Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Approach for the Creation of Digital Twins

The rapid advancements in computing, storage, communications, and networking technologies have enabled the creation of Digital Twins (DTs). A DT is a digital representation of a real-world physical component, product, or equipment. A DT can be used for 3-D design, testing, simulation, and prototyping prior to the manufacturing of the physical component. Once a physical component is in operation, a DT can be used for configuration, monitoring, diagnostics, and prognostics. It is expected that DTs will gain significant attention in the foreseeable future, and will play a key role in Industry 4.0. However, today's approaches, systems, and technologies leveraged for the creation of DTs are mostly centralized and fall short of providing trusted data provenance, audit, and traceability. Also, data related to transactions, logs, and history are not secure or tamper-proof. In this paper, we propose a blockchain-based creation process of DTs to guarantee secure and trusted traceability, accessibility, and immutability of transactions, logs, and data provenance. Our proposed approach uses smart contracts to govern and track transactions initiated by participants involved in the creation of DTs. Our approach also employs decentralized storage of interplanetary file systems to store and share DTs data. Moreover, we present details on our system design and architecture, implementation, and algorithms. Furthermore, we provide security and cost analysis, and show how our approach fulfills the requirements of DTs process creation. We make the smart contract code for creating DTs publicly available on Github.
When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?

Social media analytics is very critical in modern supply chain operations management (SCOM). However, in terms of methods, conducting social media analytics (SMA) for SCOM faces many challenges. Problems such as data accuracy (e.g., fake data), user privacy, data security, etc. are all present. Recently, with the emergence of blockchain technology (BCT), many new social media apps and platforms are developed. Motivated by the fact that (i) SMA is important for SCOM, (ii) the traditional social media (TSM) has insufficiency, and (iii) the “blockchain technology supported social media” (BSM) platforms have emerged, we explore whether and how the BSM would enhance social media analytics for SCOM. To be specific, by reviewing papers published in leading SCOM journals, we identify the applications and limitations of SMA for SCOM. Then, we conduct real case studies to examine the shortcomings of the TSM platforms and reveal features of their BSM counterparts. We investigate how the blockchain technology would potentially improve the use of SMA for SCOM. Finally, a future research agenda is proposed.
Deep reinforcement learning for imbalanced classification

Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning, in which we formulate the classification problem as a sequential decision-making process and solve it by a deep Q-learning network. In our model, the agent performs a classification action on one sample in each time step, and the environment evaluates the classification action and returns a reward to the agent. The reward from the minority class sample is larger, so the agent is more sensitive to the minority class. The agent finally finds an optimal classification policy in imbalanced data under the guidance of the specific reward function and beneficial simulated environment. Experiments have shown that our proposed model outperforms other imbalanced classification algorithms, and identifies more minority samples with better classification performance.
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation

Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there are still gaps toward an image prior that captures rich image semantics including color, spatial coherence, textures, and high-level concepts. This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. As shown in Fig. 1, the deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images. It also enables diverse image manipulation including random jittering, image morphing, and category transfer. Such highly flexible restoration and manipulation are made possible through relaxing the assumption of existing GAN inversion methods, which tend to fix the generator. Notably, we allow the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN. We show that these easy-to-implement and practical changes help preserve the reconstruction to remain in the manifold of nature images, and thus lead to more precise and faithful reconstruction for real images. Code is available at https://github.com/XingangPan/deep-generative-prior.
FBI inspired meta-optimization

This study developed a novel optimization algorithm, called Forensic-Based Investigation (FBI), inspired by the suspect investigation–location–pursuit process that is used by police officers. Although numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters. The performance of parameter-free FBI was validated using four experiments: (1) The robustness and efficiency of FBI were compared with those of 12 representations of the top leading metaphors by using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling problem associated with a highway construction project. The experiment demonstrated that FBI yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness. (3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its performance was compared with those of the three winners in CEC to validate its effectiveness. (4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark functions to 1000. FBI is efficient because it requires a relatively short computational time for solving problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves high-dimensional problems. Given that the experiments demonstrated FBI’s robustness, efficiency, stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this study provided the scientific community with a metaheuristic optimization platform for graphically and logically manipulating optimization algorithms.
A hybrid recommender system for recommending relevant movies using an expert system

Currently, the Internet contains a large amount of information, which must then be filtered to determine suitability for certain users. Recommender systems are a very suitable tool for this purpose. In this paper, we propose a monolithic hybrid recommender system called Predictory, which combines a recommender module composed of a collaborative filtering system (using the SVD algorithm), a content-based system, and a fuzzy expert system. The proposed system serves to recommend suitable movies. The system works with favorite and unpopular genres of the user, while the final list of recommended movies is determined using a fuzzy expert system, which evaluates the importance of the movies. The expert system works with several parameters – average movie rating, number of ratings, and the level of similarity between already rated movies. Therefore, our system achieves better results than traditional approaches, such as collaborative filtering systems, content-based systems, and weighted hybrid systems. The system verification based on standard metrics (precision, recall, F1-measure) achieves results over 80%. The main contribution is the creation of a complex hybrid system in the area of movie recommendation, which has been verified on a group of users using the MovieLens dataset and compared with other traditional recommender systems.
Implementing Grover Oracles for Quantum Key Search on AES and LowMC

Grover’s search algorithm gives a quantum attack against block ciphers by searching for a key that matches a small number of plaintext-ciphertext pairs. This attack uses $$O(\sqrt{N})$$ calls to the cipher to search a key space of size N. Previous work in the specific case of AES derived the full gate cost by analyzing quantum circuits for the cipher, but focused on minimizing the number of qubits. In contrast, we study the cost of quantum key search attacks under a depth restriction and introduce techniques that reduce the oracle depth, even if it requires more qubits. As cases in point, we design quantum circuits for the block ciphers AES and LowMC. Our circuits give a lower overall attack cost in both the gate count and depth-times-width cost models. In NIST’s post-quantum cryptography standardization process, security categories are defined based on the concrete cost of quantum key search against AES. We present new, lower cost estimates for each category, so our work has immediate implications for the security assessment of post-quantum cryptography. As part of this work, we release Q# implementations of the full Grover oracle for AES-128, -192, -256 and for the three LowMC instantiations used in Picnic, including unit tests and code to reproduce our quantum resource estimates. To the best of our knowledge, these are the first two such full implementations and automatic resource estimations.
Underwater image enhancement with global–local networks and compressed-histogram equalization

Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global–local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.
Noisier2Noise: Learning to Denoise From Unpaired Noisy Data

We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise.
Adversarial super-resolution of climatological wind and solar data

Significance Global climate simulations are typically unable to resolve wind and solar data at a resolution sufficient for renewable energy resource assessment in different climate scenarios. We introduce an adversarial deep learning approach to super resolve wind and solar outputs from global climate models by up to 50×. The inferred high-resolution fields are robust, physically consistent with the properties of atmospheric turbulence and solar irradiation, and can be adapted to domains from regional to global scales. This resolution enhancement enables critical localized assessments of the potential long-term economic viability of renewable energy resources.
A Blockchain-Enabled Deduplicatable Data Auditing Mechanism for Network Storage Services

Since network storage services achieve widespread adoption, security and performance issues are becoming primary concerns, affecting the scalability of storage systems. Countermeasures like data auditing mechanisms and deduplication techniques are widely studied. However, the existing data auditing mechanism with deduplication cannot solve the problems such as high cost and reliance on trusted third parties in traditional approaches, and it also faces the problem of repeated auditing of data shared by multiple-tenant. This article proposes a blockchain-based deduplicatable data auditing mechanism. We first design a client-side data deduplication scheme based on bilinear-pair techniques to reduce the burden on users and service providers. On this basis, we achieve a trustworthy and efficient data auditing mechanism that helps to check data integrity by using both the blockchain technique and bilinear pairing cryptosystem. The blockchain system is used to record the behaviors of entities in both data outsourcing and auditing processes so that the corresponding immutable records can be used to not only ensure the credibility of audit results but also help to monitor unreliable third-party auditors. Finally, theoretical analysis and experiments reveal the effectiveness and performance of our scheme.
Transparency and trust in artificial intelligence systems

Assistive technology featuring artificial intelligence (AI) to support human decision-making has become ubiquitous. Assistive AI achieves accuracy comparable to or even surpassing that of human experts. However, often the adoption of assistive AI systems is limited by a lack of trust of humans into an AI’s prediction. This is why the AI research community has been focusing on rendering AI decisions more transparent by providing explanations of an AIs decision. To what extent these explanations really help to foster trust into an AI system remains an open question. In this paper, we report the results of a behavioural experiment in which subjects were able to draw on the support of an ML-based decision support tool for text classification. We experimentally varied the information subjects received and show that transparency can actually have a negative impact on trust. We discuss implications for decision makers employing assistive AI technology.
Fault diagnosis based on extremely randomized trees in wireless sensor networks

Wireless Sensor Network (WSN) being highly diversified cyber–physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or faults in WSN is a challenging issue due to the diversity of deployment and the limitations in the sensors’ resources. In this paper, supervised machine learning-based technique is considered to scrutinize the behavior of sensors through their data for the detection and diagnosis of faults. Most of the faults that commonly occur in WSN are considered: hardover, drift, spike, erratic, data-loss, stuck, and random fault. A trusted dataset published online by the researchers at the University of North Carolina composed of temperature and humidity sensor measurements of multi-hop scenario was acquired and the aforementioned faults were simulated in non-faulty (normal) data. Events from fault occurrences were generated to replicate realistic scenarios of WSN. To detect and diagnose the faults in timely manner, we adopt an ensemble learning-based lightweight technique called Extremely Randomized Trees or Extra-Trees. The proposed Extra-Trees-based detection scheme has the ability of robustness towards signal noise and strong reduction of bias and variance error. The performances of the proposed scheme were compared with those of the state-of-the-art machine learning algorithms such as support vector machine, random forest, neural network, and decision tree. Performance evaluation shows the efficiency of the proposed scheme in terms of accuracy, precision, and F1-score. In addition, the proposed scheme has low training time compared to state-of-the-art approaches.
Multi-Temporal Recurrent Neural Networks for Progressive Non-uniform Single Image Deblurring with Incremental Temporal Training

Blind non-uniform image deblurring for severe blurs induced by large motions is still challenging. Multi-scale (MS) approach has been widely used for deblurring that sequentially recovers the downsampled original image in low spatial scale first and then further restores in high spatial scale using the result(s) from lower spatial scale(s). Here, we investigate a novel alternative approach to MS, called multi-temporal (MT), for non-uniform single image deblurring by exploiting time-resolved deblurring dataset from high-speed cameras. MT approach models severe blurs as a series of small blurs so that it deblurs small amount of blurs in the original spatial scale progressively instead of restoring the images in different spatial scales. To realize MT approach, we propose progressive deblurring over iterations and incremental temporal training with temporally augmented training data. Our MT approach, that can be seen as a form of curriculum learning in a wide sense, allows a number of state-of-the-art MS based deblurring methods to yield improved performances without using MS approach. We also proposed a MT recurrent neural network with recurrent feature maps that outperformed state-of-the-art deblurring methods with the smallest number of parameters.
TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning

With the advancement in sensor technology and the proliferation of low-cost electronic circuits, Internet of Things (IoT) is emerging as a promising technology for realization of smart cities. However, challenges such as security, privacy, trust, scalability, verifiability, and centralization prevent faster adaptations of IoT-driven smart cities. Thus, in this paper, a Trustworthy Privacy-Preserving Secured Framework (TP2SF) for smart cities is presented. This framework includes three modules namely: a trustworthiness module, a two-level privacy module, and an intrusion detection module. In trustworthiness module, address-based blockchain reputation system is designed. In the two-level privacy module, a blockchain based enhanced Proof of Work (ePoW) technique is simultaneously applied with Principal Component analysis (PCA) to transform data into a new reduced shape for preventing inference and poisoning attacks. In the intrusion detection module, an optimized gradient tree boosting system (XGBoost) is deployed. Finally, due to inherited strengths and weaknesses of Fog–Cloud architecture, we present a blockchain-IPFS integrated Fog–Cloud infrastructure namely, CloudBlock and FogBlock to deploy proposed TP2SF framework in smart city. The TP2SF framework is evaluated using two realistic IoT-based datasets, namely ToN-IoT and BoT-IoT. The findings indicate the superiority of TP2SF framework over other state-of-the-art techniques in both nonblockchain and blockchain systems.
History Repeats Itself: Human Motion Prediction via Motion Attention

Human motion prediction aims to forecast future human poses given a past motion. Whether based on recurrent or feed-forward neural networks, existing methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention-based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW evidence the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at https://github.com/wei-mao-2019/HisRepItself .