Reviewerly/exHarmony huggingface dataset:
Selecting the Best Part from Multiple Laplacian Autoencoders for Multi-view Subspace Clustering

The multi-view subspace clustering attracts much attention in recent years. Most methods follow the framework of fusing the affinity graph learned in each view. In this framework, both the fusion strategy and built graph of each view are very important. In this paper, we propose novel methods for multi-view subspace clustering to address these two aspects. On the one hand, we adopt the autoencoders with Laplacian regularization to construct the affinity graph in each view. Compared with previous work employing the autoencoders, the Laplacian term in our method can guide the learned latent representation favoring affinity extraction. Besides, we also discuss the reasons for adding Laplacian regularization. On the other hand, we propose a novel fusion strategy distinguished from the related literature. If the affinity graph of some view is not extracted well, the performance of previous fusion strategies will be seriously affected. Since our strategy can choose the best part from each affinity graph, it can overcome this limitation to some extent. Extensive experimental results on multiple benchmark data sets confirm the effectiveness of our method.
BuresNet: Conditional Bures Metric for Transferable Representation Learning

As a fundamental manner for learning and cognition, transfer learning has attracted widespread attention in recent years. Typical transfer learning tasks include unsupervised domain adaptation (UDA) and few-shot learning (FSL), which both attempt to sufficiently transfer discriminative knowledge from the training environment to the test environment to improve the model's generalization performance. Previous transfer learning methods usually ignore the potential conditional distribution shift between environments. This leads to the discriminability degradation in the test environments. Therefore, how to construct a learnable and interpretable metric to measure and then reduce the gap between conditional distributions is very important in the literature. In this article, we design the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation with convergence guarantee. CKB provides a statistical and interpretable approach, under the optimal transportation framework, to understand the knowledge transfer mechanism. It is essentially an extension of optimal transportation from the marginal distributions to the conditional distributions. CKB can be used as a plug-and-play module and placed onto the loss layer in deep networks, thus, it plays the bottleneck role in representation learning. From this perspective, the new method with network architecture is abbreviated as BuresNet, and it can be used extract conditional invariant features for both UDA and FSL tasks. BuresNet can be trained in an end-to-end manner. Extensive experiment results on several benchmark datasets validate the effectiveness of BuresNet.
Unsupervised Image Anomaly Detection and Segmentation Based on Pretrained Feature Mapping

Image anomaly detection and segmentation are important for the development of automatic product quality inspection in intelligent manufacturing. Because the normal data can be collected easily and abnormal ones are rarely existent, unsupervised methods based on reconstruction and embedding have been mainly studied for anomaly detection. But the detection performance and computing time require to be further improved. This article proposes a novel framework, named as pretrained feature mapping (PFM), for unsupervised image anomaly detection and segmentation. The proposed PFM maps the image from a pretrained feature space to another one to detect the anomalies effectively. The bidirectional and multihierarchical bidirectional PFM are further proposed and studied for improving the performance. The proposed framework achieves the better results on well-known MVTec AD dataset compared with state-of-the-art methods, with the area under the receiver operating characteristic curve of 97.5% for anomaly detection and of 97.3% for anomaly segmentation over all 15 categories. The proposed framework is also superior in terms of the computing time. The extensive experiments on ablation studies are also conducted to show the effectiveness and efficiency of the proposed framework.
Blockchain-based Life Cycle Assessment System for ESG Reporting

With the increasing popularity of Environmental, Social, and Governance (ESG) reporting, doubts about the reliability and completeness of ESG disclosures also grow. Those concerns may be alleviated by Life Cycle Assessment (LCA), a widely used approach for a complete and comprehensive evaluation of environmental impacts. However, LCA is limited in the areas of reliable data collection and management. Blockchain can solve this problem by reliably storing and distributing data from various sources without tampering via its decentralized and secure nature. This study aims to explore the influence of LCA and blockchain on ESG reporting. We propose a blockchain-based LCA system and demonstrate its potential applications using data regarding Tesla’s electric vehicles. The system automatically cross-validates ESG disclosures from companies of an entire value chain and generates an integrated report to present overall ESG performance. Our results shed light on utilizing emerging technologies to enable reliable, complete, and accurate ESG reporting.
Generation and Detection of Face Morphing Attacks

Failure of facial recognition and authentication system may lead to several unlawful activities. The current facial recognition systems are vulnerable to different biometric attacks. This research focuses on morphing attack detection. The research proposes a robust detection mechanism that can deal with variation in age, illumination, eye and head gears. A deep learning based feature extractor along with a classifier is adopted. Additionally, image enhancement and feature combination are proposed to augment the detection results. A versatile dataset is also developed that contains Morph-2 and Morph-3 images, created by sophisticated tools with manual intervention. Morph-3 images can give more realistic appearance and hence difficult to detect. Moreover, Morph-3 images are not considered in the literature before. Professional morphing software depicts more realistic morph attack scenario as compared to the morphs generated in the previous work from free programs and code scripts. Eight face databases are used for creation of morphs to encompass the variation. These databases are Celebrity2000, Extended Yale, FEI, FGNET, GT-DB, MULTI-PIE, FERET, FRLL. Results are investigated using multiple experimental setups and it is concluded that the proposed methodology gives promising results.
End-to-End Image Classification and Compression With Variational Autoencoders

The past decade has witnessed the rising dominance of deep learning and artificial intelligence in a wide range of applications. In particular, the ocean of wireless smartphones and IoT devices continue to fuel the tremendous growth of edge/cloud-based machine learning (ML) systems, including image/speech recognition and classification. To overcome the infrastructural barrier of limited network bandwidth in cloud ML, existing solutions have mainly relied on traditional compression codecs such as JPEG that were historically engineered for human-end users instead of ML algorithms. Traditional codecs do not necessarily preserve features important to ML algorithms under limited bandwidth, leading to potentially inferior performance. This work investigates application-driven optimization of programmable commercial codec settings for networked learning tasks such as image classification. Based on the foundation of variational autoencoders (VAEs), we develop an end-to-end networked learning framework by jointly optimizing the codec and classifier without reconstructing images for a given data rate (bandwidth). Compared with the standard JPEG codec, the proposed VAE joint compression and classification framework achieves classification accuracy improvement by over 10% and 4%, respectively, for CIFAR-10 and ImageNet-1k data sets at data rate of 0.8 bpp. Our proposed VAE-based models show 65%–99% reductions in encoder size, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1.5$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 13.1$ </tex-math></inline-formula> improvements in inference speed, and 25%–99% savings in power compared to baseline models. We further show that a simple decoder can reconstruct images with sufficient quality without compromising classification accuracy.
Energy and Reliability-Aware Task Scheduling for Cost Optimization of DVFS-Enabled Cloud Workflows

Due to the increasing complexity, the execution of workflow applications on cloud typically involves a large number of virtual machines (VMs), which makes the cost as well as energy consumption a great concern. To alleviate this issue, more and more cloud service providers introduce new pricing policies considering Dynamic Voltage and Frequency Scaling (DVFS), where users are charged on the basis of allocated CPU frequencies together with various combinations of VM configurations and prices. However, the customizable CPU frequencies make resource provisioning and scheduling harder to achieve a cost-optimal solution. The things become even worse, since lowering CPU voltages of VMs will increase their chance of suffering soft errors, which results in a high rate of completion time failures of workflow applications. To address the above problem, this paper proposes a novel task scheduling method for the purpose of cost optimization based on the genetic algorithm. By introducing new genetic operators and frequency scaling scheme for DVFS-enabled cloud workflows, our approach can quickly figure out cost-optimal resource provisioning and task scheduling solutions by allocating tasks to appropriate VMs with specific operating frequencies under energy, reliability, makespan and memory constraints. Extensive experiments on various well-known scientific workflow benchmarks validate the effectiveness of the proposed method. Comparing with state-of-the-art methods, our approach can significantly reduce the overall cost and energy consumption without violating the given constraints.
FedPCC: Parallelism of Communication and Computation for Federated Learning in Wireless Networks

The advances of both computation and communication technologies facilitate the exploitation of massive data generated by mobile devices. It is attractive to leverage these data and computation resources to train high-performance machine learning (ML) models. In traditional ML methods, all data are uploaded to servers for model training, which incur issues of large overhead and privacy concerns. Federated learning (FL) was proposed to address these issues. In this paper, we propose FedPCC, an efficient approach based on the parallelism of communication and computation among devices for FL in wireless networks. FedPCC considers the difference in communication and computation capabilities between different devices and optimizes the training schedule for devices selected in each round. Instead of dividing an FL training round into separate communication and computation steps, FedPCC protocol arranges devices to download the global model sequentially and then start local model training immediately once finishing downloading, which allows better utilization of communication and computation resources. Specifically, we formulate an optimization problem to minimize the time for each training round and propose an algorithm to solve the problem based on the trade-off of devices’ communication and computation capabilities. FedPCC uses a heuristic algorithm to make slow devices start relatively earlier, which can shorten the time in one training round, thus reducing the entire training time. We conduct extensive experiments to demonstrate that the proposed protocol outperforms the existing protocols under different system settings.
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., Last.fm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.
Chaos Elite Harris Hawk Optimization Algorithm to Solve Chemical Dynamic Optimization Problems

Dynamic optimization problems in chemical engineering are often described by complex differential equations, In general, Due to the complexity of the model established in practice, it is not easy to solve it with an accurate algorithm, so the study of numerical methods to solve such problems has received much attention.As a new computing model, intelligent optimization algorithm has attracted more attention in solving dynamic optimization problems because of their easy operation.Based on the analysis of the Harris Hawk Optimization algorithm, this paper proposes the Chaos Elite Harris Hawk Optimization algorithm (CEHHO), which is used to improve the performance of CEHHO using control vector parameterization to solve dynamic optimization problems of the chemical industry.First of all, when the population is initialized, the population is initialized by Opposition-based learning Logistic chaos, which improves the diversity of the population and the quality of the solution.Second, the linear decreasing escape energy factor is changed to a nonlinear decreasing escape energy factor to balance the exploration and exploitation capabilities of the algorithm.Finally, through the mutation strategy guided by elite individuals, the algorithm can jump out of the local optimum.We use 8 test functions and 5 classical chemical optimization problems to evaluate the feasibility of the algorithm and compare and analyze the research results with other solving methods, showing the superiority of the CEHHO algorithm.
The Transmon Qubit for Electromagnetics Engineers: An introduction

One of the most popular approaches being pursued to achieve a quantum advantage with practical hardware is the use of superconducting circuit devices. Although significant progress has been made over the previous two decades, substantial engineering efforts are required to scale these devices so they can be used to solve many problems of interest. Unfortunately, much of this exciting field is described using technical jargon and concepts from physics that are unfamiliar to a classically trained electromagnetics engineer. As a result, this work is often difficult for engineers to become engaged in.
Privacy-Preserving Deduplication of Sensor Compressed Data in Distributed Fog Computing

Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.
A Robust, Quantization-Aware Training Method for Photonic Neural Networks

The computationally demanding nature of Deep Learning (DL) has fueled the research on neuromorphics due to their potential to provide high-speed and low energy hardware accelerators. To this end, neuromorphic photonics are increasingly gain attention since they can operate in very high frequencies with very low energy consumption. However, they also introduce new challenges in DL training and deployment. In this paper, we propose a novel training method that is able to compensate for quantization noise, which profoundly exists in photonic hardware due to analog-to-digital (ADC) and digital-to-analog (DAC) conversions, targeting photonic neural networks (PNNs) which employ easily saturated activation functions. The proposed method takes into account quantization during training, leading to significant performance improvements during the inference phase. We conduct evaluation experiments on both image classification and time-series analysis tasks, employing a wide range of existing photonic neuromorphic architectures. The evaluation experiments demonstrate the effectiveness of the proposed method when low-bit resolution photonic architectures are used, as well as its generalization ability.
Hybrid Uncalibrated Visual Servoing Control of Harvesting Robots With RGB-D Cameras

Visual servoing (VS) control has seen wide adoption in harvesting robots. However, parameter calibration is cumbersome, which makes the use of VS robotic systems inconvenient. Besides, dynamic fruits usually lead to a degeneration of control while tracking. To overcome the drawbacks, we present a new image-based uncalibrated visual servoing (IBUVS) control approach, consisting of a hybrid visual configuration and an adaptive tracking controller, referred to as hybrid-IBUVS. Specifically, our hybrid-IBUVS employs an eye-in-hand camera and a fixed red–green–blue-depth camera to construct a hybrid VS system, basing on multiobject detection and edge-computing technologies. Meanwhile, we also propose adaptive laws to online estimate the uncalibrated parameters of the cameras and robot dynamics. Furthermore, our hybrid-IBUVS uses an adaptive tracking controller to guarantee the harvesting robot to track a predefined trajectory to approach a fruit target. By Lyapunov stability theory, asymptotic convergence of the proposed control scheme is rigorously proven. Experimental results demonstrate the effectiveness of the proposed scheme. All shown results supported the research claims.
Revamping Cloud Gaming With Distributed Engines

While cloud gaming has brought considerable advantages for its customers, from the point of view of cloud providers, multiple aspects related to infrastructure management still fall short of such a service. Indeed, different from traditional cloud-ready applications, modern game engines are still based on monolithic software architectures. This aspect precludes the applicability of fine-grained resource management and service orchestration schemes, ultimately leading to poor cost-effectiveness. To mitigate these shortcomings, we propose a cloud-oriented distributed engine for gaming (CODEG). Thanks to its distributed nature, CODEG is capable of fully exploiting the resource heterogeneity present in cloud data centers, while providing the possibility of spanning its service on multiple network layers up to the edge clouds.
Accelerating Decentralized and Partial-Privacy Data Access for VANET via Online/Offline Functional Encryption

Vehicles share information via Vehicular Ad-hoc Network (VANET) that is considered the foundation of the intelligent transportation system. Access control from functional encryption (FE) allows secure sharing over encrypted data with both utilization and privacy. However, low-powerful vehicles and the untrusted server incurs that FE-based access control mechanisms are less practical with client-side inefficiency and access unreliability. In this work, we put forth ORAC, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">an <u>O</u>ptimized encrypted <u>A</u>ccess <u>C</u>ontrol system with partial privacy and <u>R</u>eliability</i> . ORAC is built on top of functional encryption that guarantees data partial privacy and data utilization in VANET, and separates the data access into the offline phase and the online phase, such that most of decryption computation could be done in the offline phase and vehicles could execute the real decryption efficiently. By embedding blockchain transactions into identity records and data storage, ORAC protects data accessed by vehicles from tampering and thereby manages data transfers with high reliability. It can be instantiated directly from pairing-based assumptions and features only the decryption complexity of two exponentiation and two multiplication operations. We give a formal security proof of the instantiation in the semantic security model. We also prototype our scheme by implementing ORAC over Ethereum with two types of smart contracts and the evaluation can demonstrate its efficiency optimization.