Reviewerly/exHarmony huggingface dataset:
Lightweight Privacy-Preserving Scheme Using Homomorphic Encryption in Industrial Internet of Things

The emerging technologies, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">smart sensors, 5G/6G wireless communication, artificial intelligence, etc.</i> , have been maturing the future Internet of Things (IoT) by connecting the massive number of devices, which are expected to consistently collect and transmit real-time data to support business intelligence in an efficient and privacy-preserving way. The IoT can afford businesses predictive maintenance, improve field service, asset tracking, and further enhance customer satisfaction and facility management in industrial sectors. However, the privacy concern in IoT is a big challenge in IoT applications and services. This work proposed a lightweight privacy-preserving scheme based on homomorphic encryption in the context of the IoT, in which we investigated and analyzed the privacy issues between the data owners, untrustworthy third-party cloud servers, and the data users. Meanwhile, computationally efficient homomorphic algorithms are proposed to guarantee the privacy protection for the data users. Experimental results demonstrate that the proposed scheme can effectively prevent privacy breaches in IoT.
Colon cancer prediction on histological images using deep learning features and Bayesian optimized SVM

Colon cancer is one of the highest cancer diagnosis mortality rates worldwide. However, relying on the expertise of pathologists is a demanding and time-consuming process for histopathological analysis. The automated diagnosis of colon cancer from biopsy examination played an important role for patients and prognosis. As conventional handcrafted feature extraction requires specialized experience to select realistic features, deep learning processes have been chosen as abstract high-level features may be extracted automatically. This paper presents the colon cancer detection system using transfer learning architectures to automatically extract high-level features from colon biopsy images for automated diagnosis of patients and prognosis. In this study, the image features are extracted from a pre-trained convolutional neural network (CNN) and used to train the Bayesian optimized Support Vector Machine classifier. Moreover, Alexnet, VGG-16, and Inception-V3 pre-trained neural networks were used to analyze the best network for colon cancer detection. Furthermore, the proposed framework is evaluated using four datasets: two are collected from Indian hospitals (with different magnifications 4X, 10X, 20X, and 40X) and the other two are public colon image datasets. Compared with the existing classifiers and methods using public datasets, the test results evaluated the Inception-V3 network with the accuracy range from 96.5% - 99% as best suited for the proposed framework.
Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale)

Online media platforms are increasingly using algorithms to select and present relevant information to their audiences. This highlights the importance of exploring whether people are aware of algorithmic content recommendations. Although some studies have already investigated algorithmic awareness, no standardized instrument has been developed yet to assess this construct. In this study, we therefore developed and validated the Algorithmic Media Content Awareness Scale (AMCA-scale). This scale contains four underlying dimensions: 1) users’ awareness of content filtering, 2) users’ awareness of automated decision-making, 3) users’ awareness of human-algorithm interplay, and 4) users’ awareness of ethical considerations. In validating the scale, results revealed strong psychometrics properties. The AMCA-scale was also successfully tested for three different online platforms: Facebook, YouTube, and Netflix, showing its robustness over different environments. Based on these findings, we conclude that the AMCA-scale offers scholars a valid, reliable and robust tool to measure algorithmic awareness.
Fault-Tolerant Quantum Computation with Static Linear Optics

The scalability of photonic implementations of fault-tolerant quantum computing based on Gottesman-Kitaev-Preskill (GKP) qubits is injured by the requirements of inline squeezing and reconfigurability of the linear optical network. In this work we propose a topologically error-corrected architecture that does away with these elements at no cost—in fact, at an advantage—to state preparation overheads. Our computer consists of three modules: a two-dimensional (2D) array of probabilistic sources of GKP states; a depth-four circuit of static beam splitters, phase shifters, and short delay lines; and a 2D array of homodyne detectors. The symmetry of our proposed circuit allows us to combine the effects of finite squeezing and uniform photon loss within the noise model, resulting in more comprehensive threshold estimates. These jumps over both architectural and analytical hurdles considerably expedite the construction of a photonic quantum computer.Received 9 April 2021Accepted 15 November 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040353Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingOptical quantum information processingQuantum computationQuantum information architectures & platformsQuantum information processing with continuous variablesTopological quantum computingQuantum Information
Design and Implementation of CryptoCargo: A Blockchain-Powered Smart Shipping Container for Vaccine Distribution

A large number of shipments are moved everyday domestically and internationally. A considerable number of items such as food, commodities, and pharmaceutical drugs are prone to damage in transit. This can be caused due to various reasons such as improper storage conditions and exposure to air or sunlight. The Internet of Things (IoT) has been used to enhance fundamental shipment tracking by improving transparency and visibility to such transport systems. This paper introduces a blockchain-powered smart container system (CryptoCargo) that monitors the conditions of the shipment and detects any violations that may damage its contents. These violations are recorded on the blockchain via smart contracts, which provides a secure and immutable storage thereby improving its trustworthiness in an inherently trustless environment comprising of multiple stakeholders. We present the design and implementation of CryptoCargo including architectural concerns and implementation details using a test Ethereum blockchain platform and cloud services. Moreover, we present details of thorough evaluation of the system to validate its function as well as to assess its effectiveness with respect to performance efficiency and real-time operation. We have made our smart contract code publicly available on Github.
Recurrent Exposure Generation for Low-Light Face Detection

Face detection from low-light images is challenging due to limited photons and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A natural solution is to borrow the idea from <i>multi-exposure</i>, which captures multiple shots to obtain well-exposed images under challenging conditions. High-quality implementation/approximation of multi-exposure from a single image is however nontrivial. Fortunately, as shown in this paper, neither is such high-quality necessary since our task is <i>face detection</i> rather than <i>image enhancement</i>. Specifically, we propose a novel <i>Recurrent Exposure Generation (REG)</i> module and couple it seamlessly with a <i>Multi-Exposure Detection (MED)</i> module, and thus significantly improve face detection performance by effectively inhibiting non-uniform illumination and noise issues. REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions. The proposed method, named <i>REGDet</i>, is the first &#x2018;detection-with-enhancement&#x2019; framework for low-light face detection. It not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection. Moreover, as clearly shown in our experiments, REG can be flexibly coupled with different face detectors without extra low/normal-light image pairs for training. We tested REGDet on the DARK FACE low-light face benchmark with thorough ablation study, where REGDet outperforms previous state-of-the-arts by a significant margin, with only negligible extra parameters.
A panoramic survey of natural language processing in the Arab world

research-article Share on A panoramic survey of natural language processing in the Arab world Authors: Kareem Darwish Hamad Bin Khalifa University, Doha, Qatar Hamad Bin Khalifa University, Doha, QatarView Profile , Nizar Habash New York University Abu Dhabi, United Arab Emirates New York University Abu Dhabi, United Arab EmiratesView Profile , Mourad Abbas (CRSTDLA), Bouzareah, Algeria (CRSTDLA), Bouzareah, AlgeriaView Profile , Hend Al-Khalifa King Saud University, Riyadh, Saudi Arabia King Saud University, Riyadh, Saudi ArabiaView Profile , Huseein T. Al-Natsheh Mawdoo3, Jordan Mawdoo3, JordanView Profile , Houda Bouamor Carnegie Mellon University, Doha, Qatar Carnegie Mellon University, Doha, QatarView Profile , Karim Bouzoubaa Mohammed V University, Rabat, Morocco Mohammed V University, Rabat, MoroccoView Profile , Violetta Cavalli-Sforza Al Akhawayn University, Ifrane, Morocco Al Akhawayn University, Ifrane, MoroccoView Profile , Samhaa R. El-Beltagy Newgiza University, Cairo, Egypt Newgiza University, Cairo, EgyptView Profile , Wassim El-Hajj American University of Beirut, Beirut, Lebanon American University of Beirut, Beirut, LebanonView Profile , Mustafa Jarrar Birzeit University, Palestine Birzeit University, PalestineView Profile , Hamdy Mubarak Hamad Bin Khalifa University, Doha, Qatar Hamad Bin Khalifa University, Doha, QatarView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 4April 2021 pp 72–81https://doi.org/10.1145/3447735Online:22 March 2021Publication History 6citation2,881DownloadsMetricsTotal Citations6Total Downloads2,881Last 12 Months805Last 6 weeks54 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
A survey on deep learning-based Monte Carlo denoising

Abstract Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its flexibility and generality. However, the integration has to balance estimator bias and variance, which causes visually distracting noise with low sample counts. Existing solutions fall into two categories, in-process sampling schemes and post-processing reconstruction schemes. This report summarizes recent trends in the post-processing reconstruction scheme. Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning, by training neural networks to reconstruct denoised rendering results from sparse MC samples. Many of these techniques show promising results in real-world applications, and this report aims to provide an assessment of these approaches for practitioners and researchers.
Deep Learning in Medical Image Analysis

Over recent years, deep learning (DL) has established itself as a powerful tool across a broad spectrum of domains in imaging-e [...].
Drug Package Recommendation via Interaction-aware Graph Induction

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collaborative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance.
Target-adaptive Graph for Cross-target Stance Detection

Target plays an essential role in stance detection of an opinionated review/claim, since the stance expressed in the text often depends on the target. In practice, we need to deal with targets unseen in the annotated training data. As such, detecting stance for an unknown or unseen target is an important research problem. This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. More concretely, we explore a novel solution of constructing heterogeneous target-adaptive pragmatics dependency graphs (TPDG) for each sentence towards a given target. An in-target graph is constructed to produce inherent pragmatics dependencies of words for a distinct target. In addition, another cross-target graph is constructed to develop the versatility of words across all targets for boosting the learning of dominant word-level stance expressions available to an unknown target. A novel graph-aware model with interactive Graphical Convolutional Network (GCN) blocks is developed to derive the target-adaptive graph representation of the context for stance detection. The experimental results on a number of benchmark datasets show that our proposed model outperforms state-of-the-art methods in cross-target stance detection.
Public cognition of the application of blockchain in food safety management—Data from China’s Zhihu platform

Food safety is a worldwide problem, mainly caused by environmental pollution, production and management of enterprises, government supervision failure and other reasons. Blockchain technology, as an emerging technology, has strong pertinency and applicability to food traceability due to its characteristics of decentralization, traceability and immutability. Therefore, it is expected to be effectively applied in food safety management and improve the guarantee level of food safety. However, for some reasons, blockchain has not been widely used in food safety management. So further study on the advantages and challenges of its application is beneficial to the sustainable development of food enterprises, consumers and the government. Its application can provide traceable information for food companies, consumers and governments. This can not only ensure the food safety of enterprises, but also improve the level of government supervision, so as to establish the trust between consumers, enterprises and the government, which improves the efficiency and level of food safety management. Based on the information adoption model, this study takes 202 text data related to blockchain and food safety on the Zhihu platform as the research object. On the basis of summarizing part of the public opinions, this paper puts forward the viewpoints of argument on the application of blockchain in food safety management. Then, Tobit regression is used to conduct a quantitative analysis on the influencing factors of the acceptance and adoption of relevant text content on Zhihu platform. Finally, it is found that four factors are significant at a confidence level of 0.1, specifically as follows. The two viewpoints of Advantages of Blockchain Technology and High Costs have significant promotion effects on the public’s recognition and adoption but two viewpoints of Promotion of National Policy Opinions and Blockchain Requires Supervision have significant negative effects on the public’s recognition and adoption. Based on the regression results and combined with the current technological level and development status of blockchain technology, this study provides relevant policy suggestions, which has a positive effect on improving food sustainability.
Application of the novel harmony search optimization algorithm for DBSCAN clustering

At present, the DBSCAN clustering algorithm has been commonly used principally due to its ability in discovering clusters with arbitrary shapes. When the cluster number K is predefined, though the partitional clustering methods can perform efficiently, they cannot process the non-convex clustering and easily fall into local optimum. Thereby the concept of K-DBSCAN clustering is proposed in this paper. But the basic DBSCAN has a crucial defect, that is, difficult to predict the suitable clustering parameters. Here, the well-known harmony search (HS) optimization algorithm is considered to deal with this problem. By modifying the original HS, the novel harmony search (novel-HS) is put forward, which can improve the accuracy of results as well as enhance the robustness of optimization. In K-DBSCAN, the novel-HS is used to optimize the clustering parameters of DBSCAN to obtain better clustering effect with the number of K classifications. Experimental results show that the designed clustering method has superior performance to others and can be successfully considered as a new clustering scheme for further research.
A trustworthy and incentivized smart grid energy trading framework using distributed ledger and smart contracts

The smart grid integrated with renewable energy sources (RESs) allows to perform peer to peer selling of energy via Local Energy Market (LEM). However, it faces the challenges of lack of transparency and verification of energy distribution, single-point failure in the energy data management, user’s privacy, non-incentivized trading and lack of trust on the energy trading. To address these problems, we propose a trustworthy and incentivized framework for smart grid energy trading using distributed ledger technology and smart contracts. We propose different smart contracts viz; energy injection into smart grid, energy bidding to submit energy demand, energy trading and energy utilization. These contracts run on an ethereum blockchain platform with proof of stake (PoS) consensus mechanism to record energy trading related data. We have applied an iterative Vickrey–Clarke–Grove (Vickrey auction) method for the incentivized energy trading in context of both prosumers and consumers. The proposed framework offers trustworthy and incentivized trading of energy, participants’ privacy, data transparency and no single point failure. The experimental results of the proposed framework are derived in terms of average cost of energy injection and bidding transactions, throughput and the incentivized trading by considering different test scenarios.
RAMBO: Resource Allocation for Microservices Using Bayesian Optimization

Microservices are becoming the defining paradigm of cloud applications, which raises urgent challenges for efficient datacenter management. Guaranteeing end-to-end Service Level Agreement (SLA) while optimizing resource allocation is critical to both cloud service providers and users. However, one application may contain hundreds of microservices, which constitute an enormous search space that is unfeasible to explore exhaustively. Thus, we propose RAMBO, an SLA-aware framework for microservices that leverages multi-objective Bayesian Optimization (BO) to allocate resources and meet performance/cost goals. Experiments conducted on a real microservice workload demonstrate that RAMBO can correctly characterize each microservice and efficiently discover Pareto-optimal solutions. We envision that the proposed methodology and results will benefit future resource planning, cluster orchestration, and job scheduling.
Speckle Noise Reduction for OCT Images Based on Image Style Transfer and Conditional GAN

Raw optical coherence tomography (OCT) images typically are of low quality because speckle noise blurs retinal structures, severely compromising visual quality and degrading performances of subsequent image analysis tasks. In our previous study (Ma et al., 2018), we have developed a Conditional Generative Adversarial Network (cGAN) for speckle noise removal in OCT images collected by several commercial OCT scanners, which we collectively refer to as scanner T. In this paper, we improve the cGAN model and apply it to our in-house OCT scanner (scanner B) for speckle noise suppression. The proposed model consists of two steps: 1) We train a Cycle-Consistent GAN (CycleGAN) to learn style transfer between two OCT image datasets collected by different scanners. The purpose of the CycleGAN is to leverage the ground truth dataset created in our previous study. 2) We train a mini-cGAN model based on the PatchGAN mechanism with the ground truth dataset to suppress speckle noise in OCT images. After training, we first apply the CycleGAN model to convert raw images collected by scanner B to match the style of the images from scanner T, and subsequently use the mini-cGAN model to suppress speckle noise in the style transferred images. We evaluate the proposed method on a dataset collected by scanner B. Experimental results show that the improved model outperforms our previous method and other state-of-the-art models in speckle noise removal, retinal structure preservation and contrast enhancement.
A Survey on Knowledge Graph-Based Recommender Systems

To solve the cognitive overlord problem and information explosion, recommender systems have been using to model the user interest. Although recommender systems have been developed for decades, there still exists many problems such as cold start and data sparsity. Thus, the knowledge graph is introduced into the recommendation domain to alleviate these problems. We collect papers related to the knowledge graph-based recommender systems in recent years to summarize their fundamental knowledge and main ideas, including the usage of the knowledge graph in the recommender systems and user interest models. Finally, we propose several future directions aiming to make some progress.