time and near‐real‐time decisions. The framework is thoroughly assessed and investigated through multiple statistical tests under three historical earthquake events. These analyses demonstrate that the overall framework leads to excellent prediction power and, on average, has an accuracy above 85% for hazard‐consistent early‐warning trigger classification.
Risk monitoring strategy for confidentiality of healthcare information

Concrete privacy endeavours to give Confidentiality, Integrity, and Availability (CIA) measures to secure traffic streams in sensitive healthcare applications are a necessity. When talking about the access of sensitive healthcare data or the confidentiality of highly esteemed data, the first thing that comes to mind is that it should be secured. Sensitive healthcare data needs to be protected to restrict illegal access, exposure, and/or manipulation. As there is no such protection by which we can make our systems fully secure, the most acceptable methodology is to perform layered modelling of safety measures. This paper intends to provide a mathematical description of the Modular Encryption Standard (MES) and the augmentation of condition-centric risk monitoring of confidential information to provide a layered model for securing healthcare data confidentiality. Decision-making regarding the risk monitoring strategy of MES is augmented using a machine learning approach based on a Fuzzy Inference System amalgamated with Neural Networks. Result analysis shows that MES has less than a 0.005 error-rate and a 97% precision-rate, which elucidates its desideratum towards increasing security risks.
It’s Complicated: The Relationship between User Trust, Model Accuracy and Explanations in AI

Automated decision-making systems become increasingly powerful due to higher model complexity. While powerful in prediction accuracy, Deep Learning models are black boxes by nature, preventing users from making informed judgments about the correctness and fairness of such an automated system. Explanations have been proposed as a general remedy to the black box problem. However, it remains unclear if effects of explanations on user trust generalise over varying accuracy levels. In an online user study with 959 participants, we examined the practical consequences of adding explanations for user trust: We evaluated trust for three explanation types on three classifiers of varying accuracy. We find that the influence of our explanations on trust differs depending on the classifier’s accuracy. Thus, the interplay between trust and explanations is more complex than previously reported. Our findings also reveal discrepancies between self-reported and behavioural trust, showing that the choice of trust measure impacts the results.
Niching chimp optimization for constraint multimodal engineering optimization problems

Two significant concerns need to be addressed to handle multimodal problems: classifying various local/global optima and preserving these optimum values until the termination. Besides, a comprehensive local search ability is also a need to achieve the exact global optima. Chimp Optimization Algorithm (ChOA) is a recently swarm intelligence algorithm that needs less parameter tuning. The ChOA, on the other hand, is prone to early convergence and fails to strike a balance between exploration and exploitation when it comes to resolving multimodal challenges. In order to overcome these concerns, this paper embeds the niching technique in ChOA (NChOA) that includes the personal best qualities of PSO and a local search technique. To evaluate the NChOA’s performance, we analyze it against fifteen frequently utilized multimodal numerical test functions, ten complex IEEE CEC06-2019 suit tests, and twelve constrained real-world optimization problems in a variety of engineering fields, including industrial chemical producer, process design and synthesis, mechanical design, power system, power-electronic, and livestock teed ration problems. The results indicate that the NChOA outperforms several benchmark algorithms and sixteen out of eighteen state-of-the-art algorithms by an average rank of Friedman test greater than 81% for 25 numerical functions and twelve engineering problems while outperforming jDE100 and DISHchain1e + 12 by 22% and 41%, respectively. The Bonferroni-Dunn and Holm tests demonstrated that NChOA outperforms all benchmark and state-of-the-art algorithms for all numerical functions and engineering tasks while performing comparably to jDE100 and DISHchain1e + 12. The proposed NChOA, we believe, can be used to address difficulties involving multimodal search spaces. Additionally, NChOA is more broadly applicable than rival benchmarks to a broader range of engineering applications.
On Pearl’s Hierarchy and the Foundations of Causal Inference

chapter Share on On Pearl's Hierarchy and the Foundations of Causal Inference Authors: Elias Bareinboim Columbia University Columbia UniversitySearch about this author , Juan D. Correa Columbia University Columbia UniversitySearch about this author , Duligur Ibeling Stanford University Stanford UniversitySearch about this author , Thomas Icard Stanford University Stanford UniversitySearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 507–556https://doi.org/10.1145/3501714.3501743Online:04 March 2022Publication History 0citation26DownloadsMetricsTotal Citations0Total Downloads26Last 12 Months26Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Aspect-level sentiment classification based on attention-BiLSTM model and transfer learning

Aspect-level sentiment classification, a fine-grained sentiment analysis task which provides entire and intensive results, has been a research focus in recent years. However, the performance of neural network models is largely limited by the small scale of datasets for aspect-level sentiment classification due to the challenges to label such data. In this paper, we propose an aspect-level sentiment classification model based on Attention-Bidirectional Long Short-Term Memory (Attention-BiLSTM) model and transfer learning. Based on Attention-BiLSTM model, three models including Pre-training (PRET), Multitask learning (MTL), and Pre-training & Multitask learning (PRET+MTL) are proposed to transfer the knowledge obtained from document-level training of sentiment classification to aspect-level sentiment classification. Finally, the performance of the four models is verified on four datasets. Experiments show that proposed methods make up for the shortcomings of poor training of neural network models due to the small dataset of the aspect-level sentiment classification.
Context-related video anomaly detection via generative adversarial network

Video anomaly detection is a challenging task because of the scarcity and ambiguity of abnormal event samples in videos. Predicting future content based on continuous video content provides a key clue for anomaly detection. However, most existing methods ignore the video context, which is important for understanding content. To address this issue, we propose a context-related video anomaly detection method combined with a generative adversarial network. Specifically, given a video frame, we first generate its content with some continuous frames before and after this frame by using a two-branch generator network, and then minimize the prediction errors between the generated frames and its ground truth. In addition, to explore the bidirectional relations well, we minimize the content loss between the generated frames and the ground truth in two directions. In the anomaly detection phase, the final anomaly score is calculated by combining the two directional generators obtained in the training phase. Results of experiments on three benchmark datasets demonstrate the effectiveness of the proposed method.
THE EFFECT OF SELECTED MARKETING ACTIVITIES AND PROMOTIONS ON THE CONSUMERS BUYING BEHAVIOR

The core purpose of this study is to determine the impact of electronics marketing (word of mouth), perceived value, and social networking on customer buying behaviour through customer trust in the context of cosmetic industry of Jordan. To accomplish the main research objective, the study has followed SEM technique for analysing collected data. For data collection, the use of survey questionnaire has been made, in which data was collected from total 223 respondents. The results of this study confirm the significant impact of perceived value on customer trust and customer buying behaviour, whereas electronic marketing (word of mouth) was found to have insignificant impact on both customers buying behaviour and customer trust. Findings also revealed the significant impact of social networking networks on customer buying behaviour. However, its impact on customer trust was insignificant. With respect to mediating effect, customer trust significantly mediates the association between perceived value and customer buying behaviour.
A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies

Deep neural networks have achieved remarkable success in machine learning, computer vision, and pattern recognition in the last few decades. Recent studies, however, show that neural networks (both shallow and deep) may be easily fooled by certain imperceptibly perturbed input samples called adversarial examples. Such security vulnerability has resulted in a large body of research in recent years because real-world threats could be introduced due to the vast applications of neural networks. To address the robustness issue to adversarial examples particularly in pattern recognition, robust adversarial training has become one mainstream. Various ideas, methods, and applications have boomed in the field. Yet, a deep understanding of adversarial training including characteristics, interpretations, theories, and connections among different models has remained elusive. This paper presents a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition. We start with fundamentals including definition, notations, and properties of adversarial examples. We then introduce a general theoretical framework with gradient regularization for defending against adversarial samples - robust adversarial training with visualizations and interpretations on why adversarial training can lead to model robustness. Connections will also be established between adversarial training and other traditional learning theories. After that, we summarize, review, and discuss various methodologies with defense/training algorithms in a structured way. Finally, we present analysis, outlook, and remarks on adversarial training.
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering

Knowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding. One limitation of existing solutions is that they capture relevant knowledge from text-only knowledge bases, which merely contain facts expressed by first-order predicates or language descriptions while lacking complex but indispensable multimodal knowledge for visual understanding. How to construct vision-relevant and explainable multimodal knowledge for the VQA scenario has been less studied. In this paper, we propose MuKEA to represent multimodal knowledge by an explicit triplet to correlate visual objects and fact answers with implicit relations. To bridge the heterogeneous gap, we propose three objective losses to learn the triplet representations from complementary views: embedding structure, topological relation and semantic space. By adopting a pretraining and fine-tuning learning strategy, both basic and domain-specific multimodal knowledge are progressively accumulated for answer prediction. We outperform the state-of-the-art by 3.35% and 6.08% respectively on two challenging knowledge-required datasets: OK-VQA and KRVQA. Experimental results prove the complementary benefits of the multimodal knowledge with existing knowledge bases and the advantages of our end-to-end framework over the existing pipeline methods. The code is available at https://github.com/AndersonStra/MuKEA.
Automated prostate cancer grading and diagnosis system using deep learning-based Yolo object detection algorithm

Developing an artificial intelligence-based prostate cancer detection and diagnosis system that can automatically determine important regions and accurately classify the determined regions on an input biopsy image. The Yolo general-purpose object detection algorithm was utilized to detect important regions (for the localization task) and to grade the detected regions (for the classification task). The algorithm was re-trained with our prostate cancer dataset. The dataset was created by annotating 500 real prostate tissue biopsy images. The dataset was split into train/test parts as 450/50 real prostate tissue images, respectively, before the data augmentation process. Next, the training set consisting of 450 labeled biopsy images was pre-processed with the data augmentation method. This way, the number of biopsy images in the dataset was increased from 450 to 1776. Then, the algorithm was trained with the dataset and the automatic prostate cancer detection and diagnosis tool was developed. The developed tool was tested with two test sets. The first test set contains 50 images that are similar to the train set. Hence, 97% detection and classification accuracy has been achieved. The second test set contains 137 completely different real prostate tissue biopsy images, thus, 89% detection accuracy has been achieved. In this study, an automatic prostate cancer detection and diagnosis tool was developed. The test results show that high-accuracy (high-performance) prostate cancer diagnosis tools can be developed using AI (computer vision) methods such as object detection algorithms. These systems can decrease the inter-observer variability among pathologists, and help prevent the time delay in the diagnosis phase.
Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare industry by leveraging its capabilities to improve personalized healthcare services by enabling seamless communication of medical data. IoMT facilitates prompt emergency responses and provides improved quality of medical services with minimum cost. With the advancement of modern technology, progressively ubiquitous medical devices raise critical security and data privacy concerns through resource constraints and open connectivity. Vulnerabilities in IoMT devices allow unauthorized access for potential entry into healthcare and sensitive personal data. In addition, the patient may experience severe physical damage with the attack on IoMT devices. To provide security to IoMT devices and privacy to patient data, we have proposed a novel IoMT framework with the hybridization of Bayesian optimization and extreme learning machine (ELM). The proposed model derives encouraging performance with enhanced accuracy in decision-making process compared to similar state-of-the-art methods.