Reviewerly/exHarmony huggingface dataset:
FedDyn: A dynamic and efficient federated distillation approach on Recommender System

Federated Learning (FL) is a popular distributed machine learning paradigm that enables devices to work together to train a centralized model without transmitting raw data. However, when the model becomes complex, mobile devices’ communication overhead can be unacceptably large in traditional FL methods. To address this problem, Federated Distillation (FD) is proposed as a federated version of knowledge distillation. Most of the recent FD methods calculate the model output (logits) of each client as the local knowledge on a public proxy dataset and do distillation with the average of the clients’ logits on the server side. Nevertheless, these FD methods are not robust and perform poorly in the non-IID (data is nonindependent and non-identically distributed) scenario such as Federated Recommendation (FR). In order to eliminate the non-IID problem and apply FD in FR, we proposed a novel method named FedDyn to construct a proxy dataset and extract local knowledge dynamically in this paper. In this method, we replaced the average strategy with focus distillation to strengthen reliable knowledge, which solved the non-IID problem that the local model has biased knowledge. The average strategy is a dilution and perturbation of knowledge since it treats reliable and unreliable knowledge equally important. In addition, to prevent inference of private user information from local knowledge, we used a method like local differential privacy techniques to protect this knowledge on the client side. The experimental results showed that our method has a faster convergence speed and lower communication overhead than the baselines on three datasets, including MovieLens-10OK, MovieLens-IM and Pinterest.
Extracting Semantic Knowledge From GANs With Unsupervised Learning

Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations learned by generative models and in particular, Generative Adversarial Networks (GANs). Previous works on the interpretation of GANs reveal that GANs encode semantics in feature maps in a linearly separable form. In this work, we further find that GAN's features can be well clustered with the linear separability assumption. We propose a novel clustering algorithm, named KLiSH, which leverages the linear separability to cluster GAN's features. KLiSH succeeds in extracting fine-grained semantics of GANs trained on datasets of various objects, e.g., car, portrait, animals, and so on. With KLiSH, we can sample images from GANs along with their segmentation masks and synthesize paired image-segmentation datasets. Using the synthesized datasets, we enable two downstream applications. First, we train semantic segmentation networks on these datasets and test them on real images, realizing unsupervised semantic segmentation. Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations.
Autoencoder-Based Recommender System Exploiting Natural Noise Removal

Collaborative filtering (CF) is a widely used technique in recommender systems by automatically predicting the user’s latent interests based on many users’ historical rating data. To improve the performance of the CF-based recommender systems, users’ rating data should be pre-processed to avoid noise and enhance data reliability. Many researchers studied anomaly detection to remove malicious noise caused by shilling attacks, but anomalies can still exist in non-attacked real user data, which is called natural noise, as the ratings of users can be impacted by unpredictable factors such as other users’ ratings and anchoring bias. In this paper, we propose an autoencoder-based recommendation system for exploiting the ability of both anomaly detection and CF. The proposed system detects the natural noise in the rating data based on the reconstruction errors after training. By removing the detected natural noise, CF can predict the unrated ratings with noise-free data. Our experiments show that the proposed model showed better performance than the traditional method by reducing the error by up to 5% compared to the method that does not consider natural noise detection and reducing the error by up to 4% compared to the conventional rating classification based natural noise detection methods.
Few-shot encrypted traffic classification via multi-task representation enhanced meta-learning

Encrypted traffic classification requires identifying the services and programs running behind the content-invisible traffic data for improving quality of service and providing security assurance. Mainstream solutions achieve reliable performance by training on large-scale datasets. However, with the continuous emergence and development of encryption services, collecting and labeling sufficient amounts of encrypted traffic becomes impractical. Therefore, it is critical to utilize the few labeled data for accurate encrypted traffic classification. In this paper, we propose a Multi-task Representation Enhanced Meta-learning model (MetaMRE) for few-shot encrypted traffic classification. Specifically, we design a flow discrepancy enhancement module that combines supervised learning and clustering-based unsupervised learning to boost the discrepancy of encrypted traffic representations from a few labeled data. Moreover, MetaMRE introduces a multi-task collaborative meta-learning module that makes full use of non-target task data to learn the optimal initialization parameters suitable for the encrypted traffic classification, and then only a small amount of labeled encrypted traffic is required to adapt to the target classification task. Extensive evaluations on various real-world datasets show that the MetaMRE outperforms existing state-of-the-art methods and copes well with version updates and cross-domain problems in encrypted traffic classification.
A dual auditing protocol for fine-grained access control in the edge-cloud-based smart home

Devices and edges of the Internet of Things (IoT) are working together to broaden the sensing capability of the IoT. The protection of personal sensory information privacy in the smart home has gotten more attention. To enable secure data sharing under the scenario of the smart home, attribute-based encryption (ABE) has been applied widely. As a general rule, the sophisticated calculation of ABE is difficult under resource-constrained devices. The appearance of edge computing alleviates this dilemma, where an edge server (ES) is responsible for computing all heavy cryptographic operations and uploading massive device data to a cloud. Nevertheless, the ES and the cloud may be attacked artificially in reality. Accordingly, to check the correctness of the ES data and the integrity of the cloud data, we design a lightweight dual auditing scheme, where the first adopts the aggregation of data blocks and signatures to check the integrity of the cloud data, and the second constructs an efficient Zero-Knowledge (ZK) proof to verify the correctness of the ES data in a lightweight way. To prevent collusion attacks, the legitimacy of the data user is authenticated by a smart contract. The simulation experiment shows that our multiple auditing scheme is relatively efficient.
Floor plan recommendation system using graph neural network with spatial relationship dataset

The purpose of this study was to develop a recommendation system that, in the pre-design phase, quickly and easily search adequate floor plans satisfying the client requirements about the spatial relationship type using artificial intelligence (AI) technology. In this study using a graph dataset representing the spatial relationship between entities, we propose a deep neural network approach using SimGNN and shallow networks with teacher–student learning to compute graph similarity, measured by graph edit distance, fast and accurately during the search operation in the recommendation system. The prediction errors between the GED score (ground truth) and the predicted score were small enough to employ the neural networks for the recommendation system instead of using GED, which takes a long calculation time. The proposed recommendation systems based deep networks also suggested floor plans satisfying given conditions on the spatial relationship with high accuracy.
ECH<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e12960" altimg="si212.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>OA: An Enhanced Chimp-Harris Hawks Optimization Algorithm for copyright protection in Color Images using watermarking techniques

This paper proposes a hybrid Enhanced Chimp-Harris Hawks Optimization Algorithm (ECH3OA) that combines both enhanced Chimp Optimization algorithm (ChOA) and Harris Hawks optimization algorithm (HHO). HHO is a robust algorithm in solving optimization issues. However, it suffers from a balancing problem between exploitation and exploration. On the other hand, ChOA algorithm is another powerful algorithm with the capability of balancing between exploitation and exploration. However, ChOA suffers from low convergence and falling in the local minimum. The novelty of ECH3OA as a proposed optimization algorithm is concentrated in three contributions. The first is an enhanced ChOA that uses Levy Flight to improve its exploration phase. The second contribution of the algorithm is proposing an updated formula to calculate escaping energy of the prey as a selector between exploitation and exploration. Finally, the four cases of exploitation using HHO are used in the proposed algorithm. The effectiveness of the proposed algorithm is proved by comparing its performance with other eleven state-of-the-art algorithms on 23 standard benchmark function, 30 benchmark function of different modalities from CEC2017, 10 complex functions from CEC2019, and six standard real world engineering problems. Friedman average rank and Wilcoxon rank-sum (p-value) tests are applied as non-parametric statistical tests. Among the different benchmark suits, ECH3OA is ranked first among the other algorithms. We also applied ECH3OA for copyright protection in Color Images using Crypto-Watermarking Techniques. The results show better durability and transparency level of the watermark as compared to recent studies.
Vulnerability Analysis and Security Assessment of Secure Keyboard Software to Prevent PS/2 Interface Keyboard Sniffing

Online security threats have arisen through Internet banking hacking cases, and highly sensitive user information such as the ID, password, account number, and account password that is used for online payments has become vulnerable. Many security companies have therefore researched protection methods regarding keyboard-entered data for the introduction of defense techniques. Recently, keyboard security issues have arisen due to the production of new malicious codes by attackers who have combined the existing attack techniques with new attack techniques; however, a keyboard security assessment is insufficient here. The research motivation is to serve more secure user authentication methods by evaluating the security of information input from the keyboard device for the user authentication, including Internet banking service. If the authentication information input from the keyboard device is exposed during user authentication, attackers can attempt to illegal login or, worst, steal the victim's money. Accordingly, in this paper, the existing and the new keyboard-attack techniques that are known are surveyed, and the results are used as the basis for the implementation of sample malicious codes to verify both a security analysis and an assessment of secure keyboard software. As a result of the experiment, if the resend command utilization attack technique is used, 7 out of 10 companies' products expose keyboard information, and only 1 company's products detect it. The fundamental reason for these vulnerabilities is that the hardware chip related to the PS/2 interface keyboard does not provide security facilities. Therefore, since keyboard data exposure does not be prevented only by software, it is required to develop a hardware chip that provides security facilities.
A Virtual Multi-Ocular 3D Reconstruction System Using a Galvanometer Scanner and a Camera

A novel visual 3D reconstruction system, composed of a two-axis galvanometer scanner, a camera with a lens, and a set of control units, is introduced in this paper. By changing the mirror angles of the galvanometer scanner fixed in front of the camera, the boresight of the camera can be quickly adjusted. With the variable boresight, the camera can serve as a virtual multi-ocular system (VMOS), which captures the object at different perspectives. The working mechanism with a definite physical meaning is presented. A simple and efficient method for calibrating the intrinsic and extrinsic parameters of the VMOS is presented. The applicability of the proposed system for 3D reconstruction is investigated. Owing to the multiple virtual poses of the camera, the VMOS can provide stronger constraints in the object pose estimation than an ordinary perspective camera does. The experimental results demonstrate that the proposed VMOS is able to achieve 3D reconstruction performance competitive with that of a conventional stereovision system with a much more concise hardware configuration.
A Unified Point Multiplication Architecture of Weierstrass, Edward and Huff Elliptic Curves on FPGA

This article presents an area-aware unified hardware accelerator of Weierstrass, Edward, and Huff curves over GF(2233) for the point multiplication step in elliptic curve cryptography (ECC). The target implementation platform is a field-programmable gate array (FPGA). In order to explore the design space between processing time and various protection levels, this work employs two different point multiplication algorithms. The first is the Montgomery point multiplication algorithm for the Weierstrass and Edward curves. The second is the Double and Add algorithm for the Binary Huff curve. The area complexity is reduced by efficiently replacing storage elements that result in a 1.93 times decrease in the size of the memory needed. An efficient Karatsuba modular multiplier hardware accelerator is implemented to compute polynomial multiplications. We utilized the square arithmetic unit after the Karatsuba multiplier to execute the quad-block variant of a modular inversion, which preserves lower hardware resources and also reduces clock cycles. Finally, to support three different curves, an efficient controller is implemented. Our unified architecture can operate at a maximum of 294 MHz and utilizes 7423 slices on Virtex-7 FPGA. It takes less computation time than most recent state-of-the-art implementations. Thus, combining different security curves (Weierstrass, Edward, and Huff) in a single design is practical for applications that demand different reliability/security levels.
A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

The architecture of cloud–edge collaboration can improve the efficiency of Internet of Things (IoT) systems. Recent studies have pointed out that using IoT terminal devices as destinations for computing offloading can promote further optimized allocation of computational resources. However, in practice, this idea encounters the problem that participants might lack the motivation to take over computational tasks from others. Although the edge and the terminal are provided with symmetrical positions in collaborative offloading, their computational resources and capabilities are asymmetric. To mitigate this issue, this paper designs a distributed strategy for the trading of computational resources. The most prominent feature of our strategy is its multi-preference optimization objective that takes into account the overall satisfaction with task delay, energy cost, trading prices, and user reputation of participants. In addition, this paper proposes a system architecture based on the Blockchain-as-a-Service (BaaS) design to give full play to the good distributed technology features of blockchain, such as decentralization, traceability, immutability, and automation. Meanwhile, BaaS delivers decentralized identifier (DID) based distributed identity infrastructure for the distributed computational resource trading stakeholders as well. In the simulation evaluation, we compare our trading strategy based on a matching mechanism called multi-preference matching (MPM) to trading using the classical double auction (DA) matching mechanism. The results show that our computational resource trading strategy is able to offload and execut more tasks, achieving a better throughput compared to the DA-based strategy.
Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine

The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough of an indicator of COVID-19 severity and survival, but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.
The Bayesian lens and Bayesian blinkers

I discuss the benefits of looking through the 'Bayesian lens' (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing 'Bayesian blinkers' (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to understand widely used statistical methods (including confidence intervals and [Formula: see text]-values), as well as teachers of statistics and practitioners who wish to avoid the mistake of overemphasizing philosophy at the expense of practical matters. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
Construction of a base ontology to represent accident expertise knowledge

Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. For example, accident expertise eases accident understanding by describing how it happened and by identifying its causes and consequences. As a result, the design of accident expertise in a convenient human–machine structure will enable the querying, reasoning, and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations of accident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations. Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodology and implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application of extension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA) for civil aviation safety.
Bayesian clustering of multiple zero-inflated outcomes

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.