Reviewerly/exHarmony huggingface dataset:
DBTP2SF: A deep blockchain‐based trustworthy privacy‐preserving secured framework in industrial internet of things systems

Abstract By providing ubiquitous connectivity, effective data analytics tools, and better decision support systems for improved market competitiveness, the industrial internet of things (IIoT) promises creative business models in different industrial domains. However, the conventional IIoT architecture can no longer provide adequate support for such an enormous device as the number of nodes, and network size increases. Therefore, several challenges, such as security, privacy, centralization, trust, and integrity prevents faster adaptation of IIoT applications. To address aforementioned challenges, we present a deep blockchain‐based trustworthy privacy‐preserving secured framework (DBTP2SF) for IIoT environment. This framework comprises of three modules, namely, trust management module, a two‐level privacy‐preservation module, and an anomaly detection module. In trustworthiness module, blockchain (BC)‐based address reputation system is proposed. In the two‐level privacy module a BC‐based enhanced proof of work technique is simultaneously applied with AutoEncoder, to transform cyber‐physical system data into a new reduced form that prevents inference and poisoning attacks. In the anomaly detection module, deep neural network is deployed. Finally, due to various limitations of current Cloud‐Fog infrastructure, we present a BC‐interplanetary file systems integrated Cloud‐Fog architecture, namely, BlockCloud and BlockFog to deploy proposed DBTP2SF framework in IIoT environment. The experiment is conducted using IIoT‐based realistic dataset, namely, ToN‐IoT. The performance analysis shows that the proposed approach outperforms using transformed dataset over peer privacy‐preserving intrusion detection strategies, and has obtained accuracy of 98.97%, and detection rate of 93.87%. Finally, we have shown the superiority of DBTP2SF framework over some of the recent state‐of‐art techniques in both non‐BC and BC‐based IIoT system.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results

This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the low-resolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. Due to this massive information loss, it is hard to accurately restore the missing information. Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM. Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain. However, this also imposes an additional requirement, as the generated frames need to be consistent along time.
Sentiment Analysis using Word2vec-CNN-BiLSTM Classification

Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. In order to solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text.
Verifiable Multikeyword Search Encryption Scheme With Anonymous Key Generation for Medical Internet of Things

The development of the Internet of Things (IoT) has given birth to new applications and services. Accordingly, because the IoT application system collects a large amount of data containing sensitive information, there are also new challenges in data security and privacy preservation. To locate the medical IoT (mIoT), this article proposes a multikeyword searchable encryption scheme based on attributes supporting the data privacy preservation and integrity verification of the electronic health files stored on the IoT cloud platform on the basis of ensuring the application business functions of mIoT: 1) to ensure data confidentiality and fine-grained search authorization, the attribute-based encryption mechanism is introduced to encrypt the symmetric key. Moreover, anonymous key generation tampers semitrusted center authority’s (CA) decryption of all ciphertexts of users and access policy is hidden to prevent access policy from leaking; 2) the convergence key is adopted to encrypt electronic health records (EHRs), thereby providing double verification of the correctness of the search results and partial decryption results; 3) a time-division mechanism is also established to provide the access validity of shared EHRs; and 4) fixed length ciphertext and outsourcing decryption mechanisms are used to reduce the calculation on the user side, in which the majority of the decryption calculations are done by the cloud server. Formal proofs and performance evaluation show that the proposed scheme can achieve data security, keyword security, integrity verification, hidden policy, and collusion resistance, and has high computational efficiency and search efficiency.
Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0

There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.
Policy mirror descent for reinforcement learning: linear convergence, new sampling complexity, and generalized problem classes

We present new policy mirror descent (PMD) methods for solving reinforcement learning (RL) problems with either strongly convex or general convex regularizers. By exploring the structural properties of these overall highly nonconvex problems we show that the PMD methods exhibit fast linear rate of convergence to the global optimality. We develop stochastic counterparts of these methods, and establish an $${{\mathcal {O}}}(1/\epsilon )$$ (resp., $${{\mathcal {O}}}(1/\epsilon ^2)$$ ) sampling complexity for solving these RL problems with strongly (resp., general) convex regularizers using different sampling schemes, where $$\epsilon $$ denote the target accuracy. We further show that the complexity for computing the gradients of these regularizers, if necessary, can be bounded by $${{\mathcal {O}}}\{(\log _\gamma \epsilon ) [(1-\gamma )L/\mu ]^{1/2}\log (1/\epsilon )\}$$ (resp., $${{\mathcal {O}}} \{(\log _\gamma \epsilon ) (L/\epsilon )^{1/2}\}$$ ) for problems with strongly (resp., general) convex regularizers. Here $$\gamma $$ denotes the discounting factor. To the best of our knowledge, these complexity bounds, along with our algorithmic developments, appear to be new in both optimization and RL literature. The introduction of these convex regularizers also greatly enhances the flexibility and thus expands the applicability of RL models.
Sentiment Analysis with Machine Learning Methods on Social Media

Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.
MIEGAN: Mobile Image Enhancement via a Multi-Module Cascade Neural Network

Visual quality of images captured by mobile devices is often inferior to that of images captured by a Digital Single Lens Reflex (DSLR) camera. This paper presents a novel generative adversarial network-based mobile image enhancement method, referred to as MIEGAN. It consists of a novel multi-module cascade generative network and a novel adaptive multi-scale discriminative network. The multi-module cascade generative network is built upon a two-stream encoder, a feature transformer, and a decoder. In the two-stream encoder, a luminance-regularizing stream is proposed to help the network focus on low-light areas. In the feature transformation module, two networks effectively capture both global and local information of an image. To further assist the generative network to generate the high visual quality images, a multi-scale discriminator is used instead of a regular single discriminator to distinguish whether an image is fake or real globally and locally. To balance the global and local discriminators, an adaptive weight allocation is proposed. In addition, a contrast loss is proposed, and a new mixed loss function is developed to improve the visual quality of the enhanced images. Extensive experiments on the popular DSLR photo enhancement dataset and MIT-FiveK dataset have verified the effectiveness of the proposed MIEGAN.
Parallel Complement Network for Real-Time Semantic Segmentation of Road Scenes

Real-time semantic segmentation is in intense demand for the application of autonomous driving. Most of the semantic segmentation models tend to use large feature maps and complex structures to enhance the representation power for high accuracy. However, these inefficient designs increase the amount of computational costs, which hinders the model to be applied on autonomous driving. In this paper, we propose a lightweight real-time segmentation model, named Parallel Complement Network (PCNet), to address the challenging task with fewer parameters. A Parallel Complement layer is introduced to generate complementary features with a large receptive field. It provides the ability to overcome the problem of similar feature encoding among different classes, and further produces discriminative representations. With the inverted residual structure, we design a Parallel Complement block to construct the proposed PCNet. Extensive experiments are carried out on challenging road scene datasets, i.e., CityScapes and CamVid, to make comparison against several state-of-the-art real-time segmentation models. The results show that our model has promising performance. Specifically, PCNet* achieves 72.9% Mean IoU on CityScapes using only 1.5M parameters and reaches 79.1 FPS with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 2048$ </tex-math></inline-formula> resolution images on GTX 2080Ti. Moreover, our proposed system achieves the best accuracy when being trained from scratch.
Edge-Based Blockchain Architecture for Event-Driven IoT using Hierarchical Identity Based Encryption

IoT (Internet of Things) sensors usually generate a large amount of data that is shared with a centralized cloud to provide various services. Traditional IoT architecture is heavily centralized, where data stored in a cloud environment is prone to several kinds of threats. As such, both data generated by sensors as well as IoT network shall be protected. Blockchain is an emerging technology that can be used in a wide spectrum of applications for better protection. However, the traditional blockchain protocol for Bitcoin cannot be used for IoT data protection because Bitcoin is a payment system, and the IoT ecosystem has a different architecture and requirements. Hence, more suitable blockchain architecture and encryption schemes for IoT are needed. In this paper, we propose a privacy-preserving blockchain architecture for IoT using Hierarchical Identity Based Encryption (HIBE). The proposed architecture fits well with event-driven IoT devices and uses the edge and the cloudlet computing paradigm as well as HIBE to preserve the privacy. Furthermore, the efficiency of the proposed architecture is demonstrated using a real-life case, namely, traffic speed radars. The security of the proposed architecture is analyzed and is verified through theoretical analysis that considers the existence of a malicious adversary. The performance of the proposed architecture is evaluated by conducting extensive experiments using the Contiki OS. The experimental results provide the system and network performance of the proposed architecture.
Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs

We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework (Kairouz et al., 2019). Unique challenges to the traditional ERM problem in the context of FL include (i) need to provide privacy guarantees on clients' data, (ii) compress the communication between clients and the server, since clients might have low-bandwidth links, (iii) work with a dynamic client population at each round of communication between the server and the clients, as a small fraction of clients are sampled at each round. To address these challenges we develop (optimal) communication-efficient schemes for private mean estimation for several ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> spaces, enabling efficient gradient aggregation for each iteration of the optimization solution of the ERM. We also provide lower and upper bounds for mean estimation with privacy and communication constraints for arbitrary ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> spaces. To get the overall communication, privacy, and optimization performance operation point, we combine this with privacy amplification opportunities inherent to this setup. Our solution takes advantage of the inherent privacy amplification provided by client sampling and data sampling at each client (through Stochastic Gradient Descent) as well as the recently developed privacy framework using anonymization, which effectively presents to the server responses that are randomly shuffled with respect to the clients. Putting these together, we demonstrate that one can get the same privacy, optimization-performance operating point developed in recent methods that use full-precision communication, but at a much lower communication cost, i.e., effectively getting communication efficiency for “free”.
Exploring the Sustainability of the Intermediary Role in Blockchain

Traditional business models involve cost, market development, distribution channels, business partnerships, and supply chain management. The development of the digital economy and digital network technology has engendered a shift away from the traditional model of operation. Intermediaries have long played an essential role in promoting the benefits of economic activities, but new technology is increasingly replacing intermediaries in their roles of connecting players, such as, involving, committing, and mobilizing players. Potential conflicts of interest must therefore be further resolved, avoided, or mitigated. Blockchain technology, as a tool for keeping immutable and digital records, can address increasingly complex issues in global value chains to pursue sustainable development. It attempts to realize the trust mechanism and has been redefining the function of intermediaries. This study used a multiple-case study approach to examine how blockchain technology affects intermediate functionality. We evaluated the industry’s use of blockchains to assess how the processes were reshaped and how the intermediary roles were refined. On the basis of the findings, we propose three potential changes for the roles of intermediaries to improve operational efficiency.
A Survey of Low-Energy Parallel Scheduling Algorithms

High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parallel</i> scheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely: 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.
Reliability-Aware Cost-Efficient Scientific Workflows Scheduling Strategy on Multi-Cloud Systems

Nowadays, more and more computation-intensive scientific applications with diverse needs are migrating to cloud computing systems. However, the cloud systems alone cannot meet applications’ requirements at all times with the increasing demands from users. Therefore, the multi-cloud systems that can provide scalable storage and computing resources become a good solution. The main challenges for such systems are multiple billing mechanisms, virtual resources heterogeneity, and systems reliability. In response to these challenges, we first build a multi-cloud systems fault-tolerant workflow scheduling framework, which tries to improve the scientific applications execution reliability and reduce their execution cost. Then, we use Weibull distribution to analyze task execution reliability and hazard rate, which is used to duplicate task with high execution hazard rate. Third, we integrate different multi-cloud providers’ billing mechanism into the proposed scheduling framework, and this workflow scheduling problem is mathematically formulated as an optimization problem. Fourth, we define the DAG tasks cost-efficient bottom level, and propose a fault-tolerant cost-efficient workflow scheduling algorithm (FCWS) that minimizes application execution cost, time while ensuring their reliability. Simulation experiments for performance evaluation were conducted based on two real-world applications: Epigenomics and LIGO. The results clearly demonstrate that our proposed FCWS algorithm outperforms existing FR-MOS, CWS in terms of cost and reliability, and FCWS is also better than CWS and inferior to FR-MOS in term of makespan.
Exploring author gender in book rating and recommendation

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of these patterns reflect important real-world phenomena driving interactions between the various users and items; other patterns may be irrelevant or reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to one dimension of social concern, namely content creator gender. Using publicly available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms tend to propagate at least some of each user’s tendency to rate or read male or female authors into their resulting recommendations, although they differ in both the strength of this propagation and the variance in the gender balance of the recommendation lists they produce. The data, experimental design, and statistical methods are designed to be reusable for studying potentially discriminatory social dimensions of recommendations in other domains and settings as well.