Reviewerly/exHarmony huggingface dataset:
The value of Blockchain technology implementation in international trades under demand volatility risk

The extant literature has conceptually argued that Blockchain technology can provide both financial and operational benefits to firms in international trades. However, no studies have systematically and analytically investigated the effectiveness of Blockchain. This study introduces analytical models that consider the implementation of Blockchain technology in international trades, to test whether the technology improves an exporting firm's performance under demand volatility risk. The simulation and numerical analysis results show that the reduced lead time and decreased ocean transport cost under Blockchain enable the exporting firm to increase shipment via the ocean, which reduces the quantity shipped via air. In addition, the reduced total unit cost for ocean and air transports leads the firm to effectively reserve spaces for air transport, which implies that Blockchain makes the firm more proactive while preparing a backup plan to more effectively and efficiently react to demand realisation. We conclude that Blockchain can be beneficial for firms facing considerable demand volatility in international trades, and that such effectiveness could be more effective for firms that prioritise minimising lost sales (by efficiently utilising ocean and effectively exploiting air) over minimising excess quantity shipped by ocean.
Performance analysis of Chaotic Multi-Verse Harris Hawks Optimization: A case study on solving engineering problems

In recent years, several optimization algorithms are proposed, one of them is Multi-Verse Optimizer (MVO). In this paper, a modified version of MVO is proposed, called CMVHHO, which uses the chaos theory and the Harris Hawks Optimization (HHO). The main aim of using the chaotic maps in the proposed method is to determine the optimal value for the parameters of the basic MVO. Besides, the HHO is used as a local search to improve the ability of the MVO to exploit the search space. The performance of the CMVHHO is conducted using a set of chaotic maps to determine the most suitable map, as well as, the different experiments are performed to determine which parameter has the largest effect on the effectiveness of the MVO. Moreover, the performance of the CMVHHO is compared with a set of state-of-the-art algorithms to find the best solution for global optimization problems. Furthermore, the proposed CMVHHO with the best map is applied to solve four well-known engineering problems. The experimental results illustrate that the chaotic Circle map is the best map among all maps because it improved the performance of the CMVHHO, as well as the HHO, affected positively in the behavior of the proposed algorithm. The CMVHHO showed the best results than other algorithms in terms of the performance measures as well as in engineering problems and it outperformed the state-of-the-art algorithms in all problems.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.
Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning

For matching pedestrians across disjoint camera views in surveillance, person re-identification (Re-ID) has made great progress in supervised learning. However, it is infeasible to label data in a number of new scenes when extending a Re-ID system. Thus, studying unsupervised learning for Re-ID is important for saving labelling cost. Yet, cross-camera scene variation is a key challenge for unsupervised Re-ID, such as illumination, background and viewpoint variations, which cause domain shift in the feature space and result in inconsistent pairwise similarity distributions that degrade matching performance. To alleviate the effect of cross-camera scene variation, we propose a Camera-Aware Similarity Consistency Loss to learn consistent pairwise similarity distributions for intra-camera matching and cross-camera matching. To avoid learning ineffective knowledge in consistency learning, we preserve the prior common knowledge of intra-camera matching in the pretrained model as reliable guiding information, which does not suffer from cross-camera scene variation as cross-camera matching. To learn similarity consistency more effectively, we further develop a coarse-to-fine consistency learning scheme to learn consistency globally and locally in two steps. Experiments show that our method outperformed the state-of-the-art unsupervised Re-ID methods.
Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature

The petrochemical industry is the top priority of the national economy and sustainable development. For the purpose of improving the energy efficiency in the petrochemical industry, an energy optimization and prediction model based on the improved convolutional neural network (CNN) integrating the cross-feature (CF) (CF–CNN) is proposed. The CF can combine the correlation between features to obtain the input of the CNN, which can avoid over-fitting problems caused by fewer features. Then the CNN is designed as a three-layer structure and the Rectified Linear Unit (ReLU) is introduced to achieve better generalization capability and stability with boiler fluctuations in the petrochemical industry. The developed method has better performances of modeling accuracy and applicability than that of the back-propagation (BP) neural network and the extreme learning machine (ELM) on University of California Irvine (UCI) benchmark datasets. Furthermore, the developed method is applied to establish an energy optimization and prediction model of ethylene production systems in the petrochemical industry. The experimental results testify the capability of the proposed method. Meanwhile, the average relative generalization error is 2.86%, and the energy utilization efficiency increases by 6.38%, which leads to reduction of the carbon emissions by 5.29%.
GUARDIAN: Blockchain-Based Secure Demand Response Management in Smart Grid System

Smart grid (SG) is an emerging technology which provides many services to the end users and utilities, such as load management, frequency regulation, and grid stability. Although many solutions exist to provide these services in a secure manner, but these solutions are not adequate keeping in view of the heavy cryptographic primitives execution on these devices. Hence, in this article, GUARDIAN, a blockchain-based secure demand response management scheme is presented so as to take energy trading decisions securely for managing the overall load of residential, commercial, and industrial sectors. In GUARDIAN, the miner nodes, which are block verifiers, are selected using their power consumption and processing power. These nodes are responsible for authenticating the energy transactions in SG. The energy transaction is initialized by an end user which creates the block of transaction to trade the energy. The miner nodes then validate these blocks and adds these in the blockchain. The successful energy trade occurs only for the blocks which are in the blockchain. The proposed scheme is lightweight in terms of communication and computation costs. Moreover, the results obtained demonstrate the effectiveness of proposed scheme for secure demand response management in the SG.
On the negation of a Dempster–Shafer belief structure based on maximum uncertainty allocation

Probability theory and Dempster–Shafer theory are two germane theories to represent and handle uncertain information. Recent study suggested a transformation to obtain the negation of a probability distribution based on the maximum entropy. Correspondingly, determining the negation of a belief structure, however, is still an open issue in Dempster–Shafer theory, which is very important in theoretical research and practical applications. In this paper, a negation transformation for belief structures is proposed based on maximum uncertainty allocation, and several important properties satisfied by the transformation have been studied. The proposed negation transformation is more general and could be totally compatible with existing transformation for probability distributions.
TrustAccess: A Trustworthy Secure Ciphertext-Policy and Attribute Hiding Access Control Scheme Based on Blockchain

Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in fine-grained access control to achieve the secure data sharing. However, most of the existing CP-ABE access control schemes involve intermediary entities, which might suffer from a high trust-building cost, single point of failure and so on. Due to the decentralization and transparency of blockchain, some blockchain-based access control schemes are proposed to address these problems, but bring new challenges, such as the privacy leakage of access policy or attribute. In this paper, we propose a new trustworthy secure ciphertext-policy and attribute hiding access control scheme based on blockchain, named TrustAccess, to achieve trustworthy access while guaranteeing the privacy of policy and attribute. For one thing, to make the existing hidden policy CP-ABE more efficient and scalable for blockchain, we propose an optimized hidden policy CP-ABE, named OHP-CP-ABE, to ensure policy privacy while satisfying the large universe access requirement. For another thing, we use the multiplicative homomorphic ElGamal cryptosystem to ensure the attribute privacy during authorization validation. Finally, we theoretically prove the security of our TrustAccess from the aspects of blockchain operations and OHP-CP-ABE. Comprehensive comparisons and extensive experiments are conducted to demonstrate the advantages of our TrustAccess.
See-Through-Text Grouping for Referring Image Segmentation

Motivated by the conventional grouping techniques to image segmentation, we develop their DNN counterpart to tackle the referring variant. The proposed method is driven by a convolutional-recurrent neural network (ConvRNN) that iteratively carries out top-down processing of bottom-up segmentation cues. Given a natural language referring expression, our method learns to predict its relevance to each pixel and derives a See-through-Text Embedding Pixelwise (STEP) heatmap, which reveals segmentation cues of pixel level via the learned visual-textual co-embedding. The ConvRNN performs a top-down approximation by converting the STEP heatmap into a refined one, whereas the improvement is expected from training the network with a classification loss from the ground truth. With the refined heatmap, we update the textual representation of the referring expression by re-evaluating its attention distribution and then compute a new STEP heatmap as the next input to the ConvRNN. Boosting by such collaborative learning, the framework can progressively and simultaneously yield the desired referring segmentation and reasonable attention distribution over the referring sentence. Our method is general and does not rely on, say, the outcomes of object detection from other DNN models, while achieving state-of-the-art performance in all of the four datasets in the experiments.
A Stereo Attention Module for Stereo Image Super-Resolution

In stereo image super-resolution (SR), exploiting both intra-view and cross-view information is significant but challenging. As existing single image SR (SISR) methods are powerful in intra-view information exploitation, in this letter, we propose a generic stereo attention module (SAM) to extend arbitrary SISR networks for stereo image SR. Specifically, we apply two identical pretrained SISR networks to stereo images. The extracted stereo features at different stages are fed to SAMs to interact cross-view information. Finally, the intra-view and cross-view information is incorporated by SISR networks for stereo image SR. Experiments on the KITTI2012, KITTI2015 and Middlebury datasets have demonstrated the effectiveness of our scheme. Using SAM, we can exploit cross-view information while maintaining the superiority of intra-view information exploitation, resulting in notable performance gain to SISR networks. Moreover, SRResNet equipped with our SAM outperforms the state-of-the-art stereo SR methods. Source code is available at https://github.com/XinyiYing/SAM.
Geometry Guided Pose-Invariant Facial Expression Recognition

Driven by recent advances in human-centered computing, Facial Expression Recognition (FER) has attracted significant attention in many applications. However, most conventional approaches either perform face frontalization on a non-frontal facial image or learn separate classifier for each pose. Different from existing methods, this paper proposes an end-to-end deep learning model that allows to simultaneous facial image synthesis and pose-invariant facial expression recognition by exploiting shape geometry of the face image. The proposed model is based on generative adversarial network (GAN) and enjoys several merits. First, given an input face and a target pose and expression designated by a set of facial landmarks, an identity-preserving face can be generated through guiding by the target pose and expression. Second, the identity representation is explicitly disentangled from both expression and pose variations through the shape geometry delivered by facial landmarks. Third, our model can automatically generate face images with different expressions and poses in a continuous way to enlarge and enrich the training set for the FER task. Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on both controlled and in-the-wild benchmark datasets including Multi-PIE, BU-3DFE, and SFEW. The code is included in the supplementary material.
A Multi-Scale Guided Cascade Hourglass Network for Depth Completion

Depth completion, a task to estimate the dense depth map from sparse measurement under the guidance from the high-resolution image, is essential to many computer vision applications. Most previous methods building on fully convolutional networks can not handle diverse patterns in the depth map efficiently and effectively. We propose a multi-scale guided cascade hourglass network to tackle this problem. Structures at different levels are captured by specialized hourglasses in the cascade network with sparse inputs in various sizes. An encoder extracts multi-scale features from color image to provide deep guidance for all the hourglasses. A multi-scale training strategy further activates the effect of cascade stages. With the role of each sub-module divided explicitly, we can implement components with simple architectures. Extensive experiments show that our lightweight model achieves competitive results compared with state-of-the-art in KITTI depth completion benchmark, with low complexity in run-time.
Shuffled shepherd optimization method: a new Meta-heuristic algorithm

Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.
Preserving Data Privacy via Federated Learning: Challenges and Solutions

Data have always been a major priority for businesses of all sizes. Businesses tend to enhance their ability in contextualizing data and draw new insights from it as the data itself proliferates with the advancement of technologies. Federated learning acts as a special form of privacy-preserving machine learning technique and can contextualize the data. It is a decentralized training approach for privately collecting and training the data provided by mobile devices, which are located at different geographical locations. Furthermore, users can benefit from obtaining a well-trained machine learning model without sending their privacy-sensitive personal data to the cloud. This article focuses on the most significant challenges associated with the preservation of data privacy via federated learning. Valuable attack mechanisms are discussed, and associated solutions are highlighted to the corresponding attack. Several research aspects along with promising future directions and applications via federated learning are additionally discussed.
SoK: Understanding the Prevailing Security Vulnerabilities in TrustZone-assisted TEE Systems

Hundreds of millions of mobile devices worldwide rely on Trusted Execution Environments (TEEs) built with Arm TrustZone for the protection of security-critical applications (e.g., DRM) and operating system (OS) components (e.g., Android keystore). TEEs are often assumed to be highly secure; however, over the past years, TEEs have been successfully attacked multiple times, with highly damaging impact across various platforms. Unfortunately, these attacks have been possible by the presence of security flaws in TEE systems. In this paper, we aim to understand which types of vulnerabilities and limitations affect existing TrustZone-assisted TEE systems, what are the main challenges to build them correctly, and what contributions can be borrowed from the research community to overcome them. To this end, we present a security analysis of popular TrustZone-assisted TEE systems (targeting Cortex-A processors) developed by Qualcomm, Trustonic, Huawei, Nvidia, and Linaro. By studying publicly documented exploits and vulnerabilities as well as by reverse engineering the TEE firmware, we identified several critical vulnerabilities across existing systems which makes it legitimate to raise reasonable concerns about the security of commercial TEE implementations.
Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds

In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele-medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology. In particular, both public and hybrid Blockchain scenarios are discussed and assessed using the Ethereum platform.
Predicting the citation counts of individual papers via a BP neural network

Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., ‘citations in the first two years’, ‘first-cited age’, ‘paper length’, ‘month of publication’, and ‘self-citations of journals’, and the other features contribute only slightly to the prediction.
Improved Binary Sailfish Optimizer Based on Adaptive<i>β</i>-Hill Climbing for Feature Selection

Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhancing the performance of the overall prediction or classification model. Over the years, meta-heuristic optimization techniques have been applied for FS, as these are able to overcome the limitations of traditional optimization approaches. In this work, we introduce a binary variant of the recently-proposed Sailfish Optimizer (SFO), named as Binary Sailfish (BSF) optimizer, to solve FS problems. Sigmoid transfer function is utilized here to map the continuous search space of SFO to a binary one. In order to improve the exploitation ability of the BSF optimizer, we amalgamate another recently proposed meta-heuristic algorithm, namely adaptive β-hill climbing (AβHC) with BSF optimizer. The proposed BSF and AβBSF algorithms are applied on 18 standard UCI datasets and compared with 10 state-of-the-art meta-heuristic FS methods. The results demonstrate the superiority of both BSF and AβBSF algorithms in solving FS problems. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization.
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis

Race and gender have long sociopolitical histories of classification in technical infrastructures-from the passport to social media. Facial analysis technologies are particularly pertinent to understanding how identity is operationalized in new technical systems. What facial analysis technologies can do is determined by the data available to train and evaluate them with. In this study, we specifically focus on this data by examining how race and gender are defined and annotated in image databases used for facial analysis. We found that the majority of image databases rarely contain underlying source material for how those identities are defined. Further, when they are annotated with race and gender information, database authors rarely describe the process of annotation. Instead, classifications of race and gender are portrayed as insignificant, indisputable, and apolitical. We discuss the limitations of these approaches given the sociohistorical nature of race and gender. We posit that the lack of critical engagement with this nature renders databases opaque and less trustworthy. We conclude by encouraging database authors to address both the histories of classification inherently embedded into race and gender, as well as their positionality in embedding such classifications.